Phylogenetic Identification of Pathogenic and Endophytic Fungal Populations in West Coast Douglas-Fir (Pseudotsuga Menziesii) Foliage

AN ABSTRACT OF THE THESIS OF

Hazel A. Daniels for the degree of Master of Science in Sustainable Forest Management presented on September 19, 2017.

Title: Phylogenetic Identification of Pathogenic and Endophytic Fungal Populations in West Coast Douglas-fir (Pseudotsuga menziesii) Foliage.

Abstract approved: ______

James D. Kiser

Douglas-fir provides social, economic, and ecological benefits in the Pacific Northwest

(PNW). In addition to timber, forests support abundant plant and animal biodiversity and provide socioeconomic viability for many rural communities. Products derived from Douglas- fir account for approximately 17% of the U.S. lumber output with an estimated value of $1.9 billion dollars. Employment related to wood production accounts for approximately 61,000 jobs in Oregon. Timberland also supports water resources, recreation, and wildlife habitat. Minor defoliation has previously been linked to Swiss Needle Cast, associated with the fungus

Phaeocryptopus gaeumannii, however, unprecedented large-scale defoliation began in the 1990s and has increased since, leading to decreased growth and yield. Areas affected areas by SNC exceed 500,000 acres in Oregon. Defoliation symptoms are inconsistent with predicted effects of

P. gaeumannii, and targeted chemical control has had mixed results. While the microbiome community of conifer needles is poorly described to date, we hypothesize the full interstitial microbiome complex is involved in disease response in conifers.

There are at least three known pathogenic endophytes of Douglas-fir, in addition to a large number of endophytes with undetermined host relationships. In order to understand the mechanistic dynamics of needle cast, it is necessary to uncover the underlying cause of the symptoms. The first step is establishing a baseline inventory of endophytes in PNW Douglas-fir needles.

Two-year old needles were collected from two sites each in Oregon and Washington, for a total of four sites. Study sites varied in elevation (185 – 860m) and mean annual precipitation

(250 – 1,575 mm/year). DNA extraction and sequencing revealed 46 unique isolates from 39 taxa in Douglas-fir needles gathered from four test sites in Oregon and Washington. Rates of infection for all needles was 39%, with infection ranging from 5% to 60% among seed sources.

The results suggest that the probability of endophyte occurrence at the cool, wet site, was 2.8 (p

< 0.0001) times higher than the warm, dry site, and 2.2 (p = 0.0007) times higher than the warm, wet site. Non-local needles at the warm, dry site were 13.6 (p < 0.0001) times more likely to be infected than needles from the local seed source. The environmental variable that seemed most positively correlated with endophyte presence was winter relative humidity. There was strong evidence for the influence of seed source type on the relationship between continentality (the difference between mean warmest month and mean coldest month temperatures) and mean average number of endophytes.

Long-term silvicultural management of PNW forests will benefit as a result of this study.

It is the first study to use modern molecular techniques to uncover the fungal endophytic communities of West-Coast Douglas-fir foliage. It is vital to understand the complete etiology of the needle cast disease affecting Douglas-fir, and whether to focus on mitigating a single-species

pathogen, or a number of different pathogens. Because forests are a long-term strategy, it is critical to understand the ecological implications of disease in this important tree species.

Phylogenetic Identification of Pathogenic and Endophytic Fungal Populations in West Coast Douglas-fir (Pseudotsuga menziesii) Foliage.

by Hazel A. Daniels

A THESIS

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Master of Science

Presented September 19, 2017 Commencement June 2018

Master of Science thesis of Hazel A. Daniels presented on September 19, 2017

APPROVED:

Major Professor, representing Sustainable Forest Management

Head of the Department of Forest Engineering, Resources & Management

Dean of the Graduate School

I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.

Hazel A. Daniels, Author

ACKNOWLEDGEMENTS

There are probably a thousand people who have helped me reach this point in my life; I know I haven’t walked this road alone. Chief among those is Jim Kiser, my advisor and advocate, who has empowered me to think big (and small) and aim high. A good mentor, one whose core values align with your own, and with whom you can be honest in the face of despair, is priceless. For this reason and others, I feel especially fortunate to have Jim on my side. I am also fortunate to have Jeff Morrell, Dave Shaw, and Jay Pscheidt as committee members. Their collective experience, guidance, and helpful questions have made me a better researcher (I hope!).

My thanks to Connie Harrington, USFS, for granting me access to the Seed Source

Movement Trial sites in my study, and helping me stay warm at the Washington sites. Thanks also to Starker Forests, Inc. for allowing me to access one of these sites on their property, and

Chris Poklemba, USFS, for accompanying me to Southern Oregon.

Thanks to Jed Cappalazzi for his tireless enthusiasm and goodwill, despite my constant barrage of questions and various spills in the lab. He welcomed me into the Morrell lab, and helped me learn new techniques and keep better records. My sincere appreciation to Amy

Klocko, Steve Strauss, Anna Magnusen, and Michael Freitag for allowing me the use of their lab space and hardware. Thanks also to Sarath Vega Gutiérrez, Daniel Way, Daniel Trebelhorn for assistance in DNA extraction, to Lisa Ganio and Ariel Muldoon for statistical advising, to Dave

Shaw and Glenn Howe for support and advice, and to Jared LeBoldus and his entire lab group for being so welcoming after I lost my office.

I’d especially like to thank the College of Forestry, Department of Forest Engineering,

Resources and Management for their support of my research, and Madison Dudley, Erika

Hanna, and Chelsea Durling for being absolute rock stars. I thank you for the often- unrecognized administrative and care work that keeps our department running.

I am grateful to Jennifer Franklin, Ðenita Hadžiabdić Guerry, and Mark Windham and the University of Tennessee, for seeing and fostering my inner forest pathologist. I dedicate this thesis in part to the memory of Lisa Vito, who kept me company in the lab, taught me how to hold a pipette, and made every day full of laughter.

I also dedicate this thesis in part to my mother, Beth Daniels, and aunt, Dianne Muncey.

Your love and encouragement have always made me feel invincible. Thanks to my patient and forgiving partner, Josh Goben, who always follows me when I wander into the woods, and to

Ashnod and Jem for comforting me in the way only cats can do. I also thank my CGE family, who help keep my spirits high with solidarity, potlucks, and radical conversation.

Finally, I gratefully recognize and give thanks to the Kalapuya people, on whose land this university sits.

CONTRIBUTION OF AUTHORS

Jim Kiser and Hazel Daniels conceived and designed the study. Ariel Muldoon and Connie

Harrington helped with some aspects of study design. Hazel Daniels collected, analyzed, and interpreted data, with the assistance of Jim Kiser. Hazel Daniels wrote this thesis, with critical revisions by Jim Kiser.

TABLE OF CONTENTS

Page

1 General introduction ...... 1

1.1 What are endophytes ...... 4

1.2 Endophyte classifications ...... 5

1.3 Endophyte infections ...... 7

1.4 Other roles of endophytes ...... 9

1.5 Endophytes as pathogens ...... 12

1.6 Endophytic pathogens of Douglas-fir ...... 13

1.7 Literature cited ...... 23

2 Phylogenetic identification of pathogenic and endophytic fungal populations in

West Coast Douglas-fir (Pseudotsuga menziesii) foliage ...... 32

2.1 Introduction ...... 32

2.2 Objectives ...... 36

2.3 Methods ...... 37

2.3.1 Traditional plate culturing ...... 41

2.3.2 DNA extraction ...... 43

2.3.3 Data Analysis ...... 44

2.4 Results ...... 50

2.4.1 Objective 1: Quantify baseline inventories of fungal endophytes of

Douglas-fir for each of four geographical locations, for trees grown from

local seed sources and those grown from non-local seed sources ...... 50

TABLE OF CONTENTS (Continued)

Page

2.4.2 Objective 2: Compare fungal communities found within local

seed sources to those found within non-local seed sources ...... 51

2.4.3 Objective 3: Characterize fungal communities in association with

Phaeocryptopus gaeumannii that may be additionally implicated in

Douglas-fir needle-cast...... 65

2.5 Discussion ...... 66

2.6 Summary ...... 71

2.7 Literature cited ...... 72

3 Conclusions ...... 79

3.2 Literature cited ...... 82

4 Bibliography ...... 85

5 Appendix I: Modified cetrimonium bromide (CTAB) DNA extraction protocol for fungi from culture ...... 96

6. Appendix II: Supplemental Ordination Figures ...... 97

LIST OF FIGURES

Figure Page

1.1 One example of direct penetration of a fungal spore into plant tissue. Once the spore adheres to the plant surface, it creates a germ tube from which an appressorium will differentiate. The penetration peg breaks the plant tissue surface, sending threads of invasive fungal hyphae within the plant to begin colonization. Penetration can also occur just below the cuticle or between plant cells ...... 8

1.2 Stages in development of a disease cycle. Adopted from Agrios (2005) ...... 8

1.3 Pseudothecia of Phaeocryptopus Gaeumannii, the causal pathogen of Swiss Needle Cast. The image shows pseudothecia (black spots), fruiting bodies of the pathogen, occluding the stomata (white spots) of Douglas-fir needles. Photo used with permission from Dave Shaw, Swiss Needle Cast Cooperative Director (July 21, 2017). Photo credit R. Mulvey ...... 17

2.1 Locations of four study sites, part of the Douglas-fir Seed Source Movement Trials (SSMT), a long-term, large-scale reciprocal provenance study of Douglas-fir. Regions vary in characteristics such as annual precipitation and elevation, and are meant to represent large-scale variations in geographic and climatic conditions experienced by Douglas-fir in the Pacific Northwest ...... 40

2.2 Example layout of sample collection from Nortons collection site. From each block (black box), one local seed source (NL) and two foreign seed sources (NA, NB) were collected and examined. Circles represent the theoretical locations of plots within blocks ...... 41

2.3 Bar plot showing the percent infection rate for each seed source, grouped by planting site. Infection rates ranged from 5% in the Stone Nursery local seed source to 60% in a Buckhorn2 non-local seed source ...... 55

2.4 Two-way cluster analysis of endophytes by seed source (ESS) data ...... 55

2.5 Two-way cluster analysis of endophytes by needle (EN) data ...... 56

LIST OF FIGURES (Continued)

Figure Page

2.6 Non-metric scaling (NMS) ordination of endophytes by needle (EN) data, with sample units (needles, represented by triangles in figure), variables (species, represented by blue dots), and environmental factor joint biplot overlay. The three fungal species labeled represent the three strongest correlations to the axes of the figure. The two strongest environmental variables with correlations with either axis were RH_wt and TD. RH_wt (winter relative humidity) is most positively correlated with Axis 1 (r = 0.506), while continentality (TD) is most negatively correlated with the same axis (r = -0.513) ...... 60

2.7 Difference in mean average number of endophytes per seed source by interaction between seed source type and continentality (TD). Black circles represent difference, bars indicate 95% confidence intervals. The gray bar represents the 20% biological threshold (±29.9 mean average number of endophytes) ...... 64

2.8 Difference in mean average number of endophytes per seed source by interaction between seed source type and winter relative humidity (RH_wt). Black circles represent difference, bars indicate 95% confidence intervals. The gray bar represents the 20% biological threshold (±29.9 mean average number of endophytes) ...... 64

2.9 Difference in mean average number of endophytes per seed source by interaction between seed source type and mean coldest month temperature. Black circles represent difference, bars indicate 95% confidence intervals. The gray bar represents the 20% biological threshold (±29.9 mean average number of endophytes) ...... 65

LIST OF TABLES

Table Page

1.1 Differentiation between functional classes of endophytes through various criteria. Adapted from Rodriguez et al. (2009) ...... 6

2.1 Climate variables associated with seed sources (top) and planting sites (bottom) of the Seed Source Movement Trial (SSMT), a long-term, large-scale reciprocal provenance study of Douglas-fir ...... 38

2.2 Seed sources sampled at each site. Planting sites are part of the Seed Source Movement Trial (SSMT), a long-term, large-scale reciprocal provenance study of Douglas-fir ...... 39

2.3 Environmental and experimental variables utilized in community data analysis ...... 46

2.4 Taxa and frequency of endophyte species cultured and sequenced from 185 Douglas-fir needles from four sites in Oregon and Washington. The number of endophytes successfully sequenced totals 221 out of 318 isolates. Sequences were reported at 95% or greater nucleotide matches ...... 52

2.5 Endophyte infection rates for each seed source sampled at four sites in Oregon and Washington ...... 53

2.6 Odds ratios comparing incidence of infection within and between four sites in Oregon and Washington ...... 53

2.7 Alpha, beta, and gamma species diversity of endophytes in Douglas-fir needles from EN and ESS data sets ...... 57

2.8 Multi-response Permutation Procedures results for ESS data set. Note that multiple comparison p-values were not corrected for multiple comparisons (i.e. Bonferroni) ...... 57

2.9 Multi-response Permutation Procedures results for EN data set. Note that multiple comparison p-values were not corrected for multiple comparisons (i.e. Bonferroni) ...... 58

2.10 Stress in relation to dimensionality (number of axes) comparing 50 runs on the real data with 50 runs on randomized data from the EN data set. The p-value is the proportion of randomized runs with stress less than or equal to the observed stress. The two-dimensional solution is highlighted for use in further analysis...... 59

LIST OF TABLES (Continued)

Table Page

2.11 Stress in relation to dimensionality (number of axes) comparing 50 runs on the real data with 50 runs on randomized data from the ESS data set. The p-value is the proportion of randomized runs with stress less than or equal to the observed stress. The two-dimensional solution is highlighted for use in further analysis...... 59

2.12 ANOVA output for interaction of continentality (TD) and seed source type on mean average number of endophytes within each planting site. Response: mean average number of endophytes per seed source ...... 62

2.13 ANOVA output for interaction of winter relative humidity (RH_wt) and seed source type on mean average number of endophytes within each planting site. Response: mean average number of endophytes per needle ...... 62

2.14 ANOVA output for interaction of mean coldest month temperature (MCMT) and seed source type on mean average number of endophytes within each planting site. Response: mean average number of endophytes per needle ...... 62

Page 1

Chapter 1: General Introduction

Forestry is an important industry in Oregon and the greater Pacific Northwest (PNW).

The Pacific temperate rainforest zone in North America, which is home to some of the most highly productive forests in the world, stretches from Alaska to California, and from the Pacific Coast mountain range to the ocean. The temperate rainforest zone is characterized by high rainfall and moderate temperatures year-round, which provide ideal conditions for conifer growth (Waring and Franklin, 1979). In Oregon alone, there are more than 12 million hectares (nearly 30 million acres) of forest land, a portion of which has been managed and harvested since the end of World

War II (OFRI, 2017). The dominant tree in most Oregon forests west of the Cascade Range is

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco).

Douglas-fir is an evergreen conifer, a softwood tree that typically reaches heights of 20 –

38 meters (80 – 120 feet), but can reach heights of up to 100 meters (70-330 feet) tall (Ritóková et al., 2016; Sibley, 2009). Though native to western North America, this productive species is planted throughout the world, including France, Germany, Chile and New Zealand (Ledgard et al., 2005). There are two currently recognized varieties of Douglas-fir in the United States: Rocky

Mountain Douglas-fir (P. menziesii (Mirb.) Franco var. glauca (Beissn.) Franco) and coast Douglas- fir (P. menziesii (Mirb.) Franco var. menziesii). Coast Douglas-fir grows primarily in the temperate rainforest zone, while Rocky Mountain Douglas-fir grows further inland (Hermann, 1982).

Because of its quality wood and high productivity, Douglas-fir remains one of the most important commercial forest species in the PNW (Spies and Franklin, 1991). Decades of research focusing

Page 2 on this singular species have resulted in shorter rotation lengths and maximized growth and yield, further cementing Douglas-fir as an integral part of forestry in the PNW (Talbert and

Marshall, 2005).

Douglas-fir forests in Oregon offer more than just commercial gains, they also provide ecosystem services. In addition to the timber growth potential, Douglas-fir forests provide habitat for abundant plant and animal biodiversity. Major plant species in Douglas-fir forests include such understory plant species as hazel (Corylus cornuta Marshall), Oregon grape (Mahonia nervosa

(Pursh) Nutt.), Pacific poison oak (Toxicodendron diversilobum (Torr. & A. Gray) Greene), salal

(Gaultheria shallon Pursh), trailing blackberry (Rubus ursinus Cham. & Schltdl.), vine maple (Acer circinatum Pursh), and western swordfern (Polystichum munitum (Kaulf.) C. Presl.). Important primary and secondary tree species include bigleaf maple (Acer macrophyllum Pursh), red alder

(Alnus rubra Bong.), western hemlock (Tsuga heterophylla (Raf.) Sarg.), Sitka spruce (Picea sitchensis

(Bong.) Carr, and western redcedar (Thuja plicata [D.] Don.) (Cole et al., 2017). There are dozens of bird and mammal species, including northern spotted owl (Strix occidentalis caurina Merriam), red-breasted nuthatch (Sitta canadensis L.), western tanager (Piranga ludoviciana Wilson),

American marten (Martes americana Turton), black-tailed deer (Odocoileus hemionus columbianus

Richardson), marbled murrelet (Brachyramphus marmoratus Gmelin), northern flying squirrel

(Glaucomys sabrinus Shaw), and short-tailed weasel (Mustela erminea L.) (Dimock II et al., 1976;

Hagar et al., 2004; Linnell et al., 2017; Parks et al., 1999). Each of these species fulfills a specific ecological niche that shapes and is shaped by the landscape.

Page 3

Oregon forests bring socioeconomic viability to many rural communities. In some rural counties, the forest industry provides nearly a third of their economic base (OFRI, 2017). Wood production occurs on about 41% of the state’s timber land base. It accounts for approximately

17% of the U.S. lumber output, with an estimated value of $1.9 billion dollars (OFRI, 2015).

Employment related to wood production accounts for approximately 61,000 jobs in Oregon and has grown since the last report (OFRI, 2017). The additional 59% of the timberland base supports water resources, recreation, and wildlife habitat (OFRI, 2015).

Though Douglas-fir is the dominant tree species in Oregon’s managed forests, it is not immune to pests and pathogens. A number of fungal diseases affect Douglas-fir, including laminated root rot, black stain root disease, and various canker diseases (Hansen and Lewis,

1997). Additionally, coastal Douglas-fir forests are susceptible to needle cast disease. The last two decades have seen a serious upsurge in needle cast incidence and severity, particularly in younger stands of Douglas-fir, to the point of being considered epidemic (Shaw et al., 2011). The primary pathogens implicated have been two common fungal foliar diseases; Swiss Needle Cast (SNC) disease caused by the fungus Phaeocryptopus gaeumannii (T. Rohde) Petr. (Shaw et al., 2011) and

Rhabdocline needle cast caused by Rhabdocline sp. (Parker and Reid, 1969). The pathogens associated with these two diseases can be endophytes, organisms that live within the foliage of trees.

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What are endophytes?

Endophytes, like mycorrhizal fungi, are found in most plant species in some form of symbiosis (Carroll, 1986). Fungi often take center stage when discussing endophytes, but there are also a vast number of bacterial endophytes at work (Chanway, 1996). The term endophyte was first used in print by de Bary in 1866 to characterize all those organisms colonizing internal plant tissues, contrasting with epiphytes which subsist on exterior plant surfaces. This definition of an endophyte encompasses many life-history strategies, including latent pathogens, saprotrophs, and mutualistic symbionts (Carroll, 1986). However, it can also include organisms for which endophytism is only a temporary stage in their life cycles. The definition was therefore updated to include only those organisms that form “inapparent infections within leaves and stems of healthy plants,” excluding known fungal pathogens (Carroll, 1988). Mycorrhizal fungi were additionally excluded as mutualist organisms that, while colonizing internal root tissues, were also present in the rhizosphere. True endophytes, it was argued, only emerged at host tissue senescence (Carroll, 1988; Sherwood and Carroll, 1974; Stone et al., 2004). Petrini (1986) argued that this definition necessarily excluded those fungi that existed in a long-term epiphytic phase and more importantly, those fungi that may persist as latent pathogens over long periods.

Arguments ensued, referring to the vagueness of the definition (Wennström, 1994). This, in turn, prompted a further expansion to include rusts and smuts, and a call to focus more on questions of purpose, intent, and effect on the host plant than the definition of terminology (Wilson, 1995).

A more inclusive definition was proposed to recognize that latency may evolve to parasitism at

Page 5 some point: microbes that colonize living internal tissues of plants without causing any immediate, overt negative effects (Hirsch and Braun, 1992). The inclusion of a temporal delay recognizes that endophytes are, in general, nonpathogenic (latent), but clearly appear to produce secondary metabolic compounds to facilitate survival in the interstitial plant space without apparent harm to the host (Stierle and Stierle, 2015). In addition, further modifications to the definition have been proposed, to include ‘obligate endophytes’ that are unable to grow outside the host environment, and ‘facultative endophytes’ that can exist as epiphytes or endophytes

(Gunson and Spencer-Phillips, 1994).

Endophyte classifications

Fungal endophytes are classified into two major functional groups. The first group is the clavicipitaceous endophytes (C-endophytes, Class 1) found primarily colonizing grasses. This group comprises the bulk of the literature on endophytes, primarily focused on the group’s impact on agricultural crops (Clay and Schardl, 2002). Some Class 1 endophytes offer their hosts increased resistance to herbivory from insects or mammals, while others may counter drought stress or provide other ecophysiological benefits (Rodriguez et al., 2009).

The second group contains the nonclavicipitaceous endophytes (NC-endophytes, Classes 2,

3, and 4) that are found in other plants including non-vascular plants, ferns, conifers, and angiosperms. The NC-endophytes comprise three functionally distinct groups based on life history and known ecological significance, including range of colonization and tissue type, biodiversity range, and transmission mode (Table 1.1) (Rodriguez et al., 2009).

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The NC-endophytes are primarily represented within the ascomycetes and comprise a highly diverse grouping, but with poorly understood ecological roles (Petrini, 1986). Host plants to NC- endophytes comprise all major land plant lineages and host ecosystems ranging from the tropics to the arctic tundra (Schulz and Boyle, 2005). The three functional classes of NC-endophytes are differentiated by preferential host colonization, transmission mechanisms between host generations, and in planta biodiversity.

Generally, Class 2 organisms are differentiated by colonization of both below- and above- ground tissues. Most endophytes in this class have been associated with increased biomass in roots and shoots, presumably a result of increased hormone production by plant hosts, or biosynthesis of plant hormones by endophytes (Rodriguez et al., 2009). Class 3 diversity is very high, particularly in tropical hosts (Arnold et al., 2003). Unlike Additionally, Class 3 endophytes generally are limited to localized infection, while Class 2 and 4 can invade tissues extensively

(Rodriguez et al., 2009). Class 4 endophytes, also called dark septate endophytes (DSE) are distinguished from other classes by the presence of darkly melanized septa.

Table 1.1: Differentiation between functional classes of endophytes through various criteria. Adapted from Rodriguez et al. (2009).

C-endophytes NC-endophytes Criteria Class 1 Class 2 Class 3 Class 4 Host range Narrow Broad Broad Broad Colonized tissue Shoot and rhizome Shoot, root, and rhizome Shoot and leaf Root In planta colonization Extensive Extensive Limited Extensive In planta diversity Low Low High Unknown Transmission Vertical/horizontal Vertical/horizontal Horizontal Horizontal Fitness benefits NHA NHA and HA NHA NHA NHA – Non-habitat includes drought tolerance and growth enhancement HA – Habitat benefits derived from selective pressures such as pH, temperature, and salinity

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Endophyte infections

Endophytes often have a broad range, and are rarely host-specific (see Rhabdocline parkeri

Sherwood, J.K. Stone & G.C. Carroll below) (Carroll, 1988). Likewise, other life-history strategies are equally as diverse within this group. Transmission can be via seed (Talgø et al., 2010a) or spore (Carroll, 1988), and colonization can be inter- or intracellular, systemic throughout the host or localized within a single cell (Schulz and Boyle, 2005; Stone, 1988). Infections are highly localized within bark, stems, petioles, or leaves for most endophytes of woody plants (Carroll,

1988; Petrini et al., 1993; Saikkonen et al., 1998). In the case of spore-producing fungi, a generalization of the infection cycle begins at the dissemination of primary inoculum. When the inoculum reaches a host, generally via water, wind, gravity, or animal/insect vector, it contacts the exterior tissues first, and adheres itself to the host surface through mucilaginous substances, enzyme production, or other various means (Agrios, 2005). After the spore is successfully attached to the host surface it can germinate, forming a germ tube that may differentiate into an appressorium and penetration peg, specialized appendages that penetrate the host surface

(Figure 1.1). Penetration can occur through natural openings such as stomata or lenticels, wounds, or directly into epidermal cells. Germination continues, sending threads of fungal hyphae within the plant where it colonizes tissue based on the particular relationship between host and endophyte. Eventually, the endophyte will produce a fruiting body, that will then disseminate inoculum to begin the cycle again (Figure 1.2)(Agrios, 2005).

Page 8

Figure 1.1: One example of direct penetration of a fungal spore into plant tissue. Once the spore adheres to the plant surface, it creates a germ tube from which an appressorium will differentiate. The penetration peg breaks the plant tissue surface, sending threads of invasive fungal hyphae within the plant to begin colonization. Penetration can also occur just below the cuticle or between plant cells.

Figure 1.2: Stages in development of a disease cycle. Adopted from Agrios (2005).

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Other roles of endophytes

Endophytes were previously described as latent microbes, in a state of viable dormancy sometime between the time of initial landing on the host surface but preceding germination. Prior to latency, hyphae penetrate the epidermal tissues but cause no other symptoms of disease, though others questioned whether the interstitial growth of mycelium was necessarily non- symptomatic (Verhoeff, 1974). Carroll (1988) hypothesized that endophytes demonstrate a mutualist life strategy, providing the plant host some benefit such as metabolic resistance to herbivory through a decrease in palatability. Endophytes of all functional classes have since been shown to provide a number of resources to their plant hosts in exchange for the water and nutrients they receive. Some can reduce herbivory in grasses (C-endophytes, (Clay et al., 1993;

Saikkonen et al., 1998), algae, and trees (NC-endophytes, (Carroll, 1988). The ubiquitous Douglas- fir endophyte Rhabdocline parkeri antagonizes the larvae of gall wasps, increasing larvae mortality and reducing pest stress for host trees (Sherwood-Pike et al., 1986). This same modus operandi is seen in the gall wasp/endophyte/host relationships of white spruce (Picea glauca (Moench) Voss) and California live oak (Quercus agrifolia Née) as well (Carroll, 1988). Endophytes have also expressed antagonism toward both fungal and bacterial pathogens through secondary metabolites (Carroll, 1988; Richardson et al., 2014; Schulz et al., 1999).

Secondary metabolites are naturally produced compounds that are generally thought to provide functional survival roles for the organisms producing the compounds (Tan and Zou,

2001). Structurally, secondary metabolites form a number of biochemical groups that generally

Page 10 function through strong hydroxyl groups that are highly reactive, thus driving the antimicrobial action of the metabolites (Tan and Zou, 2001). Additionally, these secondary metabolites may play a role in the ability of an endophyte to grow within its host without visible symptoms of disease (Schulz et al., 1999). High percentages of anti-microbial metabolites have been found interstitially, which further called into question the idea that endophytes are truly latent. In true latency, there would be no reason for endophytes to expend any unnecessary energy in situ for metabolite production. Demain (1980) first proposed this question in response to the overall diversity of secondary metabolites found both in vitro and in situ. It was later confirmed that these secondary metabolites serve many roles including: competitive deterrents against microbes, plants, insects, and animals; metal transporting agents; agents of symbiosis between microbes and plants, nematodes, insects, and animals; sexual hormones; and differentiation effectors

(Demain and Fang, 2000).

Interaction studies with bacteria and fungi confirmed reciprocal antagonism between organisms within the same environment (Schulz et al., 2015). Importantly, it appears that the secondary metabolites exuded by endophytes not only affect the growth of bacteria, but in some cases also alter the constituent makeup of the bacteria’s secreted compounds. The authors noted that because metabolic compounds were secreted in advance of physical contact, this suggests some form of signal molecule(s) sent ahead. Endophytes in planta find themselves vying for resources with multiple microorganisms, while also competing with plant defense responses.

Thus, the environment is one of a continuum of multiple metabolic responses. Schultz et al. (2015)

Page 11 made the point that this multi-faceted metabolite response most likely keeps all competitors at a growth restricted “standoff” (mutual antagonism) which invariably results in organisms being less likely (or able) to become pathogenic.

The secondary metabolites produced by endophytes also have biomedical importance. Those produced in planta are generally thought to be defense-related, either antagonistic to other endophytes or reactionary to plant defenses. A review of endophyte-derived cytotoxic or anti- cancerous compounds identified over 100 demonstrated compounds including several originally thought to be produced by higher plants (Kharwar et al., 2011). At the time, less than 10% of those compounds were derived from conifer species. An effort launched in 1991 to date has identified a number of compounds including anti-cancer metabolites, fungal derived polyketides, terpenes, mixed-biosynthetic pathways, peptides, and nitrogen compounds (Stierle and Stierle, 2015).

One of the most important discoveries of endophyte-derived metabolites was the identification of two endophytes from the bark of Pacific yew trees (Taxus brevifolia Nutt.) that produced the diterpenes paclitaxel (trademarked as taxol ® by Bristol-Myers Squibb) and baccatin III (10-deacetylbaccatin III 10-O-acetyltransferase) independent of the tree (Stierle et al.,

1993). Paclitaxel is an anti-cancer chemotherapy drug that is used to treat ovarian, breast, lung, head, and neck cancers (Rowinsky et al., 1993). The first endophyte was previously undescribed and designated as Taxomyces andreanae Strobel, A. Stierle, D. Stierle & W.M. Hess. The second endophyte was identified as Penicillium raistrickii G. Sm. Supplies of yew-derived paclitaxel were very limited, and collection of bark for paclitaxel production results in the death of the tree.

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However, the discovery of endophyte production of paclitaxel led to the introduction and expression of the endophytic gene responsible for synthesizing baccatin III to the enokitake mushroom Flammulina velutipes (Curtis) Singer (Han et al., 2014). To date, isolation of paclitaxel has been reported from a number of additional endophytes associated with conifers other than

Taxus sp. (Li et al., 1996; Strobel et al., 1997) as well as some angiosperms (Gangadevi and

Muthumary, 2009; Miele et al., 2012).

Endophytes as pathogens

The debate on whether to classify known pathogens as endophytes has been ongoing.

Differentiating between endophytes and endophytic pathogens is a matter of degrees. Stone

(2004) notes that endophytes are found in both healthy and diseased tissues, further highlighting the uncertainty regarding classifications. The author further states that the differences between

“endophytic” fungi and “latent pathogens” may simply reflect variances in duration of latency or degree of injury sustained by the host plant.

In general, only a few specialized pathogens can infect any one plant host, due to the combination of plant defense barriers and specialized pathogen virulence factors, phytotoxic secondary metabolites, necessary to overcome those barriers (Agrios, 2005; Heath, 1997). Disease development in a host is only possible in the presence of a pathogen with one or more genes for pathogenicity, specificity, and virulence. Plant hosts must also lack the appropriate resistance genes to combat pathogen infection (Agrios, 2005; Schafer, 1994). In the previous section, it was noted that endophytes produce toxic secondary metabolites, though only to the extent that allows

Page 13 them to exist in a tenuous balance with the plant host and other microbes. Though they have a wide host range, and the same species can sometimes be found in different hosts, endophytes can become pathogenic under the right circumstances (Junker et al., 2012).

Schultz and Boyle (2005) noted that, “The difference between a pathogen and an avirulent endophyte may depend on only one gene.” Plants can use the same defense mechanisms for endophytes and pathogens, but allow endophytes to grow interstitially with little resistance. This endophyte survival strategy may resolve itself in a few ways. The endophyte could remain quiescent, initiating germination following host senescence. Alternatively, it could exist as a latent weak pathogen, slowly growing until a critical level of biomass enabled virulence in the plant host (Schulz and Boyle, 2005). In the case of Phaeocryptopus gaeumannii, the endophyte only causes disease when pseudothecia (fruiting bodies) plug stomatal openings (Ritóková et al., 2016;

Shaw et al., 2011). In any case, the “balanced antagonism” of the host-endophyte relationship must be maintained to persist in this state.

Endophytic pathogens of Douglas-fir

Rhabdocline parkeri was initially described as an asymptomatic fungal infection of coniferous trees in western Oregon and Washington (Sherwood-Pike et al., 1986). It was differentiated from the two other species in the genus, R. pseudotsugae Syd. and R. weirii A.K. Parker & J. Reid, by morphological and biological means. R. pseudotsugae and R. weirii are both described as highly pathogenic, causing significant economic losses in Douglas-fir plantations (Parker and Reid,

1969). In contrast, R. parkeri follows the more passive endophyte survival strategy. It remains

Page 14 latent until host needle abscission, persisting within needles for as long as seven years as a localized infection within the lumen of a single epidermal or hypodermal cell before further colonization and sporulation (Sherwood-Pike et al., 1986; Stone, 1988). R. parkeri does not infect intact first year needles, but can create multiple infections on second year and older needles, with infection rates rising with age (Stone, 1987). This increase in individual infections may in part be facilitated by the increase of needle micro-epiphyte cover providing additional adhesion points for spores (Bernstein and Carroll, 1977; Carroll, 1979). Genotypic diversity of R. parkeri was found to be three times greater in foliage of old growth and adjacent young trees than in isolated or managed stands (McCutcheon and Carroll, 1993). Variability in host genotype resistance was also noted by (Todd, 1988). This endophyte can, at worst, be defined as weakly pathogenic, and provides a net benefit to the host plant as a deterrent of the gall wasp species that defoliates the tree (Stone, 1988).

Rhabdocline pseudotsugae, R. weirii, and their various subspecies are the causal pathogens of the disease Rhabdocline needle cast. They are endemic throughout the native range of Douglas- fir, but have been introduced to exotic Douglas-fir plantings in North American and Europe

(Brandt, 1960; Stone, 1997). To date, only Douglas-fir and bigcone Douglas-fir (Pseudotsuga macrocarpa (Vasey) Mayr.) are known hosts (Chastagner, 2001; Wilhelmi, 2016). One year long infection cycles coincide with Douglas-fir growth cycles (Brandt, 1960; Chastagner, 2001;

Morgenstern et al., 2013). Beginning in fall, small yellow or yellow-green spots appear on current season needles, turning brown and necrotic by winter. Spots expand in size and swell by early

Page 15 summer of the next year, turning rust brown and exposing ascomata that expel spores during moist weather conditions. Infected needles are often prematurely abscised, leaving the tree with only current year needles on the branch (Brandt, 1960; Chastagner, 2001; Morgenstern et al., 2013;

Stone, 1997). Growth and yield may be significantly reduced up to and including mortality after years of repeated infection cycles and loss of photosynthetic tissue (Morgenstern et al., 2013). In contrast with R. parkeri, Rhabdocline spp. associated with Rhabdocline needle cast do not have a lengthy latency period. Many studies have shown a considerable genetic variation in host susceptibility that can be managed by choosing appropriate genotypes for stocking or breeding programs (Brandt, 1960; Chastagner, 2001; Hoff, 1987; Merrill et al., 1990; Stone, 1997). Based on this description of disease, it may be assumed that these Rhabdocline spp. are not considered endophytes. However, one study in Germany successfully extracted and isolated R. pseudotsugae from the asymptomatic buds, cambial tissue and embryos of Douglas-fir trees, reporting that these fungi may have an endophytic stage (Morgenstern et al., 2013). The authors go on to note that other fungal pathogens have been isolated as endophytes, and that host susceptibility, pathogen point mutation, and changes in environmental conditions can all affect the virulence of a pathogen and the resistance of the host. The discovery of R. pseudotsugae within seed tissue implies that it may also persist as a systemic disease that can also be spread vertically (mother to child transmission) as well as horizontally (between unrelated members of the same species)

(Morgenstern et al., 2013).

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Phaeocryptopus gaeumannii is named as the causal pathogen of Swiss Needle Cast disease

(SNC), and is the only known pathogen within the genus (Winton et al., 2007). It was first described as a pathogen of Douglas-fir in European plantations, responsible for extensive losses in productivity there, and later in New England (Boyce, 1940, 1961; Merrill et al., 1973). Within the native range of Douglas-fir it was considered an innocuous fungal associate, as the fungus caused no apparent injury to host trees (Boyce, 1961).

Foresters and researchers began noticing a change in the virulence of P. gaeumannii around the mid 1980s, with defoliation reaching epidemic levels in younger stands of coastal Douglas-fir in Oregon and Washington by the early 1990s (Hansen et al., 2000). Modeled correlations suggest

P. gaeumannii may have been the cause of growth reductions in Douglas-fir within its native range since as far back as the 1590s (Lee et al., 2013). Koch’s postulates were fulfilled (Winton, 2001), demonstrating that P. gaeumannii was the causal agent of SNC. The disease cycle of P. gaeumannii begins with spore production in spring, coinciding with Douglas-fir bud burst (Capitano, 1999).

Spores are released and transported following periods of rainfall, and infect current season needles during their elongation period in early summer. Once a P. gaeumannii spore has germinated, an appressorium forms in an individual stoma, allowing entrance into the plant.

Hyphae continues to grow interstitially without eliciting plant defense response. This fungus acts as any other endophyte, until its growth overwhelms the plant. Pseudothecia are produced and extend out of the needle through stomatal chambers alongside threads of superficial hyphae that grow over the needle surface (Boyce, 1961; Hood, 1997; Stone et al., 2008a). Diagnostic features

Page 17 include this characteristic double band of pseudothecia that extend the length of the needle on either side of the midrib (Figure 1.3) (Boyce, 1961). Each pseudothecia only occludes a single stoma, in contrast with other foliar fungal infections of Douglas-fir which present as large spherical or oblong spots unassociated with stomata. This stomatal occlusion reduces CO2 assimilation, triggering SNC symptom expression once a threshold of occlusion occurs (Manter et al., 2000). Symptoms of SNC include chlorosis and reduced needle retention, resulting in reduced leaf area index (LAI) and losses of growth and yield (Maguire et al., 2011; Weiskittel and

Maguire, 2007).

Figure 1.3: Pseudothecia of Phaeocryptopus gaeumannii, the causal pathogen of Swiss Needle Cast. The image shows pseudothecia (black spots), fruiting bodies of the pathogen, occluding the stomata (white spots) of Douglas-fir needles. Photo used with permission from Dave Shaw, Swiss Needle Cast Cooperative Director (July 21, 2017). Photo credit R. Mulvey.

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The impact of SNC on forest industry in the PNW cannot be overstated. Current affected areas exceed 200,000 hectares (~500,000 acres) along the Oregon Coast Range (Kanaskie and Norlander,

2014). Loss of volume growth has been as high as 52% in highly infected plantations (Maguire et al., 2002; Ritóková et al., 2016). SNC was initially reported to infect younger stands, but has recently been implicated in defoliation of older needles in the lower and inner crowns as well

(Hansen et al., 2000; Manter et al., 2001; Winton et al., 2003). Many researchers have sought ways to mitigate the effects of SNC (Bansal et al., 2016; Jayawickrama et al., 2012; Manter et al., 2003).

Some focus on silvicultural techniques, while others study climate variables with relation to SNC.

Several silvicultural approaches have been suggested to minimize disease incidence. SNC symptom expression varies between genetic families (McDermott and Robinson, 1989). Planting seedlings from tolerant families may be one way to reduce SNC impact on a managed Douglas- fir stand. However, care should the taken when selecting tolerant seed stock, as maladapted seedlings are still highly susceptible to both SNC and Rhabdocline needle cast (Stone, 1997). Tree improvement programs have successfully produced many highly productive seed sources that outperform unimproved seed sources (Jayawickrama et al., 2012). However, in sites with severe disease pressure such as those experienced in the Tillamook area of northwestern Oregon, no resistant or improved seed sources have been able to overcome the disease to date (Kastner et al.,

2001). Basal area data or crown density assessment can be used to determine if a genetic family is tolerant to SNC (Johnson, 2002), and crown structure can be predicted based on other silvicultural applications in a given stand (Weiskittel et al., 2007). Manter et al. (2003) found south-facing

Page 19 slopes in the Oregon Coast Range more likely to develop higher abundance of Phaeocryptopus gaeumannii and symptom expression than other aspects, likely due to temperature and solar radiation. Avoiding those slopes may provide some reduction in SNC incidence.

Climatic variables have been targeted as one of the leading influences in SNC severity in

Oregon and Washington (Manter et al., 2005; Rosso and Hansen, 2003; Stone et al., 2008a). In one study where researchers created a regression model to predict SNC, warm wet conditions with low summer temperatures were correlated with increased disease severity (Rosso and Hansen,

2003). In other studies, winter temperatures (Stone et al., 2008b) and winter mean daily temperature and spring leaf wetness (Manter et al., 2005) were strongly correlated with pathogen abundance. Stone et al. (2008) notes that as the Earth’s climate continues to shift, an increase in the severity and distribution of SNC in the PNW is likely.

Assisted migration, the intentional movement of species’ ranges based on predicted climate models to enhance or alter natural population migration, has been studied and debated extensively as a response to climate change (Aubin et al., 2011; Hewitt et al., 2011; McLachlan et al., 2007; Pedlar et al., 2012; Vitt et al., 2010; Williams and Dumroese, 2013). While it may allow assisted migration of endangered or long-lived species away from fractured habitats, there are inherent risks to introducing new species to different ecosystems (Gray et al., 2011; McLane and

Aitken, 2012; Mueller and Hellmann, 2008). Instead of introducing new species to different ecosystems, some researchers have focused on studying assisted migration of species with natural ranges that span a diverse range of ecosystems. Douglas-fir is one such species, and as a

Page 20 result of its high productivity, it has been the focus of numerous assisted migration studies (Isaac-

Renton et al., 2014; Kranabetter et al., 2012, 2015; St. Clair and Howe, 2007). The Seed Source

Movement Trial (SSMT) is an ongoing study of Douglas-fir assisted migration in California,

Oregon, and Washington (Bansal et al., 2015b, 2015a, 2016, Gould et al., 2010, 2012; St. Clair et al.,

2005; Wilhelmi, 2016). It is a common garden, reciprocal transplant trial that encompasses test sites with many environmental conditions currently suitable for Douglas-fir. The data collected from the SSMT has been used to determine drought resistance (Bansal et al., 2015a) and cold resistance (Bansal et al., 2015b) by provenance, and to create predictive models that measure adaptation of growth phenology (Gould et al., 2010, 2012). These studies have helped land managers determine the best seed sources to use in their plantations, and have contributed meaningfully to the discussion of assisted migration of Douglas-fir. In a recent study, Wilhelmi

(2016) cautions against the use of drought tolerant Douglas-fir seed stock, as they were found to be the most susceptible to SNC and Rhabdocline needle cast. More work continues to be done on the SSMT test sites, and will continue to provide vital information on assisted migration of

Douglas-fir in the PNW.

One conundrum with the SNC story is that pathological symptoms have not followed the predicted effects of P. gaeumannii. Vertical distribution of infection, changes in targeted needle age, endemic expansion of the disease contrast dramatically with previously known effects of P. gaeumannii. As the epidemic progresses, it continues to disrupt historical foliage age-class structures and crown structures (Weiskittel et al., 2006). As a result, needle retention has dropped

Page 21 in severe cases from a norm of 4 years to a low of 1.1 years (Weiskittel et al., 2006). Reductions in tree growth have previously been linked directly to reductions in needle retention (Maguire et al., 2011). Additionally, efforts to control the disease using chlorothalonil (Hadfield, 1982;

Pscheidt and Ocamb, 2017; Skilling, 1981) and other treatments targeted to P. gaeumannii (Stone et al., 2007) have had very mixed results, particularly in later treatments.

A confounding fact is that P. gaeumannii occurs wherever Douglas-fir is found (Lee et al.,

2014), but damage from the disease manifests in a limited range (Shaw et al., 2011). Various climate variables have been hypothesized as explaining this with the general rationale that foliage loss was highest in the lower and inner portions of the crown where humidity is higher

(Chastagner and Byther, 1983; Merrill et al., 1973). However, other studies have shown the opposite effect with lower foliage retention occurring in the upper crowns where humidity is lowest (Hansen et al., 2000; Shaw et al., 2011). Additionally, if reducing humidity could limit the disease, we would expect that thinning stands should reduce disease incidence. This has not necessarily been the case, as some thinning trials have not shown such a response (Crane, 2002;

Mainwaring et al., 2005).

To understand the mechanistic dynamics of needle cast, it is necessary to uncover the underlying cause of the symptom. Though P. gaeumannii has been established as the causal pathogen, the pathogen could be antagonized or facilitated by other organisms. Therefore, the first step in determining any secondary pathogenicity or microbial antagonism is to establish a baseline inventory of endophytes in Douglas-fir needles. From there, researchers can begin to

Page 22 answer questions of interaction between P. gaeumannii and various endophytes, and investigate endophytic pathogens that cause disease in other species. One example of a potential alternative defoliating pathogen is Sydowia polyspora (Bref. & Tavel) E. Müll., which is the causal pathogen of

Current Season Needle Necrosis (CSNN) in Abies sp. and a pathogen of many other conifers

(Smerlis, 1970; Talgø et al., 2010b). A preliminary molecular study of Douglas-fir endophytes in

2013 from Tillamook, Oregon, a northern coastal region, identified S. polyspora as a potential alternative cause of defoliation that accounted for 61% of the isolates from Douglas-fir needles previously field-identified as SNC-infected. An inventory of endophytes can also allow us to determine what a “healthy” community of endophytes looks like compared to a community associated with disease, and what, if anything can be done to equilibrate back to a healthy state.

In this exploratory study, 46 unique isolates from 39 taxa were successfully sequenced and identified in Douglas-fir needles gathered from four test sites in Oregon and Washington.

Environmental conditions varied at each site, including elevation (240 – 860m) and mean annual precipitation (250 – 1,575 mm/year). Rates of infection for all needles was 39%, with infection ranging from 5% to 60% among seed sources. The results suggest that the probability of endophyte occurrence at the cool, wet site, was 2.8 (p < 0.0001) times higher than the warm, dry site, and 2.2 (p = 0.0007) times higher than the warm, wet site. Non-local needles at the warm, dry site were 13.6 (p < 0.0001) times more likely to be infected than needles from the local seed source.

The environmental variables that seemed most positively correlated with endophyte presence was winter relative humidity. The environmental variable most negatively correlated with

Page 23 endophyte presence was continentality (the difference between mean warmest month temperature and mean coldest month temperature). Cryptostroma corticale and Graphostroma sp. were most often found together. Other endophyte pairings include Xylaria hypoxylon and

Lecythophora sp., Clypeosphaeria mamillana and Hypoxylon rubiginosum, and Talaromyces ruber and

Taphrina communis. There was strong evidence for the influence of seed source type on the relationship between continentality and mean average number of endophytes, but not winter relative humidity or mean coldest month temperature.

Long-term silvicultural management of PNW forests will benefit as a result of this study. The small-scale research into the endophytic microbiome is just as important as the landscape-scale understanding of climate change and environmental variables. Both elucidate the complexities of growing and managing Douglas-fir in the PNW and elsewhere. It is vital to understand the complete etiology of the needle cast diseases affecting Douglas-fir, and whether to focus on mitigating a single-species pathogen, or several different pathogens. Because forests are a long- term strategy, it is critical to understand the ecological implications of disease in this important tree species.

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Lee, E.H., Beedlow, P.A., Waschmann, R.S., Burdick, C.A., Shaw, D.C., 2013. Tree-ring analysis of the fungal disease Swiss needle cast in western Oregon coastal forests. Can. J. For. Res. 43, 677–690. doi:10.1139/cjfr-2013-0062 Li, J., Strobel, G., Sidhu, R., Hess, W.M., Ford, E.J., 1996. Endophytic taxol-producing fungi from bald cypress, Taxodium distichum. Microbiology 142, 2223–2226. Linnell, M.A., Epps, C.W., Forsman, E.D., Zielinski, W.J., 2017. Space use, movements, and rest site use by short-tailed weasels Mustela erminea in managed forests of western Oregon. Wildl. Biol. wlb.00270. doi:10.2981/wlb.00270 Maguire, D.A., Kanaskie, A., Voelker, W., Johnson, R., Johnson, G., 2002. Growth of young Douglas-fir plantations across a gradient in Swiss needle cast severity. West. J. Appl. For. 17, 86–95. Maguire, D.A., Mainwaring, D.B., Kanaskie, A., 2011. Ten-year growth and mortality in young Douglas-fir stands experiencing a range in Swiss needle cast severity. Can. J. For. Res. 41, 2064–2076. Mainwaring, D.B., Maguire, D.A., Kanaskie, A., Brandt, J., 2005. Growth responses to commercial thinning in Douglas-fir stands with varying severity of Swiss needle cast in Oregon, USA. Can. J. For. Res. 35, 2394–2402. Manter, D.K., Bond, B.J., Kavanagh, K.L., Rosso, P.H., Filip, G.M., 2000. Pseudothecia of Swiss needle cast fungus, Phaeocryptopus gaeumannii, physically block stomata of Douglas fir, reducing CO2 assimilation. New Phytol. 148, 481–491. doi:10.1046/j.1469- 8137.2000.00779.x Manter, D.K., Kelsey, R.G., Stone, J.K., 2001. Quantification of Phaeocryptopus gaeumannii colonization in Douglas-fir needles by ergosterol analysis. Manter, D.K., Reeser, P.W., Stone, J.K., 2005. A Climate-Based Model for Predicting Geographic Variation in Swiss Needle Cast Severity in the Oregon Coast Range. Phytopathology 95, 1256–1265. doi:10.1094/PHYTO-95-1256 Manter, D.K., Winton, L.M., Filip, G.M., Stone, J.K., 2003. Assessment of Swiss Needle Cast Disease: Temporal and Spatial Investigations of Fungal Colonization and Symptom Severity. J. Phytopathol. 151, 344–351. doi:10.1046/j.1439-0434.2003.00730.x McCutcheon, T.L., Carroll, G.C., 1993. Genotypic Diversity in Populations of a Fungal Endophyte from Douglas Fir. Mycologia 85, 180–186. doi:10.2307/3760454 McDermott, J.M., Robinson, R.A., 1989. Provenance variation for disease resistance in Pseudotsuga menziesii to the Swiss needle-cast pathogen, Phaeocryptopus gaeumannii. Can. J. For. Res. 19, 244–246. McLachlan, J.S., Hellmann, J.J., Schwartz, M.W., 2007. A Framework for Debate of Assisted Migration in an Era of Climate Change. Conserv. Biol. 21, 297–302. doi:10.1111/j.1523- 1739.2007.00676.x McLane, S.C., Aitken, S.N., 2012. Whitebark pine (Pinus albicaulis) assisted migration potential: testing establishment north of the species range. Ecol. Appl. 22, 142–153. doi:10.1890/11- 0329.1

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Stone, J.K., 1988. Fine structure of latent infections by Rhabdocline parkeri on Douglas-fir, with observations on uninfected epidermal cells. Can. J. Bot. 66, 45–54. Stone, J.K., 1987. Initiation and development of latent infections by Rhabdocline parkeri on Douglas-fir. Can. J. Bot. 65, 2614–2621. doi:10.1139/b87-352 Stone, J.K., Capitano, B.R., Kerrigan, J.L., 2008a. The histopathology of Phaeocryptopus gaeumannii on Douglas-fir needles. Mycologia 100, 431–444. Stone, J.K., Coop, L.B., Manter, D.K., 2008b. Predicting effects of climate change on Swiss needle cast disease severity in Pacific Northwest forests. Can. J. Plant Pathol. 30, 169–176. doi:10.1080/07060661.2008.10540533 Stone, J.K., Polishook, J.D., White, J.F., 2004. Endophytic fungi, in: Biodiversity of Fungi. Elsevier Academic Press, Burlington, MA, pp. 241–270. Stone, J.K., Reeser, P.W., Kanaskie, A., 2007. Fungicidal suppression of Swiss needle cast and pathogen reinvasion in a 20-year-old Douglas-fir stand in Oregon. West. J. Appl. For. 22, 248–252. Strobel, G.A., Hess, W.M., Li, J.-Y., Ford, E., Sears, J., Sidhu, R.S., Summerell, B., 1997. Pestalotiopsis guepinii, a taxol-producing endophyte of the wollemi pine, Wollemia nobilis. Aust. J. Bot. 45, 1073–1082. Talbert, C., Marshall, D., 2005. Plantation productivity in the Douglas-fir region under intensive silvicultural practices: results from research and operations. J. For. 103, 65–70. Talgø, V., Brodal, G., Klemsdal, S.S., Stensvand, A., 2010a. Seed borne fungi on Abies spp. Seed Sci. Technol. 38, 477–493. doi:10.15258/sst.2010.38.2.20 Talgø, V., Chastagner, G., Thomsen, I.M., Cech, T., Riley, K., Lange, K., Klemsdal, S.S., Stensvand, A., 2010b. Sydowia polyspora associated with current season needle necrosis (CSNN) on true fir (Abies spp.). Fungal Biol. 114, 545–554. Tan, R.X., Zou, W.X., 2001. Endophytes: a rich source of functional metabolites. Nat. Prod. Rep. 18, 448–459. Todd, D., 1988. The effects of host genotype, growth rate, and needle age on the distribution of a mutualistic, endophytic fungus in Douglas-fir plantations. Can. J. For. Res. 18, 601–605. doi:10.1139/x88-087 Verhoeff, K., 1974. Latent infections by fungi. Annu. Rev. Phytopathol. 12, 99–110. Vitt, P., Havens, K., Kramer, A.T., Sollenberger, D., Yates, E., 2010. Assisted migration of plants: Changes in latitudes, changes in attitudes. Biol. Conserv. 143, 18–27. doi:10.1016/j.biocon.2009.08.015 Waring, R.H., Franklin, J.F., 1979. Evergreen coniferous forests of the Pacific Northwest. Science 204, 1380–1386. Weiskittel, A.R., Maguire, D.A., 2007. Response of Douglas-fir leaf area index and litterfall dynamics to Swiss needle cast in north coastal Oregon, USA. Ann. For. Sci. 64, 121–132. doi:10.1051/forest:2006096 Weiskittel, A.R., Maguire, D.A., Garber, S.M., Kanaskie, A., 2006. Influence of Swiss needle cast on foliage age-class structure and vertical foliage distribution in Douglas-fir plantations in north coastal Oregon. Can. J. For. Res. 36, 1497–1508.

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Weiskittel, A.R., Maguire, D.A., Monserud, R.A., 2007. Modeling crown structural responses to competing vegetation control, thinning, fertilization, and Swiss needle cast in coastal Douglas-fir of the Pacific Northwest, USA. For. Ecol. Manag. 245, 96–109. doi:10.1016/j.foreco.2007.04.002 Wennström, A., 1994. Endophyte- The misuse of an old term. Oikos 71, 535–536. Wilhelmi, N., 2016. The Effects of Seed Source and Planting Environment on Douglas-fir (Pseudotsuga menziesii) Foliage Diseases. Corvallis MS Thesis Or. State Univ. 170p. Williams, M.I., Dumroese, R.K., 2013. Preparing for Climate Change: Forestry and Assisted Migration. J. For. 111, 287–297. doi:10.5849/jof.13-016 Wilson, D., 1995. Endophyte: The Evolution of a Term, and Clarification of Its Use and Definition. Oikos 73, 274–276. doi:10.2307/3545919 Winton, L.M., 2001. Phylogenetics, population genetics, molecular epidemiology, and pathogenicity of the Douglas-fir Swiss needle cast pathogen Phaeocryptopus gaeumannii. Corvallis PhD Diss. Or. State Univ. 112p. Winton, L.M., Manter, D.K., Stone, J.K., Hansen, E.M., 2003. Comparison of biochemical, molecular, and visual methods to quantify Phaeocryptopus gaeumannii in Douglas-Fir foliage. Phytopathology 93, 121–126. Winton, L.M., Stone, J.K., Hansen, E.M., Shoemaker, R.A., 2007. The systematic position of Phaeocryptopus gaeumannii. Mycologia 99, 240–252.

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Chapter 2: Phylogenetic identification of pathogenic and endophytic fungal populations in

West Coast Douglas-fir (Pseudotsuga menziesii) foliage

Introduction

Coastal Oregon forests provide many important interregional ecological and economic benefits. In addition to the timber growth potential, coastal forests produce abundant biodiversity and provide economic viability for coastal communities. Oregon wood production occurs on about 41% of the timber land base in the state, and accounts for approximately 17% of the U.S. lumber output with an estimated value of $1.9 billion dollars on 4.2 billion board feet (OFRI,

2015). Forest industry accounts for approximately 61,000 jobs in Oregon (OFRI, 2017). The additional 59% of the timber land base supports ecosystem services, water resources, recreation, and wildlife habitat (OFRI, 2015).

Defoliation of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) dominated coastal coniferous forests in the Pacific Northwest has previously been linked primarily to Swiss Needle

Cast (SNC) disease caused by the fungus Phaeocryptopus gaeumannii (T. Rohde) Petr. (Shaw et al.,

2011). Phaeocryptopus gaeumannii was initially described as an innocuous fungal associate within the native range of Douglas-fir (Boyce, 1961), though it was known to cause extensive damage to plantations in New England, Europe, and New Zealand (Boyce, 1940; Hood and Kershaw, 1975;

Łakomy and Iwańczuk, 2010; Merrill et al., 1973). It has been described as an endophyte, a fungus that spends the majority of its life cycle within a plant’s tissues. An unprecedented defoliation epidemic has continued since the early 1990's in the coastal fog belt, particularly in younger Page 33 stands of Douglas-fir. Current affected areas are in excess of 200,000 hectares (~500,000 acres) along the Oregon Coast Range (Kanaskie and Norlander, 2014; Ritóková et al., 2016). SNC has been implicated in defoliation of older needles in the lower and inner crowns (Hansen et al., 2000;

Manter et al., 2001; Winton et al., 2003).

The story of SNC has become more complex with time. Disease symptoms have not followed predicted effects of P. gaeumannii. Distribution of infection, changes in targeted needle age, endemic expansion of the disease, as examples, contrast dramatically with previously known effects of P. gaeumannii. Since the 1990’s, the epidemic has moved to the upper crowns, and it now appears to occur on younger needles and trees of all age groups (Hansen et al., 2000; Manter et al., 2001; Winton et al., 2003). As a result, needle retention has decreased in severe cases from a norm of 4 years to a low of 1.1 years (Weiskittel et al., 2006), and losses in volume growth as high as 52% have been reported in some heavily infected plantations (Maguire et al., 2002). Maguire et al (Maguire et al., 2011) demonstrated that reductions in tree growth are linked directly to reductions in needle retention. Chemical control efforts using chlorothalonil (Hadfield, 1982;

Pscheidt and Ocamb, 2017; Skilling, 1981) and other treatments targeted to P. gaeumannii (Stone et al. 2007) have had mixed results, particularly in later treatments. In addition, these chemical treatments are uneconomical and environmentally unsafe (Caux et al., 1996).

A confounding fact is that P. gaeumannii occurs wherever Douglas-fir is found (Lee et al.,

2014), but damage from the disease manifests in a limited range (Shaw et al., 2011). Climate variables of temperature and humidity have been hypothesized as explaining this with the Page 34 general rationale that foliage loss was highest in the lower and inner portions of the crown where humidity is higher (Chastagner and Byther, 1983; Merrill et al., 1973). However, other studies have shown the opposite effect with lower foliage retention occurring in the upper crowns where humidity is lowest (Hansen et al., 2000). Additionally, if reducing humidity could limit the disease, we would expect that thinning stands should reduce disease incidence. This has not necessarily been the case, as some thinning trials have not shown such responses (Crane, 2002;

Mainwaring et al., 2005; Shaw et al., 2014).

In addition to P. gaeumannii, there are several fungi associated with Douglas-fir needles.

Rhabdocline parkeri Sherwood, J.K. Stone & G.C. Carroll has been reported as an asymptomatic fungal infection in Douglas-fir (Sherwood-Pike et al., 1986). Reported epidemiology is similar to

P. gaeumannii and was found to increase exponentially with increasing needle age even though infection rates varied (Stone, 1987). Rhabdocline pseudotsugae Syd. and R. weirii A.K. Parker & J.

Reid are both described as endemic, but virulent pathogens that cause defoliation in Douglas-fir and bigcone Douglas-fir (Pseudotsuga macrocarpa (Vasey) Mayr.) (Chastagner, 2001). These two

Rhabdocline species and their various subspecies are named as the causal pathogens of

Rhabdocline Needle Cast (Brandt, 1960; Stone, 1997).

Rhizosphaera pseudotsugae Butin & Kehr was only recently described as a new species, separate from Rhizosphaera kalkhoffii Bubák, both being common in Douglas-fir (Butin and Kehr,

2000). Other endophyte species that have been found in Douglas-fir foliage include Bispora sp.,

Cryptocline abietina Petr., Geniculosporium sp., Phyllosticta sp., and Xylaria sp. (Carroll and Carroll, Page 35

1978). It is important to note that there is relatively little information on how the various species known to inhabit Douglas-fir needle tissues interact with other members of their microbial communities.

Several studies have attempted to describe endophytes in west coast conifers, but all of these relied on traditional plate culturing and morphological identification of species (Carroll and

Carroll, 1978; Petrini and Carroll, 1981; Stone, 1986). Attempts to characterize all endophytes of coastal Oregon Douglas-fir by molecular methods have not been reported. One study utilized

DNA amplification to assess the genotypic diversity of Rhabdocline parkeri, but this study focused only on the single species, and not the entire fungal microbiome of the host (McCutcheon and

Carroll, 1993).

A preliminary molecular study of Douglas-fir endophytes in 2013 from Tillamook,

Oregon, a northern coastal region, identified a potential alternative cause of defoliation, Sydowia polyspora (Bref. & Tavel) E. Müll. (anamorph: Hormonema dematioides Lagerb. & Melin) that accounted for 61% of the isolates from Douglas-fir needles previously field-identified as SNC- infected. In Europe and Canada, S. polyspora is well known for conifer defoliation. It was recently demonstrated in Norway that this fungus is the cause of Current Season Needle Necrosis (CSNN) of Abies sp. and a pathogen of many other conifers (Smerlis, 1970; Talgø et al., 2010b). The disease, which has severely reduced the marketability of fir Christmas trees and boughs in Europe and

North America for more than 25 years, was previously reported as numerous physiological disorders with unknown etiology. In addition to Douglas-fir defoliation, S. polyspora has been Page 36 implicated as a needle cast pathogen in several studies in Canada and Europe in conifer species including Pinus sp., Abies, sp., Tsuga, sp., Larix, sp., and Picea, sp. (Phillips and Burdekin, 1992;

Smerlis, 1970; Talgø et al., 2010b; Tinivella et al., 2013). Recently, S. polyspora has been found to infect nursery conifer seed (Talgø et al., 2010a).

The number of known endophytic pathogens in addition to large numbers of endophytes of indeterminate roles make the segregation of a single pathogen as the singular cause of needle cast problematic. Although P. gaeumannii fulfilled Koch’s postulates as the cause of Swiss Needle

Cast symptoms (Winton et al., 2003), there is no information regarding the impact of the microbiome community on disease development. The lack of a baseline endophyte database creates a difficult situation for appropriate pathological inquiry, particularly in the event of a large-scale outbreak/epidemic. In-field diagnoses risk misidentification of underlying cause by a masking effect of visible conditions or symptoms. From an etiological perspective, implying a cause and effect from observation of a later stage occurrence may very well impede any type of understanding of the actual disease. A comprehensive understanding of the abiotic/biotic factors involved in needle cast of Douglas-fir is needed. This requires, in part, a more detailed look at the microbiome within the needle.

Objectives

1. Quantify baseline inventories of fungal endophytes of Douglas-fir needles for each of four

geographical locations, along gradients of site elevation, temperature, and mean annual Page 37

precipitation, for Douglas-fir trees grown from local seed sources and those grown from

non-local seed sources;

2. Compare fungal communities found within local seed sources to those found within non-

local seed sources; and,

3. Characterize fungal communities in association with Phaeocryptopus gaeumannii that may be

additionally implicated in Douglas-fir needle-cast.

The microbiome community of conifer needles is poorly described to date. We hypothesize that the full interstitial microbiome complex is involved in disease response in conifers. We hypothesize that moisture and temperature are important climatic drivers of microbiome diversity and disease etiology, especially with regards to endophytes. The highly- structured nature of the study design severely limits the scope of inference and includes only those sites and seed sources included in the study. No statistical comparisons will be made across sites, since the nested study design precludes such computations, however conclusions may still be drawn from the results of the study.

Methods

Four study sites were selected along a North-South transect from southern Oregon to central Washington. The study sites are part of the an unrelated, but ongoing study called the

Douglas-fir Seed Source Movement Trials (SSMT), a long-term, large-scale reciprocal common garden provenance study of Douglas-fir (Bansal et al., 2015a, 2015b; Ford et al., 2016; Gould et al.,

2010, 2012). Twelve different seed sources were planted at each SSMT site, representing different Page 38 geographic regions from Northern California through southern Washington (Table 2.1). Regions vary in characteristics such as annual precipitation and elevation, and are meant to represent the range of geographic and climatic conditions experienced by Douglas-fir in the Pacific Northwest.

Seedlings were planted in a complete block design consisting of four replications (blocks) at each site. Within a block, seed sources were randomly assigned to one of twelve sections. Twenty seedlings representing families within each seed source region were planted at a 3.6m by 3.6m spacing within each section. A border of Douglas-fir trees was planted around each site, and competing vegetation was controlled via herbicide application to reduce seedling competition.

At some sites, fencing was erected to exclude wildlife.

Table 2.1: Climate variables associated with seed sources (top) and planting sites (bottom) of the Seed Source Movement Trial (SSMT), a long-term, large-scale reciprocal provenance study of Douglas-fir. Mean Annual Mean Annual State Region Code Elevation (m) Temperature (°C) Precipitation (mm) California Coast CACst 156 12 1122 California Klamath CAKla 1389 11 1472 California Sierra CASierra 1469 9 1172 Oregon Coast South ORCstS 308 11 2528 Oregon Coast North ORCstN 312 10 2260 Oregon Siskiyou Low ORSisL 462 11 1067 Oregon Siskiyou High OrSisH 1062 9 1254 Oregon Cascade Low ORCasL 437 10 1628 Oregon Cascade High ORCasH 1005 8 2055 Washington Coast WaCst 179 10 2530 Washington Cascade Low WACasL 442 8 1926 Washington Cascade High WACasH 1053 6 2068 Site Name Buckhorn2 240 10 1149 Doorstop 860 8 1575 Nortons 185 10 1198 Stone Nursery 415 12 250

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The four planting sites chosen for this study varied in both elevation and mean annual precipitation (Figure 2.1). Elevations ranged from 185 m to 860 m, and precipitation from 250 mm/year to 1,575 mm/year according to 2013 data collected at the sites (Table 2.1). A nested sampling design was used to collect Douglas-fir branchlets from three different seed sources at each site (Table 2.2). Three different seed sources were sampled at each of four sites, for a total of twelve seed sources. Within each site, one seed source represented the local population, while the remaining two seed sources represented non-local populations. Non-local seed sources were chosen randomly and without replacement. The second nonlocal seed source at Doorstop was omitted, as it was a duplicate of the local seed source at Stone Nursery. Therefore, the analyses focused on the 11 remaining seed sources (Table 2.2).

Table 2.2: Seed sources sampled at each site. Planting sites are part of the Seed Source Movement Trial (SSMT), a long-term, large-scale reciprocal provenance study of Douglas-fir. Planting Site Local Seed Source Code Name Non-Local Seed Sources Code Name Buckhorn2 WACasL BL WACst BA ORCasL BB Doorstop WACasH DL CAKla DA (ORSisL—OMITTED) Nortons ORCstN NL ORCstS NA ORCasH NB Stone Nursery ORSisL SL CACst SA CASierra SB

Two to four blocks were sampled at each site, depending on the availability of living samples (Figure 2.2). For several seed sources, at least one block contained no living trees of that seed source and therefore could not be sampled. Within each section, one arbitrarily chosen tree was sampled. One branchlet was removed from the tree and stored in a labeled sample collection Page 40 bag. The position of the branchlet on each tree was varied both by vertical position (high, medium, or low) and cardinal direction (N, S, E, W). Vertical and directional variations in branchlet samples were based on the availability of branchlets within 2m (6 ft) from the ground.

Needles varied in symptom expression from symptomless to heavily infected. Some were field- identified as infected with SNC, others with Rhabdocline needle cast. Branchlets were stored in -

80C freezer until ready to use. A total of thirty-seven branchlets were collected for this study.

Figure 2.1: Locations of four study sites, part of the Douglas-fir Seed Source Movement Trials (SSMT), a long-term, large-scale reciprocal provenance study of Douglas-fir. Regions vary in characteristics such as annual precipitation and elevation, and are meant to represent large-scale variations in geographic and climatic conditions experienced by Douglas-fir in the Pacific Northwest. Page 41

Figure 2.2: Example layout of sample collection from Nortons collection site. One local seed source (NL) and two foreign seed sources (NA, NB) were collected from each block (black box). Circles represent the theoretical locations of plots within blocks.

Traditional plate culturing

Needles were removed from each branchlet and surface sterilized in a 2% bleach solution for 5 minutes, then rinsed in deionized water and transferred to a sterile 15 mL collection tube

(Schulz et al., 1993). Needles were randomly chosen from the collection tube for plating, cut into

5 or more pieces using a sterile scalpel, and placed onto a 1.5% potato dextrose agar (PDA) plate

[39 g/L Difco™ Potato Dextrose Agar (Becton, Dickinson and Company, Sparks, MD, USA)]. Each plate was evenly divided into three observation units, with one divided needle placed in each unit. Five PDA plates were used for each seed source, for a total of 15 needles from each seed source. Plates were visually observed daily for evidence of new fungal growth, which was immediately transferred to a new 1.5% PDA plate to isolate non-contaminated cultures. Original plates containing needles were discarded when non-contaminated isolations were no longer Page 42 possible. After at least fourteen days of culturing, isolates were examined and grouped based on similar colony morphology. One representative of each morphological group was chosen for processing. Care was taken not to omit any questionable isolates (i.e. questionable isolates were sequenced to prevent omission of species from results).

Effectiveness of the surface sterilization technique was tested using a sub-sample from the original needle sample population (Schulz et al., 1993). Thirty-three pre-sterilized needles were removed from stored collection tubes. Eleven plates containing 2.5% PDA agar were divided into three approximately equal observation units. Each needle was randomly imprinted into one of the observation units. Plates were sealed with parafilm, incubated at room temperature (210 C) for approximately 10 weeks, and monitored for any growth originating from the imprint area.

Two plugs of fungal tissue were taken from representative daughter plates and placed into two 250 mL Erlenmeyer flasks containing 25 mL of either 1.5% potato dextrose broth (2.4 g/L

Difco™ Potato Dextrose Broth) or 1.5% malt extract [Bacto™ Malt Extract (Becton, Dickinson and

Company)], and a third plug was placed onto fresh 1.5% PDA plate. Liquid cultures were agitated periodically to facilitate growth in suspension. PDA plates were wrapped in Parafilm-M (Fisher

Scientific) and placed into long-term storage at -25°C.

Fungal tissue was collected for molecular tests when an adequate amount of tissue had accumulated in liquid media (see Appendix I). Tissue was removed from liquid media using sterile forceps, daubed on sterilized grade 1 qualitative filter paper to remove excess media, and Page 43 placed in a sterile 1.5 mL microcentrifuge tube. Tubes were labeled with sample name, representative morphological group (if any), and date.

DNA extraction

DNA was extracted from isolates using a DNeasy Plant Mini Kit (QIAgen, Valencia, CA,

USA), according to standard protocol. Fourteen isolates could not be processed as outlined above, due to slow or absent growth in liquid media. DNA was extracted from these isolates using a

Cetrimonium bromide (CTAB) DNA extraction protocol modified from Gardes and Bruns (1996,

Appendix I).

The concentration and purity of all DNA samples were measured with a NanoDrop ND-

2000c spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE, USA). Gel electrophoresis was used to validate DNA extraction using a 1% agarose gel and a 100bp ladder.

Both positive and negative control samples were included for additional confirmation and accuracy of results. After successful electrophoresis, isolates underwent polymerase chain reaction (PCR) to amplify the internal transcribed spacer (ITS) regions ITS1 and ITS2 (White et al., 1990). PCR was performed in 35 μL reactions containing 1 μL DNA (X ng/μL), 7 μL 5x Hot

Start GoTaq Flexi Buffer (Promega, Madison, WI, USA), 5.6 μL of 25 mM Bovine Serum Albumin

(BSA), 4.2 μL of 25 mM MgCl2, 2.8 μL each of 2.5 mM 10x dNTPs, 0.35 μL of 50 μM forward primer ITS-1F, 0.35 μL of 50 μM reverse primer ITS-4, 0.15 μL Hot Start GoTaq (Promega), and

13.5 μL of sterile water. PCR amplification was performed in a 96-well plate, using a thermocycler

(Eppendorf AG, Hamburg, Germany). Steps were as follows: an initial denaturation step of 2 Page 44 minutes at 95° C, followed by 31 cycles of 94° C for 30 seconds, 50° C for 60 seconds, 72° C for 90 seconds, and a final extension at 72° C for 10 minutes. Following ITS amplification, DNA samples were again analyzed via gel electrophoresis. For each isolate, a 10 μL aliquot containing 5 μL

DNA, 1.5 μL 5% dye (QIAgen), and 3.5 μL sterile water was separated through a 2% agarose gel.

The separated amplicons were visualized with UV light using a Gel Documentation System (Bio-

Rad, Hercules, CA, USA). The remaining 25 μL from the ITS amplification was PCR purified with

ExoSAP-IT PCR Product Cleanup (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s protocol. The purified DNA was again quantified with a NanoDrop ND-2000c spectrophotometer. Isolates were then sent to the Oregon State University Center for Genome

Research and Biocomputing, where Sanger sequencing was performed with an ABI 3730 capillary sequence machine (Applied Biosystems, Branchburg, NJ, USA). Completed sequences were assembled into contigs using Sequencher 4.9 and then searched using the Basic Local Alignment

Search Tool (BLAST) to identify species (https://blast.ncbi.nlm.nih.gov/Blast.cgi Accessed 8

August 2017).

Data Analysis

Data were analyzed for community structure within needles, seed sources, and sites using

PC-ORD Version 7.0 (McCune and Mefford, 2015). Sample units (needles) were included in the data set only if they had endophytes that had been successfully isolated and sequenced.

Unidentified isolates were removed from the analysis, but needles with multiple isolates were retained so long as at least one isolate was identified. A number of the unidentified isolates were Page 45 visually identified as bacterial, which would make identification and sequencing with fungal ITS regions unlikely. From 555 needles tested, 155 needles fulfilled the above criterion.

Two data sets were created to analyze community data. The first data set, “Endophytes by Needle (EN)”, includes a species matrix (155 sample units x 46 species), which contains counts of individual endophytes on each needle. An environmental matrix (155 sample units x 29 environmental variables) contains measurements of environmental variables collected in 2013, and experimental design variables (seed source) (Table 2.3). The second data set, “Endophytes by

Seed Source (ESS)”, was created from the grouped sample units, and includes all endophytes from all needles of a seed source. Since each seed source was only collected at one site, they are within-site groups. The species matrix (11 seed sources x 46 species) contains presence/absence information for endophyte species. Presence/absence of endophytes was chosen over other methods due to the majority of needles having at least one endophyte cultured and identified.

The environmental matrix (11 seed sources x 29 environmental variables) is the same as that of the EN data set (Table 2.3). Since ESS data is combined within-site, the same environmental variables can still be used. Data were not relativized or otherwise transformed for analysis.

In order to visually assess possible community relationships between endophytes, a two- way cluster analysis was performed on both EN and ESS data sets. The analyses used Euclidean distance, Ward’s method clustering, and clustered variables by relativizing by variable maximum. Multi-Response Permutation Procedures (MRPP) was performed on EN and ESS data

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Table 2.3: Environmental and experimental variables utilized in community data analysis. Variable Definition CMD_wt Winter (Dec – Feb) Hargreaves climatic moisture deficit (mm) CMD_sp Spring (Mar – May) Hargreaves climatic moisture deficit (mm) CMD_sm Summer (Jun – Aug) Hargreaves climatic moisture deficit (mm) CMD_at Autumn (Sept – Nov) Hargreaves climatic moisture deficit (mm) RH_wt Winter relative humidity (%) RH_sp Spring relative humidity (%) RH_sm Summer relative humidity (%) RH_at Autumn relative humidity (%) RH_avg Average relative humidity (%) MWMT Mean warmest month temperature (℃) MCMT Mean coldest month temperature (℃) TD Temperature difference between MWMT and MCMT; continentality (℃) MAP Mean annual precipitation (mm) MSP Mean annual summer (May – Sept) precipitation (mm) NFFD Number of frost-free days FFP Frost-free period PAS Precipitation as snow (mm) between Aug in previous year and Jul in current year Eref Hargreaves reference evaporation (mm) CMD Hargreaves climatic moisture deficit (mm) Tave_wt Winter mean temperature (℃) Tmin_wt Winter mean minimum temperature (℃) DD_0 Degree-days below 0 ℃, chilling degree-days DD5 Degree days above 5 ℃, growing degree-days DD_18 Degree-days below 18 ℃. heating degree-days DD18 Degree-days above 18 ℃, cooling degree-days

sets using site as the grouping identifier and Sørensen distance. MRPP is a nonparametric procedure for testing the null hypothesis that there are no differences between two or more groups. It is well suited for ecological community data because it does not require traditional statistical assumptions such as normality or equal variances (McCune et al., 2002). Additionally, nonmetric multidimensional scaling (NMS) was used with the EN data set to examine variation among sample units (needles) with respect to endophytes. NMS ordinations used ranked distances to plot similar sample units closer together. The distance between points/sample units widens with increasing dissimilarity in endophyte populations (Kruskal, 1964; Mather, 1976).

NMS is well suited to data for variables that do not have linear relationships, unlike other Page 47 ordination systems (McCune et al., 2002). For analysis of the EN data set, the NMS ordination was set to the “medium” thoroughness setting on autopilot using a random starting configuration and Euclidean distance. Autopilot uses default settings, and determines the most appropriate dimensionality while avoiding local minima by using random iterations of the data. The

“medium” setting runs a maximum 200 iterations, has an instability criterion of 0.00001, and performs 50 real runs in addition to 50 randomized Monte Carlo runs (McCune and Mefford,

2015). Ties, where ranked distances were the same in two or more sample units, were not penalized (Kruskal’s primary approach) (Kruskal, 1964). To confirm that a stable solution was reached, plots of stress for all iterations were checked, along with the reported final instability.

The real data run with the lowest stress was used to interpret results. Finally, outlier analysis was performed on both EN and ESS data sets using Euclidean distance measure.

Additional statistical analysis compares odds ratios between sites and between local and non-local seed sources within sites. In retrospective studies such as this one, odds ratios best describe the binary responses for the collected data (Ramsey and Schafer, 2012). Mean average number of endophytes by seed source type (local, non-local) at various environmental variables

(Table 2.3) was also calculated. No comparisons were made across sites in the calculation of mean average number of endophytes, since the nested study design precludes such comparisons. Since non-local seed sources can come from drastically different conditions than local seed sources

(Bansal et al., 2015a; St. Clair et al., 2005; Wilhelmi, 2016), it was expected that the mean average Page 48 number of endophytes would differ between local and non-local seed sources across multiple variables.

To evaluate the difference in mean average number of endophytes between local and non- local seed sources within continuous variables included in this study (ENV-VAR), linear mixed models were designed to account for the random error associated with each observation (average number of endophytes per needle per seed source type). Analyses were performed without adjustment to the α-level, since only two comparisons were performed within sites (α = 0.95). All analyses were performed using R version 3.4.1 (R Core Team, 2016), and all R graphics were created with the ggplot2 package (Wickham, 2016). The following statistical model was used to describe the linear mixed model for the data collected from the design outlined above:

Yt = β0 + β1Xt + β2I.st + β3Xt*I.st + εt where:

Yt is the mean average number of endophytes from i seed source type from j site; i=local, non-

local; j=Stone, Buckhorn2, Nortons, Doorstop,

β0 is the mean average number of endophytes in the local seed sources when ENV-VAR is 0,

β1 is the incremental effect of ENV-VAR on the mean average number of endophytes in the

local seed source, Page 49

β2 is the incremental effect of seed source type on the mean average number of endophytes

when ENV-VAR is 0,

β3 is the incremental effect of the interaction between average number of endophytes and seed

source type on the number of endophytes at a site,

Xt is a continuous variable representing the average number of endophytes from the jth site,

I.s is 1 when the seed source is non-local and 0 otherwise,

εt is the random error associated with the tth observation, εt ~ N(0, σ2) and εt and εt’ are

independent.

In this descriptive analysis, the β values were combined to create various estimates of means. For a local seed source, β0, β1, and εt were summed to get an estimate of the mean average number of endophytes. For non-local seed sources, the estimate of the mean average number of endophytes is the product of: (β0 + β2) + (β1 + β3)Xt + εt. It should be noted that, because each site has a unique precipitation level with no overlap between sites, these two factors are confounded and interchangeable, which prevents the comparison of average mean numbers of endophytes across sites. Future studies of this kind should take care to create crossed and nested designs to remove this confounding influence.

While statistical significance is an important reporting tool, biological significance is a frequently overlooked aspect of scientific research in natural sciences. There are few studies on Page 50 which to determine if a change in the mean average number of endophytes is biologically meaningful (Carroll and Carroll, 1978; Rajala et al., 2014). However, the threshold for biological significance for the purposes of this study was set to 20% difference in mean average number of endophytes. It is important to note that biologically significant differences may not be statistically significant if the power of a study is low, as was the case here (Johnson, 1995). In this study, confidence intervals were measured and reported, as a prospective power analysis was not performed prior to data collection.

Results

Objective 1: Quantify baseline inventories of fungal endophytes of Douglas-fir for each of four geographical locations, for trees grown from local seed sources and those grown from non-local seed sources.

Of the 555 Douglas-fir needles sampled, 318 isolates were cultured from 215 needles.

Eighty-nine isolates were sorted into 54 morphological groups, of which 34 were successfully identified using BLAST lookup. There were 126 ungrouped isolates, of which 96 were successfully identified. The remaining 20 morphological groups and 30 ungrouped isolates remain unidentified due to lack of amplification during PCR, or poor-quality sequencing resulting in unsuccessful identification. In total, 46 unique isolates from 39 taxa were identified from needles (Table 2.4). Whole-needle shotgun sequencing using Next-Generation sequencing

(NGS) methods was attempted alongside Sanger sequencing, but was ultimately unsuccessful.

Failure was attributed to unsuccessful extraction due to the high phenol content of Douglas-fir Page 51 needles interfering in the fungal DNA extraction process. Additionally, the surface sterilization procedures used in this study effectively eliminated epiphytic fungi from needle surfaces. This was confirmed when none of the plates from the sterilization sub-sample showed growth originating from the imprinted areas.

Objective 2: Compare fungal communities found within local seed sources to those found within non-local seed sources.

Rates of infection for all needles was 39%, with infection ranging from 5% to 60% among seed sources (Table 2.5, Figure 2.3). Infection rates of needles from non-local seed sources at

Buckhorn2, Doorstop, and Stone Nursery were higher than those of needles from local seed sources. The odds of infection of a non-local needle at Buckhorn2 was 1.4 to 1 (Fisher’s exact test, one-sided p-value = 0.25). Non-local needles at Doorstop were 1.9 times as likely as local needles to have an infection (p = 0.099). The odds of infection of a local needle at Nortons were 2.4 times higher than those for non-local needles (p = 0.01). Infections at Stone Nursery were 13.6 times more likely in non-local than local needles (p < 0.0001). Based on our methods, the odds of finding an endophyte infection on a non-local seed source was 1.6 times higher overall (p = 0.007).

Confidence intervals for these odds ratios, and between site comparisons of infection odds ratios can be seen in Table 2.6.

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Table 2.4: Taxa and frequency of endophyte species cultured and sequenced from 185 Douglas- fir needles from four sites in Oregon and Washington. The number of endophytes successfully sequenced totals 221 out of 318 isolates. Sequences were reported at 95% or greater nucleotide matches.

Species Frequency Accession Number(s) Anthostomella conorum1 6 EU552099, KT149745 Anthostomella pinea 1 KJ406991 Apodus deciduus 1 AY681199 Aposphaeria corallinolutea 1 KY554202 Aureobasidium pullulans 4 LC277152, JX188099 Aureobasidium sp. 1 HF674760 Cladosporium perangustum 1 MF303712 Claussenomyces sp.1 2 KT264343 Clypeosphaeria mamillana2 4 KT949898 Clypeosphaeria sp. 1 JQ341099 Coniochaeta hoffmannii 1 KX869937 Crustomyces subabruptus1 24 KP814558 Cryptococcus sp. 1 KM216339 Cryptostroma corticale2 3 KR870994 Diaporthe sp.1 2 LC041016 Dothideomycetes sp. 4 KP990991 Geopyxis rehmii 2 KU932461 Graphostroma sp.1,2 13 EU715682 Helicoon fuscosporum 2 EF029203 Hormonemia sp. 2 AF013225 Hypoxylon rubiginosum2 21 AY787708 Lecythophora sp.1,2 7 KX096678, GU062252, AY219880 Melanomma pulvis-pyrius 2 KY189979 Nemania serpens 1 KU141386 Nemania sp. 1 HM123573 Ophiognomonia alni-viridis 1 JF514848 Penicillium glabrum1,2 18 KU847873, KY318471, KX099660, KX609402, KU847870 Perusta inaequalis1 2 NR_144958 Pezizomycetes sp.1 3 KX909069, KJ508333 Phaeomoniella zymoides 2 GQ154600 Phomatospora biseriata 1 KX549454 Podospora sp. 1 AM262361 Preussia bipartis2 3 GQ203774 Rhabdocline parkeri1 8 AF462428, AF462427 Rosellinia quercina 3 AB017661 Rosellinia sp.1 3 KT264658 Sordariales sp.1 2 FN548158 Sordariomycetes sp.1,2 9 KT264524, KX611007, KX611549, JQ759589, GQ153043, KP992078, GQ153206 Sporormiaceae sp. 1 KX611024 Sydowia polyspora 1 KP152486 Talaromyces ruber1 2 JX965239 Taphrina communis1 13 AF492088 Taphrina veronaerambellii 1 NR_111148 Wallemia sebi 1 KX911858 Xylaria hypoxylon1,2 35 KY204024, KX096696, GU300096 Xylaria sp.2 3 AB465207 Unidentified 97 1 Common among sites 2 Common among non/local seed sources within sites Page 53

Table 2.5: Endophyte infection rates for each seed source sampled at four sites in Oregon and Washington. Needles % Infection Site Name Type Seed Source Code Name Isolates Cultured Rate Buckhorn2 Non-local WACst BA 33 60 55% Buckhorn2 Non-local ORCasL BB 18 30 60% Buckhorn2 Local WACasL BL 22 45 49% Doorstop Non-local CAKla DA 22 45 49% Doorstop Local WACasH DL 15 45 33% Nortons Non-local ORCstS NA 15 60 25% Nortons Non-local ORCasH NB 15 45 33% Nortons Local ORCstN NL 22 45 49% Stone Non-local CACst SA 17 60 28% Stone Non-local CASierra SB 33 60 55% Stone Local ORSisL SL 3 60 5% Total: 215 555 39%

Table 2.6: Odds ratios comparing incidence of infection within and between four sites in Oregon and Washington. Confidence Interval (95%) Comparison Odds Ratio Lower Limit Upper Limit p-value Buckhorn2 Non-Local:Local 1.3671 0.6668 2.803 0.2507 Doorstop Non-Local:Local 1.913 0.8161 4.4845 0.0992 Nortons Local:Non-Local 2.3913 1.1619 4.9214 0.0143 Stone Nursery Non-Local:Local 13.5714 4.0211 45.804 <0.0001 Buckhorn2:Doorstop 1.6866 0.9835 2.8923 0.0382 Buckhorn2:Nortons 2.219 1.377 3.5758 0.0007 Buckhorn2:Stone Nursery 2.8214 1.7701 4.4969 <0.0001 Doorstop:Nortons 1.3157 0.7683 2.2531 0.1940 Doorstop:Stone Nursery 1.6728 0.9864 2.8371 0.0382 Nortons:Stone Nursery 1.2715 0.799 2.0233 0.1852

Sixteen unique taxa were found at more than one site (Table 2.4). Ten taxa were isolated within needles of both local and non-local seed sources within sites. Isolates of Graphostroma sp.,

Lecythophora sp., Penicillium glabrum, Sordariomycetes sp., and Xylaria hypoxylon were found in local and non-local needles at multiple sites. Penicillium glabrum and Sordariomycetes sp. were found at all four sites, with P. glabrum isolates at three different sites sharing the same accession number. Page 54

Similarly, Xylaria hypoxylon were isolated from needles at both Buckhorn2 and Nortons. Out of

32 isolates, 28 shared the same accession number.

Community structure analysis via two-way cluster analyses for both ESS (Figure 2.4) and

EN (Figure 2.5) revealed relationships between sample units and endophyte species. The ESS cluster analysis showed a few natural groupings in seed sources, such as those between ORSisL

(SL), CACst (SA), and WACasH (DL). There were also groupings on the species side, where some species only occured alongside specific other species. There was 8% chaining among rows in the

ESS cluster, but only 3% chaining among columns, indicating strong clustering between seed sources and between endophytes. The EN analysis was less robust, with 8% chaining among rows, but 81% chaining among columns. Because most needles had only one isolated endophyte, there were no endophyte communities at the needle level. Analysis of species richness (α diversity) by seed source (ESS data set) ranged from 1.1 for ORSisL (Stone Nursery local seed source) to 2.6 for WACst (Buckhorn2 non-local seed source), with an average of 2.1 for all sites

(Table 2.7). Analysis of endophytes by needle (data set EN) had a low average α diversity (0.2) due to the large number of needles with only a single isolated endophyte species. Between-site heterogeneity (β diversity) and species richness over a range of habitats (γ diversity) are also shown in Table 2.7. Page 55

Figure 2.3: Bar plot showing the percent infection rate for each seed source, grouped by planting site. Infection rates ranged from 5% in the Stone Nursery local seed source to 60% in a Buckhorn2 non-local seed source.

Figure 2.4: Two-way cluster analysis of endophytes by seed source (ESS) data. Page 56

Figure 2.5: Two-way cluster analysis of endophytes by needle (EN) data. Page 57

Table 2.7: Alpha, beta, and gamma species diversity of endophytes in Douglas-fir needles from EN and ESS data sets. Diversity measure Data set N α β γ Endophytes by Needles (EN) 155 0.2 35.2 7 Endophytes by Seed Source (ESS) 11 2.1 4.4 9

MRPP tested the null hypothesis of no difference between groups. For both the EN and

ESS data sets, the statistics provide strong evidence that we should reject the null hypothesis

(Tables 2.8, 2.9). In all cases, observed δ (weighted mean within-group distance) was smaller than expected by chance, and the probability of a δ equal to or less than the observed value was very small. In addition, A, the chance-corrected within-group agreement, was between 0 and 1, indicating there was more heterogeneity within groups than expected by chance. Pairwise comparisons were also performed, comparing each site to the others. The p-values for these pairwise comparisons were not corrected for multiple comparisons (i.e. Bonferroni correction).

Ten of the twelve pairwise comparisons had statistically significant p-values.

Table 2.8: Multi-response Permutation Procedures results for ESS data set. Note that multiple comparison p-values were not corrected for multiple comparisons (i.e. Bonferroni). Groups Observed δ Expected δ T p-value A Sørensen 0.66 0.80 -3.30 0.003 0.18

Multiple Comparisons (Sørensen) Buckhorn2 vs. Doorstop -1.06 NaN 0.07 Buckhorn2 vs. Nortons -1.96 0.041 0.14 Buckhorn2 vs. Stone Nursery -1.88 0.037 0.11 Doorstop vs. Nortons -1.90 0.000 0.21 Doorstop vs. Stone Nursery -0.32 NaN 0.03 Nortons vs. Stone Nursery -2.51 0.025 0.23

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Table 2.9: Multi-response Permutation Procedures results for EN data set. Note that multiple comparison p-values were not corrected for multiple comparisons (i.e. Bonferroni). Groups Observed δ Expected δ T p-value A Sørensen 0.85 0.93 -20.31 0.000 0.08

Multiple Comparisons (Sørensen) Buckhorn2 vs. Doorstop -10.48 0.000 0.05 Buckhorn2 vs. Nortons -10.34 0.000 0.04 Buckhorn2 vs. Stone Nursery -8.72 0.000 0.04 Doorstop vs. Nortons -15.26 0.000 0.07 Doorstop vs. Stone Nursery -6.88 0.000 0.05 Nortons vs. Stone Nursery -10.62 0.000 0.07

NMS results for the EN data set converged on a stable two-dimensional solution (Table

2.10). The final stress of the two-dimensional solution was 9.24, with a final instability of 0.0000 from 94 iterations. This solution also had a chance-corrected final improvement value of 0.77 (1 being a perfect fit). Dimensionality was assessed, and the two-dimensional solution offered the greatest improvement, with all subsequent solutions offering insufficient improvement for use in analysis. Cumulatively, 56.25% of variation could be attributed to the two axes chosen by the model. Endophyte species with the strongest correlations were Xylaria hypoxylon (L.) Grev. (Axis

1 r = 0.577), Taphrina communis (Sadeb.) Giesenh. (Axis 2 r = 0.352), and Crustomyces subabruptus

(Bourdot & Galzin) Jülich. (Axis 2 r = 0.350). In the NMS 2D ordination graph, winter relative humidity (RH_wt) was most positively correlated with Axis 1 (r = 0.506), while continentality

(TD; temperature difference between mean warmest month and mean coldest month temperature) was most negatively correlated with that same axis (r = -0.513) (Figure 2.6).

Longitude (r = 0.435) and autumn relative humidity (r = 0.426) had the strongest positive correlations with Axis 2, and mean annual temperature (MAT) had the strongest negative Page 59 correlation with Axis 2 (r = -0.428). PC-ORD was not able to find a useful NMS ordination for the

ESS data set, due to weakly structured data (Table 2.11). Outlier analysis did not find any outliers in either data set. Additional figures showing the correlation of various species and environmental variables with these two axes can be found in Appendix II.

Table 2.10: Stress in relation to dimensionality (number of axes) comparing 50 runs on the real data with 50 runs on randomized data from the EN data set. The p-value is the proportion of randomized runs with stress less than or equal to the observed stress. The two-dimensional solution is highlighted for use in further analysis. Stress in real data Stress in randomized data Axes Minimum Mean Maximum Minimum Mean Maximum p-value 1 17.59 26.87 40.44 29.76 32.51 35.51 0.0196 2 8.94 9.86 11.00 14.05 16.21 20.33 0.0196 3 5.28 5.82 6.51 8.43 9.34 10.72 0.0196 4 3.54 3.87 4.40 5.44 6.19 7.32 0.0196

Table 2.11: Stress in relation to dimensionality (number of axes) comparing 50 runs on the real data with 50 runs on randomized data from the ESS data set. The p-value is the proportion of randomized runs with stress less than or equal to the observed stress. No useful NMS ordination was found from this data. Stress in real data Stress in randomized data Axes Minimum Mean Maximum Minimum Mean Maximum p-value 1 27.92 39.74 52.21 19.51 35.22 51.64 0.294 2 12.99 16.32 21.79 4.86 13.48 24.78 0.451 3 5.32 6.73 9.17 2.07 2.07 9.60 0.412 4 0.55 1.39 4.85 0.01 1.91 4.60 0.196

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Figure 2.6: Nonmetric multidimensional scaling (NMS) ordination of endophytes by needle (EN) data, with sample units (needles, represented by triangles in figure), variables (species, represented by blue dots), and environmental factor joint biplot overlay. The three fungi labeled represent the three strongest correlations to the axes of the figure. The two strongest environmental variables with correlations with either axis were RH_wt and TD. RH_wt (winter relative humidity) was most positively correlated with Axis 1 (r = 0.506), while continentality (TD) was most negatively correlated with the same axis (r = -0.513).

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The environmental variables used in statistical analysis were based on the outcome of the NMS ordination as well as previous research (Manter et al., 2005; Stone et al., 2008).

Continentality (TD) and winter relative humidity (RH_wt) were chosen at the two most relevant variables from the NMS ordination, and mean coldest month temperature (MCMT) was chosen based on Stone et al. (2008) and Mater et al. (2005). Each variable was analyzed separately with relation to seed source type. Model assumptions were tested and confirmed for each interaction analysis. The relationship between mean number of fungal endophytes and each environmental variable (TD, RH_wt, MCMT) were assessed to determine if they differed between local and non-local seed sources, by averaging the total number of endophytes for a seed source type without regard to the number of needles sampled. This was to find a number of endophytes rather than a number of endophytes per needle. All endophyte counts from non-local seed sources at a site were averaged to create a mean average number of endophytes per non-local seed source. Analyses were performed on the mean average number of endophytes using

ANOVA function in R (Tables 2.11, 2.12, 2.13). There was strong evidence for the influence of seed source type on the relationship between continentality (Table 2.12) and mean average number of endophytes but not winter relative humidity (Table 2.13) or mean coldest month temperature (Table 2.14). Estimated variances (σ2) were 18.29 (TD), 20.48 (RH_wt), and 41.53

(MCMT).

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Table 2.12: ANOVA output for interaction of continentality (TD) and seed source type on mean average number of endophytes within each planting site. Response: mean average number of endophytes per seed source.

Df Sum Sq Mean Sq F-value p-value TD 1 61.972 61.972 3.388 0.139 SSType 1 81.281 81.281 4.444 0.103 TD : SSType 1 176.058 176.058 9.626 0.036 Residuals 4 73.157 18.289

Table 2.13: ANOVA output for interaction of winter relative humidity (RH_wt) and seed source type on mean average number of endophytes within each planting site. Response: mean average number of endophytes per needle.

Df Sum Sq Mean Sq F-value p-value RH_wt 1 85.033 85.033 4.153 0.111 SSType 1 81.281 81.281 3.969 0.117 RH_wt : SSType 1 144.245 144.245 7.044 0.057 Residuals 4 81.910 20.478

Table 2.14: ANOVA output for interaction of mean coldest month temperature (MCMT) and seed source type on mean average number of endophytes within each planting site. Response: mean average number of endophytes per needle.

Df Sum Sq Mean Sq F-value p-value MCMT 1 51.644 51.644 1.243 0.327 SSType 1 81.281 81.281 1.957 0.234 MCMT : SSType 1 93.402 93.402 2.249 0.208 Residuals 4 166.141 41.535

Differences between mean average number of endophytes and 95% confidence intervals were constructed based on the model outputs (Tables 2.11, 2.12, 2.13, Figures 2.7, 2.8, 2.9). The incremental effect of continentality (TD) on mean average number of endophytes for a local seed source was estimated to be 2.24 endophytes fewer than the default (local seed source with

TD of 0) (95% CI: [-4.018, -0.461]). The incremental effect of non-local seed source on mean average number of endophytes at a site with TD of 0 was estimated to be 45.76 endophytes Page 63 fewer than the default (95% CI: [-93.172, -1.643]). The incremental effect of the interaction between TD and non-local seed source was estimated to be 2.81 endophytes more than the control (95% CI: [0.295, 5.326], Figure 2.7).

The incremental effect of winter relative humidity (RH_wt) on mean average number of endophytes for a local seed source was estimated to be 2.55 endophytes more than the default

(local seed source with RH_wt of 0) (95% CI: [0.415, 4.678]). The incremental effect of non-local seed source on mean average number of endophytes at a site with RH_wt of 0 was estimated to be 221.75 endophytes more than the default (95% CI: [-3.731, 447.235]). The incremental effect of the interaction between RH_wt and non-local seed source was estimated to be 2.88 endophytes fewer than the control (95% CI: [-5.895, 0.133], Figure 2.8).

The incremental effect of mean coldest month temperature (MCMT) on mean average number of endophytes for a local seed source was estimated to be 5.57 endophytes more than the default (local seed source with MCMT of 0) (95% CI: [-2.793, 13.929]). The incremental effect of non-local seed source on mean average number of endophytes at a site with MCMT of 0 was estimated to be 11.48 endophytes more than the default (95% CI: [-4.314, 27.282]). The incremental effect of the interaction between MCMT and non-local seed source was estimated to be 6.386 endophytes less than the control (95% CI: [-18.211, 5.437], Figure 2.9). Page 64

Figure 2.7: Difference in mean average number of endophytes per seed source by interaction between seed source type and continentality (TD). Black circles represent difference, bars indicate 95% confidence intervals. The gray bar represents the 20% biological threshold (±29.9 mean average number of endophytes).

Figure 2.8: Difference in mean average number of endophytes per seed source by interaction between seed source type and winter relative humidity (RH_wt). Black circles represent difference, bars indicate 95% confidence intervals. The gray bar represents the 20% biological threshold (±29.9 mean average number of endophytes). Page 65

Figure 2.9: Difference in mean average number of endophytes per seed source by interaction between seed source type and mean coldest month temperature. Black circles represent difference, bars indicate 95% confidence intervals. The gray bar represents the 20% biological threshold (±29.9 mean average number of endophytes).

Objective 3: Characterize fungal communities in association with Phaeocryptopus gaeumannii that may be additionally implicated in Douglas-fir needle-cast.

No isolates of Phaeocryptopus gaeumannii were sequenced from any of the sampled needles. Stomatal occlusion typical of P. gaeumannii infection (Figure 1.3) was observed visually on nearly all needles at Buckhorn2, Doorstop, and Nortons planting sites, but were not seen at

Stone Nursery, observations supported by Wilhelmi (2016). Because P. gaeumannii was not isolated from any cultures, it is not possible to discuss any absolutes regarding community relationships between endophytes and P. gaeumannii. However, there were a few endophytes which were found only at Stone Nursery, the site without P. gaeumannii. Dothideomycetes sp., Page 66

Aureobasidium sp. and Aureobasidium pullulans were only found at Stone Nursery, as was Sydowia

Polyspora. Both A. pullulans and S. polyspora are known pathogens of other species (Cooke, 1959;

Talgø et al., 2010b). Twenty-seven endophytes were found at Nortons, Buckhorn2, and Doorstop, exclusive of Stone Nursery. These include species like Xylaria hypoxylon, Cladosporium perangustum, and Crustomyces subabruptus. Although only speculation, based on the data collected by Wilhelmi (2016) regarding the percentage of trees from each seed source with moderate to severe P. gaeumannii infection, it seems clear that this fungus, as well as the endophytic communities found at each site, are primarily site or climate adapted.

Discussion

Ordination demonstrated evidence that there were differences between endophyte communities among sites, and that those differences were greater than any influence provided by seed source type. The cluster analyses indicated natural grouping of the endophytes by seed source (ESS) data beyond that inherent in the test itself. The relatedness of Stone Nursery (SL and SA) and Doorstop (DL) seed sources was one example of this grouping. However, the clustering overall was grouped by site, with each site’s three seed sources (one local and two non-local) clustered closer together. This provided further evidence that sites themselves were more influential on endophyte communities than seed source.

The cluster analysis of the endophytes by needle (EN) data set demonstrated how the high beta diversity made the data difficult to analyze in its current state. Multi-Response Page 67

Permutation Procedures (MRPP) demonstrated statistically significant differences between groups, which were also reflected in the pairwise comparisons. The NMS ordination graph showed distinct separations between all sites, though there was also a significant amount of overlap. There appeared to be at least some influence of continentality and winter relative humidity on endophyte populations. These endophytes all shared the same host species,

Douglas-fir, so it should not be surprising to see many of the same species within needles at different sites. What was surprising to observe were the relatively weak correlations between specifically measured environmental factors and endophyte populations according to the NMS ordination.

The three endophytes with the strongest correlations in the NMS ordination were

Crustomyces subabruptus, Taphrina communis, and Xylaria hypoxylon. It is worth noting that these three were among the most frequently isolated endophytes, which may impact their significance in the NMS ordination. The first of these, Crustomyces subabruptus, is a latent endophyte commonly found in Fagus sylvatica in Italy and Greece (Bernicchia et al., 2007;

Dimou et al., 2002), and Quercus sp. in Russia (Spirin, 2002). Taphrina communis is a well-known pathogen associated with the disease called plum leaf curl in Prunus sp. (Wylie, 1966). The third,

Xylaria hypoxylon, is a saprophyte, a fungus that lives on dead or decaying organic matter. It has been found as an endophyte in other studies of Oregon plants, as well as the tropical palm

Livistona chinensis (Jacq.) R.Br. ex Mart., and is a generally ubiquitous species (Guo et al., 2000;

Petrini et al., 1982). In addition to these three species, it is also significant that Sydowia polyspora Page 68 was among the fungi isolated in this study. Although it was only isolated from needles collected from the California Sierra seed source at Stone Nursery, it nonetheless shows that this species exists in Douglas-fir needles in Oregon. The question of its abundance and virulence in coastal Oregon Douglas-fir remains to be answered, but it may present an alternative to the singularly Phaeocryptopus gaeumannii-focused defoliation story. Since no P. gaeumannii was found at Stone Nursery, presumably due to environmental conditions (Wilhelmi 2016), S. polyspora may fill a niche where P. gaeumannii cannot. We also found Rhabdocline parkeri, the previously-described ubiquitous endophyte of Douglas-fir. It was primarily isolated only from

Stone Nursery, with one isolate from Buckhorn2. It is unknown whether the limitations of the study obfuscated the true abundance of R. parkeri, or if it is less abundant that previously thought. Other species of note were Anthostomella pinea Crous (Langenfeld et al., 2013),

Aureobasidium pullulans (de Bary & Löwenthal) G. Arnaud (Cooke, 1959), Cladosporium perangustum Bensch, Crous & U. Braun (Oliveira et al., 2014; Pak et al., 2017), Cryptostroma corticale (Ellis & Everh.) P.H. Greg. & S. Waller (Emanuel et al., 2009), Ophiognomonia alni-viridis

(Podl. & Svrček) Sogonov (Roy and Mulder, 2014), Penicillium glabrum (Wehmer) Westling

(Bardas et al., 2009), and Rosellinia quercina R. Hartig (Huang et al., 2009; Promputtha et al., 2005;

Waldie, 1930), all pathogenic fungi known to infect various plant hosts.

The relationship between mean number of fungal endophytes and certain environmental variables differed between local and non-local seed sources for these 11 seed sources at the four sites outlined above (Table 2.2). This was separate from the ordination Page 69 results, as they focused on the species and their community structures, while the statistical analysis focused instead on counts and frequencies of endophytes regardless of species.

According to the model, continentality (TD) had little influence on the mean average number of endophytes alone, yet the interaction between TD and seed source type had a statistically significant influence on the explanatory variable. Non-local seed source regardless of environmental variable led to higher mean average number of endophytes, although the 95% confidence interval for the difference in mean average number of endophytes contained 0 in all cases (Figures 2.6, 2.7, 2.8). Based on that confidence interval, non-local seed sources could also lead to a biologically significant reduction in mean average number of endophytes, as the confidence interval included both the positive and negative 20% threshold. This was perhaps one indication that the threshold for biological meaningfulness was not appropriate for these data. Another would be the fact that none of the environmental variables demonstrated biological meaningfulness based on the estimate provided earlier, though this could also be because none of the environmental variables chosen for the study were, in reality, biologically meaningful with regard to endophyte community structure or frequency. Perhaps it is the case that some other untested environmental variables are more important in determining structure or frequency.

Seed source type, though inconclusive, still demonstrated the strongest impact on mean average number of endophytes for these seed sources and sites, with -0.20 and 0.37 additional endophytes per needle among non-local seed sources. The raw data suggested that this may be Page 70 a site-specific feature, though a stronger study design will be needed to confirm this trend.

Future studies can determine if these endophytes in the non-local seed sources are beneficial or parasitic, and what, if any, impact they have on resistance or susceptibility to SNC and

Rhabdocline needle cast. The opportunities for more thorough research, even on these limited sites, are substantial.

There are many limitations on this data, including small sample size, nested and not crossed data, and limited scope of inference. In future studies to observe relationships between environmental variables and endophyte communities, the utmost care should the taken to create a study design to avoid all of the aforementioned pitfalls. The methodology in this endophyte study inevitably influenced the results we found, and so it must also be included as a limitation (Guo et al., 2001). The exclusive use of potato dextrose agar (PDA) as the initial growth media could have affected the species of endophytes isolated. Some species will not grow on PDA, and others may be completely unculturable (Oono et al., 2015). Therefore, the results of the study are limited to only those endophytes that can grow fast enough to be isolated, and will grow on PDA media (Hyde and Soytong, 2008). Limits on time and resources necessitated the use of a single media. However, other endophyte species such as Phaeocryptopus gaeumannii do not sporulate in culture. Instead, pseudothecia must be suspended above water agar, with individual ascospores collected and grown on PDA (Winton, 2001). Observationally,

SNC and P. gaeumannii are both present at these sites, but additional time was unavailable to collect and incubate individual ascospores. Page 71

The failure to perform whole-needle shotgun sequencing was attributed to unsuccessful extraction due to the high phenol content of Douglas-fir needles interfering in the fungal DNA extraction process. Though no confirmation was available, this issue has been identified as the point of failure in other studies (Luchi et al., 2016; Wilson, 1997). Difficulties in sequencing may be the result of numerous problems (Lindahl et al., 2013), and protocols have not yet been created specifically for Douglas-fir needles. A need exists to determine a reliable method of extracting fungal DNA from Douglas-fir needles for the purposes of NGS so that researchers may better understand the composition of and interaction between members of the microbiome of Douglas- fir trees (Hyde and Soytong, 2008). The use of Next-Generation Sequencing (NGS) would have potentially allowed us to develop a deeper and wider view of the endophyte communities within

Douglas-fir needles. Isolation and growth separate from other needle endophytes, such as that needed to isolate P. gaeumannii from needles, would not provide any relevant information on community structures with relation to the pathogen. The needles used for isolation of P. gaeumannii would be different than those used to isolate other endophytes, requiring speculation to infer relationships between endophytes. With NGS, direct relationships between endophytes and pathogens within the same needle could be observed. For this reason, NGS is vital to understanding the fungal microbiome of Douglas-fir needles.

Summary

This is the first study to identify endophytes of coastal Oregon Douglas-fir through molecular methods. It has expanded the previously known list of Douglas-fir endophytes put Page 72 forth by Carroll and Carroll (1978) four-fold, and outlines fungal community structures based on environmental variables and seed source provenance within the limitations of the study. The microbial community of Douglas-fir is a topic deserving of further attention, as it may have tangible effects on plantation management and assisted migration of Douglas-fir seedlings.

Effective management of Douglas-fir is complex, and becoming ever more so with increasingly extreme climatic changes (IPCC, 2014), pathogen epidemics increasing in both number and severity, and increased pressure to meet world-wide demand for quality wood with fewer resources. This study demonstrated that diversity of fungal species is high in

Douglas-fir foliage, though overall biodiversity was low due to the limitations of the study. This may be influenced by environmental variables, including the provenance of the seed source or the climate at the growing site. Endophytes are important factors in the management of plants and forest trees (Rajala et al., 2014), so the growing interest in microbiome communities will inevitably lead to advances in best management practices, potentially including steps such as inoculation with local beneficial endophytes to stave off infection by critical mass as seen with

P. gaeumannii. The fine-grain structure of endophyte communities in Douglas-fir, especially those in relation to P. gaeumannii, and their relation to environmental conditions should be more deeply addressed in future studies.

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Smerlis, E., 1970. Notes on Sydowia polyspora. Can. J. Bot. 48, 1613–1615. doi:10.1139/b70-238 Spirin, V.A., 2002. Aphyllophoroid macromycetes in oak forests of Nizhny Novgorod Region. Mikol. Fitopatol. 36, 43–52. St. Clair, J.B., Mandel, N.L., Vance-Borland, K.W., 2005. Genecology of Douglas Fir in Western Oregon and Washington. Ann. Bot. 96, 1199–1214. doi:10.1093/aob/mci278 Stone, J.K., 1997. Rhabdocline Needle Casts, in: Hansen, E.M., Lewis, K.J. (Eds.), Compendium of Conifer Diseases. The American Phytopathological Society, St. Paul, MN, pp. 54–55. Stone, J.K., 1987. Initiation and development of latent infections by Rhabdocline parkeri on Douglas-fir. Can. J. Bot. 65, 2614–2621. doi:10.1139/b87-352 Stone, J.K., 1986. Foliar endophytes of Pseudotsuga menziesii (Mirb.) Franco: cytology and physiology of the host-endophyte relationship. Eugene PhD Diss. Univ. Or. 124p. Stone, J.K., Coop, L.B., Manter, D.K., 2008. Predicting effects of climate change on Swiss needle cast disease severity in Pacific Northwest forests. Can. J. Plant Pathol. 30, 169–176. doi:10.1080/07060661.2008.10540533 Talgø, V., Brodal, G., Klemsdal, S.S., Stensvand, A., 2010a. Seed borne fungi on Abies spp. Seed Sci. Technol. 38, 477–493. doi:10.15258/sst.2010.38.2.20 Talgø, V., Chastagner, G., Thomsen, I.M., Cech, T., Riley, K., Lange, K., Klemsdal, S.S., Stensvand, A., 2010b. Sydowia polyspora associated with current season needle necrosis (CSNN) on true fir (Abies spp.). Fungal Biol. 114, 545–554. Tinivella, F., Dani, E., Minuto, G., Minuto, A., 2013. First Report of Sydowia polyspora on Aleppo Pine (Pinus halepensis) in Italy. Plant Dis. 98, 281–281. doi:10.1094/PDIS-06-13- 0658-PDN Waldie, J.S.L., 1930. An Oak seedling disease caused by Rosellinia qnercina Hartig. Forestry 4. Weiskittel, A.R., Maguire, D.A., Garber, S.M., Kanaskie, A., 2006. Influence of Swiss needle cast on foliage age-class structure and vertical foliage distribution in Douglas-fir plantations in north coastal Oregon. Can. J. For. Res. 36, 1497–1508. White, T.J., Bruns, T., Lee, S., Taylor, J., 1990. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protoc. Guide Methods Appl. 18, 315–322. Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis (Use R!). Springer, Dordrecht, Heidelberg, London, New York. 213 pp. Wilhelmi, N., 2016. The Effects of Seed Source and Planting Environment on Douglas-fir (Pseudotsuga menziesii) Foliage Diseases. Corvallis MS Thesis Or. State Univ. 170p. Wilson, I.G., 1997. Inhibition and facilitation of nucleic acid amplification. Appl. Environ. Microbiol. 63, 3741–3751. Winton, L.M., 2001. Phylogenetics, population genetics, molecular epidemiology, and pathogenicity of the Douglas-fir Swiss needle cast pathogen Phaeocryptopus gaeumannii. Corvallis PhD Diss. Or. State Univ. 112p. Winton, L.M., Manter, D.K., Stone, J.K., Hansen, E.M., 2003. Comparison of biochemical, molecular, and visual methods to quantify Phaeocryptopus gaeumannii in Douglas-Fir foliage. Phytopathology 93, 121–126. Page 78

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Chapter 3: General Conclusions

This work expands on the known community of endophytes within coastal Douglas-fir foliage. Genetic determination of species is more robust than results produced through visual/morphological methods due to the reduction of human error throughout the process.

Utilizing more modern molecular methods such as Sanger sequencing to identify species rather than morphological traits expands on previous findings (Carroll and Carroll, 1978), revealing a more than four-fold increase in the number of known species of endophytes of West Coast

Douglas-fir.

Chapter one provided a comprehensive review of the literature on endophytes, including their uses in medicine, their potential impact on disease incidence, and the ways in which they maintain a balanced antagonism within the host-endophyte relationship. It also included a discussion on the classification of species as endophytes, and whether pathogenic microbes should be defined as endophytes. Finally, chapter one defined some known endophytes and pathogenic endophytes of Douglas-fir, including Rhabdocline parkeri, R. pseduotsugae, and

Phaeocryptopus gaeumannii, and how these fungi have shaped forest management and industry in

Oregon.

Chapter two described 46 unique isolates from 39 different taxa, and assessed fungal community structures based on environmental variables and seed source provenance. Ordination analysis revealed that continentality and winter relative humidity impacted community structure Page 80 at the site level. Needle-level community analysis was limited by the dearth of endophytes isolated from individual needles. Seed source type (local or non-local) had a statistically significant effect on endophyte frequency among isolated endophytes, as did continentality, but based on the analysis as a whole do not seem biologically meaningful. Biological significance, set at a threshold of 20% change in mean average number of endophytes per site, was not observed for any of the tested environmental variables.

Difficulties growing and isolating Phaeocryptopus gaeumannii precluded any analysis of community structure with relation to the pathogen, but visual identification of presumed P. gaeumannii reproductive structures on needles from three of the four planting sites confirmed the importance of continuing research on the microbial communities in Douglas-fir. Field- identification of P. gaeumannii pseudothecia at Buckhorn2, Doorstop, and Nortons planting sites was supported by Wilhelmi (2016). Because P. gaeumannii was not isolated directly from the needles from which community information was drawn, we can only speculate that perhaps the endophyte communities at each of these three sites are those associated with P. gaeumannii. The two-way cluster analysis in chapter two indicates that there is no singular community, nor is there a singular endophyte species in common among all three sites (Figure 2.4). Xylaria hypoxylon comes closest to being ubiquitous among the three sites, but was not isolated from Doorstop. The limitations of the study, namely using a single growth media, likely affected the endophytes isolated, and therefore the community data generated from those isolates. Page 81

Despite the limitations of the study, a number of endophytes were successfully isolated from Douglas-fir needles, including several known pathogens of trees and herbaceous plants. The most frequently isolated endophyte was Xylaria hypoxylon, a saprophytic fungus that lives on dead or decaying organic matter. Its high frequency may be due to errors in needle preparation before plating, or it may be that this species also lives as a quiescent endophyte until needle senescence. It has been found as an endophyte in other studies of Oregon plants, and is a generally ubiquitous species (Petrini et al., 1982). The next most frequently isolated endophyte was Crustomyces subabruptus, a latent endophyte found in Fagus sylvatica in Italy and Greece

(Bernicchia et al., 2007; Dimou et al., 2002), and Quercus sp. in Russia (Spirin, 2002). Hypoxylon rubiginosum (Pers.) Fr. was had the third-highest frequency of isolation from Douglas-fir needles.

It is known to cause slow-decay in Fraxinus excelsior (Boddy et al., 1987; Vasiliauskas and Stenlid,

1998). Among the less frequently isolated endophytes were a few species specifically associated with conifers. Anthostomella conorum (Fuckel) Sacc. Is an endophyte associated with Pinus and

Picea (Bartyńska and Mirski, 2005; Sieber, 1989). Anthostomella pinea is a saprotrophic pathogenic endophyte associated with Pineceae and other species (Crous et al., 2010). Coniochaeta hoffmannii

(J.F.H. Beyma) Z.U. Khan, Gené & Guarro is a wood decay fungus associated with Pinus sylvestris knotwood and deadwood (Kwaśna et al., 2017; Szewczyk et al., 2017). Geopyxis rehmii Turnau is known as a root associate of Picea abies (Vrålstad et al., 1998). Perusta inaequalis Egidi & Stielow is found in Pinus sylvestris knotwood (Szewczyk et al., 2017). Rhabdocline parkeri is a ubiquitous endophyte associated exclusively with Douglas-fir (Sherwood-Pike et al., 1986; Stone, 1988, 1987).

Sydowia polyspora is a pathogen associated with Current Season Needle Necrosis of Abies and has Page 82 also been found on multiple conifer species (Smerlis, 1970; Sutton and Waterston, 1970; Talgø et al., 2010; Tinivella et al., 2013).

Opportunities for further research include interaction studies between endophytic fungal species and known pathogens (e.g. Phaeocryptopus gaeumannii, Rhabdocline pseudotsugae) to test for antagonism or secondary pathogenicity between species. Additionally, bacterial endophytes are a relatively unexplored aspect of Douglas-fir endophyte communities, but may contribute as much as fungal endophytes to disease incidence or protection. Expanding on the impact of environmental aspects of disease incidence and endophyte community structure are another potential topic for future research. Other needle-cast pathogens, such as Sydowia polyspora, were isolated from Douglas-fir foliage. While Koch’s postulates were satisfied by P. gaeumannii, the causal pathogen of Swiss Needle Cast, it may be worth investigating whether other pathogens cause disease symptoms similar or identical to P. gaeumannii in the field. Finally, there is a need to establish reliable high-throughput methods for endophyte identification in Douglas-fir foliage to advance the molecular methodology of this important species. Current methods are inadequate to meaningfully associate fungal communities. When multiple growth media are required to cultivate isolates, the true community structure becomes fractured. High throughput sequencing will give researchers a more precise idea of which endophytes live within needles, and their relative abundances. This will provide a much more robust picture of the endophytic communities of Douglas-fir foliage. Page 83

Understanding the impacts of the microbiome on disease of west-coast Douglas-fir foliage is vital to the forest industry which supports Oregon’s economic and ecological interests. It is important to determine all sources of disease when creating management plans, in particular where even-age, single-species planning strategies are employed, in order to mitigate losses such as those experienced from the Swiss Needle Cast epidemic affecting parts of coastal Oregon.

Further research into endophytes and their community structures will strengthen these management plans for the eventuality of increased disease pressure from shifting climate variables, assisted migration, and exotic/invasive pathogens.

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Appendix I: Modified cetrimonium bromide (CTAB) DNA extraction protocol for fungi from culture

1. Turn on the heat block to 65°C. Add 300µL of 2X CTAB buffer to 1.5mL microcentrifuge tubes equal to the number of samples you have. Add a small quantity of hyphae or spore- bearing tissue to labeled tubes (a “small lump”) using sterile technique.

2. Soften tissue by freezing and thawing. Place tubes in freezer until frozen (~10 min) and incubate at 65°C; repeat two more times. Crush fungal tissue with a sterile micropestle. Freeze and thaw one more time to break fungal cell walls.

3. Incubate microcentrifuge tubes at 65°C for 40 minutes.

4. Remove tubes from heat block and add 300µL of chloroform (V:V with the CTAB) in a fume hood. Vortex well (5-10 sec.). This should result in a milky solution.

5. Centrifuge at 13,000g for 15 minutes. a. During centrifugation, label another set of 1.5 mL microcentrifuge tubes with the original tube labels.

6. Remove upper phase and put it in a clean labeled tube (being careful not to pull any of the lower phase or DNA extraction will fail). Discard lower phase in appropriate waste container.

7. Precipitate DNA with 500µL ice cold isopropanol. Invert samples to mix 2X and put samples in the freezer for at least 3 hours.

8. Take samples out of the freezer and quickly centrifuge at 13,000g for 8 minutes.

9. Carefully pour off supernatant and discard into the appropriate waste container. If you can see a pellet formed, watch that it doesn’t pour off. Wash pellets; add 500µL ice cold 70% Ethanol to each tube.

10. Invert tubes 2X and centrifuge for 5 minutes.

11. Discard supernatant into appropriate container and flick tubes to remove as much alcohol as possible. Place open tubes in the laminar flow hood (close to the filter) and let residual alcohol dry off (~ 30 min). Do not proceed until all alcohol is gone. Test for this by flicking tubes and looking for liquid residue.

12. Resuspend pellet in 50µL TE buffer. Vortex to ensure pellet has dissolved. Dilutions may be necessary if DNA quantity is too high. Page 97

Appendix II: Supplemental Ordination Figures

Figure A.1: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Anthostomella conorum and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

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Figure A.2: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Anthostomella pinea and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.3: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Apodus deciduus and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 99

Figure A.4: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Aposphaeria corallinolutea and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.5: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Aureobasidium pullulans and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 100

Figure A.6: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Aureobasidium sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.7: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Cladosporium perangustum and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 101

Figure A.8: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Claussenomyces sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.9: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Clypeosphaeria mamillana and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 102

Figure A.10: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Clypeosphaeria sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.11: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Coniochaeta hoffmannii and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 103

Figure A.12: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Crustomyces subabruptus and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.13: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Cryptococcus sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 104

Figure A.14: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Cryptostroma corticale and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.15: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Diaporthe sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 105

Figure A.16: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Dothideomycetes sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.17: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Geopyxis rehmii and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 106

Figure A.18: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Graphostroma sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.19: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Helicoon fuscosporum and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 107

Figure A.20: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Hormonemia sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.21: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Hypoxylon rubiginosum and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 108

Figure A.22: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Lecythophora sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.23: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Melanomma pulvis-pyrius and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 109

Figure A.24: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Nemania sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.25: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Nemania serpens and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 110

Figure A.26: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Ophiognomonia alni-viridis and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.27: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Penicillium glabrum and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 111

Figure A.28: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Perusta inaequalis and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.29: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Pezizomycetes sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 112

Figure A.30: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Phaeomoniella zymoides and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.31: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Phomatospora_biseriata and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 113

Figure A.32: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Podospora sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.33: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Preussia bipartis and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 114

Figure A.34: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Rhabdocline parkeri and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.35: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Rosellinia sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 115

Figure A.36: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Rosellinia quercina and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.37: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Sordariales sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 116

Figure A.38: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Sordariomycetes sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.39: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Sporormiaceae sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 117

Figure A.40: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Sydowia polyspora and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.41: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Talaromyces ruber and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 118

Figure A.42: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Taphrina communis and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.43: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Taphrina veronaerambelliiand each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 119

Figure A.44: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Wallemia sebi and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.45: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Xylaria hypoxylon and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 120

Figure A.46: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Xylaria sp. and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 121

Figure A.47: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing seed sources relationships within a stable two axis ordination, with sample units (needles, represented by triangles in figure), species (represented by blue dots in figure), and environmental factor joint biplot overlay.

Page 122

Figure A.48: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between winter (Dec – Feb) Hargreaves climatic moisture deficit (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.49: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between autumn (Sept – Nov) Hargreaves climatic moisture deficit (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 123

Figure A.50: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between average Hargreaves climatic moisture deficit (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.51: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between summer (Jun – Aug) Hargreaves climatic moisture deficit (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 124

Figure A.52: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between spring (Mar – May) Hargreaves climatic moisture deficit (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.53: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between degree-days above 18℃ (cooling degree-days) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 125

Figure A.54: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between degree-days below 0℃ (chilling degree-days) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.55: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between degree-days below 18℃ (heating degree-days) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 126

Figure A.56: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between degree-days above 5℃ (growing degree-days) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.57: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between elevation and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 127

Figure A.58: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between Hargreaves reference evaporation (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.59: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between frost-free period and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 128

Figure A.60: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between latitude and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.61: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between longitude and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 129

Figure A.62: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between mean coldest month temperature (℃) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.63: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between mean annual precipitation (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 130

Figure A.64: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between mean annual temperature (℃) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.65: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between mean warmest month temperature (℃) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 131

Figure A.66: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between mean annual summer (May – Sept) precipitation (mm) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.67: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between number of frost-free days and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 132

Figure A.68: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between precipitation as snow (mm) between Aug in previous year and Jul in current year and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.69: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between winter relative humidity and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 133

Figure A.70: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between autumn relative humidity and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.71: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between average relative humidity and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 134

Figure A.72: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between summer relative humidity and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.73: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between spring relative humidity and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 135

Figure A.74: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between winter mean minimum temperature (℃) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.

Figure A.75: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between winter mean temperature (℃) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay. Page 136

Figure A.75: Non-metric multidimensional scaling (NMS) of endophytes by needle (EN) data, showing the correlation between temperature difference between MWMT and MCMT; continentality (℃) and each axis within a stable two axis ordination, with sample units (needles, represented by triangles in figure) and environmental factor joint biplot overlay.