Examining Ectomycorrhizal Communities of Ponderosa Pine and Lodgepole Pine in the South-Central Oregon Pumice Zone

AN ABSTRACT OF THE THESIS OF

Maria Osuna Garcia for the degree of Master of Science in Forest Science presented on June 28, 2013. Title: Examining Ectomycorrhizal Communities of Ponderosa Pine and Lodgepole Pine in the South-central Oregon Pumice Zone

Abstract approved:

______Jane E. Smith

Background information is presented that provides historical perspectives on the field of mycology in the Pacific Northwest and its role in forest management. The series of events and decisions that have led to previous studies (or lack of studies) in the field also dictate the directions of current research. Culture, philosophy, and history all play a role in the questions that may be asked today. Examining the past gives light to the questions that are asked in the present and future. Forest ecosystems of the Pacific Northwest are changing as a result of climate change. Rise of global temperatures, decline of winter precipitation, earlier loss of snowpack, and increased summer drought are altering the range of Pinus contorta. As environmental conditions change, Pinus ponderosa may establish within the historic Pinus contorta range. Successful pine species migration will be constrained by the distribution or co-migration of ectomycorrhizal fungi (EMF). Knowledge of the linkages among soil fungal diversity, community structure, and environmental factors is critical to understanding the organization and stability of pine ecosystems. The objectives of this study are to establish an informational foundation of the EMF communities of P. ponderosa and P. contorta in the Deschutes National Forest and to examine soil characteristics associated with community composition. We examined EMF root tips of P. ponderosa and P. contorta in soil cores and conducted soil chemistry analysis for P. ponderosa. !"#$%&#'()*(+,&"'&-,&'!"#$%$%%&'()"$*+,-&'.'.+,/$*$)$#(01-"23$0&0.',)*' 4#$%52"(6-$%%&-$01(,/"'*01(),)&'()'20&-'78(%$#9$391',)*'78(*$#:"3$01(#0(%'+0/"#3'' .+,/$*$)$#(#443'5"/"'$2(6$(&0$#'()'78(*$#:"3$018''7-"/"'5,#')0'#(8)(9(+,)&'

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Examining Ectomycorrhizal Communities in Ponderosa Pine and Lodgepole Pine in the South-central Oregon Pumice Zone

by Maria Osuna Garcia

A THESIS

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Master of Science

Presented June 28, 2013 Commencement June 2014

Master of Science thesis of Maria Osuna Garcia presented on June 28, 2013.

APPROVED:

______Major Professor, representing Forest Science

______Head of the Department of Forest Ecosystems and Society

______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

______Maria Osuna Garcia, Author

ACKNOWLEDGEMENTS

I would like to thank my advisor Dr. Jane E. Smith, for giving me the opportunity to work on this project. Jane, thank you for your kindness and your patience throughout the years. I would also like to thank my committee members Dr. Paul Doescher and Dr.

Melanie Jones for their support throughout this process. I am grateful to Dr. Dan Luoma for providing support, mentorship, and advice. I am also thankful to Dr. Bruce McCune for providing the resources to tackle the datasets that came along with this project. I would also like to thank all the people at the Corvallis Forestry Sciences Lab that assisted me throughout the project: Levi Davis, and Tara Jennings, Doni McKay, and Joyce

Eberhart.

I am thankful for my support system at home, Norman Forsberg and Alexandra

Hesbrook; you are wonderful friends and have been there for me through some of my biggest challenges. Thank you for your love and support. I would also like to thank Ed

Mitchell for being willing to spend his time teaching me about community analysis and getting me closer to being successful. Of course, I would like to thank my family for risking their lives, crossing the border, and starting a new life in a foreign country, so that

I could have better opportunities such as this one. All the hard work that went into this project is done in their honor.

Most importantly, I would like to thank my undergraduate advisor Dr. Kathleen

Treseder because she was gracious enough to give an undergrad a chance to be involved in science and the opportunity to learn about the world of forestry. I also want to thank all the members in the Treseder lab for teaching me so much, and the California Alliance for Minority Participation Program at the University of California, Irvine for providing

the funds to make it possible for me to make it this far. Finally, I am grateful for the

National Science Foundation Graduate Research Fellowship Program and the USDA

Forest Service Pacific Northwest Research Station, Land and Watershed Management

Program for providing financial support for this endeavor.

TABLE OF CONTENTS

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LIST OF FIGURES

Figure Page

Figure 1. Map showing general area of study site in Central Oregon and distribution of sampling sites in the Deschutes National Forest...... 45'

Figure 2. Percent occurrence of dominant ectomycorrhizal fungus operational taxonomic units in Pinus ponderosa by soil core (black) and site (grey)...... 46'

Figure 3. Percent occurrence of dominant ectomycorrhizal fungus operational taxonomic units in Pinus contorta by soil core (black) and site (grey)...... 47'

Figure 4. NMS ordination of Pinus ponderosa and Pinus contorta soil cores in EMF species space...... 48'

Figure 5. NMS ordination of ponderosa pine sites in EMF species space with superimposed joint plot...... 49'

Figure 6. Linear relationship between Mineralizable N and species richness ...... 52'

LIST OF TABLES

Table Page

Table 1. Pinus ponderosa Sites...... 31'

Table 2. Operational Taxonomic Units of Pinus ponderosa ...... 32'

Table 3. Operational Taxonomic Units of Pinus contorta...... 41'

Table 4. Codes for environmental variables and respective correlations with ordination axes...... 50'

Table 5. Soil Chemistry, Pinus ponderosa...... 51'

Examining Ectomycorrhizal Communities of Lodgepole Pine and Ponderosa Pine in the South-central Oregon Pumice Zone

CHAPTER 1 – HISTORICAL PERSPECTIVES

Many years ago, when Oregon State University was still Oregon’s Agricultural

College, a budding mycologist, by the name of Helen Margaret Gilkey began what became an epidemic in mycological research in the Pacific Northwest (Trappe 1975).

Although Gilkey was originally trained in the botanical sciences, her endeavors in mycology were largely influenced by a truffle mycologist at the University of

California, Berkeley by the name of Professor W.A. Setchell, who was known to collect truffles under eucalyptus trees in Berkeley, California. In 1918, promptly after publishing her doctoral thesis, Gilkey was hired as a faculty member of Oregon

Agricultural College alongside another newly hired budding mycologist, Sanford

Myron Zeller (Trappe et al. 2009). The interesting bit is that neither was hired as a truffle taxonomist, but rather as pathologists, yet thus began a series of investigations that laid the foundation for mycological research in the Pacific Northwest.

Although Gilkey and Zeller’s research dealt primarily with the taxonomic investigation of truffle species, it had been well known (since 1887) that the microscopic fungi which associated with the roots of trees were responsible for the development of truffles and mushrooms (Trappe and Berch 1985). This infection of tree roots by microscopic fungi, is termed “mycorrhizal symbiosis.” The word

“mycorrhiza” is derived from the Greek word roots mycos- and rhiza-, which translate respectively to fungus and root. Mycorrhizal fungi establish beneficial relationships

2 with trees such as Douglas-fir and pine all over the Pacific Northwest. The relationship is described as symbiotic, because the host tree provides photosynthates to the colonized fungi in exchange for nutrients, such as nitrogen and phosphorus, in the soil. Ectomycorrhizal fungi (EMF) form an external sheath of hyphae around the fine roots of the host tree and extend mycelium outward into the soil, reaching larger volumes of soil than roots of trees alone could access. In addition, EMF are a critical component of the forest ecosystem because they link aboveground and belowground components of biogeochemical cycles in forest ecosystems (Treseder and Allen 2000), protect roots from pathogens, and help maintain soil structure (Amaranthus 1998).

The first correct recognition of the symbiotic nature of EMF occurred in 1885 by Albert Bernhard Frank in Prussia (Trappe et al. 2009). Even though the microscope had already been well established as an essential tool for conducting scientific research well before the period of Gilkey and Zeller, mycologists gravitated towards examining sporocarps rather than mycorrhizae. Likely, it was much more feasible to track and investigate fungi by unearthing hypogeous fruiting bodies than to look at the colonized roots of trees. Furthermore, there was an economic and culinary incentive to digging up certain species of truffles from the ground, which helped to support the research endeavors of Oregon’s earliest mycologists.

Zeller and Gilkey continued their research on truffle taxomomy in Oregon for thirty years, and Gilkey continued publishing documents on truffle taxonomy even after her retirement in 1951, up until 1963. Mycologists, Alexander H. Smith and

Rolfe Singer began exploring western fungi in Oregon and Idaho well before 1958

3 (Singer and Smith 1958) and by 1966 Smith published what was “A Preliminary

Account of the North American Species Rhizopogon.” Here, I would like to emphasize the word “Preliminary,” because it is astounding that after several decades of research, mycologists working in the Pacific Northwest had only begun to scratch the surface of the intricate science that involved identification of hypogeous fungi. With that publication, Smith officially established Rhizopogon as “the world’s largest and most complex of all truffle genera” and by his death in 1986, was touted as having

“established a legacy of advancing our knowledge” of truffle fungi in the Pacific

Northwest (Trappe et al. 2009).

Defining the Pacific Northwest

In the book The Pacific Northwest, An Interpretive History, it is recognized that a consensus of the Pacific Northwest boundaries is difficult to come by (pg 1) but a map recognizing the “Major Cities and Towns of the Pacific Northwest” is presented with major cities mainly encompassing all of Idaho, Washington and Oregon

(Schwantes 1996). In “Diversity, Ecology, and Conservation of Truffle Fungi in

Forests of the Pacific Northwest,” where a lot of truffle and mycological history can be found, the Pacific Northwest is defined as an area that consists of Northern

California, most of Oregon, Washington, most of Idaho, and a portion of Montana and

British Columbia (Trappe et al. 2009). Yet, in publications on mycology by the

Pacific Northwest Research Station, the majority of studies that are identified as pertaining to the Pacific Northwest have taken place in an area sandwiched between the coast range and the Cascade Mountains primarily in stands of Douglas-fir. In

4 Oregon, the highest concentration of areas in which mycological studies take place is west of the Cascades in Douglas-fir stands (Fogel 1976). A contributing factor to keeping research west of the Cascade Range was the establishment of the HJ

Andrew’s Experimental Forest, which encouraged mycologists at Oregon State

University and at the Pacific Northwest Research Station to set up studies in old growth Douglas-fir (Hunt and Trappe 1987; Luoma 1988; Luoma et al. 1991; Smith et al. 2002).

When it comes to belowground investigations of mycorrhizal fungi east of the

Cascade Range, the studies are but a handful. The very first account of the evaluation of mycorrhizae on pine was by Ernest Wright, professor of forest pathology at Oregon

State University (Trappe, Personal communication, (Wright 1957), in a study that examined survival of mycorrhizal associated and non-mycorrhizal associated ponderosa pine seedlings in the Deschutes National Forest. In 1963, a study was published that was the first attempt to identify mycorrhizal fungi in ponderosa pine

(Pinus ponderosa) and lodgepole pine (Pinus contorta) in the Pacific Northwest, with conclusions indicating Cenococcum graniforme, now named Cenococcum geophilum, as the dominant mycorrhizal species (Wright 1963). Even though C. graniforme was identified as the dominant species, Wright‘s research is limited because it does not describe many of the other species that make up the community composition of the root systems of pine. It may be the case, that C. graniforme resulted as a highlighted species in comparison to other species because C. graniforme ectomycorrhizae exhibit unique and distinct morphological characteristics that are easily identifiable in

5 comparison to other EMF. In 1971, Wright once again delves into mycorrhizal research and attempts to describe EMF on Douglas-fir and ponderosa pine. Again there is an emphasis on C. graniforme as the dominant mycorrhizal type and any mycorrhizae encountered in the study that deviated from the morphology of C. graniforme are identified as “other” (Wright 1971). In addition, it is imperative to point out that Wright’s research was decades prior to the use of molecular tools, which allow researchers to make a more thorough examination of microscopic organisms such as mycorrhizae colonizing the roots of trees.

The utilization of molecular tools and development of protocols to examine fungi at a molecular level revolutionized the field of mycology because scientists interested in studying belowground fungal communities could now identify EMF root tips down to genus and/or species. Amplification and direct sequencing of fungal

DNA (White et al. 1990) gave mycologists the ability to break free from the limitations of having to rely on morphological characteristics to identify particular fungi in forest stands. Furthermore, the incorporation of DNA technology in fungal research made it more feasible to explore larger geographic areas and provide a thorough investigation of the community structure of fungal species colonizing forest soils. Molecular tools also lifted the limitation of having to rely on fruiting bodies as a way to identify fungi and remain tied to a particular geographic area where fruiting bodies are plentiful.

Breaking Through to the Other Side

It was not until 1997 that studies of EMF in eastern Oregon were once again

6 picked up by researchers investigating post-fire Pezizales and EMF communities of ponderosa pine in the Blue Mountains of eastern Oregon (Smith et al. 2004; Fujimura et al. 2005; Smith et al. 2005). At this point in time, researchers aimed at examining a mycorrhizal fungi in ponderosa pine using molecular tools. The execution of these studies was largely influenced by an incentive to understand fire dynamics in a system that had been pushed out of balance due in part to limited understanding of the ecological role of fire in forest ecosystems and to the relentless “Smokey the Bear

Syndrome,” a Euro-American belief that all fire is destructive and strong efforts should be made to contain or eliminate it (Boyd 1999). A streak of fire suppression in eastside forests led to high fuels and a higher risk of stand replacing fire; a state of imbalance for an ecosystem that benefits from burning (Forest History Society 2013).

A shift in the popular mentality of the need for fire suppression and a shift in the

Forest Service policies incited a desire to understand the role of fire in forest ecosystems, and with that came prescribed burn treatments which once again opened up a window of opportunity to take a look at the effects of fire on mycorrhizal fungi that lay buried underneath the earth’s surface (Smith et al. 2004; Smith et al. 2005).

Similarly today, we are faced with a new dilemma and a desire to restore and repair ecosystem processes that have been affected by our actions. In the last decade, there has been a tremendous shift in attitudes concerning the impacts of anthropogenic activities, which are causing a change in the earth’s climate. Climate change is no longer a topic that concerns only atmospheric chemists and earth system scientists, but a topic that is now relevant to politicians, farmers, forest managers, and even citizens

7 of the world. In 2010, a study was published that suggested that there would be a strong decline in lodgepole pine by the end of the 21st century (Coops and Waring

2011). This brings us to the current research, which aims to characterize the EMF community composition of ponderosa pine and lodgepole pine in the Deschutes

National Forest.

Projected climate change models suggest that lodgepole pine will decline in the

NW United States by the end of the 21st century (Coops and Waring 2011) and variations in climatic conditions, including earlier spring warming, may favor establishment of ponderosa pine within the historic lodgepole pine range (Coops and

Waring 2011). Climate change may also affect soil moisture levels and thereby influence fungal communities. Successful pine species migration will be constrained by the distribution or co-migration of fungal symbionts, so knowledge of the linkages among soil fungal diversity, community structure, and environmental factors is critical to understanding the organization and stability of pine ecosystems (Simard and Austin

2010). In addition, EMF are a critical component of the forest ecosystem because they link aboveground and belowground components of biogeochemical cycles in forest ecosystems (Treseder and Allen 2000). Nitrogen (N), phosphorus (P), and carbon (C) are key nutrients in soil systems and are important for fungal nutrition. The availability of these nutrients is known to control mycorrhizal abundance because plants will invest more C in mycorrhizal fungi when N and P are limiting to plant growth (Mosse and Phillips 1971; Treseder 2004). Conversely, mycorrhizal abundance is expected to decline if N or P availability increases in the soil, resulting in C-limited

8 mycorrhizae (Read 1991). Belowground biogeochemical dynamics contributing to the availability of N, P, and C in the soil may in turn drive EMF community composition

(Lilleskov et al. 2002) so it is important to analyze the biogeochemical structure of the soil and how it affects the formation of EMF communities.

We need to understand the state of ecosystems now so that we can predict how they may respond to climate change scenarios and so that we can better manage the system to meet particular objectives. Not only will this study provide information that will help forest managers deal with climate change scenarios, but will help expand mycological research in the Pacific Northwest by crossing the boundaries of the

Cascade Range and establish an informational foundation about the state of EMF community structure of pine in pumice soils of the Deschutes National Forest.

9 CHAPTER 2 - ECTOMYCORRHIZAL COMMUNITIES OF PONDEROSA PINE AND LODGEPOLE PINE IN THE SOUTH-CENTRAL OREGON PUMICE ZONE

Introduction

Forest ecosystems of the Pacific Northwest are changing as a result of the effects of climate change (Vose et al. 2012). The rise of global temperatures, decline of winter precipitation, earlier loss of snowpack, and increased summer drought are altering the range of economically and ecologically important tree species, such as lodgepole pine (Pinus contorta Douglas ex Loudon)---a species that thrives in low- temperature zones also known as “frost pockets”. Additional stressors, such as concurrent bark beetle infestations, are contributing to the demise of this widely distributed early seral-stage tree species. In fact, a recent study based on climate change models suggests that lodgepole pine will decline in the NW United States by the end of the 21st century (Coops and Waring 2011). As climate change transforms the lodgepole pine zone into a warmer drier environment, drought tolerant tree species such as ponderosa pine (Pinus ponderosa Lawson) may establish within the historic lodgepole pine range (Coops and Waring 2011).

Pine species migration is a complex process that requires the examination of ecological linkages such as the belowground and aboveground components of a forest ecosystem. For a tree species to most effectively compete in a particular region, environmental conditions such as temperature, precipitation, water-availability, photosynthetic capacity, and soil conditions must be at an optimal level to favor one

10 tree species over another. In addition, trees need to form symbiotic relationships with mycorrhizal fungi for optimal survival and growth (Jones and Smith 2004). Successful pine species migration will be constrained by the distribution or co-migration of belowground fungal symbionts (Perry et al. 1990). The impacts of climate change may change belowground fungal communities (Pickles et al. 2012) so that they may be a limiting factor in tree migration. Furthermore, knowledge of the linkages among soil fungal diversity, community structure, and environmental factors is critical to understanding the organization and stability of pine ecosystems (Simard and Austin

2010).

Mycorrhizal fungi establish obligate beneficial relationships with pine. The relationship is termed symbiotic, because the host tree provides photosynthates, sources of carbon (C), to the colonized fungi in exchange for nutrients in the soil such as nitrogen (N) and phosphorus (P). Ectomycorrhizal fungi (EMF) form an external sheath of hyphae around the fine roots of the host tree and extend mycelium outward into the soil reaching further ground than roots of trees alone could access (Smith and

Read 2008). Furthermore, EMF are a critical component of the forest ecosystem because they link aboveground and belowground components of biogeochemical cycles in forest ecosystems (Treseder and Allen 2000). Specifically, fungi are known to be important players in the decomposition and mineralization of organic matter

(Schimel and Bennett 2004). Nitrogen, P, and C are key nutrients in soil systems and are important for fungal nutrition. The availability of these nutrients is known to control mycorrhizal abundance because plants will invest more C in mycorrhizal fungi

11 when N and P are limiting to plant growth (Mosse and Phillips 1971; Treseder 2004).

Conversely, mycorrhizal abundance is expected to decline if N or P availability increases in the soil, resulting in C-limited mycorrhizae (Read 1991). Belowground biogeochemical dynamics contributing to the availability of N, P, and C in the soil may in turn drive EMF community composition (Lilleskov et al. 2002; Treseder 2004) so it is important to analyze the biogeochemical structure of the soil and how it affects the formation of EMF communities.

Many of the factors that contribute to the formation of EMF communities in soil systems are based on specificity phenomenon of mycorrhizal fungi (Molina et al.

1992). In specificity phenomenon theory, mycorrhizal fungi may vary in host range from narrow (associating only with a single plant genus or family) (Massicotte et al.

1994; Bruns et al. 2002) to broad (associate across a diversity of plant genera, families, and orders) (Molina and Trappe 1982). Conversely, host-receptivity defines the numbers and diversity of mycorrhizal fungi that are accepted by a particular host.

Ecological specificity is a result of biotic and abiotic factors that control the ability for plants to form relationships with particular fungi in the soil and also plays a role in

EMF community formation (Molina et al. 1992). The interactions among host- specificity, host receptivity, and ecological specificity are important to consider when examining the formation of EMF communities in host specific soil systems. Indeed, mycorrhizae play a key role in the establishment of plant species in a particular environment and the presence or absence of particular mycorrhizal fungi might determine the composition of plant species in forest stands.

12 Common mycorrhizal networks (CMN), defined as the interconnection of mycorrhizal fungal hyphae and two or more root systems via mycorrhizal fungal hyphae (Simard and Durall 2004), may also be important for the establishment of new seedlings in a forest stand. Common mycorrhizal networks may be beneficial to plant communities when they help distribute resources between plants within the community and may increase the rate at which new seedlings become infected by fungal symbionts. If CMNs already exist in stands of ponderosa pine and lodgepole pine, the transition from a lodgepole pine dominated stand to a ponderosa pine dominated stand is likely to occur.

Lodgepole pine is currently a widely distributed tree species in eastern Oregon.

The Pinus contorta Zone, found on the pumice plateau, formed as a result of the eruption of Mount Mazama 6,600 years ago (Volland 1985). Previous studies of lodgepole pine EMF species in other systems have found Cenococcum geophilum,

Thelephora spp., Mycelium Radicis Atrovirens, Suillus spp., Russula spp., and

Piloderma spp. dominating the community composition (Bradbury 1998; Durall et al.

1999; Byrd et al. 2000; Douglas et al. 2005; Jones et al. 2012). The Pinus ponderosa

Zone extends in a 35-40 mile wide range within the pumice/ash deposits from Mount

Mazama, located south of Bend, OR (Simpson 2007). Overall, very few studies of

EMF on ponderosa pine have been conducted (Kotter and Farentinos 1984; Stendell et al. 1999; Barroetavena et al. 2005; 2007), and even fewer studies on the mycorrhizal fungi of pine in Oregon have been conducted east of the Cascade range (Wright 1957;

Wright 1963; Wright 1971; Smith et al. 2004; Fujimura et al. 2005; Smith et al. 2005).

13 No previous studies have utilized molecular tools to examine community composition and structure of EMF of ponderosa pine and lodgepole pine in the Deschutes National

Forest in Central Oregon.

The dry forests of Central Oregon are ideal for examining fungal microbial communities that associate with pines. The objectives of this observational study are to establish an informational foundation of the EMF communities of ponderosa pine and lodgepole pine in Central Oregon and investigate the role of soil chemistry and its effects on EMF community structure. We specifically focus our efforts on the examination of soil chemistry relationships with ponderosa pine because it is one of the tree species expected to replace lodgepole pine under future climate change conditions. The information gathered through this study will provide insights into the driving forces behind the formation of EMF communities in pine, supplementing the current knowledge for developing management strategies in a system anticipated to shift with impending climate change.

Methods

Study Area

This study was conducted east of the Oregon Cascade mountain range in the

Deschutes National Forest (Oregon, USA; Figure1). Sites consisted of intermixed lodgepole pine and ponderosa pine stands of the south-central Oregon pumice zone.

Understory shrub communities include Arctostaphylos patula Greene, Purshia tridentata Pursh DC, Festuca idahoensis Elmer, and Ceanothus velutinus Douglas ex

14 Hook (Franklin and Dyrness 1988).

Sampling Design

Locations of intermixed ponderosa pine and lodgepole stands in the Deschutes

National Forest were acquired (Chaylon Shuffield, personal communication (Shuffield

2011), and utilized to establish 17 sites for this study (Figure 1). Site centers were recorded using GPS (Table 1). Randomly selected trees were flagged, painted, and their GPS coordinates were recorded. A random number table and the second hand of a watch randomly determined soil core collection sites. A total of 5 soil cores were collected from 5 ponderosa trees in each of the 17 sites for a total of 85 cores. Since we sampled from intermixed stands, a ponderosa pine tree needed to be at least 3 canopy diameter lengths away from any lodgepole pine tree in order to meet our sampling criteria. In addition, 26 lodgepole pine cores were collected from pure lodgepole stands within the general area of our ponderosa pine stands in the Deschutes

National Forest to compare and contrast the EMF communities of ponderosa pine.

Soil Core Collection

The upper duff layer, consisting primarily of pine needles, was removed and a soil corer (5 cm diameter) was used to sample to a depth of 10 cm. Each core was placed into a zip-lock plastic bag and kept in a cooler on ice while in the field and at

4°C during the week of collection. Soil cores were transported back to the lab within 1 day and stored in a cold room (4°C) until processing. All soil core samples were collected July 2011.

15 Processing of Soil Cores

Soil cores were processed from July 2011 through September 2011. Soil was sieved (2 mm) out of each core sample and the remaining roots were washed with water and examined with stereomicroscopy at 10x magnification. All live EMF root tips were collected per core and grouped based on morphological characteristics such as color, shape, and surface hyphal formations (Agerer 1993). The total number of

EMF root tips in each core was recorded for 5 soil cores from each site. One tip of each morphotype per core was washed in a fine sieve using water, placed into a 0.5mL tube, air-dried overnight, and used for DNA extraction.

DNA extraction, amplification, and sequencing

DNA was extracted from dried EMF root tips using the Sigma Extract-N-

Amp™ kit (Sigma, Dorset, UK). A crushed dried root tip was placed into a 0.5mL tube and 10 µl of extraction solution was added. The sample was then incubated at

95°C for 10 min in a thermocycler (BioRad DNA Engine PTC 0200). Following the incubation, 20 µl of dilution solution was added to the extraction solution and lightly vortexed. Samples were stored at -20°C until amplification by Polymerase Chain

Reaction (PCR). DNA amplification was carried out in 15 ul reactions using Promega

GoTaq™ and universal fungal primers ITS1f and ITS4 (White et al. 1990).

Amplifications were performed with initial denaturation at 95°C for 2 min, followed by 35 cycles of 94°C for 30 s, 50°C for 1 min, and 72°C for 1 min. 30 s, with a final extension of 72°C for 10 min. Successful PCR products were purified using ExoSAP-

IT™ (USB, Cleveland, OH, USA). Positive amplicons were directly sequenced at the

16 University of Washington on an ABI 3730xl DNA analyzer using ABI reagents

(Applied Biosystems Foster City, CA) and Sanger sequencing determined fungal species affinities.

Chromatograms were examined, edited, and corrected manually using the

Geneious Pro 5.5.6 Program. Sequences were assembled into Contigs using the

Assembly feature of Geneious and setting the maximum overlap identity parameter to

97%. Sequences were entered into The National Center for Bioscience Informatics

(NCBI) Basic Local Alignment Search tool (BLAST) to determine fungal sequence identities. Names were assigned to OTU’s using a sequence similarity criteria of

!97% for species, !95% for genera, and "95% to family. When possible, sequences that were 97% or more identical were assigned the same OTU. Sequences were further examined using the alignment feature of Geneious Pro. Parameters were set to use

MAFFT v6.814b alignment tool and setting the algorithm to “Auto.” Once aligned, sequences were manually cross-checked to determine whether they could be assigned to the same OTU. For this analysis, BLAST hits for Cenococcum geophilum were grouped into one OTU.

Soil Chemistry

For soil chemistry analysis soil was sieved (2 mm) from each core sample, composited by site, homogenized, and air-dried. All soil chemistry analyses were conducted at the Oregon State University Central Analytical Lab following the methods of Horneck et al. (1989) (Horneck et al. 1989). Soil pH was tested using a

1:2 soil to water ratio. The dilute acid-fluoride method was used to analyze Bray-P.

17 Ammonium (NH4) and nitrate (NO3)were extracted using the KCL extraction method

(Horneck et al. 1989) and quantified using the Alpkem Flow Solution autoanalyzer.

Mineralizable N was measured using the anaerobic incubation method. Total N was measured using the Kjeldahl procedure. Total Kjeldahl P (TKP) was digested in a solution of sulfuric acid, potassium sulfate and a catalyst. The resulting orthophosphate was determined using an Astoria Pacific flow solution analyzer (Will

Austin, Personal Communication). Carbon and N were measured using pure oxygen combustion on the Leco CNS-2000 Macro Analyzer.

Statistical Analysis

Data Structure

The presence-absence matrices constructed for this analysis are based on a total of 81 out of 85 ponderosa pine cores because 4 out of the 85 cores failed to give reliable sequences for use in this analysis. All matrices that include lodgepole pine data have a total of 26 cores in the matrix.

Multivariate Analyses of OTU presence and absence

PC-ORD software version 6.0 (McCune and Mefford 2011) was utilized to run non-parametric multivariate statistical analysis. Multi-response Permutation

Procedures (MRPP) (Mielke and Berry 2001) with Sørensen distance measure were used to test the null hypothesis of no difference between groups (EMF fungal species

OTUs of lodgepole pine and ponderosa pine). The Sørensen's distance is a relative distance measure and is typically used to analyze presence/absence data (McCune et al. 2002). MRPP analysis provides a p value for a test of the hypothesis of no

18 difference between groups and an A statistic that represents the chance-corrected within group agreement and is a measure of effect size (McCune et al. 2002). When

A=0, the groups are no more or less different than expected by chance; when A=1 all sample units are identical within each group. A stratified random sample approach was used to account for differences in the number of samples between ponderosa pine and lodgepole pine. The “Random Sample” option of PC-ORD was used to select 26 random cores out of the total 81 ponderosa pine cores. A bootstrapped confidence interval was calculated for the MRPP statistic to estimate the variability within by repeatedly sampling the data over 1000 iterations. Non-metric multidimensional scaling was used to provide a graphical representation of community relationships between ponderosa pine and lodgepole pine, where points closer to one another on the ordination have more similar EMF fungal communities than points further apart. The

Sørensen distance measure was used to calculate similarities in communities and the settings in PC-ORD were set on “Autopilot Mode” which includes a random starting configuration and a maximum number of iterations of 500 with 100 runs with real data. The final instability criterion was set to 0.000001. An NMS ordination with the same configurations was also generated for ponderosa pine communities and a joint plot of environmental variables was superimposed on the ordination.

The number of EMF types per soil core (species richness) was used as response variables. One-factor ANOVA was used for comparisons among sites.

Linear regression was used to examine relationships between species richness and soil chemistry. To meet the assumptions of normality and constant variance (Sabin and

19 Stafford 1990), species richness was square-root transformed. Analyses were carried out with StatView software v5.1 (SAS Institute 1999).

Results

Ectomycorrhizal Community Structure of Ponderosa Pine and Lodgepole Pine

This observational study, conducted to determine the similarities or differences between the EMF communities of ponderosa pine and lodgepole pine, yielded 440 usable DNA sequences (Tables 2 & 3). Dominant OTUs of both pine species in this system were Cenococcum geophilum Fr. , Inocybe flocculosa Saccardo, and

Rhizopogon salebrosus A.H. Smith (Figures 2 & 3).

EMF commmunities did not differ between ponderosa pine and lodgepole pine at the species level (MRPP: A=0.001 90% CI -0.0024

EMF communities of ponderosa pine and lodgepole pine at the species level when singletons (OTUs that occurred only once) were eliminated from the dataset (MRPP:

A=0.01, 90% CI -0.0042

20 Soil Chemistry of Ponderosa Pine

Soil chemistry variables with the highest variability across sites were NO3,

NH4, mineralizable N, Bray-P, and C with relative standard deviations greater than

23%. In contrast, we observed low variability in pH across our sites with a relative standard deviation of about 3% (Table 4).

Environmental relationships of EMF communities on Ponderosa Pine

Our NMS ordination resulted in three dimensions and explained 70% of the variation in the dataset. Axis one explained 17% of the variation, axis two explained

25% of the variation, and axis three explained 28% of the variation. The strongest environmental variables related to ponderosa pine EMF composition were mineralizable N, Bray-P, NO3, NH4, C, TKP, and elevation. Elevation was positively correlated with axis 1 and NO3 and NH4 were negatively correlated with axis 1.

Mineralizable N and Bray-P were positively correlated with axis 3 (Figure 5, Table 5).

We found a significant linear relationship with mineralizable N and square- root EMF species richness. Mean square-root EMF species richness treated as dependent on mineralizable N produced a regression model of Y= 1.436+ 0.21*X;

R2=0.234, p=0.04 (Figure 6).

Discussion

Dominant EMF fungi of ponderosa pine and lodgepole pine

The Ascomycete Cenococcum geophilum was widespread across sites and the most encountered EMF in ponderosa pine and lodgepine cores. These findings are consistent with the morphological examination of root tips by Ernest Wright (Wright

21 1963; Wright 1971). It is not surprising that Cenococcum was a dominant type in our study since Cenococcum geophilum [Cenococcum graniforme (Sow.) Fred & Winge] is considered the world’s most recognized and widely distributed ectomycorrhizal fungus (Massicotte et al. 1992). Our results also indicated that Basdiomycete fungi

Rhizopogon salebrosus and Inocybe flocculosa were dominant in our pine forests. The results of this study are consistent with other studies that have reported Rhizopogon spp. on pine. Rhizopogon spp. are typical in Pinus and the genus is considered the largest of hypogeous EMF fungi with the largest assemblage of species occurring in

Pinaceae dominated areas of the Pacific Northwest (Massicotte et al. 1999; Kennedy and Bruns 2005).

It has been demonstrated, in greenhouse experiments, that ponderosa pine has the ability to associate with several Rhizopogon species including R. arctostaphylli, R. ellenae, R. subcaerulescens, R. truncatus, R. rubescens, and R. flavofibrillosus

(Massicotte et al. 1999). While our results are consistent with those of the greenhouse study at the genus level, our field studies indicate that there is an inconsistency with the types of mycorrhizae that colonize pine roots in vitro vs. in situ at the species level.

Inocybe flocculosa has not been previously identified as a species that associates with ponderosa pine in studies based on the observations of sporocarps

(Barroetavena et al. 2007). This study, however, was able to capture the

Basidomycete EMF as a dominant species. The use of molecular tools may have been the contributing factor to this finding. Overall, the results of this study are consistent

22 with the idea that dominance by a few species may be a common feature of EMF fungal communities (Gehring et al. 1998).

Common mycorrhizal networks

The result of no difference between the EMF community of the two pines supports the hypothesis that the ponderosa pine and lodgepole pine may be forming

CMN in the Deschutes National Forest. Common mycorrhizal networks may be especially important for an ecosystem that is expected to transition with climate change because established CMN may be beneficial for the survival of replacement tree seedlings such as those of ponderosa pine. Previous studies demonstrate that

EMF networks may help enhance EMF seedling survival and growth (Nara 2006;

McGuire 2007) and may facilitate distribution of resources within the system (Simard and Durall 2004). Furthermore, CMN studies have shown that seedlings that can tap into CMN are more likely to survive in stressed or harsh environments (Borchers and

Perry 1990; Horton et al. 1999; Marler et al. 1999). Thus, ponderosa pine seedlings may have a better chance of survival due to the presence of mycorrhizal networks established previously by lodgepole pine.

Relationship of elevation and EMF communities

Our NMS analysis indicates that elevation may be a factor driving community structure in ponderosa pine. Some studies suggest that elevation gradients are correlated with xeric to mesic gradients in ecosystems (Allen and Peet 1990) but further study would be needed to determine whether there is a correlation between the elevation gradient and a moisture gradient in these sites.

Soil Chemistry

Fungi are versatile and can exist over a wide pH range (pH 4-9) (Brady and

Weil 1996). The pH of the soils sampled in this study ranged between 6.27-6.91 in ponderosa pine sites. Macronutrient and micronutrient availability in soil is related to pH and plant available N increases when the pH of the soil is above 5.5 (Brady and

Weil 1996). Phosphorus is more plant available when soil pH is between 6-7, because iron and aluminum phosphate become more soluble in the soil (Brady and Weil 1996).

The presence of nitrogen-fixing understory shrubs such as Ceonothus velutinus and

Purshia tridentata may also be contributing to N enrichment of these soils (Busse et al. 2007), but further studies would need to be conducted to examine the effects of nitrogen fixation on biogeochemical cycles and in order to confirm interactions of the understory shrub community with pine roots. The results of the NMS analysis suggest that mineralizable N, NO3, NH4, Bray-P, TKP and C are the main biogeochemical driving factors for the community composition in ponderosa pine sites. Nitrogen, P, and C are essential nutrients that are used by EMF fungi and the results of this study are consistent with other studies indicating that N, P, and C are driving biogeochemical factors of EMF communities in soil (Lilleskov et al. 2002; Treseder

2004).

24 Relationship between mineralizable N and species richness

The results of simple linear regression suggest that species richness increases as mineralizable N increases. Time-point of sample collection may have been the factor leading to this result. Mineralizable N is a measure of the fraction of organic nitrogen that can potentially be mineralized by soil microbes to produce NH4 and NO3

(Robertson et al. 1999). Most mineralization occurs during the growing season when the soil is moist and warm. Central Oregon consists of dry ecosystems with the growing season spanning between late May through June. Our samples were collected in July when the soils were dry and water availability was low. We may have detected an increase in species richness because at our sample time-points, micro-organisms did not have conditions conducive to mineralizing organic nitrogen into plant- available inorganic forms. Thus, tree species may have invested in different species of fungi as a strategy because they may have been N-limited. Alternatively, belowground competition may be driving the relationship between species richness and mineralizable N. In a system where mycorrhizal fungi are present, root nitrifiers, heterotrophs, and other microbial organisms may also exist. If there is a release of inorganic N as a result of mineralization, it cannot be assumed that this will be taken up by mycorrhizal fungi and tree roots alone (Brady and Weil 1996). Fungi may have to compete with all organisms in the system for inorganic nitrogen (Norton and

Firestone 1996), thus, trees may invest in many species of fungi as a way to create a competitive advantage.

Conclusions

We found that ponderosa pine and lodgepole pine, located within the

Deschutes National Forest, share the same dominant EMF fungal species. This finding supports the conclusion that ponderosa pine may be able to successfully establish within the historic lodgepole pine range in a climate change scenario and dominant EMF assemblages may be conserved. However, temporal studies would be necessary to confirm this. Ponderosa pine and lodgepole pine might be forming CMN in the soil system and knowledge of the presence of fungal networks in the Deschutes

National Forest may prove helpful to forest managers. For example, if an assisted migration approach is considered as a management strategy (Kranabetter et al. 2012),

EMF networks may help enhance EMF seedling survivorship and growth in this system. In addition, the biogeochemistry and nutrient availability data presented in this research may be useful to determine optimal conditions for the survival of ponderosa pine in this system. Furthermore, the results of this study indicate that the availability of C, N, and P in this system will be important for the formation of EMF communities, which in turn are essential for the survival of migrating tree species.

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Table 1. Pinus ponderosa Sites

Pinus Ponderosa Site Locations and Elevations Site Latitude (N) Longitude (W) Elevation (M) 4380 43°43'46" 121°34'15" 1306 BHB 43°26'06" 121°17'04" 1438 C1 43°44'34" 121°45'00" 1358 C4ER 43°36'00" 121°42'05" 1315 C6A 43°39'43" 121°51'33" 1400 C6B 43°40'11" 121°50'51" 1396 C7 43°45'29" 121°38'51" 1345 C8 43°41'57" 121°22'38" 1451 DEAD LOG 43°35'04" 120°54'59" 1546 FIN. BUTTE 43°36'36" 121°23'22" 1480 FIRE BUTTE 43°38'52" 120°59'11" 1561 HWY31 43°28'24" 121°23'26" 1435 ICE CAVE 43°35'07" 121°03'15" 1490 KB 43°26'06" 121°21'02" 1452 LB 43°46'45" 121°10'45" 1829 MOFF. BUTTE 43°30'29" 121°27'11" 1326 PB 43°47'28" 121°25'07" 1322

Table 2. Operational Taxonomic Units of Pinus ponderosa

Site Tree Sample # Accession # Analysis Name Max Identity 4380 1 223 FN550920.1 Inocybe tarda 92% 4380 2 244 JN943885.1 Cenococcum geophilum 97% 4380 2 240 AF377094.1 Gautieria 2 94% 4380 2 245 AM882713.2 Inocybe 14 94% 4380 2 241 FN669236.1 Piloderma 20 90% 4380 3 216 JN943885.1 Cenococcum geophilum 95% 4380 3 212 FN550920.1 Inocybe contig 3 95% 4380 3 218 FN550920.1 Inocybe contig 3 95% 4380 3 219 DQ469291.1 Piloderma 3 91% 4380 3 215 AY880931.1 Rhizopogon salebrosus 97% 4380 3 213 EF458011.1 Rhizopogon salebrosus 97% 4380 3 217 AJ534914.1 Tomentella 2 92% 4380 3 214 AF349699.1 Tricholoma 4 88% 4380 6 234 HM189968.1 Tomentella 4 94% 4380 7 211 HQ604314.1 Inocybe lanuginosa 97%

BHB 1 560 JN943893.1 Cenococcum geophilum 96% BHB 1 567 HQ604726.1 Cortinarius fulvescens 94% BHB 1 562 HQ604516.1 Inocybe flocculosa 97% BHB 1 559 GQ267482.1 Rhizopogon luteorubescens 98% BHB 1 566 GQ267482.1 Rhizopogon luteorubescens 97% BHB 1 563 HQ914339.1 Rhizopogon salebrosus 98% BHB 1 561 FJ845443.1 Tricholoma 6 89% BHB 1 558 FJ845443.1 Tricholoma myomyces 99% BHB 2 580 EU427331.1 Cenococcum geophilum 99% BHB 2 584 HQ604726.1 Cortinarius fulvescens 91% BHB 2 585 JF908023.1 Geopora 2 87% BHB 2 581 HQ604404.1 Inocybe 2 94% BHB 2 582 HQ914339.1 Rhizopogon 12 96% BHB 2 583 AJ534914.1 Tomentella 2 92% BHB 3 554 FN669236.1 Piloderma 24 91% BHB 3 556 AJ534914.1 Tomentella 2 94% BHB 5 571 EU427331.1 Cenococcum geophilum 99% BHB 5 569 JN655614.1 Phialocephala 6 95% BHB 5 572 L54107.1 Suillus pseudobrevipes 97%

BHB 6 549 JN943885.1 Cenococcum geophilum 97% BHB 6 541 JF908130.1 Inocybe 1 93% BHB 6 544 DQ469291.1 Piloderma 3 91% BHB 6 550 HQ914321.1 Rhizopogon salebrosus 95% BHB 6 545 AY880941.1 Suillus brevipes 99% BHB 6 548 AB286068.1 Tricholoma 2 81%

C1 1 206 AJ515418.1 Rhizopogon 19 85% C1 1 202 AF377175.1 Rhizopogon15 94% C1 3 176 AY010281.1 Piloderma 1 80% C1 3 179 HQ914321.1 Rhizopogon salebrosus 97% C1 3 177 HM189966.1 Thelephora terrestris 97% C1 3 180 HM189966.1 Thelephora terrestris 97% C1 3 171 HQ215826.1 Tomentella 7 94% C1 6 195 AY239347.1 Gymnomyces 1 94% C1 6 192 AY239317.1 Gymnomyces 2 85% C1 6 194 JQ711951.1 Piloderma spp 89% C1 7 196 JF899555.1 Hebeloma sacchariolens 98% C1 4 187 HQ604084.1 Agaricales 1 89% C1 4 188 FN669188.1 Cortinarius 9 94% C1 4 189 AF377167.1 Rhizopogon arctostaphyli 93%

C4-ER 3 153 DQ469291.1 Piloderma 2 90% C4-ER 3 155 JF834358.1 Russula 4 92% C4-ER 4 136 JF908784.1 Boletopsis 1 95% C4-ER 4 131 EF457902.1 Boletopsis grisea 98% C4-ER 4 132 DQ680181.1 Rhizopogon 20 96% C4-ER 5 146 JN943894.1 Cenococcum geophilum 92% C4-ER 5 140 HQ604213.1 Inocybe auricoma 99% C4-ER 5 142 HQ604213.1 Inocybe auricoma 98% C4-ER 5 147 FN550920.1 Inocybe contig 3 95% C4-ER 6 M149 HQ604516.1 Inocybe flocculosa 99% C4-ER 6 148 EU846312.1 Leucogastraceae OTU1 88% C4-ER 7 163 JN943898.1 Amphinema 5 94% C4-ER 7 166 JN943905.1 Amphinema 8 96% C4-ER 7 164 FN669208.1 Hydnum 1 93% C4-ER 7 168 JF908112.1 Inocybe 16 95% C4-ER 7 169 AF377167.1 Rhizopogon arctostaphyli 94%

C6A 1 31 HQ604813.1 Inocybe jacobi 99% C6A 1 33 HQ604813.1 Inocybe jacobi 99% C6A 1 34 AY880944.1 Rhizopogon 8 88% C6A 2 762 JN943898.1 Amphinema 7 92% C6A 2 761 EU103612.1 Phialocephala 5 93% C6A 3 770 JN943893.1 Cenococcum geophilum 95% C6A 4 763 EU669372.1 Rhizopogon elipsoporus 97% C6A 5 769 EU563921.1 Hysterangium 2 82% C6A 5 767 GU067764.1 Phialocephal fortinii 83% C6A 5 766 DQ365663.1 Piloderma 6 96% C6A 5 765 DQ365674.1 Piloderma 9 95%

C6B 1 96 JN943898.1 Amphinema 5 97% C6B 1 95 AY606311.1 Cadophora 2 96% C6B 1 94 FN550919.1 Inocybe 3 96% C6B 1 97 GQ249398.1 Suillus volcanalis 99% C6B 4 98 JN943893.1 Cenococcum geophilum 81% C6B 4 99 EU862208.1 Clavulina 1 90% C6B 4 101 AF377167.1 Rhizopogon arctostaphyli 100% C6B 5 104 JF834355.1 Russula albonigra 99% C6B 6 93 JX561240.1 Sistotrema sp. 97% C6B 6 91 FJ845443.1 Tricholoma 5 87% C6B 7 90 EU526006.1 Russula cascadensis 99%

C7 1 291 JN943898.1 Amphinema 6 97% C7 1 290 AF058303.1 Rhizopogon burlinghamii 73% C7 1 289 EU669372.1 Rhizopogon ellipsoporus 96% C7 1 M286 U83467.1 Tomentella spp. 88% C7 4 264 FN669236.1 Piloderma 21 93% C7 4 266 HM190011.1 Tomentellopsis 1 92% C7 4 265 AF266708.1 Wilcoxina rehmii 1 97% C7 5 255 DQ469291.1 Piloderma 13 82% C7 5 257 EU837230.1 Rhizopogon bacillisporus 97% C7 6 281 FJ039589.1 Cortinarius 4 92% C7 6 279 AF377167.1 Rhizopogon arctostaphyli 98% C7 6 280 JQ711917.1 Tomentella bryophila 92% C7 6 276 HM590873.1 Tricholoma 1 85%

C7 7 253 EU427331.1 Cenococcum geophilum 95% C7 7 M248 HQ604516.1 Inocybe 9 95% C7 7 251 JF834352.1 Russula 3 87%

C8 1 18 HQ604726.1 Cortinarius fulvescens 84% C8 1 15 DQ097870.1 Cortinarius hemitrichus 99% C8 1 20 AF071440.1 Rhizopogon ochraceorubens 98% C8 2 27 EU427331.1 Cenococcum geophilum 99% C8 2 30 AF377094.1 Gautieria 1 91% C8 2 29 GQ249393.1 Suillus quiescens 81% C8 2 28 AF266708.1 Wilcoxina rehmii 1 99% C8 3 21 DQ822822.1 Rhizopogon salebrosus 96% C8 3 26 AF266708.1 Wilcoxina rehmii 1 99% C8 5 55 EU427331.1 Cenococcum geophilum 99% C8 5 49 HQ604726.1 Cortinarius fulvescens 93% C8 5 52 EF685051.1 Lactarius delciosus 90% C8 5 53 EF685051.1 Lactarius deliciosus 97% C8 5 56 DQ469291.1 Piloderma 11 91% C8 5 48 EU669372.1 Rhizopogon ellipsoporus 99% C8 5 51 EU669372.1 Rhizopogon ellipsoporus 99% C8 5 50 AF377157.1 Rhizopogon salebrosus 99% C8 4 43 JN943910.1 Amphinema 9 97% C8 4 44 JN943910.1 Amphinema 9 99% C8 4 45 JN943910.1 Amphinema 9 99% C8 4 35 EU427331.1 Cenococcum geophilum 95% C8 4 38 AF377167.1 Rhizopogon arctostaphyli 100% C8 4 36 L54107.1 Suillus pseudobrevipes 96% C8 4 37 GQ249398.1 Suillus volcanalis 99% C8 4 39 GQ249398.1 Suillus volcanalis 98% C8 4 47 GQ249398.1 Suillus volcanalis 99%

DL 1 608 EU427331.1 Cenococcum geophilum 93% DL 1 612 DQ093752.1 Cenococcum geophilum 93% DL 1 613 GQ159814.1 Cortinarius 11 93% DL 1 610 HQ604726.1 Cortinarius fulvescens 93% DL 1 611 JF907866.1 Cortinarius vernus 98% DL 1 606 AY880931.1 Rhizopogon 14 95% DL 1 607 AJ534914.1 Tomentella 2 93%

DL 1 603 FJ845443.1 Tricholoma 7 86% DL 2 597 JN943885.1 Cenococcum geophilum 97% DL 2 601 HQ283095.1 Geopora 1 90% DL 2 598 HQ604216.1 Inocybe glabrescens 99% DL 2 602 AF377157.1 Rhizopogon salebrosus 98% DL 2 599 AJ534914.1 Tomentella 1 91% DL 2 600 FJ845443.1 Tricholoma myomyces 99% DL 3 591 EU346870.1 Cenococcum geophilum 94% DL 3 594 HQ604731.1 Cortinarius 6 96% DL 3 595 AY309962.1 Hebeloma collariatum 97% DL 3 593 JN022511.1 Hysterangium 3 76% DL 3 596 FN550920.1 Inocybe contig 3 95% DL 4 630 JN943885.1 Cenococcum geophilum 95% DL 4 631 GQ159814.1 Cortinarius 11 93% DL 4 M629 HQ604516.1 Inocybe 13 96% DL 4 625 FN550919.1 Inocybe 3 94% DL 4 627 EU669319.1 Rhizopogon 1 82% DL 4 628 FJ845443.1 Tricholoma myomyces 99% DL 7 617 JN943962.1 Cortinarius 2 87% DL 7 616 HQ604731.1 Cortinarius fulvescens 97% DL 7 621 HQ604731.1 Cortinarius fulvescens 97% DL 7 624 FN550919.1 Inocybe 3 94% DL 7 623 HQ604516.1 Inocybe 7 92% DL 7 615 HQ604516.1 Inocybe flocculosa 97% DL 7 618 HQ604516.1 Inocybe flocculosa 97% DL 7 619 AJ534914.1 Tomentella 2 93%

FIN. 1 758 JN943885.1 Cenococcum geophilum 94% BUTTE FIN. 1 759 GQ159869.1 Cortinarius 8 78% BUTTE FIN. 1 756 HM190137.1 Helotiales 3 80% BUTTE FIN. 1 M750 HQ604516.1 Inocybe flocculosa 98% BUTTE FIN. 1 753 EF685051.1 Lactarius 3 96% BUTTE FIN. 1 752 AY078133.1 Phialocephala 1 85% BUTTE FIN. 1 751 AY010280.1 Piloderma fallax 97% BUTTE FIN. 1 757 AF062927.1 Rhizopogon 6 95% BUTTE FIN. 1 754 HM189968.1 Tomentella 4 93% BUTTE

FIN. 1 755 HM189968.1 Tomentella 4 95% BUTTE FIN. 2 738 DQ365653.1 Piloderma 5 90% BUTTE FIN. 2 739 HM240541.1 Russula 2 92% BUTTE FIN. 2 740 HM240541.1 Russula 2 87% BUTTE FIN. 2 741 AF377195.1 Tricholoma 8 79% BUTTE FIN. 3 748 EU427331.1 Cenococcum geophilum 92% BUTTE FIN. 3 743 FN669173.1 Clavulina 2 97% BUTTE FIN. 3 749 FN669173.1 Clavulina 3 95% BUTTE FIN. 3 745 HQ604719.1 Cortinarius casimiri 99% BUTTE FIN. 3 744 DQ365674.1 Piloderma 8 87% BUTTE FIN. 3 742 AF377167.1 Rhizopogon arctostaphyli 94% BUTTE FIN. 3 746 AF377167.1 Rhizopogon arctostaphyli 96% BUTTE FIN. 4 735 HM060322.1 Inocybe strobilomyces 100% BUTTE FIN. 4 730 DQ469285.1 Piloderma 10 87% BUTTE FIN. 4 732 AY010280.1 Piloderma fallax 98% BUTTE FIN. 4 734 AJ810045.1 Rhizopogon 10 84% BUTTE FIN. 7 736 HQ201354.1 Agaricales 4 90% BUTTE FIN. 7 737 JN943893.1 Cenococcum geophilum 94% BUTTE

FIRE 1 686 JN133916.1 Amphinema 3 82% FIRE 1 685 EU821662.1 Cortinarius 5 90% FIRE 1 684 HQ604600.1 Inocybe 15 89% FIRE 2 693 JN133916.1 Amphinema 4 82% FIRE 2 690 EU427331.1 Cenococcum geophilum 93% FIRE 2 688 AY669679.1 Cortinarius 1 91% FIRE 2 694 HQ604726.1 Cortinarius fulvescens 93% FIRE 3 703 HQ604090.1 Agaricales 5 90% FIRE 3 704 HQ604719.1 Cortinarius casimiri 96% FIRE 3 702 FN550920.1 Inocybe tarda 94% FIRE 4 705 DQ469291.1 Piloderma 15 76% FIRE 4 708 HQ914256.1 Rhizopogon salebrosus 96% FIRE 4 706 FJ876183.1 Tomentella 3 94% FIRE 5 697 JF899555.1 Hebeloma sacchariolens 98% FIRE 5 M696 HQ604516.1 Inocybe flocculosa 97%

FIRE 5 695 HQ914265.1 Rhizopogon salebrosus 94%

HWY31 2 M107 HQ604516.1 Inocybe 8 93% HWY31 3 113 AF377157.1 Rhizopogon salebrosus 98% HWY31 3 110 AB263122.1 Rhodotorula 1 88% HWY31 3 112 JF834356.1 Russula 1 79% HWY31 3 111 AF266708.1 Wilcoxina rehmii 1 99% HWY31 4 114 JN943885.1 Cenococcum geophilum 95% HWY31 4 115 FN669236.1 Piloderma 18 95% HWY31 5 124 FN550920.1 Inocybe tarda 94% HWY31 5 125 JF695015.1 Rhizopogon 17 96% HWY31 7 122 FN669212.1 Inocybe 18 95%

HWY31 7 M119 FN669236.1 Piloderma 25 83% HWY31 7 120 AF377157.1 Rhizopogon salebrosus 98% HWY31 7 121 HM189969.1 Tomentella 5 92% ICE 1 513 JN943925.1 Amphinema 10 93% CAVE ICE 1 512 JN943885.1 Cenococcum geophilum 95% CAVE ICE 1 511 JN580857.1 Inocybe silvae-herbaceae 97% CAVE ICE 1 514 EF685051.1 Lactarius deliciosus 100% CAVE ICE 2 522 JN943926.1 Amphinema 2 100% CAVE ICE 2 516 JN943893.1 Cenococcum geophilum 93% CAVE ICE 2 515 HQ604726.1 Cortinarius fulvescens 97% CAVE ICE 2 521 HQ604242.1 Inocybe subcarpta 98% CAVE ICE 2 520 DQ469291.1 Piloderma 14 89% CAVE ICE 2 518 DQ365674.1 Piloderma 7 94% CAVE ICE 2 517 AF071440.1 Rhizopogon ochraceorubens 98% CAVE ICE 2 519 AF377157.1 Rhizopogon salebrosus 98% CAVE ICE 3 504 JN943893.1 Cenococcum geophilum 92% CAVE ICE 3 505 GQ267482.1 Rhizopogon luteorubescens 98% CAVE ICE 3 508 AF062934.1 Rhizopogon vulgaris 97% CAVE ICE 3 510 EF644117.1 Tomentella 8 96% CAVE ICE 4 499 HQ604515.1 Agaricales 2 87% CAVE ICE 4 M498 HQ604516.1 Agaricales 6 95% CAVE ICE 4 M501 HQ604516.1 Inocybe 11 90% CAVE

ICE 4 503 AJ534914.1 Tomentella 2 92% CAVE ICE 6 528 EF685047.1 Lactarius barrowsii 98% CAVE ICE 6 526 HM036610.1 Phialocephala 3 80% CAVE ICE 6 523 L54107.1 Suillus pseudobrevipes 96% CAVE ICE 6 527 L54107.1 Suillus pseudobrevipes 97% CAVE

KB 1 62 EU427331.1 Cenococcum geophilum 99% KB 1 64 FN550920.1 Inocybe tarda 90% KB 1 63 AF377167.1 Rhizopogon arctostaphyli 100% KB 3 84 EF685051.1 Lactarius deliciosus 100% KB 3 88 EF685051.1 Lactarius deliciosus 94% KB 3 82 DQ469291.1 Piloderma 12 88% KB 3 86 AF377167.1 Rhizopogon arctostaphyli 100% KB 3 83 AF377157.1 Rhizopogon salebrosus 99% KB 3 85 AF377157.1 Rhizopogon salebrosus 99% KB 3 87 AF266708.1 Wilcoxina rehmii 1 99% KB 4 77 EU427331.1 Cenococcum geophilum 99% KB 4 81 HQ604719.1 Cortinarius casimiri 95% KB 4 79 GU166481.1 Helotiales 2 93% KB 4 78 FN550920.1 Inocybe contig 3 95% KB 5 68 HM190137.1 Phialocephala fortini 98% KB 7 69 HQ604726.1 Cortinarius fulvescens 92% KB 7 71 HM176572.1 Hypocrea 1 85% KB 7 66 AF377157.1 Rhizopogon salebrosus 99% KB 7 74 FJ845443.1 Tricholoma myomyces 99% KB 3 80 AB211277.1 Cenococcum geophilum 86%

LB 1 417 HQ604516.1 Inocybe flocculosa 97% LB 1 423 AY010280.1 Piloderma fallax 98%

LB 1 422 AY880938.1 Suillus pseudobrevipes 1 98% LB 2 424 HQ604806.1 Inocybe jacobi 98% LB 2 425 HQ604806.1 Inocybe jacobi 96% LB 2 427 FR827862.1 Sacosphaera 88% LB 3 M436 HQ604516.1 Inocybe flocculosa 100% LB 3 435 HQ914321.1 Rhizopogon salebrosus 97% LB 4 440 AJ810038.1 Rhizopogon 3 95% MOF. 2 303 AF377167.1 Rhizopogon arctostaphyli 96% BUTTE

MOF. 2 312 AF377157.1 Rhizopogon salebrosus 99% BUTTE MOF. 2 308 AJ534914.1 Tomentella 2 92% BUTTE MOF. 2 306 AJ889982.1 Tomentella cf. sublilacina 90% BUTTE MOF. 2 305 FJ845443.1 Tricholoma myomyces 99% BUTTE MOF. 2 307 FJ845443.1 Tricholoma myomyces 99% BUTTE MOF. 2 302 EF458013.1 Wilcoxina 2 96% BUTTE MOF. 2 304 AF266708.1 Wilcoxina Rehmii 1 97% BUTTE MOF. 3 M296 HQ604516.1 Inocybe flocculosa 98% BUTTE MOF. 3 297 AJ534914.1 Tomentella 1 94% BUTTE MOF. 3 299 AJ534914.1 Tomentella 2 94% BUTTE MOF. 3 301 AM086447.1 Tomentellopsis 2 93% BUTTE MOF. 3 300 FJ845443.1 Tricholoma myomyces 99% BUTTE MOF. 5 318 FN550920.1 Inocybe contig 3 95% BUTTE MOF. 5 320 FN550920.1 Inocybe contig 3 95% BUTTE MOF. 5 317 EU819536.1 Tricholoma 10 86% BUTTE MOF. 5 319 AF266708.1 Wilcoxina rehmii 1 98% BUTTE

PB 1 373 HQ604726.1 Cortinarius fulvescens 94% PB 1 371 AY010280.1 Piloderma 4 95% PB 1 374 AF377167.1 Rhizopogon arctostaphyli 100% PB 1 372 GQ267482.1 Rhizopogon luteorubescens 94% PB 2 362 JN943885.1 Cenococcum geophilum 96% PB 2 364 HQ604726.1 Cortinarius fulvescens 98% PB 2 356 AF377167.1 Rhizopogon arctostaphyli 98% PB 2 367 GQ267482.1 Rhizopogon luteorubescens 97% PB 4 M333 HQ604516.1 Inocybe flocculosa 96% PB 4 327 GQ406458.1 Laccaria amethysteo- 99% occidentalis PB 4 331 GQ406458.1 Laccaria amethysteo- 99% occidentalis PB 4 329 GQ267482.1 Rhizopogon 2 75% PB 4 328 AF377167.1 Rhizopogon arctostaphyli 98% PB 6 336 HQ604084.1 Inocybe 4 95% PB 6 340 FJ845418.1 Lactarius deliciosus 98% PB 6 342 AF071440.1 Rhizopogon ochraceorubens 98% PB 6 338 AF377157.1 Rhizopogon salebrosus 98%

Table 3. Operational Taxonomic Units of Pinus contorta

Site Tree Sample # Accession # Analysis Name Max Identity BHB PICO 575 EU427331.1 Cenococcum geophilum 99% BHB PICO 573 JF908249.1 Inocybe 20 94% BHB PICO 578 AY606285.1 Phialocephala 2 90% BHB PICO 576 DQ469291.1 Piloderma 2 90% BHB PICO 574 FJ845443.1 Tricholoma myomyces 99%

C1 PICO 182 HQ604719.1 Cortinarius casimiri 99% C1 PICO 183 HQ604719.1 Cortinarius casimiri 99% C1 PICO 181 HQ604242.1 Inocybe subcarpta 99%

C4-ER PICO 159 JN943891.1 Cenococcum geophilum 96% C4-ER PICO 162 HQ604806.1 Inocybe jacobi 97% C4-ER PICO 158 AF274770.1 Pseudotomentella 1 96% C4-ER PICO 161 AJ810040.1 Rhizopogon 9 95%

C6A PICO 5 EU427331.1 Cenococcum geophilum 99% C6A PICO 8 GU234029.1 Cortinarius inconspicuus 98% C6A PICO 6 AY880931.1 Rhizopogon salebrosus 97% C6A PICO 2 FJ845440.1 Suillus brevipes 99% C6A PICO 1 GQ249389.1 Suillus brevipes 2 90% C6A PICO 9 HM189964.1 Thelephora terrestris 99% C6A PICO 7 HM189966.1 Thelephora terrestris 97%

C7 PICO 275 JN943893.1 Cenococcum geophilum 89% C7 PICO 267 FJ845440.1 Suillus brevipes 96% C7 PICO 273 HM189966.1 Thelephora terrestris 97% C7 PICO 269 HQ215807.1 Tomentella 6 96% C7 PICO 270 HQ215807.1 Tomentella 6 96% C7 PICO 271 HQ215807.1 Tomentella 6 93%

C8 PICO M58 HQ604516.1 Inocybe 12 94% C8 PICO 58 HQ604516.1 Inocybe 5 94% C8 PICO 60 HQ604516.1 Inocybe flocculosa 97% C8 PICO 59 AF071438.1 Rhizopogon 11 89% C8 PICO 61 DQ517421.1 Tricholoma 3 90% DAVIS PICO1 416 HQ604516.1 Inocybe flocculosa 97% DAVIS PICO1 411 FN669236.1 Piloderma 22 94% DAVIS PICO1 415 HM189966.1 Thelephora terrestris 97%

DAVIS PICO2 387 FJ039683.1 Cortinarius viridipes 97% DAVIS PICO2 382 DQ469291.1 Piloderma olivaceum 97% DAVIS PICO2 385 EF458015.1 Rhizopogon ochraceorubens 97% DAVIS PICO2 386 AF377157.1 Rhizopogon salebrosus 97% DAVIS PICO2 389 FJ845441.1 Suillus tomentosus 2 96% DAVIS PICO3 398 DQ365632.1 Hysterangium 1 87% DAVIS PICO3 392 JN580876.1 Inocybe17 90% DAVIS PICO3 390 FJ845430.1 Russula densifola 99% DAVIS PICO3 391 FJ845430.1 Russula densifola 99%

DAVIS PICO4 380 GQ162811.1 Amphinema 1 96% DAVIS PICO4 376 FN550919.1 Inocybe 3 94% DAVIS PICO4 379 AM490946.1 Pseudotomentella humicola 100% DAVIS PICO4 381 AF062927.1 Rhizopogon 7 96% DAVIS PICO4 375 DQ822822.1 Rhizopogon salebrosus 97% DAVIS PICO4 377 EF458017.1 Suillus tomentosus 1 97%

DAVIS PICO5 406 HQ604726.1 Cortinarius fulvescens 93% DAVIS PICO5 401 JF908076.1 Hygrophorus 1 95% DAVIS PICO5 399 AJ515411.1 Rhizopogon 18 90% DAVIS PICO5 402 M91613.1 Rhizopogon 21 92% DAVIS PICO5 404 AF377157.1 Rhizopogon salebrosus 93% DAVIS PICO5 409 DQ822822.1 Rhizopogon salebrosus 99%

Fin. Butte PICO 715 EU557316.1 Cadophora 1 94% Fin. Butte PICO 716 JN943885.1 Cenococcum geophilum 94% Fin. Butte PICO 729 GQ159818.1 Cortinarius 10 84% Fin. Butte PICO 718 HQ650744.1 Cortinarius 12 95% Fin. Butte PICO 723 AF389170.1 Cortinarius 7 96% Fin. Butte PICO 728 HQ604213.1 Inocybe auricoma 94% Fin. Butte PICO 714 DQ469291.1 Piloderma 16 77% Fin. Butte PICO 722 FN669236.1 Piloderma 17 84% Fin. Butte PICO 724 AY010280.1 Piloderma fallax 98% Fin. Butte PICO 727 AY880945.1 Rhizopogon 5 81% Fin. Butte PICO 717 EF458011.1 Rhizopogon salebrosus 97% Fin. Butte PICO 725 DQ822822.1 Rhizopogon salebrosus 97%

Fin. Butte PICO 720 AY880931.1 Rhizopogon salebrosus 95% Fin. Butte PICO 712 JQ711917.1 Tomentella bryophila 99%

Fire PICO 682 GU166481.1 Helotiales 1 92% Fire PICO 672 JN655616.1 Helotiales 4 87% Fire PICO 670 HQ604235.1 Inocybe 21 91% Fire PICO 673 AF377167.1 Rhizopogon arctostaphyli 96% Fire PICO 680 EU837230.1 Rhizopogon bacillisporus 94% Fire PICO 675 U83487.1 Thelephora 1 96% Fire PICO 674 AJ889980.1 Thelephora 2 94% Fire PICO 677 AF274770.1 Thelephoraceae 1 86% Fire PICO 678 AF274770.1 Thelephoraceae 2 83% HD PICO1 476 FJ845418.1 Lactarius deliciosus 97%

HD PICO2 M495 HQ604516.1 Inocybe flocculosa 97% HD PICO2 496 AF377177.1 Rhizopogon 16 91% HD PICO2 493 GQ267482.1 Rhizopogon luteorubescens 93% HD PICO2 494 AJ534914.1 Tomentella 2 93% HD PICO2 492 EF458013.1 Wilcoxina 3 88% HD PICO2 490 AF266708.1 Wilcoxina rehmii 1 97%

HD PICO3 459 GQ401354.1 Amanita 1 89% HD PICO3 464 FN550920.1 Inocybe contig 3 95% HD PICO3 461 DQ822823.1 Rhizopogon vulgaris 96% HD PICO3 467 AJ534914.1 Tomentella 2 94%

HD PICO4 473 FJ717527.1 Cortinarius 3 86% HD PICO4 471 HQ604516.1 Inocybe flocculosa 97% HD PICO4 470 FN669236.1 Piloderma23 90%

HD PICO5 479 FN550920.1 Inocybe 22 88% HD PICO5 481 FN550920.1 Inocybe 23 94% HD PICO5 478 AY606285.1 Phialocephala 4 81% HD PICO5 480 EU669372.1 Rhizopogon ellipsoporus 94%

HWY31 PICO 129 HQ604813.1 Inocybe jacobi 99% ICE PICO 530 JF908175.1 Agaricomycete 1 79% ICE PICO 537 JN943893.1 Cenococcum geophilum 83% ICE PICO 531 HQ604809.1 Inocybe jacobi 99% ICE PICO 533 DQ469291.1 Piloderma 3 91%

ICE PICO 536 HQ914321.1 Rhizopogon salebrosus 2 97% ICE PICO 535 AF158017.1 Rhizopogon smithii 99% ICE PICO 529 AJ534914.1 Tomentella 1 92% ICE PICO 539 AF266708.1 Wilcoxina rehmii 1 98%

KB PICO 70 EU427331.1 Cenococcum geophilum 99% KB PICO 482 JN943894.1 Cenococcum geophilum 90% KB PICO 485 AF377173.1 Rhizopogon 13 79% KB PICO 486 AF377134.1 Rhizopogon 4 72% KB PICO 75 FR838002.1 Sistorema 90% KB PICO M487 FJ845443.1 Tricholoma myomyces 97% KB PICO 72 AF266708.1 Wilcoxina Rehmii 1 99%

LB PICO M457 HQ604516.1 Inocybe 10 93% LB PICO 456 AY880941.1 Suillus brevipes 99%

Mof. Butte PICO1 654 JF899547.1 Amanita 2 81% Mof. Butte PICO1 653 JN943893.1 Cenococcum geophilum 94% Mof. Butte PICO1 656 AJ534914.1 Tomentella 2 90% Mof. Butte PICO1 655 AF377209.1 Tricholoma 9 92% Mof. Butte PICO1 657 EF458013.1 Wilcoxina 4 92%

Mof. Butte PICO3 649 HQ604516.1 Agaricales 3 85% Mof. Butte PICO3 650 HQ604516.1 Inocybe flocculosa 95% Mof. Butte PICO3 648 HM485339.1 Tuber2 89%

Mof. Butte PICO4 634 HQ604213.1 Inocybe auricoma 91% Mof. Butte PICO4 640 HQ604213.1 Inocybe auricoma 95% Mof. Butte PICO4 M637 HQ604516.1 Inocybe flocculosa 98% Mof. Butte PICO4 636 DQ822823.1 Rhizopogon 22 86% Mof. Butte PICO4 635 HM485339.1 Tuber1 88%

Mof. Butte PICO5 643 EF685058.1 Lactarius 1 77%

Figure 1. Map showing general area of study site in Central Oregon and distribution of sampling sites in the Deschutes National Forest

Figure 2. Percent occurrence of dominant ectomycorrhizal fungus operational taxonomic units in Pinus ponderosa by soil core (black) and site (grey). Ectomycorrhizal fungus analysis names are represented along the x-axis.

90% Dominant OTUs in Pinus ponderosa 80%

70%

60% core site 50% 40% 30%

% Occurence 20% 10% 0%

Figure 3. Percent occurrence of dominant ectomycorrhizal fungus operational taxonomic units in Pinus contorta by soil core (black) and site (grey). Ectomycorrhizal fungus analysis names are represented along the x-axis.

(!"# Dominant OTUs in Pinus contorta '$"#

'!"# core site

&$"#

&!"#

%$"# %!"#

% Occurrence % Occurrence $"#

!"#

Figure 4. NMS ordination of Pinus ponderosa and Pinus contorta soil cores in EMF species space.

Figure 5. NMS ordination of ponderosa pine sites in EMF species space with superimposed joint plot. Vectors show direction and magnitude of correlation between sample units and environmental variables. Axis 1 explained 17% of the variation and Axis 2 explained 25% of the variation. Proportion of variance represented by each axis is based on the r 2 between distance in the ordination space and distance in the original space. Environmental variables with an r2 greater than 0.150 are plotted.

Table 4. Codes for environmental variables and respective correlations with ordination axes. Figure 5 displays these variables in conjunction with EMF communities ordination of ponderosa pine graph.

Table 5. Soil Chemistry for Ponderosa Pine

Site pH Bray-P NO3-N NH4-N Mineralizable N C TKN TKP 4380 6.8 37 0.2 0.2 10.7 1.55 509.2 749.2 BHB 6.6 42 0.5 0.4 18.1 1.47 470.3 585.2 C1 6.7 32 0.6 0.6 13.5 1.94 508.9 664.9 C4ER 6.5 37 0.3 0.4 9.7 1.92 530.9 561.5 C6A 6.4 33 0.2 0.2 11.9 2.71 680.0 525.0 C6B 6.6 34 0.3 0.3 10.5 1.91 464.6 548.2 C7 6.4 63 0.3 0.5 18.2 2.68 615.4 888.1 C8 6.7 55 0.2 0.3 21.7 1.75 546.5 705.0 DEAD LOG 6.6 33 0.9 0.4 24.1 1.03 563.5 423.4 FIN. BUTTE 6.3 60 0.5 0.5 14.7 2.43 748.1 739.5 FIRE BUTTE 6.7 37 0.6 0.3 23.8 1.21 536.0 468.9 HWY 31 6.8 80 0.6 0.3 22.8 2.18 644.3 569.5 ICE CAVE 6.6 54 0.2 0.6 22.2 1.58 467.7 550.0 KB 6.3 65 9.0 1.4 33.1 2.54 914.6 473.3 LB 6.9 31 0.4 0.3 9.1 1.64 571.7 579.1 MOFF. BUTTE 6.6 71 0.9 0.8 27.1 2.49 821.3 534.8 PB 6.8 49 0.4 0.2 12.5 1.48 413.5 671.2 Average !"!# $%"%# &"'# '"(# &%")# &")# (**"!# !'+"+# RSD +"%# ,+"!# +&)",# !("!# ,)"$# +%"&# +,"'# &)"*#

Figure 6. Linear relationship between mineralizable N and species richness.