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The Impact of Polypore Fungi on Growth and Physiology of Yellow Birch and Molecular Detection of Fungal Pathogens in Live

Trees

by

Erin Elizabeth Mycroft

A thesis submitted in conformity with the requirements for the degree of Masters of Science in Forestry

Faculty of Forestry University of Toronto

© Copyright by Erin Elizabeth Mycroft 2010

ii

The Impact of Polypore Fungi on Growth and Physiology of

Yellow Birch and Molecular Detection of Fungal Pathogens in Live

Trees

Erin Elizabeth Mycroft

Masters of Science in Forestry

Faculty of Forestry

University of Toronto

2010

Abstract

Pathogenic fungi, such as polypore fungi that infect live sapwood, decrease quality and value of

wood; however their effects on canopy physiology and growth have been little examined. This

study examines how Fomes fomentarius, a species of polypore fungus affects canopy physiology

in Betula alleghaniensis. A mobile canopy lift enabled the collection of leaf physiology,

morphology and chemistry data from canopies of infected, damaged, and control trees. A

molecular protocol developed to detect and identify polypore fungi in live trees confirmed that F.

fomentarius was the major species present in infected trees. Infected trees exhibited reductions

in physiological performance and growth, along with higher leaf carbon and chlorosis. While

some characteristics of fungal infection were consistent with a mechanism involving partial

xylem occlusion, patterns did not resemble those of a simple drought response. Likely, other

factors such as fungal toxins or host defense mechanisms also contribute to these patterns.

iii

Acknowledgments

This Master‟s thesis could have not been accomplished without the help and support of a number

of people. First, I would like to thank my supervisor, Dr. Sean Thomas, for the opportunities and

scientific inspiration that he provided me with during this degree. I would also like to thank my

committee members Dr. Jean-Marc Moncalvo and Dr. Martin Hubbes, who have been generous

in sharing their expertise throughout the progress of my work. Thank you to everyone who got

up early in the morning to help me in the field: Moe Luksenberg, Matt O‟Hara, Jessica Stokes,

Jonathan Schurman and Heather McLeod. A special thanks to Rajit Patankar for sharing not only

the early mornings, but also his friendship, scientific insights, and understanding with me.

Thanks also to all the other members of the Thomas lab, especially Michael Fuller for his

guidance and encouragement. Thank you to everyone at the ROM in the LMS lab, especially

Kristen Choffe and Simona Margaritescu, who were instrumental in the success of the molecular

portion of this work. Thank you to Dr. Peter Schleifenbaum and the staff at Haliburton Forest

and Wildlife Reserve for the opportunity to work and study in a picturesque environment for the

past two summers. Thank you also to the National Science and Engineering Research Council of

Canada who provided me with generous funding for this degree. Finally, I would like to thank

my family and friends, and especially John Thaler for lending hands, thoughts, and shoulders

whenever they were needed. This thesis is dedicated to my Mom, who shared with me her love

and passion for the botanical world.

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Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ............................................................................................................................... viii

List of Figures ................................................................................................................................ ix

List of Appendices ........................................................................................................................ xii

Chapter 1 General Introduction and Literature Review .................................................................. 1

1.1 Pathogenic and wood-decay fungi ...................................................................................... 1

1.1.1 Effects on forests ..................................................................................................... 2

1.2 Metabolic requirements of wood-decay fungi .................................................................... 4

1.2.1 Mechanisms of fungal infection .............................................................................. 5

1.2.2 Tree defense mechanisms ....................................................................................... 6

1.2.3 Effects on tree physiology ...................................................................................... 7

1.2.4 Growth responses to pathogens .............................................................................. 8

1.2.5 Aging trees and pathogens ...................................................................................... 8

1.3 Detection of tree pathogens ................................................................................................ 9

1.4 Study Organisms ............................................................................................................... 11

1.4.1 Polypore fungi ....................................................................................................... 11

1.4.2 Fomes fomentarius ................................................................................................ 11

1.4.3 Betula alleghaniensis ............................................................................................ 12

1.5 The focus of this thesis ..................................................................................................... 12

Chapter 2 The Impact of Fomes fomentarius on Growth and Canopy Physiology of Betula

alleghaniensis ........................................................................................................................... 14

2.1 Abstract ............................................................................................................................. 14

2.2 Introduction ....................................................................................................................... 14

2.2.1 Effects of pathogenic wood-decay fungi on forests .............................................. 14

v

2.2.2 Study species ......................................................................................................... 15

2.2.3 Effects of pathogens on trees ................................................................................ 16

2.2.4 Ontogenetic traits .................................................................................................. 17

2.2.5 Focus of this study ................................................................................................ 17

2.3 Methods ............................................................................................................................. 18

2.3.1 Study site and canopy access ................................................................................ 18

2.3.2 Gas-exchange measurements ................................................................................ 19

2.3.3 Leaf morphometrics .............................................................................................. 20

2.3.4 Leaf chemistry ...................................................................................................... 20

2.3.5 Dendrochronological analysis ............................................................................... 21

2.3.6 Molecular analysis ................................................................................................ 21

2.3.7 Statistical analysis ................................................................................................. 21

2.4 Results ............................................................................................................................... 23

2.4.1 Gas exchange parameters ...................................................................................... 23

2.4.2 Leaf morphometrics .............................................................................................. 28

2.4.3 Leaf chemistry ...................................................................................................... 33

2.4.4 Chlorosis ............................................................................................................... 37

2.4.5 Growth .................................................................................................................. 38

2.4.6 Relationships among variables ............................................................................. 40

2.5 Discussion ......................................................................................................................... 50

2.5.1 Gas exchange parameters ...................................................................................... 50

2.5.2 Leaf chemistry ...................................................................................................... 51

2.5.3 Chlorosis ............................................................................................................... 53

2.5.4 Herbivory .............................................................................................................. 54

2.5.5 Growth .................................................................................................................. 54

2.5.6 Leaf morphology ................................................................................................... 55

vi

2.5.7 Potential mechanisms ............................................................................................ 55

2.5.8 Comparison to ontogenetic traits .......................................................................... 57

2.6 Conclusion ........................................................................................................................ 58

Chapter 3 Molecular Detection of Polypore Fungal Infection in Live Woody Tissue of

Yellow Birch ............................................................................................................................ 60

3.1 Abstract ............................................................................................................................. 60

3.2 Introduction ....................................................................................................................... 60

3.2.1 Culturing ............................................................................................................... 61

3.2.2 PCR ....................................................................................................................... 61

3.2.3 Focus of this study ................................................................................................ 64

3.3 Methods ............................................................................................................................. 64

3.3.1 Field sampling ....................................................................................................... 64

3.3.2 Initial extraction attempts ..................................................................................... 65

3.3.3 DNA extraction and purification .......................................................................... 66

3.3.4 DNA amplification and visualization ................................................................... 67

3.3.5 Cloning .................................................................................................................. 68

3.3.6 Sequencing and analysis ....................................................................................... 69

3.4 Results ............................................................................................................................... 70

3.4.1 DNA Isolation from wood .................................................................................... 70

3.4.2 Amplification and visualization ............................................................................ 70

3.4.3 Cloning .................................................................................................................. 72

3.4.4 ITS sequences – Genbank database similarities. .................................................. 72

3.5 Discussion ......................................................................................................................... 78

3.5.1 Molecular protocol development .......................................................................... 78

3.5.2 Fungi detected ....................................................................................................... 78

3.5.3 Methodological considerations ............................................................................. 80

vii

3.5.4 Implications ........................................................................................................... 84

3.6 Conclusion ........................................................................................................................ 85

Chapter 4 ....................................................................................................................................... 86

4.1 Overview ........................................................................................................................... 86

4.2 Impacts of infection on physiology, morphology and growth .......................................... 86

4.3 Molecular detection of infection ....................................................................................... 87

4.4 Current limitations, implications and future directions .................................................... 88

References ..................................................................................................................................... 91

Appendix ..................................................................................................................................... 105

viii

List of Tables

Table 2.1. Results of ANOVA describing effects of tree condition (Tmt), canopy stratum

(stratum) and the interaction between the two (Tmt*Stratum) on measured physiological,

morphological and growth characteristics of B. alleghaniensis. .................................................. 47

Table 2.2 Equations of the non-linear least squares estimates describing the relationship between

photosynthetic rate (Amax ) and stomatal conductance (gs) ........................................................... 48

Table 2.3. Summary of ANCOVA results for relationships between Amax and morphological,

chemical, and growth parameters by treatment (tree condition). .................................................. 49

Table 3.1 Summarizing table indicating tree ID number, clone number (if applicable), tree

condition, and respective PCR, cloning, and Genbank results ..................................................... 75

ix

List of Figures

Figure 1.1 Relationships between semivariance of maximum photosynthetic assimilation rate of

upper canopy leaves, mid- canopy leaves, and lower canopy leaves and distance between trees in

degrees latitude and longitude. ..................................................................................................... 23

Figure 2.2. Observed photosynthetic rate (Amax) in B. alleghaniensis across tree „condition‟

treatments and canopy strata. ........................................................................................................ 25

Figure 2.3. Observed stomatal conductance (gs) in B. alleghaniensis across tree „condition‟

treatments and canopy strata.. ....................................................................................................... 26

Figure 2.4. Instantaneous water-use-efficiency (mmol CO2/mol H2O) observed in B.

alleghaniensis across tree „condition‟ treatments and canopy strata. ........................................... 27

Figure 2.5. Integrated water-use-efficiency (δ13

C (‰)) in B. alleghaniensis, measured with

respect to the Pee Dee Belemnite standard. Observations are shown across tree „condition‟


Figure 2.6. Observed leaf area (cm2) in B. alleghaniensis across tree „condition‟ treatments and

canopy strata. ................................................................................................................................ 29

Figure 2.7. Observed leaf mass area (g/cm2) in B. alleghaniensis across tree „condition‟


Figure 2.8. Observed leaf length (cm) in B. alleghaniensis across tree „condition‟ treatments and

canopy strata. ................................................................................................................................ 31

Figure 2.9. Observed leaf tissue density (g/cm3) in B. alleghaniensis across tree „condition‟


Figure 2.10. Percent observed herbivory in B. alleghaniensis across tree „condition‟ treatments

and canopy strata. .......................................................................................................................... 33

Figure 2.11. Leaf nitrogen content (by mass (g/g)) in B. alleghaniensis across tree „condition‟


x

Figure 2.12. Leaf nitrogen content (by area (g/cm2)) in B. alleghaniensis across tree „condition‟

treatments and upper, middle, and lower canopy strata.. .............................................................. 35

Figure 2.13. Observed leaf carbon content (by mass (g/g)) in B. alleghaniensis across tree

„condition‟ treatments and upper, middle, and lower canopy strata. ............................................ 36

Figure 2.14. Observed leaf carbon content by area ((g/cm2)) in B. alleghaniensis across tree

„condition‟ treatments and canopy strata.. .................................................................................... 37

Figure 2.15.Observed percent chlorosis in B. alleghaniensis across tree „condition‟ treatments

and canopy strata (low, middle and upper). .................................................................................. 38

Figure 2.16. The five-year average radial growth increment (mm/year) in B. alleghaniensis

across tree „condition‟ treatments: control, physically damaged, and infected with F. fomentarius.

....................................................................................................................................................... 39

Figure 2.17. The five-year average basal area increment (mm2 /year) in B. alleghaniensis across

tree „condition‟ treatments: control, physically damaged, and infected with F. fomentarius. ..... 40

Figure 2.18. Relationships between stomatal conductance (gs) and net carbon dioxide

assimilation (Amax) in B. alleghaniensis for all three „condition‟ treatments: control, physically

damaged, and infected with F. fomentarius. ................................................................................. 41

Figure 2.19. Relationship between nitrogen per leaf area (g/cm2) and net carbon dioxide



Figure 2.20. Relationship between carbon per leaf area (g/cm2) and net carbon dioxide



Figure 2.21. Relationship between leaf mass per area (g/cm2) and net carbon dioxide assimilation

(Amax) in B. alleghaniensis for all three „condition‟ treatments: control, physically damaged, and

infected with F. fomentarius.. ....................................................................................................... 45

xi

Figure 2.22. Observed relationship between basal area increment (mm2/year) and net carbon

dioxide assimilation (Amax) in B. alleghaniensis for all three „condition‟ treatments: control,

physically damaged, and infected with F. fomentarius. Only upper canopy leaves were analyzed.

....................................................................................................................................................... 46

Figure 3.1. Electrophoresis gel depicting amplified DNA in the ITS region from each tree in this

study using ITS8F and ITS6R primers ......................................................................................... 71

Figure 3.2. Image of electrophoresis gel showing amplified ITS clones using primers ITS8F and

ITS6R. The samples have not yet been cleaned, as indicated by smears near the well and under

the bands. ...................................................................................................................................... 73

Figure 3.3 Image of electrophoresis gel showing amplified ITS clones using primers ITS8F and

ITS6R following a purification step. Bands are much sharper than in Figure 3.2, indicating that

the contaminants had been successfully removed from the DNA. ............................................... 74

xii

List of Appendices

Table A 1. Estimated extracted DNA concentrations and corresponding 260/280nm and

260/230nm ratios for DNA samples from each tree. .................................................................. 105

Table A 2. PCR Recipe (25 μl DNA amplification reaction and Cloning PCR Reaction) ........ 107

Table A 3. Primer Sequences ...................................................................................................... 108

Table A 4, Thermocycler Settings (DNA amplification reaction and cloning reaction) ............ 109

Table A 5. Sequencing PCR Recipe, using a total of 10 ng template DNA. Calculations shown

are for 1 μl or 4 μl template DNA. .............................................................................................. 110

Table A 6. Thermocycler Settings (Sequencing Reaction) ......................................................... 111

1

Chapter 1 General Introduction and Literature Review

1.1 Pathogenic and wood-decay fungi

Wood-decay fungi have a tremendous impact on forests, from both an ecological and economic

perspective (Lewis and Lindgren 2000). Fungi play an important role in forest ecosystems

through the cycling of nutrients, creation of gaps and habitat formation, maintenance of host

population fitness through selection (e.g. Castello et al. 1995, Hennon 1995, Stubblefield et al.

2005), and influence on succession (Holah et al. 1997, Haack and Byler 1993).

Fungi are biologically diverse, and although most tree pathogens belong to the Basidiomycota

(e.g. Ganoderma spp., Phellinus spp. and Fomes spp.) and the Ascomycota (e.g. Taphrina

betulina , the cause of witches‟ brooms on Betula; sudden oak death caused by Phytophthora

ramarum), and some diseases and rots are caused by members of the imperfect fungi (e.g.

brunchorstia dieback of conifers caused by Gremmeniella abietina, fusicoccum bark canker of

oak caused by Fusicoccum quercus, and soft-rot caused by Paecilomyces variotii) (Butin 1995,

Schmidt 2006).

Among fungal pathogens, the type and form of infection vary considerably. Some pathogenic

fungi are parasitic and saprophytic, and thus can infect live sapwood and decay dead wood for

nutrition as well (Manion 1981). Most pathogenic fungi are also specialized to infect certain

parts of a tree. Fungi such as Pythium spp. cause „damping off‟ diseases in seeds and seedlings

(Augspurger 1984, 2007). Wilt diseases affect water movement in trees and cause the leaves to

wilt, such as in Dutch elm disease, caused by the ascomycetes Ophiostoma ulmi and O. novo-

ulmi (Manion 1981, Temple and Horgen 2000, Schmidt 2006). Additionally, some fungi are

specialized to infect reproductive tissues, such as species in the genus Taphrina which can affect

alder catkins (Butin 1995).

Root-rot, butt-rot and stem-rot pathogens tend to have broad host ranges (Butin 1995, Lewis and

Lindgren 2000). These fungi have large influences on tree and forest dynamics, as they

deteriorate the structural integrity of the tree, eventually leading to complete degradation. This,

in turn affects ecological processes as will be discussed later. Root and stem-rot pathogens have

a variety of colonization strategies; some species are restricted entirely to using live trees as a

2

substrate, whereas others will initially infect heartwood and then move to sapwood, and yet some

initially infect live sapwood but continue decay even after the tree is dead (such as Fomitopsis

pinicola and Laetiporus sulphurous) (Schmidt 2006).

Root and butt-rot fungi are among the most widely studied fungal pathogens in forests, and have

received a good deal of attention because of their capacity to attack young and vigorous trees

(Tainter and Baker 2006). Root-rots in particular increase vulnerability of trees to windthrow

(e.g. Manion 1981, Whitney et al. 2002) and are principal initiators of canopy gaps in some

forests (e.g. Worrall and Harrington 1988). Some of these fungi are solely confined to infecting

root systems (e.g. Rhizina undulata) (Butin 1995), whereas others will initially infect roots but

eventually spread to the bole (e.g. Armillaria spp.) (Butin 1995, Tainter and Baker 2006). Three

of the most common root-rot pathogens include Phellinus weirii, Heterobasidion annosum and

Armillaria spp. (Manion 1981).

Stem decay and heart rot fungi on the other hand, are not as commonly studied as root rot fungi,

so considerably less is known about their biology and impact on forests (Hennon 1995). This

broad group, which includes polypore fungi, typically attack the sapwood and/or heartwood of

living trees, often infecting sapwood and decomposing heartwood before the tree is dead. A

number of these species also advance to the phloem and disrupt the physiology of the tree

(Hennon 1995). Some of these fungi are primary colonizers, whereas others follow in a

succession (Hennon 1995, Durall et al. 1996).

1.1.1 Effects on forests

Historically, forest pathogens have been viewed as having negative impacts on forests, as they

typically reduce the economic viability of the stand (Haddow 1938, Lewis and Lindgren 2000,

Manion 2003) However, recent reviews have emphasized the need for a „healthy balance of

disease‟ in forest ecosystems (Castello et al. 1995; Manion 2003), and have highlighted the need

to better understand the ecological roles of these organisms and the effects that forest

management has on them (Lewis and Lindgren 2000, Sippola 2004). Pathogenic wood-decay

fungi may considerably influence the structure and dynamics of forest communities (Franklin et

al. 1987, Castello et al. 1995, Hansen and Goheen 2000), and play an important role in the

maintenance and health of forest ecosystems, through the breakdown of complex carbohydrates,

cellulose, and lignin in wood and subsequent nutrient recycling, elimination of less competitive

3

genotypes, regulation of host and pathogen species distribution, creation of canopy gaps and

habitat (Waring et al. 1987, Castello et al. 1995, Hansen and Goheen 2000, Augspurger 2007).

1.1.1.1 Canopy gaps

Woody decay pathogens, such as polypore fungi, influence forest structure through the formation

of canopy gaps, which facilitate horizontal and vertical heterogeneity in a forest stand (Hansen

and Goheen 2000, Stubblefield et al. 2005). In general, canopy gaps positively influence the

richness and composition of understory vegetation through regeneration (e.g. Bendel et al.

2006b, Holah et al. 1993) as a result of increased light availability. Studies have found that

heart, butt and more aggressive root rot pathogens contribute significantly to the creation of

forest gaps (Worrall and Harrington 1988, Bendel et al. 2006a). While stem-infecting fungi such

as Polypores are not necessarily as aggressive as these pathogens, they are important canopy-gap

initiators in forests that do not frequently experience large-scale disturbances (Hennon 1995,

Lewis and Lindgren 2000). These pathogens tend to create smaller canopy gaps than root-rot

pathogens, because the time between infection and bole breakage can be quite long (Hennon

1995).

1.1.1.2 Habitat formation

Fungal pathogens are also integral in the formation of habitat conditions within forests (Franklin

et al. 1987, Hennon 1995, Stubblefield et al. 2005). Through wood degradation and nutrient

cycling, root and stem pathogens initiate conditions suitable for other fungi, plants, insects and

vertebrates to become established (Hennon 1995). For instance, woodpeckers rely on heart-rot

fungi such as polypores to soften wood for them, and cavity nesting birds reside in infected trees

and snags to minimize the energy they exert in excavating their nests (Walters 1991 (as cited by

Castello et al. 1995), Stubblefield et al. 2005). Additionally, infected trees with weakened roots

that fall provide shelter for mice and other small rodents (Stubblefield et al. 2005). Thus, wood-

decay fungi contribute significantly to stand health and biodiversity through habitat creation

(Franklin et al. 1987, Manion 2003).

Polypore fungi are the most important decomposers of woody debris in boreal forests (Renvall

1995 (as cited by Junninen 2007)), and an estimated 20-25% of all boreal forest fungal species

depend on decomposed wood for reproduction (Siitonen 2001). Fungi occupy substrates in a

succession, altering and creating habitat suitable for the successor species; thus, a number of

4

polypore fungal species which are dependent on the later stages of wood decay are particularly

sensitive to forest type (Lindblad 1998, Sippola 2004, Junninen 2007).

In Scandinavian countries, intensive forest management has resulted in a dramatic decline in

coarse woody debris in forests, thus decreasing polypore habitat and leaving many of the late-

successional polypores endangered (Lindblad 1998, Junninen 2007). There are a number of

conservation initiatives and forest management methods now being employed to protect

polypores in these countries (Junninen 2007), and „red-listed‟ endangered polypores are

increasingly used as forest health indicators for management planning (Sippola 2004).

1.1.1.3 Post-harvest mortality

A number of studies have found that forest management practices have a pronounced influence

the incidence of decay fungi in trees (e.g. Morrison and Mallett 1996, Lewis and Lindgren 2000,

Durall et al. 2005). Wounds inflicted on trees caused by skidding operations, or stumps left

behind are easily colonized by pathogenic fungi (Morrison and Mallett 1996) which then spread

to other trees. Harvesting can also upset the host-pathogen balance (Lewis and Lindgren 2000).

In Haliburton forest, one study found that fungal diversity was greater in old growth stands when

compared to harvested and horse-harvested stands, however there was no difference in overall

fungal infection rates (Shuter 2002). Furthermore, some management practices can affect wind

dynamics and tree exposure, thus increasing fungal-induced mortality in trees due to windthrow

(Whitney et al. 2002). While post-harvest mortality has been documented in the literature, there

has not been much attention directed to the role of fungi in this process (Morrison and Mallett

1996).

1.2 Metabolic requirements of wood-decay fungi

Pathogenic wood-decay fungi differ in their metabolic requirements. In general, these fungi can

be grouped into three different categories of rot, based on which wood molecules they are

capable of degrading (Schwarze et al. 2000). Brown rot fungi degrade both cellulose and

hemicelluloses in wood, but lack the enzymes to degrade lignin. The wood becomes brittle and

loses most of its strength. Fungi which cause white rot contain enzymes which facilitate the

degradation of cellulose, hemicelluloses, and lignin. The enzymes of white rot fungi do not

diffuse very far, resulting in „pockets‟ of degraded wood surrounded by intact wood (Manion

5

1981, Tainter and Baker 1996, Schwarze et al. 2000, Schmidt 2006). In the past, associations

have been made linking white rots with hardwoods and brown rots with conifers; this

relationship, however, is not completely consistent (Manion 1981) as some white rots infect

conifers (e.g. Phellinus pini) and some brown rots infect hardwoods (e.g. Laetiporus sulphurous)

(Schmidt 2006). Furthermore, there is no finite correlation between the phylogenetic relationship

of fungi to one another, and the molecules that those taxa break down. Soft rot fungi degrade

cellulose and hemicelluloses, but do not usually infect living trees (Schmidt 2006).

1.2.1 Mechanisms of fungal infection

Stem infecting fungi usually gain access to the tree via spore infection of open wounds in the

main stem, crown, or roots (Butin 1995, Schwarze et al. 2000). The release of spores commonly

occurs under certain environmental conditions; for example, Fomes fomentarius tends to release

spores at lower temperatures (Schwarze et al. 2000). Spores are then transmitted by wind, rain,

or human/animal vectors (Hennon 1995, Schwarze et al. 2000). While major wounds constitute a

large proportion of overall infection, minor wounds such as branch wounds are likely to play a

role in infection as well (Hennon 1995). However, the probability of infection tends to be

positively correlated with wound size (Schwarze et al. 2000). Various types of fungi have

numerous strategies for penetrating a host, such as adherence to the host using enzymes followed

by either enzymatic or mechanical penetration (Knogge 1998, Schwarze et al. 2000). Spore

germination is induced by a number of molecular signals, typically initiated by an increase in

water content of the spore (Schwarze et al. 2000). The success of colonization then depends on

a number of biotic and abiotic factors, including the age of the host and the amount of moisture

in the wood (Boddy and Rayner 1983, Schwarze et al. 2000). For simultaneous white rot fungi in

particular, once the fungus has established itself, hyphae secrete enzymes which degrade

hemicelluloses, celluloses and lignin at a similar rate (Schwarze et al. 2000), thus creating a lysis

zone which penetrates the cell walls (Schmidt et al. 2006). As the decay progresses, hyphae grow

in the lumen and progressively attack the remaining cell walls from the lumen (Schwarze et al.

2000). In Fomes fomentarius and Ganoderma applanatum (simultaneous and selective white rots

respectively), the damage to cell walls is generally greater in earlywood than in latewood

(Schmidt et al. 2006). It is important to note, however, that scientists do not know detailed

colonization processes for the majority of fungal species (Schwarze et al. 2000).

6

Fungal toxins may also play a role in the development of plant disease. There are two main

types of toxins involved in plant disease, nonspecific toxins and host-selective (specific) toxins

(Scheffer and Livingston 1984). Nonspecific toxins cause obvious damage to plant tissues and

are known to be involved in disease development regardless of whether the plant is the host of

the fungus producing the toxin (Scheffer and Livingston 1984), while host-selective toxins are

produced by a fungus restricted to specific host plants, and only cause symptoms in the host

plants. In addition to toxins, hormones, extracellular enzymes and proteins may also cause

disease-like symptoms in host plants (Scheffer and Livingston 1984, Van Alfen 1989, Bowden et

al. 1990, Whiteford and Spanu 2002).

While the effects of toxins on plants vary considerably, physiological effects may include

changes in CO2 fixation and respiration, changes in membrane permeability and even

degradation of cell membranes and pigments, synthesis of proteins, manipulation of water

potentials and nutrient release from cells (Scheffer and Livingston 1984, Van Alfen 1989,

Peterson and Aylor 1995, Snoeijers et al. 2000). For example, Ophistoma ulmi and Ophistoma

novo-ulmi, the fungi that cause Dutch elm disease, secrete a hydrophobic protein known as

cerato-ulmin (CU) (Bowden et al. 1994, Temple and Horgen 2000). These hydrophobic proteins,

known as hydrophobins, are considered common to all filamentous fungi (Whiteford and Spanu

2002). Studies have shown that CU production is correlated with the aggressiveness of

Ophistoma isolates, and has been shown to cause wilt via embolisms in xylem vessels by

stabilizing air bubbles (Temple and Horgen 2000, Whiteford and Spanu 2002). However, there

is a debate surrounding its role in the virulence of O.ulmi and O.novo-ulmi, as targeted disruption

of CU does not decrease virulence of Ophistoma (Temple and Horgen 2000).

1.2.2 Tree defense mechanisms

Trees exhibit a variety of mechanisms which aid in pathogen resistance. Live, sound wood

contains small amounts of anti-microbial compounds, and thick bark acts as a passive defense

against pathogens (Yamada 2001). Compartmentalization of decay in trees, or „CODIT‟ is a

model that describes a set of physical and chemical defense mechanisms used by trees to isolate

infected tissues and resist further spread of the pathogen (Shigo 1984). Compartmentalization

works through four „walls‟ (axial, radial, tangential, barrier zone) which act to defend against the

spread of wood-decay fungi. Parenchyma cells create physical and chemical barriers to

7

movement of hyphae using gums, tyloses, and cellulases (Shigo 1984, Schwarze et al. 2000,

Yamada 2001). Another common physiological response is the swelling of cell walls, or

formation abscission layers to segregate the pathogen and avoid further invasion of hyphae

(Vance 1980, Schwarze et al. 2000). Chemical deposits may also occlude xylem elements,

preventing the spread of hyphae (Yamada 2001). Another induced form of defense involves the

production of antimicrobial compounds, which are toxic to many fungi and bacteria (Tainter and

Baker 1996, Manion 2003, Lambers et al. 2006), and walls of xylem and parenchyma cells may

also become increasingly lignified and/or suberized (Yamada 2001). Although much about

compartmentalization and defense has been learned in the past few decades (Manion 2003),

knowledge of antimicrobial defenses in trees is still at a preliminary stage (Pearce 1996).

1.2.3 Effects on tree physiology

Tree physiology is affected by fungal pathogens in a variety of ways, as defence compounds,

hydraulic conductance, metabolic processes, hormones and growth may be altered when a tree is

inoculated with a pathogen (Kozlowski 1969, Yamada 2001, Schmidt et al. 2006).

A number of fungal pathogens are known to have a negative effect on the hydraulic conductivity

of trees, due to the compartmentalization that often occurs during their invasion (Schwarze et al.

2000). Pathogens that infect roots often alter the supply of water and nutrients to the tree

(Froelich et al. 1977). Similarily, stem pathogens commonly interfere with water movement, and

fungi may produce molecules such as hydrophobins which may cause embolisms (Guéard et al.

2000, Cherubini et al. 2002, Whiteford and Spanu 2002). In one particular study, authors found

that Scots pine infected with a blue-stain fungus decreased hydraulic conductivity in areas of the

sapwood by up to 60% (Guéard et al. 2000).

Studies have found that fungal pathogens generally have negative effects on gas exchange

processes in trees, although the majority of studies to date focus on ascomycete stem or leaf wilt

pathogens (Luque et al. 1999, Berger et al. 2007, Clemenz et al. 2008). To date, no data exists

on what effects polypore or other wood-decay fungi which infect live trees have on gas

exchange. In studies which have been done on other pathogens, decreases in photosynthetic rate

have been attributed to a wide variety of mechanisms including: physiological drought responses

due to a restriction of water transport (e.g. Luque et al. 1999), physical blockages of stomata

(Manter et al. 2000), damage to photosynthetic machinery resulting from fungal toxins (e.g. Van

8

Alfen 1989), or feedback inhibition due to accumulation of starch in leaves (e.g. Berger et al.

2007, Clemenz et al. 2008). Generally, respiration tends to increase following inoculation, and

subsequently declines after the sporulation stage of the fungus (Tainter and Baker 1996). An

increase in the pentose pathway has been noted to accompany the rise in respiration and aids the

production of phenolics for defence (Tainter and Baker 1996).

It is evident that more experimental and observational studies are needed to fully understand and

evaluate the physiogical responses and gas exchange processes in trees exposed to root and stem-

rot pathogens. Knowledge of how these key processes work is necessary for a more complete

understanding of tree and stand-level responses to fungal pathogens.

1.2.4 Growth responses to pathogens

In general, root and stem pathogens negatively affect secondary growth (e.g. Kozlowski 1969,

Froelich et al. 1977, Whitney 1995, Cherubini et al. 2002), and the overall quality of wood,

which ultimately decreases the economic value of a tree or stand (Tainter and Baker 1996). For

example, Froelich and others (1977) found a marked decrease in the height and diameter growth

of slash pine as little as six years after being infected with Fomes annosus (compared to trees

with little or no infection). It has been suggested that when trees are under stress and

photosynthesis or other physiological processes are downregulated, carbon allocation is altered

and secondary (stem) growth is compromised (Kozlowski 1969, Froelich et al. 1977, Dobbertin

2005).

1.2.5 Aging trees and pathogens

As a tree‟s ability to defend itself typically decreases in over-mature trees, the age of the host

tree likely affects the probability of infection (Kozlowski 1969, Schwarze et al. 2000, Boege and

Marquis 2005, but see Ishida et al. 2005). Furthermore, the incidence of pathogenic fungal

infection has been noted to increase with age (Whitney 1995), particularly in stem infecting

fungi (Manion 1981, Hennon 1995).

Recently, an increasing number of studies have investigated changes physiological and

morphological traits in trees as they age (e.g. Thomas and Ickes 1995, Thomas and Winner 2002,

Ishida et al. 2005, Thomas 2010). For example, trees very late in ontogeny tend to exhibit

reduced shoot and diameter growth, a reduction in photosynthetic capacity and stomatal

9

conductance, increased water use efficiency, decreased leaf area, reductions in leaf thickness and

density, decreased leaf nitrogen content and photosynthetic nitrogen use efficiency, increased in

leaf carbon content and reduced allocation to defense (Kozlowski 1969, Boege and Marquis

2005, Ishida et al. 2005, Thomas 2010).

A number of characteristics of very old trees are common to those observed in existing studies of

tree pathogens, such as a decrease in photosynthetic efficiency (Luque et al. 1999, Peterson and

Aylor), reductions in shoot and stem growth (Kozlowski 1969, Froelich et al. 1977), and

decreased leaf area (Parker 1986, Thomas 2010). Thus, characteristics of trees late in ontogeny

may be influenced by an accumulation of pathogen infection over the years. To date, there have

been no studies which have compared physiological and morphological traits which vary

throughout ontogeny with traits associated with infection.

1.3 Detection of tree pathogens

There are many difficulties associated with the study of forest pathogens. Firstly, relatively long

periods of time are needed to adequately study forest pathology, as both trees and pathogens

develop slowly in comparison with the pathology of agricultural crops (Pearce 1996).

Furthermore, many species (such as Armillaria, Heterobasidion and Ganoderma) are not well

delineated, and further studies are required before their ecological role can be better understood

(Moncalvo et al. 1995, Hoff et al. 2004).

Additionally, detection of decay fungi in wood is another challenge in forest pathology. Often,

fruiting bodies are the first external indication of presence (Butin 1995) although sporocarps are

not always present, which makes this technique unreliable (Johannesson and Stenlid 1999,

Schmidt 2006). In these cases, hyphae in wood can be stained with a chitin stain (Chen and

Johnson 1983, Eikenes et al. 2005) and microscopic characters can be used to distinguish

species (Butin 1995, Schmidt 2006). Cultures of mycelia isolated from wood can also be used to

identify fungal species, however this is a time-consuming task as some fungal species may be

unculturable altogether, and even experienced mycologists often have trouble distinguishing

species (Tainter and Baker 1996, Johannesson and Stenlid 1999).

In addition to traditional microscopic and culturing methods, spectroscopic techniques have also

been used to detect fungal presence in wood. In a recent study, Fackler et al. (2007) scanned

10

infected wood cores using Rapid Fourier-transform near infrared (FT-NIR) spectroscopy. This

technique detects changes in the cellulose, hemicellulose and lignin components of wood, and

was able to distinguish between brown, white and soft rots after as few as 5 days of decay

(Fackler et al. 2007). However, this technique requires extensive calibration and cannot identify

individual fungal species (Fackler et al. 2007).

It has been argued that the use of an increment corer may lead to further infection of the tree by

providing a path for fungal invasion (Butin 1995, Larsson et al. 2004). Some non-invasive

methods have been developed to detect decay; for instance, the relative impedance in situ

examination (RISE) method is based on estimating the resistivity in a tree using four electrodes

and comparing resistivity with other trees (Larsson et al. 2004). Although this method does not

work well on frozen wood and can be affected by drought, it is able to distinguish between

various types of decay (Larsson et al. 2004). Other methods of non-invasive detection have also

been used, including magnetic resonance imagery and gamma ray computer tomography (see

Larsson et al. 2004).

Advances have also been made with the use of molecular techniques for detection and

identification of fungal pathogens. These methods provide researchers with objective

measurements as well as a suite of genetic information, enhancing the capability to accurately

determine species, identify individuals within a population, and further delineate phylogenies.

Protein-based techniques such as SDS polyacrylamide gel electrophoresis (SDS-PAGE) and

isozyme analyses have been used, as well as fatty acid profiles and DNA-based techniques

including restriction fragment length polymorphisms (RFLP‟s), ribosomal DNA sequencing,

microsatellites, and microarrays (Hoff et al. 2004).

Identification methods each have advantages and limitations. One study assessed three different

methods of assessment for fungal composition and abundance: sporocarp counts, culturing

mycelia and direct amplification of the internal transcribed spacer (ITS) region of rRNA using

terminal rapid fragment length polymorphism (T-RFLP) (Allmér et al. 2006). Sporocarp counts

and mycelia cultures revealed greater species richness than did direct amplification. However,

sporocarp counts poorly reflected their actual abundance in wood. The T-RFLP method was

efficient in detecting common species but overlooked rarer species present in wood. Culturing

techniques bias the results because species favoured by culture media appear more abundant

11

(Allmér et al. 2006). In a similar study, Johannesson and Stenlid (1999) successfully identified

fungal species by comparing RFLP‟s of the ITS region.

1.4 Study Organisms

1.4.1 Polypore fungi

Polypore fungi are wood-decay fungi belonging to the commonly termed group “bracket fungi”,

a polyphyletic group belonging to the phylum Basidiomycota and order Aphyllophorales

(Gilberston and Ryvarden 1986, Barron 1999). The majority of polypores belong to the family

Polyporaceae, and are characterized by persistent, perennial fruiting bodies and a poroid

hymenophore, or spore bearing surface. Polypore fungi are obligate phytophages, have the

ability to break down both lignin and cellulose, and may be found on live and/or dead trees

(Gilbertson and Ryvarden 1986).

1.4.2 Fomes fomentarius

Fomes fomentarius ((L. ex Fr.) Lowe) is a perennial wood-decaying polypore, often found on

birch or beech trees. It most commonly infects stems, causing white rot through the simultaneous

degradation of lignin, cellulose and hemicelluloses (Schwarze et al. 2000). F. fomentarius is

widespread; it is found in deciduous forests throughout the northern hemisphere. F. fomentarius

is both parasitic and saprobic, often infecting live trees and persisting after the tree has died

(Schwarze et al. 2000). The sporocarps of F. fomentarius are perennial, and appear grey and

hoof-shaped, with a creamy white pore surface (Bossenmaier 1997, Barron 1999). The perennial

layers of F. fomentarius can be seen in the cross-section of the sporocarp (E. Mycroft, personal

observation). In Haliburton forest, south-central Ontario, the fruiting bodies of F. Fomentarius or

Ganoderma applanatum (another common Polypore species) were observed on 70% of all post-

harvest tree mortalities caused by fungal infection (Martin 2005). F. fomentarius is closely

related to another species of polypore, Phellinus ignarius (previously Fomes annosus). Phellinus

tends to be slightly more aggressive (Schmidt 2006), and is visually distinguished from F.

fomentarius by a blackened, cracked sporocarp with a rusty brown hymenium (Barron 1999).

When F. fomentarius infects a tree, the hyphae grow mainly in the vessels and along the xylem

rays. The decay begins first in the earlywood, and then progresses to the latewood (Schwarze et

al. 2000). F. fomentarius has been noted as a primary decay wood-rotting polypore (Heilmann-

12

Clausen 2001), and may even stimulate the growth of secondary decay fungi and late stage

specialists (Heilmann-Clausen and Boddy 2005). Despite the ubiquity and prevalence of this

fungus in the forest, it is surprising that little research has examined the interactions of F.

fomentarius with living trees.

1.4.3 Betula alleghaniensis

Betula alleghaniensis (Yellow Birch) is an important deciduous tree species in the Great Lakes–

St. Lawrence, Deciduous, and Acadian hardwood forest regions of Canada and north-eastern

United States (Hosie 1979, Burns and Honkala 1990). It is moderately shade tolerant, often

found in association with Acer, Fagus, Tilia, Tsuga, and Pinus species. B. alleghaniensis thrives

on loamy well-drained soils, but can grow on a variety of soil types (Hosie 1979).

Economically, yellow birch is an important source of hardwood lumber, as the wood is hard and

strong (Burns and Honkala 1990). In comparison with other diffuse-porous species, Yellow

birch tends to be susceptible to injury and decay (Ohman 1970, Houston 1971), which decreases

wood quality and results in economic loss (Burns and Honkala 1990).

1.5 The focus of this thesis

The general objective of this thesis research is to examine how polypore fungal infection affects

tree growth and physiology, and to develop a molecular detection method for polypore and other

woody-decay species in wood from living trees.

While a substantial amount of literature exists on physiological symptoms of leaf wilt pathogens

and stem pathogens (e.g. Luque et al. 1999, Aldea et al. 2006, Berger et al. 2007, Clemenz et al.

2008), there is no data on how polypore fungal infection affects tree growth and physiology.

Furthermore, these studies are typically conducted ex-situ with saplings. In this thesis, the use of

a mobile forest canopy lift (Scanlift240, Finland) presented a unique opportunity to examine

physiology and morphology of mature trees infected with polypore fungi, specifically Fomes

fomentarius, in-situ to see how infection plays a role in the natural environment.

Recent work in the Thomas lab has examined patterns in tree ontogeny and results of previous

studies (Thomas 2010) suggest that polypore fungal infection may play a role in driving some of

the ontogenetic characteristics observed in ageing trees. The use of the canopy lift enabled this

13

study to examine if leaf traits in trees with fungal infection resemble those found in later

ontogenetic stages of trees at the same site where these previous studies had been conducted.

One aspect of studying infected trees that had yet to be addressed, was the development of a

method of assessing whether or not the „infected‟ trees used in the study were actually infected

with F. fomentarius, and to ensure that „control‟ trees did not contain any detectable trace of F.

fomentarius infection. Previous studies have been successful in developing methods of detection

and identification of decay from wood chips and woody debris (e.g. Allmér et al. 2006, Adair et

al. 2002, Fisher 2008). However, fewer studies have developed techniques to identify fungal

communities from living trees. To date, there have been no studies investigating the

physiological effects of forest pathogens on trees that had determined whether or not control

and/or damaged trees were in fact, absent of infection. Thus, the need for a molecular method to

detect infection in live trees arose.

The first data chapter of this thesis is entitled, “The Impact of Fomes fomentarius on Growth and

Canopy Physiology of Betula alleghaniensis” and examines how physiological and

morphological characteristics are affected by infection with F. fomentarius, what morphological

characters may be correlated with physiological changes, and how overall tree growth is affected

by infection. It also briefly compares the results of this study to characteristics found in the later

ontogenetic stages of trees. Finally, potential mechanisms for the physiological symptoms

observed in this study are discussed.

The second data chapter of this thesis focuses on the development of a molecular protocol for the

detection and identification of woody decay basidiomycete fungi in live standing trees and is

entitled, “Molecular detection of polypore fungal infection in live woody tissue of yellow birch”.

The aim of this study is to confirm the presence of F. fomentarius in the trees used in the first

data chapter, and to detect any infection present prior to sporocarp development in damaged

and/or asymptomatic trees.

14

Chapter 2

The Impact of Fomes fomentarius on Growth and Canopy Physiology of Betula alleghaniensis

2.1 Abstract

Pathogenic fungi, such as polypore fungi that infect live sapwood, considerably decrease overall

quality and value of merchantable wood, but their effects on canopy physiology and growth have

been little examined. This study examines how Fomes fomentarius, a widespread species of

polypore fungus that infects live trees, affects canopy physiology of yellow birch (Betula

alleghaniensis), a common tree species in the Great Lakes region of eastern North America. A

mobile canopy lift at the Haliburton Forest and Wildlife Reserve (Haliburton, Ontario, Canada)

was used to collect data on leaf physiology, morphology and chemistry from canopies of visibly

infected, damaged (but not visibly infected), and non-damaged trees. Trees infected with F.

fomentarius showed large reductions in stomatal conductance and net photosynthetic

assimilation compared to non-infected and damaged trees. Leaves of infected trees exhibited

higher carbon content and a greater degree of chlorosis; however, there were no significant

differences in leaf area, length, leaf mass per area, herbivory rate, or leaf nitrogen content when

compared to non-infected trees. Average radial growth increment and basal area increment was

also considerably reduced in infected trees. While some physiological effects of fungal infection

were consistent with a mechanism involving partial occlusion of xylem conduits, patterns here

do not solely follow those of a typical drought response. It is likely some other factor, such as

fungal-induced defense responses or toxins may also contribute to these patterns. Although the

mechanisms are not fully elucidated, the results of this study demonstrate that polypore fungal

infection has a clear effect on canopy tree physiology and growth.

2.2 Introduction

2.2.1 Effects of pathogenic wood-decay fungi on forests

Tree pathogenic fungi considerably decrease overall quality, quantity and value of merchantable

wood through effects on structural degradation and tree growth and mortality (Burns and

Honkala 1990, Tainter and Baker 1996). Live trees in partially harvested areas are especially

vulnerable to infection, as open wounds from skidders and falling trees are readily colonized by

15

pathogens (Vasiliauskas 2001, Lewis and Lindgren 2000, Durall et al. 2005) however, the

majority of polypore fungi do not attack live trees (Schwarze et al. 2000). Wood-decay fungi

such as polypore fungi are important for decomposition and nutrient cycling (Hennon 1995,

Haack and Byler 1993). Previous studies have demonstrated the importance of carbon and

nutrient acquisition for proper allocation to tree defense mechanisms (Matson and Waring 1984,

Sandnes and Solheim 2002). Nevertheless, surprisingly little is known about the effect polypore

fungi have on tree physiology, particularly with regard to gas exchange and other aspects of leaf

function.

Forest management practices have the ability to influence the incidence of decay fungi in trees

(e.g. Morrison and Mallett 1996, Lewis and Lindgren 2000, Durall et al. 2005). While some

studies have documented a higher occurrence of fungal infection in managed stands (e.g.

Morrison and Mallett 1966, Lewis and Lindgren 2000, Vasiliauskas 2001, Durall et al. 2005),

one study in Haliburton forest found that management had no effect on overall fungal

frequencies, although the composition of fungal communities differed between old growth and

managed stands (Shuter 2002). Wounds inflicted on trees caused by skidding operations, or

stumps left behind are easily colonized by pathogenic fungi (Morrison and Mallett 1996) which

spread to other trees. Consequently, decay fungi can play a major role in post-harvest mortality

temperate deciduous forests (Vasiliauskas 2001, Martin 2005).

2.2.2 Study species

The polypores are a polyphyletic group of fungi from the phylum Basidiomycota, belonging to

the order Polyporaceae plus a number of other related groups. As the majority of polypores are

woody decay fungi, they play an important role in forest ecosystems through the cycling of

nutrients, creation of forest canopy gaps and formation of habitat (Gilbertson and Ryvarden

1986, Hennon 1995). Approximately 2% of living stems in Haliburton Forest show signs of

polypore fungal infection through the presence of fungal fruiting bodies (Shuter 2002, E.

Mycroft 2008, unpublished data). However, this is likely a substantial underestimate, as there

could be infected trees upon which sporocarps have not yet developed.

Fomes fomentarius ((L. ex Fr.) Lowe) is a perennial wood-decaying basidiomycete, often found

on birch or beech trees. It most commonly infects stems, causing white rot through the

simultaneous degradation of lignin, cellulose and hemicelluloses (Schwarze et al. 2000). F.

16

fomentarius is widespread; it is found in deciduous forests throughout the northern hemisphere.

F. fomentarius is both parasitic and saprobic, often infecting live trees and persisting after the

tree has died (Schwarze et al. 2000). In Haliburton forest, the fruiting bodies of F. fomentarius

or Ganoderma applanatum (another common polypore species) were observed on 70% of all

post-harvest tree mortalities caused by fungal infection (Martin 2005). When F. fomentarius

infects a tree, the hyphae grow mainly in the vessels and along the xylem rays – the decay begins

first in the earlywood, then progresses to the latewood (Schwarze et al. 2000). F. fomentarius has

been noted as a primary decay wood-rotting polypore (Heilmann-Clausen 2001), and may even

stimulate the growth of secondary decay fungi and late stage specialists (Heilmann-Clausen and

Boddy 2005). Despite the ubiquity and prevalence of this fungus in the forest, little research has

examined its interactions with living trees.

Betula alleghaniensis (Yellow Birch) is an important deciduous tree species in the Great Lakes–

St. Lawrence, Deciduous, and Acadian hardwood forest regions of Canada and north-eastern

United States (Hosie 1979, Burns and Honkala 1990). It is moderately shade tolerant, often

found in association with Acer, Fagus, Tilia, Tsuga, and Pinus species. B. alleghaniensis thrives

on loamy well-drained soils, but can grow on a variety of soil types (Hosie 1979).

Economically, yellow birch is an important source of hardwood lumber, as the wood is hard and

strong (Burns and Honkala 1990). In comparison with other diffuse-porous species, Yellow

birch tends to be susceptible to injury and decay (Ohman 1970, Houston 1971), which decreases

wood quality and results in economic loss (Burns and Honkala 1990).

2.2.3 Effects of pathogens on trees

There has been substantial work done on the effects that root and stem pathogens have on wood

quality and growth rate of trees. For instance, root and stem pathogens in general tend to

decrease both radial growth rate (Froelich et al. 1977, Whitney 1995, Luque et al. 1999,

Cherubini et al. 2002) and the overall quality of wood (Tainter and Baker 1996), leading to a

decrease in the economic value of a tree or stand. One study examining the effect of root-rot

pathogens on tree ring growth found that mountain pines infected with Heterobasidion annosum

and Armillaria ceased stem growth decades (up to 31 years) before crown death was evident

(Cherubini et al. 2002). In contrast, Froelich and others (1977) found a marked decrease in the

17

height and diameter growth of slash pine as little as six years after being infected with Fomes

annosus when compared to trees with little or no infection.

There has also been a good deal of focus on woody tissue responses at and/or near the site of

infection (e.g. Shigo 1984, Boddy and Rayner 1993). However, the impact of fungal pathogens

at the leaf level remains largely unexplored. Furthermore, surprisingly little attention has been

given to the effects that pathogens have on primary metabolism and overall physiological

performance of plants (Berger et al. 2007). In recent years, there has been a growing body of

literature on the effects of leaf pathogens on plant physiology (see Berger et al. 2007 for review),

but considerably less work has been done on stem pathogens.

2.2.4 Ontogenetic traits

Incidence of pathogenic fungal infection has been noted to increase with age (Whitney 1995),

and allocation to tree defense is thought to be low in over-mature, nearly senescent trees (Boege

and Marquis 2005). Recent studies have examined physiological and morphological traits in

trees as they age, (e.g. Thomas and Ickes 1995, Thomas and Winner 2002, Ishida et al. 2005,

Thomas 2010) and have hypothesized that ontogenetic patterns may in part be associated with

biotic interactions, such as pathogen infection (Thomas and Winner 2002).

2.2.5 Focus of this study

To date, there has been little to no research done investigating the impact of polypore fungi on

the physiology of trees, especially mature canopy trees. However, there are a handful of studies

which have examined the effect that ascomycete stem pathogens have on photosynthetic

performance of plants. In general, these pathogens decrease photosynthetic assimilation rates and

induce a number of other physiological changes, however the mechanism by which this occurs

varies, as both pathogens and hosts are diverse (Tainter and Baker 1996, Luque et al. 1999,

Berger et al. 2007). Some studies have reported a decrease in photosynthetic rate due to a

restriction of water transport resulting in physiological drought responses, such as decreased

stomatal conductance (e.g. Luque et al. 1999) or even physical blockage of stomata (Manter et

al. 2000). Another suggested mechanism may be linked to accumulation of sugars and starch in

leaves (Berger et al. 2007) and feedback inhibition (Clemenz et al. 2008). Alternate mechanisms

18

of photosynthetic downregulation may have to do with creation the production of toxins by fungi

(Van Alfen 1989), or leaf nitrogen re-allocation (Tavernier et al. 2007).

The goal of this study is to understand how the growth and canopy physiology of Betula

alleghaniensis is affected by infection by Fomes fomentarius. It is hypothesized that

photosynthetic assimilation and stomatal conductance will be reduced while water-use-efficiency

will be increased in infected trees, that physiological stress will be evident in morphological

characteristics such as increased herbivory, chlorosis and decreased leaf nitrogen content, and

that infected trees will have significantly lower growth rates when compared to healthy trees. It

is also hypothesized that patterns related to fungal infection will reflect those seen in the later

stages of ontogeny, in near-senescent trees. The questions addressed in this study are to (i) How

are gas-exchange parameters, leaf morphology and leaf chemistry impacted by fungal infection?

(ii) Does infection affect tree growth? (iii) Are morphological characters correlated with changes

in physiology? (iv) Do physiological and morphological patterns of fungal infection resemble

those of trees late in ontogeny? (v) What mechanisms are consistent with observed physiological

responses to fungal infection?

2.3 Methods

2.3.1 Study site and canopy access

This study was conducted at the Haliburton Forest and Wildlife Reserve Ltd., near Haliburton,

Ontario, Canada. Canopy access was achieved with the use of a mobile forest canopy lift

(Scanlift240, Finland), which enabled morphological and physiological measurements to be

taken within the canopy, up to 24 m from the ground. A total of 24 B. alleghaniensis trees were

selected for this study, chosen in groups of three according to their diameter at breast height

(DBH) and crown class. Crown class was qualitatively determined by a single individual

(E.Mycroft) using a crown exposure class assessment ranked from 1 (understory trees

completely overtopped) to 5 (emergent trees with crown completely exposed). Crown class

assessment was modified from Clark and Clark (1992), see Thomas (2010) for a complete

description of each class. For each group, one infected tree, one damaged tree, and two „control‟

trees were examined. Infected trees were defined as having at least one live, visible sporocarp of

Fomes fomentarius and an unhealed scar larger than 5cm x 5cm in area. Damaged trees were

defined as having no visible fungal sporocarps, but at least one unhealed bark scar larger than

19

5cm x 5cm. Controlled trees had no visual signs of infection, and little to no physical damage.

The geographic coordinates of each tree were recorded using a GPS (GPSmap 60CS, Garmin,

<15m accuracy) for spatial analysis.

2.3.2 Gas-exchange measurements

Maximum light-saturated photosynthetic rate (Amax), stomatal conductance (gs) and transpiration

(E) were measured using a LI-6400 gas exchange system (Li-COR, Lincoln, Nebraska). Gas-

exchange measurements were performed on the most recently expanded leaf on the branch,

between the hours of 07:00 and 12:15 from July 15 through August 18, 2008, and July 3 through

August 28, 2009. A preliminary test examining variation in photosynthetic rates among fully-

expanded leaves along a branch did not reveal any large variation in Amax. Preliminary light

curves on B. alleghaniensis leaves indicated that light saturation occurred at photon flux levels of

~1000 μmol m -2

s-1

. Measured leaves were maintained at 50-70% humidity and carbon dioxide

concentration of 400 ppm, (approximated by measuring ambient CO2 concentrations), and light

levels were maintained at a photon flux of 1000 μmol m -2

s-1

, provided by a red SI-355 LED

light source (LI-COR Inc.,. Lincoln, Nebraska). Once placed within the chamber, leaves were

allowed to stabilize within the chamber for 10 min, or until readings were stable for at least one

minute (determined visually with LI-6400 graphics system). Three readings, 20 seconds apart

were taken and averaged to obtain final values.

For each tree, 7-12 gas exchange measurements were collected, distributed evenly between the

lower canopy (lowest 25% of the tree crown), mid-canopy (middle 50% of the crown), and upper

canopy (upper 25% of the crown). Within each vertical strata category, the measurements were

taken randomly by visually dividing each canopy level into tenths, and then a random number

was chosen which corresponded to the tenth of the crown that was measured.

Following measurements, instantaneous water-use-efficiency (iWUE) was determined, defined

as the ratio of maximum photosynthetic assimilation (Amax) to transpiration (E) and calculated

using the equation:

1) iWUE(μmol CO2/mol H2O)= Amax (μmol CO2 m -2

s-1

)/E(mol H2O m -2

s-1

)

20

2.3.3 Leaf morphometrics

Leaf lamina length, herbivory and chlorosis measurements were taken on ten leafs of a branch

adjacent to the gas exchange leaf. Herbivory and chlorosis were measured by visually estimating

the percentage of original leaf material affected, and binned into a group reflecting the closest

value to the estimate: (1%, 5%, 10%, 25%, 50%, 75%, 90%, 95%, 99%). Leaves with less than

1% herbivory were assigned to the 1% group. Herbivory estimates for the first ten leaves on

each branch were made by two individuals (E. Mycroft and M. O‟Hara or J. Schurman) and

compared. In the case where greater than eight of ten estimates differed more than one bin

group, then the leaves were re-assessed and an average taken. Leaf lengths for the same first ten

leaves on each branch, and distance between each leaf or leaf scar were measured to the nearest

0.1 cm.

Leaf area and thickness measurements were collected for all gas-exchange leaves and were

completed within 6 hours of leaf collection. Leaf area was determined using a Li-COR 3100 leaf

area meter (Li-COR, Lincoln, Nebraska). Each leaf was measured twice, and the mean leaf area

(±0.1cm2) was used. Leaf thickness was determined using a low-force micrometer (Mitutoyo

Corporation, Japan, No. 227-101) on three areas of each leaf, two at the base of the leaf on either

side of the petiole, and one near the tip of the leaf. Measurements were taken between the leaf

veins, to increase measurement consistency. The three measurements were recorded, and the

mean leaf thickness (±0.01 mm) was used in analyses.

Leaves were dried at 40.6°C for at least 48 hours, then were transferred to paper envelopes, and

stored. Leaf mass was measured with a toploading balance (Denver Instrument Company,

Colorado), accurate to 0.001g. Leaf mass per unit area (LMA) and leaf tissue density were

calculated using the following equations:

x) LMA = Dry Leaf Mass (g)/Leaf Area (cm2)

x) Leaf tissue density = LMA (g/cm2)/ leaf thickness(cm)

2.3.4 Leaf chemistry

One leaf (the one for which gas-exchange was measured) from each sample set was analyzed for

carbon (C) and nitrogen (N). Approximately 0.2 g of leaf tissue was weighed, packaged in a

21

6x8mm tin capsule and analyzed for total C and N, assessed with an ECS 4010 Elemental

Combustion System (Costech Analytical Technologies, Inc. Valencia, California). Leaf carbon

was expressed by mass (Cmass % (g/g)) and area (Carea (g/cm2)), and leaf nitrogen was expressed

by mass (Nmass % (g/g)), and by area (Narea (g/cm2)).

Stable carbon isotopes (δ13

C (‰)) were measured as a proxy for integrated water-use-efficiency

for trees in 2008 only. Samples were ground and pooled within each canopy strata from each tree

and analyzed for 13C/12C ratio at the University of California at Davis Stable Isotope

Laboratory using a continuous flow isotope ratio mass spectrometer (Europa Hydra 20/20). The

isotopic ratios were expressed in the delta notation (δ) notation with respect to the Pee Dee

Belemnite (PDB) standard.

2.3.5 Dendrochronological analysis

Tree cores for dendrochronological analysis were taken 0.5 m above ground level on each tree,

mounted on plywood and sanded until the rings were distinguishable. Cores were scanned with a

high-resolution scanner, and the 5-year average increment width (mm) and average basal area

increment (BAI) (mm2) for the most recent (2003-2008) years were analyzed using WinDendro

(Regent Instruments, Inc.) and measured to the nearest 0.1 mm. This technique has been widely

used in the literature (See Payette et al. 1990; Gradowski and Thomas 2006) to average out

annual variation in growth due to climate factors.

2.3.6 Molecular analysis

Molecular analyses were conducted on five wood samples from each tree, to confirm the

presence of pathogens in infected trees, and the absence of pathogens in controls. Samples were

analysed by DNA extraction from wood, followed by amplification of the ITS region of rDNA

using the primers ITS8F and ITS6R, cloning, and sequencing. Details of the method can be

found in section 3.3 (Chapter 3, methods section) of this thesis.

2.3.7 Statistical analysis

Statistical analyses were performed with R version 2.6.0 (The R foundation for Statistical

Computing 2007). Analysis of Variance (ANOVA) was used to determine significant

differences among treatments, using DBH as a covariate in the analysis. Variables with non-

22

normal distributions (iWUE, average herbivory, LMA) were log-transformed to meet the

assumptions of the ANOVA. Tukey's „Honest Significant Difference‟ was calculated to

determine confidence intervals on the differences between the means of the levels for the canopy

strata and treatment factors. Estimated chlorosis could not be transformed and was subjected to

the non-parametric, rank-transformed Kruskal-Wallis multiple comparisons test. To examine the

relationships between variables, analysis of covariance (ANCOVA) was employed. A non-linear

least squares estimation was used to determine the equations for the curves describing the

relationship between Amax and gs, and was based on the model described in Medrano et al.

(2002). Goodness of fit was described using Akaike‟s Information Criterion (AICc) and

compared to a null model.

A number of studies have observed spatial autocorrelations in a variety of systems, including

fungal communities, soils and soil water gradients (Adelman et al. 2008), all of which are factors

that may have an influence on spatial variation in transpiration (Adelman et al. 2008) and other

tree physiological processes (Lambers et al. 2006). To analyze spatial patterns, a semivariogram

analysis was applied to all of the physiological and morphological variables collected with

respect to individual tree locations. The analysis was conducted in R v. 2.6.0, code is available

from E. Mycroft. For all of the variables measured, visual examination of semivariograms

indicated that there was no significant spatial autocorrelation, indicating that all points are

independent at each of the canopy levels. For example, the semivariogram plotting Amax versus

distance reveals no clear „range‟, or distance at which there is no further decrease in spatial

autocorrelation (Figure 1.1), thus indicating spatial autocorrelation remains relatively uniform

across the samples. Semivariograms of measured physiological and morphological

characteristics versus distance show no evidence for spatial variability (e.g. Figure 1.1),

indicating that pathogen effects were stronger than spatially autocorrelated environmental effects

among the trees sampled.

23

Figure 1.1 Relationships between semivariance of maximum photosynthetic assimilation rate

(Amax) of (A) upper canopy leaves, (B) mid- canopy leaves, and (C) lower canopy leaves and distance

between trees in degrees latitude and longitude. Dotted lines represent 95% confidence intervals.

2.4 Results

The presence of pathogens in the trees was confirmed by molecular analyses of five drilled wood

samples per tree. All infected trees were confirmed to be infected with F. fomentarius. One

infected tree (5I1) also contained a species of the genus Willopsis, an ascomycete yeast. One of

the damaged trees (1D1) was also found to contain F. fomentarius and a species of Udeniomyces,

a basidiomycete yeast, however sporocarps of F. fomentarius had not yet developed on the tree.

Two other damaged trees were found to contain Phoma sp. and Cryptococcus sp., as well as

Epicoccum sp., respectively. There were no fungal species amplified from control (undamaged)

trees (Appendix, Table A1).

2.4.1 Gas exchange parameters

In general, canopy physiology varied strongly with tree condition. There were marginally

significant differences in maximum rates of photosynthesis (Amax) across treatments (p =

0.0513), with infected trees exhibiting mean observed Amax values (6.24 μmol CO2 m-2

s-1

24

±0.759) approximately 26% lower than control trees (8.43 μmol CO2 m-2

s-1

±0.493, Tukey‟s

HSD p=0.022) (Figure 2.2). However, there were no significant differences in Amax between

infected and damaged trees (8.05 μmol CO2 m-2

s-1

± 0.694, Tukey‟s HSD p=0.116) or damaged

and control trees (Tukey‟s HSD p=0.904). There were also significant differences in Amax values

across canopy levels (p =0.0002), with lower leaves being the least productive and upper leaves

being the most productive. Differences were most pronounced in the upper canopy (Figure 2.2).

However, there were no significant canopy x condition interactions (p = 0.808).

25

Figure 2.2. Observed photosynthetic rate (Amax) in B. alleghaniensis across tree ‘condition’

treatments (control, physically damaged, infected with F. fomentarius) and upper, middle, and

lower canopy strata. Error bars represent one standard error, letters above error bars represent

differences between treatments at the P<0.05 level.

26

Figure 2.3. Observed stomatal conductance (gs) in B. alleghaniensis across tree ‘condition’




Stomatal conductance values of infected trees were significantly lower (0.079 mol m-2

s-1

±0.010)

than control trees (0.122 mol m-2

s-1

± 0.0113) or damaged trees (0.139 mol m-2

s-1

± 0.0142) (p =

0.0138, Table 1; Tukey‟s HSD p=0.0401, p=0.0085, respectively). There was no significant

difference between control and damaged trees (Tukey‟s HSD p=0.547). As expected,

conductance also varied among canopy treatments (p= 0.0171), but again there were no

interactive effects between canopy level and tree condition (p=0.404) (Figure 2.3).

Instantaneous water-use-efficiency tended to be lower in infected trees (5.69 mmol CO2/mol

H2O ±0.609), but was not significantly different from damaged (6.05 mmol CO2/mol H2O

±0.318) or control trees (6.59 mmol CO2/mol H2O ±0.384) (p=0.261, Table 2.1, Figure 2.4).

Integrated water use efficiency, measured with δ13

C (‰), also showed no significant differences

among tree conditions (p=0.985, Table 2.1, Figure 2.5). There were also no canopy level by tree

condition interactive effects on either instantaneous water use efficiency (p= 0.860) or integrated

water use efficiency (p= 0.816, Table 2.1).

27

Figure 2.4.Observed instantaneous water-use-efficiency (mmol CO2/mol H2O) in B. alleghaniensis

across tree ‘condition’ treatments (control, physically damaged, infected with F. fomentarius) and

upper, middle, and lower canopy strata. Error bars represent one standard error, letters above

error bars represent differences between treatments at the P<0.05 level.

28

Figure 2.5. Observed integrated water-use-efficiency (δ13

C (‰)) in B. alleghaniensis, measured with

respect to the Pee Dee Belemnite standard. Observations are shown across tree ‘condition’




2.4.2 Leaf morphometrics

Leaf area (cm2) did not vary among tree conditions (p =0.330, Table 2.1, Figure 2.6), nor did leaf

mass per unit area (LMA) (g/cm2) (p =0.488, Table 2.1, Figure 2.7), leaf tissue density (g/cm

3) (p

=0.468, Table 2.1, Figure 2.8), leaf length (p =0.891, Table 2.1, Figure 2.9), or herbivory (p =

0.569, Table 2.1, Figure 2.10). There were also no significant canopy strata by treatment

interactive effects on leaf area (p = 0.329), LMA (p = 0.689), leaf tissue density (p= 0.268), leaf

length (p= 0.819), or herbivory (p= 0.174).

29

Figure 2.6. Observed leaf area (cm2) in B. alleghaniensis across tree ‘condition’ treatments

(control, physically damaged, infected with F. fomentarius) and upper, middle, and lower canopy

strata. Error bars represent one standard error, letters above error bars represent differences

between treatments at the P<0.05 level.

30

Figure 2.7. Observed leaf mass area (g/cm2) in B. alleghaniensis across tree ‘condition’ treatments


strata. Error bars represent standard error, letters above error bars represent differences between

treatments at the P<0.05 level.

31

Figure 2.8. Observed leaf length (cm) in B. alleghaniensis across tree ‘condition’ treatments


strata. Error bars represent one standard error, letters above error bars represent differences

between treatments at the P<0.05 level.

32

Figure 2.9. Observed leaf tissue density (g/cm3) in B. alleghaniensis across tree ‘condition’




33

Figure 2.10. Observed herbivory in B. alleghaniensis across tree ‘condition’ treatments (control,

physically damaged, infected with F. fomentarius) and upper, middle, and lower canopy strata.

Error bars represent one standard error, letters above error bars represent differences between



Leaf nitrogenmass differed significantly among tree conditions (p=0.0154, Table 2.1, Figure 2.11).

It is interesting to note that damaged trees (2.55 g/g ± 0.08) had lower nitrogenmass than control

trees (2.87 g/g ±0.06) (Tukey‟s HSD p= 0.011), while there was no significant difference

between infected and control, and infected and damaged (Tukey‟s HSD p=0.594, p=0.198

respectively). However, nitrogenarea on the other hand, did not significantly differ among tree

conditions (p = 0.225, Table 2.1, Figure 2.12). There was no significant difference in nitrogen by

canopy strata for nitrogenmass, however this was marginally significant when expressed as

nitrogenarea, with higher nitrogen in the upper canopy (p=0.423, 0.068 respectively). No

significant canopy strata by treatment interactions were found for either nitrogenmass (p =0.493)

or nitrogenarea (p =0.897, Table 2.1).

34

Infected trees had significantly greater concentrations of leaf carbon by mass (47.9 g/g ±0.38)

than damaged (46.2g/g ±0.47) trees (Tukey‟s HSD p= 0.004), however the difference between

infected and control (47.0 g/g ± 0.19) trees or damaged and control trees was not statistically

significant (Tukey‟s HSD p= 0.137, p= 0.146) (Figure 2.13). In terms of leaf carbon by area,

there was a marginal statistical difference between tree conditions (p= 0.073), with infected (1.75

g/cm2

±0.13) trees having greater carbonarea content than damaged (1.50 g/cm2

±0.073) or control

(1.50 g/cm2

±0.074) (Figure 2.14). Leaf carbon levels also did not vary by canopy level (p

=0.796), and there were no canopy strata by treatment interactions for either carbon mass or

carbon area (p = 0.917, Table 2.1).

Figure 2.11. Observed leaf nitrogen content by mass (g/g) in B. alleghaniensis across tree

‘condition’ treatments (control, physically damaged, infected with F. fomentarius) and upper,

middle, and lower canopy strata. Error bars represent one standard error, letters above error bars

represent differences between treatments at the P<0.05 level.

35

Figure 2.12. Observed leaf nitrogen content by area (g/cm2) in B. alleghaniensis across tree




36

Figure 2.13. Observed leaf carbon content by mass (g/g) in B. alleghaniensis across tree ‘condition’

treatments (control, physically damaged, infected with F. fomentarius) and upper, middle, and lower canopy

strata. Error bars represent one standard error, letters above error bars represent differences between


37

Figure 2.14. Observed leaf carbon content by area (g/cm2) in B. alleghaniensis across tree




2.4.4 Chlorosis

The degree of chlorosis was significantly greater in infected (7.55% ±1.73) trees compared to

control (1.51% ±0.145) and damaged (2.40% ±0.917) trees (Table 2.1, Figure 2.15). There was

no difference in the level of chlorosis between control and damaged trees (Kruskal-Wallis

multiple comparisons test). In fact, the degree of chlorosis in leaves of infected trees was

observed to be almost four-fold (394.6%) greater than in damaged or control leaves (Figure

2.14).

38

Figure 2.15.Observed percent chlorosis in B. alleghaniensis across tree ‘condition’ treatments:

control, physically damaged, and infected with F. Fomentarius, and along canopy strata (low,

middle and upper). Error bars represent standard error, letters above error bars represent


2.4.5 Growth

The five-year average growth increment and basal area increment varied significantly among tree

condition types (p<0.0001, p=0.0002 respectively, Table 2.1). Infected trees tended to have the

lowest growth area increment of any treatment (1.22 mm/year ± 0.142), but did not significantly

differ from damaged trees (1.67 mm/year ± 0.199; Tukey‟s HSD p= 0.156). When expressed in

terms of basal area increment, infected trees had significantly lower growth rates (27.04

mm2/year ± 3.22) than control trees (44.94 mm

2/year ± 3.56, Tukey‟s HSD p=0.0049), but not

damaged trees (33.88 mm2/year ± 4.38, Tukey‟s HSD p=0.520) (Figures 2.16, 2.17).

39

Figure 2.16. The five-year average radial growth increment (mm/year) in B. alleghaniensis across

tree ‘condition’ treatments: control, physically damaged, and infected with F. fomentarius. Error

bars represent one standard error, letters above error bars represent differences between


40

Figure 2.17. The five-year average basal area increment (mm2 /year) in B. alleghaniensis across tree

‘condition’ treatments: control, physically damaged, and infected with F. fomentarius. Error bars

represent one standard error, letters above error bars represent differences between treatments at

the P<0.05 level.

2.4.6 Relationships among variables

Non-linear curves described the relationship between photosynthetic rate (Amax) and stomatal

conductance (gs) (Figure 2.18), although the relationship between the two variables was not

significantly different among treatments (p=0.1285). Equations of the nonlinear least squares

estimates can be found in Table 2.2. There was adequate support for the model, with an AICc

value of 280.7 compared with the null model (y~1) value of 356.3.

41

Figure 2.18. Relationships between stomatal conductance (gs) and net carbon dioxide assimilation

(Amax) in B. alleghaniensis for all three ‘condition’ treatments: control, physically damaged, and

infected with F. fomentarius. Best-fitting correlation curves are based on parameter relationships

described in Medrano et al. (2002). Equations of the lines are found in Table 2.2.

Overall, there was little correlation between photosynthetic capacity and leaf nitrogen content

expressed in terms of area (R2

adj = 0.0621) (Figure 2.19). However, the slope of the line

describing the relationship between Amax and Narea for infected trees did vary from that of

damaged or control trees (p=0.0247). In general, Amax in the leaves of infected trees tended to

decrease with increasing nitrogen content, while Amax in the leaves of control and damaged trees

tended to increase (Figure 2.19). Nitrogen content in control trees explained more variation in

42

Amax than in damaged or control trees (Table 2.3). There was no heterogeneity of slopes found

for this relationship.

The relationship between photosynthetic capacity and leaf carbon content by area was also fairly

weak (R2

adj = 0.0529, Figure 20). Infected trees had a significantly different slope than that of

damaged or control trees (p=0.0197), as there were a few observations where high leaf carbon

content was associated with low photosynthetic rate (Figure 2.20). The interaction term was

omitted, as there was weak evidence for heterogeneity of slopes (p=0.0982).

Leaf mass area explained more variation in photosynthetic capacity than did leaf nitrogen or

carbon (R2

adj= 0.325) (Figure 2.21). There was heterogeneity of slopes noted for the damaged

factor (p=0.023), which in general had a shallower slope than infected or control trees.

Generally, infected trees tended to have lower Amax for similar LMA values, especially for larger

values of LMA (Figure 2.21). Across treatments, leaf area, leaf length, and leaf tissue density in

control trees explained more variation in Amax than the same variables in damaged or infected

trees, whereas herbivory explained very little variation in Amax (Table 2.3). Chlorosis and Amax

were negatively correlated with one another (Spearman‟s rho= -0.326), indicating that as the

level of chlorosis increased, Amax decreased.

Photosynthetic capacity of upper canopy leaves explained very little variation in average annual

basal area increment (R2

adj = 0.0723). There was little evidence that the slopes of the relationship

between Amax and BAI were significantly different than zero for control, damaged or infected

trees (Control: p=0.881; Damaged: p=0.180; Infected: p=0.576) (Table 2.3, Figure 2.22). There

was also no heterogeneity of slopes found for this relationship (p= 0.374).

43

Figure 2.19. Observed relationship between nitrogen per leaf area (g/cm2) and net carbon dioxide

assimilation (Amax) in B. alleghaniensis for all three ‘condition’ treatments: control, physically

damaged, and infected with F. fomentarius.

44

Figure 2.20. Observed relationship between carbon per leaf area (g/cm2) and net carbon dioxide



45

Figure 2.21. Observed relationship between leaf mass per area (g/cm2) and net carbon dioxide



46

Figure 2.22. Observed relationship between basal area increment (mm2/year) and net carbon

dioxide assimilation (Amax) in B. alleghaniensis for all three ‘condition’ treatments: control,

physically damaged, and infected with F. fomentarius. Only upper canopy leaves were analyzed.

47

Table 2.1. Results of ANOVA describing effects of tree conditionh (Treatment), canopy stratum

(stratum) and the interaction between the two (Tmt*Stratum) on measured physiological,

morphological and growth characteristics of B. alleghaniensis. Results of the Kruskal-Wallis

multiple comparisons test is shown for chlorosis. P-values <0.05 are in bold.

Variable

(transformation) Source df SS MS F P

Amax Tmt 2 53.67 26.83 3.8369 0.0270

Stratum 2 0.000194 0.1340 0.1340 0.0001

Tmt*Stratum 4 11.17 2.79 0.3993 0.6899

Conductance Tmt 2 0.033224 0.016612 5.1130 0.0089

Stratum 2 0.0317267 0.015874 4.8859 0.0108

Tmt*Stratum 4 0.013267 0.003317 1.0208 0.6147

Water Use Efficiency

(log)

Tmt 2 0.4472 0.2236 1.8074 0.1729

Stratum 2 0.0085 0.0042 0.0343 0.9663

Tmt*Stratum 4 0.1068 0.0267 0.2159 0.9286

13C (Delta PDB) Tmt 2 0.195 0.097 0.0618 0.9790

Stratum 2 25.243 12.621 8.0097 0.0019

Tmt*Stratum 4 2.440 0.610 0.3870 0.8598

Carbon (mass) Tmt 2 12.218 6.109 3.8902 0.0258

Stratum 2 1.264 0.632 0.4023 0.6705

Tmt*Stratum 4 1.485 0.371 0.2363 0.9168

Carbon (area) Tmt 2 0.8452 0.4226 2.7370 0.0733

Stratum 2 0.21103 1.0552 6.8335 0.0022

Tmt*Stratum 4 1.0661 0.2265 1.726 0.1568

Nitrogen (mass) Tmt 2 1.1643 0.5821 4.4659 0.0154

Stratum 2 0.2275 0.1137 0.8726 0.4229

Tmt*Stratum 4 0.4481 0.1120 0.8594 0.4934

Nitrogen (area) Tmt 2 0.0030 0.0015 1.5295 0.2251

Stratum 2 0.0055 0.0028 2.8145 0.0680

Tmt*Stratum 4 0.0011 0.0003 0.2685 0.8971

Average leaf length Tmt 2 0.614 0.307 0.1562 0.8558

48

Stratum 2 21.22 10.610 5.3991 0.0071

Tmt*Stratum 4 3.055 0.764 0.3887 0.8190

Leaf Area Tmt 2 121.5 60.8 1.1298 0.3300

Stratum 2 817.5 408.8 7.6013 0.0012

Tmt*Stratum 4 253.6 63.4 1.1791 0.3294

Average herbivory

(log)

Tmt 2 0.3672 0.1836 0.5687 0.5769

Stratum 2 0.3495 0.1747 0.5418 0.5554

Tmt*Stratum 4 2.0746 0.5186 1.6485 0.1744

Leaf Mass per Area

(log)

Tmt 2 0.11328 0.05664 1.3718 0.2622

Stratum 2 2.35982 1.17991 28.5777 3.090 e-9

Tmt*Stratum 4 0.09324 0.02331 0.5646 0.6894

Leaf Tissue Density Tmt 2 0.002417 0.001208 0.5629 0.5728

Stratum 2 0.04093 0.020465 9.5319 0.0003

Tmt*Stratum 4 0.011486 0.002871 1.3374 0.2677

Average annual

growth

Tmt 2 11.234 5.617 11.1332 6.953 e-5

Basal Area Increment Tmt 2 4032.4 2016.2 9.8093 0.0002

Table 2.2 Equations of the non-linear least squares estimates describing the relationship between

photosynthetic rate (Amax ) and stomatal conductance (gs) (Figure 2.17).

Treatment Equation

Control Amax=(19.87713*(gs/1000))/(0.14872+(gs/1000))

Damaged Amax=(19.5157*(gs/1000))/(0.1849+(gs/1000))

Infected Amax= (21.0058*(gs/1000))/(0.1724+(gs/1000))

49

Table 2.3. Summary of ANCOVA describing relationships between Amax (μmol CO2 m-2

s-1

) and

morphological, chemical, and growth parameters by tree condition (treatment). P-values <0.05 are

indicated in bold.

Variable Source df SS MS F P

Leaf Area (cm2) Main Effect 1 13.54 13.54 1.8585 0.1778

Treatment 2 46.43 23.22 3.1866 0.0483

Main* Tmt 2 36.74 18.37 2.5214 0.0887

Leaf Length (cm) Main Effect 1 8.49 8.49 0.9792 0.3263

Treatment 2 54.90 27.45 3.1671 0.0491

Main* Tmt 2 33.13 16.56 1.9111 0.1567

Leaf Mass per Area

(g/cm2)

Main Effect 1 126.137 126.137 23.7836 9.02e-06

Treatment 2 28.252 14.126 2.6635 0.0784

Main* Tmt 2 30.369 15.184 2.8631 0.0653

Leaf Tissue Density

(g/cm3)

Main Effect 1 69.40 69.40 11.7546 0.001134

Treatment 2 23.20 11.60 1.9650 0.1495

Main* Tmt 2 57.91 28.96 4.9043 0.0108

Herbivory (%) Main Effect 1 0.48 0.48 0.0550 0.81529

Treatment 2 54.84 27.42 3.1292 0.0509

Main* Tmt 2 35.35 17.67 2.0169 0.1419

Carbonmass (g/g) Main Effect 1 0.09 0.09 0.0114 0.91523

Treatment 2 57.20 28.60 3.6566 0.0315

Main* Tmt 2 46.59 23.29 2.9785 0.0582

Carbonarea (g/cm2) Main Effect 1 4.58 4.58 0.6496 0.42350

Treatment 2 43.90 21.95 3.1159 0.05171

Main* Tmt 2 34.37 17.18 2.4395 0.0960

Nitrogenmass (g/g) Main Effect 1 0.05 0.05 0.0056 0.94084

Treatment 2 54.21 27.10 3.0451 0.0545

Main* Tmt 2 4.94 2.47 0.2776 0.7585

Nitrogenarea (g/cm2) Main Effect 1 8.07 8.07 1.0936 0.29981

Treatment 2 48.63 24.31 3.2929 0.0439

Main* Tmt 2 34.00 17.00 2.3022 0.1087

Basal Area Increment

(cm2/year)

Main Effect 1 13.73 13.73 1.6292 0.20665

Treatment 2 55.32 27.66 3.2820 0.0443

Main* Tmt 2 16.87 8.43 1.0006 0.3736

50

2.5 Discussion

In general, results indicate photosynthetic assimilation and stomatal conductance are negatively

impacted by infection (Figures 2.2, 2.3), consistent with the initial hypotheses presented at the

beginning of this chapter. However, there was no change in water-use-efficiency (either

instantaneous or integrated) as was initially hypothesized (Figures 2.4, 2.5). In further response

to the first research question proposed in the introduction of this chapter, leaf morphological

characteristics (Figures 2.6-2.9) did not appear to be impacted by infection, nor did herbivory

(Figure 2.10). Leaf carbon was found to be higher in infected trees (Figures 2.13, 2.14), however

contrary to the initial hypothesis, leaf nitrogen was not affected (Figures 2.11, 2.12). Chlorosis in

leaves of infected trees was also much greater than in damaged or control trees (Figure 2.15). In

response to the second research question, results suggest that polypore fungal infection

negatively affected growth, as infected trees exhibited lower average annual growth than control

trees over the past five seasons (Figures 2.16, 2.17). Furthermore, results suggest that

morphological characters were not strongly correlated with changes in photosynthetic

assimilation associated with infected trees (Table 2.3). The fourth research question proposed in

this chapter had to do with whether or not physiological and morphological patterns of fungal

infection resemble those of trees late in ontogeny. Results indicate that while polypore fungal

infection may contribute to some characteristics of trees late in ontogeny (e.g. Amax, gs, leaf

carbon), it does not explain all the patterns typically observed in very large (i.e. old, mature) B.

alleghaniensis trees (Thomas 2010). Finally, the fifth question proposed asked what potential

mechanisms could explain the observed impacts of fungal infection on tree physiology. While

fungal-induced xylem occlusion (e.g. through compartmentalization or induction of embolisms)

may have a negative impact on Amax and gs through drought stress, not all the patterns seen here

would be expected from a typical drought stress response. Thus, here it is suggested that there is

likely another factor associated with F. fomentarius infection that alters host leaf chemistry and

physiology.

2.5.1 Gas exchange parameters

This study represents the first data on the physiological effects of polypore fungal infection on

live hardwood trees. Leaf-level maximal photosynthetic assimilation rate (Amax) and stomatal

51

conductance were significantly reduced in B. alleghaniensis infected with F. fomentarius. We

found the average photosynthetic assimilation rate of infected trees was 26% lower than control

trees, in conjunction with a 35% reduction in stomatal conductance (Figures 2,3; Table 2.1).

These results are comparable to previous fungal plant pathogen studies, which found reduced

stomatal conductance in infected versus non-infected plants (Luque et al. 1999, Goicoechea et al.

2001, Clemenz et al. 2008). Decreases in stomatal conductance typically lower intercellular CO2

concentration, thus limiting Amax (Lambers et al. 2006). For example, Luque et al. (1999) found

a decrease in photochemical efficiency and stomatal conductance when cork trees were infected

with any of the pathogens Phytopthora cinnamomi, Hypoxylon mediterraneum and

Botryosphaeria sterensii (which cause ink disease, necrosis, and trunk canker, respectively).

In the present study, an increase in instantaneous photosynthetic water use efficiency (iWUE)

was not found, indicating that Amax and transpiration were reduced proportionately in infected

trees (Figure 2.4, Table 2.1). There was also no significant difference in integrated water use

efficiency (δ13

C) found (Figure 2.5, Table 2.1). This is an interesting result, as an increase in

iWUE would be expected in a typical response to drought stress (Reich et al. 1989, Lambers et

al. 2006), as would occur had the reduction in photosynthesis been due to a decrease in stomatal

conductance as a result of fungal induced xylem blockage and water stress. The lack of

significant „treatment x canopy stratum‟ interaction indicates that there was no canopy level in

which Amax, gs, or iWUE were significantly affected by treatment more than another.


2.5.2.1 Leaf carbon

Our results indicate that infected leaves had significantly greater leaf carbon expressed on both a

mass and area basis relative to non-infected and damaged trees (Figure 2.13, 2.14). Non-

structural carbohydrates (e.g. sugars) have lower carbon content than structural carbohydrates

(e.g. lignin, cellulose) (Poorter et al. 1992). Thus, an increase in total carbon content likely

reflects a greater amount of structural carbon relative to non-structural carbon (i.e. starch, sugars)

in infected trees than non-infected trees (Figures 2.13, 2.14) (Thomas 2010). This is an

interesting result, as a number of studies have found converse results (i.e. an increase in non-

structural carbon). For example, leaf pathogen infection generally leads to the accumulation of

sugars as a carbohydrate sink in the leaves as a result of photosynthetic down-regulation and

52

increased demand for assimilates (see review by Berger et al. 2007). In another study, leaf

starch was found to accumulate in leaves, likely as a result of decreased assimilate transport from

leaves to roots due to pathogen-induced phloem damage in Phytophthora alni infected Alnus

trees (Clemenz et al. 2008). However, the response of sugar levels in leaves does vary depending

on the plant-pathogen interaction (Berger et al. 2007).

There are a number of mechanisms which may explain the observed increase in leaf C found in

the present study. For instance, filamentous fungi are known to produce elicitors that can induce

host defences to increase lignification in cell walls, and lignin content has been observed to

increase following inoculation of root and leaf pathogens (Vance 1980). It is thought that

lignification of cell walls may help restrict enzyme and toxin diffusion from the fungus to the

host, and likewise restrict water and nutrient transport from the host to the fungus (Vance 1980).

Although this may be the case, increased lignification would likely be reflected in higher LMA

or leaf tissue density, as high LMA is often associated with thicker, more lignified cell walls

(Lambers et al. 2006). Our results show little difference in LMA or leaf tissue density of

infected trees (Figures 2.7, 2.9; Table 2.1), which suggests that increased carbon in leaves of

infected trees was not due to increased lignifications of cell walls. It is also possible that the

ratio of structural to non-structural carbon increased as a result of the fungus inducing starch

mobilization for its own growth, or fungal-induced tree defense mechanisms mobilizing carbon

in response to increased demand for assimilates (e.g. compartmentalization or production of

phytoalexins) (Berger et al. 2007). Finally, it may be that the reduction in photosynthetic rate

was so drastic that very little starch was able to accumulate in leaves. This may be reflected in

the few infected tree observations with very low Amax and high carbon values (Figure 2.20).

2.5.2.2 Leaf nitrogen

Pathogens are known to affect nitrogen mobilization in plants, which is similar to nitrogen

resorption processes which occur during leaf senescence, as the genes responsible for nitrogen

remobilization are upregulated by stress and are normally active during senescence (Solomon et

al. 2003, Lambers et al. 2006, Tavernier et al. 2007). Toxins produced by fungal pathogens may

also interfere with nitrogen metabolism (Snoeijers et al. 2000). In the present study, leaf nitrogen

content differed between control and damaged trees when expressed in terms of mass, but there

was no significant difference in nitrogenmass between infected and damaged trees, nor was there a

significant difference in nitrogen content among treatments when expressed in terms of area

53

(Table 2.1). Neither nitrogenmass nor nitrogenarea exhibited significant difference in nitrogen

content between infected and control trees (Figures 2.11, 2.12). Thus, in the present study there

is no clear evidence for fungal-induced nitrogen mobilization in infected trees. In general, leaf

nitrogen and photosynthetic capacity are well correlated (Lambers et al. 2006). However, leaf

nitrogen content (expressed in terms of mass or area) did not explain much of the variation in

Amax in damaged or infected trees (Figure 2.19, Table 2.3), which suggests that that reduced

nitrogen content was not responsible for the decrease in Amax observed in infected trees.

2.5.3 Chlorosis

The levels of chlorosis in infected trees were almost four-fold greater in infected trees compared

to control or damaged trees. This result is in concurrence with other studies which have found

decreases in chlorophyll content with fungal pathogen infection (Kozlowski 1969, Goicoechea et

al. 2001). Leaf chlorophyll levels are known to be closely related to nitrogen availability and

Amax, and are negatively correlated with plant stress and senescence (Kozlowski 1969, Merzlyak

and Gitelson 1995, Baltzer and Thomas 2005, Gitelson et al. 2009). However, our results show

that leaf chlorophyll levels were reduced in infected trees despite the fact that there was no

significant change in leaf nitrogen (Figures 2.11, 2.12, 2.15). There was also a relatively high

correlation between chlorosis and Amax (Spearman‟s rho= -0.3261). This result may suggest that

a decrease in Amax could have been driven by degradation of chlorophyll, or vice versa. As there

was no significant difference in chlorosis between control and damaged trees (Figure 2.15), it is

likely that some factor specific to F. fomentarius is responsible for the decrease in chlorophyll

content of leaves in infected trees observed here. It is possible that water stress due to xylem

occlusion may have been responsible for the decrease in chlorophyll content observed here

(Larcher 1995). Another potential explanation is that chemical signals, such as pathogenic

toxins could contribute to chlorosis (Peterson and Aylor 1995), possibly through the failure of

photoprotective mechanisms resulting in damage to the photosynthetic machinery and

destruction of chloroplast components (Balachandran and Osmond 1994).

A number of previous studies have employed leaf reflectance measurements and indices to

identify and quantify foliar pigments (see review by Ustin et al. 2009). Leaf chlorophyll content

is a useful indicator for measuring plant stress (Gitelson et al. 2009, Ustin et al. 2009).

Additionally, chlorophyll in stressed plants declines more rapidly than carotenoids, making the

54

ratio of chlorophyll to carotenoids another useful tool (Gitelson and Merzlyak 1994, Sims and

Gamon 2002). Furthermore, anthocyanin production is thought to be induced by a number of

plant stresses, including pathogens, wounding, desiccation, low temperature and UV radiation

(Chalker-Scott 1999, Close and Beadle 2003, Gitelson et al. 2009). It would therefore be

interesting to examine carotenoids and anthocyanins in leaves of infected trees in addition to

chlorophyll.

2.5.4 Herbivory

Herbivory is known to significantly alter gas exchange parameters, secondary defense

compounds, and growth in trees (Dobbertin 2005, Aldea et al. 2006). In the present study, no

significant differences were found in the level of herbivory among infected, damaged and control

trees (Table 2.1). Herbivory also did not explain significant variation in Amax. Thus, results

indicate that differences in Amax, gs, leaf carbon, chlorosis and growth found in this study are

likely not herbivory-induced changes.

2.5.5 Growth

Infected trees had significantly reduced annual growth when compared with non-infected control

trees (Table 2.1). This result is consistent with the findings of a number of other studies which

have found that fungal pathogen infection is associated with a reduction in growth rate

(Kozlowski 1969, Froelich et al. 1977, Whitney 1995, Cherubini et al. 2002), often in

conjunction with a disruption in physiological activity such as decreased stomatal conductance

and photosynthetic capacity (Kozlowski 1969, Luque et al. 1999). While infected and damaged

trees both had significantly lower growth rates when compared with control trees, infected trees

and damaged trees did not have significantly different growth rates. The weak correlation

between basal area increment and Amax for all three tree conditions suggests that BAI was fairly

independent of Amax. (Figure 2.22, Table 2.3). It is possible that differential allocation of carbon

may explain this result, however as only upper leaves were analyzed, a larger sample size may be

needed to clarify the relationship between Amax and BAI. Nevertheless, there is a strong trend of

reduced growth in infected trees relative to control and damaged trees (Figures 2.16, 2.17).

55

2.5.6 Leaf morphology

Along with stomatal conductance and Amax, fungal infection has been found to negatively impact

leaf area (Parker 1986). Water stress is also known to have a negative effect on leaf expansion

due to reduced turgor (Lambers et al. 2006). However, the present study showed no evidence of

reduced leaf area (Figure 2.6) or length (Figure 2.8) in infected trees when compared with

damaged or control trees (Table 2.1), as may be expected if fungal infection induced water stress

in infected trees. There was also no significant variation in LMA or leaf tissue density found

among control, damaged or infected trees (Figures 2.7, 2.9; Table 2.1). Leaves with higher LMA

tend to have thicker, more lignified cell walls than leaves with low LMA (Lambers et al. 2006).

Thus, as mentioned above, this result may suggest that the increase in carbon found in infected

trees was not due to increased lignification of cell walls, as this would have been reflected in

LMA or leaf tissue density. The relationship between Amax and both LMA and leaf tissue density

was quite strong in control trees (R2

adj=0.623; R2

adj=0.446, respectively), however this

relationship was not as strong in damaged (R2

adj=-0.0482; R2

adj=-0.0085, respectively) or

infected trees (R2

adj=0.175; R2

adj=0.138 respectively) (Table 2.3). One potential explanation for

this is difference may be that leaves of control trees with high LMA were more densely packed

with photosynthetic tissues (e.g. mesophyll cells), whereas leaves of damaged and infected trees

with high LMA had greater allocation to mechanical tissues. Another more likely explanation is

that infection does not affect leaf performance until after leaf formation and expansion, which is

consistent with the lack of difference in leaf area and leaf length (Figures 2.8, 2.9). Overall, our

results indicate that the strong reduction in Amax observed in infected trees was not related to

changes in leaf gross morphology.

2.5.7 Potential mechanisms

2.5.7.1 Water stress

There are a number of mechanisms which may explain the reduction of stomatal conductance

observed in this study. One potential mechanism is the fungal induction of water stress (Bowden

et al. 1990, Goicoechea et al. 2001, Lambers et al. 2006). When plants encounter water stress,

stomata close to conserve water loss through transpiration, thus reducing the supply of CO2 and

decreasing Amax (Lambers et al. 2006). For example, Aldea et al. 2006 found that Quercus

vetulina and Cercis canadensis infected with Phylosticta fungus (leaf spot) had low stomatal

56

conductance when compared to non-infected trees. In that study, reduced stomatal conductance

in turn decreased intercellular CO2 concentration, thus reducing photosynthetic efficiency by

more than 25% (Aldea et al. 2006). The present study is the first to note a similar reduction of

Amax and gs in trees infected with species of polypore fungi.

Water stress is a common symptom of fungal pathogen infections (e.g. Peterson and Aylor 1995,

Luque et al. 1999, Goicoechea et al. 2001), and previous studies have found reduced stomatal

conductance and decreased Amax to be highly correlated with low leaf water potential in infected

plants (e.g. Bowden et al. 1990, Habermann et al. 2003). Fungal pathogens are known to

interfere with water movement through xylem tissues via embolism or physical blockage of the

plant‟s xylem (Van Alfen 1989, Simonin et al. 1994, Vannini and Valentini 1994, Guéard et al.

2000, Goicoechea et al. 2001, Cherubini et al. 2002, Lambers et al. 2006). This has been

observed to occur in a number of fungal pathogens including dutch elm disease (Newbanks et al.

1983, Temple and Horgen 2000), oak wilt (Kozlowski 1969 but see Simonin et al. 1994), blue-

stain fungus (Guéard et al. 2000), and Verticillium wilt (Kozlowski 1969, Bowden et al. 1990).

For example, Vannini and Valentini (1994) found that spread of Hypoxylon mediterraneum in

the xylem of Quercus cerris was significantly correlated with a decrease in hydraulic

conductivity of the xylem; the authors suggested that H. mediterraneum used embolized vessels

to spread mycelia throughout the host (Vannini and Valentini 1994). In the present study, Amax

and gs were significantly lower in infected trees relative to damaged and control trees (Figure

2.2, 2.3), which is consistent with other studies which have found a simple drought stress

response to infection (Luque et al. 1999, Goicoechea et al. 2001). However, the usual increase in

iWUE that accompanies a decrease in Amax and gs when broadleaved trees are under water stress

(Reich et al. 1989, Lambers et al. 2006) was not found (Figure 2.4). This is an interesting result,

as it suggests that while the reduction in Amax and gs may have been in part due to water stress

resulting from fungal induced blockage of the xylem, there is clearly more going on than just a

simple „drought stress‟ response to infection. A priority for future studies should be to measure

hydraulic conductance and leaf water potential to more clearly assess what, if any, impact F.

fomentarius has on water relations.

57

2.5.7.2 Toxins, hormones, proteins

Toxins, hormones, enzymes or proteins produced by fungi may also affect stomatal closure and

Amax reductions in infected plants (Van Alfen 1989, Bowden et al. 1990). The effects of toxins

on plants vary considerably, and depend on a complex interaction between the pathogen, the

host, and the environment (Van Alfen 1989). Toxins are known to affect plant function in a

number of ways, including: induction of stomatal closure followed by Calvin cycle inhibition,

degradation of cell membranes and pigments, manipulation of water potential surrounding plant

cells, nutrient release from cells, uncoupling electron transport leading to ATP deficiency, and

increasing resistance to water flow in the xylem (Van Alfen 1989, Peterson and Aylor 1995,

Snoeijers et al. 2000, Temple and Horgen 2000, Whiteford and Spanu 2002). For example,

Ophiostoma ulmi and Ophiostoma novo-ulmi, the fungi that cause Dutch elm disease secrete a

hydrophobic protein known as cerato-ulmin (CU) (Temple and Horgen 2000). These

hydrophobic proteins, known as hydrophobins, are considered common to all filamentous fungi

(Whiteford and Spanu 2002). Studies have shown that CU production is correlated with the

aggressiveness of Ophiostoma isolates, and has been shown to cause wilt via embolisms in

xylem vessels by stabilizing air bubbles (Temple and Horgen 2000, Whiteford and Spanu 2002).

In another instance, Clemenz et al. (2008) suggested that observed decreases in Amax, stomatal

conductance were caused by either toxins produced by the pathogen or hormonal imbalances in

the leaves after stem infection (Clemenz et al. 2008). In the present study, it is possible that

pathogen-produced toxins may have played a role, given that: (i) we did not observe a reduction

in leaf length or area in infected trees as may be expected to result from fungal induced water

stress (Figures 2.8, 2.9); (ii) there was not a corresponding increase in iWUE (Figure 2.4) as

would normally be expected with a decrease in gs (Figure 2.3) (Lambers et al. 2006); and (iii) the

increase leaf carbon content seen here would not be expected as a typical symptom of a drought

stress response.

2.5.8 Comparison to ontogenetic traits

Previous studies have suggested that biotic interactions may have a pronounced effect on the

physiological performance of trees throughout ontogeny (Thomas and Winner 2002, Thomas

2010). With particular reference to fungal interactions, we find that there is partial evidence for

this hypothesis. A recent study conducted on B. alleghaniensis at the same site (Haliburton

Forest and Wildlife Reserve, Ltd) found decreased Amax with a slight decrease in gs and increase

58

in leaf carbon in very large trees (50+ cm DBH) (Thomas 2010). These patterns parallel those of

infected trees in the present study (Figures 2.2, 2.3, 2.13). However, Thomas (2010) also found

increased WUE, leaf thickness, leaf tissue density, and LMA, as well as decreased leaf area and

leaf nitrogen content in large trees, which were not reflected in our results of infected trees

(Figures 2.4-2.9, 2.11-2.12). While there is some evidence that polypore fungal infection may

contribute to some ontogenetic effects (e.g. Amax, gs, leaf carbon) it clearly does not explain all

the trends noted in ageing trees. However, the trends reported in this study do not negate the

hypothesis that biotic interactions have strong effects on tree physiology, as some galling

arthropods (e.g. mites) have been shown increase through ontogeny (Thomas et al. 2010) and

have a pronounced effect on physiological performance (Rajit Patankar, personal

communication).

2.6 Conclusion

The majority of previous studies examining the effects of fungal infection on physiology,

morphology and growth have focused mainly on foliar pathogens, and this is the first study to

examine the effects of polypore fungi. The results of this study confirm our initial hypothesis

that photosynthetic assimilation and stomatal conductance would be negatively affected in

infected trees; however, we did not observe an increase in water use efficiency as expected.

Overall, leaf morphological traits and leaf nitrogen were not significantly different among

control, damaged and infected trees, however there was an increase in leaf carbon in infected

trees which likely reflects an increase in structural carbon and/or decrease in labile carbon.

Leaves of infected trees also exhibited a greater degree of chlorosis than non-infected trees.

While the relationship between LMA and Amax was strong in control trees, it was relatively weak

in damaged and infected trees. Growth of infected trees was significantly reduced compared to

control and damaged trees, which supports our hypothesis that infected trees would have lower

annual growth when compared to healthy trees. In general, our results indicate that the strong

pattern observed in Amax of infected trees was not entirely explained by morphological traits,

which may suggest that F. fomentarius infection does not affect leaf physiological performance

until after leaf formation and expansion has occurred.

The results presented here indicate that morphological leaf traits in infected trees did not

necessarily reflect those consistent with later ontogenetic stages of trees. However physiological

59

parameters (i.e. decreased Amax and gs) and leaf carbon content were in line with results of a

recent study (Thomas 2010). Thus, fungal infection may play a partial role in driving some

ontogenetic patterns.

While fungal-induced xylem blockage via tree compartmentalization or induction of embolisms

may contribute to the morphological and physiological patterns seen in this study, it is clear that

they do not simply follow a typical drought response. For instance, we did not observe a

corresponding decrease in iWUE with stomatal conductance which would normally occur in

drought situations, leaf carbon content would not be expected to change under drought stress,

and we did not observe a reduction of leaf area which would be expected as water stress often

reduces leaf turgor and expansion (Lambers et al. 2006). Thus, results suggest that there may

also be some other factor associated with F. fomentarius which alters host physiology, such as

fungal-produced toxins.

Future studies could focus on water relations in infected trees, including leaf water potential and

hydraulic conductance to determine to what extent the physiological changes observed here are

driven by water stress. It would also be of interest to examine how reproductive allocation is

affected by F. fomentarius infection, as fungal pathogens have been found to increase

reproductive effort, likely through changes in host hormonal balance (Goicoechea et al. 2001).

This would be interesting to take into consideration, as studies have noted reduced growth in

trees during years with heavy seed crops (e.g. Woodward et al. 1994). If infection indeed has an

effect on reproductive allocation, this may explain a portion of the growth reduction seen in

infected trees (Figure 2.16, 2.17), although, changes in leaf size which are expected to be

associated with greater allocation to reproductive structures were not noted (Thomas and Ickes

1995, Thomas 2010). Furthermore, it would be interesting to quantify the effect that F.

fomentarius has on leaf pigmentation by measuring leaf reflectance. Recent advances make

these measurements quick and simple to use, and have the ability to provide insightful

information on relative abundances of pigments such as anthocyanin, carotenoid and chlorophyll

content in leaves (Ustin et al. 2009). Inoculation studies would also be useful, as all trees would

be inoculated with the same amount of inoculant and at the same time, which would enable the

examination of how F. fomentarius infection progresses and affects leaf morphology and

physiology through time. Although the mechanisms may not yet be fully elucidated, the results

of this study demonstrate that polypore fungi have a clear effect on tree physiology and growth.

60

Chapter 3

Molecular Detection of Polypore Fungal Infection in Live Woody

Tissue of Yellow Birch

3.1 Abstract

A DNA-based method for molecular detection of wood-decay fungi in living trees is described.

The method involves direct DNA extraction from wood, amplification of the ITS region of

rDNA using the recently developed ITS8F and ITS6R primers, cloning and sequencing. Here,

the method was tested with six „infected‟ trees with visible Fomes fomentarius sporocarps, six

„damaged‟ trees with significant bark damage but no visible sporocarps, and twelve „control‟

trees which had no significant damage or sporocarps present. Positive and negative controls were

used to verify the procedure. We found molecular evidence to confirm the presence of Fomes

fomentarius in all six infected trees. Three of the six damaged trees had evidence of fungal

endophytes or yeasts. We found no evidence of wood-decay fungal presence in any of the control

trees. The method reported here is a rapid and sensitive analysis which does not require the

development of reference libraries or time-consuming primer design. The ability to detect and

identify fungal species in live trees at an early stage of infection will undoubtedly be of

substantial aid to our understanding of how fungal pathogens, woody tissue endophytes and

commensals affect physiological processes in trees.

3.2 Introduction

The early detection and identification of pathogens, such as wood-decay fungi, is a long-standing

challenge in the field of forest pathology. Often, fruiting bodies are the first external indication

of wood-rotting fungal presence trees (Butin 1995). Many wood-decay fungi, such as polypore

fungi, are perennial and have sporocarps that persist year-round (Barron 1999), so identification

and detection based on sporocarp identification is a convenient method. However, sporocarps

may not develop until long after the initial infection (if at all), and are therefore not always

present (e.g. Johanesson and Stenlid 1999, Allmér et al. 2006). Furthermore, this method relies

heavily on only one fungal life-history stage and does not consider spore and mycelial presence,

61

which may result in an inaccurate reflection of species composition, as fungal species differ in

resource allocation (Gardes and Bruns 1996, Allmér et al. 2006). Although the sporocarp count

technique of wood-rotting fungal detection and identification may be somewhat accurate, it is

still an uncertain technique (Johannesson and Stenlid 1999, Schmidt 2006), and a more reliable

method for detection of fungi in wood is needed.

3.2.1 Culturing

Another traditional method of identifying fungi in wood involves culturing and isolating mycelia

from wood and examining microscopic hyphal characters to distinguish species (e.g. Butin

1995, Allmér et al. 2006, Schmidt 2006). However, culturing is often a difficult task as many

fungi have very specialized substrate requirements, may be unculturable, or the culture media

itself may select for faster growing species leading to biased results (Rayner and Boddy 1988,

Anderson and Cairney 2004, Allmér et al. 2006). Furthermore, even if culturing is successful,

species identification based on hyphal morphology is time consuming (Oh et al. 2003, Allmér et

al. 2006), and is heavily dependent on the skill of the mycologist involved (Johannesson and

Stenlid 1999); even experienced mycologists have expressed difficulty distinguishing species

using hyphal characteristics (Johannesson and Stenlid 1999, Nicolotti et al. 2009).

3.2.2 PCR

Relatively recent advances in the use of polymerase chain reaction (PCR) have resulted in the

design of molecular techniques for detection and identification of fungal pathogens. These

techniques have proven to be rapid and effective (Gugielmo et al. 2007, Nicolotti et al. 2009),

providing researchers with objective measurements and a suite of genetic information, including

enhanced capability to accurately determine species, identify individuals within a population,

and further delineate phylogenies (Johanesson and Stenlid 1999, Hoff et al. 2004, Moncalvo

2005).

3.2.2.1 Molecular markers

Nuclear encoded ribosomal RNA (rDNA) genes are commonly used markers in molecular

systematic studies (e.g. Anderson and Cairney 2004, O‟Brien et al. 2005, Porter et al., 2008), as

well as in molecular identification databases. Ribosomal DNA genes have also been suggested

for use in the Barcode of Life project (Seifert 2009; Vialle et al. 2009). As rDNA is multicopy

62

in nature in found in a high proportion in the fungal genome, it is a relatively simple region to

amplify using DNA samples which may be degraded or dilute (Nazar et al. 1991, Gardes and

Bruns 1993, Hoff et al. 2004). The ITS region of rDNA has been used by many molecular

studies looking to identify to species (e.g. Johanesson and Stenlid 1999, Jasalavich et al. 2000,

Porter et al. 2008), as it provides greater taxonomic resolution than other commonly used rDNA

regions, such as 18S rDNA (Lord et al. 2002, Anderson and Cairney 2004). The internal

transcribed spacers are non-coding regions of DNA which are highly variable among species, but

relatively conserved within species (e.g. Kårén et al. 1997, Jasalavich et al. 2000, Garbelotto

2004), which is useful for species delineation. The ITS region is located between the 18S and

28S rRNA genes, and includes the 5.8S rRNA gene (Nazar et al. 1991, Gardes and Bruns 1993,

Anderson and Cairney 2004) In most fungi, the ITS region is between 600 and 800 base pairs in

length, which enables it to be readily amplified (Gardes and Bruns 1993) and easily sequenced.

The primer pair ITS1F and ITS4B are commonly used to amplify the ITS1 and ITS2 regions of

rDNA, and are specific to basidiomycete fungi (Gardes and Bruns, 1993). Recently, the primers

ITS8F and ITS6R were developed to enhance sequencing success (Dentinger et al. 2009).

Currently, there is extensive fungal ITS data deposited in online databases (e.g. Assembling the

Fungal Tree of Life (AFTOL), Fungal Environmental Sampling and Informatics Network

(FESIN), NCBI Genbank), which makes it well suited for identification of fungal DNA from

environmental samples.

3.2.2.2 Molecular environmental sampling methods

Assessing fungal DNA from environmental samples (such as soil or wood) is often a challenge,

as these substrates usually contain a complex of species (Jasalavich et al. 2000, Lord et al. 2002)

which need to be isolated. While there is a growing body of literature on the analysis of fungal

communities from soil environments (e.g. O‟Brien et al. 1995, Anderson and Cairney 2004,

Porter et al. 2008), there have only been a handful of studies using molecular techniques to

detect and identify fungal species directly from wood. Most commonly, these studies involve

an optimized extraction protocol to extract fungi directly from wood and amplification of a

targeted region of rDNA, followed by a combination of immunological methods, taxon-specific

primers and multiplex PCR (e.g. Gugielmo et al. 2007, Nicolotti et al. 2009), restriction

fragment length polymorphism (RFLP) (e.g. Johannesson and Stenlid 1999, Jasalavich et al

63

2000, Lord et al. 2002, Allmér et al.2006), cloning, and/or sequencing (Fisher et al. 2008, Porter

et al. 2008).

Taxon-specific primers have been used to identify and detect decay fungi directly from wood

(e.g. Moreth and Schmidt 2000), and recent developments in multiplex PCR assays using taxon-

specific primers have proven to be successful for detecting wood decay in live trees (Gugielmo

et al. 2007, Nicolotti et al. 2009). This process involves the development of species-specific

primers using nuclear or mitochondrial ribosomal DNA regions, and uses optimized PCR

reactions to simultaneously amplify target sequences. Using this method, Gugielmo et al. (2007)

and Nicolotti et al. (2009) were able to correctly identify 82-83% of wood decay fungi present,

and confirmed their results with field samples of living trees with evident decay and fruiting

bodies. However, this method has not been tested to see if fungal presence is detectable in trees

without visible sporocarps. Overall, multiplex PCR is a sensitive and rapid diagnostic method for

identifying wood-decay fungi in living trees, and is well-suited to studies which have a few

specific species of interest. However, the development of individual primers for each species

may be a time consuming process, and the approach ignores non-target organisms, limiting the

fungal diversity captured to a few select species.

Another commonly used technique used to identify fungi from cultured mycelia as well as

directly from wood is Restriction Fragment Length Polymorphism (RFLP) (e.g. Johanesson and

Stenlid 1999, Jasalavich et al. 2000). This technique involves extraction of DNA, amplification,

and digestion using restriction enzymes. When run on a gel, individual species can be identified

based on examination of the relative fragment lengths and compared to known species in a

reference library. Terminal RFLP is a newer technique used for analyzing DNA extracted from

environmental samples containing multiple species (Allmér et al. 2006). In T-RFLP, taxon-

specific primers are labelled with a fluorescent tag and run through a sequencer. Several studies

have successfully used T-RFLP combined with ITS primers to identify saproxylic fungal

communities in inoculated wood and slash piles at a relatively early stage of infection (e.g.

Allmér et al. 2006). In a similar study, Johannesson and Stenlid (1999) used ITS primers

combined with RFLP to identify fungal species from standing trees and snags. They extracted

DNA from both cultured mycelia and wood, however the ITS region only amplified successfully

from six out of 108 wood samples (Johannesson and Stenlid 1999). It is not clear whether this

study used positive controls to determine if the lack of amplification was due to the procedure, or

64

due to a lack of fungal DNA in the samples. While RFLP is a promising method in terms of its

relative ease and rapid turnaround time for results, the development of a reference library and

choice of restriction enzymes can be a time consuming process.

3.2.3 Focus of this study

While there is a growing body of literature on detection and identification of decay from wood

chips and woody debris (e.g. Allmér et al. 2006, Adair et al. 2002, Fisher 2008), there are fewer

studies which have focused on molecular techniques to identify wood-decay fungi in living trees

with visible decay. Living trees present a special problem as sapwood is often more difficult to

extract DNA from than heartwood, due to the amount of PCR-inhibitors in sapwood (Schwarze

et al. 2000). Furthermore, these methods have only been tested on living trees with evident

decay, based on sporocarp presence. To my knowledge, Johannesson and Stenlid (1999) is the

only study to date which has attempted to extract fungal DNA directly from the wood of living,

asymptomatic trees. They were able to do so for six samples of alive and decayed trees, but there

were no asymptomatic trees from which they were able to extract DNA.

The objectives of this study were (i) to develop a molecular protocol for the detection and

identification of woody decay fungi in live standing trees; (ii) to confirm the presence of Fomes

fomentarius in live „infected‟ trees from chapter 2; and (iii) to determine if any infection present

prior to sporocarp development can be detected in damaged and/or asymptomatic trees.

3.3 Methods

3.3.1 Field sampling

This study was conducted at the Haliburton Forest and Wildlife Reserve Ltd., near Haliburton,

Ontario, Canada. Canopy access was achieved with the use of a mobile forest canopy lift

(Scanlift240, Finland), which enabled morphological and physiological measurements to be

taken within the canopy, up to 24m from the ground.

A total of 24 B. alleghaniensis trees were selected for this study, chosen in groups of three

according to their diameter at breast height (DBH) (±5cm) and crown class. Crown class was

qualitatively determined by a single individual (E. Mycroft) using a crown exposure class

assessment ranked from 1 (understory trees completely overtopped) to 5 (emergent trees with

crown completely exposed). Crown class assessment was modified from Clark and Clark

65

(1992); see Thomas (2010) for a complete description of each class. For each group, one

infected tree, one damaged tree, and two „control‟ trees were examined. Infected trees were

defined as having at least one live, visible sporocarp of Fomes fomentarius and an unhealed scar

larger than 5cm x 5cm in area. Damaged trees were defined as having no visible fungal

sporocarps, but at least one unhealed scar larger than 5cm x 5cm. Controlled trees had no visual

signs of infection, and little to no physical damage.

To obtain wood samples from along a vertical gradient from each of the study trees, a cordless

drill was used to collect shavings from five approximately equidistant locations on each tree.

Samples were taken at 0.5m, 3m, 6m, 9m, and 12m above the ground for each tree, with the help

of an aerial lift. In the case of branching, the sample was taken from the largest branch. The drill

was inserted between 5cm and 7cm deep into the tree using a 0.5cm drill bit to obtain at least 0.3

g of drill shavings. To avoid cross-contamination of samples, the drill bit was sterilized between

each sample by dipping it in a 70% alcohol solution and was then flamed using a lighter. Two

dead yellow birch snags with F. fomentarius fruiting bodies were selected and served as positive

controls. Wood samples were stored in sterile tissue culture tubes, and kept in a refrigerator

freezer (approximately -15°C) overnight, until transport to a permanent location in a -20°C

freezer. For each tree, the wood samples were mixed in equal amounts (by mass) to make one

grouped sample. Wood tissue was collected between July 3 and August 28, 2009.

3.3.2 Initial extraction attempts

Initial DNA isolation attempts from wood were made from wood cores taken with an increment

tree core borer. Wood was carefully sliced with a razor into thin sections, and 0.1g of wood was

used to isolate DNA. DNA isolation was accomplished using the UltraClean® Soil DNA

Isolation Kit (MO BIO Laboratories, Inc., Carlsbad CA), using 0.1g of wood tissue. The

traditional protocol was followed once as described in the isolation kit manual. Following this

trial the Alternative Protocol was attempted, with a number of minor modifications. At step two,

a 5mm glass bead was added to the Fastprep tube, and a Fastprep FP120 (Bio 101, Thermo

Electronic Corporation) was used to pulverize the wood tissue at level 5.5 for 25 seconds.

Samples were placed on ice for at least one minute, and the Fastprep step was repeated 5-7 times,

or until wood tissue appeared to be homogenous. At step five of the isolation, 200μl of Inhibitor

Removal Solution was added. At step six, the alternative lysis method was used to prevent DNA

66

shearing. After adding solution S1, the tube was vortexed for 3-4 seconds. The IRS solution was

then added, the tubes vortexed for 3-4 seconds, and then heated to 70°C for 5 minutes. The tubes

were vortexed for another 3-4 seconds, heated to 70°C for another 5 minutes, and finally

vortexed again for 3-4 seconds. DNA extracted with this method was met with difficulty in

downstream applications (e.g. PCR amplification), and after a number of attempts an alternate

DNA isolation protocol was developed, as described below.

3.3.3 DNA extraction and purification

Fungal DNA from wood cores was extracted directly using the E.Z.NA Soil DNA Kit (D5625-

01, Omega Bio-Tek Inc.) extraction protocol, with a number of modifications. In a 2.0 ml „Fast

Prep‟ tube, 0.1g of wood drill shavings were added to the 500 g of glass beads provided in the

kit, as well as one large 5mm glass bead and 1.0 ml of SLXMlus buffer. The tubes were mixed

using a Fastprep FP120 (Bio 101, Thermo Electronic Corporation) at level 5.5 for 25 seconds,

and then placed on ice for at least one minute. This procedure was repeated a total of five times.

To each sample, 100 μl of Buffer DS was added, and vortexed to mix. Tubes were then

incubated for 10 min at 70°C, vortexed once halfway through the incubation period. Tubes were

then centrifuged at 3000 rpm for 3 minutes, 800 μl of the supernatant was transferred to a new

1.5 ml tube, and 270μl of Buffer P2 was added and vortexed to mix. Samples were incubated on

ice for 5 min, then centrifuged for 5 min at full speed (13,200 x g), and supernatant was

transferred to a 2ml tube. The contents of each tube were measured, and a 0.7 volume of 99%

isopropanol was added. DNA was precipitated by centrifuging at 13 000 x g for 10 min, and

supernatant was discarded by inverting the tube on a paper towel for one minute. Warmed (70°C)

elution buffer (200μl) was added to each tube, the samples were incubated at 70°C for 20

minutes, and then 100μl of HTR Reagent was added using a wide-bore tip. Samples were

vortexed for 10s to mix, and then they were incubated at room temperature for 2 min. After

centrifuging at full speed for 2 min, the supernatant was transferred to a new tube. Equal volume

(~265μl) of XP1 buffer was added, and samples were vortexed. Equilibration buffer (100μl) was

then added directly onto the HiBind DNA column, and centrifuged at 10 000 x g for 1 minute.

After discarding flow-through, each sample was applied to a separate HiBind DNA column, and

centrifuged at 10 000 x g for one minute. Flow-through was discarded, and 300μl of XP1 Buffer

was added and spun at maximum speed for one minute. Columns were then placed in new

collection tubes, and washed with 700μl of SPW wash buffer, and centrifuged at 10,000 x g for 1

67

minute, flow-through was discarded, and the wash step was repeated, followed by a final

centrifugation at 13,200 x g for 2 minutes. Columns were inserted into a clean 1.5ml

microcentrifuge tube, 50 μl of Elution Buffer (@ 70°C) was directly applied to the HiBind

matrix, and then incubated at 70°C for 15 min. The tubes were centrifuged at full speed (13,200

x g) for one minute to elute the DNA. Extracted DNA was stored in a -20°C freezer for future

use.

Prior to amplification, the extracted DNA was purified using the QIAquick PCR purification kit

(QIAGEN, July 2002), following the protocol described in the QIAspin handbook. Five (5)

volumes of buffer PB (240μl) were added to one volume of PCR sample (48μl) and were mixed.

The mixture was transferred to a QIAquick spin column placed in a microcentrifuge tube, and

the contents were spun for one minute at maximum speed. The flow-through was discarded, the

column was washed with 0.75ml of buffer PE, and the tube was spun again for one minute at

maximum speed. The flow-through was again discarded, and the dry column was spun for one

minute. Each QIAquick column was placed in a clean 1.5ml centrifuge tube, and 30μl of buffer

EB was carefully added to the center of the membrane. Each column was left to stand for one

minute, and then was centrifuged for one minute at maximum speed. Extracted DNA was

quantified using a NanoVue™ Spectrophotometer (GE Healthcare).

3.3.4 DNA amplification and visualization

Two trials were performed using separate DNA extractions in the exact same manner, except the

cloning step was left out in the first trial. Methods will be given here according to the second

trial.

The ITS region of the fungal ribosomal DNA was amplified using the primer pair ITS8F and

ITS6R (Dentinger et al. 2009) in a Polymerase Chain Reaction (PCR) (see Appendix Tables A2,

A3, and A4 for PCR recipe, primer sequences, and thermocycler settings respectively).

Following amplification, PCR products were cleaned using a QIAGEN MinElute PCR

Purification kit, eluting 20ul of PCR in the final elution step.

Visualization of cleaned, amplified PCR product was performed using 1% agarose gel with 4.5

μl ethidium bromide added for DNA visualization. PCR product (4μl) was migrated at ~100 V

for 20-30 min, along with a negative control, positive control, and 4μl of low mass DNA ladder

68

to determine the molecular weight. The gel was illuminated under UV light, and samples

containing ITS bands (~800bp) were prepared for the cloning procedure.

3.3.5 Cloning

Transformation of PCR product into bacterial cells was accomplished using the QIAGEN PCR

Cloning plus Kit, using the ligation protocol outlined in the QIAGEN PCR Cloning Handbook

(QIAGEN 2001). The ligation master mix, pDrive cloning vector DNA, and distilled water were

thawed and kept on ice throughout the process. The ligation-reaction mixture (QIAGEN 2001)

was pipette-mixed, and incubated at 4°C overnight. The following afternoon, one LB agar petri

plate containing ampicillin was prepared for each sample to be cloned (see QIAGEN PCR

Cloning Handbook 2001 for recipe).

The transformation procedure followed the transformation protocol outlined in the QIAGEN

PCR Cloning Handbook (QIAGEN 2001). The QIAGEN EZ competent cells were thawed on

ice, and SOC medium was warmed to room temperature. Exactly 2μl of ligation-reaction mixture

was added per tube of QIAGEN EZ competent cells. Each tube of cells was divided among three

samples to three to prevent overcrowding of colonies on the petri plates. The cells were gently

mixed by flicking, and incubated on ice for 5 minutes. The tubes were then heated on a 42°C

heating block for 30 seconds, and then incubated on ice for another 2 minutes. SOC medium

(250μl) was then added to each tube, and the entire tube was plated directly onto the LB agar

plates. The plates were incubated at room temperature until the transformation mixture had been

absorbed into the agar. Plates were then inverted and incubated at 37°C overnight.

The following morning, plates were incubated at 4°C for at least two hours to allow the colour to

develop in colonies for which the lacZ alpha gene was present. A blue colour indicated cells that

did not take up the vector DNA. Five white coloured colonies were picked using a 1-10μl

pipette tip, and cells were carefully deposited in a PCR plate well containing 20μl of prepared

PCR master mix (see Appendix table A2 for recipe). The PCR plate was kept on a cold block

throughout the process.

Cloned DNA sequences were amplified using the primers ITS8F and ITS6R (See Appendix A4

for thermocycler program) and subsequently cleaned with the QIAGEN MinElute Kit, eluting

15μl of cleaned DNA in the final step.

69

To visualize the cloned PCR products, 4ul of each cloned DNA sample was run along with a low

mass DNA ladder (4μl) on a 1% agarose gel with 4.5ul ethidium bromide. One sample (5-1) did

not amplify, and so was excluded from the sequencing process. The brightness of each band was

compared with that of the DNA ladder, and a standard amount (10ng) of DNA for each sample

was used for the sequencing reactions.

3.3.6 Sequencing and analysis

Sequencing reactions were set up using a standard recipe (Appendix Table A5), and a

standard thermocycler programme (Appendix Table A6). To precipitate, 2μl of a 50:50 sodium

acetate and EDTA mixture was added to each sample and mixed thoroughly. Following this,

25μl of chilled 100% ethanol was added to each sample and the entire volume was transferred

into 1.5ml microcentrifuge tubes. After sitting for 20 minutes, the tubes were spun for an

additional 20 min at 13,200 x g. The supernatant was discarded, the samples were cleaned with

50μl of 70% ethanol and spun for an additional 10 min at 13,200 x g. The supernatant was again

discarded, and the samples spun for 10 s. The remaining ethanol was drawn out with a 10μl

micropipette, carefully ensuring that the pellet was not disturbed. The tube caps were left open

and covered with tinfoil for 10 min to dry thoroughly. Finally, 15ul of Hi Di was added to each

sample, and samples were transferred to a 96 well plate for sequencing. DNA was denatured at

95°C for 2 min, and then placed on a cooling block prior to loading on an ABI capillary

sequencer (Prism 3100 or 3730, Center for the Analysis of Genome Evolution and Function,

University of Toronto).

Alignment and editing of ITS sequences was performed using Sequencher v.4.1.2

(GeneCodes Corporation, Ann Arbor, MI) and sequences were identified to species with the

NCBI Basic Local Alignment Search Tool (BLAST) on GenBank (Benson et al. 2008).

Uncultured/environmental sample sequences deposited on GenBank were excluded from the

analysis.

70

3.4 Results

3.4.1 DNA Isolation from wood

Initial DNA isolation attempts from wood using a MoBio Soil DNA Kit (MO BIO Laboratories,

Inc., Carlsbad CA) were met with difficulty in downstream applications (e.g. PCR

amplification). These results were likely due to PCR inhibitory substances, such as tannins,

phenols or salts which remained in the extracted DNA. Subsequent attempts were made using

the E.Z.NA soil DNA (Omega Bio-Tek Inc., Norcross GA) isolation kit, which employs a

greater number of purification steps. Again, difficulties were met in downstream applications. It

was later found that a post-DNA isolation clean-up procedure using the QIAGEN QIA quick

PCR purification kit was sufficient to remove any remaining PCR inhibitors. Using this

combination, DNA was successfully extracted from all 24 wood samples. Relatively uniform

concentrations of total DNA across samples were obtained using this method, confirmed by

spectrophotometric measurements (see Appendix Table A1). DNA concentrations ranged from

5.6 - 35 ng/μl with an average of 14.26 ± 1.41 ng/μl (mean ± standard error).

3.4.2 Amplification and visualization

Only 9 of the 24 wood DNA samples were successfully amplified (Figure 3.1). Of the 9 samples

which amplified, 6 were obtained from infected trees and 3 were obtained from damaged trees.

The positive control confirmed that the amplification process was indeed successful for total

DNA samples containing fungal DNA, and the negative control confirmed that there was no

contamination. There were no bands that developed in control trees.

This result is consistent with the initial trial, where 4 of 6 infected trees, 3 of 6 damaged trees,

and 4 of 12 control tree samples were successfully amplified, however not all of these were

successfully sequenced (Table 3.1).

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Figure 3.1. Electrophoresis gel depicting amplified DNA in the ITS region from each tree in this

study using ITS8F and ITS6R primers. Ladder is a high-mass molecular weight marker. Not all

bands which were visible under the UV light are apparent in the photograph. Lane codes

correspond to tree names shown in Table 1. The middle character of each code corresponds to the

tree condition: I – ‘infected’, D-‘damaged’ , C- ‘control’.

72

3.4.3 Cloning

Gel electrophoresis following the cloning step and subsequent amplification of clones revealed

that the cloning was successful overall. Clone numbers 1-1 through 4-1 produced especially

strong bands, indicating a high concentration of template DNA, with the exception of one clone

from tree 1I1 (clone 1-5, see Figure 3.2). Sample 7-1 from tree 1D1 did not run quite as far on

the gel as the rest of the samples (Figure 3.2), indicating that it was likely a longer fragment.

Shadows in the gel (Figure 3.2) indicated that there were residual contaminants in the cloned

product DNA following the amplification. The cloned product was cleaned using a QIAGEN

MinElute kit (QIAGEN 2008), and a subsequent gel (Figure 3.3) revealed that the contaminants

had been successfully removed.

3.4.4 ITS sequences – Genbank database similarities.

The ITS region of rDNA was successfully cloned and amplified in all infected trees, and

sequences obtained confirm the presence of Fomes fomentarius in all six of these trees. All

clones which were identified as Fomes fomentarius had 100% sequence coverage, with 98-99%

sequence similarity, with the exception of one clone. Tree 1I1 had one clone (1-1) with only

87% sequence coverage, and a maximum similarity of 92% (Table 3.1). These results are similar

to the initial trial (without cloning), with four infected trees returning sequences for Fomes

fomentarius, none of the damaged trees returning sequences, and one of the control trees

returning as Penicillium urticae, which is an endophytic fungi belonging to the Ascomycota

(Table 3.1).

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ITS6R. The samples have not yet been cleaned, as indicated by smears near the well and under the

bands. A high-mass molecular weight marker was used. The clone number is indicated above each

lane, and the tree number corresponding to the clone number can be found in Table 3.1.

74


ITS6R following a purification step. Bands are much sharper than in Figure 3.2, indicating that the

contaminants had been successfully removed from the DNA. A high-mass molecular weight marker

was used. The clone number is indicated above each lane, and the tree number corresponding to

the clone number can be found in Table 3.1.

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Table 3.1 Summary of tree ID number, clone number (if applicable), tree condition, and the respective

PCR, cloning, and Genbank results. The results of the preliminary trial are also shown. Genbank

results (species, sequence coverage, E-value and maximum identity) are given for the first five matches

in BLAST. ‘+’ indicates a positive response; ‘-‘ indicates a negative response. Following the cloning

procedure, one clone from the each of the first five trees was omitted from sequencing due to limitations

in plate wells.

Clone Number

Tree ID

Tree Condition

Results Prelim.

Trial

PCR Result

Cloning Result

Top Genbank Hit, Genbank Accession

Number

Sequence Coverage

E-value

Max. Identity

Result

1-1 1I1

Infected +

+ Fomes fomentarius

EF155498.1 87% 0.0 92%

Fomes fomentarius

1-2 1I1 + Fomes fomentarius

EF155498.1 99-100% 0.0 98-99%

1-3 1I1 + Omitted n/a n/a n/a


EF155498.1 100% 0.0 98-99%

1-5 1I1 - Not sequenced n/a n/a n/a

2-1 2I1

Infected +

+ Fomes fomentarius

EF155498.1 100% 0.0 98-99%

Fomes fomentarius


EF155498.1 100% 0.0 98-99%


EF155498.1 100% 0.0 98-99%



EF155498.1 100% 0.0 98%

3-1 3I1

Infected +

+ Fomes fomentarius

EF155498.1 100% 0.0 98%

Fomes fomentarius



EF155498.1 100% 0.0 98-99%


EF155498.1 100% 0.0 98-99%


EF155498.1 100% 0.0 98%

4-1 4I1

Infected +

+ Omitted n/a n/a n/a

Fomes fomentarius


EF155498.1 100% 0.0 98%


EF155498.1 100% 0.0 98%


EF155498.1 100% 0.0 98-99%

4-5 4I1 +

Fomes fomentarius EF155498.1 100% 0.0 98-99%

5-1 5I1 Infected + + Fomes fomentarius

EF155498.1 100% 0.0 98% n/a


76


EF155498.1 100% 0.0 98%


EF155498.1 100% 0.0 98%

5-5 5I1 + Willopsis sp. EF194844.1 87-100% 0.0 84-87%

6-1 6I1

Infected +

+ Fomes fomentarius

EF155498.1 100% 0.0 98%

n/a


EF155498.1 100% 0.0 98-99%


EF155498.1 100% 0.0 98%


EF155498.1 100% 0.0 98%


EF155498.1 100% 0.0 98%

7-1 1D1

Damaged +

+ Udeniomyces sp.

AF444402.1 89-100% 0.0 to

1e-165 85-98%

Amplified did not

sequence

7-2 1D1 + Nonsense sequence n/a n/a n/a

7-3 1D1 + Fomes fomentarius

EF155498.1 100% 0.0 98%


EF155498.1 100% 0.0 98-99%


EF155498.1 100% 0.0 98%

8-1 2D1

Damaged +

+ Phoma sp.

AB465199.1 100% 0.0 99%

n/a

8-2 2D1 + Phoma sp.

AB465199.1 100% 0.0 100%

8-3 2D1 + Cryptococcus sp.

DO000318.1 55-56% 0.0 94-95%

8-4 2D1 + Phoma sp.

AB465199.1 100% 0.0 100%

8-5 2D1 + Phoma sp.

AB465199.1 100% 0.0 100%

9-1 3D1

Damaged +

+ Epicoccum sp.

FJ210554.1 100% 0.0 99%

n/a

9-2 3D1 + Epicoccum sp.

FJ210554.1 100% 0.0 99%


FJ210554.1 100% 0.0 99%


FJ210554.1 100% 0.0 99%


FJ210554.1 100% 0.0 99%

4D1

Damaged - n/a n/a n/a n/a n/a Amplified

did not sequence

77

5D1

Damaged - n/a n/a n/a n/a n/a Amplified

did not sequence

6D1 Damaged - n/a n/a n/a n/a n/a n/a

1C1 Control - n/a n/a n/a n/a n/a n/a


3C1 Control

- n/a n/a n/a n/a n/a Amplified

did not sequence



6C1 Control


did not sequence



3C2 Control


did not sequence


5C2 Control

- n/a n/a n/a n/a n/a Penicillium urticae


It is interesting to note that although tree 5I1 had three clones identified as F. fomentarius, there

was also one clone (5-5) which had very low similarity with sequences in the Genbank Database.

BLAST results indicated an expectation value (E-value) of 1e-143 to 5e-154 with maximum

similarity of 84-87% to members of the yeast genus Willopsis (Ascomycota; Saccharomycetales;

Saccharomycetaceae) (Table 3.1). All of the following top hits are with other members of the

Saccharomycetaceae, in the genera Pichia, Candida, and Wickherhamomyces. It is therefore

quite likely that this sequence represents a yeast in the family Saccharomycetaceae, but it cannot

be identified to genus. It is also remarkable that no closely related sequence was ever reported

from environmental samples, especially given the number of yeast ITS sequences currently

available in Genbank (~4121 sequences, June 27 2010, Genbank).

Of the six damaged trees, fungal rDNA was successfully amplified for only three (Table 3.1). In

tree 1D1, three of five clones were identified as F. fomentarius with 100% sequence coverage

and 98-99% sequence similarity. Clone 7-1 from tree 1D1 which appeared to be a larger

fragment from the gel electrophoresis, returned a nonsense sequence. The remaining clone from

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tree 1D1 had its top five matches to Udeniomyces sp., with 89-100% query coverage, E-values

from 0.0 to 1e-165, and maximum identity of 85-98% (Table 3.1). Udeniomyces sp. is a genus

of Basidiomycete yeasts, belonging to the Tremellomycetes. The following top hits for this

clone were Cryptococcus sp. and Mrakia sp. which are other types of basidiomycete yeasts,

belonging to the families Sporidiobolaceae and Cystofilobasidiaceae respectively. In tree 2D1,

four out of five clones were identified as Phoma sp., with 100% sequence coverage, and 99-

100% similarity. One clone returned top matches for Cryptococcus sp. with a maximum identity

of 95%, but only 55-56% query coverage and E-values ranging from 3e-94 to 6e-97. Clones

from tree 3D1 all returned sequences for Epicoccum sp., with high similarity of 99% and 100%

query coverage (Table 3.1).

3.5 Discussion

3.5.1 Molecular protocol development

The primary goal of this study was to design a molecular protocol for the detection and

identification of woody decay fungi in live standing trees. Through the development of a

molecular technique involving direct DNA isolation from wood followed by cloning and

sequencing, wood-decay fungi were successfully isolated and identified from trees with

sporocarps as well as from damaged trees without visible sporocarps. Performed directly from

wood samples, this method bypasses the often long and involved process of culturing and

permits the amplification of fungal species which may not be culturable (Johannesson and

Stenlid 1999). Furthermore, the time-consuming development of species-specific primers or

RFLP libraries is not necessary. The protocol described here provides reliable and relatively

rapid identification of basidiomycete and some ascomycete fungi in the sapwood of living trees,

and was confirmed by a separate trial. Details and further considerations pertaining to this

procedure will be discussed later in this chapter.

3.5.2 Fungi detected

The second objective of this study was to confirm the presence of Fomes fomentarius in live

trees with visible sporocarps. Molecular results confirmed the presence of F. fomentarius in all

six of the „Infected‟ trees on which F. fomentarius sporocarps were observed (Table 3.1). This

result suggests that in this study, sporocarp presence was a generally good indicator of fungal

79

presence. However, while sporocarp presence may indicate the presence of one particular

species of fungus, it is important to keep in mind that there may be other fungal species present

which do not have visible sporocarps. The cloning step in our protocol is critical for enabling the

identification of multiple species from an individual sample. For instance, in one of the six

infected trees (5I1), the molecular analysis indicates that F. fomentarius was found in

conjunction with a member of the Ascomycete yeast family Saccharomycetaceae genus

Williopsis (Table 3.1). Previous studies have also found that molecular techniques may not even

detect complete diversity as reflected by sporocarp presence (Allmér et al. 2006, Gugielmo et al.

2007, Fisher 2008) as a result of primer and sampling biases, which will be discussed later.

Thus, while sporocarp presence was a good predictor of infection, it clearly does not reflect the

total diversity of fungi present in live wood.

In addition to detecting fungi in trees with visible signs of infection, wood-decay fungi were also

detected in trees where sporocarps had not yet developed. In three of the six damaged trees

examined in this study, there was molecular evidence of fungal species inhabiting the wood.

Fomes fomentarius was found in one of these trees, in conjunction with a member of the

basidiomycete yeast genus Udeniomyces (Table 3.1).

Another damaged tree was found to contain isolates of Phoma sp. and Cryptococcus sp. (Tree

2D1, Table 3.1). Members of the genus Phoma are plant endophytes, which live in a putatively

mutualistic association with a plant host (Yang et al. 1994). Species of Phoma have been shown

to play a role in the production of antibiotic compounds in Taxus wallachiana (Yang et al. 1994).

The genus Cryptococcus belongs to the Basidiomycota, family Tremellaceae (Genbank

Database). One species of Cryptococcus, C. neoformans, is an agent which causes

cryptococcosis in humans and is known to be found in the decaying hollows of living trees

(Lazera et al. 2000). Recent studies have confirmed that C. neoformans produces a laccase

enzyme (Williamson 1994), which is likely involved in lignin degradation (Lazera et al. 2000).

It is therefore plausible that the Cryptococcus species found in our study may be also associated

with wood decay processes in B. alleghaniensis, as wood decay processes are known to occur in

an ecological succession (Hennon 1995, Durall et al. 1996).

The third damaged three which returned a positive fungal ID was 3D1, which was most closely

matched in Genbank with Epicoccum sp.. Members of the genus Epicoccum, specifically

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Epicoccum nigrum are known secondary invaders of plant material, also found in soil (Arenal et

al. 2000). The role of Epicoccum sp. here is not clear. It is interesting to note, however, that a

relatively recent study suggested on the basis of ITS sequences that E. nigrum is synanamorphic

with Phoma epicoccina (Arenal et al. 2000), a member of a genus found in tree 2D1.

Of the „control‟ trees in this study (which had no visible signs of infection and no significant

trunk damage), there were none that contained fungal DNA which successfully amplified ITS.

Spectrophotometer readings following DNA extraction indicate that the isolation procedure

(Table A1, appendix) and positive controls successfully amplified in parallel to these samples.

This evidence indicates that the amplification procedure itself was successful. This is not

surprising, as these were trees which were fairly intact and healthy, with few visible entry points

for fungal colonization. It is therefore most likely that very little or no basidiomycete DNA was

present in these samples. However, this result does not necessarily imply an absence of fungal

DNA altogether. The primers ITS8F and ITS6R used in this study were designed specifically for

basidiomycetes (Detinger et al. 2009), so there is the potential that other fungal taxa may have

been present wood, but were simply not amplified.

Results of the initial trial using the same primers revealed only one „control‟ sample (5C2)

containing the ascomycete fungus Penicillium urticae (Table 3.1), which is an endophytic fungi

belonging to the Ascomycota (Genbank Database, May 8 2010). It is likely that the differences

between the first and second trial were due to the differences in wood sample used, as separate

DNA extractions were conducted for each trial using combined wood samples from five areas of

each tree.

3.5.3 Methodological considerations

The sampling technique used in this study is relatively non-invasive and non-destructive, as

DNA extraction requires as little as 0.1g of wood for each sample. Furthermore, specialized

equipment such as an increment core borer is not required; samples can simply be collected with

the use of a common cordless drill. It is important to note, however, that care should be taken in

how the wood is extracted from the tree in an effort to prevent cross-contamination and reduce

accidental inoculation of study trees. Previous studies have noted that wounds from core borers

and drills may cause discolouration and potentially lead to colonization of fungi in the wound

(Butin 1995, Dujesiefken et al. 1999, Larsson et al. 2004). A variety of treatments to prevent

81

infection of the bore hole have been assessed, but none have been found to be advantageous

(Dujesiefken et al. 1999). To prevent cross-contamination of samples in this study, a simple

sterilization technique using 70% ethanol and a lighter flame was used on the drill bit prior to

drilling each sample.

The amount of wood and area sampled from each tree is another factor that should be considered

when attempting to detect and/or identify woody-decay agents in standing trees. As wood

samples required for molecular analysis are very small relative to the large size of the tree, the

probability of the true diversity of the tree being reflected in the molecular outcome is likely

small. In the current study, sampling along the trunk in a vertical gradient was done in attempt to

capture fungal diversity along a vertical gradient throughout the tree. Future studies may

consider using more samples in an effort to better capture diversity. However, there is a trade-off

as more samples may increase the risk of accidentally inoculating a healthy tree.

3.5.3.1 DNA isolation procedures

Optimization of the DNA extraction process was found to be particularly important as the

sapwood samples contained PCR inhibitors. Preliminary DNA extraction trials were

unsuccessful following attempts with two different DNA isolation kits, a number of modification

to extraction protocols, and various thermocycler regimes. It was found that an additional

purification procedure to clean the DNA of inhibitory compounds such as polyphenol and

tannins was necessary. This is not surprising, as difficulty extracting DNA from sapwood has

been previously documented (Schwarze et al. 2000), and a number of studies have also

emphasized the importance of the isolation protocol and inclusion of a purification step to

decrease the interference of wood extractives and decay by-products with the DNA (Adair et al.

2002, Oh et al. 2003, Gugielmo et al. 2007, Nicolotti et al. 2009).

3.5.3.2 Amplification

The primers ITS8F and ITS6R used in this study were developed to avoid non-specific

amplification products, such as homo- and hetero-dimers (commonly known as primer-dimers)

(Dentinger et al. 2009) which are commonly associated with the primers ITS1F and ITS4B.

While ITS8F and ITS6R are very similar to ITS1F and ITS4B in that they amplify the ITS1, 5.8S

and ITS2 regions of rDNA, they are more specific towards Agaricomycetes, the class of fungi to

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which the polypore fungi belong. This particular primer set was chosen for this investigation, as

polypore fungi were the main focus of the study. Therefore, we likely recovered the majority of

Agaricomycetes present, in addition to some other members of Basidiomycota and Ascomycota.

These groups encompass the majority of wood-decay fungi in addition to a number of other

species which would likely have a predominant effect on the physiology of the tree. Therefore,

while using this method in combination with the primers ITS8F and ITS6R is probably not

optimal for studies investigating entire fungal communities in wood, it is quite useful for studies

of decay-inducing members of Agaricomycetes (Basidiomycota). For broader community

studies, the protocol described here could be easily adapted for use with other primer sets

designed to specifically amplify other fungal phyla, for example, primer sets ITS1/ITS4A or ITS

5/ITS4A specifically for Ascomycota.

When using universal primers, one particular challenge is a potential bias in the number of initial

strands in the originally extracted mixed DNA sample versus what is amplified, as primers may

have a higher affinity toward one template than another, and would affect the ability to detect

other taxa (see Polz and Cavanaugh 1998 (as cited by Anderson and Cairney 2004), O‟Brien et

al. 2005, Porter et al. 2008). This effect can be lessened through the reduction of PCR cycles

during amplification, and by using high concentrations of template DNA (Anderson and Cairney

2004). However, this may result in a lower final concentration of amplified DNA. It would be

interesting to use the same template DNA and reduce the number of PCR cycles and observe any

differing results.

The use of a positive control is important to ensure that the process is effective at each step in the

protocol. In this study, positive controls confirmed that negative amplification results were

really due to a lack of target DNA, rather than a problem with the procedure itself. Previous

studies (e.g. Johannesson and Stenlid 1999) have returned negative results but did not (to our

knowledge) use positive controls to determine whether it was a lack of fungal DNA in the

sample itself, or if the procedure was in fact ineffectual.

While a number of studies have claimed that molecular methods provide a more complete

representation of fungal communities in environmental samples than sporocarp counts or

culturing alone (e.g. Hunt et al. 2004, O‟Brien et al. 2005), other studies have found that

molecular techniques may not detect complete diversity as reflected by sporocarp presence

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(Allmér et al. 2006, Gugielmo et al. 2007, Fisher 2008). It is likely that molecular methods are

most effective in combination with other traditional methods of assessment, such as sporocarp

counts and culturing (e.g. Allmér et al. 2006, Fisher 2008). For example, Allmér et al. (2006)

assessed three different methods of assessment for fungal composition and abundance: sporocarp

counts, culturing mycelia and direct amplification of the internal transcribed spacer (ITS) region

of rRNA using terminal rapid fragment length polymorphism. They found that sporocarp counts

and mycelia cultures revealed greater species richness than did direct amplification. However,

sporocarp counts poorly reflect their actual abundance in wood. The T-RFLP method was

efficient in detecting common species but overlooked rarer species present in wood. Culturing

techniques bias the results because species favoured by culture media appear more abundant

(Allmér et al. 2006).

Although we were able to detect the DNA of certain wood-decay fungal species from wood

samples, it is difficult to confirm whether or not these pathogens were still viable in the sample

(Garbelotto 2004). It is possible that the tree may have already „compartmentalized‟ this fungus,

halting its progression throughout the tree. However, even if this is the case, it is still likely that

the pathogen may have an effect on the physiological performance of the tree as a result of

decreased hydraulic conductivity. One method of determining the viability of the pathogen

would be to examine the samples for RNA, as RNA would degrade quickly after the cells die

(Narayanasamy 2008).

It should also be mentioned that Genbank results should be interpreted with care, as Genbank

currently does not have a thorough verification system, making it difficult to determine whether

an organism has been accurately identified and if the taxonomy is correct (Hoff et al. 2004). For

example, the second return on clone 5-5 was 84% similarity to Gastrodia elata, which is a plant

species in the Orchidaceae family native to Asia. This sequence was likely a species of Willopsis

isolated from the orchid, and mistakenly uploaded to Genbank as Gastrodia elata. Genbank also

has low representation of fungi from some substrates (Hoff et al. 2004), so it may be difficult to

find a proper match for the target sequence. Furthermore, some ITS sequences also amplify

angiosperm DNA (Anderson and Cairney 2004), so results should be interpreted with care.

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3.5.3.3 Detection of decay

While the molecular method described in this chapter can effectively identify and describe

agents of decay, it is difficult to quantify the extent of this decay. Traditional methods such as

shigometers and the four-point resistivity method (also known as RISE) quantify the extent of

decay by measuring woody tissue resistance to a current (Butin 1995, Larsson et al. 2004).

However, little information pertaining to the fungal species involved can be gleaned from this

technique. Real-Time PCR (RT-PCR) is another molecular technique which has been used in

combination with taxon-specific primers, and is able to quantify the amount of target DNA

present in the template mixture (Garbelotto 2004, Mumford et al. 2006). This technique has been

increasingly used to study a variety of plant pathogens, from fungi to bacteria and viruses

(Mumford et al. 2006). RT-PCR may provide more ecologically relevant information than

traditional PCR methods. For example, researchers may be able to distinguish template strands

which are in small quantities (likely spores or contamination) from those in high abundance

(indicative of an infection) (Garbelotto 2004). When used in conjunction with inoculation

studies, this technique can be effective in monitoring spread of infection over time. However,

RT-PCR may not be practical for every lab as it is a relatively expensive method, requiring

significant capital investment (Mumford et al. 2006).

3.5.4 Implications

As Phoma sp., Epicoccum sp., and Cryptococcus sp. were found in damaged trees but not

undamaged trees, it is unlikely that these species are mutualists with B. alleghaniensis.

Furthermore, these species were also not detected in infected trees. Although this could be due

to PCR or primer biases, it is likely that Fomes was able to out-compete these other taxa.

As F. fomentarius was only detected in one damaged trees, and in no control (undamaged, no

visible infection) trees, this suggests that the latent period where it is present in live sapwood is

not long, as the tree dies shortly thereafter. Throughout the course of this study, it was noted that

many trees visibly infected with F. fomentarius exhibited rapid signs of decline over a one-year

period. In fact, two infected trees which had been measured in 2008 had little to no live growth

remaining in the 2009 season (E. Mycroft, unpublished). In another case also in Haliburton

forest, a number of live B. alleghaniensis with F. fomentarius sporocarps in 2006 had no live

growth remaining in 2008 (S.C. Thomas, personal communication). It seems likely that once F.

85

fomentarius establishes itself it proliferates widely, contributing quickly to tree death. Another

explanation may be that once the sporocarps have formed the fungus has exhausted its nutrient

supply and the tree rapidly declines. The results of this study also indicate that F. fomentarius is

the major species present in infected trees (Table 3.1), so it is reasonable to infer that

physiological effects noted in Chapter 2 are indeed being driven by this fungal species.

3.6 Conclusion

In summary, the PCR-based method of amplifying and cloning the ITS region of fungal

ribosomal DNA directly from wood samples described here was successfully able to confirm the

presence of woody decay fungi in both infected and damaged live standing trees. This technique

involves a straightforward collection method, followed by a rapid extraction, amplification and

cloning process with the use of universal primers. This is also an effective technique for the

detection of species which cannot be cultured, have not produced visible fruiting bodies, and for

the discovery and identification of cryptic species, such as the possible new yeast species

isolated in this study. As fungal species vary in their aggressiveness and invasion strategy,

methods such as the one described here which enable the detection and identification of fungal

agents during the early stages of decay may allow researchers to better predict the rate of decay

progression within and among trees (Schwarze et al. 2000, Guglielmo et al. 2007). As such, it

would be useful to conduct further tests on trees of different species, as well as fungi of different

species to confirm the technique.

In the future, inoculation studies with live trees would be useful to determine the sensitivity of

the molecular protocol described in this study. Inoculation studies combined with RT-PCR

would also be informative in monitoring the extent of infection over time. With the recent

development of portable RT-PCR instruments, testing could take place in the field (Mumford et

al. 2006). New developments such as high-throughput DNA extraction methods (e.g. Xin et al.

2003) could be applied to this type of assay to assess multiple samples simultaneously, thus

enabling researchers to acquire valuable information about fungal pathogens and their effect on

forest stands in a relatively short amount of time thus enhancing early detection capabilities. The

ability to detect and identify fungal species in live trees at an early stage of infection will

undoubtedly be of substantial aid to our understanding of how fungal pathogens such as polypore

fungi, woody tissue endophytes and commensals affect physiological processes in trees.

86

Chapter 4

Synthesis: Effects of polypore fungal infection on

B. allegnaniensis and molecular detection of pathogens

4.1 Overview

This thesis represents the first study to examine the effect of polypore fungal infection on tree

growth and physiology, confirmed with a molecular protocol designed to detect polypore fungal

infection from live trees. Generally it was observed that trees infected with F. fomentarius had

significantly different patterns of growth and physiology than damaged and control trees.

Although fungal-induced water stress via xylem occlusion or embolism likely played a role in

this, results suggest that fungal toxins or induced host defense mechanisms may have also

contributed to the patterns seen in this study. The development of a molecular protocol to detect

and identify polypore fungi in live trees indicated that F. fomentarius was the major species

present in infected trees. Thus, it is reasonable to infer that the physiological effects in infected

trees are in fact being driven by F. fomentarius. The following sections summarize the major

findings from each chapter of this thesis and discuss some of the limitations and implications of

this work.

4.2 Impacts of infection on physiology, morphology and growth

Chapter two of this thesis examined how characteristics of canopy physiology, morphology and

chemistry and overall tree growth may be affected by infection with F. fomentarius, and what

morphological characters may be correlated with physiological traits in infected trees. It also

briefly compared patterns found in infected trees with those typical of trees later in ontogeny and

suggested potential mechanisms for the physiological symptoms observed here. To my

knowledge there are no other studies which examine the effects of polypore fungal infection on

canopy physiology. Photosynthetic capacity (Amax) and stomatal conductance (gs) were

significantly lower in infected trees compared to damaged and control trees (Figures 2.2,2.3), but

there was no difference in water-use-efficiency (Figure 2.4, Figure 2.5, Table 2.1). Higher levels

of leaf carbonmass, carbonarea and chlorosis (Figures 2.13 - 2.15) were also observed in infected

87

trees. Growth was significantly lower in infected trees compared to control trees (Figures

2.16,2.17), however there were few differences in leaf morphological traits (Figures 2.6- 2.9).

Generally, morphological traits in infected trees did not explain the variation in Amax. This may

indicate that fungal infection affects leaf physiological performance after leaf formation and

expansion. These results suggest that although fungal-induced xylem blockage resulting from

tree compartmentalization (Shigo 1984, Schwarze et al. 2000) or induction of embolisms

(Temple and Horgen 2000) may contribute to the morphological and physiological patterns

observed, it is clear that a typical drought response is not the only mechanism. It is possible that

there may also be some toxic substance associated with F. fomentarius infection, and/or induced

host defense that also alters host physiology (Vance 1980, Scheffer and Livingston 1984, Van

Alfen 1989, Whiteford and Spanu 2002, Berger et al. 2007).

Results also suggest that fungal infection may play a partial role in driving some ontogenetic

patterns. While morphological leaf traits in infected trees did not reflect those consistent with

later ontogenetic stages of trees, physiological parameters (i.e. decreased Amax and gs) and leaf

carbon resembled traits of very large (i.e. late in ontogeny) B. alleghaniensis trees found in a

recent study (Thomas 2010) at the same field site.

4.3 Molecular detection of infection

In the second data chapter of this thesis, the development of a novel molecular protocol

involving direct extraction of fungal DNA from live standing trees was described. This technique

involved a straightforward collection method, followed by a rapid extraction, amplification and

cloning process with the use of newly developed primers (Dentinger et al. 2009). As it is

performed directly from wood samples, this method bypasses the involved processes of

culturing, development of species-specific primers and RFLP libraries. The molecular results

confirmed that F. fomentarius was the major species present in infected trees of the first data

chapter and identified the presense of other fungal species prior to sporocarp development in

damaged trees (Table 3.1). While a number of damaged trees contained fungal species

(e.g.Cryptococcus sp., Phoma sp., Epicoccum sp.), there was no fungal DNA detected in control

trees (Table 3.1). As F. fomentarius was only detected in one damaged trees, and in no control

(undamaged, no visible infection) trees. This suggests that the latent period where it is present in

live wood is likely not long, as the tree dies shortly thereafter. Or, it may be that once the

88

sporocarps have formed the fungus has proliferated throughout the tree to the point that its

nutrient supply is exhausted, and the tree rapidly declines thereafter.

4.4 Current limitations, implications and future directions

There were several limitations in methodology throughout this thesis which should be addressed

in future work. Firstly, one of the most difficult obstacles was obtaining an adequate sample size

for this study. As the canopy lift enabled access to the canopy, the trees used in this study

needed to be easily accessed from roads or skid-trails. As discussed in Chapter 3, F. fomentarius

appears to have a relatively short latent period in live trees, so it was a considerable challenge to

identify infected trees which were both living and readily accessible by the canopy lift.

Secondly, a number of other fungal species were identified in damaged trees, which may have

had an effect on growth and physiology in these trees. Furthermore, one damaged tree which did

not have any visible sporocarps was found to contain F. fomentarius (Table 3.1). For future

work, it would be valuable to treat this tree differently in the analysis, as it may have obscured

differences between infected and damaged trees. Thirdly, the present study only identified

fungal presence using sporocarp counts and molecular techniques. Culturing techniques, while

sometimes biased because of the culturing media used, have been shown to reveal additional

species that neither sporocarp counts nor molecular techniques were able to detect (Allmér et al.

2006, Fisher 2008). Thus, it would be interesting to include culturing in future studies as an

additional method of fungal detection and identification.

Quantifying chlorosis patterns using leaf reflectance would also be a fascinating element of

future work. As described in Chapter 2, there have recently been a number of leaf reflectance

parameters described which can be used to characterize chlorophyll (Gitelson and Merzlyak

1994, Ustin et al. 2009), anthocyanins (Close and Beadle 2003, Gitelson et al. 2009) and

carotenoids (Sims and Gamon 2002) in leaves, which may shed more light on the mechanisms

occurring with polypore infection, as leaf pigments can be useful indicators of plant stress (Ustin

et al. 2009). Furthermore, characterising reflectance signals of woody-decay fungal infection

could have useful applications for detection of infection using remote sensing techniques

(Nilsson 1995).

89

While compartmentalization is known to occur in trees infected with woody decay fungi (Shigo

1984), it is difficult to identify compartmentalization zones non-destructively. Recently, there

have been developments in sensitive thermal imaging cameras which are able to detect spatial

patterns of temperature across leaf surfaces, and can be used to examine patterns of pathogen

infection (e.g. water loss patterns) (Aldea et al. 2006). If used on a broader scale, this type of

technology may enable researchers to „map out‟ spatial components of infection in the canopy in

a non-destructive manner. Using something such as thermal cameras to image an entire tree may

give insight into compartmentalization within the tree, and thus would enable examination of

how sectorality influences canopy physiology.

A potential extension of the molecular work would be to examine how fungal communities in

sapwood of living trees compare with those of snags and downed woody debris. A handful of

studies have examined fungal communities in soil and downed woody debris (e.g. Heilmann-

Clausen 2001, Heilmann-Clausen and Boddy 2005, Fisher 2008, Porter et al. 2008), however

live trees remain a relatively unexplored environment. A few of the molecular results here show

relatively low maximum identity scores in Genbank (e.g. Willopsis sp.,Udeniomyces sp.) (Table

1), indicating the specific species we found may have not been entered in Genbank at the time of

the study, or the species may be undescribed altogether. Thus, living trees may be one

environment containing a number of undescribed or cryptic fungal species (Hawksworth and

Rossman 1997). It would also be of interest to examine the community interactions of these

fungi, as previous studies on wood plugs in vitro indicate that early decay fungi (such as those in

live trees) have a strong influence on the establishment of successive fungal colonizers

(Heilmann-Clausen and Boddy 2005).

Observations from this study also provide a novel insight into the interactions between F.

fomentarius and B. alleghaniensis. As discussed in Chapter 3, observations here suggest that

once F. fomentarius establishes itself in a tree it proliferates widely, contributing quickly to tree

death. Alternatively, another explanation may be that once the sporocarps have formed the

fungus has exhausted its nutrient supply and the tree rapidly declines. If it is indeed the case that

F. fomentarius has a relatively short latent period in trees, it seems unlikely that this particular

pathogen is a major driver of long-term ontogenetic changes typically observed in ageing trees.

In future studies, it would be interesting to conduct inoculation studies with trees to describe the

time-progression of infection. In addition, as trees varying in size would have vary in their

90

allocation of resources to defense (Boege and Marquis 2006), conducting inoculation studies on

trees from a variety of size classes would provide further insight into how the severity of fungal

infection and effectiveness of tree defence mechanisms vary throughout ontogeny.

In summary, the work presented in this thesis demonstrates the physiological, morphological,

chemical and growth impacts of polypore fungal infection in mature temperate deciduous trees,

suggests potential mechanisms for the patterns observed, and describes the development of a

molecular method for the detection of fungal pathogens in live trees. The ability to detect and

identify fungal species in live trees at an early stage of infection will undoubtedly be of

substantial aid to our understanding of how fungal pathogens such as polypore fungi affect

physiological processes in trees.

91

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Appendix

Table A 1. Estimated extracted DNA concentrations and corresponding 260/280nm and 260/230nm

ratios for DNA samples from each tree.

Tree ID Tree Condition DNA concentration

(ng/ul)

260/280 260/230

1I1 Infected 5.6 1.302 0.647

2I1 Infected 34.5 1.86 1.408

3I1 Infected 14.7 1.68 1.47

4I1 Infected 14.4 1.725 0.776

5I1 Infected 14.3 1.599 0.753

6I1 Infected 35.0 1.79 1.373

1D1 Damaged 10.0 1.77 0.909

2D1 Damaged 19.5 1.771 0.780

3D1 Damaged 8.6 1.365 0.972

4D1 Damaged 13.4 1.836 0.705

5D1 Damaged 13.8 1.554 0.671

6D1 Damaged 15.0 1.622 0.789

1C1 Control 10.2 1.632 0.618

1C2 Control 12.6 1.702 0.697

2C1 Control 12.8 1.684 0.674

2C2 Control 14.8 1.595 0.686

3C1 Control 10.3 1.471 0.542

3C2 Control 12.8 1.561 0.522

4C1 Control 9.6 1.752 0.612

4C2 Control 14.2 1.747 0.647

106

5C1 Control 10.9 1.632 0.603

5C2 Control 9.4 1.979 0.699

6C1 Control 12.1 1.662 0.603

6C2 Control 13.8 1.852 0.627

107

Table A 2. PCR Recipe (25 μl DNA amplification reaction and Cloning PCR Reaction)

Mixed PCR Reaction (25

μl)

Cloning PCR Reaction

(20ul)

1.25mM dNTPs 2.5 μl 2 μl

PCR Water 14.25 μl 13.4 μl

Platinum Taq

(Invitrogen) 0.25 μl

0.2 μl

EH 2.5 μl 2 μl

Forward Primer –

ITS8F (10mM) 1.5 μl

1.2 μl

Reverse Primer –

ITS6R (10mM) 1.5 μl

1.2 μl

DNA product 2.5 μl [pipette tip touched to

colony]

108

Table A 3. Primer Sequences

Primer Name Sequence (5‟→3‟) Reference

ITS1F CTTGGTCATTTAGAGGAAGTAA Gardes and Bruns, 1993

ITS4B TCCTCCGCTTATTGATATGC White et al. 1990

ITS8F AGTCGTAACAAGGTTTCCGTAGGTG Dentinger et al. 2009

ITS6R TTCCCGCTTCACTCGCAGT Dentinger et al. 2009

109

Table A 4, Thermocycler Settings (DNA amplification reaction and cloning reaction)

Temperature (°C) Time (min:sec)

94 2:00

94 0:30

55 0:30

72 1:00

Steps 2-4 repeated 30 times

72 7:00

4 ∞

110

Table A 5. Sequencing PCR Recipe, using a total of 10 ng template DNA. Calculations shown are

for 1 μl or 4 μl template DNA.

1 μl Template DNA 4 μl Template DNA

Big Dye ® Terminator v. 1.1

(Applied Biosystems; Foster

City, CA)

0.5 μl 0.5 μl

Big Dye ® Buffer (Applied

Biosystems; Foster city, CA)

2 μl 2 μl

Primer (10mM) 1 μl 1 μl

5M Betaine 2 μl 2 μl

PCR Water 0.5 μl 3.5 μl

111

Table A 6. Thermocycler Settings (Sequencing Reaction)

Temperature (°C) Time (min:sec)

96 2:00

96 0:10

50 0:05

60 0:04

Steps 2-4 repeated 30 times

4 ∞

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