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.
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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
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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‟
treatments and canopy strata.. ....................................................................................................... 28
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‟
treatments and canopy strata.. ....................................................................................................... 30
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‟
treatments and canopy strata.. ....................................................................................................... 32
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‟
treatments and canopy strata.. ....................................................................................................... 34
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
assimilation (Amax) in B. alleghaniensis for all three „condition‟ treatments: control, physically
damaged, and infected with F. fomentarius. ................................................................................. 43
Figure 2.20. Relationship between carbon 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 2.21. 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’
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.
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’
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.
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
(control, physically damaged, infected with F. fomentarius) and upper, middle, and lower canopy
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
(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.
32
Figure 2.9. Observed leaf tissue density (g/cm3) 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.
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
treatments at the P<0.05 level.
2.4.3 Leaf chemistry
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
‘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.
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
treatments at the P<0.05 level.
37
Figure 2.14. Observed leaf carbon content by area (g/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.
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
differences between treatments at the P<0.05 level.
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
treatments at the P<0.05 level.
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
assimilation (Amax) in B. alleghaniensis for all three ‘condition’ treatments: control, physically
damaged, and infected with F. fomentarius.
45
Figure 2.21. Observed 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.
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 Leaf chemistry
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).
71
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).
73
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. 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
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. 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.
75
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
1-4 1I1 + Fomes fomentarius
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
2-2 2I1 + Fomes fomentarius
EF155498.1 100% 0.0 98-99%
2-3 2I1 + Fomes fomentarius
EF155498.1 100% 0.0 98-99%
2-4 2I1 + Omitted n/a n/a n/a
2-5 2I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
3-1 3I1
Infected +
+ Fomes fomentarius
EF155498.1 100% 0.0 98%
Fomes fomentarius
3-2 3I1 + Omitted n/a n/a n/a
3-3 3I1 + Fomes fomentarius
EF155498.1 100% 0.0 98-99%
3-4 3I1 + Fomes fomentarius
EF155498.1 100% 0.0 98-99%
3-5 3I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
4-1 4I1
Infected +
+ Omitted n/a n/a n/a
Fomes fomentarius
4-2 4I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
4-3 4I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
4-4 4I1 + Fomes fomentarius
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
5-2 5I1 + Omitted n/a n/a n/a
76
5-3 5I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
5-4 5I1 + Fomes fomentarius
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
6-2 6I1 + Fomes fomentarius
EF155498.1 100% 0.0 98-99%
6-3 6I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
6-4 6I1 + Fomes fomentarius
EF155498.1 100% 0.0 98%
6-5 6I1 + Fomes fomentarius
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%
7-4 1D1 + Fomes fomentarius
EF155498.1 100% 0.0 98-99%
7-5 1D1 + Fomes fomentarius
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%
9-3 3D1 + Epicoccum sp.
FJ210554.1 100% 0.0 99%
9-4 3D1 + Epicoccum sp.
FJ210554.1 100% 0.0 99%
9-5 3D1 + Epicoccum sp.
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
2C1 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
4C1 Control - n/a n/a n/a n/a n/a n/a
5C1 Control - n/a n/a n/a n/a n/a n/a
6C1 Control
- n/a n/a n/a n/a n/a Amplified
did not sequence
1C2 Control - n/a n/a n/a n/a n/a n/a
2C2 Control - n/a n/a n/a n/a n/a n/a
3C2 Control
- n/a n/a n/a n/a n/a Amplified
did not sequence
4C2 Control - n/a n/a n/a n/a n/a n/a
5C2 Control
- n/a n/a n/a n/a n/a Penicillium urticae
6C2 Control - n/a n/a n/a n/a n/a n/a
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
80
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
82
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
83
(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.
84
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 ∞