Dissertation- Bryn Sitkiewicz

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.


Date: 25/8/2015

Statement 2:

This dissertation is the result of my own independent work/investigation except where

otherwise stated.


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.


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

[v]

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


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


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.


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


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.

Alexander, S. A. "Annosus root disease hazard rating, detection, and management strategies in the southeastern

United States." on Research and Management of Annosus Root Disease (Heterobasidion annosum) in

Western North America (1989): 111.

Asiegbu, Fred O., Aleksandra Adomas, and J. A. N. Stenlid. "Conifer root and butt rot caused by

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