Approaches to Predicting 

Potential Impacts of Climate 

Change on Forest Disease: 

An Example With Armillaria Root Disease

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Paper

RMRS-RP-76

July 2009

Ned B. Klopfenstein, Mee-Sook Kim, John W. Hanna, 

Bryce A. Richardson, and John Lundquist
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Klopfenstein, Ned B.; Kim, Mee-Sook; Hanna, John W.; Richardson, Bryce A.; Lundquist, 

John E. 2009. Approaches to predicting potential impacts of climate change on forest 

disease: an example with Armillaria root disease. Res. Pap. RMRS-RP-76. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 10 p.

Abstract

Predicting climate change influences on forest diseases will foster forest management 

practices that minimize adverse impacts of diseases. Precise locations of accurately identified pathogens and hosts must be documented and spatially referenced to determine which 

climatic factors influence species distribution. With this information, bioclimatic models can 

predict the occurrence and distribution of suitable climate space for host and pathogen species 

under projected climate scenarios. Predictive capacity is extremely limited for forest pathogens 

because distribution data are usually lacking. Using Armillaria root disease as an example, 

predictive approaches using available data are presented.

Keywords: climate change, forest diseases, Armillaria, bioclimatic models, forest pathogens, 

global warming, MaxEnt

Authors

Ned B. Klopfenstein is a Research Plant Pathologist, with the U.S. Forest Service, Rocky 

Mountain Research Station, Moscow, ID.

John W. Hanna is a Biological Technician, with the U.S. Forest Service, Rocky Mountain 

Research Station, Moscow, ID.

Bryce A. Richardson is a Research Plant Geneticist, with the U.S. Forest Service, Rocky 

Mountain Research Station, Provo, UT.

Mee-Sook Kim is an Assistant Professor, with the Department of Forest Resources, College 

of Forest Sciences, Kookmin University, Seoul, Korea.

John E. Lundquist is a Forest Entomologist, with the U.S. Forest Service, State and Private 

Forestry, Forest Pest Management, Pacific Northwest Research Station, Anchorage, AK.
USDA Forest Service Res. Pap. RMRS-RP-76. 2009 1

Introduction ____________________

Climate change is expected to cause dramatic, direct 

impacts on the health of forests worldwide. For some 

areas of the world, computer models predict that many 

tree species will become maladapted (i.e., ill-suited to 

survive, grow, and reproduce in the ambient environment) 

to the predicted climate change over the next century 

(e.g., Rehfeldt and others 2006). Impact on forest health 

will be even greater if damage from diseases and insects 

is enhanced by climate change (Dukes and others 2009; 

Klienjunas and others 2008; McCarthy and others 2001; 

Sturrock 2007). Thus, it is critical to consider the impacts 

of disease when predicting impacts of climate change. 

If impacts of climate change on forest disease can be 

predicted, then appropriate management practices can 

be implemented to minimize disease impacts.

Many types of tree diseases threaten the health and 

sustainability of forests. In this paper, we focus on a 

root disease. Climate change will likely have significant 

influences on tree root diseases, such as Armillaria root 

disease (Ayres and Lombardero 2000; Kliejunas and 

others 2008; McCarthy and others 2001; Sturrock 2007). 

Climate change could alter patterns of root disease by 

1) direct effects on the development, survival, reproduction, dispersal, and distribution of hosts and pathogens; 

2) physiological changes in tree defenses; and/or 3) indirect effects from changes in the abundance of mutualists 

and competitors (Ayres and Lombardero 2000).

The goal of this paper is to discuss approaches to 

predict impacts of climate change on root disease; however, similar approaches could be applied to other forest 

diseases. Our work with Armillaria root disease is used 

as an example.

Approaches to Predicting Potential Impacts 

of Climate Change on Forest Disease: 

An Example With Armillaria Root Disease

Ned B. Klopfenstein, Mee-Sook Kim, John W. Hanna, 

Bryce A. Richardson, and John Lundquist

Forest Disease: an Interaction of Host, 

Pathogen, and Environment_______

As a first step in predicting tree diseases, the conditions 

that are necessary for disease to occur must be considered. 

For disease to occur, a virulent pathogen and susceptible 

host must occur together at a time when the environmental 

conditions are favorable for the development of disease. 

This concept, known as the “Plant Disease Triangle,” is 

a fundamental principle in basic plant pathology (fig. 1). 

Thus, we must consider the pathogen, host, and environment when developing approaches to predict where forest 

diseases will occur.

Figure 1—The plant disease triangle. Plant disease 

occurs when a virulent pathogen comes into contact with a 

susceptible host at a time when the environment is favorable 

for the development of disease.
2 USDA Forest Service Res. Pap. RMRS-RP-76. 2009

Pathogens That Cause Armillaria 

Root Disease ___________________

The Armillaria genus contains many species that can 

cause Armillaria root disease. Because the pathogen 

must be accurately identified at a species or subspecies 

level to allow predictions of disease, we will focus on 

one species, Armillaria solidipes (a currently recognized 

older name for A. ostoyae ) (Burdsall and Volk 2008). 

This pathogen has a wide distribution across the Northern 

Hemisphere (fig.2) and has a wide host range that includes 

most conifers and several hardwood species. Armillaria 

solidipes plays diverse ecological roles in forests, ranging 

from being a beneficial saprophyte to a virulent pathogen. 

The pathogen inhabits diverse environments and has the 

potential for long-term and widespread occupancy of a 

site. For example, one vegetative clone of A. solidipes

is predicted to occupy 965 ha, with an age estimate of 

1,900 to 8,650 years (Ferguson and others 2003). In addition, significant genetic diversity has been demonstrated 

within A. solidipes (Hanna and others 2007), and it may 

have the potential to hybridize with other, closely related 

Armillaria species (Kim and others 2006).

Armillaria solidipes causes tree mortality, which 

ranges from being extensive within a disease center to 

being diffuse across a stand. Cumulative mortality from 

Armillaria root disease can reach 15-20% at 20 years 

of age (Morrison and Pellow 1994; Whitney 1988). 

Furthermore, root disease caused by this pathogen can 

also result in significant growth loss (e.g., up to 40% 

volume loss over 4 to 8 years in 18-year-old Douglas-fir, 

Pseudotsuga menziesii), frequently in the absence of readily observable symptoms (Cruickshank 2000; Morrison 

and others 2000). Although A. solidipes produces mushrooms in autumn under some environmental conditions, 

it is more consistently found 1) as rhizomorphs (root-like 

structures that occur on the root surface, under the bark, 

or in the soil); 2) as mycelial fans that occur under the 

bark; or 3) in decayed wood.

Hosts of Armillaria Root Disease___

While A. solidipes has an extremely wide host range 

across the Northern Hemisphere, host susceptibility varies 

with geographic location. In western North America, 

A.solidipes can cause major growth losses in Douglas-fir 

or true firs (Abies spp.; Kile and others 1991). Because of 

its economic value and widespread occurrence in western 

North America, we focus here on Douglas-fir to predict 

potential impacts of Armillaria root disease.

Figure 2—Distribution of Armillaria solidipes (depicted in red) (Hanna 2005).
USDA Forest Service Res. Pap. RMRS-RP-76. 2009 3

Determining the Potential 

Distribution of the Pathogen ______

Accurate baseline information on pathogen occurrence is needed to develop predictions of the pathogen’s 

current distribution. Survey plots, scattered across ranging 

geographic areas and diverse climates, represent a 

reliable method for obtaining baseline information 

on pathogen distribution (fig. 3). For a chronic root 

pathogen such as A. solidipes, which is not readily 

detected on a site based on above-ground symptoms, 

surveys of all host species on plots must be quite thorough and include excavation of tree roots. Samples 

collected from fruiting bodies, rhizomorphs, mycelial 

fans, or decayed wood are all acceptable for establishing fungal cultures (fig. 3). Detailed plot information, 

such as precise location (e.g., GPS coordinates at +/– 30-m 

resolution or better), elevation, slope, aspect, soil 

type, habitat type, and other environmental data, is 

also needed. Because pathogen samples must be accurately characterized using DNA-based methods, it is 

recommended that pathogens be placed into culture to 

facilitate further study.

Accurate identification of the pathogen species, 

subspecies, or population is critical to establishing a data 

set on pathogen distribution. Because it is difficult to 

identify many forest pathogens, such as Armillaria spp., 

based on vegetative morphology, DNA-based diagnostic 

methods are particularly useful to help validate taxonomic identification (fig. 4). For this example, cultures 

of A. solidipes were identified using PCR and DNA 

sequencing of the intergenic spacer-1 (IGS-1) region 

of the ribosomal DNA (rDNA) (Kim and others 2006).

This approach establishes a data set from plots with 

confirmed presence or absence of accurately identified 

A. solidipes. A large data set is preferred — the larger 

the data set the more accurate will be the predictions of 

distribution.

Figure 3—Survey plots of Armillaria root disease. A. Available presence/absence data for 

Armillaria spp. in western North America, including approximately 400 0.04-ha plots were 

established in western North America; B. Roots of each tree species were thoroughly 

inspected for the pathogen. Precise location and associated collection data were recorded; 

C. Rhizomorphs of Armillaria spp. were collected (photo courtesy of the Intermountain Forest 

Tree Nutrition Cooperative); D. Mycelial fans of Armillaria spp. were also collected (photo 

courtesy of the Intermountain Forest Tree Nutrition Cooperative).
4 USDA Forest Service Res. Pap. RMRS-RP-76. 2009

Determining the Potential 

Distribution of the Host___________

As with the pathogens, it is also necessary to have 

baseline information on the distribution of the host-tree 

species. Although general range information is available for many tree species, more accurate predictions of 

distributions must rely on survey plot data that records 

species presence or absence at precise geographic locations (e.g., GPS coordinates) and elevations. Fortunately, 

survey plot data are available for several tree species. For 

example, the U.S. Forest Service, Forest Inventory and 

Analysis (FIA) National Program maintains numerous 

permanent plots on forest and nonforest lands that have 

associated plot data for tree species and other plant species (Alerich and others 2004; Bechtold and Patterson 

2005). Data sets from such survey plots are critical to 

developing models to predict the present distribution of 

forest species.

Climate Surfaces ________________

Understanding how climatic factors vary across the 

global landscape is necessary to determine the influence 

of climatic factors on plant disease. Based on information 

from numerous weather stations, various methods are 

available that create a climate surface, which is spatially 

interpolated climate data on a landscape grid (Hijmans 

and others 2005). Climate data are available from several 

sources, such as WorldClim (http://www.worldclim.org/; 

Hijmans and others 2005), Daymet (http://www.daymet.

org/), Rehfeldt and others (2006; http://forest.moscowfsl.

wsu.edu/climate/index.html), and others. The climate 

surfaces from these sources allow estimations of multiple 

climatic factors, such as monthly temperatures, monthly 

moisture, and complex interactions among multiple factors, for a specific geographic location and elevation. For 

example, the climate surface for Degree Days >5 °C, 

a measure of warmth, is shown across western North 

America (fig. 5A).

Models to Predict Present Distribution 

of the Host and Pathogen __________

Models to predict species distribution are based on 

the data set of precise locations where the species is 

known to be present or absent. Climate surfaces are used 

to estimate values for climate variables for each location. The analysis determines which climatic factors or 

Figure 4—Identification of Armillaria species. A. Samples of Armillaria spp. were established in 

culture; B. Polymerase Chain Reaction (PCR) was used to amplify the intergenic spacer-1 (IGS-1) 

region of ribosomal DNA (rDNA); C. DNA sequences of IGS-1 were used to confirm the identification 

of A. solidipes or other species.
USDA Forest Service Res. Pap. RMRS-RP-76. 2009 5

interactions predict the presence and absence of the host 

species. The critical climate variables are incorporated 

into a bioclimatic model that predicts the probability 

of suitable climate space (i.e., the geographic area that 

possesses a climate suitable for species survival) for the 

species across the landscape within the area that is being 

analyzed. For example, Rehfeldt and others (2006) used 

a Random Forest classification tree algorithm to model 

suitable climate space for Douglas-fir across the landscape 

(fig. 6B). This prediction was based on ca. 120,000 plots 

(20,171 plots with Douglas-fir present) that were available 

from FIA (U.S. Forest Service) and resulted in an error 

of omission approaching 0 and an error of commission 

of less than 9%. Lower sample numbers can be used 

for predictive models, but the user must determine the 

acceptable level of accuracy for their purposes.

It is difficult to predict pathogen distribution with 

high accuracy because data sets confirming pathogen 

occurrence are usually quite limited in comparison 

with host data sets. For predicting the potential current 

distribution of A. solidipes, we based our prediction on 

ca. 400 survey plots of which A. solidipes was found 

on 202 plots. Several different bioclimatic models can 

be used to predict A. solidipes distribution. Figure 6A 

shows a probability distribution for suitable climate 

space generated using Maximun Entropy modeling 

(MaxEnt; Phillips and others 2006; Phillips and Dudik 

2008) that is based on presence-only data. This spatial 

model could correspond to the present distribution of 

A. solidipes if this species has expanded to its ecological breadth and climate is the primary factor that 

influences its distribution.

Figure 5—Example of current and future climate surface for one climatic factor (Degree Days >5 °C ) using the CGCM3 model 

and the A2 “worst-case” greenhouse-gas scenario provided by The Intergovermental Panel on Climate Change (IPCC) (Rehfeldt 

and others 2006). A. Current climate surface; B. Projected future climate surface for the year 2090.
6 USDA Forest Service Res. Pap. RMRS-RP-76. 2009

Figure 6—Examples of approaches to predict current suitable climate space for Armillaria solidipes and a susceptible 

host, Douglas-fir (Pseudotsuga menziesii), in the interior northwestern United States. A. Preliminary example of an 

approach to predict suitable climate space for A. solidipes using MaxEnt and the climate data and variables of 

Rehfeldt and others (2006); B. Predicted suitable climate space for Douglas-fir using Random Forest classification 

(Rehfeldt and others 2006). Colors represent the number of tree votes cast, analogous to the probability of 

occurrence. Yellow = 50 to 75, Red 76 to 100 votes.

Models to Predict Future Climate 

Surfaces _______________________

General Circulation Models (GCMs) are a class of computer models that forecast weather and climate change. 

GCMs can be used with various greenhouse gas emission 

scenarios, such as those proposed by the Special Report on 

Emissions Scenarios (SRES), an intergovernmental panel 

on climate change (IPCC), to predict the future global 

climate surface (McCarthy and others 2001). Examples 

of GCMs include 1) CGCM3 — Canadian Centre for 

Climate Modeling and Analysis (Flato and Boer 2001); 

2) HadCM3 — Hadley Center (Gordon and others 2000); 

and 3) GFDL CM2.1 — Geophysical Fluid Dynamics 

Laboratory (Delworth and others 2006). SRES scenarios 

are based on different assumptions of population growth, 

economic development, and technological development. 

Three common SRES scenarios that represent low, 

moderate, and high estimations of future atmospheric 

CO2 concentrations include the SRES B1 (atmospheric 

CO2 concentration stabilizing at just below 550ppm by 

2100), SRES A1B (CO2 rate still rising but at a slower 

rate to 700ppm by 2100), and the “worst case” SRES A2 

(increasing CO2 shows no sign of slowing down with 

800ppm by 2100) (Peterson and others 2008). Figure 5 

shows an example of current and future projections of 

the climate surface for one climatic factor (Degree Days 

>5 °C) produced by the CGCM3 model and the A2 

“worse case” SRES scenario. However, according to the 

Global Carbon Project (2008), the most recent records of 

the annual mean growth rate of atmospheric CO2 show 

levels have increased faster than projected. The suitable 

climate space for host and pathogen species across the 

landscape can then be projected onto the future climate 

surfaces showing potential changes in species distributions based on the climate change scenario.

Models to Predict Future Suitable 

Climate Space for Hosts and 

Pathogens Under Changing Climate 

Scenarios ______________________

Future climate surfaces produced by a GCM and 

specified scenario for greenhouse-gas emissions serve 

as the basis for bioclimatic modeling of suitable climate 

space for hosts and pathogens across future landscapes. 

Different models and greenhouse gas emission scenarios 

produce different future climate surfaces, and projections 

of climate change are variable. Further comparisons 

A B
USDA Forest Service Res. Pap. RMRS-RP-76. 2009 7

among the GCMs and discussion of their reliability are 

beyond the scope of this paper. However, predictions of 

climate change impacts on forest disease are dependent 

on an accurate GCM projection.

Figure 7 shows examples of approaches to predict 

suitable climate space for A. solidipes and Douglas-fir 

within the interior northwestern United States. In these 

examples for the decade 2060, minor shifts in the probability of suitable climate space for A. solidipes are apparent in this region. In contrast, the probability of suitable 

climate space for Douglas-fir is markedly decreased. 

If this projection holds true, it is reasonable to assume 

that many Douglas-fir stands in this region may become 

stressed due to climate maladaptation. Such stress could 

render trees more susceptible to Armillaria root disease.

For such prediction models, it must be noted that predicted suitable climate space for a species does not necessarily correspond to the future distribution of that species. 

Future species distribution will depend on many factors 

such as population structure, migration, regeneration, 

competition, susceptibility to insect attack and diseases, 

and other interacting factors. The realized distribution of 

a species will be determined by the interactions among 

diverse biological, climatic, and environmental factors.

Toward Determining When and 

Where Disease Will Occur ________

Predictions of potential distribution of forest pathogens 

and hosts under current and changing climate will provide valuable insights about where hosts and pathogens 

might co-occur and thus, where diseases might occur. It 

is important to note, however, that the co-occurrence of 

suitable climate space for the pathogens and their host(s) 

does not necessarily result in disease. The establishment 

and severity of disease depends on many interacting 

factors, including: seasonal or annual weather, microclimatic factors, age and vigor of host plants, pathogen 

inoculum potential, host tree density, stand history, past 

management practices, host species composition and 

age, localized populations of hosts and pathogens, 

occurrence of pathogen vectors, biological control 

agents, and several other factors. One aspect of changing climate is the increased variability in weather and 

a greater likelihood of extreme weather (e.g., high 

temperatures, low temperatures, drought, excessive rain 

events) (IPCC 2001). Weather events could increase 

or decrease the level of forest disease depending on 

the weather event and the pathosystem. Predictions of 

Figure 7—Examples of approaches to predict future suitable climate space for Armillaria solidipes and a susceptible 

host, Douglas-fir (Pseudotsuga menziesii), in the interior northwestern United States. A. Preliminary example of 

an approach to predict future suitable climate space for A. solidipes in the year 2060 using MaxEnt, CGCM3 GCM 

(Canadian Centre for Climate Modeling and Analysis, SRES A1B), and climate data from Rehfeldt and others (2006); 

B. Predicted suitable climate space for Douglas-fir using Random Forest classification (Rehfeldt and others 2006). 

Colors represent the number of tree votes cast, analogous to the probability of occurrence. Yellow = 50 to 75, Red 

76 to 100 votes.

A B
8 USDA Forest Service Res. Pap. RMRS-RP-76. 2009

forest disease occurrence will likely improve when other 

contributing factors are incorporated into classification 

models. Because of lacking prerequisite information, 

it will likely be challenging to develop approaches to 

accurately predict the spatial distribution of some forest 

diseases. For this reason, many assessments of forest 

disease must still rely on information obtained at the 

stand level.

Regarding our Armillaria root disease example, it 

appears that A. solidipes and Douglas-fir are both 

broadly well adapted to the existing climate in the 

interior northwestern U.S. (fig. 6). However, Armillaria 

root disease is currently quite severe on Douglas-fir in 

this region. Because Armillaria root disease is generally 

more severe on trees predisposed by stress, it seems 

reasonable that this disease could become even more 

severe in the future if Douglas-fir is subjected to climate 

stress, such as is projected for the interior northwestern 

U.S. (Rehfeldt and others 2006: fig. 6B). Thus, we might 

expect increased likelihood of disease if 1) host trees are 

maladapted to climate or subjected to other stress agents 

such as seasonal weather (e.g., moisture or temperature 

extremes), insect attack, wounding, etc. or 2) past stand 

history or management has contributed to increased 

inoculum potential (McDonald and others 1987) such 

as by leaving colonized stumps or selectively removing 

tolerant tree species. Forest management decisions can 

be based on predictions of host and pathogen distribution 

coupled with other stand information.

Forest Management Applications __

Predictions of forest disease incidence and severity 

under current and changing climate scenarios are useful 

to managers for strategic and management planning. 

Several management coping strategies have been proposed, including 1) favoring forest species and populations 

that are adapted to future climatic conditions; 2) altering 

species composition and diversity to favor resistant/tolerant hosts in areas where disease presence is predicted; 

3) changing forest structure and age patterns at landscape 

scales; and 4) other strategies, such as reducing inoculum 

potential by removing its source, avoiding wounding or 

stressing trees, and increasing biological control activity. 

These suggestions are largely based on historical information; however, managers may face environmental conditions that have no historical precedent. For this reason, 

it is difficult to base proposed management actions on 

scientifically rigorous research. Managers must recognize that a changing climate will likely create novel 

conditions for which previous experience is unavailable. 

Thus, an increased emphasis is needed on strategies that 

consider ecosystem adaptability.

Regarding the Armillaria root disease example, a number 

of management options are available depending on the situation and management objectives. These options could 

include 1) selectively removing species that are maladapted or predicted to become maladapted; 2) planting 

with seed-sources that are well-adapted to the predicted 

climate during the tree’s lifespan; 3) selectively favoring 

seral species (e.g., Pinus spp. or Larix spp.) that are more 

tolerant of Armillaria root disease and adapted to the 

predicted climate; 4) avoiding wounding of trees, which 

may facilitate pathogen infection; 5) employing treatments 

that reduce or do not exacerbate tree stress; 6) applying treatments that minimize woody substrates that can 

serve to build up the inoculum potential; or 7) other less 

developed practices, such as fostering biological control. 

As with most forest management, management practices 

must be selected for the specific stand and objectives.

Additional Information Needed to 

Refine Disease Predictions _______

Approaches that are currently available for predicting 

climate influences on forest disease provide a solid foundation for forest planning. However, further refinements 

will improve accuracy and utility of these approaches. 

Of critical need is more information on the current distribution of accurately identified pathogens. Prediction 

models can be further improved by including additional 

information, such as more precise climate data, other 

geographic information (e.g., slope, aspect, topography), 

and other environmental data (e.g., soil type, habitat type). 

Improved precision will be achieved by understanding 

the population structure of hosts and pathogens and the 

adaptation of these populations to climatic factors. Other 

interacting biotic factors, such as the distribution and 

activities of biological control agents or vectors, also 

influence the occurrence of forest disease and should 

be considered where they are relevant. Once climatic 

influences on host and pathogen distribution are better 

understood, information is frequently needed to determine 

the climatic conditions, seasonal/annual weather, and 

microclimates that are suitable for the development of 

disease. Many forest diseases may also be influenced 

by inoculum potential, stand history, stand vigor, stand 

age, stand structure, stand density, and many other 

interacting factors that may require assessment at the 

stand level. Thus, continued surveys and research can 

provide necessary information for refining predictions 

of climate influence on diverse forest diseases.
USDA Forest Service Res. Pap. RMRS-RP-76. 2009 9

Methods and information are available to continue 

refining predictions of climatic influences on forest 

disease. Geographic data (e.g., slope, aspect, and 

topography) can be obtained from various landscape 

modeling software (e.g., GoogleTM Earth, DeLorme 

Xmap®, ESRI’s ArcGIS and ArcMap) and websites 

(e.g., USGS). Similarly, soils data are available for 

many regions from diverse sources, such as government 

agencies (e.g., Natural Resource Conservation Service) 

or commercial sources. Populations and phylogenetic 

relationships of hosts and pathogens can be determined 

by the application of genetic markers (Hamelin 2006; 

Kim and others 2005; Richardson and others 2005). 

Predicting the distribution of biological control agents 

or vectors can be accomplished in a manner similar to 

predictions for the hosts and pathogens. Furthermore, 

insights on the role of host maladaptation and disease 

development can be gained from disease surveys that 

include hosts growing on the margin of their range. If 

warranted, remote sensing tools (e.g., light detection and 

ranging — LIDAR, satellite imagery, spectral analysis) 

can be used to obtain stand-level information such as 

stand vigor, age, structure, and density. Other factors 

that contribute to disease incidence, such as stand 

history and inoculum potential, could be determined 

for the individual stands of interest.

An integration of diverse tools and data is necessary 

to improve predictions for climatic influence on forest 

disease. Reliable predictions are especially critical to 

address and mitigate future impacts of climate change on 

forest disease, which will likely be quite severe in many 

regions if this issue is neglected. Fortunately, approaches 

are currently available to develop models for predicting 

the potential impacts of climate change on forest disease. The health of our future forests is dependent upon 

continued efforts to develop and refine these models in 

conjunction with other predictive tools.

Acknowledgments_______________

We thank Phil Cannon, C.G. “Terry” Shaw, Nick 

Crookston, Rona Sturrock, and Jonalea Tonn for reviews 

of an earlier version of this manuscript. This paper was 

supported by the USDA Forest Service Global Change 

Research Funding, Research Joint Venture Agreement 

07-JV-11221662-078, and the new faculty research program 2009 of Kookmin University in Korea. Mention 

of trade names does not constitute endorsement of the 

U.S. Forest Service.

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Notes


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