dissertation- bryn sitkiewicz
TRANSCRIPT
Using modelling to predict expected timber yields in red pine monocultures
and in mixed species stands to assess timber losses due to Annosum root rot
in the Midwestern United States
A dissertation submitted in partial fulfilment of the requirements for the degree
of Master of Science (MSc) in Environmental Forestry, Bangor University
By Bryn Sitkiewicz
BSc Forestry Management (2014, University of Wisconsin – Stevens Point)
School of Environment, Natural Resources and Geography Bangor University
Gwynedd, LL57 2UW, UK www.bangor.ac.uk
Submitted in September, 2015
[i]
DECLARATION This work has not previously been accepted in substance for any degree and is not being
concurrently submitted in candidature for any degree.
Candidate: ............................................. Bryn Sitkiewicz
Date: 25/8/2015
Statement 1:
This dissertation is being submitted in partial fulfilment of the requirements for the
degree of Master of Science.
Candidate: ............................................. Bryn Sitkiewicz
Date: 25/8/2015
Statement 2:
This dissertation is the result of my own independent work/investigation except where
otherwise stated.
Candidate: ............................................. Bryn Sitkiewicz
Date: 25/8/2015
Statement 3:
I hereby give consent for my dissertation, if accepted, to be available for photocopying
and for interlibrary loan, and for the title and summary to be made available to outside
organisations.
Candidate: ............................................. Bryn Sitkiewicz
Date: 25/8/2015
[ii]
Abstract
Forest landowners are required to make decisions about species compositions based upon their goals
and their accepted level of risk. Previous studies have shown that landowners in the Midwestern
United States will plant red pine monocultures if they desire a high profit margin. A model-based
study was performed to illustrate the actual timber yield of stands with differing species compositions
in the presence and absence of Annosum root rot. Timber stands were cruised to determine basal
areas. These basal areas were used as a base to create a model used to simulate scenarios of future
timber yields of differing species compositions. It was found that when Annosum root rot is present in
a timber stand, stands containing a diverse species composition and have undergone several thinnings
will have a significantly higher actual timber yield than identically managed red pine monocultures. It
was further found that when trees are spaced closer together, there is a higher loss of timber due to
Annosum root rot. It is likely that landowners who have a high tolerance of risk will continue to plant
red pine monocultures, regardless of an impending Annosum root rot infection. Landowners who have
a lower risk tolerance are more likely to plant a mixture of species to counter the risk of a species-
specific disease.
[iii]
Acknowledgements
Thank you, Dr. Mark Rayment, for the guidance and support that you have given me throughout
this process. The motivation and the pep talks that you gave me in your office were much
appreciated, and the way that you were able to make sense of the jumble of questions and
concerns that I had in my head was invaluable. It was an honour and a pleasure working with you
this year.
A special thanks to Kevin Burns and UW-Stevens Point for allowing me to access school owned
tree stands at the Tree Haven research station and for providing me with equipment to complete
my research.
Also, thank you to Kyoto Scanlon of the Wisconsin Department of Natural Resources for
providing me with current information on the Annosum root rot situation in Wisconsin and for
connecting me with landowners with infected stands.
[iv]
Table of Contents
DECLARATION .................................................................................................................................................. I
ABSTRACT ......................................................................................................................................................... II
ACKNOWLEDGEMENTS .............................................................................................................................. III
1 – INTRODUCTION .......................................................................................................................................... 1
2 – LITERATURE REVIEW .............................................................................................................................. 2
2.1 – BACKGROUND ............................................................................................................................................ 2 2.1.1 - History of Heterobasidion annosum in the United States ................................................................... 2 2.1.2 - Heterobasidion annosum Life Cycle ................................................................................................... 3
2.1.2.1 - Heterobasidion annosum Reproduction ........................................................................................................ 3 2.1.2.2 – Process of Infection ...................................................................................................................................... 4
2.1.3 – Role of Beetles.................................................................................................................................... 5 2.2 - SIGNS AND SYMPTOMS ................................................................................................................................ 5
2.2.1 Fungus Identification ............................................................................................................................ 5 2.2.2 Tree Symptoms ...................................................................................................................................... 5
2.3 - SUSCEPTIBLE SITES ..................................................................................................................................... 6 2.3.1 – Soil Texture ........................................................................................................................................ 6 2.3.2 – ph Level .............................................................................................................................................. 6 2.3.3 – The Landscape ................................................................................................................................... 6
2.4 – IMPACTS OF HETEROBASIDION ANNOSUM .................................................................................................. 7 2.4.1 – Susceptible Hosts ............................................................................................................................... 7 2.4.2 - Heterobasidion annosum in Wisconsin ............................................................................................... 7
2.5 – MANAGEMENT STRATEGIES ....................................................................................................................... 7 2.5.1 – Chemical Control ............................................................................................................................... 8 2.5.2 – Biological Control ............................................................................................................................. 8 2.5.3 – Silvicultural Treatments ..................................................................................................................... 8
2.5.3.1 – Spacing ......................................................................................................................................................... 8 2.5.3.2 – Thinning Regime .......................................................................................................................................... 9 2.5.3.3 – Species Choice ............................................................................................................................................. 9 2.5.3.4 – Fire Management ........................................................................................................................................ 10 2.5.3.5 – Salvage Harvest .......................................................................................................................................... 10
2.6 – MONOCULTURES VERSUS MIXED SPECIES STANDS .................................................................................. 10 2.6.1 – Monocultures ................................................................................................................................... 10
2.6.1.1 – Simplicity of Monocultures ........................................................................................................................ 10 2.6.1.2 – Convenience of Monocultures .................................................................................................................... 11
2.6.2 - Mixed Species Stand ......................................................................................................................... 11 2.6.2.1 – Biodiversity in Mixed Stands ..................................................................................................................... 11 2.6.2.2 – Facilitation and Interspecific Competition .................................................................................................. 12
2.7 – RISK ......................................................................................................................................................... 12 2.7.1 – Risk Awareness ................................................................................................................................ 12 2.7.2 – Risk Management ............................................................................................................................. 13 2.7.3 – Financial Implications of Risk ......................................................................................................... 14
2.7.3.1 – Risk Integration .......................................................................................................................................... 14 2.7.3.2 – Risk Return Curves..................................................................................................................................... 14 2.7.3.3 – Future Discounting ..................................................................................................................................... 16
2.8 – USING MODELLING IN FORESTRY APPLICATIONS ..................................................................................... 16 2.8.1 – Spatially Explicit Models ................................................................................................................. 16 2.8.2 – Empirical versus Process Models .................................................................................................... 17
2.8.2.1 – Empirical Models ....................................................................................................................................... 17 2.8.2.2 – Process Models ........................................................................................................................................... 18
2.8.3 – Forest Vegetation Simulator (FVS) .................................................................................................. 18 2.8.3.1 – Western Root Disease (WRD) .................................................................................................................... 18
3 – METHODOLOGY ....................................................................................................................................... 19
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3.1 –COLLECTING DATA IN THE FIELD .............................................................................................................. 19 3.1.2 – Description of Sites .......................................................................................................................... 19 3.1.3 – Timber Cruise Preparation .............................................................................................................. 20
3.1.3.1 – Plot Selection .............................................................................................................................................. 20 3.1.3.2 – Advantages and Limitations of Variable Plot Sampling ............................................................................. 21 3.1.3.3 – Equipment .................................................................................................................................................. 21
3.1.4 – Timber Cruises of Sites .................................................................................................................... 22 3.1.5 – Determination of Basal Area ........................................................................................................... 22
3.2 –USING THE FVS MODEL TO COLLECT DATA ............................................................................................. 23 3.2.1 – Model Preparation ........................................................................................................................... 23 3.2.2 – Creation of the Models ..................................................................................................................... 24 3.2.3 – Running the Model ........................................................................................................................... 25 3.2.4 – Additional Models ............................................................................................................................ 26 3.2.5 – Limitations of FVS Modelling .......................................................................................................... 26
3.3 – SENSITIVITY ANALYSIS ............................................................................................................................ 27
4 – RESULTS ...................................................................................................................................................... 27
4.1 – MODEL OUTPUT FOR DATA COLLECTED IN FIELD .................................................................................... 27 4.2 – MODEL OUTPUTS FOR THEORETICAL STANDS .......................................................................................... 28
4.2.1 – Stands Spaced at 2.4m x 2.4m .......................................................................................................... 28 4.2.2 – Stands Spaced at 2.1m x 2.1m .......................................................................................................... 30 4.2.3 – Basal Area Losses across Both Spacings ......................................................................................... 32 4.2.4 – Additional Scenarios ........................................................................................................................ 34
5 – DISCUSSION ................................................................................................................................................ 36
5.1 – SPECIES COMPOSITION AND ANNOSUM ROOT ROT INFECTION ................................................................. 36 5.2 – THINNING REGIMES AND ANNOSUM ROOT ROT INFECTION ..................................................................... 37 5.3 – TREE SPACING AND ANNOSUM ROOT ROT ............................................................................................... 39 5.4 – OTHER FACTORS AFFECTING TIMBER YIELD ........................................................................................... 40
6 – CONCLUSION ............................................................................................................................................. 41
LITERATURE CITED ...................................................................................................................................... 44
List of Figures and Tables
Figure 1 9
Figure 2 20
Figure 3 26
Figure 4 28
Figure 5 31
Figure 6 33
Figure 7 34
Figure 8 35
Figure 9 36
Figure 10 37
Figure 11 40
Figure 12 41
Table 1 38
Table 2 39
[1]
1 – Introduction
Making a forestry related decision is a high pressure activity since a single decision can
potentially impact timber yield and economic return for generations to come. Forest
managers must predict future environmental conditions such as climate change and pest
breakouts as well as future economical characteristics such as the demand for different
species of timber. As the field of forestry is constantly evolving, more tools are becoming
available to aid forest managers in making sound decisions. A spatially explicit forest
modelling programme is one such tool that can be used to portray expected future timber
yields of tree stands under differing environmental conditions. This allows forest managers to
weigh different management strategies and select which option best meets his or her
objectives depending on the risk the landowner is willing to take.
This study aims to investigate the effectiveness of a forest model used to predict potential
disease impacts prior to planting. In Wisconsin, red pine (Pinus resinosa) is a desirable
timber species, but it is associated with Annosum root rot (Heterobasidion annosum). Despite
high red pine timber losses due to this disease in the past decade, landowners continue to
plant red pine monocultures. The model developed in this study may be used to determine
whether or not planting red pine in mixed species stands results in a higher expected timber
yield than a red pine monoculture when considering the impact of Annosum root rot.
Four different species compositions are simulated in this study. The first is a red pine
monoculture consisting of 100% red pine. The second is a mixed stand containing 60% red
pine, 15% northern pin oak, 10% balsam fir, 7.5% quaking aspen, and 7.5% red maple. The
third is a mixed stand containing 40% red pine, 20% northern pin oak, 15% balsam fir, 12.5%
quaking aspen, and 12.5% red maple. The fourth is a stand containing 50% red pine and 50%
quaking aspen. The percentage of red pine in each stand represents a different level of risk,
based off of risk return curves created by Knocke (2008) as detailed further in chapter 2. In
order to validate the model created in this study, data collected in the field is used as
indicators of how accurate the model’s outputs are. In this study, several hypotheses are
examined:
[2]
1). A red pine monoculture that has been infected by Heterobasidion annosum will
have less expected timber yield at the end of a 70 year rotation than a red pine
monoculture that has not been infected by Heterobasidion annosum.
2). Planting red pine in a stand of mixed species will result in greater expected timber
yield at the end of a 70 year rotation than the expected timber yield of a red pine
monoculture if Heterobasidion annosum enters both stand types in the same years.
3). Performing a single thinning in a stand will reduce the rate of Heterobasidion
annosum infection and will result in a higher expected timber yield than infected
stands that have been thinned more than once.
4). Planting trees at a wider spacing will reduce the rate of Heterobasidion annosum
infection and will result in higher expected timber yield than stands that have trees
spaced closer together.
There are several published scientific articles that juxtapose the yields of monocultures and
mixed stands infected by Annosum root rot using models and data collected in the field.
However, these articles examine species that were identified as hosts to the disease, such as
Norway spruce and Scots pine, decades before red pine hosts were discovered. There is very
little material that examines expected timber yield in mixed stands containing red pine
infected with Annosum root rot. This study may benefit forest managers in areas where red
pine is a primary merchantable timber species since it provides predicted timber yield for a
variety of planting options in a landscape where Annosum root rot is a threat.
2 – Literature Review
2.1 – Background
2.1.1 - History of Heterobasidion annosum in the United States
Heterobasidion annosum is recognised as one of the most devastating diseases that affects
conifers in the north temperate region of the world (Scanlon 2008). Heterobasidion annosum
was first discovered in the United States in 1909 by E.P. Meineke. Meineke observed the first
documented case of Heterobasidion annosum in the United States on a Monterey pine (Pinus
radiata) in California (Smith 1989). The disease remained in the northwest United States
[3]
until the United States entered World War II. During the war, woody material from the
northwest was introduced to military camps across the country, primarily to the southeast.
Much of this transported woody material was infected with the Heterobasidion annosum
fungus (Asiegbu et al. 2008). As the disease expanded across the country, an increased
interest in tree disease prevention arose. The discovery and the awareness of Heterobasidion
annosum coincided with the evolution of the relatively new field of forestry (Smith 1989),
and stemmed research and interest in forest pathology. The diseases remained in the north-
western and the south-eastern parts of the United States for most of the 20th
century, and
infected the forests of the Lake States towards the end of the century. In 1993, the first case
of Heterobasidion annosum was discovered in Wisconsin (Scanlon 2008) and became the
destructive force that it is today in red pine plantations.
2.1.2 - Heterobasidion annosum Life Cycle
In order to comprehend the impact that Heterobasidion annosum is having on conifer species
in the United States and the effect that the fungus has on forest management, it is important to
understand how Heterobasidion annosum reproduces and how the fungus infects its host.
This knowledge can aid in formulating strategies to prevent or reduce the likelihood of the
disease entering a tree stand.
2.1.2.1 - Heterobasidion annosum Reproduction
The Heterobasidion annosum fungus is heterothallic (sex resides in different individuals),
multiallelic (affected by multiple genes), and unifactorial (an inherited characteristic is
dependent on a single gene) (Stambaugh 1989). These characteristics result in a fungus that is
very genetically diverse. This genetic variation means that it is difficult to find hosts that are
resistant to the disease. There are two strains of the fungus. The strain that is found in
Wisconsin is the P-strain, which primarily affects pines. There is also the S-strain in other
parts of the country which affects fir, hemlock, and Douglas-fir (Frankel 1998).
Heterobasidion annosum sexual reproduction begins when individual basidiospores give rise
to both male and female homokaryotic material. Male and female homokaryotic material fuse
together through the hyphae to form mycelium that is capable of fruiting. The mycelium
created is called a dikaryon, which form clamped septa during mitotic division of nuclei
(Chase 1989).
[4]
2.1.2.2 – Process of Infection
Heterobasidion annosum infects its host by releasing basidiospores upon maturity. The
spores are most often produced when the temperature is between 23 – 26oC and can be
dispersed up to 90m from the fungal source once airborne (Schwingle et al. 2003). Once
temperatures reach 35 oC, the fungus becomes inactive and can no longer produce
basidiospores (Otrosina and Cobb 1989).
The fungal disease enters a tree plantation once the basidiospores land on tree stump surfaces
(Chase and Ullrich 1983). A stump can remain susceptible to basidiospores invasion for up to
45 days upon being cut (Otrosina and Cobb 1989). The disease can survive in the stump for
up to 62 years after the tree has been felled (Asiegbu et al. 2008). From the stump, the
disease will then move downward to the root collar and to the roots, since Heterobasidion
annosum can penetrate and degrade woody tissue, lignin, and cellulose (Schwingle et al.
2003). Living trees surrounding the infected stump and roots can then become infected by the
disease if their roots are grafted or touching the infected roots (Chase and Ullrich 1983).
Once inside a living tree, the fungus moves up through the roots and enters the bole of the
tree as seen in Figure 1. The fungus then spreads at an average growth rate of 20cm to 50cm
annually (Asiegbu et al. 2008). Since Heterobasidion annosum spreads vegetatively from tree
to tree, the disease can be passed from generation to generation (Lygis et al. 2004).
Figure 1: Spread of Heterobasidion annosum through a tree stand. Source: G. Stanosz, U. Wisc - Madison
[5]
2.1.3 – Role of Beetles
Species of beetles (Dendroctonus valens and Hylastes porculus) have been observed to
facilitate Heterobasidion annosum invasion (Erbilgin and Raffa 2001). Beetles act as vectors
since they can transport basidiospores between stumps of felled trees. They spread the fungus
underground from the roots of an infected tree to those of an uninfected tree (Otrosina and
Cobb 1989).
Heterobasidion annosum can also facilitate a beetle invasion. Pine engraver beetles attack
trees that have been stressed by biotic or abiotic factors, and colonise conifers that have been
infected with Heterobasidion annosum. This is because the fungal root disease causes a
reduction in the tree’s ability to withstand pest invasions (Erbilgin and Raffa 2001).
2.2 - Signs and Symptoms
It is important to be able to identify Heterobasidion annosum by its appearance and by the
symptoms of infected trees. Detecting the pathogen early is crucial in creating an action plan
against the disease.
2.2.1 Fungus Identification
Fruiting bodies of Heterobasidion annosum begin to appear near a tree’s root collar in the
beginning of July (Schwingle et al. 2003). These fruiting bodies – called conks – can be
found in or on stumps from felled trees, under the forest’s duff layer on the root collar, or on
the roots of windthrown trees. The conks are shelf-like in appearance and have distinct
furrows along the edges. The furrows are dark brown with creamy white margins. The lower
surface contains many tiny pores (Frankel 1998). The conks are found on the stump or
directly under the duff layer on the root collar. On the exterior of roots of infected trees, dull
white ectotrophic mycelium can be found. The mycelium is one of the mechanisms that is
used to spread the disease through root connectionism (Schmitt 1989).
2.2.2 Tree Symptoms
Trees that have been infected with Heterobasidion annosum produce resinous white streaks
speckled with black flecks (Schmitt 1989). Reddish brown staining can be seen on the
exterior of the roots and on the lower stem (Frankel 1998). As the disease spreads, the tree’s
growth rate becomes stunted and its crown becomes thinner (Scanlon 2008). Butt rot will
appear in some species of infected fir (Frankel 1998). The crown of an infected tree will
appear rounded in shape (Byler 1989). Crown symptoms appear 3-8 years following a fungal
invasion (Schwingle et al. 2003). As trees near death, they will produce an abundant cone
[6]
crop (Byler 1989). If Heterobasidion annosum enters a stand, these symptoms will appear in
pockets within the forest. These pockets are called zones of mortality, from which the
epicentre expands outwards as more trees die (Erbiligin and Raffa 2001).
2.3 - Susceptible Sites
Different sites of conifer forests have varying susceptibilities to fungal root diseases. The
texture of the soil, the ph level of the soil and the landscape where the forest is on all
contribute to how hazardous a site is regarding Heterobasidion annosum breakout.
Recognizing the hazards of a site is important for a forest manager who must decide on the
care that must be taken when performing silvicultural prescriptions within the forest.
2.3.1 – Soil Texture
Some sites may be at more risk of Annosum root rot than others. Soil has the strongest
influence in the development of this disease since it provides the growth mechanism for root
diseases (Stambaugh 1989). In 1989, an Annosum hazard system was created based on a
site’s soil type. A site with a low hazard contains soils with clay and clay loams. An
intermediate hazard site contains loams and silt loams. A high hazard site contains any type
of sandy soil (Alexander 1989). A site with sandy soil is at most risk when there is an A
horizon (Alexander 1989) containing sand for at least 25 centimetres into the soil horizon
(Schwingle et al. 2003).This is detrimental for red pine plantations, since red pine prefers
sandy sites to grow.
2.3.2 – ph Level
Soil ph levels also play a role in the susceptibility of sites to Heterobasidion annosum. Soil
that is alkaline (ph > 6) is considered hazardous for fungal invasion. When soils are acidic,
there is rarely a tree mortality rate of over 5% if the disease enters the site (Stambaugh 1989).
2.3.3 – The Landscape
The landscape can influence whether a site is susceptible to the disease. Heterobasidion
annosum is commonly found in forests planted on former agriculture land (Schwingle et al.
2003) as well as on forested land that contains grass cover, or similar vegetation (Alexander
1989). Old forest soils are less susceptible to inoculation (Schwingle et al. 2003). Other
conditions where Heterobasidion annosum thrive include sites that have a fluctuating water
table (Pukkala et al. 2005) and sites that are susceptible to high levels of air pollution
(Stambaugh 1989).
[7]
2.4 – Impacts of Heterobasidion annosum
Heterobasidion annosum has infected valuable timber species across the United States and
has been reported to reduce timber yields over time. Red pine monocultures in Wisconsin are
experiencing high timber losses due to the disease. The mortality of trees is unavoidable once
the fungus enters a site.
2.4.1 – Susceptible Hosts
Although there are some reports of hardwood trees acting as hosts to Heterobasidion
annosum, conifers are much more susceptible (Scanlon 2008). In Wisconsin, red pine, white
pine, and red cedar have been reported hosts of the disease (Schwingle et al. 2003). Trees of
all ages are susceptible to the fungal disease (Asiegbu et al. 2008), but infection is most likely
to occur on stands that have undergone a first rotation (Pukkala et al. 2005).
2.4.2 - Heterobasidion annosum in Wisconsin
In Wisconsin, there has been a decline in red pine monocultures that are between the ages of
30-50 years due to the susceptibility of the species to the disease (Erbilgin and Raffa 2001).
This means that these stands can potentially contain 55% less basal area than red pine stands
that have not been affected by the disease (Frankel 1998). This has negative implications for
the forestry industry in Wisconsin since pines occupy 15% of Wisconsin’s total volume of
merchantable timber (Scanlon 2008).
Once a tree becomes infected with the disease, there is no way for it to recover (Asiegbu et
al. 2008). The fungus kills its host by slowly decaying the roots as well as destroying the
cambium that surrounds the root collar (Frankel 1998). Trees infected with the disease will
stay alive for many years (Byler 1989) and it is almost impossible to control the spread of the
fungus once it is present in a site (Scanlon 2008). Norway spruce has been observed to
survive an infection for the longest period of time over any other coniferous species (Pukkala
et al. 2005).
2.5 – Management Strategies
There are several management strategies that can be implemented in order to reduce or
mitigate the impact of the disease. Chemical and biological control methods may prove to be
effective in keeping the fungus out of a stand. Also, silvicultural tools can be put into place to
defend against the disease.
[8]
2.5.1 – Chemical Control
Applying specific chemicals to the stumps of felled trees is a common practice during a tree
harvest or a thinning. Granular borax is the primary chemical used, and is sprayed on a stump
immediately after a cutting. The chemical is designed to kill any basidiospores that may try to
inoculate stump surfaces (Alexander 1989). Applying granular borax has been an effective
preventative tool. However, it incurs an additional cost during a thinning or a harvesting and
in sites that have been severely infected by Heterobasidion annosum, granular borax is
useless (Scanlon 2008).
2.5.2 – Biological Control
Another tool available on the market to help prevent Heterobasidion annosum from entering
a site is Phlebia gigantean applications in the form of a suspension spray. Phlebia gigantean
is a natural fungal competitor of Heterobasidion annosum and may help to control the
pathogen if it has entered a stand (Alexander 1989).
2.5.3 – Silvicultural Treatments
Silvicultural tools are some of the most powerful means of defence that a forest manager may
have at his or her disposal when it comes to lessening the impact that Heterobasidion
annosum has on a stand. Planting trees at an optimal space, performing minimal thinnings,
and selecting to plant resistant species are all techniques that can be carried out to protect the
site from the fungal disease.
2.5.3.1 – Spacing
Planting individual trees farther apart from their neighbours may reduce the incidence of
Annosum root rot in a stand. Initially planting the trees further apart increases the length of
time before an initial thinning is needed (Stambaugh 1989). Basidiospores from the fungus
enter the stand through stump surfaces. By pushing the first thinning forward in time by
planting individuals further apart, there is a lengthened time period in which there is an
absence of stump surfaces that can be exposed to disease (Linden and Volbrecht 2002).
Wider spaces combined with mixed species planting can result in a reduction in potential root
contacts from infected individuals (Stambaugh 1989). The mixed species serve to buffer
infected roots from coming into contact with roots from susceptible individuals that have not
been infected. Asiegbu et al. (2008) observed that combining wide spacing with mixed
species planting can result in higher yield than pure plantations with normal spacing under
diseased conditions.
[9]
2.5.3.2 – Thinning Regime
Carefully planning a thinning prescription can help mitigate the effects that Heterobasidion
annosum can have on a stand of trees. A forest manager can reduce the chance of a stand
becoming infected by the disease by performing a thinning regime outside summer months
when basidiospores are dispersed (Asiegbu et al. 2008). It may be beneficial to perform these
thinning regimes when it is hotter than 35 oC; this is the temperature when the fungus
becomes inactive. This is practical in only certain regions of the country – such as the
southeast – that experience these high temperatures (Otrosina and Cobb 1989).
A forest manager can modify the intensity of a thinning to protect against Heterobasidion
annosum. This is done by reducing the amount of thinnings within a stand’s rotation.
Decreasing the amount of thinnings reduces the amount of opportunities that a fungus has to
enter a stand (Pukkala et al. 2005). Fewer thinnings can be accomplished by widening the
spacing between trees upon initial planting (Stambaugh 1989). Petersen (1989) suggests that
a rotation length for a stand of trees should not exceed 120 years if Heterobasidion annosum
is a threat. Minimizing the wounding of individual trees during a harvest or a thinning can
also prevent opportunities for the fungal disease to cause infection (Petersen 1989).
Selecting to perform a pre-commercial thinning may also be detrimental to the health of a
tree stand. Pre-commercial thinnings are executed early on in the rotation when young trees
that do not contain any economic value are removed. Heterobasidion annosum does not
discriminate by the age of a stump during infection (Asiegbu et al. 2008), so performing pre-
commercial thinnings may increase the risk of the fungus entering a stand. One study
observed that the rate of infection within hemlock stands that had been pre-commercially
thinned were eight times higher than that of hemlock stands that were not pre-commercially
thinned (Otrosina and Cobb 1989).
2.5.3.3 – Species Choice
Planting species of trees that are resistant to Heterobasidion annosum infection may help
prevent the disease from entering the stand in the first place (Asiegbu et al. 2008), and may
cleanse an infected site from the disease in the long term (Lygis et al. 2004). Several studies
indicate that deciduous trees are less susceptible to the disease than coniferous trees (Lygis et
al. 2004). Although there are records of most pine species in Wisconsin contracting the
disease, there are few reported incidences of white pine developing Heterobasidion annosum
[10]
(Schwingle et al. 2003). Spruce trees have minimal reports of infection (Linden and Volbecht
2002).
2.5.3.4 – Fire Management
Some studies indicate that prescribing a burning on a site can reduce Heterobasidion
annosum infection. One study observed that seven years following a prescribed burn, the
occurrence of Heterobasidion annosum was 55% less in plots that had been burned than
similar plots that had not been burned (Stambaugh 1989). A prescribed burn may be suitable
for a stand depending on the species within the site as well as where the site is located.
2.5.3.5 – Salvage Harvest
If a site is infected with the disease, a salvage harvest may be the only option. Salvage
opportunities are scarce within an infected stand since the rate of trees that are killed per year
is relatively small compared to a large disturbance, such as wind, in which a salvage harvest
would be more practical (Frankel 1998).
2.6 – Monocultures versus Mixed Species Stands
Depending on the goals of a land manager, he or she may choose to plant a monoculture or a
mixed species stand. Monocultures provide the advantage of ease and simplicity, whereas
mixed stands provide protection through biodiversity. Planting a mixed species stand may
combat Annosum root rot from entering a stand.
2.6.1 – Monocultures
A monoculture is a stand of trees containing identical species. Red pine in Wisconsin is most
often planted in a monoculture (Martin and Ek 1984). The practice of planting monocultures
is popular due to the idea that it produces maximum yield for a desirable species and that it is
easy and simple to manage.
2.6.1.1 – Simplicity of Monocultures
There are several reasons why planting monocultures is the preferred choice for land owners
in the timber industry. One of the largest reasons why monocultures are so popular is because
they are very easy to manage compared to mixed stands. This is because a forest manager is
able to concentrate all of his or her resources on favouring a single desirable species. Planting
monocultures is easy because only a single species is needed from a nursery (Piotto 2007).
Stand management is simple since row thinnings are performed usually under five times
during the rotation age of the stand. This results in an ununiformed harvest (Aikman and
Watkinson 1979).
[11]
2.6.1.2 – Convenience of Monocultures
Another practical reason for planting a monoculture is that fires are easy to control within
them. This is because there are trails and rows put into place that the fire crew can access
(Gadgill and Bain 1999). Monocultures are favourable in that they can be planted in any
advantageous pattern (Gadgill and Bain 1999). For example, trees within a monoculture can
be planted in the shape of chevrons. There is evidence that suggests that wind movement
through chevron planted monocultures reduces the chances of windfall damage (Niklas
1998).
Monocultures can be planted with species that are genetically modified to perform better in
different environments. Pinus taieda (L.) Englemann is a spruce that has been genetically
modified to resist fusiform rust (Gadgill and Bain 1999). Although a genetically modified
Heterobasidion annosum species has not been developed, it is still a possibility.
2.6.2 - Mixed Species Stand
A mixed species stand differs from a monoculture in that it contains more than one species.
Mixed stands are seen as more natural than monocultures, and many landowners are
beginning to discover the benefits of carrying out a mixed planting scheme (Kelty 2006).
Biodiversity conservation is one of the largest benefits associated with a mixed species stand,
and in some cases, they can result in higher timber yields than monocultures (Piotto 2007).
2.6.2.1 – Biodiversity in Mixed Stands
A mixed species stand may be more difficult to manage than a monoculture, but it contains
many advantages. Biodiversity conservation is one of the main benefits of planting a mixed
stand (Piotto 2007). A site containing more than one species of trees serves to protect the
overall stand if a species-specific threat enters the site. If a species-specific disease enters a
mixed stand, only a portion of the overall population will suffer, rather than the entirety. It is
more difficult for a pest or a pathogen to find a proper host if the concentration of hosts is
diluted by unsusceptible species (Kelty 2006). The diversity of trees in a mixed stand also
leads to the creation of diverse habitats. A range of habitats may support a range of natural
enemies to any pest species that enters the stand (Kelty 2006).
This tactic can be applied with the strategy of increasing the space between individual trees
when planting a forest. If Heterobasidion annosum infects an individual red pine, then a wide
space as well as an unsusceptible species may serve to buffer the further contraction of the
[12]
disease (Stambaugh 1989). This is why loses from an outbreak of Heterobasidion annosum in
a mixed plantation have been recorded to be lower than an outbreak in a monoculture
(Asiegbu et al. 2008).
In southern Sweden, Norway spruce and Scots pine were planted in the same stand.
Monocultures of each species were also planted. Each of the three sites was inoculated with
Heterobasidion annosum. After ten years, the mixed stand had a significant lower incidence
of the disease than the monoculture counterparts, due to the lack of root contact between each
of the two species. The best results were achieved when the mixtures was 50% Norway
spruce and 50% Scots pine. (Linden and Volbrecht 2002).
2.6.2.2 – Facilitation and Interspecific Competition
In some circumstances, a higher timber yield has been reported in mixed species plantations
over monocultures. This stems from higher diameter growth as a result of facilitation and
interspecific competitive production. Facilitation from nitrogen fixing species, such as alder,
has been observed in mixed species stands. According to the research of Piotto (2007), non-
nitrogen fixing species grow at a greater rate in the presence of these nitrogen fixing species.
2.7 – Risk
2.7.1 – Risk Awareness
Although planting a mixed species stand may seem to be the most logical route when
considering the threat of Heterobasidion annosum, there are many factors that play a role in a
landowner’s decision to manage a forest. According to Petersen (1989), some landowners are
ignorant to the devastation that root diseases can bring to a stand of trees, and there are not
enough forest managers in the field today who have taken any formal pathology course.
As reported by Lidskog and Sjodin (2014), landowners will act differently when faced with
risk due to several reasons. In Sweden in 2005, there was a devastating storm that felled 250
million trees; 80% of these trees were spruce since spruce trees are more vulnerable in storms
than other species. After this event, awareness of wind devastation among Swedish
landowners increased from 48% to 84%. This means that landowners become more aware of
a risk once they experience the risk first hand. However, 95% of these landowners still
replanted spruce to replace the fallen timber.
[13]
The landowners gave several reasons as to why they still planted spruce, even though their
risk awareness about wind damage increased. Economic pressure plays a factor, since it is
cheaper to plant spruce over other species. Spruce is not the only species that is threatened by
external factors; other species of trees may be susceptible to other biotic or abiotic elements.
Uncertainty about future climate change was another concern. Also, the landowners were
familiar with spruce and were not certain on how well other species would grow on their soil;
they lacked the knowledge to manage other species. Lidskog and Sjodin (2014)’s findings in
this case study can be applied to other situations across the globe, such as landowners in
Wisconsin continuing to plant red pine plantations despite the threat of Heterobasidion
annosum. It is understandable that a land manager might not want to abandon his or her way
of managing a forest in order to embrace the knowledge of an outsider (Lidskog and Sjodin
2014).
2.7.2 – Risk Management
Since an investment in timber is a long term commitment due to lengthy rotation ages, it may
be important for a forest manager to take the proper steps to manage the risk involved.
According to Gardiner and Quine (2000), there are three main steps of risk management. The
process begins with a risk analysis in which potential hazards are identified and their
likelihood is estimated. The second step is risk handling. This phase involves implementing
alternative management strategies and calculating the opportunity cost of handling the risk
through different management techniques versus not handling the risk. Lastly, the risk control
phase implements the alternative management strategies and evaluates them through time
(Gardiner and Quine 2000).
According to Hanewinkel et al. (2009), there are three primary questions that a forest
manager needs to answer in the first step of risk management. First, it is important to
determine what can go wrong. Next, a forest manager should identify how likely it is that
something can go wrong. Lastly, the consequences of something going wrong should be
identified. The answers to these questions are not straightforward. The probability of specific
hazards differs within the spatial scale, and stakeholders do not all share a similar awareness
of risk. (Hanewinkel et al. 2009).
In the risk handling phase, there are two ways that risk can be handled: cause-oriented or
effect-oriented. Cause-oriented handling involves avoiding risk. This could mean ceasing to
[14]
harvest or thin a forest to prevent Heterobasidion annosum from entering a site, or increasing
the stability of a forest by planting species that are resistant to disease. The goal of effect-
oriented handling is to decrease the damage of a risk, but not decrease the probability of the
damage actually happening. An example of effect-oriented risk handling is to diversify the
forest by planting a mixture of species, or insuring the timber with an insurance agency
(Hanewinkel 2009).
2.7.3 – Financial Implications of Risk
Investing in standing timber is considered a risky expenditure. This is because it is a long
term investment since tree stands may have lengthy rotations. There are many uncertainties
associated with forest investments such as fluctuating timber prices and the ambiguous
assumption that interest rates are held constant over the entire time period of the investment.
Therefore, it is important for a stakeholder to understand how to determine the risks involved
in forest investments.
2.7.3.1 – Risk Integration
Calculating the risk of a financial investment in timber has several challenges that are
associated. First, there may be multiple coinciding threats that need to be assessed. Another
challenge is being able to determine the net value of a stand at different rotation periods
considering associated risks (Hanewinkel et al. 2009).
Integrating risk into a financial investment involves four phases. First, a framework must be
created and analysed. This includes a development of all potential scenarios and hazards
associated with a timber investment. Next, the probability for each hazard must be
determined. This includes an estimation of the amount of damage associated with each hazard
as well as the probability of that damaging happening. The third part of the risk integration
process is an estimation of cost. The cost of risk reducing actions is compared to the cost of
not including risk reducing actions in a management plant. Lastly, the best choice of action is
selected based off of the previous benefit-cost analyses (Kurz et al. 2008).
2.7.3.2 – Risk Return Curves
One technique to determine the optimal species composition, depending on a stakeholder’s
acceptable risk, it to create risk return curves. A risk return curve is a graph that portrays
levels of risk involved with planting different percentages of a tree species within a mixture.
The risk return curve is then displayed on a graph containing utility curves, which are
standardised curves that reflect weak, normal, and strong risk-aversion scenarios. The y-axis
[15]
contains standard deviations of the net present value of the species, and the x-axis portrays
the net present value of the species. The slope of each curve portrays the intensity of risk-
aversion, and the optimum percentage of the tree species within a mixture is where the slope
of the risk return curve and the slope of the utility curve meet. Any point lower than this is
less risky, and any point higher than this value is more risky (Knocke 2008).
Figure 2 is an example of a risk return curve. The risk return curve where k=0 shows what
risk is to be expected when combinations of spruce/beech affect the amount of risk. This
differs from the simple straight-line curve of k=+1, where the amount of risk and return grow
proportional to the amount of Norway spruce within the mixture. The normal equal utility
curve meets the risk-return curve where k=0 (for Norway spruce) at 54%. The other 46% is
allocated to European beech in this example. Norway spruce is a more valuable species than
European beech, yet it is more prone to disease and therefore more risky to plant. Hence,
planting 54% Norway spruce is the most justifiable mixture to plant in order to obtain a high
profit from the stand without taking a large risk. Depending on the amount of risk that a
landowner is willing to accept, a strong risk-aversion curve or a weak risk-aversion curve
(not illustrated in Figure 2) can be used. (Knocke 2008).
Figure 2: Risk return curve for Norway spruce in a European beech/ Norway spruce mixture
(Knocke 2008). A forest containing 100% Norway spruce is depicted on the right hand side of
the chart, and a forest containing 100% beech is depicted on the left hand side.
[16]
2.7.3.3 – Future Discounting
Future discounting is a rate at which future benefits and costs are converted to a net present
value. It is important to be aware of the future discount rate when investing in a forest to
determine if it is worth taking a risk on the investment. If the net present value is greater than
0, then the project is efficient. If the net present value is below zero, then the investment is
not worthwhile (Hepburn 2006). When a discount rate is high (5% or higher), a present day
investment is not economically practical since there is little incentive to replant trees after one
rotation (Samuelson 1976).
A constant discount rate is most commonly used; a discount rate of 3% would remain at 3%
until the final stand rotation. A constant discount rate is risky for long term investments since
it ignores uncertainty of the future and assumes that the yield of the forest will not be
hindered by devastating abiotic and biotic disturbances. This has caused a trend of
stakeholders investing in short-term investments rather than in long-term investments.
A future discount rate that declines through time better protects the stakeholder from such
uncertainties. This is because an unknown hazard that may reduce timber yield is balanced by
a lower discount rate in the future (Hepburn 2006).
2.8 – Using Modelling in Forestry Applications
Investing in timber may have a high risk since it is a long term investment with a range of
uncertainties. It is impossible to look into the future, but spatially explicit models are useful
in portraying the outputs of possible scenarios under a variety of management strategies and
external biotic and abiotic factors. An effective model that assists land managers in making
decisions in regards to the threat of Heterobasidion annosum will produce yield outputs
based on the primary and secondary rates of infection, root contacts, and the development of
decay within individual trees (Asiegbu et al. 2008). The Western Root Disease extension
within the Forest Vegetation Simulator is one such model. Every model has its advantages
and disadvantages for different situations, and it is important to consider a specific problem
or objective before selecting an appropriate model (Korzukhin 1995).
2.8.1 – Spatially Explicit Models
Spatially explicit models have become a valuable tool for land managers studying the
population dynamics of a forest within a specific scale. A model that is considered to be
spatially explicit incorporates a population simulation within a landscape and its spatially
[17]
distributed features (Dunning 1995). A spatially explicit model’s output will reflect the
response of trees within a constantly changing environment and the output is tailored to
individual situations since habitat-specific information is needed to run the model. This
allows managers to consider adaptive management strategies regarding species choice and
silvicultural treatments (Walters 1986).
These types of models are useful in portraying possible outcomes of a catastrophic event that
can impact the landscape at a large scale such as a wind event, insect outbreak, or disease
outbreak (Levin 1992). Using a spatially explicit model can help a land manager to compare
management techniques within complicated landscapes and can improve one’s ability to
understand how a landscape and its features correlate with tree growth (Dunning 1995).
Some models can be non-spatially explicit. These are useful in studying isolated processes
within the landscape. The physiography of the landscape is ignored since the arrangement of
habitats and tree stands are not taken into consideration (Dunning 1995).
2.8.2 – Empirical versus Process Models
2.8.2.1 – Empirical Models
Forest managers tend to favour using empirical models to aid decision making. Empirical
modelling is implemented when predications of management strategies are needed. The
output contains quantitative answers based off of yield and growth tables of different species
that have been pre-written into the model. An empirical model is the simpler of the two
models since the answers are produced in a short amount of time and based off of levels of
precision and accuracy that have been programmed (Korzukhin 1995).
Empirical models are most useful when they are used to produce statistical relationships
among data collected in the field in order to describe gathered knowledge and relate it to
management strategies. There are several limitations to using this type of model. Empirical
models are not as flexible as process models since the specifications used to create a model
must remain the same for every new condition or object that is added (Leersnijder 1992).
Empirical models are not as effective as process models if an increased database of
knowledge is required; the data that is inserted into the model is directly measured from the
specific condition that is designed to make the prediction (Wissel 1992).
[18]
2.8.2.2 – Process Models
While an empirical model is used as a tool for predicting relationships and describing
knowledge, a process model is used as a tool for understanding relationships and developing
knowledge. This is because a process model is a representation of a hypothesis of how forest
structure and forest processes function (Korzuhkin 1995). Because process models revolve
around knowledge that is unknown, there are many parameters that are required to run the
models. Process models are most useful in situations where principle mechanisms are known
after there is an accumulation of knowledge through the use of empirical models (Wissel
1992).
Many claim that process models are limited since their high complexity makes it difficult to
produce a clear picture or prediction. Running a process model can also be more time
consuming. They do not produce as accurate or as precise outputs as empirical knowledge
since rigorous statistical testing cannot ensue (Korzuhkin 1995).
2.8.3 – Forest Vegetation Simulator (FVS)
The Forest Vegetation Simulator (FVS) is designed to predict forest stand dynamics in
United States forests. It is the most widely used forest modelling programme in the United
States. Agencies that regularly use FVS include the United Stated Department of Agriculture
(USDA) Forest Service, the United States Department of Interior (USDI) Bureau of Land
Management, the USDI Bureau of Indian Affairs, and many other state agencies (Dixon
2002). FVS is a spatially explicit empirical model.
FVS is designed to summarise current stand conditions, predict future stand outputs under
potential environmental factors and silvicultural prescriptions, and update tree inventory
statistics. This is a valuable tool for forest managers who are constantly under pressure to
create and carry out stand management alternatives in order to meet different objectives
(Dixon 2002).
2.8.3.1 – Western Root Disease (WRD)
Western Root Disease (WRD) is an expansion within the FVS modelling system. According
to Pukkala et al. (2005), WRD is the most comprehensive Heterobasidion annosum
modelling programme available to forest managers. WRD enables the user to juxtapose
future yields of healthy stands and Heterobasidion annosum infected stands and can be
manipulated to portray outputs under different silvicultural prescriptions. The output of WRD
[19]
is in the form of tables containing information on the basal area of stands under different
conditions, as well as in the form of visual graphics and charts. Impacts of various levels of
the disease can be portrayed throughout different stages of management regimes (Frankel
1998).
3 – Methodology
3.1 –Collecting Data in the Field
3.1.2 – Description of Sites
To determine if Heterobasidion annosum has a significant impact on red pine plantations, a
timber cruise took place in an uninfected red pine plantation and in an infected red pine
plantation. The two plantations were located on similar sites within Portage County,
Wisconsin and were planted in the mid 1960’s. The soil type of each plantation was
determined by using the USDA (United States Department of Agriculture) Web Soil Survey,
which is a soil survey tool that is free to the public and can be found at:
http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.
After completing this survey, it was determined that the soil of both red pine sites consisted
of Plainfield loamy sand; the A horizon contains loamy sand, and the B2 and B3 horizons
contain sand. The slope of both of these sites is 0-2%. It is important to sample plantations on
similar sites in order to reduce the amount of variables that may impact expected timber
yield, other than Heterobasidion annosum.
To compare the expected timber yield of mixed plantations containing red pine with the
expected timber yield of the infected and uninfected red pine plantations, six different sites
were found in Lincoln County, Wisconsin. These sites were located on land owned by
University of Wisconsin – Stevens Point (named Tree Haven). The sites contained the
following species composition: infected and non infected mixed species containing
approximately 60% red pine, infected and non infected mixed species containing
approximately 40% red pine, and infected and non infected mixed species containing
approximately 50% red pine and 50% aspen.
[20]
All six mixed stands of trees were planted in the mid 1960’s, and upon completion of the
USDA Web Soil Survey, it was determined that the sites primarily consisted of Vilas-Sayner
loamy sands with 0-2% slopes.
Each of the eight stands has been thinned twice, starting in year 30. A third thinning is
scheduled to occur in each stand within the next 5 years. In all cases, the infected stands
experienced their first Annosum root rot infection after the first thinning in year 30. Each of
the eight stands was also planted at an initial spacing of approximately 2.4m x 2.4m.
3.1.3 – Timber Cruise Preparation
3.1.3.1 – Plot Selection
A timber cruise took place in all eight sites to collect data on basal area and trees per hectare.
A three hectare tract was selected in each stand to perform the timber cruise. Based on the
amount of plots per hectare that Linden and Volbrecht (2002) sampled in their study on the
susceptibility of Norway spruce to Heterobasidion annosum, it was determined that 12 plots
would be sampled within each site. These plots were mapped using ArcMap. The location of
the plots was determined using stratified random sampling. The chain multiplier was used to
accomplish this. The formula for the chain multiplier is:
. By
using the chain multiplier, the distance between each plot was calculated to be 2 chains, after
rounding down to the nearest whole number. The first plot was randomly selected by using
Microsoft Excel’s random number generator to generate two numbers between the chain
multiplier and half of the chain multiplier (2 and 1) to determine the x and y axes of the first
plot. The geographic position of each plot was than imputed into a handheld Recon field
computer with a built in GPS (Figure 3).
[21]
3.1.3.2 – Advantages and Limitations of Variable Plot Sampling
Variable plot sampling was used to collect data from the different forest plots. The advantage
of using variable plot sampling is that it is more time efficient than fixed radius sampling.
This type of sampling is useful in an even-aged tree plantation since all of the standing timber
has uniform in size and age, overall. Variable plot sampling can be limited in bushy areas or
areas where there is limited visibility. This is because some of the trees may not be accounted
for.
3.1.3.3 – Equipment
There are several tools that were used to complete variable plot sampling of the eight sites. A
10 BAF (Basal Area Factor) prism was used. A 10 BAF prism was used over a 20 BAF
prism; although a 20 BAF prism is more time efficient since it only includes large trees in a
sample plot, a 10 BAF prism leads to more accurate data since more trees are sampled. A
tape measure was also needed in order to measure trees deemed as “borderline” through the
prism. Diameter tape was used to measure the DBH (diameter at breast height) of trees that
were determined to be within the plot using the 10 BAF prism. A Recon field computer was
brought to record data, and a hardhat was worn for protection.
Figure 3: Twelve plots mapped through a Recon field computer of the uninfected red
pine plantation cruised in Portage County, Wisconsin
[22]
3.1.4 – Timber Cruises of Sites
The first tract to be sampled was the uninfected red pine plantation. The location of the first
plot was located using the GPS within the handheld Recon field computer. The 10 BAF prism
was held at the centre of the plot. Moving clockwise around the prism, the diameters were
measured in each tree that was determined to be inside the plot. If it was difficult to
determine if a tree was in or out of the plot (a borderline tree), a tape measure was used to
determine the distance from the plot centre to the bole of the tree. The diameter of the
borderline tree was then multiplied by the plot radius factor (2.75 for a 10 factor prism). If
this number was greater than the distance from plot centre, then the tree was considered to be
inside the plot. This procedure was repeated at each of the twelve plots for each uninfected
site.
The second tract to be sampled was at the red pine plantation that had been infected by
Heterobasidion annosum. The same method of data collection that took place in the
uninfected pine plantation occurred at this tract. Additionally, data about whether or not a tree
inside a plot was infected by the disease was recorded. By brushing away the litter layer of
each tree inside each plot, the root collar was exposed to examine signs of fungus. The crown
of each tree was also observed for signs and symptoms of the disease. This method of
determining the presence of Heterobasidion annosum within a tree was limited since some
signs or symptoms may have been overlooked. For instance, small ectotrophic mycelium –
which is a mechanism used to spread the disease through the roots – can be found on
exploratory roots deep within the soil. Expertise on Heterobasidion annosum identification
may be needed to reduce this limitation. This procedure was repeated for all twelve plots
within each infected site.
3.1.5 – Determination of Basal Area
After all twelve plots within each of the eight sites had been cruised, the basal area was
calculated in each site. For each of the eight sites, the basal area for each tree sampled within
each of the twelve plots was determined using the formula . This
number was then multiplied by the BAF of 10 used to cruise the plots to extrapolate the basal
area to a per hectare basis. Then, the mean of the twelve plots was calculated to determine the
final basal area at each site.
For infected stands, the formula P = P(max) + P(inf) was used to determine the actual basal
area, where P is the actual production, P(max) is the proportion of area uninfected, and P(inf)
[23]
is 1 minus the proportion of area uninfected. P(max) and P(inf) were determined in ArcGIS
by calculating the geometry of the infected pockets (Figure 4).
3.2 –Using the FVS Model to Collect Data
Using the species compositions and basal areas determined in the field for each stand type,
theoretical models based on differing parameters (thinning regimes, species composition,
spacing) were able to be created to expand the findings in the field to a variety of situations.
3.2.1 – Model Preparation
In order to simulate the possible expected timber yields that result in planting different tree
stands containing red pine in an area where Annosum root rot is a threat, several models were
created using the Forest Vegetation Simulator (FVS) as described in 2.8.3. A total of four tree
data files were first created. The first was a red pine monoculture consisting of 100% red
pine. The second was a mixed stand containing 60% red pine, 15% northern pin oak, 10%
balsam fir, 7.5% quaking aspen, and 7.5% red maple. The third was a mixed stand containing
40% red pine, 20% northern pin oak, 15% balsam fir, 12.5% quaking aspen, and 12.5% red
maple. The fourth was a stand containing 50% red pine and 50% quaking aspen. The species
were chosen based on their occurrence within the data collected in the field for the eight sites.
1700 trees per hectare were used, and the number of individuals was put into each tree data
file based on the predetermined percentiles. Site index curves that had been developed by the
Figure 4: Delineation of infected pockets in infected pine plantation in Portage County,
Wisconsin to determine the P(max) and the P(inf).
[24]
US Forest Service were then looked at to determine the height of each species at age 20. A
site index of 65 was used since it is the median index number on all curves and an age of 20
was used since it is the first age available on each curve. Stocking charts developed by the US
Forest Service were then looked at to determine the DBH (diameter at breast height) given
the amount of trees per hectare used in the simulation. The height and the DBH were then
computed into the tree data files at age 20.
After the tree data files were created, stand list files were made for each simulation. Each
stand list file had a location code of 906, which is a code number that is used to simulate
climatic conditions in central Wisconsin. A basal area factor of 10 was also computed into
each stand file, since this was the basal area factor that was used to collect data in the field. A
stand size of 1 hectare and a site index of 65 were also put into the file. Lastly, the previously
created tree files were uploaded into each individual stand. Each stand file was then uploaded
into a different location file and given a unique location name to be used in upcoming
simulations.
3.2.2 – Creation of the Models
Four types of models were created for each stand type: a model simulating expected timber
yield with an absence of Annosum root rot in both a stand that has undergone one thinning
and a stand that has undergone three thinnings, and a model simulating expected timber yield
with the presence of Annosum root rot for both a stand that has undergone one thinning and a
stand that has undergone three thinnings. Each of the sixteen stands (four stand types with
four models for each stand) was given a time scale of 70 years. Natural regeneration was
removed from each model.
Single thinned stands were given a thinning in year 40. This was done by adding a
mechanical thinning management scheme into each model with a retention rate of 70% of the
number of standing trees at year 40. The removal of individual trees was done in a way that
retained the initial desired species mixture (e.g. 60% red pine – 15% northern pin oak – 10%
balsam fir – 7.5% quaking aspen – 7.5% red maple). Stands that were to be thinned thrice
were given a thinning in years 30, 40, and 50. A mechanical thinning management was added
into these models with a retention rate of 70% of the number of standing trees at each of the
thinning years. These thinnings also retained the initial desired species mixtures.
[25]
Models containing Annosum root rot infections were then created for single thinned stands
and triple thinned stands. In reality, a stand can become infected with annosum after exposed
tree stumps are present after a thinning has taken place. Thus, a new annosum infection was
programmed to occur after a thinning in each model designed to simulate expected timber
yield with a presence of the disease. According to Asiegbu et al. (2008), the disease travels at
an annual rate of 20-50cm. Therefore, the model was designed to simulate the spread of
annosum at a median annual rate of 35cm. Since Heterobasidion annosum spreads outwards
in a circular pocket, the radius of the circle was designed to increase at the rate of 35cm
annually. Thus, the proportion of red pine trees that fell within the expanding radius was
killed in the model. This adjusted mortality rate was added to the natural mortality rate
already in place in the model.
3.2.3 – Running the Model
After each model was constructed, the simulations were run. The output of the simulations
were viewed in the form of a main output file containing stand and stock tables as well as in
the form of a stand visualisation system which provides a 3D drawing of the stand changing
through time (Figure 5). In an Excel spreadsheet, the basal area of each stand was recorded at
intervals of 10 years, as found within the main output file. Using these data, a graph
containing basal area data from both infected and uninfected stands was created. Two
different graphs were created: one representing the data from stands that had been thinned
once, and another representing the data from stands that had been thinned three times.
[26]
3.2.4 – Additional Models
After the first set of sixteen stands were simulated through the FVS modelling system, an
additional sixteen tree files and stand files were created. The parameters were the same for
these new files, except the trees per hectare increased to 2223. Due to the decrease in spacing
between neighbouring trees, the rate at which Heterobasidion annosum spreads through a
stand increased to 50cm per year when the models for the infected stands were built. These
sixteen new stands were then run in the same manner as the previous sixteen and depicted on
two separate graphs using basal area data from the model’s output recorded using Excel.
3.2.5 – Limitations of FVS Modelling
Although building a FVS model is advantageous in that it is spatially explicit and portrays the
outcome of possible management strategies under different biotic and abiotic factors, it is still
limited by the uncertainties of the future. An infinite amount of models can be made for a
desired managed forest, and a land manager may not have the time to weigh every possibility.
Figure 5: Example of Forest Vegetation Simulator 3D visual output. Portrayal of
aftermath of an infected mixed stand containing 40% red pine following a second
thinning in year 50.
[27]
The models created in this study only take disease into consideration. It is possible that some
of the species selected for the stand mixtures are more susceptible to other environment
factors, such as wind throw, than other species.
3.3 – Sensitivity Analysis
The data collected in section 3.1 was then converted into different tree, stand, and location
files as described in section 3.2.1. The model was then run for each of the 8 sites to juxtapose
the basal areas of actual data collected in the field with data from the theoretical stand
compositions.
A sensitivity analysis was performed to determine the degree which the model-created curves
can vary using the data collected in the field. The mean basal area from the sampled subplots
was calculated for each stand. The standard deviation in each stand was calculated, and then
used to determine the standard error of the mean. Error bars were then added into the curves
created from the model by using the standard error of the mean of each stand sampled in the
field.
4 – Results
4.1 – Model Output for Data Collected in Field
The output of the model that was based on actual data collected in the field suggests that the
final rotation of an uninfected red pine monoculture will result in a higher basal area (41.1m2)
than the uninfected mixed stands containing 40%, 50%, and 60% red pine (37.2m2, 39.9m
2,
and 37.2m2
respectively) . The model suggests that an Annosum infected stand containing
50% red pine and 50% quaking aspen will result in a higher basal area (22.4m2) than the red
pine monoculture, the mixed stand containing 40% red pine, and the mixed stand containing
60% red pine (13.8m2, 21.3m
2, and 19.8m
2 respectively). Figure 6 suggests that an infected
red pine plantation will have a statistically significantly lower final basal area than all other
stand types.
[28]
4.2 – Model Outputs for Theoretical Stands
The data and expected timber yields illustrated in Figure 6 were used to create theoretical
timber stands for the model used in this study. The following results are based on the data
collected in the field, yet have differing parameters (species composition, spacing, thinning
regime).
4.2.1 – Stands Spaced at 2.4m x 2.4m
The output of the model containing stands planted at a spacing of 2.4m x 2.4m and thinned in
years 30, 40, and 50 suggest that planting a red pine monoculture will result in a higher basal
area (44.6m2) than a mixed stand containing 40% red pine (34.6m
2), 50% red pine (41.1 m
2),
or 60% red pine (39.5 m2) at the end of a 70 year rotation in the absence of Heterobasidion
annosum. The output of this model suggests that when Heterobasidion annosum enters a
stand, planting a mixed stand containing 50% red pine and 50% quaking aspen results in the
highest basal area (31.4 m2), and a red pine monoculture results in the lowest (28.8 m
2).
When the standard error of the mean (±5% of the basal area, calculated using the field data) is
added to the output of the model, then a red pine monoculture will have a statistically
significantly higher basal area than the mixed stands. When Heterobasidion annosum has
infected a stand, then a red pine monoculture will have a statistically significantly lower final
Figure 6: Scatter plot of the basal area per hectare of stands that have been thinned in years 30,
40, and 50 and contain trees spaced 2.4m x 2.4m
[29]
basal area than stands containing 50% and 40% red pine, but not a statistically significantly
higher final basal area than stands containing 60% red pine. In all scenarios, Figure 7
suggests that all stand types that have not been infected by Heterobasidion annosum have a
statistically significantly higher yield than every stand type that has been infected by the
disease. Figure 7 differs from Figure 6 since the rate of growth defined in the model was
initially programmed to be slightly higher than the growth rate determined in the field for
each individual species.
Figure 8 indicates that a red pine monoculture will contain a higher basal area (55.7m2) than
mixed stands containing 40% red pine (47.1 m2), 50% red pine (50.6 m
2), or 60% red pine
(51.6 m2) when Heterobasidion annosum is absent and when seedlings are initially spaced at
an interval of 2.4m x 2.4m. The output of the model portrayed in Figure 4 suggests that when
Heterobasidion annosum is present, a red pine monoculture still results in a higher basal area
(47.1 m2) than the mixed stands. When the standard error from the data collected in the field
(±5% of the basal area) is added to the model’s output, a red pine monoculture has a
statistically significantly higher basal area than the mixed stands when Heterobasidion
annosum is both present and absent. Figure 8 suggests that almost all stand types that have
Figure 7: Scatter plot of the basal area per hectare of stands that have been thinned in years 30,
40, and 50 and contain trees spaced 2.4m x 2.4m
[30]
not been infected by Heterobasidion annosum have statistically significantly higher basal
areas than every stand type that has been infected by the disease. The exception is that the
final basal area of the uninfected mixed stand containing 40% red pine is not statistically
significantly higher than the final basal area of the red pine monoculture.
4.2.2 – Stands Spaced at 2.1m x 2.1m
Figure 9 illustrates that when a thinning in years 30, 40, and 50 occur, a red pine monoculture
will have a higher final basal area (45.8m2) than mixed stands containing 40% red pine (36.9
m2), 50% red pine (40.6 m
2), or 60% red pine (39.5 m
2) when Heterobasidion annosum is not
a factor and when a spacing interval of 2.1m x 2.1m is used. When Heterobasidion annosum
enters the stand after each thinning, then the model output in Figure 9 suggests that a red pine
monoculture will have a lower final basal area (23.5 m2) than the mixed stands. When the
standard error from the data collected in the field (±5% of the basal area) is added to the
model’s output, the red pine monoculture has a statistically significantly higher basal area
than all of the mixed stands when Heterobasidion annosum is absent and has a statistically
significantly lower basal area than all of the mixed stands when Heterobasidion annosum is
present. All stand types that have not been infected by Heterobasidion annosum have a
Figure 8: Scatter plot of the basal area per hectare of stands that have been thinned in year 30
and contain trees spaced 2.4m x 2.4m
[31]
statistically significantly higher yield than every stand type that has been infected by the
disease.
Figure 10 suggests that when a single thinning in year 30 occurs on stands that have been
spaced at an interval of 2.1m x 2.1m, a red pine monoculture will have a greater final basal
area (55.7m2) than the mixed stands containing 40% red pine (46 m
2), 50% red pine (52.7
m2), and 60% red pine (52.5 m
2) when Heterobasidion annosum has not entered the sites.
When Heterobasidion annosum is present after the thinning in year 30, then the red pine
monoculture will also have a higher basal area (44.4 m2) than the mixed stands containing
40% red pine (36.2 m2), 50% red pine (43.2 m
2), and 60% red pine (39.3 m
2).
When the standard error from the data collected in the field (±5% of the basal area) is added
to the model’s output, then the red pine monoculture has a statistically significantly greater
final basal area than the mixed stands containing 40% red pine when Heterobasidion
annosum is absent, but not a statistically significantly higher final basal area than the mixed
Figure 9: Scatter plot of the basal area per hectare of stands that have been thinned in years 30,
40, and 50 and contain trees spaced at an interval of 2.1m x 2.1m
[32]
stand containing 60% red pine or 50% red pine when Heterobasidion annosum is absent.
When Heterobasidion annosum enters the stand after the thinning in year 30, then the red
pine monoculture has a statistically significantly higher basal area than the mixed stands
containing 60% red pine or 40% red pine, but not statistically significantly higher basal area
than the mixed stand containing 50% red pine. Almost all stand types that have not been
infected by Heterobasidion annosum have statistically significantly higher basal areas than
every stand type that has been infected by the disease. The exception is that the final basal
area of the uninfected mixed stand containing 40% red pine is not statistically significantly
higher than the final basal area of the red pine monoculture.
4.2.3 – Basal Area Losses across Both Spacings
When considering every thinning regime and tree spacing used in this model for every stand
type, Table 1 shows that planting a red pine monoculture at a spacing of 2.1m x 2.1m and
performing three thinnings within a 70 rotation period will result in the highest proportion of
basal area lost (48.7%) when Heterobasidion Annosum is present. Planting a mixture of 50%
red pine and 50% quaking aspen at a spacing of 2.4m x 2.4m and performing a single
thinning during the 70 year rotation will result in the lowest proportion of basal area lost
(10.4%) when Heterobasidion annosum is present.
Figure 10: Scatter plot of the basal area per hectare of stands that have been thinned in year 30
and contain trees spaced at an interval of 2.1m x 2.1m
[33]
Despite the heavy timber losses within each red pine monoculture scenario, the highest actual
basal areas were found in both red pine monocultures that had been thinned once (55.8m2).
The actual lowest basal area was found in the thrice-thinned mixed stand containing 40% red
pine (37.0m2).
% Basal Area (BA) Lost in Each Stand
# of Thinnings
% Red Pine (RP)
Final BA Uninfected (m2) Final BA Infected (m2)
% BA Lost
100 44.5 28.8 35.3%
3 60 39.5 29.3 25.8%
50 41.0 31.3 23.7%
2.4m x 2.4m 40 38.3 31.0 19.1%
100 55.8 47.3 15.2%
1 60 51.5 43.8 15.0%
50 50.8 45.5 10.4%
40 47.3 42.0 11.2%
100 45.8 23.5 48.7%
3 60 39.5 24.5 38.0%
50 40.8 27.5 32.6%
2.1m x 2.1m 40 37.0 27.8 24.9%
100 55.8 44.3 20.6%
1 60 52.5 39.3 25.1%
50 52.8 43.3 18.0%
40 46.0 36.3 21.1%
When comparing all of mixed stand types to the red pine monocultures in an environment
where Heterobasidion annosum is present, Table 2 suggests that a mixed stand that has
undergone a single rotation will have a lower proportion of final basal area than a red pine
monoculture in both spacing types. Specifically, planting a mixed stand containing 40% red
pine at a spacing interval of 2.1m x 2.1m will result in the largest negative difference in basal
area (-22%). When the stands are thinned three times within a 70 year period, then the mixed
stands will all have a higher proportion of final basal area than the red pine monocultures.
Table 1: Comparison of the final actual basal areas within each stand as well as the percentage of the basal area
lost when Heterobasidion annosum is present in a stand for all mixtures within every spacing type and thinning
treatments used in this study’s model. Red font indicates the lowest values found and green font indicates the
highest values found.
[34]
Planting a mixed stand containing 40% red pine will result in the highest positive difference
in basal area (+15.5%).
4.2.4 – Additional Scenarios
The future is uncertain and an infinite amount of unknown disturbances and diseases may
arise and may impact timber yield in years to come. Scenarios that explore other high-
mortality disease disturbances – as yet unknown – were also explored in this study.
Figure 11 suggests that when a hypothetical disturbance that kills 50% of all red pines in a
stand every 10 years following a thinning in years 30, 40, and 50, a red pine monoculture will
have a lower basal area (2.3m2) than the basal areas of mixed species stands containing 40
red pine (21.6 m2), 50% red pine (18.8 m
2), or 60% red pine (11.1 m
2).
%Difference in Basal Area (BA) Between Mixtures and Red Pine (RP) Monocultures
Spacing # of
Thinnings %RP Final BA Infected
(m2) Final BA of Infected 100%RP
(m2) %
Difference
60 29.3 28.8 1.7%
3 50 31.3 28.8 8.0%
2.4m x 2.4m 40 31.0 28.8 7.1%
60 43.8 47.3 -8.0%
1 50 45.5 47.3 -4.0%
40 42.0 47.3 -12.6%
60 24.5 23.5 4.1%
3 50 27.5 23.5 14.5%
2.1m x 2.1m 40 27.8 23.5 15.5%
60 39.3 44.3 -12.7%
1 50 43.3 44.3 -2.3%
40 36.3 44.3 -22.0%
Table 2: The difference in basal areas (expressed as a percentage) between all of the thinning treatments and
the tree spacings of the mixed stands and the basal areas of all of the thinning treatments and the tree
spacings of the red pine monocultures
[35]
Figure 12 suggests that when a hypothetical disturbance that kills 50% of all red pines in a
stand every 10 years following a thinning in year 30, a red pine monoculture will have a
lower basal area (5.1m2) than the basal areas of mixed species stands containing 40% red pine
(26.7 m2), 50% red pine (24.4 m
2), or 60% red pine (20.9m
2).
Figure 11: Scatter plot of the basal area per hectare of stands that have been thinned in years
30, 40, and 50 and contain trees spaced at an interval of 2.4m x 2.4m
[36]
5 – Discussion
5.1 – Species Composition and Annosum Root Rot Infection
The results indicate that under certain conditions and management prescriptions, planting red
pine within a mixed species stand results in a higher expected timber yield than planting a red
pine monoculture. However, the results also indicate that this is not always the case. If
Annosum root rot does not enter a stand, then planting a red pine monoculture results in a
higher expected timber yield. This result is opposite to the findings of Piotto (2007) which
claim that a higher expected timber yield is found in mixed species stands since there is a
lack of intraspecific competition. Kelty (2006) suggests that the growth rate of an individual
tree is higher in a mixed species plantation, rather than in a monoculture. Individual tree
growth was not a component that was measured in this study since the FVS model produces
data based on a population as a whole.
The results in this study show that the optimal percentage of red pine in a mixed stand is
50%. This is because the 50% red pine mixture yielded a higher basal area than the mixtures
Figure 12: Scatter plot of the basal area per hectare of stands that have been thinned in year 30
and contain trees spaced at an interval of 2.4m x 2.4m
[37]
containing 40% and 60% red pines in all scenarios. This finding matches that of Linden and
Volbrecht (2002). In Linden and Volbrecht’s research, it was found that planting a 50%
mixture is the most favourable mixture for alleviating the impact of Heterobasidion annosum
on a stand. Linden and Volbrecht used Norway spruce as the target species as opposed to red
pine. This may provide evidence that 50% is the ideal proportion for every target species
within a mixed stand, but more research on this needs to be performed.
The stand containing the 50% red pine mixture only contained one other species: quaking
aspen. Quaking aspen was selected as the second species in this stand since it provided a
greater yield than northern pin oak, red maple, or balsam fir when paired with red pine.
Despite this, quaking aspen did not grow as well in the other mixtures. This may be because
quaking aspen is a very shade intolerant species, and northern pin oak and red maple may
have hindered its growth. Due to only two species being present, the stand containing 50%
red pine is less diverse than the mixtures containing 40% and 50% red pine. Thus, planting a
50% mixture has a higher associated risk, even though it resulted in a higher expected timber
yield in this study. Quaking aspen is associated with a fatal disease in Wisconsin called
Hypoxylon canker. If both red pine and quaking aspen contract Annosum root rot and
Hypoxylon canker respectively, then the yield will likely be lower than the yield simulated in
this model.
In this study, the percentage of lost basal area due to Annosum root rot in any type of stand
ranges from 10.4% to 48%, with an average of 24% basal area lost. The results of Frankel
(1998) indicate that stands infected with Heterobasidion annosum have 55% less basal area
than uninfected stands – almost doubling the average percentage found in the present study.
This may be due to the fact that Frankel (1998) studied trees with Annosum root rot in the
Pacific Northwest United States. This region of the United States has had more severe
outbreak of Annosum root rot than the outbreaks found in the Lake States, which may explain
this discrepancy. The rotation lengths of the stands that Frankel (1998) observed were longer
than those used in the present study. Heterobasidion annosum spreads exponentially, so the
longer that it is in a stand, the more trees it will infect.
5.2 – Thinning Regimes and Annosum Root Rot Infection
Without taking into consideration the possibility of an Annosum root rot infection, a land
owner in Wisconsin whose priority is to maximise yield and economic return will likely
[38]
choose to plant a red pine monoculture. This study indicates that if an Annosum root rot
infection occurs, it is better to have planted a mixed stand of trees in some cases. According
to the results of this study, if a stand is thinned three times and a new Annosum infection
occurs after each thinning, then planting a mixed stand will result in a higher yield. This is
because stumps from the thinnings facilitate the Heterobasidion annosum fungus to enter the
root systems of the stand, as detailed in 2.2.2.
The model developed in this study assumes that there is one new Annosum root rot infection
after every thinning, which is a rate that agrees with the average rate of 3.3 new annosum
pockets per hectare per thinning that Scanlon (2008) found for Portage County, Wisconsin.
The findings in this study indicate that if a new pocket of Annosum develops after each
thinning of a stand that is thinned three times during its cycle, then a higher yield will be
achieved by having infected red pine in a mixed stand of trees rather than having infected red
pine in a monoculture.
If a stand is only thinned once, Heterobasidion annosum is given fewer chances to enter the
stand. This study indicates that planting red pine will result in a higher expected timber yield
than planting a mixed stand when there is an Annosum root rot outbreak in all stand types
and the stand is only thinned once during its cycle. The lesser damage caused by
Heterobasidion annosum in stands that have only been thinned once is congruent with the
findings of Petersen (1989). Petersen (1989) found that losses can be reduced by managing a
stand in short rotations. Similarly, as seen in the results of this study, the basal area of stands
infected by Heterobasidion annosum begins to decline at age 60. A manager may consider
performing a salvage harvest at this time to avoid further damage.
When Heterobasidion annosum has infected a stand, this study indicates that timber volume
losses are higher in red pine stands than in mixed stands in every scenario with the exception
of the stands spaced at 2.1m x 2.1m and have been thinned once. In contrast to this finding,
Gadgil and Bain (1999) provide results that claim timber losses due to insects and diseases
are typically less in intensively managed monocultures than in mixed stands. Gadgil and Bain
(1999) claim that this is because a mixed stand will typically be thinned more frequently –yet
more sparsely – than a monoculture, since individual trees are marked for removal to favour
the growth of neighbouring trees. There may be a greater opportunity for Heterobasidion
annosum to enter a mixed stand since more thinnings occur.
[39]
Knocke et al. (2008)’s results oppose the results of Gadgil and Bain (1999) and are more
similar to the findings within this study. Knocke et al. (2008) also used a model to illustrate
that a mixed stand may produce a higher yield than a monoculture when considering a pest or
disease outbreak. The model in the present study was programmed to perform the same
amount and intensity of thinning regimes for both monoculture and mixed stands, which may
be why the results differ from those of Gadgil and Bain (1999).
The exception is that the 2.1m x 2.1m spaced mixed stands containing 60% and 40% red pine
suffered a greater percentage of timber loss than the red pine monoculture. One possible
reason for this is that the timber species other than red pine did not grow well under the
increased density of trees per hectare. Thus, a loss of red pine would have a larger impact on
the total basal area lost within the mixed stands.
5.3 – Tree Spacing and Annosum Root Rot
The model developed in this study was programmed to inoculate trees at a lower rate in
stands that had wider spacing. When the spacing between individual trees decreased from
2.4m x 2.4m (1700 trees per hectare) to 2.1m x 2.1m (2223 trees per hectare), the results
indicate that the stand preferences are the same in the 2.1m x 2.1m as the 2.4m x 2.4m. A red
pine plantation will have the highest yield when Heterobasidion annosum is both absent and
present but only one thinning regime occurs, and a 50% mixture of red pine and quaking
aspen will have the highest yield when Heterobasidion annosum is present and three thinning
regimes occur.
The basal areas of all stands that were spaced at 2.1m x 2.1m intervals were greater than their
counterparts in the stands spaced at 2.4m x 2.4m intervals. The findings of Stambaugh (1989)
show that there is a lower infection rate in stands that have wider spaces between individual
trees. Stambaugh claims that this is because fewer root contacts exist to spread
Heterobasidion annosum. The higher basal areas in the 2.1m x 2.1m spaced stands do not
imply that the rate of infection was less than the 2.4m x 2.4m spaced stands. The higher basal
areas in the stands that had lower spacing may be due to the fact that there were a greater
number of trees in these stands. By comparing the QMDs (quadratic mean diameters) of the
stands within each spacing type, individual trees within the 2.1m x 2.1m stands had lower
[40]
diameters than those within the 2.4m x 2.4m stands. This is likely because there was an
increased amount of competition for resources in the 2.1m x 2.1m stands.
5.4 – Other Factors Affecting Timber Yield
There are many variables that may have an impact on the yield of a tree stand containing red
pine other than Annosum root rot. For this study, a site was selected that is suitable for all
species simulated in the model, yet there are still many uncertainties that can potentially
cause high mortality rates for different species. For example, red maple and quaking aspen
are two species that are highly susceptible to wind throw. There is a natural mortality rate that
is already programmed into the model, yet a heavy wind event in a given year may exceed
that programmed rate. A significant drought or a flooding can potentially impact the yield at
the end of a cycle, too. An infinite number of disturbance combinations can be modelled
using FVS, but the future is uncertain.
Erbilgin and Raffa (2000)’s results indicate that the outcome of timber yield cannot be
predicted based on a single stressor, and that the decline of red pine plantations in Wisconsin
is due to complex abiotic and biotic interactions. The findings of Gardiner and Quine (2000)
support the idea that a model cannot realistically encompass every possible scenario to
completely eliminate risk, but a model can help minimize it. The results of this study only
provide a piece of this complex puzzle, yet the results do show that Annosum root rot can
play a role in reducing the basal areas in stands containing red pine.
Several Heterobasidion annosum preventative measures were not programmed into the
model. Neither biological control nor chemical control (as described in 2.5.1 and 2.5.2) was
taken into consideration during the creation of the FVS model. According to Kliejunas et al.
(2006), applying Sporax® (the most widely used granular borax fungicide used to treat
stumps for Heterobasidion annosum) on the stumps of felled trees can reduce Heterobasidion
annosum infection rates by up to 90%. The economical feasibility of applying Sporax®
varies among landowners since there are several factors that affect the cost such as equipment
availability, cost of labour, and price of fungicide. Determining if using chemical or
biological control is economically feasible given different stand types is an area in need of
future exploration.
[41]
Another preventative variable that was not used in this study was time of year. The results of
Asiegbu et al. (2008) indicate that to avoid Annosum root rot, a thinning treatment should be
performed once temperatures reach 35oC – when Heterobasidion annosum becomes inactive.
This was left out of the model since the average high summer temperature in Wisconsin is
25oC (U.S. Climate Data 2015).
6 – Conclusion The results of this study suggest that Annosum root rot reduces the amount of basal area in
stands containing red pine, regardless of the number of thinnings undergone in the stands or
the spacing between individual trees, leading to the acceptance of hypothesis 1. Although it
was hypothesised that red pine monocultures suffer a greater loss of timber yield than mixed
stands containing red pine when infected with Heterobasidion annosum, the model developed
in this study suggests that this is not always the case, leading to the rejection of the second
hypothesis. It was hypothesised that stands that have undergone multiple thinnings will have
a lesser expected timber yield at the end of a 70 year rotation than stands that have undergone
a single thinning when exposed to Heterobasidion annosum. According to the model in this
study, which was verified by data collected in the field, this hypothesis can be accepted.
Hypothesis 4 can be rejected since insufficient evidence suggested that a wider spacing of
trees did not significantly decrease the instance of Heterobasidion annosum in a stand.
The use of a spatially explicit model to predict future expected timber yield, such as the one
created in this study using FVS, can be employed by forest managers as a helpful tool to
make sound decisions when considering damaging agents such as Heterobasidion annosum.
However, there are still opportunities for improvement. For instance, a model that considers
multiple abiotic and biotic disturbances may help to further reduce the risk that stakeholders
take when investing in standing timber. Although data was collected in the field to validate
the model that was developed, sampling a larger number of tree stands may be useful to
further increase the confidence interval and decrease the standard error of the model’s output.
The model based on the collected data only portrayed stands that were thinned three times
and were spaced at 2.4m x 2.4m. This is because this spacing/thinning regime stand type was
the only stand type that was found to encompass all eight different tree stands studied in this
report. This may be due to the very specific nature of the tree stands. Fitting all eight tree
stand types into the three remaining stand types (e.g. stands thinned once at a 2.1m x 2.1m
[42]
spacing) may require a broader scope of research outside of central Wisconsin, or even within
other lake states. This would lead to a more accurate model, since the model in this present
study assumes that because the data collected in the field validates the trends within the
stands thinned thrice at a 2.4m x 2.4m spacing, the other stand types are validated as well.
Another suggestion to improve future research is to include an economic analysis within the
model. Although a mixed stand of trees may have a higher final basal area than a red pine
monoculture when Heterobasidion annosum is present, the red pine monoculture may contain
timber that is more valuable; therefore the monoculture may potentially result in a higher
profit than the mixed stand containing more basal area. Timber prices are constantly
fluctuating, which may prove to be a challenging development.
In this study, collaboration occurred with the lead pathologist of the Wisconsin DNR
(Department of Natural Resources), which helped facilitate connecting with landowners who
owned stands described in this study. Although some landowners granted permission to
access their property, there were several landowners (specifically, those who owned stands
infected with Heterobasidion annosum) who did not feel comfortable with research being
performed on their property. An outreach programme centred around Annosum root rot
awareness and preventative measures may prove to be beneficial in increasing the number of
landowners willing to participate in a study such as this one.
This study recommends that a landowner considering growing red pine in Wisconsin should
use a model, such as the one developed in this study, to his or her advantage. Spatially
explicit models provide some of the most accurate answers available to landowners and the
models created in this study offer different planting options for landowners who have
differing levels of risk. While this study indicates that the lowest timber losses result from
planting a mixed stand over a monoculture when considering a lethal stand infection, it is up
to the landowner’s personal approach to risk to make decisions regarding species
composition. While there are many landowners who are eager to experiment with new
silvicultural techniques, there are arguably even more landowners who prefer to stick to the
management practices that they are familiar with. It is impossible to predict the future. While
the model created in this study offers possible future outcomes when considering a realistic
[43]
threat to the landscape, it is important for landowners to realise that Annosum root rot is just
one piece to the puzzle of interacting environmental factors.
[44]
Literature Cited
Aikman, D. P., and A. R. Watkinson. "A model for growth and self-thinning in even-aged monocultures of
plants." Annals of Botany 45.4 (1980): 419-427.
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