Investigating the Spatial and Temporal Dynamics of Fungal Endophyte Community Structure in Quercus lobata and its Mistletoe Parasite, Phoradendron villosum

Brendan Palmieri May 2016

Investigating the Spatial and Temporal Dynamics of Fungal Endophyte Community Structure in Quercus lobata and its Mistletoe Parasite, Phoradendron villosum

An Honors Thesis Submitted to the Department of Biology in partial fulfillment of the Honors Program STANFORD UNIVERSITY

by Brendan Palmieri May 2016

Acknowledgments

First, I would like to thank Kabir Peay and the all the members of Peay Lab for helping me along the way and always welcoming me into their lab. I want to particularly thank Nora Dunkirk for teaching me so many of the critical tools to complete this project. I would like to thank Rodolfo Dirzo for always supporting my research ambitions. Thank you for your mentorship and instruction over the last four years and for introducing me to the world of ecology. This project would not have been possible without John Schroeder, my main mentor over the past three years. Thank you for showing me what it means to do research, and for helping me at every step of the process. I would also like to thank Po-Ju Ke and Manpreet Dhami for going out of their way to help me with molecular work. I would not have been able to prepare my library without the help provided by Po-Ju and Manpreet and am incredibly grateful. Finally, I would like to thank Nona Chiariello for supporting this project through the Jasper Ridge Mellon Grant.

Table of Contents

List of Figures & Tables ...... 2

Abstract ...... 4

Introduction ...... 5

Methods ...... 10

Results ...... 17

Discussion ...... 23

Works Cited ...... 34

Data Appendices ...... 39

List of Figures & Tables

Figure 1: Aerial Image of study site at Jasper Ridge Biological Preserve.

Figure 2: Boxplots displaying average read depth in each sampling period.

Figure 3a: Ordination of the community dissimilarity of all samples from May and June.

Figure 3b: Ordination of the community dissimilarity of Q. Lobata samples from May and June.

Figure 3c: Ordination of the community dissimilarity of P. villosum samples from May and June.

Figure 4a: Ordination of the community dissimilarity of Q. lobata and P. villosum samples in April.

Figure 4b: Ordination of the community dissimilarity of Q. lobata and P. villosum samples in May.

Figure 4c: Ordination of the community dissimilarity of Q. lobata and P. villosum samples in June.

Figure 5a: Ordination of the community dissimilarity of Q. lobata samples in April with the neighborhood index value of each sample represented by color.

Figure 5b: Ordination of the community dissimilarity of Q. lobata samples in May with the neighborhood index value of each sample represented by color.

Figure 5c: Ordination of the community dissimilarity of Q. lobata samples in June with the neighborhood index value of each sample represented by color.

Figure 6a: Boxplot of the number of OTU’s observed in May and June samples.

Figure 6b: Boxplot of the number of OTU’s observed in May and June Q. lobata samples.

2 Figure 6c: Boxplot of the number of OTU’s observed in May and June P. villosum samples.

Figure 7a: Plot of fungal richness and fungal diversity, as calculated by the Shannon

Index, of Q. lobata and P. villosum samples in April.

Figure 7b: Plot of fungal richness and fungal diversity, as calculated by the Shannon

Index, of Q. lobata and P. villosum samples in May.

Figure 7c: Plot of fungal richness and fungal diversity, as calculated by the Shannon

Index, of Q. lobata and P. villosum samples in June.

Table 1: Eight most abundant taxa in April.

Table 2: Eight most abundant taxa in May.

Table 3: Eight most abundant taxa in April.

Table 4a: GLM P-value output for model incorporating May and June samples.

Table 4b: GLM P-value output for model incorporating all samples in April, May and

June.

Table 4c: GLM P-value output for model incorporating Q. lobata samples in April, May and June.

Table 4d: GLM P-value output for model incorporating infected Q. lobata samples in

April, May and June.

3 Abstract

Fungal endophytes have been found in the asymptomatic tissue of every plant species analyzed to date. These cryptic and highly diverse symbionts often have profound impacts on host fitness and ecology, yet much is still unknown about the dynamics of endophyte communities. To investigate the temporal and spatial variation in foliar endophyte community structure, I collected leaf samples from a deciduous tree, Quercus lobata, and its evergreen, canopy parasite, Phoradendron villosum, over a three-month period. Using molecular sequencing data, I show that the endophyte communities of both deciduous and evergreen hosts undergo strong temporal changes in composition.

Furthermore, I found that the endophyte communities of Q. lobata and P. villosum converged within a month, suggesting widespread host generalism among endophytic fungi. My results suggest that horizontal transmission between hosts is the main vector of foliar endophytic infection, supporting much of the prior research into non- clavicipitaceous endophytes. This study also aimed to investigate the hypothesis that P. villosum may serve as an endophyte reservoir for Q. lobata during leaf abscission.

Preliminary data suggests that the presence of P. villosum may systemically impact the foliar endophyte communities of its canopy host, suggesting vascular transmission of fungal endophytes. This study has demonstrated the complex and dynamic nature of endophyte communities and highlights the need for future work addressing these topics.

4 Introduction

Over 100 years of research has demonstrated that all plants have symbiotic relationships with fungi in their natural environments (De Bary, 1879; Arnold & Lutzoni,

2007; Rodriguez et al., 2009). Fossil evidence suggests that these symbioses date back to the first terrestrial plants, over 400 million years ago, suggesting that fungal symbionts have played a critical and sustained role in the development of terrestrial ecosystems

(Redecker et al., 2000; Krings et al., 2007). Research has shown that fungal symbionts often have strong impacts on the fitness and evolution of their hosts and, in turn, influence plant population dynamics and community structure (Rodriguez et al., 2009;

Afkhami et al., 2014; Busby et al., 2016). However, much remains unknown about the ecology of these ubiquitous interactions. Here, I focus on interactions between foliar endophytes and their woody angiosperm hosts in a California oak woodland, a subset of plant-fungal interactions that is among the least well studied due to the cryptic and highly diverse nature of the endophytic fungal symbionts.

The term endophyte refers to fungi that live entirely within the tissue of their host and, for some or all of their lifecycle, are asymptomatic (Wilson, 1995). Endophytic fungi are found in all plant tissue types; however, this study addresses only foliar endophytes as the high turnover rate of leaf tissue provides a unique opportunity to study questions of community dynamics. The majority of past endophyte research has focused on clavicipitaceous endophytes (C- endophytes): a monophyletic group of host-specific endophytes that infect select grass species (Clay & Schardl, 2002). C-endophytes are transmitted vertically through seeds and produce mycotoxins and alkaloids that serve as chemical defenses for their host, suggesting an almost exclusively mutualistic

5 relationship between host and symbiont (Clay et al., 1993; Faeth, 2002; Clay & Schardl,

2002; Rodriguez et al., 2009). The research bias towards C-endophytes is due to the economic and agricultural importance of some of the grasses infected. As a result, there is a deficit of research focusing on non-clavicipitaceous (NC) endophytes, which are the main endophytes of large woody plants (Rodriguez et al., 2009).

NC-endophytes represent a diverse, polyphyletic assemblage of fungi with the majority belonging to the Ascomycota (Saikkonen et al., 2004). Unlike C-endophytes, which have narrow host ranges and are vertically transmitted, the majority of NC- endophytes are transmitted horizontally though sexual or asexual spores, or sometimes via hyphal fragments (Arnold & Herre, 2003; Hoffman & Arnold, 2008). The contagious spread of these NC-endophytes results in complex and variable endophyte-host associations, far different than the strict mutualistic associations observed between C- endophytes and their grass hosts.

Recent research has demonstrated that some NC-endophytes can improve host fitness by providing herbivory defense (Bing & Lewis, 1991; Wagner & Lewis, 2000), protecting against fungal pathogens (Campanile et al., 2007), increasing thermal tolerances (Redman et al., 2002), and decreasing lesion formation and leaf death (Arnold et al., 2003). However, other research has demonstrated negative impacts on the plant host, such as reductions in photosynthetic efficiency (PINTO et al., 2000), and increased water loss (Arnold & Engelbrecht, 2007). The variability of these interactions is due to the extreme phylogenetic diversity of the endophytes in question, and it is unlikely that any single ecological effect is universal among all endophyte-host associations (Arnold &

Lutzoni, 2007). In order to acquire a more complete understanding of the complex

6 ecology of endophytes, one must first understand the fundamental aspects of the endophyte symbioses. Particularly important, yet unanswered, are questions regarding the impact of host affinity, temporal change and inoculum sources on endophyte community structure.

Multiple studies have investigated questions of endophyte host affinity by analyzing the communities of distantly related host species that co-occur in a given region; however, these studies have yielded conflicting results. Suryanarayanan &

Senthilarasu (2000) found distinct and non-overlapping endophyte communities between

Cuscuta reflexa, a canopy parasite, and its angiosperm hosts. Similarly, research in lowland Panama demonstrated that distantly-related hosts growing in close proximity hosted distinct endophyte communities (Arnold et al., 2000). In contrast, Cannon &

Simmons (2002) showed that the endophyte communities from distantly related angiosperms in Guyana were similar and indistinguishable. Multiple other studies have also shown widespread host generalism among endophytes (Suryanarayanan et al., 2004;

2005; Mohali et al., 2005). As such, there is no consensus regarding the degree of host specificity in woody angiosperms, particularly in temperate systems.

Research on the temporal dynamics of endophyte community structure has also fostered conflicting data (Rodrigues, 1994; Wilson & Carroll, 1994; Arnold & Herre,

2003; Unterseher et al., 2007; Scholtysik et al., 2012). Some research suggests that endophyte infection density is positively correlated with leaf age (Rodrigues, 1994), while other research shows no impact of leaf age on infection density (Arnold & Herre,

2003). Studies focusing on the seasonal changes in endophyte community composition have been more conclusive (Rodrigues, 1994; Wilson & Carroll, 1994; Unterseher et al.,

7 2007; Scholtysik et al., 2012), consistently showing significant seasonal variations with regards to either infection density (Rodrigues, 1994) or community composition

(Unterseher et al., 2007; Scholtysik et al., 2012). For example, infection density of

Quercus garryanna with a common endophyte, Discula quercina, was positively correlated with higher rainfall in the wet season (Wilson & Carroll, 1994). Other studies seem to indicate that rainfall is large determinant of horizontal spore transmission.

Almost no research has investigated the sources or reservoirs of endophyte inoculum in temperate systems. As such, there is uncertainty about what serves to infect the emergent leaves of deciduous trees. Some research has addressed these questions in a tropical forest system, however the findings are likely not directly comparable to

California oak woodland ecosystems given the significant differences in canopy structure. Arnold and Herre (2003) investigated endophyte colonization of Theobroma cacao in the Neotropics. They found that the extent of exposure to aerial and epiphytic inoculum was the main factor determining infection density (Arnold & Herre, 2003).

However, in oak woodland ecosystems, there is less structural complexity to the canopy, and thus, there are few aerial inoculum sources above the newly emergent leaves.

Unterseher et al. (2007) demonstrated that there were significant spatial differences in endophyte community composition within the canopy of a single temperate angiosperm, likely due to endophyte-specific preferences towards differing light regimes. Some hypothesize that observed canopy differences in endophyte community composition is determined by the differing thickness of leaf cuticles, with understory leaves hosting more soft, think leaves (Scholtysik et al., 2012). However, there is no explanation of how or why these endophytes arrived in the emergent leaves.

8 My project aims to address some of these questions about host affinity, temporal change and inoculum sources by analyzing the foliar endophyte communities in the valley oak, Quercus lobata, and its common mistletoe parasite, Phoradendron villosum using DNA metabarcoding techniques. Q. lobata is a foundational species in California oak woodland ecosystems, and is frequency parasitized by P. villosum, a hemiparastic epiphytic mistletoe that grows in the canopy of its host and extracts water and nutrients

(Panvini & Eickmeier, 1993). Q. lobata is a deciduous angiosperm that undergoes leaf abscission in winter months. The mistletoe, however, is evergreen and remains in the canopy of its host year round. Research has shown that foliar endophytes are lost during abscission and the majority of emergent leaves in the spring are free of endophytic infection (Scholtysik et al., 2012). Emergent leaves are infected over time either via exposure to spores from the environment or through the transmission of resident non- foliar endophytes within the plant’s vascular system (Toti et al., 1993; Scholtysik et al.,

2012). Thus, this mistletoe-valley oak parasitism provides an exceedingly interesting and simple system to study the dynamics of endophyte community structure, with particular focus on the topics of inncolum sources, host affinity and temporal variation.

First, I hypothesize that the evergreen mistletoe will serve as a reservoir for endophytes that will infect the newly emergent leaves of its deciduous host. I expect to observe greater foliar endophyte diversity in valley oaks infected by mistletoe. Further, as endophyte transmission between the valley oak and the mistletoe is presumably due to spore and hyphal fragment transmission, I expect differences in endophyte diversity between the leaves of a single infected host tree based on proximity to the mistletoe plant.

Second, despite being exposed to the same environmental inoculum, I expect that

9 mistletoe and valley oak endophyte communities will remain distinctly different due to endophyte host specificity.

Lastly, this study addresses the temporal changes in endophyte community structure of an evergreen and deciduous host. I hypothesize that over a three-month sampling period the endophyte community composition of both hosts will change significantly due to variable abiotic conditions. However, I expect that the endophyte communities in the leaves of Q. lobata will undergo far greater change as the newly emergent leaves rapidly accumulate endophytes. Additionally, I expect the fungal richness in Q. lobata samples to increase throughout the sampling period, with the emergent leaves behaving as a sort of primary successional substrate (Connell & Slatyer,

1977). In contrast, I predict that the endophyte diversity and community structure of P. villosum remain more stable, given the more stable foliar substrate provided by the evergreen leaves (Clements, 1936). Ultimately, understanding these questions regarding the factors that influence the community dynamics of these widespread endophytes is critical to a complete understanding of the plant-endophyte symbiosis.

Methods

Study Site and Sampling Design

Sampling was conducted at Jasper Ridge Biological Preserve (JRBP), located in the eastern foothills of the Santa Cruz Mountains (37˚24’ N, 12˚13’ 30’’ W; Fig. 1).

JRBP is a 1,189-acre preserve closed to the public and managed by Stanford University for research and education purposes. In a 5-hectare region of JRBP, I identified five Q. lobata individuals infected by a single P. villosum mistletoe. For each of the five

10 mistletoe-infected Q. lobata, an uninfected Q. lobata of similar size was identified within a 40-meter radius (mean distance between pairs: 24.2 meters, range: 12m to 39m). This created a paired sampling design, where each mistletoe infected Q. lobata was paired with a control, uninfected Q. lobata. The paired sampling serves to control for potential confounders to endophyte community composition including local variation in environmental factors, such as aspect, slope, and local plant community composition. The diameter at breast height (DBH) and a neighborhood index (NI) value of each Q. lobata was calculated, along with the canopy height of each P. villosum individual, as possible covariates. The NI value was determined by the formula:

NI = (# of trees within a 15m radius) * (Σ DBH).

The fifteen meter radius was determined based on data showing an exponential drop in spore number with distance from spore sources, suggesting that local dispersal effects likely attenuate within a few meters (Norros et al., 2012; Peay et al., 2012)

Sampling occurred once a month for three months, resulting in a total of three sampling events from April to June. Only leaves that had no visible pathogen damage were collected. Five leaves were collected from each mistletoe individual and ten leaves were taken from each Q. lobata. All the leaves collected from a given paired unit

(defined as the infected Q. lobata, P. villosum, and control Q. lobata) were obtained from the same canopy height, as determined by the height of the mistletoe infection. For the infected Q. lobata, five leaves were collected directly next to the mistletoe and five leaves were collected from the opposite side of the canopy, at the furthest distance possible. For consistency in terminology, the mistletoe samples and the Q. lobata samples collected directly next to the mistletoe are termed a interior paired unit.

11 Furthermore, the Q. lobata samples of the interior paired unit will be referred to as the adjacent samples while the Q. lobata samples collected from the opposite side of the canopy will be referred to as the distant samples. Finally, ten leaves were collected from the control Q. lobata at the same canopy height as the mistletoe in the paired Q. lobata.

While the canopy height was consistent, the location in the canopy was randomized for all ten leaves collected in the control Q. lobata. In total, this resulted in 25 leaves collected from each paired unit at a standardized canopy height, and a total of 125 leaves collected at each sampling event. In total, 375 leaves were collected over the sampling period. After collection, leaves were placed into a minus 80 freezer to preserve the endophyte communities (within 2 hours of collection).

Library Preparation

DNA extractions and molecular work began after all sampling was completed.

Before DNA extraction, the leaves were surfaced sterilized to remove any epiphytic fungi or bacteria. The surface sterilization protocol was based on protocols adapted from

Kumaresan and Suryanarayanan (2001) and Dobranic (1994). Leaves were first placed in

70% ethanol for 5 seconds, and then moved to 8.25% sodium hypochlorite for 60 seconds. Finally the leaves were rinsed in sterile water for 10 seconds. After surface sterilization, 0.25 grams of each leaf was removed using a cork borer. This leaf tissue was loaded into the MO BIO PowerSoil® DNA Isolation Kit to isolate genomic DNA (MO

BIO Laboratories, Inc., Carlsbad, CA, USA).

After the DNA extractions were completed, fungal DNA was amplified using a polymerase chain reaction (PCR) with fungal specific primers, ITS1F and ITS2 (White et

12 al., 1990; Gardes & Bruns, 1993; Smith & Peay, 2014). This protocol amplifies the internal transcribed spacer situated between the small-subunit ribosomal RNA and large- subunit ribosomal RNA genes. This region is recognized as the universal fungal barcode sequence and is ubiquitously used in fungal ecology studies (Manter & Vivanco, 2007;

Schoch et al., 2012). For high throughput sequencing, we used a single PCR with primer constructs that included Illumina adapters and a unique 12 basepair molecular identification tag (Smith & Peay, 2014). Illumina PCR mixtures contained 0.2 µL of

Phusion High Fidelity DNA Polymerase (New England Biolabs, Ipswich, MA, USA),

4.00 µL of 5x PCR buffer (New England Biolabs, Ipswich, MA, USA), 0.40 µL of 10X each deoxynucleotide triphosphate (dNTP), 1.00 µL of 10 µM forward primer, 1.00 µL of 10 µM reverse primer, 1 µL of 1:10 diluted DNA template, and water up to 20 µL.

Thermocyler conditions were set with an initial denaturation step of 98˚C for 30 s, followed by 35 cycles of 10 s at 98˚C (denaturation), 30 s at 51˚C (annealing) and 30 s at

72˚C (extension). The final step was a final 5-min extension at 72˚C

After PCR, the individual samples were cleaned and normalized using the Charm

Biotech Just-A-Plate 96 PCR Purification and Normalization Kit (Charm Biotech, San

Diego, CA, USA). The samples were then pooled and quantified fluorescently with the

Quibit dsDNA HS kit (Life Technologies Inc., Gaithersburg, MD, USA). The library was quality checked for concentration and amplicon size using the Agilent 2100 Bioanalyzer

(Agilent Technologies, Santa Cara, CA, USA) at the Stanford Functional Genomics

Facility, Stanford University, CA. Results from the bioanalyzer showed a high concentration of small amplicons (>100 bp primer dimers), so a second size selective clean up protocol was conducted using Agencourt AMPure XP Beads (Beckman Coulter

13 Inc., Brea, CA, USA) at a ratio of 0.8: 1 beads to pooled library product . Finally, the library was sent to the Stanford Function Genomics Facility for an Illumina MiSeq sequencing run.

Sequencing Data Preparation

Illumina raw data were processed using USEARCH scripts (Edgar, 2010). Raw sequence reads were trimmed to remove primer sequences and low quality ends using the cutadapt function. This step trimmed reads at a minimum quality score of 20, allowed for

20% error rates in primer mismatches, and removed any reads of less than 50 basepairs.

This trimming step implemented the Burrows Wheeler Aligner (BWA) algorithm. Due to the poor quality of reverse reads in this data set, I chose to conduct analyses solely on the forward read, Read 1. However, single reads have been shown to produce comparable (or better) results than paired reads (Nguyen et al., 2014). Read 1 was further quality filtered using the usearch7 fastq_filter, which discarded reads with greater than 25% expected errors for all bases in the read. Singletons were removed using the sortbysize function, and OTU’s were clustered at 97% sequence similarity using the usearch7 cluster_otus function, which includes a de-novo chimera detection. The output OTU table was created using the USEARCH script uc2otutab.py. Further reference based chimera detection was conducted using the usearch7 –uchime_ref function. Finally, taxonomy was assigned by mapping representative sequences from each OTU against the UNITE database using the assign_taxonomy.py script.

14 Statistical Analysis

The output data from the QIIME pipeline was exported into R version 3.2.2.

Statistical analysis utilized the ‘phlyoseq’, ‘vegan’ and ‘MVAbund’ packages in R

(Dixon, 2003; Wang et al., 2012; McMurdie & Holmes, 2013). Given the low sequencing depth, sequence reads from identical treatments were merged to increase the power of community dissimilarity and richness analyses. Two distinct merged data sets were created to fulfill the demands of these different analyses. The first merged data set

(MergeTen) consisted of all subsamples from a given individual from a given sampling period. This means that each paired unit (consisting of 5 mistletoe leaves, and 10 leaves from each oak tree) was merged into one sample from each individual (1 mistletoe, 1 infected oak, 1 control oak). In total, this data set consisted of 45 samples (15 samples from the three sampling periods).

The second dataset (MergeFive) was created in order to facilitate more accurate comparisons of richness between the mistletoe and oak individuals, in addition to allowing for analyses of the impact of intra-canopy proximity to mistletoe. This merged data set consisted of 1 merged sample for each mistletoe individual, 2 merged samples for each infected oak tree, where the leaves were merged based on proximity to the mistletoe, and 2 samples for each control oak tree, where the ten individual samples were randomly merged into two equal sized samples. This MergeFive dataset consisted of 75 samples.

The sample sequence counts were standardized using a formula adopted from

John Schroeder (Ph.D. candidate, Dirzo Lab, Stanford University):

!"#$%#&%'()% !"#$%& !"#$% = ! × ( ! ) !"#(!)

15 Where t is the value to which the samples are being standardized, x is the sample in question, and the sum(x) is the total sequence counts of all samples. Standardization was used in place of rarefaction in order to preserve useful data, particularly given low sequence counts. April samples had extremely low sequencing depth, so April samples were standardized against the median of April counts. May and June samples were standardized against the median of May counts. Given April’s low sequence count and separate standardization values, no valid comparisons of community composition could be made between April and May or June. However, within month analyses were still valid across all three months, and community composition was reliably analyzed between

May and June.

I tested for differences in community composition among treatments using the

‘adonis’ function in the R-package vegan. This function implements a Permutational

Multivariate Analysis of Variance Using Distance Matrices (Anderson, 2001). Matrices were obtained using the distance function in the ‘phyloseq’ package and calculated based on the Bray Curtis Dissimilarity Index (Bray & Curtis, 1957). The adonis function partitions the dissimilarity provided by the distance matrix according to a source of variation and then uses permutation-tests to determine significance (Grossmann, 2015).

In addition to adonis, multiple General Linear Models (GLM) were fit to the data to investigate the taxa that contribute most to the observed dissimilarity. The GLMs were implemented using the manyglm() function in the R-package mvabund, using a negative binomial distribution of the data (Wang et al., 2012). The function fits a separate GLM to each taxon using a common set of explanatory variables. A test statistic is calculated based on the difference in log-likelihood between the observed distribution and the null

16 hypothesis model. After fitting a model for each taxa, a second step uses resampling- based hypothesis testing to make inferences about the environmental variables impacting community-level and taxon specific variations in abundance. We report adjusted p-values to account for multiple hypothesis testing.

We also tested to see how our treatments influenced the richness and diversity of fungi. Richness was measured as the observed number of OTUs within each standardized sample from the MergeFive sample set Diversity was measured using the Shannon

Diversity Index, which incorporates both richness and the relative abundance of OTUs :

!

!′ = !!!" !! !!!

th Where pi= the proportion of sequences belonging to the i OTU in the dataset. The

MergeFive sample set was also used for diversity measures. To determine the impact of a given treatment (i.e. month; infected status; canopy location; NI) on the richness or diversity of a sample, an Analysis of Variance (ANOVA) was conducted. The significance of a given treatment was reported from the results of the ANOVA.

Results

Sequencing Results

The Illumina MiSeq run yielded a total of 12,192, 839 raw sequence reads. Of these reads, 2.3499% were identified and assigned to the samples based on the custom indexed barcodes. This low indexing was due to a nearly failed indexing run, with a Q30 quality score of 2.94%. An attempt at pairing reads from Read 1 and Read 2 resulted in 0 exact overlaps among 214,216 pairs. Thus, only the unpaired sequence reads from Read

1 (Q30 of 68.31%) were used for downstream analysis, and Read 2 data were

17 disregarded. A total of 238,250 reads were recovered from Read 1 and passed through the initial quality filtering steps. After further quality filtering (as described in the Methods),

41,070 reads remained for downstream analysis. These reads were grouped into a total of

710 OTUs (at 97% sequence similarity), 589 of which were identified as belonging to the kingdom Fungi and retained for analysis.

There was a significant difference in the average number of sequence reads per sampling period (ANOVA: F2,71 = 18.66, P < 3.08e-07; Fig. 2). Analysis showed that each successive sampling period had greater sequencing depth per sample: April had an average of 106.875 reads per sample (SEApril = 20.98622), May had an average of 537.52 reads per sample (SEMay= 123.3777), and June had 1002.68 average reads per sample

(SEJune=124.7873, Fig. 2). Given the low sequencing depth in April, the data from April was excluded in temporal community dissimilarity comparisons. The data from May and

June were both standardized to the median of the sample sequencing depth in May, in order to allow for comparisons of community composition and richness. The data from

April was standardized to the median sequencing depth per sample in April.

Temporal Changes in Community Dissimilarity and Fungal Richness

A test of community dissimilarity showed significant differences in community composition between the samples collected in May and June (ADONIS: R2= 0.09011, P

< 0.001; Fig. 3A). There was also a significant difference in fungal richness between these two sampling periods, with June samples hosting a higher average number of

2 observed fungal OTU’s (F1,48 = 4.334, P= 0.0427, Multiple R = 0.08281, Fig. 6A). The mean number of observed OTU’s per sample in June was 43.48 (SEJune= 3.463197),

18 compared to the 33.48 OTU’s (SEMay = 3.328273) observed on average in May.

However, there was no significant difference in diversity, as calculated with the Shannon

2 Index (F1,48 = 4.334, P= 0.7842, Multiple R = 0.00157). The two host species were analyzed individually to determine if the endophyte communities of both hosts were undergoing temporal changes. In Q. lobata, significant differences were found between the May and June endophyte communities (ADONIS: R2= 0.08646, P < 0.001; Fig. 3B).

However, May and June Q. lobata endophyte communities had similar levels of species

2 richness and diversity (Observed: F1,38 = 2.197, P = 0.1465, Multiple R = 0.05466;

2 Simpson: F1,38 = 0.01908, P = 0.8909, Multiple R = 0.0005019, Fig. 6B). The P. villosum endophyte communities were also significantly different between the two sampling periods (ADONIS: R2= 0.22678, P = 0.01; Fig. 3C). However, similar to the oak samples, the difference in fungal richness and diversity was not significant, despite a trend suggesting more OTU’s per sample in June (Observed: F1,8 = 2.248, P = 0.1722,

2 Multiple R = 0.2194, Fig. 6C).

Impact of Host Species on Community Dissimilarity and Fungal Richness

A significant difference was found between the endophyte communities in oak and mistletoe samples in April (ADONIS: R2= 0.18849, P < 0.002; 4A). However, this host specific difference in community composition was indistinguishable in samples collected in May and June (ADONIS: May: R2= 0.06033, P < 0.786, Fig. 4B; June: R2=

0.0781, P < 0.25, Fig 4C). The fungal richness was assessed for each host species in all three sampling periods. There was no significant difference in endophyte richness

2 between hosts in April or May (April: F1,22 = 0.769, P= 0.39, Multiple R = 0.03377, Fig.

19 2 7A; May: F1,23 = 0.0358, P= 0.8516, Multiple R = 0.001554, Fig. 7B). However, the

June mistletoe samples had significantly higher species richness than oak samples (F1,23 =

2 5.399, P = 0.0293, Multiple R = 0.1901, Fig. 7C).

Impact of P. villosum Infection on Q. lobata Communities

There was no significant difference in the endophyte communities of infected and control hosts across any of the sampling periods (ADONIS: April: R2= 0.08461, P <

0.597; May: R2= 0.07756, P < 1; June: R2= 0.10326, P < 0.503). Likewise, there was no difference in average fungal richness between infected and control hosts at any point

2 (April: F1,8 = 0.1928, P= 0.6722, Multiple R = 0.02354; May: F1,8 = 1.327, P= 0.2825;

2 2 Multiple R = 0.1423; June: F1,8 = 0.0002246, P= 0.9884, Multiple R = 2.807e-05).

Additionally, no difference was found in the community composition between the adjacent and opposite canopy treatments within infected oaks (ADONIS: April: R2=

0.08352, P < 0.778; May: R2= 0.08723, P < 0.858; June: R2= 0.08817, P < 0.827).

Similarly, there was no distinguishable difference in fungal richness between these two

2 treatments (April: F1,8 = 0.6259, P= 0.4517, Multiple R = 0.07256; May: F1,8 = 0.1476,

2 2 P= 0.7108; Multiple R = 0.01812; June: F1,8 = 0.01242, P= 0.914, Multiple R =

0.00155).

Differences in Community Composition based on Environmental and Morphological

Differences

A strong and significant correlation was found in the April sampling period between NI and the endophyte community composition of Q.lobata (ADONIS: R2=

20 0.29109, P < 0.007; Fig. 5A). There is also a significant positive relationship in April between NI and the fungal richness of a given sample, with NI explaining about 55% of

2 the variation in fungal richness across samples (F1,8 = 9.96, P= 0.01348, Multiple R =

0.5546, Correlation Coefficient = 13.344). This relationship between NI and endophyte community composition is not present in May or June (ADONIS: May: R2= 0.07455, P <

0.978, Fig 5B; June: R2= 0.12319, P < 0.272, Fig 5C). Likewise, the correlation between

NI and fungal richness disappears after April (May: F1,8 = 0.06036, P= 0.8121, Multiple

2 2 R = 0.007489; June: F1,8 = 5.294, P= 0.0504, Multiple R = 0.3982). However, NI was correlated with higher fungal diversity in June (F1,8 = 13.94, P= 0.00576).The basal area of the host tree had no significant correlation with community composition in any of the three sampling periods (ADONIS: April: R2= 0.19525, P < 0.102; May: R2= 0.1429, P <

0.077; June: R2= 0.07549, P < 0.921). Basal area also did not impact fungal richness or

2 diversity (April: F1,8 = 0.6206, P= 0.4535, Multiple R = 0.072; May: F1,8 = 0.2873, P=

2 2 0.6066, Multiple R = 0.03466; June: F1,8 = 0.07514, P= 0.7909, Multiple R =

0.009305).

Impact of P. villosum on adjacent and opposite Q. lobata samples

The community dissimilarity of infected oak samples and their mistletoe parasites were analyzed to determine if the dissimilarity between oak and mistletoe communities varied depending on the location of the collected oak leaf. Permutations in the Adonis test were constrained to sample units, in order to control for other variables that may impact composition. No relationship between canopy proximity to mistletoe and

21 community similarity was found in any of the three months. (ADONIS: April: R2=

0.05466, P < 0.747; May: R2= 0.04674, P < 0.963; June: R2= 0.05359, P < 0.775).

Results from Generalized Linear Models

Multiple GLM’s were fit to the abundance data to supplement t the distance-based methods. Firstly, the importance of sampling period was addressed via a GLM fit to the

May and June data. . The GLM results demonstrated that the sampling month was a significant predictor variable (Table 4a, p<0.001). Next, GLMs were run on the data from each month to analyze the importance of host species and sample pair. These models showed that sample pair was significant across all three months, indicating that trees in close proximity share more similar species (Table 4b). Additionally, I found that the species of host was a significant explanatory variable in April and June (Table 4b). In order to address the impact of NI, BA, sample pair, and oak host type (i.e. infected or control), GLMs were fit to the oak samples from each sampling period. These GLMs showed that sample pair and host type were highly significant in each month, while NI was significant in April and June (Table 4c). BA was not found to be a significant explanatory variable in any month (Table 4c). Another set of models was fit to the data from infected oak samples across each sampling period in order to address the impact of sample pair and canopy proximity to mistletoe. These models showed that sample pair was significant in May and June, and proximity was not significant in any sampling period (Table 4d)

22 Discussion

The vast majority of prior endophyte studies have isolated fungal endophytes from plant tissue via culturing on some type of agar medium (Rodrigues, 1994; Arnold et al., 2001; Kumaresan & Suryanarayanan, 2001; Kaneko et al., 2003; Suryanarayanan &

Thennarasan, 2004; Hoffman & Arnold, 2008; Hashizume et al., 2008; Thongsandee et al., 2011). Some of these studies have utilized morphological characteristics to identify the fungal isolates to the morphospecies level (Arnold et al., 2001; Suryanarayanan &

Thennarasan, 2004), while other more recent studies have identified the isolates via DNA sequencing (Stefani & Bérubé, 2006). However, despite advances in the identification of fungal isolates, these studies are inherently limited in their scope due to the limitations of culturing. Research has shown a strong culturing bias, which selects for certain fast growing fungal species (Arnold et al., 2007). As such, isolation of endophytic fungi via culturing fails to capture the entire community, in turn limiting one’s ability to analyze the dynamics of endophyte community composition and richness.

To date, only a few studies have utilized modern high-throughput sequencing techniques to study foliar endophytes (Jumpponen & Jones, 2009; Zimmerman &

Vitousek, 2012). Some of the studies that have been conducted have either focused on the entire phyllosphere (Jumpponen & Jones, 2009; Kembel & Mueller, 2014) or solely on the endophytic bacterial communities (Shi et al., 2014). Only a small number of studies have isolated and analyzed the foliar fungal endophyte communities using high- throughput sequencing, and none of these studies have investigated topics of temporal community change or inoculum sources in a temperate system (Zimmerman & Vitousek,

2012). While the data set obtained in this study had relatively low read depth and counts

23 when compared to average Illumina MiSeq outputs, the sequencing depth is greater than what has been published and analyzed from 454 pyro sequencing (Jumpponen & Jones,

2009). As such, this study is the first of its kind to explore the dynamics of endophyte community structure in a temperate system using NGS techniques.

While sequence counts are generally not viewed as informative data, the unique nature of this sequencing run suggests that sequencing depth and total counts may indicate fungal abundance. First, I found that the average read count increased significantly and by orders of magnitude each sampling period. Second, the percentage of non-fungal host plant sequence reads (belonging to either P. villosum or Q. lobata) decreased from 30% in April to only 5% in June. A high proportion of non-specific amplification may be the result of low fungal DNA template in the original samples, resulting in less specific ITS primer attachments during the PCR process. Thus, I hypothesize that samples accumulated a greater amount of endophytic fungi throughout the sampling periods allowing for greater and more accurate PCR amplification and superior sequencing depth. Research has shown that newly emergent leaves are often free of endophytic infection, supporting this speculation (Scholtysik et al., 2012).

Interestingly, the mistletoe samples in April have similarly low average read depth, suggesting that recent emergence is not the sole explanation to low read counts.

Remarkably, the mistletoe sample read counts are indistinguishable from the oak samples in each sampling period. If read count is taken as a proxy for endophyte infection density, then this suggests that mistletoe leaves have similar or identical infection density during these three sampling periods. Unfortunately, these hypotheses cannot be addressed with the data at hand, as this data only allows for within sample comparisons of relative

24 abundance. Perhaps further research should explore questions about the endophyte infection density of evergreen plants in primarily deciduous forests using QPCR or other methods that allow for estimations of DNA quantity.

One of the most salient findings of this study was the significant and distinct temporal separation of the endophyte communities. The community structure of the samples collected during these May and June were significantly different, and the sampling period explained about 10% of the variation in dissimilarity. These findings were also supported by the results of the generalized linear models, which showed a strongly significant effect of sampling period on taxon abundance trends. As oak leaves had recently emerged and were presumably free of endophytic infection upon emergence,

I predicted to observe a temporal change in community composition as the endophytic community underwent primary successional changes. This prediction was supported, as the oak endophyte communities did in fact undergo significant changes across sampling periods.

The more constant foliar environment of the evergreen mistletoe foliage was predicted to host a more stable community that would in turn prevent colonization or invasion from new fungal endophytes. However, we observed almost identical temporal change between the mistletoe and oak communities. The mistletoe endophytic communities collected in May were significantly and substantially different than those found in June. Furthermore, the sampling period explained more of the variation in the mistletoe endophyte communities than it did in the oak endophyte communities. Such findings question the assumption that evergreen leaves have to reach a stable equilibrium in terms of their endophyte communities. Perhaps the natural turnover rate of evergreen

25 leaves is high enough to prevent any sort of stable equilibrium with respect to endophyte communities, as hypothesized.

Alternatively, these findings could indicate that the observed temporal change in endophyte communities is not a function of successional dynamics, but rather varies based on abiotic differences between sampling periods. This is supported by the simultaneous and identical change in both the communities of the host species.. Past research has demonstrated that abiotic conditions, particularly microclimatic parameters such as sun exposure and temperature, impact and stratify endophyte communities

(Osono & Mori, 2004; Scholtysik et al., 2012). Multiple studies have also demonstrated seasonal differences in endophyte communities (Kaneko et al., 2003; Unterseher et al.,

2007; Scholtysik et al., 2012). Thus, it seems plausible that the abiotic changes, particularly in regard to temperature, rainfall and light regimes, may be the driving force for temporal change.

Given the phylogenetic and morphological differences between mistletoe and oak, in addition to the phenological differences, we expected distinct, yet diminishing, community dissimilarity between the two hosts throughout the sampling period due to endophyte host affinity (Arnold et al., 2000). In April, there was significant community separation between mistletoe and oak samples. However, the endophyte communities of these different hosts became indistinguishable in the two later sampling periods. Given these findings, I suggest that the community differences observed in April are the result of phenological differences, and not due to endophyte host specificity. The oak leaves likely had not been exposed to the same volume or diversity of fungal spores, resulting in an earlier successional community than the mistletoe samples. The elimination of

26 distinguishable community dissimilarity in May and June suggests widespread host generalism among the endophytic fungi. These findings support much of research into

NC endophytes that suggests horizontal transmission which, as is ubiquitously observed, encourages greater host ranges (Saikkonen et al., 2004; Arnold & Lutzoni, 2007;

Rodriguez et al., 2009). These findings could suggest that the convergence of endophyte communities in May and June is due in part to horizontal transmission between the mistletoe and oak, or it could simply suggest that both host species were exposed to similar aerially borne fungal inoculum. In order to disentangle these two hypotheses, we must focus on the sample units of this experimental design.

Each sample unit consisted of a mistletoe infected oak and a control, uninfected oak. For each infected oak, five samples were collected directly adjacent to the mistletoe individual, and five samples were collected on the opposite side of the canopy. Thus, if mistletoe were serving as a reservoir for endophytic fungi and transmitting these endophytes to the Q. lobata host via spore or hyphal fragments, then we would expect to observe greater community similarity between the adjacent leaves than the distant leaves.

This is simply a function of spore dispersal, as the volume of fungal spores dispersed decreases rapidly with distance (Peay et al., 2012). Our analyses showed no significant grouping based on location in the canopy. Additionally, there were no differences in average fungal richness or diversity between distant and adjacent leaves across any of the sampling periods. This could be a function of limited replication (n=5), but given these findings we cannot confirm the hypothesis that mistletoe serves as reservoir.

Another method at addressing this reservoir hypothesis is to analyze the differences in community structure between the control and infected Q. lobata samples of

27 a given paired unit. This analysis is investigating the possibility that mistletoe systemically impacts the endophyte communities of its host, rather than locally infecting adjacent leafs. Adonis results suggested no relationship between Q.lobata infection status and community structure during any of the sampling periods. However, the GLM results indicate a strong and significant relationship between community structure and infection status across all three sampling periods. These conflicting results are likely a function of the limitations of distance-based methods of analysis used in Adonis.

Numerous studies have shown that distance-based methods of analyses have numerous limitations when addressing questions about multivariate abundance data

(Wang et al., 2012). Furthermore, many studies have found that significant patterns determined via GLM’s were undetectable by standard distance-based methods (Wang et al., 2012; Grossmann, 2015). While the statistical validity of these tests is out of the scope of this study (see Wang:2012eq; Grossmann:2015is for further discussion), these

GLM results suggest at least a trend in endophytic community structure based on the presence or absence of a mistletoe infection. While this finding should be verified via further replication, it suggests a potentially interesting and novel role of epiphytic parasites. Namely, this variation in host wide community structure indicates there may be some extent of systemic and vascular transmission of fungal endophytes between the mistletoe and its host. Given the fact that mistletoe invades the vascular system of its host

(Panvini & Eickmeier, 1993), and that some fungal endophytes are transmitted via vasculature, this finding is not inconceivable. Further research must be conducted to clarify the impact of mistletoe on the endophytic communities of its host.

28 The neighborhood index serves as a metric to quantify the local plant density around each individual sampled. Presumably, if the transmission of fungal spores is the primary vector for endophyte inoculation, then one would expect that greater local plant density would result in a larger endophytic spore bank and thus result in greater fungal richness. This hypothesis was supported by the April data, as neighborhood index was a highly significant predictor of both community dissimilarity and fungal richness. In fact,

NI explained approximately 55% of the variation in fungal richness observed during the

April sampling period. This strong positive relationship between local plant density and fungal richness supports the notion of widespread horizontal endophyte transmission between different hosts. In other words, our findings suggest that neighboring plants are strongly involved in endophyte transmission and infection. The April endophyte community structures were also strongly grouped based on NI, and NI accounted for approximately 29% of the variation in community dissimilarity. This was the strongest single predictor of community dissimilarity observed across all treatments and across all sampling periods.

Despite the strong relationship in April between community composition and NI, there was no apparent relationship in the later sampling periods using the distance-based method of analysis. Likewise, the relationship between richness and NI was not present in May or June. However, in June we found that NI was significantly related to endophyte diversity, a metric that incorporates both richness and evenness. These intuitive findings suggest that local plant density is a critical factor in shaping the early successional communities in deciduous plants, most likely by providing a greater volume of endophytic fungal spores. The loss of signal relating NI and community dissimilarity

29 in May and June suggests that NI only affects the immediate rate at which the endophytic community is assembled, but that all leaves in this 5 ha region, regardless of the local plant densities, will encounter and be infected by the same fungal species. It is worth noting that this relationship between NI and community composition was not present for the mistletoe individuals, supporting the suggestion that NI is only important during early successional stages.

It is also worth noting that once again the GLM results conflicted slightly with the findings of the distance-based Adonis. The GLMs showed that NI was significant in both

April and June sampling periods, and not significant in May. The significance of NI in

June is not particularly troubling, as we observed a significant effect of NI in the June richness regression model. The GLMs address each taxon individually, so are more powerful in picking up minor variations in community structure. Thus, we remain confident of our findings regarding NI, namely that it is important in both April and June.

A question emerges regarding the source of the positive relationship between NI and fungal diversity in June. I suggest two possibilities, either that the local plant density continues to impact and promote fungal diversity in June, or alternatively that the greater diversity is an artifact of the earlier successional community richness observed in April.

A second suggestion rests on the dynamics of succession rather than June inoculation. I argue that the early species richness of these samples in April ultimately leads to a more diverse community in June. In this explanation, NI, and thus local plant density, is only important in determining the richness and diversity of the early community, which then in turn influences the successional trajectory of the community. This hypothesis is highly testable and warrants further research.

30 While this study was not focused on the natural history or ecology of specific endophyte taxa, the temporal fluctuations of abundance of individual taxa can be analyzed to infer possible ecological niches or successional strategies. We analyzed the top eight most abundant taxa across each sampling period to determine if there were any obvious trends. One of the most remarkable initial observations was that the fourth most abundant taxa in April, Archaeorhizomyces sp., was the 181st most abundant taxa in May and the 389th most abundant taxa in June. Likewise, the single most abundant species in

June, Epicoccum nigrum, was only the 37th and 24th most abundant species in May and

April, respectively. The dramatic fluctuation in abundance was similar for two of the other top 5 most abundant taxa in June, namely Mortierella camargensis and

Mycosphaerellaceae sp. The fluctuation was especially remarkable for

Mycosphaerellaceae sp, the fourth most abundant taxa in June, which was the 56th most abundant taxa in May, and entirely absent from the April samples. It seems likely that the strong temporal separation of endophyte communities is driven by the successional changes from potentially fast dispersing, fast colonizing endophytes to more competitive, yet less effectively dispersing taxa. These questions should be addressed in the future via experimental manipulations to understand the role of priority effects, and possible deterministic successional trajectories.

This study has provided a new understanding of the dynamic nature of endophyte communities and supported many of the fundamental hypothesis regarding NC endophytes. First, we found that community composition was consistently and primarily influenced by the sampling period, and, at most, weakly influenced by other abiotic factors. Despite spatial separation and local abiotic variation between hosts, all endophyte

31 communities underwent similar successional changes in composition and structure. This strong temporal grouping across all host plants indicates that the endophyte taxa observed are widespread and ubiquitous throughout this study site, and not limited by dispersal.

This brings into question the range and biogeography of these endophytes, and suggests inquiry into the endophyte community variation across space. While some research has looked at fungal variation on a continental scale (Glassman et al., 2015), none has addressed variation at the ecosystem level, and it would be interesting to see if these same temporal groupings occur in the closely adjacent chaparral and mixed woodland ecosystems. These findings also support the notion that horizontal transmission is the primary vector for NC endophyte infection, evident by the strong impact of local plant density on endophyte community composition. This suggests that horizontal transmission between hosts of various taxa is critical to the assemblage of endophyte communities.

This study suggests that mistletoe infection may systemically impact the endophyte communities of its host, either by vascular transmission of fungi or through wide spread spore dispersal. However, this finding is only supported by the generalized linear model analysis and requires further attention. The quality of these sequencing data prohibit more definite conclusions regarding the role of mistletoe, but there are suggestions of horizontal transmission between host plants, evident by the community convergence of mistletoe and oak endophytes. I hypothesize that the abundance of fungal inoculum may be too large to find the clear signal of horizontal transmission between mistletoe and oak, especially with the limited read depth. However, the community convergence of mistletoe and oak does conclusively show remarkable and widespread host generalism among fungal endophytes. It appears that these endophytes have no

32 preference despite contrasting phylogeny and phenology. This finding supports much of the previous research in NC endophytes that suggests broad host ranges (Rodriguez et al.,

2009).

Ultimately, this study has demonstrated that foliar endophyte communities are highly dynamic and variable, suggesting a potential model to study fundamental questions in ecology regarding succession and community assembly. The preliminary findings here open up a myriad of research opportunities, enabled by the advances in

NGS technology. Firstly, I encourage research with a greater time scale that addresses the annual variation in endophyte communities of an evergreen host in a partially or primarily deciduous forest. While no conclusions can be drawn from sequencing depth, I suspect that deciduous hosts may have reduced endophyte density during periods of evergreen abscission. Secondly, these results incite the need for experimental manipulation to more fully understand the dynamics of community change. In particular,

I am interested in the importance of priority effects and early successional communities in determining more mature community composition. It is clear that understanding this ubiquitous and widespread symbiosis remains a critical component to a more complete understanding of terrestrial ecology.

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38 Data Appendices

Figure 1. Aerial image of the study site at JRBP. Host trees are labeled based on infected status (infected or control) and paired unit number.

.

39 2500 1500 500 Average Read Depth Average 0 April May June Sampling Period

Figure 2. Average read depth in each sampling period. The central rectangle of each boxplot corresponds to the interquartile range (IQR), and the central line indicates the median value for each sampling period. Whiskers correspond to maximum and minimum values, except in May where the upper whisker is taken to be 1.5 X IQR and the outlying values are represented as unfilled circles.

40

A B C

Figure 3. A) Ordination of the community dissimilarity of all samples from May and June sampling periods. Samples from June are red, while samples from May are blue. Mistletoe samples are represented by circles and oak samples are represented by triangles. B) Ordination of the community dissimilarity of Q. lobata samples from May and June, represented by blue and red circles, respectively. . C) Ordination of the community dissimilarity of P. villosum samples, represented by blue and red circle respectively.

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A B C

Figure 4. Ordinations of community dissimilarity between mistletoe and oak samples using bray dissimilarity matrices. Red circles represent mistletoe samples, and blue circles represent oak samples .A) Ordination of the community dissimilarity in April. B) Ordination of the community dissimilarity in May. C) Ordination of the community dissimilarity in June.

42 A B C

Figure 5. Ordinations of the community dissimilarity between Q. lobata samples in the three sampling periods. The color of the circle corresponds to the neighborhood index value. A) Ordination of the dissimilarity of oak samples in April. B) Ordination of the dissimilarity of oak samples in May. C) Ordination of the dissimilarity of oak in June.

43 A B C

Figure 6. Boxplots indicating the number of OTU’s observed in A) all the samples, B) the oak samples, C) and the mistletoe samples in May and June. The orange rectangles correspond to June samples, and the red rectangles correspond to May samples. The central rectangle of each boxplot corresponds to the interquartile range, and the central line indicates the median value for each sampling period. Whiskers correspond to maximum and minimum values,

44 A B C

Figure 7. Plots of fungal richness and diversity for A) April, B) May, and C) June samples. The left panel of each plot corresponds to observed OTU counts, and the right panel corresponds to the Shannon diversity index. Red and blue circles represent mistletoe and oak samples, respectively.

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Table 1: Eight Most Abundant Taxa in April Kingdom Phylum Class Order Family Genus Species Fungi Ascomycota Sordariomycetes Xylariales Unidentified Unidentified Xylariales sp Fungi Ascomycota Pezizomycotina Dothideomycetes Capnodiales Cladosporium Cladosporium cladosporiodes Fungi Basidiomycota Agaricomycetes Atheliales Atheliaceae Unidentified Atheliaceae sp Fungi Ascomycota Archaeorhizomy- Archaeorhizom- Archaeorhizo- Archaeorhizo Archaeorhizomyces cetes ycetales mycetaceae myces sp Fungi Basidiomycota Agaricomycetes Polyporales Meruliaceae Phlebia Phlebia subserialis Fungi Basidiomycota Tremellomycetes Tremellales Incertae sedis Cryptococcus Cryptococcus podzolicus Fungi Basidiomycota Agaricomycetes Atheliales Atheliaceae Unidentified Atheliaceae sp Fungi Ascomycota Sordariomycetes Hypocreales Incertae sedis Ilyonectria Ilyonectria mors- panacis

Table 2: Eight Most Abundant Taxa in May Kingdom Phylum Class Order Family Genus Species Fungi Basidiomycota Agaricomycetes Atheliales Atheliaceae Unidentified Atheliaceae sp Fungi Ascomycota Pezizomycoti Dothideomycetes Capnodiales Cladosporium Cladosporium cladosporiodes Fungi Basidiomycota Agaricomycetes Atheliales Atheliaceae unidentified Atheliaceae sp Fungi Basidiomycota Agaricomycetes Atheliales Atheliaceae Piloderma Piloderma sp Fungi Ascomycota Dothideomycetes Pleosporales Incertae sedis Phoma Phoma calidophila Fungi Basidiomycota Tremellomycetes Trichosporoles Trichosporoceae Trichosporon Trichosporon moniliiforme Fungi Zygomycota Incertae sedis Mortierellales Mortierellaceae Mortierella Mortierella humilis Fungi Ascomycota Dothideomycetes Dothideales Dothioraceae Aureobasidium Aureobasidium pullulans

46 Table 3: Eight Most Abundant Taxa in June Kingdom Phylum Class Order Family Genus Species Fungi Ascomycota Dothideomycetes Pleosporales Pleosporaceae Epicoccum Epicoccum nigrum Fungi Ascomycota Pezizomycoti Dothideomycetes Capnodiales Cladosporium Cladosporium cladosporiodes Fungi Zygomycota Incertae sedis Mortierellales Mortierellaceae Mortierella Mortierella camargensis Fungi Ascomycota Dothideomycetes Capnodiales Mycosphaerellaceae Unidentified Mycosphaerella ceae sp Fungi Basidiomycota Agaricomycetes Atheliales Atheliaceae Piloderma Piloderma sp Fungi Ascomycota Dothideomycetes Pleosporales Incertae sedis Phoma Phoma calidophila Fungi Basidiomycota Tremellomycetes Trichosporoles Trichosporoceae Trichosporon Trichosporon moniliiforme Fungi Zygomycota Incertae sedis Mortierellales Mortierellaceae Mortierella Mortierella humilis

Table 4a: GLM P-value output including May and June samples

Explanatory Factor Month 0.001

Table 4b: GLM P-value output including all samples in the listed month

Explanatory Factor April May June

Host Species 0.029 0.098 0.050 Sample Pair 0.005 0.016 0.002

Table 4c: GLM P-value output including all oak samples in the listed month Explanatory Factor April May June NI 0.040 0.092 0.030 BA 0.052 0.057 0.054 Sample Pair 0.003 0.001 0.001 Host type 0.001 0.001 0.002

Table 4d: GLM P-value output including infected oak samples in the listed month Explanatory Factor April May June Infected Status 0.309 0.200 0.193 Sample Pair 0.060 0.013 0.005

47