United States Department of Agriculture

Forest Rocky Mountain General Technical Report

Service Research Station RMRS-GTR-361 August 2017

Restoring Whitebark Pine Ecosystems in 

the Face of Climate Change

Robert E. Keane, Lisa M. Holsinger, Mary F. Mahalovich, and Diana F. Tomback
Keane, Robert E.; Holsinger, Lisa M.; Mahalovich, Mary F.; Tomback, Diana F. 2017. Restoring 

whitebark pine ecosystems in the face of climate change. Gen. Tech. Rep. RMRS-GTR-361. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 123 p.

Abstract

Whitebark pine (Pinus albicaulis) forests have been declining throughout their range in western 

North America from the combined effects of mountain pine beetle (Dendroctonus ponderosae) 

outbreaks, fire exclusion policies, and the exotic disease white pine blister rust (Cronartium 

ribicola). Projected warming and drying trends in climate may exacerbate this decline; however, 

whitebark pine has a wide climatic tolerance because of its broad distribution coupled with high 

genetic diversity. A rangewide whitebark pine restoration strategy (Keane et al. 2012b) was 

developed recently to inform restoration efforts for whitebark pine across Federal, State, and 

Provincial land management agencies. This strategy, however, did not address the effects of 

climate change on existing whitebark pine populations and restoration efforts. In this report, 

we present guidelines for restoring whitebark pine under future climates using the rangewide 

restoration strategy structure. The information to create the guidelines came from two sources: 

(1) a comprehensive review of the literature and (2) a modeling experiment that simulated various 

climate change, management, and fire exclusion scenarios. The general guidelines presented 

here are to be used with the rangewide strategy to address climate change impacts for planning, 

designing, implementing, and evaluating fine-scale restoration activities for whitebark pine by 

public land management agencies.

Keywords: whitebark pine, climate change, fire regime, blister rust, mountain pine beetle, Clark’s 

nutcracker, seed dispersal, regeneration, upper subalpine, restoration

Photo credits: All photos were taken by Forest Service employees except as noted.

Cover photos: This cover is similar to the cover for the rangewide strategy (Keane et al. 2012b); 

which is the companion document to this publication, but the photos are different. In the center are 

the actions and guiding principles of the restoration strategy and clockwise from top: wildfire in a 

subalpine fir; a natural seedling after a wildfire; a sawyer removing subalpine fir in a restoration 

cutting; encroaching whitebark pine into sagebrush grasslands; a prescribed burn in whitebark 

pine; thousands of rust-resistant whitebark pine seedlings; treatments called nutcracker openings in 

whitebark pine forests. All photos taken by Bob Keane.

All Rocky Mountain Research Station publications are published by U.S. Forest Service employees and are in the 

public domain and available at no cost. Even though U.S. Forest Service publications are not copyrighted, they 

are formatted according to U.S. Department of Agriculture standards and research findings and formatting cannot 

be altered in reprints. Altering content or formatting, including the cover and title page, is strictly prohibited.
Authors

Robert E. Keane is a Research Ecologist with the U.S. Forest Service, Rocky Mountain Research 

Station at the Missoula Fire Sciences Laboratory in Missoula, Montana. He received his B.S. 

degree in Forest Engineering from the University of Maine, Orono; his M.S. degree in Forest 

Ecology from the University of Montana; and his Ph.D. in Forest Ecology from the University of 

Idaho. His most recent research includes (1) developing ecological computer simulation models for 

exploring landscape, fire, and climate dynamics, (2) sampling, describing, modeling, and mapping 

fuel characteristics, and (3) investigating the ecology and restoration of whitebark pine.

Lisa M. Holsinger is a GIS Specialist for the Rocky Mountain Research Station. Holsinger 

received her B.S. degree in Biological Sciences from U.C. Davis and her M.S. degree in fisheries 

at the University of Washington. She has developed and analyzed geospatial and attribute data 

for simulation models (FireBGCv2, FSim, WX-FIRE, LANDSUM) for fire ecology research directed 

towards understanding landscape level interactions between changing climate, fire regimes, 

vegetation and fuel management.

Mary F. Mahalovich is a Regional Geneticist with the U.S. Forest Service, Northern, Rocky 

Mountain, Southwestern, and Intermountain Regions, Forestry Sciences Laboratory in Moscow, 

Idaho. She received her B.S. degree in Forest Management at Northern Arizona University, her 

M.S. degree in Forest Genetics from the University of California, Berkeley, and a Ph.D. in Forestry 

and Genetics from North Carolina State University. Her program management and research 

includes genecology, identifying, harnessing and deploying blister rust-resistant five-needle pines, 

and gene conservation.

Diana F. Tomback is a Professor in the Department of Integrative Biology, University of Colorado 

Denver, an Evolutionary Ecologist and Conservation Biologist, and volunteer Director of the 

Whitebark Pine Ecosystem Foundation. She received her B.A. and M.A. degrees in Zoology from 

the University of California at Los Angeles, and her Ph.D. in Biological Sciences from the University 

of California at Santa Barbara. Her current research examines several aspects of the interaction 

between Clark’s nutcrackers and whitebark pine, including: (1) timeframe of postfire regeneration 

of whitebark pine, (2) detecting changes in nutcracker seed dispersal services as whitebark pine 

declines, and (3) the role of whitebark pine in vegetation dynamics at treeline.

The use of trade or firm names in the publication is for reader information and does not imply 

endorsement by the U.S. Department of Agriculture of any product or service.

i
Executive Summary

Whitebark pine (Pinus albicaulis) forests are declining across most of their range in North America 

because of the combined effects of mountain pine beetle (Dendroctonus ponderosae) outbreaks, 

fire exclusion policies, and the exotic pathogen Cronartium ribicola, which infects five-needle white 

pines and causes the disease white pine blister rust. Predicted changes in climate may exacerbate 

whitebark pine decline by (1) accelerating succession to more shade tolerant conifers, (2) creating 

environments that are unsuitable for the species, (3) increasing the frequency and severity of 

mountain pine beetle outbreaks and wildland fire events, and (4) facilitating the spread of blister 

rust. Yet, whitebark pine tolerates a variety of stressful conditions and the broad genetic diversity to 

adapt to changes in climate and disturbance.

The ongoing decline in this high-elevation tree species poses serious consequences for upper 

subalpine and treeline ecosystems and, as a result, whitebark pine is a candidate species for 

listing under the Endangered Species Act. The large, nutritious seeds produced by this pine are an 

important food for many bird and mammal species, such as the endangered grizzly bear (Ursus 

actos horribilis), and whitebark pine communities provide nesting sites and habitat for many 

other wildlife species. Whitebark pine seeds are dispersed long distances by Clark’s nutcrackers 

(Nucifraga columbiana), which cache seeds in a variety of terrain and plant community types, 

including recent burns and other disturbed areas. Unclaimed seeds often germinate and produce 

hardy seedlings. These seedlings can survive on harsh, arid sites, and act as nurse trees to less 

hardy conifers and vegetation.

Because more than 90 percent of whitebark pine forests exist on public land in the United States 

and Canada, a rangewide whitebark pine restoration strategy (Keane et al. 2012b) was developed 

to coordinate and inform restoration efforts across Federal, State, and Provincial land management 

agencies. This restoration strategy, however, failed to fully address the projected effects of climate 

change on whitebark pine restoration efforts and existing populations.

In this report, we present guidelines for restoring whitebark pine under future climates using the 

rangewide restoration strategy structure. General restoration guidelines considering effects of 

climate change are given for each of the strategy’s guiding principles: (1) promote resistance 

to blister rust, (2) conserve genetic diversity, (3) save seed sources, and (4) employ restoration 

treatments. We then provide specific guidelines for each of the strategy’s actions: (1) assess 

condition, (2) plan activities, (3) reduce disturbance impacts, (4) gather seed, (5) grow seedlings, 

(6) protect seed sources, (7) implement restoration treatments, (8) plant burned areas, (9) monitor 

activities, and (10) support research.

We used information from two sources to account for climate change impacts on whitebark pine 

restoration activities. First, we conducted an extensive and comprehensive review of the literature 

to assess climate change impacts on whitebark pine ecology and management. Second, we 

augmented this review with results from a comprehensive simulation experiment using the spatially 

explicit, ecological process model FireBGCv2. This modeling experiment simulated various climate 

change, management and fire exclusion scenarios. We also ran FireBGCv2 to evaluate the effects 

of specific rangewide restoration actions with and without climate change. We analyzed two 

simulated response variables (whitebark pine basal area, proportion of the landscape dominated by 

whitebark pine) to explore which restoration scenarios are likely to succeed.

Our findings indicate that management intervention actions such as planting rust-resistant 

seedlings and employing proactive restoration treatments, can return whitebark pine to the high 

mountain settings of western North America to create resilient upper subalpine forests for the 

future. The report is written as companion guide to the rangewide restoration strategy for planning, 

designing, implementing, and evaluating fine-scale restoration activities for whitebark pine by 

addressing climate change impacts. 

ii
Acknowledgments

This effort was mostly funded by the Great Northern Landscape Conservation Cooperative 

(GNLCC). We are grateful to the members of the Whitebark Pine Ecosystem Foundation for 

reviewing this document. We also thank Molly Retzlaff, U.S. Forest Service, Rocky Mountain 

Research Station for extensive editing and quality control; Regan Nelson, Crown of the Continent 

Conservation Initiative, Andy Bower, U.S. Forest Service, Shawn McKinney, USGS and Katie 

Ireland, Montana State University for extensive reviews of various versions of the document. 

iii
iv

Contents

1. Introduction ..............................................................................................................................1

1.1 This Report ............................................................................................................................2

1.2 Rangewide Restoration Strategy Summary...........................................................................2

2. Whitebark Pine Ecology.............................................................................................5

2.1 Autecology .............................................................................................................................5

2.2 Clark’s Nutcracker Interactions..............................................................................................6

2.3 Mycorrhizae ...........................................................................................................................7

2.4 Community Characteristics....................................................................................................8

2.5 Genetics...............................................................................................................................10

2.6 Disturbance..........................................................................................................................11

2.6.1 Wildland Fire................................................................................................................11

2.6.2 Mountain Pine Beetle...................................................................................................12

2.6.3 White Pine Blister Rust ................................................................................................14

2.7 Current Status......................................................................................................................15

3. Climate Change Impacts: A Literature Review ......................................................16

3.1 Climate Projections..............................................................................................................17

3.1.1 Understanding Climate Change Predictions................................................................17

3.1.2 Climate Projections for the Range of Whitebark Pine..................................................21

3.1.3 Understanding Species Response to Climate Change................................................23

3.2 Species Responses.............................................................................................................28

3.2.2 Regeneration ...............................................................................................................30

3.2.1 Reproduction................................................................................................................31

3.2.3 Growth and Mortality....................................................................................................31

3.3 Community Response .........................................................................................................33

3.4 Disturbance Changes ..........................................................................................................33

3.4.1 Wildland Fire................................................................................................................34

3.4.2 Mountain Pine Beetle (MPB)........................................................................................35

3.4.3 White Pine Blister Rust (WPBR)..................................................................................36

3.4.4 Interactive Effects ........................................................................................................37

3.5 Nutcracker Responses ........................................................................................................39

4. The Simulation Effort ...............................................................................................42

4.1 The Model............................................................................................................................42

4.2 The Simulation Experiment..................................................................................................44

4.3 Independent Model Runs.....................................................................................................49

4.4 Simulation Results...............................................................................................................49

4.4.1 Whitebark Pine Abundance..........................................................................................49

4.4.2 Forest Species Community Response.........................................................................55

4.4.3 Fuel Characteristics .....................................................................................................62

4.4.4 Fire Regimes................................................................................................................62

4.4.5 Fire Effects...................................................................................................................65

4.4.6 Lynx and Grizzly Bear Habitat Suitability .....................................................................66
4.5 Simulation Summary............................................................................................................69

4.5.1 Findings .......................................................................................................................69

4.5.2 Simulation Limitations..................................................................................................73

5. Climate change adaption strategies and tactics for restoring 

 whitebark pine ecosystems..................................................................................74

5.1 Climate Change Adaptation Primer .....................................................................................75

5.2 Guiding Principles for Adaptation Strategies and Approaches ............................................79

5.2.1 Promote Rust Resistance ............................................................................................79

5.2.2 Conserve Genetic Diversity .........................................................................................79

5.2.3 Save Seed Sources .....................................................................................................81

5.2.4 Employ Restoration Treatments...................................................................................82

5.3 Management actions ...........................................................................................................84

5.3.1 Assess Condition .........................................................................................................84

5.3.2 Plan Activities...............................................................................................................86

5.3.3 Reduce Disturbance Impacts.......................................................................................88

5.3.4 Gather Seed.................................................................................................................90

5.3.5 Grow Seedlings............................................................................................................92

5.3.6 Protect Seed Sources..................................................................................................92

5.3.7 Implement Treatments .................................................................................................94

5.3.8 Plant Seedlings............................................................................................................96

5.3.9 Monitor Activities..........................................................................................................98

5.3.10 Conduct Research ...................................................................................................100

6. Discussion ..............................................................................................................102

6.1 Restoring Whitebark Pine in the Face of Climate Change ................................................102

6.2 Caveats..............................................................................................................................103

7. Summary .................................................................................................................105

8. References ..............................................................................................................106

v
Figure 1—Declining whitebark pine forests across the species range. (a) mountain pine beetle (MPB) mortality 

in central Idaho, (b) white pine blister rust (WPBR) mortality in west-central Montana, (c) WPBR mortality in 

the Great Burn of Idaho, and (d) extensive WPBR mortality in the Bob Marshall Wilderness Area of Montana 

(photos a,b,c, Bob Keane, USDA Forest Service; photo d, Steve Arno, USDA Forest Service, retired).

a b

c d
USDA Forest Service RMRS-GTR-361. 2017. 1

Upper subalpine whitebark pine (Pinus albicaulis

Engelm.) forests are rapidly declining throughout 

western North America because of the interaction 

among and cumulative effects of historical and 

current mountain pine beetle (MPB, Dendroctonus 

ponderosae Hopkins Coleopteran: Curculionidae, 

Scotlytinae) outbreaks, more than 90 years of fire exclusion policies, and white pine blister rust, caused by 

the introduced pathogen Cronartium ribicola (Keane 

and Arno 1993; Kendall and Keane 2001; Murray 

and Rasumussen 2003; Schwandt 2006; Tomback 

and Achuff 2010) (fig.1). To make matters worse, 

many scientists believe that projected warmer future 

climates will severely reduce high elevation whitebark 

pine habitat, thereby restricting populations to mountaintops or to the northern parts of its range (Funk and 

Saunders 2014; Koteen 1999; McKenney et al. 2007; 

Schrag et al. 2007; Warwell et al. 2007). While it is 

widely held that whitebark pine will decline under 

future climates, the magnitudes and directions of 

whitebark pine ecosystem responses to projected climate changes are still unknown. This may confound 

attempts at restoring this valuable ecosystem.

The loss of this iconic high-elevation tree species 

has serious consequences for high mountain ecosystems, both in terms of the impacts on biodiversity 

and in losses of valuable ecosystem processes and 

services (Funk and Sanders 2014; Lee 2003; Tomback 

and Achuff 2010; Tomback et al. 2001a). The large, 

nutritious seeds produced by whitebark pine are an 

important food for birds and mammals, and whitebark 

pine communities provide important habitat for many 

wildlife species (Lorenz et al. 2008; Tomback and 

Kendall 2001). Whitebark pine seedlings survive on 

harsh, high elevation sites and, when fully grown, 

often act as nurse trees to less hardy conifers and 

undergrowth vegetation (Callaway 1998; Callaway 

et al. 1998; Tomback et al. 2001b). At upper subalpine elevations, mature whitebark pine trees help to 

regulate snowmelt and reduce soil erosion (Farnes 

1990). For these collective functions, whitebark pine 

is considered both a keystone species for promoting 

community diversity and a foundation species for 

promoting community stability (Ellison et al. 2005; 

Mills et al. 1993; Paine 1995; Tomback and Achuff 

2010; Tomback et al. 2001a). The loss of whitebark 

pine also potentially impacts fire regimes, recreational 

experiences, and aesthetic perceptions (Keane et al. 

2002a; McCool and Freimund 2001; Tomback and 

Achuff 2010; Tomback et al. 2001a). As a result, 

whitebark pine is a candidate species for listing under 

the Endangered Species Act (FWS 2011).

There are many compelling reasons to restore 

whitebark pine forests (Tomback et al. 2001a). As 

mentioned, losing a foundation and keystone species 

due to exotic diseases and adverse management actions might result in the decline of the many species 

of flora and fauna that depend on whitebark pine, such 

as grizzly bears, Clark’s nutcrackers, and blue grouse 

(Dendragapus obscurus). Changes in ecosystem 

services, such as reduced late summer streamflows 

may also result (Tomback et al. 2001b). In addition, 

healthy whitebark pine forests maintain resilient upper 

subalpine ecosystems in the face of climate change, 

particularly at the “climate change fronts” of high 

elevations and northern-most latitudes (Tomback and 

Achuff 2010). Whitebark pine forests may be more resilient to climate change than those forests that might 

replace them. For example, the late successional 

spruce-fir forests have more shade-tolerant conifers 

that pose greater risks to society, such as higher fire 

hazard, lower biodiversity, and greater water demand, 

than whitebark pine forests (Keane et al. 2012b). 

These spruce-fir forests may also provide fewer ecosystem services, such as late summer irrigation water, 

snowpack stability, and wildlife habitat.

A detailed restoration strategy was recently developed to coordinate restoration activities across the 

entire range of whitebark pine at several spatial scales 

(Keane et al. 2012b). This “rangewide strategy” is 

an important reference for researchers and managers 

involved in coordinating efforts to restore declining 

whitebark pine forests. In short, the strategy outlines 

several broad actions for whitebark pine restoration 

1. Introduction
2 USDA Forest Service RMRS-GTR-361. 2017.

including protecting putative rust-resistant trees to 

provide seeds for natural rust-resistant regeneration, 

collecting seed from proven rust-resistant (elite or 

plus) trees to grow genetically diverse rust-resistant 

seedlings for planting, and allowing wildfire and 

management treatments to create competition-free 

growing spaces for planting rust-resistant seedlings.

The strategy, however, is critically lacking validation of these actions under future climates because it 

doesn’t consider the impacts of climate change or the 

interactions between climate change, the recommended management actions, and other environmental 

stressors. Recommendations in the strategy do not 

address how to select the best areas to plant, burn, and 

treat given a changing climate, or how to determine 

how much of the landscape needs to be treated to 

ensure sustainability of whitebark pine forests.

1.1 This Report

This report provides managers with the latest 

information to integrate climate change impacts into 

plans to restore whitebark pine forests. It is intended 

to be a companion to the Keane et al. (2012b) rangewide strategy by addressing climate change impacts 

for each of the strategy’s guiding principles and 

restoration actions. The report begins with some basic 

ecological principles and knowledge that are important in addressing climate change effects on whitebark 

pine forests (Section 2). We then present a general 

summary of possible climate change projections for 

whitebark pine habitats and address the impacts of 

those projections on whitebark pine populations by 

summarizing information from the literature (Section 

3). Next, we present simulation modeling effort that 

was used to help develop climate change considerations in the rangewide restoration strategy (Section 

4). The FireBGCv2 model was used in a carefully designed experiment to simulate climate change impacts 

on landscape dynamics. Section 5 presents climate 

change considerations and recommendations for each 

of the rangewide restoration strategy’s guiding principles and restoration actions based on the literature 

review and FireBGCv2 simulation experiment. We 

also used independent runs of FireBGCv2 to illustrate 

potential ecosystem responses to restoration actions 

under changing climates. Finally, we discuss the limitations and caveats of this report (Section 6).

Predicting the response of whitebark pine to 

projected climate change is an immensely complicated task involving myriad complex ecological 

interactions for whitebark pine at multiple spatial 

and temporal scales. Climate warming, for example, 

may foster more high elevation fires that will burn 

more whitebark pine forests and kill trees that may be 

genetically resistant to blister rust, thereby reducing 

regeneration potential. Thus, it is nearly impossible to 

comprehensively assess the magnitude and direction 

of all impacts of climate change on all whitebark pine 

forests. We have attempted to address some of these 

interactions using the FireBGCv2 model, but only 

two landscapes could be simulated due to computational and logistical considerations. The model also 

lacks a simulation of other stressors in new climates. 

Therefore, we used an approach that integrates qualitative assessments from the climate change literature 

with the FireBGCv2 simulation results to develop recommendations for implementing effective restoration 

measures. Since most interactions occur at fine scales, 

such as the tree- and stand-level, we recommend that 

information in this report be modified to incorporate 

local conditions in restoration actions.

1.2 Rangewide Restoration Strategy 

Summary

The rangewide whitebark pine restoration strategy 

consists of a set of principles with associated actions 

to guide the design, planning, and implementation of 

restoration activities throughout the range of whitebark pine (Keane et al. 2012b) (fig. 2). The guiding 

principles (in bold type) represent broad areas of 

emphasis that should be addressed when restoring 

whitebark pine.

The most important guiding principle is to ensure 

that future populations of the species have some resistance to blister rust by increasing the total number 

of trees with genetic resistance to the blister rust 

pathogen (Promote rust resistance). The full genetic 

diversity across the range of whitebark pine must be 

preserved for the future by collecting and archiving 

seeds and growing and planting genetically diverse 
USDA Forest Service RMRS-GTR-361. 2017. 3

seedlings (Conserve genetic diversity). Mature, 

seed-producing, putatively rust-resistant whitebark 

pine trees in regions that are experiencing rapid 

decline must be protected from other native or exotic 

disturbances so that the apparent rust-resistant seed 

can be harvested in the future (Save seed sources). In 

areas where whitebark pine forests are declining due 

to insects, disease, or advanced succession, it might 

be appropriate for proactive or passive restoration 

treatments to create sustainable whitebark pine populations (Employ restoration treatments).

These principles are used to guide whitebark pine 

restoration plans at various spatial scales and are 

implemented using a set of ten possible management 

actions. One or more of these actions constitute 

meaningful steps toward restoring whitebark pine 

ecosystems.

1. Assess condition. Conduct assessments that 

document the status and trend of whitebark pine 

forests within regions.

2. Plan activities. Develop plans and design 

possible treatments for restoring whitebark pine 

ecosystems.

3. Reduce disturbance impacts. Implement proactive 

measures to reduce blister rust, mountain pine 

beetle, and other disturbance impacts on whitebark 

pine forests.

4. Gather seed. Collect seeds from trees that are 

proven rust-resistant or phenotypically rustresistant in areas exposed to blister rust, and from 

trees not tested in areas yet to be exposed to blister 

rust for archiving genetic diversity and variation.

5. Grow seedlings. Grow whitebark pine seedlings 

from seeds of proven (genetically tested in a rust 

screening process) rust-resistant trees.

6. Protect seed sources. Protect valuable rustresistant, seed-producing whitebark pine from 

future mortality caused by disturbance, climate 

change, and advanced competition.

Figure 2—The rangewide 

whitebark pine 

restoration strategy with 

the Guiding Principles (in 

green) implemented by 

the set of management 

actions (in blue) (Keane 

and others 2012b).
4 USDA Forest Service RMRS-GTR-361. 2017.

7. Implement treatments. Create conditions that 

encourage whitebark pine regeneration, conserve 

seed sources, and promote rust resistance.

8. Plant seedlings. Plant rust-resistant seedlings 

or sow seeds directly in treated or burned areas, 

especially in areas experiencing heavy whitebark 

pine mortality.

9. Monitor activities. Pre- and post-activity field 

sampling is critical to document the success or 

failure of restoration treatments.

10. Conduct research. Researchers must continuously 

develop new and more efficient methods and 

techniques for effective ecosystem restoration.

The rangewide strategy provides direction at six 

spatial scales of analysis (fig. 3): (1) coarse scale, the 

whitebark pine’s entire range in the United States; 

(2) regional scale using the U.S. Forest Service Pacific 

Northwest and Northern Regions as examples; (3) forest scale that is equivalent in size to National Forests 

and National Parks; (4) landscape scale, which could 

be watersheds, management units, or landforms; 

(5) stand scale where most proactive restoration activities take place; and (6) tree level, where intensive 

treatments are needed to protect individual whitebark 

pines.

Figure 3—The five scales of the rangewide whitebark pine restoration strategy from tree (~10 m2) 

through stand (103 m2) to landscape (105 m2) to Park or Forest (107 m2) to Region (1010 m2) 

and its entire range in the United States (Keane and others 2012b).
USDA Forest Service RMRS-GTR-361. 2017. 5

Restoration efforts, especially those that consider 

climate change, demand some basic knowledge of the 

ecology of species to be restored. While the Keane et 

al. (2012b) rangewide strategy includes extensive background material, we included this section to provide a 

brief synopsis of whitebark pine ecology to understand 

the potential response of the species to climate change. 

The material was taken from Arno and Hoff (1990), 

Keane et al. (2012b), and Tomback et al. (2001a).

2.1 Autecology

Whitebark pine forms extensive forests in the upper 

subalpine areas of the northern Rocky Mountains of 

the U.S. and the southern Rocky Mountains in Canada 

(fig. 4). It is abundant, with a patchy distribution on the 

eastern slopes of the Cascades and Coast Ranges and 

at the northern end of its distribution in the Canadian 

Rockies and Coast Ranges of British Columbia. It is 

also present but confined to specialized environments 

in the Sierra Nevada and in northern Nevada (Arno 

and Hoff 1990; Day 1967). Whitebark pine typically 

is less abundant on limestone soils, except in wetter 

areas near and north of the Canadian border (reviewed 

in Weaver 2001). In the northernmost Canadian Rocky 

Mountains, whitebark pine grows exclusively on siliceous soils as opposed to limestone soils.

2. Whitebark Pine Ecology

Figure 4—The range of whitebark pine from left, Arno and Hoff (1990) and right, Keane et al. (2011). Whitebark pine is a major 

seral species in the upper subalpine zone in these areas (seral sites) and, in some areas, whitebark pine is the only species 

able to maintain dominance because of cold, windy, droughty conditions (climax sites).
6 USDA Forest Service RMRS-GTR-361. 2017.

Whitebark pine is a long-lived tree of moderate 

shade tolerance (Minore 1979). It is common to find 

mature whitebark pine trees well over 400 years of 

age, especially on harsh growing sites (fig. 5). The 

oldest known individual is more than 1,275 years 

(Luckman et al. 1984; Perkins and Swetnam 1996). 

Whitebark pine is slow-growing in both height and 

diameter, and it rarely grows faster than most of its 

competitors except on the most severe sites (Arno 

and Hoff 1990). In fact, it rarely exhibits rapid height 

growth even on the most productive sites. Whitebark 

pine is one of the most drought tolerant species in 

the upper subalpine of North America (Arno and 

Hoff 1990). It reaches reproductive maturity by 40 to 

60 years and doesn’t reach optimal cone production 

capability until it is well over 200 years old (Arno and 

Hoff 1989).

2.2 Clark’s Nutcracker Interactions

Whitebark pine seeds are primarily dispersed by 

Clark’s nutcracker (Nucifraga columbiana Wilson), a 

bird related to crows, ravens, and jays (avian Family 

Corvidae) (fig. 6). The Clark’s nutcracker and whitebark pine coevolved mutualistic interaction where 

both species gain from the relationship (Lanner 1982; 

Tomback 1978, 1982, 1983; Tomback and Linhart 

1990). Whitebark pine has evolved dependence on 

nutcrackers to disperse its large wingless seeds, and 

in turn, nutcrackers utilize fresh and stored whitebark 

pine seeds as an important food source. Nutcrackers 

bury thousands of whitebark pine seeds each year as 

food stores in small clusters or “seed caches” across 

diverse forest terrain (Hutchins and Lanner 1982; 

Tomback 1982). Seeds in some of the caches are retrieved by nutcrackers primarily in spring and summer 

Figure 5—Typical appearance of a mature whitebark pine on 

a climax site. This tree is over 400 years old and displays 

the lyrate growth form that is commonly observed in the 

species.

Figure 6—A Clarks nutcracker harvesting seed from whitebark 

pine cones (photo courtesy of Diana Tomback from Keane 

et al. 2011).
USDA Forest Service RMRS-GTR-361. 2017. 7

months as an important food source for themselves 

and their young. However, not all seed caches are 

recovered, particularly after a large cone crop. Moist 

conditions from snowmelt and summer rains stimulate 

germination in the seeds; this leads to regeneration 

newly established whitebark pine forests (Tomback 

1982; Tomback et al. 2001c). Although whitebark 

pine depends nearly exclusively on nutcrackers, 

nutcrackers are not dependent on whitebark pine and 

harvest, cache, and eat seeds of other large-seeded 

pines (Tomback 1978, 1998).

In years with cone production, nutcrackers begin 

to harvest seeds as early as mid-July, removing pieces 

of unripe seeds from unripe, resinous cones, to feed 

themselves or their dependent juveniles (Tomback 

1978). Throughout the summer, nutcrackers also 

retrieve whitebark pine seed caches made the previous year (Tomback 1978; Vander Wall and Hutchins 

1983). Nutcrackers compete with pine squirrels for 

whitebark pine seeds—the Douglas squirrel or chickaree (Tamiasciurus douglasii) of the Pacific ranges 

and the widely distributed American red squirrel (T. 

hudsonicus)—which cut and store cones as a winter 

food supply. When cone crops are small, most of the 

seed crop will be depleted between the pine squirrels 

and early nutcracker foraging (e.g., McKinney and 

Tomback 2007, 2011).

Nutcrackers select diverse microsites for caching, 

burying caches 1 to 3 cm under various substrates, 

such as forest litter, mineral soil, gravel, or pumice. 

Nutcrackers place a large proportion of their caches at 

the base of trees; these microsites tend to experience 

early snowmelt. Nutcrackers also cache next to fallen 

trees and rocks, in fallen trees, in open terrain, among 

plants, on rocky ledges, and in rock fissures (Tomback 

1978). They place some of their caches in trees, 

tucking seeds in cracks, holes, and under the bark, 

which may allow easier access to seeds, especially in 

regions with heavy winter snowpack (Lorenz et al. 

2008; Tomback 1978). They will also cache whitebark 

pine seeds in the ground at and above treeline among 

patches of krummholz conifers. Whitebark pine seeds 

may be cached near source trees or transported 32 km 

or farther, often to higher or to lower elevations, 

where whitebark pine cannot grow as well (Lorenz et 

al. 2011; Tomback 1978). Nutcrackers appear to cache 

across a variety of terrain within their home range, but 

also within communal storage areas that are within 3 

to 4 km of source trees (Tomback 1978; Hutchins and 

Lanner 1982).

2.3 Mycorrhizae

Whitebark pine, like other western conifers, requires ectomycorrhizal fungi (ECM) for survival in 

the cold, infertile upper subalpine environs (Mohatt 

et al. 200; Read 1998). Mycorrhizae considerations 

should be taken into account in management strategies 

to help ensure establishment, maintenance, and conservation of this pine species under changing climates 

(see Keane et al. 2012b for details). Ectomycorrhizal 

fungi are important because they (as species or 

strains) vary in host specificity; soil preference; host 

age requirements; dispersal strategies; ability to 

enhance nitrogen (N) or phosphorus (P) uptake; types 

of N and P accessed; and protective abilities against 

pathogens, drought, heavy metals, and soil grazers 

(Tedersoo et al. 2009). There are 7,000 to 10,000 

species of ectomycorrhizal fungi associated with 

trees and woody shrubs (Taylor and Alexander 2005) 

(fig. 7). Whitebark pine hosts only a small subset of 

ecotomycorrhizae, currently assessed at fewer than 50 

species (Cripps and Grime 2009; Mohatt et al. 2008; 

Molina and Trappe 1994). Fourteen of those species 

are shared with limber pine (Cripps and Antibus 

2010). Practices and impacts potentially threatening 

to the maintenance of ectomycorrhizal diversity in 

the soil include tree-cutting, soil removal, mechanical 

disturbance, soil compaction, erosion, mining activities, liming, N-deposition, fertilization, high severity 

fire, reduction of tree age diversity, and promotion of 

certain grasses (Cripps and Antibus 2010). Additional 

Figure 7—Ecomycorrhzae on whitebark pine (photo 

courtesy of Cathy 

Cripps, Montana 

State University).
8 USDA Forest Service RMRS-GTR-361. 2017.

detrimental effects may result from the removal of 

certain understory or reservoir plants, woody debris, 

nurse trees, and other microsite components (reviewed by Wiensczyk et al. 2002). In general, these 

practices should be minimized to maintain high ECM 

fungal diversity in the soil.

Fire can affect ectomycorrhizal communities in 

soil in different ways, depending on the severity of 

the fire, forest type, and other factors (Cairney and 

Bastias 2007). High intensity fires may eliminate ectomycorrhizal communities because of the deep depth 

of heating in the soil, the loss of original tree hosts, 

and changes in abiotic conditions, including an increase in soil surface temperature (Neary et al. 1999). 

Although some fungal species survive and rapidly 

recolonize after fire, others do not, and consumption 

of the duff layer can inhibit establishment of some 

ectomycorrhizal fungi (Smith et al. 2005). Five years 

after a high severity fire, seedlings were partially 

colonized by suilloids, likely due to availability of a 

nearby inoculum (an adjacent unburned forest), the 

presence of dispersal vectors (deer and small mammals), and a management plan that included planting 

1 year after the burn (Trusty and Cripps 2010). Under 

other circumstances, recolonization of suilloids might 

take decades.

2.4 Community Characteristics

Whitebark pine forests occur in two high mountain 

biophysical settings (fig. 8). On productive, upper 

subalpine sites, whitebark pine is the major seral 

species that is eventually replaced, in the absence of 

disturbance, by the more shade-tolerant subalpine fir 

(Abies lasiocarpa (Hook.) Nutt.), Engelmann spruce 

(Picea engelmannii Parry ex Engelm), or mountain 

hemlock (Tsuga mertensiana (Bong.) Carrière), 

depending on geographic region (Arno and Weaver 

1990). These sites, referred to as “seral whitebark pine 

sites” here, support upright, closed-canopy forests in 

the upper subalpine zone, just above or overlapping 

with the elevational limit of the shade-intolerant 

lodgepole pine (Pinus contorta Douglas ex Louden) 

(Arno and Weaver 1990; Pfister et al. 1977). These 

two species can often share dominance of a site. 

Other minor to rare species found with whitebark pine 

on these sites in the northern Rocky Mountains are 

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), 

western larch (Larix occidentalis), western white pine 

(Pinus monticola Dougl.), and limber pine (Pinus 

flexilis James).

Biophysical settings where whitebark pine is the 

major, and sometimes only, tree species able to successfully dominate in high-elevation settings are called 

“climax whitebark pine sites.” Climax sites are often 

harsh and severe, and occur above the seral sites and 

just below tree line on relatively dry, windswept, cold 

slopes (Arno 1986; Arno and Weaver 1990; Steele 

et al. 1983) (fig. 9a). The stands tend to be open and 

well-spaced, sometimes mixed with other species, 

such as subalpine fir, spruce, and lodgepole pine in 

Figure 8—Elevational range of 

whitebark pine showing the two 

site types often associated with 

this species (Arno 2001). The 

climax type is dominated by 

whitebark pine because it is the 

only species able to inhabit these 

inhospitable environments. The 

seral type is where the shadeintolerant whitebark pine is 

eventually replaced by the more 

shade tolerant subalpine fir, 

spruce, and mountain hemlock.
USDA Forest Service RMRS-GTR-361. 2017. 9

the Rocky Mountains (fig. 9b). Co-occurring species 

in seral communities vary geographically and include 

western white pine (Pinus monticola), foxtail pine 

(Pinus balfouriana), red fir (Abies magnifica), and 

mountain hemlock (Tsuga mertensiana) (Tomback 

and Achuff 2010). On climax sites, these species may 

co-occur with whitebark pine, but only as scattered individuals with truncated growth forms and they rarely 

dominate (Arno and Hoff 1990; Arno and Weaver 

1990; Cooper et al. 1991; Pfister et al. 1977). Alpine 

larch (Larix lyallii) is often found on north-facing 

climax whitebark pine sites, often in association with 

sub-surface water (Arno and Habeck 1972). Whitebark 

pine also occurs as krummholz, elfin forests, clusters, 

groves, tree islands, and timber atolls in the alpine 

treeline ecotone (Arno and Hoff 1990; Resler and 

Tomback 2008; Tomback 1989; Tomback et al. 2014) 

and as a minor seral species in lower subalpine sites 

(Cooper et al. 1991; Pfister et al. 1977).

In the absence of fire, whitebark pine is eventually replaced by shade-tolerant competitors, namely 

subalpine fir, spruce, and mountain hemlock on the 

more productive seral whitebark pine sites (Arno and 

Hoff 1990; Campbell and Antos 2003; Keane 2001b) 

(fig. 9c,d). It can take 50 to 250 years for subalpine fir 

to replace whitebark pine in the overstory depending 

on the local environment and previous fire history 

(Arno and Hoff 1990; Keane 2001b; Kipfmueller and 

Kupfer 2005). Successional processes and rates vary 

considerably among and within the species range 

Figure 9—Typical whitebark pine stands on the two site types: (a,b) climax sites where whitebark is often the only tree species 

able to gain dominance (photos courtesy of Steve Arno) and (c,d) seral sites where whitebark pine is often found with 

lodgepole, subalpine fir, and spruce, and is often outcompeted by fir and spruce in the absence of fire (photos by Bob 

Keane, USDA Forest Service). 

a b

c d
10 USDA Forest Service RMRS-GTR-361. 2017.

(Campbell and Antos 2003; Keane 2001b). On seral 

sites, whitebark pine may occur as a minor to major 

seral component depending on biophysical settings, 

available seed sources, and disturbance type and 

severity.

2.5 Genetics

A key component of any whitebark pine restoration program is the planting of rust-resistant seed 

or seedlings for reforestation (Keane and Parsons 

2010b; Mahalovich and Dickerson 2004; Mahalovich 

and Foushee, submitted; Mahalovich et al. 2006). 

However, the planting of seed away from its source 

of collection increases the risk of maladaptation, 

which could lead to reduced growth and survival 

(Campbell 1979; Rehfeldt 1994). Seed transfer should 

be guided by natural levels of genetic variation and 

local adaptation in adaptive traits specific to the species in question (Hufford and Mazer 2003; McKay et 

al. 2005; Morgenstern 1996). Understanding genetic 

structure and levels of inbreeding are also necessary 

for predicting the possible effects of climate change 

(St. Clair et al. 2005).

Current predictions of future climates may complicate seed transfer. A diverse mixture of seed sources 

can balance suitability of resultant seedlings to current 

and future environments (Bower and Aitken 2008). 

The degree of diversity, however, must be balanced 

to offset possible adverse impacts of outbreeding 

depression. An individual trees’s genetic makeup and 

its interaction with the environment determine the 

amount of variation in measurable characteristics such 

as growth, survival, and tolerance to biotic and other 

stressors, such as disease resistance, insect tolerance, 

drought tolerance, and cold hardiness.

Genetic variation is critical because it provides 

the raw material for adaptation to new environments 

and it is the foundational component of biodiversity. 

The amount and structure of genetic variation within 

a population are influenced by many factors including gene flow, mutation, genetic drift, and selection 

(Frankham et al. 2002). Knowledge of a species’ 

genetic structure is essential to ensure that management activities do not adversely affect the amount 

and patterns of genetic diversity. Moreover, because 

whitebark pine is an outcrossed species (unrelated 

genetic material is introduced to the breeding line) 

(Bower and Aitken 2007; Jørgensen and Hamrick 

1997; Mahalovich and Hipkins 2011), it is imperative 

that management activities do not adversely contribute to an increase in inbreeding.

A few studies have assessed the genetic variation 

of whitebark pine in adaptive traits such as cold 

hardiness, growth, phenology, stem form, and disease 

resistance. Mahalovich et al. (2006) found significant 

differences between seed sources for late winter cold 

injury (cold hardiness), survival, height growth, and 

blister rust resistance. Most of the differentiation is 

attributed to broadscale differences among geographic 

regions, with sources from the Greater Yellowstone 

Area distinct from other sources in Montana, Idaho, 

and eastern Washington. Cold hardiness and rust 

resistance show weak and opposite geographic patterns, with sources in the Pacific Northwest having 

higher rust resistance and lower cold hardiness, and 

southeastern sources having lower rust resistance and 

higher cold hardiness. Bower and Aitken (2006) found 

that the level of cold hardiness varies throughout the 

year from below –70 °C in the winter to –9 °C in 

the summer. Acclimation and de-acclimation to cold 

often occur rapidly over a period of 2 to 3 weeks in 

the fall and spring, respectively; however, even during 

the period of active shoot elongation, whitebark pine 

shows greater hardiness to cold than most conifers. 

Most trait correlations are favorable; unfavorable correlations with cold hardiness can be managed through 

zoning, a restricted selection index (Mahalovich et 

al. 2006, Mahalovich and Foushee, submitted), or 

site-specific planting prescriptions to avoid frost 

pockets and swales (McCaughey et al. 2009). Family 

heritabilities for survival, height, cold hardiness, and 

blister rust resistance are moderate to high (h2

F = 

0.68–0.99) (Mahalovich et al. 2006). Gentle clines in 

elevation for height and rust resistance and moderate 

clines in cold hardiness characterize whitebark pine as 

having a generalist adaptive strategy in the Northern 

Rockies. Moreover, adaptation to heterogeneous environments does not appear to be as strongly related to 

phenotypic plasticity as western white pine (Potter et 

al., in prep.).
USDA Forest Service RMRS-GTR-361. 2017. 11

2.6 Disturbance

2.6.1 Wildland Fire

Whitebark pine fire regimes often contain elements 

of all three types of fire severities: non-lethal, mixed 

severity, and stand-replacing (Arno and Hoff 1990; 

Barrett 2008; Campbell et al. 2011; Larson et al. 2010; 

Morgan et al. 1994b; Murray 2008; Siderius and 

Murray 2005) (fig. 10). Some whitebark pine stands 

may experience low-intensity, non-lethal surface fires 

because of sparse surface and canopy fuel loadings and 

unique topographical settings. These sites are mostly 

found in the southern parts of the species’ range in the 

Rocky Mountains or on high, dry ridges and represent 

less than 10 percent of existing whitebark pine forests 

(Keane et al. 1994; Morgan et al. 1994b). Whitebark 

pine can survive low-intensity surface fires better 

than most of its competitors, especially subalpine fir, 

because it has somewhat thicker bark, higher and thinner crowns, and deeper roots (Arno and Hoff 1990; 

Morgan and Bunting 1990; Ryan and Reinhardt 1988). 

Non-lethal surface fires have historically maintained 

whitebark pine dominance in the overstory and prolonged whitebark pine cone production by delaying 

succession (Keane 2001b).

The more common mixed-severity fire regime is 

characterized by fire severities that are highly variable in space and time, creating complex patterns of 

tree survival and mortality on the landscape (Murray 

et al. 1998; Romme and Knight 1981; Siderius and 

Murray 2005) (fig. 10). Mixed-severity fires can 

occur at 60 to 300+ year intervals and sometimes 

Figure 10—The three severities of fire that occur in whitebark pine ecosystems. The low severity surface fire is a low intensity 

fire that creeps along on the ground killing small seedlings, saplings, and fire-sensitive trees. The mixed severity fire burns 

in a patchy pattern with some patches having high tree mortality and other patches have low tree mortality. The standreplacement fire kills most (>90 percent) trees in large patches (Keane and others 2012b; photos taken by R. Keane, USDA 

Forest Service and anonymous photographers).

Stand-replacement fire

Low severity surface fire

Mixed severity fire
12 USDA Forest Service RMRS-GTR-361. 2017.

over 500 years, depending on drought cycles, fuel 

conditions, landscape burn history, and frequencies 

of high wind events (Arno and Hoff 1990; Morgan 

et al. 1994b; Walsh 2005). Individual mixed-severity 

fires can include patches of non-lethal surface fires 

with differential mortality mixed with patches of variable mortality stand-replacement fires (Morgan et al. 

1994b). Sometimes fires burn in sparse ground fuels at 

low severities, killing the smallest trees and the most 

fire-susceptible overstory species, often subalpine fir 

(Walsh 2005). Severity increases if the fire enters areas 

with high fuel loads or under high wind or drought 

conditions because these situations facilitate ignition 

of tree crowns, thereby creating patches of fire-killed 

trees (Lasko 1990). Burned patches vary widely in size 

depending on topography and fuels; these openings 

provide important seed caching habitat for the Clark’s 

nutcracker (Norment 1991; Tomback et al. 1990).

Many whitebark pine forests in the northern Rocky 

Mountains, Cascades, and Greater Yellowstone Area 

historically experienced periodic large, standreplacement fires that occurred at intervals of over 

250 years) (fig. 10). Stand-replacement fires also 

occurred within mixed-severity fire regimes, but as 

infrequent events (Morgan and Bunting 1989; Romme 

1980). Stand-replacement fires are usually wind-driven 

and often originate in lower, forested stands (Murray 

et al. 1998), and they create large burned patches that 

may be distant from tree seed sources (Beighley and 

Bishop 1990). Whitebark pine has an advantage over 

its competitors in that it readily colonizes large, standreplacement burns because its seeds are transported 

great distances by Clark’s nutcracker (Lorenz et al. 

2008; Tomback 1982, 2005). Nutcrackers can disperse 

whitebark pine seeds up to 100 times farther (over 

10 km) than wind can disperse seeds of subalpine fir 

and spruce (McCaughey et al. 1985; Tomback et al. 

1990, 1993). Since nutcrackers often cache in open 

sites with many visual cues, stands burned by mixedor stand-replacement fire provide favorable sites for 

seed caching and competition free seedling growth 

(McCaughey and Weaver 1990; Sund et al. 1991; 

Tomback 1998). Murray et al. (1995) found that larger 

burns were associated with a greater volume per hectare of whitebark pine as compared to smaller burns in 

the Bitterroot Mountains.

Whitebark pine benefits from wildland fire because 

it is better adapted to surviving fire and also to regenerating in burned areas than associated shade-tolerant 

trees (Arno and Hoff 1990). Without fire, most seral 

whitebark pine forests would be successionally replaced by subalpine fir or some other shade-tolerant 

high-elevation species. Fire, whitebark pine, and the 

Clark’s nutcracker form an important high-mountain 

ecological triangle (Tomback 1989). Fire burns areas 

thereby enhancing fine-scale patterns thereby increasing visual cues that then facilitate nutcracker caching. 

Unretrieved whitebark pine seed cached in burned areas can germinate and become seedlings that can grow 

into mature trees unfettered by competition.

2.6.2 Mountain Pine Beetle

The primary insect that kills whitebark pine trees 

is the mountain pine beetle (MPB) (Arno and Hoff 

1990). Mountain pine beetles range from the Pacific 

Coast east to the Black Hills of South Dakota and from 

northern British Columbia and western Alberta south 

into northwestern Mexico. It is a native, cambialfeeding bark beetle of all the western pines, including 

western white, limber, ponderosa, and lodgepole pines. 

Its entire life cycle is spent beneath the bark of host 

trees, except when adults emerge from brood trees in 

the early summer and fly in search of new host trees. 

Trees defend themselves with various secondary 

chemicals and by exuding pitch, but these matchheadsize beetles can overwhelm these defenses with a 

mass attack strategy through sheer numbers of beetles 

(Logan and Powell 2001).

Beetles develop through four stages: egg, larva, 

pupa, and adult (Amman and Cole 1983). Adults mate 

and then lay eggs that hatch into larvae that develop 

in the phloem of the tree through the pupa stage, thus 

completing the life cycle—a 1-year or univoltine life 

cycle. Larval growth is aided by one of two symbiotic 

fungi—Grosmannia clavigera or Ophiostoma montium

(Adams and Six 2007; Six 2003; Six and Paine 1998). 

Adult beetles deposit the fungi in the tree as they excavate galleries where the females lay eggs. The fungi 

colonize the phloem and sapwood of the infected tree 

with the fungal hyphae providing nutrition to beetle 

larvae (Six 2003). Adult beetles also feed on fungal 

spores in the pupal chambers before emergence and 
USDA Forest Service RMRS-GTR-361. 2017. 13

dispersal from the host tree. One to several months after the tree is infested by MPB, the sapwood discolors 

to a bluish tint caused by the fungi.

Beetle life cycles take 1 to 2 years, and many 

empirical, laboratory, and modeling research studies 

have been undertaken to understand the causes of life 

cycle variability (Bentz et al. 1991; Logan and Powell 

2001; Powell et al. 1996) (fig. 11). Over most of its 

range, the beetle has one generation per year, but two 

generations have been observed in high-elevation 

areas, including whitebark pine forests, when warm 

temperatures prevail (Logan and Bentz 1999). The 

traditionally cold environment of the whitebark pine 

forest creates an unfavorable heat balance for beetle 

development in most years (Amman 1973; Logan 

and Bentz 1999). Mountain pine beetles have mechanisms to survive in sub-zero temperatures; however, 

sustained, sub-freezing temperatures may result in 

mortality in all life stages (Amman and Cole 1983; 

Regniere and Bentz 2007). Beetle development is 

under direct temperature control (Logan and Bentz 

1999), and warm temperatures favor successful brood 

development, beetle survivorship, and successful attacks (Amman 1972, 1973; Bentz et al. 1991; Logan 

and Bentz 1999; Reid and Gates 1970). Generally, epidemics collapse due to one of two factors: an extreme 

cold snap (less than –18 °C) in early fall or late spring 

and winter temperatures below –37 °C (Cole et al. 

1989); or a lack of susceptible host trees.

Once inside the bark, the MPB disrupts the connectivity of the water transport system of the tree, 

damaging the tree by mechanically girdling the stem 

as adults and larvae create galleries in the phloem. 

More importantly, the MPB introduces a blue stain 

fungus that inhibits water transport and may eventually 

kill the tree (Six 2003; Six and Paine 1998). Some 

trees have the ability to exude pitch at the attack sites 

as a defense mechanism, but these are infrequent in 

whitebark pine. Whitebark pine, like other pines, may 

sustain “strip attacks” where a vertical portion of 

inner bark is killed while the rest of the stem is unaffected and continues to transport water and nutrients. 

Foliage of successfully attacked trees generally fades 

uniformly through the crown from yellowish green to 

shades of orange, rust and red. Whitebark pine crowns 

generally fade to a rust color the year after attack, but 

may take 2 to 3 years in some hearty individuals.

The severity of the current MPB outbreak in high 

elevation pines is attributed to warmer winters that 

have increased survival and warmer summers that 

have allowed some proportion of broods to shift from 

a 2-year life cycle to a 1-year life cycle (Bentz et al. 

2011, 2014; Dooley 2012; Logan and Powell 2001; 

Logan et al. 2010). Within the past decade, 47 million 

ha across all pine types in the Rocky Mountains have 

been affected by the mountain pine beetle (Raffa et al. 

2008). Recent climate projections indicate this trend 

will continue in the future, with a possible increase in 

the frequency of back-to-back MPB outbreaks subjecting surviving host trees for further attacks (reviewed in 

Mahalovich 2013).

Figure 11—The complex life cycle of the mountain pine beetle (taken from Logan and McFarlane 2010). The eggs 

are laid inside the bark in mid-summer and then hatch and overwinter as larva in galleries. 
14 USDA Forest Service RMRS-GTR-361. 2017.

2.6.3 White Pine Blister Rust

White pine blister rust (WPBR) is an exotic fungal 

disease caused by the fungus Cronartium ribicola and 

infects all five-needle pines including sugar, western 

white, limber, southwestern white, bristlecone, and 

foxtail pines (Burns et al. 2007; Geils et al. 2010; 

McDonald and Hoff 2001). It was introduced to 

eastern North America in the 1890s and into western 

North America in the first decade of the 20th century 

on infected eastern white pine nursery stock grown in 

France and shipped to Vancouver British Columbia. 

Since then, the pathogen has spread across all or part 

of the ranges of all five-needle pines in the United 

States and Canada, except for Great Basin bristlecone 

pine. It was once thought that the dry environments of 

western North America were inhospitable to WPBR, 

but the rust has since infected pines in even the most 

severe and hostile climates (Burns et al. 2008; Resler 

and Tomback 2008; Tomback and Achuff 2010).

WPBR has a complex life cycle involving five different spore types on three groups of alternate hosts. 

Blister rust cankers on the white pine hosts produce 

aeciospores, which transmit the disease to the alternate 

hosts. Shrubs of genus Ribes are the most common 

hosts, but herbaceous plants of the genera Pedicularis

and Castilleja also serve as hosts (Geils et al. 2010; 

McDonald et al. 2006). Basiodiospores produced 

by the alternate hosts are fragile, short-lived spores 

that infect pines by entering needle stomata. This 

stage of the life cycle is most climatically limited, 

requiring moderate temperatures and high humidity 

for spore production and transmission to pines (Van 

Arsdel 1967; Van Arsdel et al. 1956). As reviewed 

in Mahalovich (2013), regional geography and local 

physiographic features dictate whether pine infections 

originate predominately from sources within the pine 

stand (local spread) or from distant sources (longdistance dispersal) (Zambino 2010).

There are two modes of WPBR dispersal:

1. Diffusion. Basidiospores may be dispersed by 

diffusion at the local scale, where the density of 

spores for deposition and infection from a single 

source declines in magnitude with the square root 

of distance. In this mode, basidiospores usually 

travel only a short distance, averaging 300 m since 

the delicate spores are vulnerable to desiccation and 

sunlight (Kinloch 2003). Local spread (diffusion) 

and intensification of blister rust in five-needle 

pines progresses concentrically and relatively 

slowly (Kinloch 2003).

2. Turbulent mixing. Long distance flow can occur 

at multiple scales as a result of distinct air masses 

with a combination of laminar flow and disruption 

by turbulent mixing (Van Arsdel et al. 2006).

The range of basidiospore dispersal also varies 

by position of blister rust cankers within an infested 

crown. Hunt (1983) found that within western white 

pine stands in British Columbia, cankers near the 

ground resulted from Ribes spp. inoculum produced in 

the stand, whereas cankers high in the crown resulted 

when inoculum from a distant source was carried into 

the stand by down-slope air flow. Thus, average distance estimates of basidiospore transport range from 

15 m (diffusion) to 27 km (turbulent mixing), where 

patterns of pine infection and distances of effective 

dissemination also vary from year-to-year with annual 

variation in the weather (Zambino 2010). Lastly, moist 

air masses in late summer form the backdrop of wave 

years, where basidiospores can survive traveling farther over wider geographic areas (Van Arsdel 1967).

Spores that germinate within the needle tissue 

produce hyphae, which then grow into the vascular 

system and the needle stem (see McDonald and Hoff 

2001; Geils et al. 2010 and references therein). The 

hyphae grow down the branch and eventually into 

the tree bole. There, 2 to 3 years later, it eventually 

forms a canker in which spore structures called pycnia 

produce pycniaspores, which are not infectious but 

are needed for fertilization by insects. Aeciospores are 

produced from fertilized pycnia found in bright orange 

blisters in the early summer of the following year. 

These aeciospores are hardy with thick walls and can 

travel long distances to infect Ribes spp. Aeciospore 

transmission to Ribes spp. has been estimated to be as 

far as 300 km (Fujioka 1992) to 500 km during wave 

years (Mielke 1943). Wave-year events can either be 

a significant local intensification of rust infection or a 

long-distance jump of blister rust (Mahalovich 2013). 

Wave years need not be particularly wet; however, 

“dry” events are typically preceded by 1 or 2 wet years 
USDA Forest Service RMRS-GTR-361. 2017. 15

(Fujioka 1992). Fragile orange urediniospores are produced on the underside of the alternate host leaves and 

infect other alternate hosts throughout the summer. In 

the fall, teliospores form on infected Ribes spp. leaves, 

and these germinate to form the basiodiospores that 

infect the pine.

WPBR damages and then kills whitebark pine trees 

from the top down by girdling branches and stems 

(Hoff 1992), which reduces cone production before 

death. Kinloch (2003) argued that the most insidious 

impact of rust is its destruction of host natural regeneration, which consequently alters natural succession. 

This reduction in regeneration potential is further 

exacerbated by a suite of interactions among vertebrate 

predation and pine cone production that are altered 

both directly and diffusely by disproportionate whitebark pine mortality and reduced seed cone availability.

Current estimates of whitebark pine resistance to 

WPBR in high rust-mortality areas ranged from 33 

percent in a small sample (n = 3) (Hoff et al. 1980) to 

47 percent (n = 108) (Mahalovich et al. 2006) in the 

Inland Northwest and 26.3 percent (n = 43) on the 

Pacific Coast (Sniezko et al. 2007). Seeds from the 

healthy trees in stands heavily infected and damaged 

by blister rust would have the highest probability of 

having some resistance to blister rust. Resistance to 

rust can be manifest in many mechanisms or adaptations including no spotting (failure of the germinating 

basidiospore to penetrate the stomatal cavity), 

premature shedding of infected needles, fungicidal 

short-shoot reaction, bark reactions walling off canker 

development with callous tissue, and canker tolerance (Hoff 1992; Hoff et al. 1980; Mahalovich 2013; 

Zambino and McDonald 2004). If whitebark pine 

continues to decline, the surviving (rust-resistant) trees 

that have these resistance mechanisms will be at such 

low numbers that virtually all of their seeds will be 

eaten and little of the resistance will be passed to the 

future generations (McKinney and Tomback 2007). 

Some landscapes in the northern Rocky Mountains 

contain so few undamaged trees and apparent rustresistant whitebark pine seed sources that there is 

major concern that whitebark pine seed dispersal is not 

occurring at any magnitude. Between the pine squirrels cutting down cones and nutcrackers consuming 

unripe seeds, there may be few ripe seeds available for 

seed caching.

2.7 Current Status

The rapid decline of whitebark pine across most 

of its range in North America is mostly a result of 

multiple concurrent human-caused and natural events 

(Arno 1986; Kendall 1995; Kendall and Keane 2001; 

Raffa et al. 2008; Tomback et al. 2001b; Zeglen 2007; 

for recent reviews, see Tomback and Achuff 2010 and 

Tomback et al. 2011a). First, several major natural 

MPB outbreaks over the last 70 years have killed many 

cone-bearing whitebark pine trees across its range 

(Arno 1986; Baker et al. 1971; Waring and Six 2005). 

The most recent MPB outbreak, which began in the late 

1990s, apparently has achieved unprecedented intensity 

and geographic extent, driven by higher temperatures 

that many associate with anthropogenic climatewarming (Logan and Powell 2001).

Next, an extensive and successful fire exclusion 

program in western North America for most of the 20th

century has reduced the area that can be potentially 

inhabited by the shade-intolerant whitebark pine. 

Because there were fewer fires, late successional whitebark pine communities became increasingly common 

and early successional communities increasingly rare. 

This resulted in a cumulative decline of whitebark pine 

at a landscape scale over time (Keane and Arno 1993; 

Kipfmueller and Kupfer 2005; Morgan and Bunting 

1990).

And finally, the potentially most threatening events 

were multiple introductions of the exotic fungal pathogen Cronartium ribicola to the western United States 

and Canada in the early 1900s (Hunt 1983), which 

causes WPBR (Geils et al. 2010; McDonald and Hoff 

2001). Whitebark pine was historically considered 

the most susceptible of the five-needle white pines to 

blister rust (Hoff et al. 1980; Kendall and Keane 2001; 

Schwandt 2006). More recent rust screening trials 

from 2006 to 2014 involving larger and geographically 

widespread samples have shown that whitebark pine 

exceeded western white pine seedlings in percent rust 

resistance in two of the three tests (Mahalovich and 

Foushee, submitted).
16 USDA Forest Service RMRS-GTR-361. 2017.

The cumulative interactions of these three agents 

have resulted in a rapid decrease in mature whitebark 

pine, particularly in the more mesic parts of its range 

(>80 percent mortality in northern Idaho, northwestern 

Montana, northern Cascades) (Campbell and Antos 

2000; Elderd et al. 2008; Fiedler and McKinney 

2014; Keane and Arno 1993; Six and Adams 2007). 

Moreover, predicted changes in the Pacific Northwest 

climate brought about by global climate change could 

further accelerate the decline of this important tree species by directly influencing the regeneration, growth, 

and mortality of whitebark pine and indirectly by increasing the frequency, intensity, and duration of MPB, 

WPBR, and fire (Blaustein and Dobson 2006; Logan 

and Powell 2001; Romme and Turner 1991; Running 

2006).

When incorporating climate change into restoration 

strategies, it is important to evaluate effects of climate 

change across all of the ecological interactions and 

processes that influence whitebark pine abundance. 

All life stages, including reproduction, seed dispersal, 

regeneration, phenology, and mortality, should be 

considered, not just growth response (Koteen 1999). 

All ecosystem processes, specifically disturbance regimes, hydrology, and successional dynamics, should 

also be appraised. Climate change impacts on spatial 

processes, such as seed dispersal and disturbance 

spread, must also be assessed (Chuine 2010; Loehle 

and LeBlanc 1996; Price et al. 2001).

Moreover, climate change effects should be assessed at multiple spatial and temporal scales to 

ensure all interactions are appropriately evaluated 

(Gardner et al. 1996; Keane et al. 2015). Regeneration 

and mortality responses, for example, are best evaluated at the tree level, while disturbance dynamics 

are more appropriately assessed at landscape scales. 

While climate change may adversely affect whitebark 

pine populations across its entire range (Chang et al. 

2014; Hansen and Phillips 2015; Koteen 1999), climate response must be evaluated locally to determine 

any exceptional regions of continued dominance and 

the most efficient set of restoration goals (Keane et 

al. 2008b). Maintaining whitebark pine on the high 

mountain landscape using controlled wildfires and 

prescribed fires, for example, may only be possible in 

a narrow set of situations dictated by local constraints 

(e.g., smoke regulations, nearby developments, 

whitebark pine abundance). Restoration actions are 

probably more effective if implemented at landscape 

scales (see Section 4). Matching restoration objectives and climate change impacts with local landscape 

conditions will more effectively create resilient and 

resistant landscapes that are crucial for future conservation of whitebark pine (Craig 2009; Diggins et al. 

2010).

Predicted warming, drought, and highly variable 

precipitation patterns have the potential to significantly impact whitebark pine ecosystems (Bartlein et 

al. 1997; Romme and Turner 1991), but the directions 

and magnitudes of these changes on high elevation 

landscapes in the range of whitebark pine are still 

largely unknown (Loehman et al. 2011b). Some 

aspects of whitebark pine ecology, such as drought 

tolerance, may enable the species to adjust to rapid 

climate change. Because of its long life span and phenotypic plasticity, whitebark pine is a hardy species 

that may have the adaptations to live through major 

climatic cycles such as those experienced in the past 

(Arno and Hoff 1990).

This Section 3 describes some important aspects 

of whitebark pine ecosystems that may make them 

more or less susceptible to climate change impacts 

over the next century. First, a climate projection was 

taken from the Northern Region Assessment Program 

vulnerability assessment for the northern Rocky 

Mountains (Keane et al., in press). Then, various ecological aspects of whitebark pine that are important 

to assessing climate change responses are discussed: 

species life cycles, genetics, and succession dynamics. Next, changes in the biophysical environment 

as a consequence of climate change are evaluated. 

Following that are some assessments of how climate change may alter those disturbance processes 

important in whitebark pine dynamics, specifically 

3. Climate Change Impacts: A Literature Review
USDA Forest Service RMRS-GTR-361. 2017. 17

fire, MPB, and WPBR responses to climate change. 

Finally, possible climate change influences on the 

Clark’s nutcracker are discussed. The climate projections for the whitebark pine’s range are summarized 

in table 1. Responses of whitebark pine ecosystems 

to these climate projections are presented in table 2 

for reference when evaluating the mitigating actions 

recommended in Section 5.

3.1 Climate Projections

3.1.1 Understanding Climate Change 

Predictions

Predicting spatially explicit daily weather for the 

next century is an incredibly difficult task considering the complexity of the earth’s interacting physical 

and biological systems. These predictions are usually 

made using global circulation models (GCMs), which 

are three-dimensional physical models that simulate 

weather and climate using conservation of mass and 

momentum approaches (Edenhofer et al. 2011). Many 

GCMs are currently used to simulate future climate at 

coarse scales. Although it is clear that all models are 

predicting warming climates because of increasing 

atmospheric greenhouse gases (GHGs), specifically 

CO2 (Stocker et al. 2013), there is a great deal of 

difference in the magnitude and rate of change across 

model projections (Roe and Baker 2007; Stainforth et 

al. 2005) (fig. 12). Not only do these GCMs simulate 

complex biophysical processes and feedback systems, 

they must also simulate society’s response to climate 

change through the use of technological innovations 

to minimize or mitigate the effects of increasing GHG 

emissions.

Societal responses to GHG emissions are usually 

accounted for in various GCM simulation scenarios 

(fig. 12). As a result, there are vast differences in 

climate forecasts among GCMs and GHG scenarios 

contributing to a high degree of uncertainty in climate 

forecasts. This diversity of projections ultimately 

makes it difficult to design restoration strategies and 

actions under future climates (Stocker et al. 2013) 

Table 1—A summary of important climate change impacts on those biophysical processes that affect vegetation 

response for the CMIP5 RCP8.5 climate scenario outputs.

Evaluation category Climate change impact

Precipitation No trends in precipitation over the last century; higher variability range wide (16% 

higher). No significant trends for the western portions of whitebark pine’s range and 

lower variation for east of the Continental Divide (CD) with a slight increase in 

precipitation.

Seasonal precipitation West of the CD there is a decrease of 30% summer precipitation, and slightly higher 

in other seasons. East of the CD, it appears there might be a slight increase. 

Precipitation in all areas are highly uncertain and variable.

Average temperature Temperatures have increased by 0.8 °C this past century. Considerable warming of 

1.2–4.8 °C for areas west of the CD and 1.6–4.6 °C for areas east of the CD. Most 

warming occurs in the daily minimum than in the daily maximum; higher nighttime 

temperatures;

Maximum summer temps Considerable warming of 1.9–5.3 °C west of CD and even higher east of the CD. 

Heat extremes will increase.

Minimum winter temps Increased by 1.1–4.6 °C; greater in the shoulder seasons. Cold extremes will decrease

Freeze days Decrease by 35 days in west of CD and 24 days east of the CD

Frost free days Increase by 35 days west of the CD and 23 days east of the CD.

Drought duration Droughts will be longer, but no indication of how much longer

Fire Increased fire frequencies (1–3 times more fire). Possible increase in fire size (mostly 

as a result of fire exclusion). Possible increases in fire severity although highly 

variable and ecosystem dependent.

Insect outbreaks Increased frequencies but depends on host and disease species.

Disease outbreaks Increased frequencies but depends on host and disease species.

Extreme weather Increased high precipitation events (>2.5 cm) by 13%.
18 USDA Forest Service RMRS-GTR-361. 2017. Table 2—Summary of the effects of climate change on whitebark pine ecosystems at multiple scales (Tree, Community, Landscape) and multiple interactions (Disturbance, Nutcracker). Columns in the table include Factor (the ecosystem characteristic or process being impacted by climate change), the Climate Effect (the effect of climate change on that factor), Impact (the severity of that effect on the factor), Direction (the trend in whitebark pine population stability), Importance (the importance of the change in the factor to the decline of whitebark pine and the effectiveness of restoration on a scale of 1-low to 10-high), and Notes (any other material deemed important). Factor Process affected Climate effect Impact Direction Importance Notes Tree Reproduction Cone crop frequency and abundance Increase Moderate Increase 4 Fine scale and coarse scale climate dynamics can influence cone crop dynamics Regeneration Germination Decrease Low No change 2 Establishment Uncertain Moderate Unknown 7 Difficult to evaluate if upper subalpine areas will be so hot and dry that seedlings will die Dispersal Uncertain Low No change 5 Little is known of nutcracker-climate relationships Growth Tree diameter and height growth Increase High Increase 5 It is unknown if understory whitebark pine seedlings and saplings will release Mortality Tree death Uncertain High Increase 7 It is unknown if gains in vigor reduce mortality rates Community Successional replacement Competitor growth Accelerate towards shade tolerant species High Decrease 8 Rate of successional replacement of whitebark pine to subalpine fir, spruce, and hemlock will increase Competitor growth and regeneration Low soil moisture causes high competitor mortality Low Increase 3 Droughty sites Disturbance Wildland fire Increased fire frequency and intensity May kill more whitebark pine, especially rust-resistant individuals High Decrease 7 Frequency might be so quick that fires burn regeneration before they reach reproductive maturity Larger and more severe fires Increased burn areas create more caching sites free from competition High Increase 7 May kill those mature, cone-bearing trees that are somewhat rust- resistant thereby causing a loss of genetic diversity MPB-caused Mortality Increased frequency and severity of outbreaks May kill more whitebark pine, especially rust-resistant individuals High Decrease 8 High mortality in cone-bearing trees resulting in a loss of rust- resistance and genetic diversity WPBR-caused mortality Increased spread Increased spore production and spread Moderate Decrease 5 Highly variable climate may facilitate mutations in WPBR
USDA Forest Service RMRS-GTR-361. 2017. 19

(figs. 12, 13). Moreover, the spatial resolutions of 

GCM predictions are usually quite coarse (1 to 5 

degrees longitude and latitude), so another suite of 

models are often used to extrapolate GCM gridded 

daily data to finer scales (4 to 50 km pixel sizes) 

(Wilby and Wigley 1997).

Unfortunately, even these fine scale weather extrapolations are still too coarse to accurately describe 

local, landscape-level climate changes for most 

whitebark pine forests because the species occurs 

in unique landscape settings (tops of mountains) 

that are highly patterned at resolutions finer than the 

resolution of most downscaled GCM extrapolations 

(e.g., 4 km). Mountain ridges, for example, are long, 

thin linear features that are only detected at scales 

below 4 km. Another confounding factor is that these 

model extrapolations have shallow temporal depth; 

most GCMs only simulate climate for 50 to 100 

years, which is often too short to detect changes in 

whitebark pine populations.

In 1995, the Coupled Model Intercomparison 

Project (CMIP; see Meehl et al. 2000) was created 

to provide climate change scientists with a spatial 

database of coupled GCM simulations to use in 

other climate change studies. CMIP also was created 

to determine why different models gave different 

Figure 12—Figure from Vano et al. (2015) showing the differences in climate projections among Global 

Circulation Models (GCMs) and two emissions scenarios (RCP4.5, RCP8.5) from the CMIP study. This 

graph shows isolines that represent constant change in vegetation carbon for the three regions: (a) 

OR/WA Coast Ranges, (b) Western Cascades, and (c) Columbia Basin. All three of these regions are 

within the range of whitebark pine. GCM positions in ∆P-∆T space are identified with performance 

ranking numbers. Blue numbers are RCP4.5 and red numbers are RCP8.5, where lighter numbers 

indicate multiple runs for the same GCM. Note the high uncertainty across all models and scenarios.
20 USDA Forest Service RMRS-GTR-361. 2017.

outputs in response to the same input and to simply 

identify “consensus” in model predictions. In CMIP, 

the complete physical climate system, including the 

oceans and sea ice, adjust to prescribed atmospheric 

concentrations of carbon dioxide (CO2). The first 

phase of CMIP, called CMIP1, collected output 

from coupled GCM control runs in which CO2, 

solar brightness and other external climatic forcings are kept constant. A subsequent phase, CMIP2, 

collected output from both model control runs and 

matching runs in which CO2 increases at the rate of 

1 percent per year. Climate projections from CMIP3 

and CMIP5 are used in this report. The CMIP3/5 

data were downscaled to an 8 km resolution and are 

stratified by the time frames 2000 to 2050 and 2050 

to 2090.

Two sets of climate change emission scenarios are 

used for illustration in this paper because of shifts in 

Edenhofer et al. (2011) scenario development. The 

B2 and A2 scenarios are part of the IPCC Special 

Report Emission Scenarios (SRES) created prior to 

2011 to describe potential effects of society’s reaction to climate change (fig. 13). The A2 scenario 

is considered the hot, dry scenario. It represents a 

world with less international cooperation and more 

self-reliance on natural resources and government 

structures (Stocker et al. 2013). In the B2 world, 

there is increased concern for environmental and 

social sustainability. Environmentally aware citizens 

across the globe develop government, business, and 

social strategies for decreasing emissions and mitigating climate change.

In the Edenhofer et al. (2011) report, the SRES 

scenarios were replaced by a set of four scenarios that 

represented the total radiative forcing of the cumulative effects of human emissions of GHGs, expressed 

in W m-2 of additional energy. These representative 

concentration pathways (RCPs) are indirectly tied to 

society’s response to climate change (fig. 14). The 

two RCP scenarios used in this report are RCP4.5 and 

RCP8.5 representing 4.5 W m-2 and 8.5 W m-2 of additional energy due to GHG emissions. Although it is 

Figure 13—A set of emission scenarios for one 

GCM showing the (a) projected CO2 emissions 

over the next century, and (b) atmospheric CO2

concentrations that those emissions will create till 

year 2100. Also shown (c) is the great uncertainty 

(shaded bars) within and across emission scenarios 

(B2, A1T, B2, A2, A1F1) especially towards the latter 

end of the century (taken from Solomon et al. 2007 

and Houghton et al. 2001. Used with permission).

2000 2020 2040 2060 2080 2100

5

10

15

20

25

CO2 emissions (Gt C/yr)

Several models

all SRES

envelope

All SRES envelope

including land-ice

uncertainty

Model average

all SRES

envelope

All

IS92

Bars show the

range in 2100

produced by

several models

(a) CO2 emissions (b) CO2 concentrations (c) SO2 emissions

(e) Sea level rise 

2000 2020 2040 2060 2080 2100 Year Year Year

50

100

150

SO2 Emissions (Millions of tonnes of sulphur per year)

A1B

A1T

A1FI

A2

B1

B2

IS92a

Scenarios

A1B

A1T

A1FI

A2

B1

B2

IS92a

Scenarios

A1B

A1T

A1FI

A2

B1

B2

IS92a

Scenarios

A1B

A1T

A1FI

A2

B1

B2

Scenarios

CO2 concentration (ppm) 300

400

500

600

700

800

900

1000

1100

1200

1300

2000 2020 2040 2060 2080 2100

2000 2020 2040 2060 2080 2100

Year

0.0

0.2

0.4

0.6

0.8

1.0

Sea level rise (metres)

The global climate of the 21st century

(d) Temperature change

All

IS92

2000 2020 2040 2060 2080 2100

Year

0

1

2

3

4

5

6

Temperature change (°C)

A1FI

A1B

A1T

A2

B1

B2

IS92a (TAR method)

Several models

all SRES

envelope

Model ensemble

all SRES

envelope

Bars show the

range in 2100

produced by

several models

0 2020 2040 2060 2080 2100

Several models

all SRES

envelope

All SRES envelope

including land-ice

uncertainty

Model average

all SRES

envelope

All

IS92

Bars show the

range in 2100

produced by

several models

CO2 emissions (b) CO2 concentrations (c) SO2 emissions

(e) Sea level rise 

2000 2020 2040 2060 2080 2100 Year Year Year

50

100

150

SO2 Emissions (Millions of tonnes of sulphur per year)

A1B

A1T

A1FI

A2

B1

B2

IS92a

Scenarios

A1B

A1T

A1FI

A2

B1

B2

IS92a

Scenarios

A1B

A1T

A1FI

A2

B1

B2

IS92a

arios

A1B

A1T

A1FI

A2

B1

B2

Scenarios

CO2 concentration (ppm) 300

400

500

600

700

800

900

1000

1100

1200

1300

2000 2020 2040 2060 2080 2100

2000 2020 2040 2060 2080 2100

Year

0.0

0.2

0.4

0.6

0.8

1.0

Sea level rise (metres)

The global climate of the 21st century

emperature change

All

IS92

00 2020 2040 2060 2080 2100

Year

A1FI

A1B

A1T

A2

B1

B2

IS92a (TAR method)

Several models

all SRES

envelope

Model ensemble

all SRES

envelope

Bars show the

range in 2100

produced by

several models

(c)
USDA Forest Service RMRS-GTR-361. 2017. 21

difficult to compare the SRES scenarios with the RCP 

scenarios, it may be convenient to think of B2 and 

RCP4.5 as the more moderate warm, dry scenarios 

and A2 and RCP8.5 as the severe hot, dry scenarios. 

Both scenarios are depicted here so that climate 

change studies using the earlier SRES A2/B2 scenarios can be linked to the more recent research that 

used RCP 4.5/8.5 scenarios in the literature synthesis 

of this section of the report.

3.1.2 Climate Projections for the Range of 

Whitebark Pine

Over the last 100 years in the Pacific Northwest, 

which encompasses most of whitebark pine’s range, 

temperatures have increased while precipitation 

amounts have changed little (Mote and Salathé 2010). 

In the Greater Yellowstone Area (GYA), however, 

recent evidence shows that there has been a decrease 

in precipitation with a larger percentage falling as 

rainfall instead of snowfall (Mahalovich 2013). From 

1895 to 2011, temperatures warmed 1.8 °C. During 

the period 1901 to 2009, observed temperatures, 

especially high nighttime minimum temperatures, 

have increased since 1980 (fig. 15). No significant 

trend in precipitation has been found, although variability appears to be 16 percent higher since 1970 

than in the preceding 75 years. Increases in extreme 

precipitation events have been modest (Mote and 

Salathé 2010).

Future projections suggest there will be greater 

warming in the northern Rocky Mountains than 

in the previous century (fig. 16). Compared to a 

baseline of 1970 to 1999, CMIP5 models project 

warming of 2–5 °C for the Pacific Northwest from 

2041 to 2070, with the lower end of warming occurring for a scenario with moderate greenhouse 

gas emissions (RCP4.5) (Mote and Salathé 2010). 

CMIP3 and CMIP5 efforts project increases of at 

least 4 °C across all seasons with the greatest warming projected for the summer; the CMIP5 RCP8.5, 

the high emissions scenario, projects 3–8 °C warming for the summer. Heat extremes are projected to 

increase, whereas measures of cold extremes are 

projected to decrease. The biggest changes will be in 

minimum temperatures. NARCCAP simulations for 

2041 to 2070 under the emissions scenario SRES to 

A2 project the duration of freeze to frost-free days 

to increase by 35 days (6 day standard deviation), 

and the number of days under 0 °C to decrease by 35 

days (6 day standard deviation) (Mote and Salathé 

2010).

Projections for precipitation are less certain 

(fig. 17). There are no significant trends in precipitation projections for the 2010 to 2100 timeframe 

for the Pacific Northwest (Mote et al. 2010). 

Despite lack of consensus among models for total 

annual precipitation, nearly all models project 

lower summer precipitation (some as much as 30 

percent lower), and higher winter, spring, and fall 

precipitation. Increased precipitation is projected 

for high elevations (above 1,800 m mean sea level 

approximately), whereas decreased precipitation is 

projected for low elevations. CMIP3 projections for 

precipitation extremes are more uncertain; moderate 

events (days >1 inch of precipitation) are projected 

to increase by 13 percent (7 percent standard deviation), but scenarios of other extremes are less certain 

(Mote et al. 2010).

10

8

6

4

2

0

-2

2000 2025 2050 2075 2100

Radiative forcing (W/m2)

RCP6

RCP4.5

RCP3PD/RCP2.6

RCP8.5

Figure 14—The new set of Representative Concentration 

Pathway scenarios used by the GCMs to predict future 

climate change. This report uses RCP4.5 and RCP8.5, 

which reflect the amount of additional radiative forcing 

caused by increased greenhouse gases (taken from Stocker 

et al. 2013).
22 USDA Forest Service RMRS-GTR-361. 2017. Figure 15—Changes in observed temperature over the last century across the contiguous United States. Colors in the map reflect temperature changes from 1991–2012 compared to the 1901–1960 average and graphs show average temperature changes by decade (source: NOAA National Climate Data Center).
USDA Forest Service RMRS-GTR-361. 2017. 23

Many other changes in climatic factors are related 

to the increases in temperature and decreases in 

precipitation. First, the number of frost-free days is 

predicted to increase substantially, especially in the 

range of whitebark pine (fig. 18). High elevation 

areas throughout whitebark pine’s range may experience an increase of over 50 frost-free days a year, a 

significant increase over historical averages (Littel 

et al. 2011). The higher temperatures would also 

result in lower snowpacks throughout the western 

United States (fig. 19). Mountain snowpacks within 

the range of whitebark pine are predicted to decrease 

by 20 to 70 percent with the greatest reductions in 

the Cascade Range of Washington and Oregon. The 

earlier snowmelt, coupled with higher temperatures, 

could result in less soil water during the growing 

season in some parts of whitebark pine’s range, 

but the already high precipitation amounts in the 

upper subalpine forests of the Pacific Northwest will 

ensure plenty of water throughout the year (fig. 20). 

Many GCMs actually predict increases in soil water 

for some high mountain areas. Climate trends are 

summarized in table 1.

3.1.3 Understanding Species Response to 

Climate Change

In general, there are four techniques used to assess effects of climate change on vegetation and any 

other resource concern. The first is expert opinion; 

this involves having experts in the fields of climate 

change, ecology, and vegetation dynamics qualitatively judge what will happen to vegetation under 

various climate change scenarios. The majority of 

papers about climate change impacts on vegetation were written by experts who have evaluated 

future climate projections and used their valuable 

Figure 16—Projected temperatures for 

two GCMs and one SRES scenario 

(A2, which is similar to RCP8.5) 

for the Pacific Northwest to year 

2100. Note the extensive warming 

in the high elevation whitebark 

pine ecosystems of the Northern 

Rocky Mountains and Cascades.
24 USDA Forest Service RMRS-GTR-361. 2017.

experience to deduce how vegetation would respond 

to different climates.

The second technique is field assessment, where 

extensive field sampling or remote sensing projects 

monitor vegetation change as the climate warms. This 

empirical technique involves establishing plots in 

networks across the landscape and detecting change 

Figure 17—Projected precipitation (mm) for two GCMs and one SRES scenario (A2, which is similar to RCP8.5) for 

the Pacific Northwest to year 2100. Note the relative little change in precipitation across the range of whitebark 

pine.

between plot measurements and correlating those 

changes to climate data. Demography studies track 

individual plants over time, rather than use periodic 

plot-level inventories, to fully understand the role 

of climate relative to other risk factors such as competition, variation in physiology and function, and 

vulnerability to insects and pathogens. 
USDA Forest Service RMRS-GTR-361. 2017. 25

Figure 18—Projected changes in the number of frost-free 

days for the contiguous United States for the A2 and B1 

scenario at year 2100. Percent increase (red) indicates 

that growing seasons in many high elevation areas in the 

western United States will increase by at least 40 days 

(source NOAA National Climate Data Center). The A2 

scenario is most similar to the RCP8.5 scenario.

50oN

40oN

30oN

120 140oW 130oW oW 80oW 70 110oW 100oW 90oW oW

Percent Change in Average Annual Snowfall (Future vs. Present) 

More 

Future

Less Future Snow Snow

-100% -90% -80% -70% -60% -50% -40% -30% -20% -10% 0% +5%

Figure 19—Projected changes 

in annual snowfall for the 

contiguous United States 

for the A2 (close to RCP8.5) 

scenario at year 2100. Percent 

increase (blue) and decrease 

(red) indicate that snowpacks 

are predicted to decrease in 

whitebark pine’s range although 

there is a great deal of variability 

from 10 percent in central 

Montana to over 70 percent in 

the Cascades (Source: NOAA 

National Climate Data Center).
26 USDA Forest Service RMRS-GTR-361. 2017.

One demographic study in the southeastern United 

States tracked more than 27,000 individuals of 40 species for about a decade to address climate-vegetation 

interactions (Clark et al. 2011). Although this field 

technique is the most reliable and most useful, it is 

often intractable because of the large areas and long 

time periods needed to sample vegetation at the appropriate scales to detect or anticipate changes as a 

result of climate.

The third method involves the use of advanced 

statistical analysis to create empirical models that 

predict climate change response. Most of the studies 

that predict the demise of whitebark pine from climate 

warming use Bioclimatic Envelope Models (BEMs) to 

project future geographical ranges (Chang et al. 2014; 

Crookston et al. 2010; Hansen and Phillips 2015; 

McDermid and Smith 2008; Rehfeldt et al. 2012; 

Warwell et al. 2007) (fig. 21). BEMs, also called 

species distributional models, niche models, or species envelope models, are developed by associating 

current climate conditions to the current distribution 

of a species of interest by means of advanced statistical modeling. Climate variables are correlated to 

species presence as determined from field data using 

statistical models to predict probabilities of species 

occurrence. Future species distributions are then 

computed using the projected future climate data, 

such as CMIP or downscaled GCM data, as inputs to 

the statistical model. A major BEM assumption is that 

current distribution of a species adequately defines the 

entire bioclimatic niche of that species. The presence 

data used to create BEMs, however, often represent 

Figure 20—Projected 

changes in soil moisture 

for the A2 and B1 

scenarios at 2050 and 

2100. Percent increase 

(blue) and decrease 

(red) are shown for the 

western United States. 

Note that most high 

elevation areas in the 

range of whitebark pine 

will experience greater 

soil moisture (Solomon 

et al. 2007).
USDA Forest Service RMRS-GTR-361. 2017. 27

current species habitat and not the actual spatial distribution of a species. Most field data do not recognize 

all places where the species is missing but could still 

exist. This is the major limitation in BEM modeling. 

Whitebark pine can be missing from many areas for 

any number of reasons, including disturbance effects, dispersal limitations, and land-use activities; 

therefore, BEM projections may underestimate future 

whitebark pine distributions.

There are other problems with the BEM approach 

that warrant discussion so results from literature 

search presented in this report can be put in the right 

context. First and most importantly, many critical 

life cycle processes, such as cone production and 

pollination, nutcracker-mediated seed dispersal, seed 

germination, seedling establishment, tree growth, 

mycorrhizae associations, competitive interactions, 

phenology, and mortality are not represented or included in BEMs; only climate and species occurrence 

are considered. Dullinger et al. (2012), for example, 

found that range shifts predicted by BEMs retracted 

by over 40 percent when seed dispersal was included 

in the prediction process. Another limitation is that 

many studies have now found that most species distributions are not in equilibrium with climate, thereby 

causing BEMs to miss those areas that are conducive 

for the species, but where the species is currently 

absent because it hasn’t migrated there yet. Moritz 

and Agudo (2013), for example, found that many 

species in the fossil record existed over a wider range 

of climates than is recorded today. BEMs assume that 

the current distribution of the species is a consequence 

of climate alone, yet we know that many other factors, 

such as fire exclusion, exotic diseases, and management actions have reduced whitebark pine occurrence 

so much so that the current distribution of whitebark 

pine is greatly contracted over historical conditions 

(Tomback et al. 2001a).

There are also scale problems in BEM analyses. 

The data used to represent climates in BEM model 

development represent a small slice of time (50 

to100 years) relative to the long time periods that 

living trees, especially the long-lived whitebark pine 

(>1000 years of age), have survived. Thus, these 

limited climate data sets rarely capture the full range 

of climate experienced by existing trees sampled in 

the field data. Along these same lines, BEM-projected 

changes in species habitat are uncorrelated to actual 

rates of change in future species distributions; the 

long-lived whitebark pine can certainly live longer 

than the 100-year projections of habitat declines. 

Climate data are often summarized as monthly or annual averages to simplify their correlation to species 

occurrence, yet we know that many climate impacts 

on species distributions result from short-term (daily) 

Figure 21—The decline in the range of whitebark pine over 

the next 80 years as predicted from a bioclimatic species 

envelope (BEM) modeling approach for the A2 (hot, dry) 

climate scenario (Warwell et al. 2007). Part (a) shows 

whitebark pine distribution modeled from NOAA weather 

station data and (b) shows whitebark pine distribution at year 

2090 under the A2 (similar to the RCP8.5) climate scenario. 

Red indicates areas where the statistical model predicts 

occurrence with greater certainty, these are the areas that the 

model predicts the species will most likely occur.

b

a
28 USDA Forest Service RMRS-GTR-361. 2017.

weather phenomena, such as early frosts. Current 

and projected climate data are too coarse spatially to 

adequately represent those local conditions that may 

impact species distribution, such as frost pockets, cold 

air drainages, microsites, and topographic shading 

(Pypker et al. 2007).

Some BEM efforts recognize species occurrence in 

the field data by setting a threshold level below which 

the species is considered absent. For example, whitebark pine is present on a field plot when it exceeds 

a minimum basal area or canopy cover requirement. 

These thresholds may be high (e.g., >10 percent 

cover), thereby excluding areas where the species currently exists at low levels but could potentially support 

greater abundances of the species. Moreover, the sampling and analysis methods used to estimate species 

characteristics to compare against thresholds are sometimes too coarse to detect the presence of a species, 

especially if the species occurs as scattered regeneration. Whitebark pine is often found as seedlings and 

saplings at low levels throughout the landscape due to 

widely dispersed nutcracker seed caching (Arno and 

Hoff 1990), yet these small and rare individuals are 

often missed by common sampling protocols. These 

limitations make it difficult to have any confidence 

in BEM projections; they are informative, but not 

prognostic, especially on short time scales of decades 

and half-centuries required by land management and at 

spatial scales of project implementation.

The last and most rigorous approach uses simulation modeling to assess climate-mediated vegetation 

responses (Keane et al. 2015; McKenzie et al. 2014). 

Future projections of climate from GCMs are input 

into simple to complex ecological models to simulate 

climate change effects on whitebark pine. Spatially 

explicit models that simulate the major biophysical 

processes that control ecosystem dynamics are important tools for exploring climate change effects on 

vegetation under possible future climates. A variety 

of existing models simulate ecological change at 

broad (global, regional) and fine (point, ecosystem, 

stand) scales (Keane et al. 2015). However, landscape 

scale models simulating areas of 100 to 250 km2 are 

perhaps the most critical for predicting climate change 

effects because a landscape scale is where many 

ecosystem processes and linkages are manifest and 

the scale at which most management decisions are 

made (Cushman et al. 2007; Littell et al. 2011). Finerscale stand models (0 to 1 km2) cannot incorporate 

important exogenous disturbance regimes because 

of their limited spatial extent. Coarse-scale models 

(1,000 km2 to global) are unable to simulate important 

plant-, species- and canopy-level competition and 

disturbance effects, such as successional shifts, community dynamics, and differential disturbance effects 

among species (McKenzie et al. 2014).

To be effective at realistically predicting climate 

change effects, models must simulate disturbances, 

vegetation, and climate as well as their interactions 

across fine multiple scales. For whitebark pine, we 

believe the processes identified in figure 22 should 

be included at a minimum. Yet few models simulate 

ecosystem processes with the mechanistic detail 

needed to realistically represent important interactions 

among landscape processes, vegetation dynamics, 

disturbance regimes, and climate. Direct interactions 

between climate and vegetation, for example, may 

be more realistically represented by simulating daily 

carbon (photosynthesis, respiration), water (evapotranspiration), and nutrient (nitrogen, phosphorous) 

dynamics at the plant level rather than by simulating 

annual vegetation development using state-andtransition modeling approaches (Keane et al. 2015). 

Moreover, few GCMs project climate at scales useful 

for landscape models.

3.2 Species Responses

There are three modes of response of a tree species 

to climate change. A species can (1) change in productivity and health in situ or within its current range; 

(2) become extirpated from its current range due to 

adverse environmental conditions; or (3) migrate into 

new areas that were not part of its historical range 

(Aitken et al. 2008). Tree species range limits, both 

in the past and into the future, are highly dynamic 

because of high climate variability. Any assessment 

of climate change impacts on species range expansion 

and contraction demands a long temporal evaluation 

(Aitken et al. 2008), which makes evaluating BEM 

results difficult. But overall, these three modes may 

provide the foundation for evaluating whitebark pine 
USDA Forest Service RMRS-GTR-361. 2017. 29

response to climate change. Predicting the future of 

whitebark pine in western North America is a great 

deal more difficult than some have theorized because 

of the complex interactions of whitebark pine with all 

the other ecological processes that form the ecosystem 

(see fig. 22).

Predicted increases in productivity for upper 

subalpine areas throughout the range of whitebark 

pine may allow whitebark pine to remain in some of 

its current geographical range (Aston 2010; Chhin 

2008). Whitebark pine may experience gains in 

diameter growth, cone production, bumper cone crop 

frequencies, and leaf area under the predicted climate. 

Recent ecological simulation modeling efforts have 

shown that whitebark pine growth will increase in the 

future (Loehman et al. 2011b). Lenihan et al. (2003) 

predicted increases in productivity in upper subalpine 

whitebark pine forests and expansion of this forest 

into the alpine areas of California. Whitebark pine 

also shows promise for being maintained in situ in the 

Northern Rockies because of high levels of genetic 

diversity (Mahalovich and Hipkins 2011; Mahalovich 

et al. 2006; Richardson et al. 2002a); moderate to 

high heritabilities of key adaptive traits; demonstrated 

blister rust resistance (Hoff et al. 1980; Mahalovich 

et al. 2006; Sneizko et al. 2007); minimal inbreeding 

(Bower and Aitken 2007; Mahalovich and Hipkins 

2011); and generalist adaptive strategies (Mahalovich, 

in prep.).

A preponderance of the studies contends that 

whitebark pine’s range will decrease in the future 

because of increasing temperatures, but most of these 

studies were conducted using BEMs (McKenney et 

al. 2007; Hamann and Wang 2006). Most qualitative, 

expert opinion synthesis papers speculate that climate 

change could “push” whitebark pine off the top of 

the mountain by moving its lower elevational limits 

above the height of the tallest peaks (Bartlein et al. 

1997; Schrag et al. 2007). Other statistical modeling 

studies, similar to Warwell et al. (2007) presented 

in figure 21, support this hypothesis by showing 

dramatic decreases in whitebark pine suitable habitat 

over the next 80 years (Chang et al. 2014; Crookston 

et al. 2010; Funk and Saunders 2014; McDermid and 

Smith 2008; Rehfeldt et al. 2012). McKenzie et al. 

(2003) predict major range retractions of whitebark 

pine in the Pacific Northwest, as do Nitschke and 

Innes (2008). Forecasts of severe droughts at the 

lower elevational limits of whitebark pine’s range (see 

Section 3.1.2) may create inhospitable conditions for 

regeneration. The projected increase in productivity 

of all species in the upper subalpine forests may also 

foster more intense competition and accelerate succession of shade tolerant tree species associated with 

whitebark pine. Also, lower elevational species, such 

as lodgepole pine and Douglas-fir, might invade the 

lower portions of the upper subalpine forest zone and 

outcompete whitebark pine.

Figure 22—The important interactions 

and feedbacks in the whitebark 

pine ecosystem to consider when 

evaluating climate change effects 

on whitebark pine ecosystems. The 

yellow boxes in the black circle 

indicate the three critical elements 

of the whitebark pine ecosystem: 

the species, wildland fire, and the 

bird. The red boxes indicate the 

important disturbance processes that 

impact whitebark pine dynamics: 

the mountain pine beetle, white pine 

blister rust, and humans. Climate is 

in the blue. The dotted line indicates 

a minor impact. Less significant 

interactions are not shown.
30 USDA Forest Service RMRS-GTR-361. 2017.

Evidence of warmer climates in paleoecological 

records indicates that whitebark pine was maintained 

and sometimes increased in some places under past 

warmer and drier climates in some parts of its range 

(Iglesias et al. 2015; Krause et al. 2015; Whitlock and 

Bartlein 1993). Whitebark pine can grow within a 

broad upper elevation zone in western North America 

but it is a poor competitor; it grows best on those 

sites that are inhospitable to other tree species. In 

fact, Arno et al. (1993) found that whitebark pine’s 

elevational range extended more than 500 feet below 

the current lower elevational limits of whitebark pine 

in the Bitterroot Mountains of Montana, USA, prior to 

the modern fire exclusion era (circa 1910). Whitebark 

pine occupies the largest latitudinal range of any fiveneedled white pine in the western United States and 

Canada, indicating some tolerance to a wide range of 

climates (Tomback and Achuff 2010).

There is other evidence that whitebark pine may 

continue to inhabit high mountain landscapes in 

warming climates. Anecdotal evidence shows that 

some whitebark pine forests are experiencing abnormally high growth rates and more frequent cone 

crops due to warmer summers and longer growing 

seasons. Loehman et al. (2011a,b) used a mechanistic 

landscape model to simulate higher productivities in 

whitebark pine in the future, but the species continued 

to decline, not from warming climates, but from 

WPBR, especially when wildland fire was not allowed to burn. Moreover, WPBR screening trials and 

other common garden (genecology) studies indicate 

whitebark pine has both the adaptive capacity and 

phenotypic plasticity to favorably respond to climate 

change (Mahalovich, in prep.). The notion that whitebark pine will stop growing and reproducing at high 

elevations under future climates is mostly unsubstantiated. It is entirely possible that as long as wildland 

fire creates areas where birds can cache seeds and 

seedlings can grow without competition, whitebark 

pine will continue to thrive throughout its range. This 

is supported for some of the region-based scenarios 

using computer modeling (Loehman et al. 2011b).

Climate change can impact each phase of the life 

cycle processes of a species including reproduction, 

regeneration, growth, and mortality. The following 

subsections are summaries from the literature about 

possible climate change effects on whitebark pine by 

life cycle processes.

3.2.2 Regeneration

Regeneration is probably the life cycle phase 

most susceptible to shifts in climate (Solomon and 

West 1993). Microsite conditions needed for successful regeneration may be so demanding that seed 

germination is rarely successful. Bunn et al. (2003) 

emphasized the importance of accounting for microsite variability in assessing climate change response; 

high elevation microsite climate amelioration, coupled 

with increased fire activity, could increase whitebark 

pine regeneration and growth as climates change. 

High snow depth and long duration snow cover often 

govern high elevation tree regeneration. Most years 

are moist enough for regeneration but snow may 

remain on the site for a long time thereby limiting the 

number of days that a seedling can actually photosynthesize and grow. Warm years in the upper subalpine 

often result in waves of regeneration and can be dated 

from seedling and sapling ages (Little et al. 1994). 

With projected temperature increases, earlier snow 

melt might give more time for seedlings to grow. 

Recent observations of invasions of subalpine meadows and open, sparsely vegetated areas (balds) by 

subalpine fir, alpine larch, and spruce are a result of a 

string of warm years with low snowpack over the last 

decade that have facilitated high mountain regeneration (Dullinger et al. 2004). Moreover, there is often 

abundant precipitation in upper subalpine settings and 

future projections indicate roughly the same; therefore, future seedling mortality from drought might be 

minimal.

Future climates and their high variability may affect the ability of the cached and forgotten whitebark 

pine seeds to germinate. Seed chilling requirements 

may not be met during mild winters (Arno and Hoff 

1990) thereby reducing germination. Germination 

may also be delayed to the driest parts of the growing season resulting in higher seedling mortality 

(McCaughey 1993; Tomback et al. 2001c). Because 

of longer growing seasons and increased warming, 

soil temperatures may be too high, especially for 

the critical initial post-germination growth stages, 

causing greater mortality to both germinants and 
USDA Forest Service RMRS-GTR-361. 2017. 31

also to established seedlings (Rochefort et al. 1994). 

Conversely, longer growing seasons may result 

in more effective establishment of whitebark pine 

seedlings through additional root growth which 

can stabilize seedlings against high snow loads or 

allow the seedlings access to more water (Keane 

and Parsons 2010a), thereby mitigating the negative 

consequences of drought. And most importantly, the 

projected longer, drier, and warmer growing seasons 

may have such high variability that infrequent hot, dry 

years may kill young seedlings, especially on southern 

slopes in lower elevation settings.

Climate change can also affect dispersal of seeds, 

especially those of competing tree species. Stronger 

winds (Gedalof et al. 2005) coupled with longer 

growing seasons may result in longer and more successful seed dispersal over a longer period of time for 

whitebark pine’s competitors. However, since whitebark pine’s seed are dispersed great distances by the 

Clark’s nutcracker (see Section 3.5) and wildland fire 

size is predicted to increase in the future (see Section 

3.4.1), whitebark pine might have a competitive 

advantage over other tree species in that it will be able 

to populate new habitats before the wind-blown seeds 

of its competitors arrive in these areas (Tomback et 

al. 1990). Longer summers and autumns might also 

mean that seed dispersal takes place when the ground 

and litter are the warmest and driest, which may adversely affect seed germination and establishment of 

competing tree species (Neilson et al. 2005). Changes 

in spatial heterogeneity due to shifts in disturbance 

regimes may also influence bird-mediated dispersal 

by shifting potential seed sources and changing patch 

size that might affect nutcracker behavior.

3.2.1 Reproduction

Whitebark pine cone and seed crops could be both 

adversely and beneficially affected by climate change. 

In high elevation, historically cold environments, increasing temperatures may lengthen growing seasons 

and thereby increase the potential for more frequent 

and more abundant cone crops with greater numbers of seed. Thus, decreases in species abundance 

and associated cone production may be offset by 

climate-driven increases in cone crops. Alternatively, 

lodgepole pine, subalpine fir, and spruce might also 

experience increased seed production and thereby 

heighten competition with whitebark pine.

Climate change and its variation may also affect 

the phenology of cone crops, but impacts may be 

minimal. Some predict higher frost mortality of 

emerging cones due to earlier onset of the growing 

season coupled with high daily temperature variability 

(Chmura et al. 2011). Others suggest that cone crops 

will be reduced in both magnitude and frequency in 

the future because of the high drought stress (Mutke 

et al. 2005). Because whitebark pine is both drought 

tolerant and cold tolerant (Bower and Aitken 2007; 

Mahalovich et al. 2006), changes in climate variability and timing may have a minimal impact on species 

cone production and reproduction. Reduced tree density due to mortality from MPB, WPBR, wildland fire, 

and stand isolation is more likely to reduce seed cone 

abundance and have an impact on future cone crops 

than climate (Rapp et al. 2013). Pease and Mattson 

(1999) characterized high mast years as those with 

more than 20 cones per tree. McKinney et al. (2009) 

report that a threshold of 900 cones ha-1 is needed 

to ensure seed dispersal by Clark’s nutcrackers. This 

threshold equates to 25 to 50 cone-bearing trees per 

ha (or 20 to 50 cone-bearing trees ha-1). For an effective pollination cloud to provide adequate pollination 

of receptive ovules, a minimum of 25 reproductively 

mature Pinus spp. per ha are required (Mahalovich 

2013). Furthermore, for wind-pollinated conifers, 50 

to 125 reproductively mature trees per ha ensure a 

genetically diverse cone crop with minimal consequences of inbreeding depression.

3.2.3 Growth and Mortality

Climate can adversely impact growth and mortality 

of whitebark pine in a number of ways (Bugmann 

and Cramer 1998; Keane et al. 2001d). Projected 

decreases in water availability may result in more 

droughty sites. Longer periods of drought might cause 

whitebark pine to close their stomata more often 

and for longer times resulting in slow growth. The 

projected increased temperatures will increase both 

maintenance and growth respiration, especially during 

periods when stomata are closed, thereby requiring 

additional photosynthetic gains to counterbalance 

respirational losses that will require even more water 
32 USDA Forest Service RMRS-GTR-361. 2017.

in the future. If photosynthesis doesn’t exceed respirational demands, then the plant becomes stressed 

thereby increasing direct mortality and its susceptibility to insect and disease attacks. Moreover, Aitken et 

al. (2008) note that the genetically controlled timing 

of the growth flush may be out of synchrony with the 

new environment, because it is occurring in drier parts 

of the season.

Projected warmer upper subalpine climates may 

have a positive impact by increasing whitebark pine 

diameter growth in established trees and decreasing 

mortality, especially in those mesic seral whitebark 

pine forests. Wu et al. (2011) found increases in plant 

growth for many forest and rangeland ecosystems 

worldwide. Earlier growing seasons with sufficient 

moisture, such as that predicted for the upper subalpine forests, would result in increased productivity 

and greater growth. This is especially true for the 

widespread higher mountain areas where cold, not 

moisture, limits tree growth. Longer, warmer growing 

seasons may result in higher vegetative productivities 

and greater biomass, especially considering the high 

amounts of precipitation that currently fall in upper 

subalpine forests. The abundant moisture may enable longer periods of tree growth at high elevations. 

Higher biomass could result in increased canopy bulk 

density and therefore, higher crown fire potential and 

more intense, severe fires and, possibly, more intense 

insect and disease outbreaks. More importantly, the 

higher amounts of biomass may increase cone crop 

abundance and frequency (Ibanez et al. 2007; LaDeau 

and Clark 2001). This increased production, however, 

may also heighten competitive interactions between 

whitebark pine and its associated species thereby 

favoring the more shade-tolerant individuals in the 

absence of disturbance.

Increased atmospheric carbon dioxide and 

greenhouse gas levels may also modify basic ecophysiological growth processes. Oxygen and carbon 

dioxide compete for active RuBisCo (the primary 

enzyme used in photosynthesis) binding sites, particularly under high temperatures. Higher atmospheric 

CO2 concentrations increases internal leaf CO2

concentrations via diffusion, thereby favoring CO2

attachment to RuBisCo sites. This results in photosynthetic increases of 2 to 250 percent, depending on 

the site and species. Increased water use efficiency for 

conifers in water-limited environments might compensate for decreases in water availability and might 

increase growth rates in water-rich environments. 

Water use efficiency is the ratio of water used for 

plant metabolism (photosynthesis and respiration) to 

the water lost to transpiration. With higher CO2 concentrations in the atmosphere, the plant would obtain 

more CO2 during the time the stomata are open resulting in lower transpiration losses. Whitebark pine may 

have the plasticity to adapt to low water conditions 

by decreasing transpiration through other avenues. 

However, this is totally dependent on many other 

ecological conditions such as nitrogen, soil nutrients, 

water, and light availability.

Whitebark pine diameter and height growth may 

be directly enhanced by the elevated CO2 levels in 

the atmosphere (Chmura et al. 2011). Physiological 

adaptations allow the plant to utilize more CO2

in photosynthesis because of higher atmospheric 

concentrations, and this increased photosynthesis 

might increase plant biomass. Leaf biomass is usually the first to increase as plants attempt to optimize 

photosynthesis by growing more photosynthetically 

active foliage (i.e., more leaf area). Increases in leaf 

biomass often lead to increases in leaf area, usually 

measured as leaf area index (ratio of leaf area to 

projected ground area), but these increases are often 

transient and greatly dependent on available nitrogen. 

Increases in leaf area index would likely result in 

greater rainfall interception, higher snow collection, 

greater canopy evaporation, and shadier forest floors, 

which might increase forest soil aridity. Elevated CO2

levels may also change root:shoot ratios, with more 

aboveground biomass and fewer roots due to higher 

levels of photosynthesis and water use efficiency. 

Increased CO2 levels and increasing temperatures 

may also interact to increase growth. The temperature 

optima for photosynthesis differs by tree species, and 

those temperature optima may change under new 

atmospheric CO2 levels. Warmer temperatures might 

be closer to the new temperature optima, especially 

during the cooler early growing season, resulting in 

higher growth. In summary, because most whitebark 

pine occurs in cool to cold environs that rarely experience moisture deficits, growth rates of whitebark 
USDA Forest Service RMRS-GTR-361. 2017. 33

pine in most areas may increase as a result of higher 

carbon dioxide levels and longer growing seasons.

3.3 Community Response

As climates warm, less hardy but more shadetolerant conifer species may be able to establish in 

those higher-elevation stands where whitebark pine 

currently is dominant (Hansen and Phillips 2015; 

Koteen 1999; Romme and Turner 1991; Schrag et al. 

2007). Succession toward fir, spruce, and mountain 

hemlock may accelerate on seral whitebark pine sites 

and initiate on climax whitebark pine sites. Many of 

whitebark pine’s shade tolerant associates can grow 

faster in height than whitebark pine with increasing productivity (Arno and Hoff 1990). Conifers 

invading from lower elevation forests, in addition 

to the shade-tolerant conifers already present, could 

out-compete whitebark pine and shift the upper 

subalpine areas to a more spruce and fir-dominated 

community composition. Moreover, as growth rates 

increase, the rate of succession may also increase, 

accelerating the shift from whitebark pine to more 

shade-tolerant species (Aston 2010). Because of intense competition, whitebark pine is often relegated 

to those high elevation sites where it is the only tree 

adapted to the harsh conditions at and below timberline (Koteen 1999). This scenario of heightened 

competition assumes that wildland fire is minimal 

across the range of whitebark pine.

It is also possible that some seral whitebark pine 

sites, such as south-facing aspects in the upper 

subalpine zones, may experience longer and more 

frequent soil water deficits throughout the summer, 

especially in drought years. Whitebark pine appears 

to have a greater ability to survive and prosper 

through periods of low available soil moisture than 

nearly all of its competitors, especially during the 

critical establishment phases (Arno and Hoff 1990; 

Callaway et al. 1998). Therefore, it may be that 

whitebark pine is able to maintain dominance on 

these sites providing low WPBR and MPB activity. 

In fact, Coops and Waring (2011) predict whitebark 

pine will expand in range under future climates in 

some areas, especially into the lower treeline.

3.4 Disturbance Changes

Climate-induced changes in disturbance regimes 

may overwhelm most direct vegetation responses to 

climate change (Dale et al. 2001). Most landscape 

and ecosystem shifts caused by global climate 

change will probably be facilitated by major modifications in disturbance regimes (Gardner et al. 1996; 

Swetnam and Betancourt 1998). Some of these shifts 

may have already been observed in some whitebark 

pine ecosystems (Larson et al. 2010; Millar et al. 

2012). Recent MPB outbreaks are killing whitebark 

pine trees at rates greater than previously documented in the historical records (Raffa et al. 2008). 

These unprecedented outbreaks are probably a result 

of warmer winter temperatures that facilitate expansion and establishment of beetle populations in the 

higher-elevation whitebark pine zone (Logan and 

Powell, 2001; Logan et al. 2003). A warmer climate 

may accelerate the spread of blister rust (Koteen 

1999; Resler and Tomback 2008) or it could disrupt 

the complex life cycle and reduce spore production 

(see Section 2.6.3).

If disturbances are predicted to increase 

(Flannigan et al. 2008; Logan and Powell 2001), 

then disturbance adaptations, not species competitive 

interactions, might determine the future composition and structure of landscapes. Whitebark pine 

has many adaptations to disturbance that might 

allow it to remain on the high-elevation landscape, 

especially in comparison to all of its competitors. 

Although climate change impacts could be severe for 

whitebark pine, they are also complex and difficult 

to predict because of disturbance interactions (Hobbs 

and Cramer 2008). Therefore, it is important that 

potential shifts in fire, beetles, and blister rust dynamics caused by changing climates be anticipated 

and built into the design, approach, and kinds of 

restoration activities across the range of whitebark 

pine. Moreover, a unique set of activities might be 

emphasized in each bioclimatic region, site, or at 

more local levels such as the stand level. The following sections detail projected changes in the regimes 

of the three major disturbance agents in whitebark 

pine forests—wildland fire, MPB, and WPBR.
34 USDA Forest Service RMRS-GTR-361. 2017.

3.4.1 Wildland Fire

Many climate change studies consistently project 

that the warmer conditions in the range of whitebark 

pine will result in large increases in the length of 

the fire season, annual number of fires, area burned, 

size, and intensity of wildfires (Flannigan et al. 2009; 

Krawchuk et al. 2009; Marlon et al. 2009). Some predict as much as 2 to 6 times more fire in the future as a 

result of climate change (Brown et al. 2004; Running 

2006; Westerling et al. 2006) (fig. 23). Loehman et al. 

(2011b) simulated increases in burned area under the 

A2 climate that were 2.5 times more than historical 

averages in a whitebark pine landscape in Glacier 

National Park. Wildfire frequency in western forests 

has increased fourfold during the period 1987 to 2003 

as compared to 1970 to 1986, while the total area 

burned increased six-fold (Westerling et al. 2006). 

Earlier snowmelt dates have been shown to correspond 

to increased wildfire frequency (Running 2006). Trouet 

et al. (2006) confirm that increases in area burned 

are tied to climate conditions. Prolonged dry and hot 

periods are generally required for large fires (Gedalof et 

al. 2005) and projected climates will likely make these 

droughts and resultant wildfires more likely (Keeton et 

al. 2007).

In most years, whitebark pine forests remain moist 

for the majority of the fire season, resulting in long 

fire return intervals (Morgan and Bunting 1990), but 

the projected warmer climates will probably result in 

greater drying that may cause earlier and longer fire 

seasons. Historically moist high elevation areas, which 

normally act as fire breaks, could become dry enough 

to carry fire in some years. This means that future 

fires might be larger because fuels are dry enough to 

facilitate fire growth across larger areas (i.e., entire 

landscapes) (Loehman et al. 2011b). Moreover, fire seasons are also predicted to be hotter and drier with deep, 

prolonged droughts that may carry over to consecutive 

years. Climate models also predict increased lightning 

and wind because the increased temperatures add more 

energy to the atmosphere (Price and Rind 1994; Reeve 

and Toumi 1999; Romps et al. 2014). The increased 

temperatures, drought, lightning, and wind, coupled 

with more fuel due to higher biomass productivity 

(Section 3.2.3), may also result in more fire starts that 

foster more intense fires (Flannigan et al. 2008, 2009). 

Fires may also be more severe, especially considering that the existing heavy accumulations of canopy 

and surface fuels, which resulted from successful fire 

exclusion over the last century, that when burned, may 

cause atypical postfire mortality in many plants and 

animals. However, Keane et al. (2008a) mention that 

fire severities may remain the same because of inherent 

fire adaptations in the biota, and only fire intensities 

may increase.

With increased fire frequency and intensity, whitebark pine may have a unique opportunity to maintain, 

or even increase, its range in the future because of 

bird-mediated (Clark’s nutcracker) seed dispersal. 

Nutcrackers can disseminate seeds great distances into 

large, severe burns well before wind can disperse the 

seeds of its competitors (Lorenz et al. 2008; Lorenz and 

Sullivan 2009; Tomback 1978, 1982; Tomback et al. 

1990). Whitebark pine seedlings are hardy, tolerant of 

poor seedbeds, and readily regenerate in large burned 

areas (Arno and Hoff 1990; McCaughey and Tomback 

2001; Tomback et al. 1990, 1995, 2001c). Whitebark 

pine morphology (deep roots, high crowns, somewhat 

thick bark) enables it to survive low to moderate 

severity fires (Ryan and Reinhardt 1988). Therefore, 

Figure 23—Projected percent increase in burned areas in the 

West for a 1 °C increase in global average temperatures 

relative to the median area burned during 1950–2003 

(National Research Council 2011). The darker colors 

indicate the greatest increases in fire with over five-fold 

more burned areas.
USDA Forest Service RMRS-GTR-361. 2017. 35

whitebark pine could be uniquely positioned as a species that may increase under future fire regimes.

A major drawback of more frequent and intense 

fire on the high elevation landscape is that these fires 

are also more likely to kill those whitebark pine trees 

that are resistant to WPBR. These rust-resistant trees 

are the foundation of the restoration strategy in two 

ways. They provide rust-resistant seeds for nutcracker 

caching into burned areas, and the cones are collected 

to grow rust-resistant whitebark pine seedlings in the 

nursery for planting in areas lacking seed sources. 

If these trees are killed by fire, the potential for seed 

dispersal by nutcrackers is reduced and with this, 

opportunities to spread genes for rust resistance to succeeding pine generations of whitebark pine.

Increased fire frequency and intensity due to climate 

change could also reduce wildland fuels by burning 

more of the landscape and creating firebreaks that 

may potentially protect rust-resistant whitebark pines 

from future fire damage. Allowing wildfires to burn, 

especially in moderate fire weather years, may increase 

the area subject to lower intensity fire in declining 

whitebark pine stands. This would reduce fuels and 

protect those valuable surviving rust-resistant trees 

against future fires that might occur in severe weather 

years. Fire in moderate years can reduce fuel in two 

ways. First, these fires have low intensities that might 

allow whitebark pine to survive, but may kill the more 

fire-susceptible subalpine fir and other potential conifer 

invaders, thereby reducing canopy fuels. This in turn 

would lower the potential for damaging crown fires 

(Keane and Parsons 2010a). Low-intensity fires can 

also creep through high elevation stands and consume 

surface fuel and reduce severities of future fires (Lasko 

1990). Fires that consume surface and canopy fuels 

may create burned areas that could stop the spread of 

future fires (Larson et al. 2010).

Future fires might also become so frequent that 

whitebark pine trees may not be able to grow to a size 

where the foliage is above lethal scorch heights. As a 

result, newly established trees may be killed by fire 

before reaching reproductive maturity. In model simulations, Holsinger et al. (2014) found that future fires 

were so frequent in a western Montana watershed that 

lodgepole pine seedlings were killed by fire before they 

could grow the 15 years needed for cone production. 

Whitebark pine trees need especially long intervals 

between fires in order to remain on the landscape 

because the species has an older age to reproductive 

maturity (>60 years) (Arno and Hoff 1990; Holsinger 

et al. 2014).

Predicted increases in fire size and intensity could 

also be detrimental to whitebark pine ecosystems in 

other ways. First, fires may be so frequent and intense 

that they reduce mycorrhizal populations (see Section 

2.3), which may have negative impacts on whitebark 

pine growth and regeneration (Keane et al. 2012b; 

Mohatt et al. 2008). Many high elevation species of 

mycorrhizae respond poorly to fire, especially standreplacing fires. The absence of whitebark pine trees on 

the burned site may cause a corresponding decline in 

mycorrhizae populations that may ultimately result in 

poor survival of whitebark pine regeneration (Mohatt 

et al. 2008, Lonergan et al. 2013). Native shrubs, which 

often share mycorrhizae with whitebark pine, may be 

reduced so much by frequent fire that their populations 

will be insufficient for maintaining healthy mycorrhizae communities.

3.4.2 Mountain Pine Beetle (MPB)

Unfortunately, climate-mediated MPB outbreaks 

have occurred in many areas within whitebark pine’s 

range (Carroll et al. 2003, 2006; Gibson et al. 2008) 

killing many cone-bearing whitebark pines (Hicke 

and Logan 2009). Most of these trees might have 

eventually died from the exotic WPBR, but those trees 

that would have survived WPBR because they were 

somewhat rust-resistant may now be targeted by the 

beetles. Logan et al. (2010) suggest that MPB might be 

the main driver of future losses in whitebark pine trees, 

especially in the Greater Yellowstone Area.

Temperature is a major driver of bark beetle 

population survival and growth (Bentz et al. 1991, 

2014; Logan and Powell 2004). Recent warming temperatures, especially daily minima, have resulted in 

increased overwintering survival of MPB, particularly 

in forests with historically severe winters (Bentz et al. 

2009). Warm temperatures in other parts of the year 

also play a significant role because they influence timing of adult emergence and generation cycles (Bentz 

et al. 2014). Slight differences in temperature, even 

within a single tree or stand, can result in dramatic 
36 USDA Forest Service RMRS-GTR-361. 2017.

differences in MPB generation time. Mountain pine 

beetles will complete their lifecycles in one year rather than two in warmer climates, and this often leads 

to increased population growth and associated beetlecaused tree mortality (Safranyik et al. 2010). If adult 

emergence occurs in early summer, a generation can 

be completed by the next fall. Completion of a generation over the winter is constrained, however, due 

to evolved thresholds for development (Bentz et al. 

2014). Short-term drought often associated with warm 

temperatures can create both a pool of weakened 

host trees and the appropriate thermal conditions for 

population outbreaks of multiple bark beetle species 

(Chapman et al. 2012; Hart et al. 2013). The greatest 

density of pine occurs at 2,000–3,000 m in elevation, 

and these stands are predicted to have greater univoltine population growth rates than historic, through 

2030–2050 (Loehman et al., in press).

One major adverse effect of MPB outbreaks on 

whitebark pine stand dynamics is that the heavy beetle-caused mortality in overstory whitebark pine may 

release the shade-tolerant but suppressed understory 

of subalpine fir and other associated conifer competitors (Amberson 2014). Once released, these trees can 

rapidly occupy the overstory canopy space vacated by 

the dying whitebark pine resulting in a swift conversion of whitebark pine forests to fir-spruce forests 

(Campbell 1998; Keane 2001a). As mentioned, these 

fir-spruce forests tend to have lower canopy bases and 

high canopy densities resulting in a dramatic increase 

in crown fire potential. Moreover, whitebark pine will 

be unable to regenerate in MPB-killed stands because 

of intense competition with the fir-spruce component.

3.4.3 White Pine Blister Rust (WPBR)

Direct responses of WPBR to climate change 

greatly depends on the conditions for dispersal and 

germination in late summer and early autumn. At 

this time, basidiospores are released from the telia on 

the undersides of Ribes spp. leaves and travel before 

landing on five-needle pine needles to germinate. This 

is a delicate time, because for WPBR to germinate on 

pine needles and for the hyphae to grow into the stomata, the spores need warm (>10 oC) and moist (>95 

percent relative humidity) conditions. Since future 

climates are predicted to be warmer, there may be 

an increased temperature window for spore infection 

of pines (Mahalovich 2013). However, there is great 

uncertainty as to whether the high relative humidity 

needed for spore germination and hyphae growth 

during the potentially warmer part of late summer 

and early autumn will occur. Increases in infection 

due to favorable fall temperatures might be negated 

by forecast decreases in humidity. This, of course, is 

all dependent on the ability of all species involved 

(WPBR, pine, and all alternate hosts) to adjust their 

phenology to the predicted newer climates.

Fall mesoscale weather events may also accelerate 

the spread of WPBR by influencing the frequency and 

length of infection periods or “wave years.” Koteen 

(1999) suggested that selective weather conditions 

required for basidiospore germination and infection of 

pine needles may be longer and more frequent in future climates for upper subalpine forests. Wave years 

occur when cooler temperatures and higher relative 

humidity or monsoon-like climatic conditions occur 

in late summer and early fall. These conditions favor 

long distance basiodiospore transport, while facilitating regional and local rust infection amplification. 

Kinloch (2003) and Sturrocka et al. (2011) speculate 

that wave years will actually decrease for most pine 

forests because of projected hotter, drier climates. 

Helfer (2014) believes that warmer temperatures 

would negatively impact rusts, where the higher concentrations of atmospheric carbon dioxide could also 

cause declines in rust populations. He also mentions 

that the highly variable and extreme weather projected 

in the future will aid in WPBR spore dispersal resulting in increases in WPBR range and higher spore 

loads on existing pines.

Indirect WPBR climate change response will be 

mediated by the host species’ ability to respond to 

climate change under changing disturbance regimes. 

Changes in the abundance of whitebark pine and 

its alternate host species from climate warming in 

the upper subalpine areas will surely change WPBR 

dynamics (Koteen 1999). White pine blister rust 

infection will certainly continue to increase in the new 

disturbance regimes that favor the alternate hosts and 

other five-needle pines, such as Pinus monticola and 

P. flexilis, to complete its life cycle. It may be that 

future climates will increase vigor of whitebark pine’s 
USDA Forest Service RMRS-GTR-361. 2017. 37

shade-tolerant competitors, in which case, increases 

in competition might stress the hosts and increase 

WPBR infection. Rust infection and mortality appear 

to occur regardless of tree condition and vigor. Thus 

it is doubtful that any direct responses of the tree or 

alternate hosts to future climates, such as increased 

growth due to a longer growing season, will enhance 

or reduce the ability of whitebark pine to ward off infections (Hoff et al. 2001; Smith and Hoffman 2001).

Climate-mediated changes in host regeneration dynamics, however, could restrict or expand host ranges, 

which could also affect the range of WPBR (Helfer 

2014). Higher leaf biomass for the pine and host 

species under warmer, enriched CO2 environments 

(see Section 3.2.3) means greater spore germination 

surfaces and a higher chance for rust infection on 

both pine and hosts. High elevation areas may experience warming temperatures along with continued 

high precipitation that may facilitate the expansion 

of the alternate hosts into areas that were historically too cold and snowy. Conversely, low elevation 

areas where Ribes spp. are currently abundant might 

experience more drought. This might cause a decline 

in their abundance and a decline in premature leaf 

fall in July, thereby disrupting completion of the rust 

cycle (Mahalovich 2013; Zambino 2010). Moreover, 

increased drought may cause extended and extensive 

stomatal closure in the pines, which would prevent 

hyphae entry. The shifting mosaics of the alternate 

hosts into new, higher elevation mesic areas are driven 

by drought in lower elevations and may result in the 

spread of WPBR into areas where it currently has not 

been detected.

3.4.4 Interactive Effects

While climate is a key driver of wildland fire, 

MPB, and WPBR dynamics, the impact of climate 

change on whitebark pine forests will most likely 

result from the interactions of these disturbances 

on high mountain landscapes (Hicke et al. 2012; 

Jenkins et al. 2012; Loehman et al. 2016; Mahalovich 

2013) (fig. 22). Most of the research exploring these 

interactions has primarily focused on the potential for 

increased fire hazard following MPB and WPBR outbreaks (see Hicke et al. 2012 for a summary). Acting 

independently or interacting synchronously in space 

and time, wildland fires, MPB outbreaks, and WPBR 

epidemics can substantially influence forest structure, composition, and function, abruptly reorganize 

landscapes, and alter biogeochemical processes such 

as carbon cycling, water supply, and nutrient cycles 

(Edburg et al. 2012; Falk 2013; Fettig et al. 2007; 

Hansen 2014; Kurz et al. 2008).

3.4.4.1 Fire and Beetles

MPB can often be found in pine trees damaged by 

fire (Davis et al. 2012; Geiszler et al. 1984; McHugh 

et al. 2003; Schwilk et al. 2006; Six and Skov 2009). 

However, MPB may make only minor contributions 

to postfire tree mortality (Geiszler et al. 1984; Jenkins 

et al. 2014; Powell et al. 2012). This mortality is 

usually limited to the immediate vicinity of the fire 

(Ryan and Amman 1996). Although fires rarely initiate 

MPB outbreaks (Mitchell and Sartwell 1974; Powell 

et al. 2012), they may maintain local MPB populations (Davis et al. 2012; Elkin and Reid 2004; Powell 

et al. 2012). Fire effects on MPB populations are 

time-dependent; fire-weakened trees, for example, are 

colonized by MPB only when the fires occurred when 

beetles were searching for new host trees (Parker et al. 

2006). Although MPB often reproduce in fire-damaged 

trees, this lasts only a few months or years after a fire 

(Davis et al. 2012; Powell et al. 2012). Wildland fires 

can affect MPB activity indirectly over longer time 

periods by altering species composition and forest 

structure (e.g., removing fire intolerant species) and 

providing increased water, light, and nutrients to surviving trees, thus improving vigor of suitable host trees 

(Fettig et al. 2007; Hessburg et al. 2005; Keeling and 

Sala 2012). Stand-replacing fires reduce the likelihood 

of MBP attack until regenerating forests have attained a 

sufficient size to attract beetles, especially when beetle 

populations are relatively low (Kulakowski et al. 2012).

Mountain pine beetle activity influences wildland 

fire by altering the quantity, type, vertical and horizontal arrangement, and chemical and moisture properties 

of dead and live biomass (fuel) (Hicke et al. 2012). In 

the endemic phase, MPBs are restricted to stressed or 

damaged trees, so there is little influence on fuels and 

subsequent fire behavior as few trees are affected (Page 

and Jenkins 2007). In the epidemic and post-epidemic 

phases, in which large beetle populations attack and 
38 USDA Forest Service RMRS-GTR-361. 2017.

kill as many as 80–95 percent of susceptible host trees 

within stands, fine surface and canopy fuels change in a 

variety of ways (Hicke et al. 2012). One to 3 years after 

the initial attack, needles of attacked trees are yellowing or red, but still attached to branches. These needles 

have lower foliar moisture contents, contributing to 

higher flammability and torching potential than green 

trees. Because aerial fuel continuity is maintained, 

active crown fire potential is high (Jenkins et al. 2014; 

Page et al. 2012). Four to 10 years after an attack, 

standing dead trees have lost their needles and small 

branches, active crown fire potential is lower than in 

non-attacked stands, but increased fine surface fuel 

loads result in higher surface fire rates of spread, flame 

lengths, and torching potential (Hicke et al. 2012; 

Schoennagel et al. 2012). The highest fire hazard is 

assumed to occur in the post-epidemic phase, decades 

after attack, as a result of high snag fall contributing 

to the accumulation of heavy, large-diameter fuels. 

These stands also have increased shrub, herb, and tree 

regeneration biomass, and the loss of forest canopy 

contributes to increased wind speeds and radiation, 

thereby drying fuels more rapidly (Jenkins et al. 2008).

3.4.4.2 Fire and WPBR

Severe fires that kill rust-resistant pine trees may 

ensure continued high WPBR mortality in the future 

because there will be fewer resistant whitebark pine 

for natural selection to increase the frequency of rustresistant genes in the subsequent progeny (Keane et 

al. 2012b). However, where rust-resistant five-needle 

pines survive fire, they can provide the seeds for 

populating future landscapes consisting of whitebark 

pine trees that are resilient to both WPBR infection and 

fire mortality (Keane et al. 2012b). Conversely, other 

studies have shown that fire exclusion has increased 

competition stress, weakening pine trees and perhaps 

facilitating rust infection and mortality (Heward et al. 

2013; Parker et al. 2006).

Rust-infected whitebark pine stands may have 

a greater potential for postfire tree mortality. Trees 

infected with WPBR are weakened with dead branches 

and tops, and therefore may be more susceptible to 

fire-caused damage and mortality (Stephens and Finney 

2002). As WPBR kills mature pine trees, dead foliage and wood added to the fuelbed may increase fire 

intensity, which in turn may increase tree mortality. 

White pine blister rust infection results in elimination 

of the shade-intolerant pine overstory, allowing shadetolerant competitors, such as subalpine fir, to quickly 

occupy the openings. This creates substantially different canopy fuel conditions, such as lower canopy base 

heights, higher canopy bulk densities, and greater canopy cover, which facilitate more frequent and intense 

crown fires (Keane et al. 2002c; Reinhardt et al. 2010). 

Shade-tolerant competitors are also more susceptible to 

fire damage, resulting in higher postfire tree mortality 

of these species in rust-infected landscapes.

3.4.4.3 MPB and WPBR

Interactions between MPB populations and WPBR 

are rarely studied. In their endemic phase, MPB 

populations may weaken five-needle pines and facilitate infection by WPBR, but these interactions are 

strongly governed by climate and biophysical environment (Bockino 2008; Tomback and Achuff 2010). 

Mahalovich (2013) outlines some concerns regarding 

correlated response in whitebark pine, particularly 

an unfavorable interaction between WPBR infection 

and MPB incidence levels that have been put forth by 

Six and Adams (2007), Bockino and Tinker (2012), 

and Dooley (2012). Host selection ratios developed 

by Macfarlane et al. (2013) assume that trees with 

more blister rust infection are prone to MPB attacks. 

Schwandt and Kegley (2008) found that mountain 

pine beetle preferred trees infected with blister rust 

in north Idaho when beetle populations were low; 

however, during outbreaks, beetles preferentially attacked trees with little or no WPBR. Dooley (2012) 

hypothesized that MPB tree selection is nonlinear and 

increased with WPBR infection levels to a point, after 

which it decreased. More importantly, MPB indirectly 

influences WPBR and vice versa through regulation 

of the tree species that are host to both disturbance 

agents, especially by killing those five-needled host 

trees that are resistant to blister rust (Campbell and 

Antos 2000).

Effects of WPBR on MPB infestations are also 

highly variable and subtle. Campbell and Antos 

(2000) found less MPB activity in trees that had high 

WPBR damage, whereas Bockino and Tinker (2012) 

found that whitebark pine selected as hosts for MPB 
USDA Forest Service RMRS-GTR-361. 2017. 39

had significantly higher WPBR infection, but this varied by tree size (diameter), stand type, and disturbance 

pattern (Larson 2011). Kulhavy et al. (1984) found 

that over 90 percent of western white pine trees infected by bark beetles had either WPBR or some type 

of root disease, whereas Six and Adams (2007) found 

little evidence of interaction effects between MPB 

and WPBR. Simulations of MPB disturbance under 

current climate result in increased mortality of both 

lodgepole and whitebark pine, with a corresponding 

increase in subalpine fir and Douglas-fir, and there is 

little change with the addition of WPBR (Clark et al. 

2017). These projected trends were enhanced under 

a warmer climate, where lodgepole pine declines are 

greater and stands are mainly replaced by Douglas-fir, 

but WPBR interaction has minor effects on species 

composition.

Between two traits, correlated response from a 

genetics perspective has a basis in linked genetic loci, 

overlapping genetic loci, or both (Mahalovich 2013). 

Plus trees in the genetics program provide a biological 

basis for testing the hypothesis that there are one or 

more unfavorable, correlated responses among traits. 

Pearson mean correlations among the plus trees do not 

indicate an unfavorable correlation between WPBR 

infection and high MPB outbreaks, whether those 

data are pooled across years or analyzed by year. 

Blister rust infection is weakly correlated with MPB 

incidence (r = 0.01, P < 0.88), as is average canker 

count (r = 0.03, P < 0.67) (Mahalovich 2015). Blister 

rust was first identified in the Greater Yellowstone 

Ecosystem in 1937; so within the recent evolutionary 

history (1.3 generations), this timeframe is not long 

enough for whitebark pine to have established a correlated response between an introduced pathogen and 

an endemic insect. Moreover, blister rust infection, 

number of cankers, and MPB incidence are weakly 

related geographically, increasing with latitude and 

longitude and decreasing with elevation.

3.4.4.4 Fire, MPB, and WPBR

Real-world studies of the complex interactions 

among all three disturbances—fire, beetles, and 

rust—are rare, but, in our opinion, the net result is 

that MPB and WPBR serve to reduce five-needle 

populations and create high fuel loads. When ignited, 

these fuelbeds may foster fires that are more intense 

than historical fires, and this may result in greater firecaused mortality for all plants within the burned area. 

However, fire, while reducing pines in the short term, 

appears to ensure their long-term persistence by eliminating competitors, as noted by Keane and Morgan 

(1994a). Fire also creates conditions suitable for the 

germination of seed cached by Clark’s nutcrackers. In 

previous modeling efforts, decades or centuries were 

required to reestablish populations of rust-resistant 

pines after die-off (such as would occur with MPB) 

(Loehman et al. 2011b). Simulated wildland fires 

killed some trees, but prevented encroachment by 

shade-tolerant, non-pine species and maintained fiveneedle pines on the landscape (Loehman et al. 2011b). 

Observationally, the greatest decline in whitebark pine 

has been found in those areas affected by both WPBR 

and MPB, but not fire (Campbell and Antos 2000).

3.5 Nutcracker Responses

Given the obligate dependency of whitebark pine 

on Clark’s nutcrackers for seed dispersal and regeneration (Tomback and Linhart 1990), the response by 

the bird to predicted climate warming and changing 

forest landscapes should generate as much concern as 

the likely changes in disturbance regimes and threats 

for whitebark pine. Nutcracker distribution and numbers will determine the distribution and continuity of 

whitebark pine (Tomback 2001, 2005). Any modeling 

of future distribution of whitebark pine should be constrained by the predicted future distribution of Clark’s 

nutcracker (Keane et al. 1990). Direct connections 

between the two species have not been properly recognized with respect to the implications for whitebark 

pine.

Nutcrackers determine the distribution of whitebark 

pine geographically, regionally, and locally, as well as 

population structure, growth forms, and other aspects 

of whitebark pine ecology. Sites selected by nutcrackers for seed caching, along with the environmental 

suitability of these sites for seed germination and 

seedling survival, determine where whitebark pine 

occurs on the landscape (Tomback 1983, 2001, 2005). 

The tendency of nutcrackers to cache seeds below and 

above the current elevational distribution of whitebark 
40 USDA Forest Service RMRS-GTR-361. 2017.

pine enables whitebark pine to respond rapidly to 

climate cooling or warming with changes in elevational and even latitudinal distribution. This influence 

is particularly important now, as climate warming is 

predicted to cause an upward and northward shift in 

whitebark pine distribution (e.g., McKenney et al. 

2007; Schrag et al. 2007; Tomback 2005; Tomback 

and Achuff 2010).

Secondly, seed dispersal by nutcrackers is responsible for the genetic population structure of whitebark 

pine at several spatial scales (Tomback 2005). At the 

local level, the seed caching mode of seed dispersal 

results in a unique fine-scale genetic population structure. Because two or more whitebark pine seeds are 

often cached together, several seedlings from a cache 

may survive and produce a “tree cluster” growth 

form. This growth form appears as a single tree with 

multiple stems, which are tightly clustered or fused at 

the base or part way up the stem. Also, because nutcrackers harvest several seeds to an entire pouch-load 

of seeds from a single tree, there is a high probability 

that the seeds within a cache are genetically related 

(Tomback and Linhart 1990). Within a given area, 

nutcrackers cache at random with respect to caches 

already present, and with seeds from different source 

trees. Therefore, seedlings among neighboring caches 

are usually unrelated, resulting in a random genetic 

pattern among clusters. Finally, nutcrackers transport 

seeds for caching typically over distances of several 

kilometers to more than 30 km. Consequently, gene 

flow from nutcracker seed dispersal occurs across 

greater distances than seed dispersal by wind, resulting in genetically diverse populations (Rogers et al. 

1999; Tomback et al. 1990). This high genetic diversity may facilitate local adaptation and whitebark pine 

resilience in the climates of the future.

Loss of cone production capacity within declining 

whitebark pine forests coupled with the tendency of 

nutcrackers to emigrate when cone crops are small 

could result in fewer seed dispersal events in many 

whitebark pine forests. Whitebark pine stands with 

extensive damage or high mortality from MPB and 

WPBR have lower cone abundance and relatively 

higher pre-dispersal squirrel and nutcracker predation 

(McKinney and Tomback 2007). Yet these stands 

could harbor a higher frequency of rust-resistant 

alleles than similar stands with low mortality if 

trees with some resistance were most likely to have 

survived over time (Hoff et al. 1994). In general, as 

cone production declines from tree mortality and 

damage, the likelihood of stand visitation by nutcrackers and seed dispersal declines (Barringer et al. 2012; 

McKinney et al. 2009).

Bioclimatic envelope models (BEMs) predict that 

whitebark pine will move to higher elevations and 

more northern latitudes (e.g., McKenney et al. 2007; 

Schrag et al. 2008) (Section 3.1.3). However, these 

assessments are made without considering interaction 

with nutcrackers, or even other mutualists such as 

mycorrhizae, and how nutcrackers will respond to 

climate change. Since nutcrackers determine the distribution of whitebark pine on several spatial scales, 

the influence of climate on nutcracker distribution 

will also affect whitebark pine distribution. One major 

limitation of the use of assisted migration in the management of whitebark pine (e.g., McLane and Aitken 

2012), for example, is that the range extension may 

not represent suitable habitat, or include the BEM, for 

nutcrackers.

What is the likely influence of a warming climate 

on nutcracker distribution? The National Audubon 

Society has simulated the effects of climate change 

for the North American bird species they consider to 

be the most climatically vulnerable. The distributional 

requirements for each species were defined in terms 

of temperature range, precipitation, and seasonal 

changes. Determination of “climate suitability” was 

based on three decades of data collected through citizen science projects including the Audubon Christmas 

Counts (December) and the North American Breeding 

Bird Surveys (usually June). Clark’s nutcrackers were 

classified as climatically vulnerable because of their 

dependence on high elevation forest communities, 

which are projected to shift in distribution and contract in areal extent (National Audubon Society 2014). 

The simulations started with the species distribution 

in the year 2000 and examined projected changes in 

winter and summer distributions in 2020, 2050, and 

2080, based on “a range of... greenhouse gas emission scenarios.” By 2080, the projections indicated 

that only 16 percent of the summer range of nutcrackers present in 2000 was usable, with an overall 
USDA Forest Service RMRS-GTR-361. 2017. 41

decrease in suitable summer range of 72 percent. Only 

25 percent of winter range was usable in 2080, with 

an overall decrease of 68 percent. Some of the new 

climatically suitable areas identified for nutcrackers 

are geographically disjunct and distant from the dependable nutcracker range and thus are unlikely to be 

occupied, or are in areas with unsuitable habitat.

For most North American birds, Christmas counts 

and breeding bird surveys provide a reasonable 

picture of distribution. For Clark’s nutcrackers, 

however, neither survey may capture their primary, 

high elevation range or the extent of their range, and 

thus provide an incomplete BEM. Nutcrackers are 

mobile, traveling widely in search of food resources, 

especially in years of poor seed production (Lorenz 

and Sullivan 2009; Tomback 1978; Vander Wall et al. 

1981). In fact, this mobility appears to result in the absence of a rangewide population genetic structure for 

nutcrackers (Dohms and Burg 2013). In December, 

when Christmas bird counts are conducted, many nutcrackers may have left their summer range (Tomback 

1998; Lorenz and Sullivan 2009). Most nutcrackers complete nesting by April and May (Tomback 

1998), whereas breeding bird surveys tend to be in 

June. Also, bird watchers conducting these surveys 

are often not in the forest types that are favored by 

nutcrackers, which limits information on bioclimatic 

tolerances.

Although we tend to associate nutcrackers with 

cold, high mountain environments, the reliable 

distribution of Clark’s nutcrackers may be quite 

broad, including a disjunct population on the high 

peak Cerro Potosí, in Nuevo León, Mexico (latitude 

25°N), and a former population in Sierra San Pedro 

Mártir, northern Baja, California. Populations are also 

found in the desert ranges of southern California and 

the sky island forests of southern New Mexico and 

Arizona (Tomback 1998). At the northern limits, the 

numbers of nutcrackers are reported to decline above 

53oN in the coastal ranges of British Columbia and 

in the Rocky Mountain distribution (Tomback 1998). 

Within some regions, nutcrackers also have a wide 

elevational distribution, even within local areas (e.g., 

Lorenz et al. 2011; Tomback 1978).

Thus, the nutcracker distribution, which encompasses 28 degrees of latitude, several hundred meters 

of local elevational range, a multitude of forest 

types and various weather and disturbance regimes, 

represents a large bioclimatic envelope that should encompass future climates as well as future distributions 

of whitebark pine. However, Clark’s nutcracker distribution may be limited by ecological requirements as 

opposed to climatic. Nutcracker populations require 

multiple seed sources, as backup for years of poor 

cone production by whitebark pine. Other important 

seed sources include ponderosa pine and Douglas-fir 

in the northern range, as well as pinyon pines and 

other high elevation five-needle white pines in more 

central and southern portions of its range (Lorenz 

et al. 2011; Tomback 1978, 1998; Vander Wall and 

Balda 1977). In the northernmost range of whitebark 

pine, only Douglas-fir—which produces small, less 

preferred seeds—serves as an alternative seed source, 

and its range ends at lower latitudes than whitebark 

pine on the eastern slope of the Rocky Mountains. 

The lack of additional seed sources at the northern 

end of the nutcracker distribution may preclude a 

dependable disperser population, which will limit the 

extent of northward shifts in whitebark pine.

Regardless of the nutcracker bioclimatic niche and 

predicted distributional shifts, loss of cone-producing 

whitebark pine as a result of WPBR and MPB infestations diminishes the energetic reward for nutcrackers 

locally, and reduces the likelihood of whitebark pine 

seed dispersal and regeneration. Maintaining or restoring healthy stands of whitebark pine is the best means 

of insuring continued stand visitation by nutcrackers 

and seed dispersal leading to regeneration, even with 

changing climate (Barringer et al. 2012; McKinney et 

al. 2009).
42 USDA Forest Service RMRS-GTR-361. 2017.

The complexity of most ecosystem processes 

and interactions preclude traditional approaches of 

predicting climate impacts such as expert opinion, 

statistical modeling, and field experiments because of 

the long time spans and large areas needed to properly 

evaluate vegetation, disturbance, and ecosystem responses to changing climates. Mechanistic ecological 

simulation of the climate, vegetation, and disturbance 

dynamics in a spatial domain is probably the best 

approach, but this field of landscape modeling is still 

in its infancy (Keane and Finney 2003; Sklar and 

Costanza 1991; Walker 1994,). Some ecosystem simulation models are missing the important interactions 

of disturbance, hydrology, and land use that might 

dictate climate effects on plant distributions (Notaro et 

al. 2007). Many landscape models also fail to include 

disturbances and spatial relationships that may be 

minor now, but are likely to be important in the future 

(e.g., exotics). This could result in still higher levels 

of uncertainty in describing future conditions.

Little is known about the interactions among 

climate, vegetation, and disturbance, along with interactions of critical plant and animal life cycle processes 

of reproduction, growth, and mortality with climate 

(Gworek et al. 2007; Ibanez et al. 2007; Keane et al. 

2001d; Lambrecht et al. 2007). Therefore, it is difficult 

to determine which interaction might be the most 

important in determining species response to climate 

change, especially in upper subalpine whitebark pine 

ecosystems (Price et al. 2001; Walther et al. 2002). 

Climate influences on interactions among different 

disturbance regimes and vegetation could create novel 

landscape behaviors (Williams and Jackson 2007; 

Williams et al. 2007). Therefore, even though landscape ecosystem modeling is not yet mature, we think 

that a simulation approach is still the best way to objectively address complex interactions among climate, 

disturbance, and vegetation. Hence, we have included 

a simulation modeling experiment in this report to 

support various climate change recommendations. We 

have also included independent model runs to illustrate 

various responses of restoration actions in the future.

4.1 The Model

FireBGCv2 is best described as a mechanistic, individual-tree, gap model that is implemented in a spatial 

domain (see Keane et al. 2011 for complete model 

documentation, also http://www.firelab.org/project/

firebgcv2-simulation-platform-and-projects [accessed 

October 2015]). The model was developed by integrating empirically derived deterministic functions 

with stochastically driven algorithms to approximate 

landscape and ecosystem behavior across time and 

space (Keane et al. 2011). Empirical and deterministic functions are used to represent those ecological 

processes, such as autotrophic respiration and photosynthesis, which are often heavily studied so the 

processes are somewhat understood and predictable. 

Stochastic functions are used to represent ecological 

processes that are highly variable, somewhat unstudied, and difficult to quantify, such as fire ignition, tree 

mortality, and snag fall. FireBGCv2 is a cumulative 

effects model that is best used to study long-term 

changes to landscape and ecosystem dynamics 

regimes rather than as a prognostic model to predict 

what will happen in the near future. The FireBGCv2 

model is best used to simulate the long-term interactions of disturbance, climate, and vegetation across 

several model runs to determine patterns and trends in 

landscape behavior and response.

FireBGCv2 simulates ecological processes across 

and within multiple spatiotemporal scales including cross-scale interactions that can drive landscape 

behaviors (fig. 24). Wildland fire ignition and spread, 

along with cone crop production and seed dispersal, 

are simulated at the landscape level at the end of each 

simulation year. Most of the action in a FireBGCv2 

simulation occurs at the stand level (fig. 24) where the 

flows of carbon, nitrogen, and water are distributed 

across various terrestrial and atmospheric components 

within the model (fig. 25). Ecological processes 

modeled at the stand level include decomposition of 

leaf and litter fall, which dictates forest floor dynamics; the interaction of precipitation and temperature, 

4. The Simulation Effort
USDA Forest Service RMRS-GTR-361. 2017. 43

which influences leaf area production and governs 

water dynamics; and photosynthesis and respiration, 

which dictate carbon dynamics. Weather and species 

phenology are simulated at the site level at a daily 

time step. Tree growth, establishment, and mortality 

are simulated at the individual tree level. Disturbance 

effects, such as fuel consumption, tree mortality, and 

soil heating, are computed at each of the scales.

All simulated processes have cross scale implications. For example, carbon is fixed by tree leaves 

(needles) via photosynthesis, which is simulated using 

solar radiation and precipitation weather inputs, and 

the carbon is then distributed to leaves, stems, and 

roots. A portion of this plant material is lost each year 

and accumulates on the forest floor in the litter, duff, 

and soil, eventually providing fuel for a fire. These 

forest floor compartments also lose carbon through 

decomposition. Nitrogen is cycled through the system 

from the available nitrogen pool.

Because FireBGCv2 has many stochastic elements, 

the model should be run for long time periods that 

are at least two to five times the longest fire return 

interval and each run should be replicated at least 

five times (Keane et al. 2012). Simulation results 

should be summarized to emphasize relative trends 

in ecological change rather than absolute change. 

And, as with most mechanistic models, the simulation 

results are best used when compared across simulation scenarios to evaluate differential patterns. Using 

a complex landscape simulation model to develop 

climate change guidelines demands thoughtful design 

of a comprehensive simulation experiment because 

the strength of modeling is in the comparison of alternative scenarios, rather than interpretation of singular 

predictions.

Figure 24—The FireBGCv2 model was designed to simulate 

multiple spatial and temporal scales. (a) A description of 

the five spatial and organizational scales implemented in 

the design. The landscape is the entire simulation area. 

Sites are the unique biophysical settings of the landscape 

based on soils, topography, aspect, and slope. Stands are 

patches of vegetation within a site of different successional 

status, and these stands are made of species of trees and 

undergrowth. (b) There are unique ecological processes 

simulated at each of the inherent scales of FireBGCv2.

a

b

Figure 25—A diagram showing 

the flow of carbon, water, and 

nitrogen to some of the many 

compartments in the FireBGCv2 

model which represent 

the basic ecophysiological 

processes needed to simulate 

climate change responses.
44 USDA Forest Service RMRS-GTR-361. 2017.

4.2 The Simulation Experiment

Our simulation experiment used FireBGCv2 to 

explore climate change impacts on whitebark pine 

and fire dynamics and their effects on ecosystem components, including interactions with insects (MPB), 

diseases (WPBR), fire management (suppression), 

and restoration activities. It would be impossible to 

simulate the complex interactions of climate, fire, and 

landscape dynamics over the entire range of whitebark 

pine. Therefore, we assessed resilience and persistence of whitebark pine under future climates on two 

landscapes that are broadly representative of a wide 

range of climate, vegetation, and fire regime types 

for whitebark pine in the northern Rocky Mountains. 

These landscapes were previously calibrated for 

FireBGCv2 execution and represent a considerable 

investment. It typically requires about 3 to 6 months 

of intensive field sampling to collect data to initialize 

and parameterize FireBGCv2 for a target landscape 

and another 6 to 8 months to prepare the initial input 

maps and files. We selected the following landscapes 

for inclusion in this project (fig. 26):

• East Fork Bitterroot River (EFBR), Bitterroot 

National Forest. A 128,000-ha dry mixed-conifer 

ecosystem with a mixed frequency and severity fire 

Montana

Idaho

East Fork Bitterroot River Basin

Crown of the Continent

0 10 20 30

Km

0 10 20 30

Km

 ̄

Legend

Continental Divide

State boundaries

Glacier National Park

Water

Whitebark pine zone

Figure 26—The two 

landscapes that were 

included in the FireBGCv2 

simulation experiment 

and that were used in the 

illustrative independent 

simulations. The landscapes 

are described in the text. 
USDA Forest Service RMRS-GTR-361. 2017. 45

regime. Whitebark pine forests currently make up 8 

to 12 percent of the landscape.

• Crown of the Continent (CROWN), including 

portions of Glacier National Park and the Flathead 

National Forest. A 100,000-ha high-elevation mesic 

mixed-conifer ecosystem with fire regimes of variable frequency and severity. Whitebark pine forests 

compose 8 to 14 percent of the landscape.

All lands with the potential to support whitebark 

pine were explicitly identified on each landscape 

using biophysical parameters and field data (fig. 

26). In FireBGCv2 simulations, each landscape was 

delineated into two zones. The first zone consists of 

areas that readily support whitebark pine communities. These are typically higher elevation subalpine 

areas, which in the EFBR basin elevation ranged from 

1,305 to 2,876 m and elevations 956 to 3,010 m in 

the CROWN landscape. The second zone is all other 

areas with lands that are generally unsuitable for 

whitebark pine.

In prior studies, we found that a fully factorial 

approach works well with a suite of factors that 

represent the important objectives of the simulation 

effort (Loehman et al. 2011b). In this project, our 

simulation objective was to evaluate the impact of climate change on whitebark pine abundance under fire 

management and restoration treatments. Therefore, 

we designed a simulation experiment using a set of 

factors that represent climate, restoration approaches, 

and fire management. We varied each factor to explore a range of treatment intensities or frequencies. 

We then replicated each simulation across all factors 

to account for model stochasticity (table 3).

The four factors included in the simulation 

experiment were (1) fire suppression level (S), (2) restoration activity (R), (3) whitebark pine planting (P), 

and (4) climate (C). Within each factor, we simulated 

a number of factors that span a reasonable array of 

potential outcomes (table 3). For S, we simulated 

three levels of fire suppression: (1) 0 percent suppression (mimics historical fire regime), (2) 50 percent 

suppression (mimics wildland fire use management 

option), and (3) 92 percent suppression (operational 

fire suppression) (Loehman et al. 2011b). For R, we 

simulated three restoration treatment strategies: 

(1) no restoration treatments, (2) moderate restoration 

where mechanical cuttings of subalpine fir (thinning, 

removal) in concert with prescribed burning were 

used to remove whitebark pine competitors across a 

small proportion (3 percent per year) of the landscape, 

and (3) extensive restoration where mechanical cuttings and prescribed burning treatments were imposed 

across a substantial portion (30 percent per year) of 

the landscape. The P factor had three treatments: 

(1) no planting, (2) planting rust-resistant whitebark 

pine seedlings at 10 ha yr-1 at a density of 275 seedlings ha-1; and (3) planting seedlings at 100 ha yr-1

at a density of 550 seedlings ha-1. Each year, seedlings were simulated to be planted on any areas that 

burned within the last 30 years either from wildfire 

or prescribed burning in an attempt to mimic a full 

restoration program. If there were insufficient newly 

burned areas for planting (i.e., no prescribed fire or 

new wildfires occurred during a particular year in the 

simulation), then seedlings were planted in old burns 

up to 50 years old. If no burned areas were available, then no seedlings were planted that year. The 

proportions of seedlings with rust resistance in these 

simulations were set at 30 percent. This is at the upper 

range of whitebark pine seedling resistance as determined from the Inland West Whitebark Pine Genetic 

Restoration Program at the USDA Forest Service 

Coeur d’Alene (ID) (Mahalovich and Foushee, submitted). However, natural levels of rust resistance at 

high spore loads similar to a wave-year event were 

estimated below 1.0 percent (Mahalovich 2015); we 

included simulations and analysis at this lower level 

as well.

The climate (C) factor had two scenarios: 

(1) historical climate and (2) a future climate 

predicted by the Coupled Model Intercomparison 

Project Phase 5 (CMIP5) with an 8.5 Representative 

Concentration Pathway (RCP). In the future climate 

scenario, average temperatures are projected at about 

5 °C above pre-industrial levels. This is an emissions scenario that predicts the highest temperature 

increases among various possible emission scenarios 

and the one scenario that appears increasingly most 

likely to occur (Peters et al. 2013). A summary of the 

RCP8.5 CMIP5 data is provided in table 1.
46 USDA Forest Service RMRS-GTR-361. 2017.

Typically, it is recommended that at least ten global 

circulation models (GCMs) be used to describe future 

climate or impacts models to sufficiently capture the 

range of uncertainty in climate projections (Mote 

et al. 2011). Due to limited time and capacity for 

FireBGCv2 simulation, we chose one GCM from the 

numerous models available. Based on an evaluation 

of a suite of GCMs by Rupp et al. (2013) for the 

Pacific Northwest and surrounding region, we chose 

the CNRM-CM5 (National Centre of Meteorological 

Research, France), which was the highest ranked 

model overall for the northern Rocky Mountains region. Landscape climate input data were taken from a 

statistical downscaling of the GCM data from CMIP5 

using the Multivariate Adaptive Constructed Analogs 

(Abatzoglou and Brown 2012) method with the 

METDATA (Abatzoglou 2011) observational dataset 

as training data. We also derived weather data for our 

historic scenario simulations from downscaled grids 

of historical climate modeled from the CNRM-CM5 

Table 3—The multifactorial simulation experiment used to assess impacts of climate change on whitebark pine 

restoration attempts. The factors and treatments were used to explore climate change effects on whitebark 

pine distribution and abundance. We performed 10 replicates of each factor. Also presented are acronyms 

of the tree species included in all simulations as referenced in other figures and tables in this section.

Factor Number of levels Values for each level

Fire suppression (S)—Levels of fire 

suppression expressed as percent of 

fires that are suppressed

3

No suppression (SN)- historical fire regime; 

Low suppression (SL)- 50% suppression 

(wildland fire use); High suppression (SH) 

92% suppression (current management)

Restoration activities (R)—Three 

levels of area treated with mechanical 

cuttings coupled with prescribed burns

3

No treatments (RN); low restoration levels 

(RL) 3% of landscape treated per year; high 

restoration (RH) 30% landscape treated per 

year

Whitebark planting (P)—Three levels 

of area treated with planting whitebark 

pine seedlings

3

No planting (PN); low planting (PL) 10 

ha per year at 275 seedlings per ha; high 

planting (PH) 100 ha per year at 550 

seedlings per ha

Climate (C)—Current and projected 

daily climate streams for 95 years 2 Historical and warm-dry (RCP8.5) 

scenarios 

Tree species and plant guilds included in the simulations

Scientific Name Abbreviation Common name

Abies lasiocarpa ABLA Subalpine fir

Pinus albicaulis PIAL Whitebark pine

Pinus contorta PICO Lodgepole pine

Pinus engelmannii PIEN Engelmann spruce

Populus tremuloides POTR Quaking aspen

Pseudotsuga menziesii PSME Douglas-fir

Pinus ponderosa PIPO Ponderosa pine

Pinus flexilis PIFL Limber pine

Laryx occidentalis LAOC Western larch

Pinus monticola PIMO Western white pine

Larix lyallii LALY Alpine larch

Thuja plicata THPL Western red cedar

Tsuga heterophylla TSHE Western hemlock

Riparian Herb RHRB Wetland herbaceous communities

Grass GRSS Grassland dominated communities
USDA Forest Service RMRS-GTR-361. 2017. 47

GCM for the baseline period of 1950–2005 (56 years). 

The GCM modelers recommend that the simulated 

historical climate be used instead of the actual observed climate in order to match the resolution and 

detail of the simulated future projections (Peters et al. 

2013). However, the average annual conditions simulated for the six weather variables input to FireBGCv2 

for the EFBR (fig. 27) and CROWN (fig. 28) were 

different from the observed historical patterns from 

weather station data and often did not match the start 

of future projections.

Each level for each factor was simulated in a 

multifactorial design with ten replicates for each of 

these simulations resulting in a total of 540 simulations (3 S x 3 R x 3 P x 2 CC x 10 reps = 540 runs) 

for each landscape. We used the same species list for 

each simulation run (shown in table 3); this species 

list dictated which species could inhabit the landscape 

during the simulation. Data for the future climate scenarios were available for years 2006–2100 (95 years). 

For comparable results, we simulated both historical 

and future scenarios for this same length of time. We 

output simulation results every 10 years to create a 

simulated time series of nine observations for each 

simulation run. For the historical simulations, we 

repeated the 56-year weather record for both study 

areas to achieve 95-year long simulations. Results 

from these simulations provided the basis for the set 

of recommendations that we crafted future whitebark 

pine management.

A suite of response variables that comprehensively 

describe ecological dynamics across the combinations 

Figure 27—Simulated and observed weather data for EFBR simulation landscape, including average annual maximum, 

minimum and mean temperature (oC), precipitation (cm), vapor pressure deficit (Pa), and solar radiation (W m-2). Historic 

observed data were taken from a weather station at Sula, Montana, and historic and future modeled data were derived 

from the CNRM-CM5 GCM. Note the following: (1) lack of transition from simulated historical (blue line) to simulated 

future (red); (2) difference between simulated historical (blue) and observed historical climate (green); and (3) the same 

precipitation predicted for the future (red: no decrease).
48 USDA Forest Service RMRS-GTR-361. 2017.

of factors detailed above was used to assess climate 

change impacts. Most importantly, we analyzed the 

response of whitebark populations to restoration 

and planting treatments at the stand scale using 

“basal area (m2 ha-1) in whitebark pine,” and at the 

landscape scale using “proportion of the simulation 

area in whitebark pine dominated stands (proportion 

landscape cover).” We summarized the basal area 

and proportion landscape cover in the whitebark pine 

zone only (upper elevation areas that readily support 

whitebark pine) (fig. 26).

We used generalized linear mixed models (GLMM) 

for repeated measures to test for significant differences in restoration, planting, fire suppression, 

and climate, and we also built GLMMs to evaluate 

historical and future climate conditions separately. 

To evaluate the amount of basal area, we used R (R 

Development Core Team 2014) with the “lme” package (Bates et al. 2014) and an AR(1) random residual 

structure to account for temporal autocorrelation. We 

also included multiple comparisons of the main treatment effects. We used GLIMMIX in SAS to evaluate 

proportion cover, with an AR(1) structure and a beta 

probability distribution (appropriate for proportion 

data), and conducted multiple comparisons of main 

effects. In addition, we developed GLMMs to include 

comparisons of plantings with rust resistance at two 

levels: 5 percent (approximate maximum native 

resistance under intense spore loads indicative of 

wave year events) and 30 percent (average WPBRresistance level in breeding program). We performed 

multiple comparisons of main treatment effects, as 

Figure 28—Simulated and observed weather data for CROWN simulation landscape, including average annual maximum, 

minimum and mean temperature (°C), precipitation (cm), vapor pressure deficit (Pa), and solar radiation (W m-2). Historic 

observed data were taken from a weather station at Sula, Montana, and historic and future modeled data were derived 

from the CNRM-CM5 GCM. Note the following: (1) lack of transition from simulated historical (blue line) to simulated 

future (red); (2) difference between simulated historical (blue) and observed historical climate (green); and (3) the same 

precipitation predicted for the future (red: no decrease).
USDA Forest Service RMRS-GTR-361. 2017. 49

well as their two-way interactions to better explore 

relationships between rust resistance and restoration, 

planting, and fire suppression.

We also qualitatively described forest community 

responses to climate change and wildfire management by summarizing percent of the upper subalpine 

landscape (whitebark pine zone) comprised by each 

tree species cover type and by six structural stages 

(seedling d.b.h. < 1 cm, sapling d.b.h. 1–10 cm, 

pole d.b.h. 10–30, mature d.b.h. 30–70 cm, large 

d.b.h. 70–120 cm, and very large d.b.h. > 120 cm). 

We also evaluated a host of other ecosystem related 

explanatory variables including fuels (canopy bulk 

density), fire (average annual area burned, landscape 

fire rotation, fire size), fire effects (tree mortality, fuel 

consumption), and wildlife habitat (lynx and grizzly 

bear) summarized to the landscape scale. Because 

whitebark pine is a keystone and foundation species 

and its distribution overlaps with other forest types 

that provide important habitat for other key conservation targets, we also included the simulation of grizzly 

bear and lynx habitat suitability in our model runs. 

The Loehman et al. (2011b) matrix that rates habitat 

suitability to combinations of cover type and structural stage was used in this step.

4.3 Independent Model Runs

The FireBGCv2 model was also used as an illustrative tool to show effects of a particular action 

or strategy on whitebark pine dynamics over the 

long term. It would have been impossible to include 

all possible factors in our multifactorial simulation 

experiment because of logistical concerns (i.e., it took 

about 4 months to conduct the full simulation experiment). Further, including all other possible restoration 

actions, such as tree protection and patterns of genetic 

variation, would have made the simulation experiment 

unwieldy. As a result, we employed the FireBGCv2 

model to illustrate important impacts and recommendations for specific cases. To support our climate 

change considerations discussed in Section 5, we ran 

the model independently of the simulation experiment 

to evaluate effects of restoration guiding principles 

and actions. We did this because responses of whitebark pine to climate change are relatively unknown 

for many geographic regions so the only reference 

available is a FireBGCv2 model run.

Independent runs used different weather data than 

our simulation experiment. The GCM CMIP5 data 

represented only 95 years into the future, which is 

often insufficient for the impacts of climate change 

to manifest. Fire regimes, for example, need several 

centuries of simulation to be described appropriately. Therefore, we used the methods presented in 

Holsinger et al. (2014) to simulate five centuries 

of climate change by offsetting the daily historical 

recorded weather data presented in figures 27 and 28 

using parameters calculated from the CMIP5 data for 

the RCP8.5 scenario (approximately 4.5 °C in temperature and 1.05 percent increase in precipitation) 

(see Holsinger et al. 2014 for details). Results from 

these independent runs are presented throughout this 

report.

4.4 Simulation Results

4.4.1 Whitebark Pine Abundance

In the EFBR, restoration treatments had the greatest success under a future climate for increasing 

both whitebark pine basal area and the proportion 

landscape cover of whitebark pine in the upper 

elevations of this landscape (figs. 29, 30; table 4). 

Basal area doubled when restoration treatments were 

implemented at either low or high levels as compared 

to no treatment. The proportion of upper subalpine 

landscape in whitebark pine was almost four times as 

high in low or high levels compared to no restoration 

(table 5). In addition, fire suppression had a significant 

but minimal effect on the proportion in whitebark 

pine in the upper subalpine with significantly higher 

canopy cover (3 to 5 percent higher) in the full suppression scenario as compared to the no suppression 

simulations (tables 4, 5).

For the historical climate, we obtained similar 

results, but also with a significant two-way interaction 

between suppression and restoration for basal area 

amount. Both climate and restoration had the greatest 

effects on the amount of basal area and proportion 

of landscape in whitebark pine. High suppression 

was also important for the landscape proportion in 

whitebark pine, where more whitebark pine was 
50 USDA Forest Service RMRS-GTR-361. 2017.

evident compared to no suppression. We found that 

the amount and proportion of whitebark pine was 

significantly higher in the EFBR under the future climate as compared to the historical (P value < 0.0001). 

Over twice as much whitebark pine was predicted 

for the future (3.72 vs. 1.69 m2 ha-1) and for proportion landscape cover (33 vs 12 percent for future and 

historical climate, respectively) (figs. 29, 30). Planting 

whitebark pine seedlings did not significantly affect 

the amount or proportion of whitebark pine in any of 

the EFBR simulations. The greatest whitebark pine 

basal areas are found under future climates with high 

restoration and fire suppression.

In contrast, simulation results for the CROWN 

landscape showed that there were significantly fewer 

whitebark pines, overall, in the future as compared to 

the historical simulations (3.33 vs 10.05 m2 ha-1 basal 

area and 0.24 vs 0.32 proportion cover for future and 

historic respectively) (figs. 31 and 32; tables 4, 5). 

Wildfire suppression had the greatest effect on basal 

area and proportion landscape cover in whitebark 

pine under both future and historical climates. In 

each climate scenario, the amount of basal area in 

whitebark pine was significantly higher with low or 

high suppression compared to without suppression. 

Similarly, the proportion of landscape in whitebark 

Figure 29— Basal area (m2 ha-1) 

of whitebark pine in upper 

elevations (whitebark pine 

zone) of the EFBR landscape 

across all scenarios 

including: historic (green) 

and future (blue) climate; fire 

suppression at none (SN), 

low (SL) and high (SH) levels; 

planting at none, low and 

high levels (PN, PL, PH); and 

restoration at none, low and 

high levels (RN, RL and RH). 

FireBGCv2 predicts greater 

whitebark pine basal areas in 

the future, especially when 

restoration measures are 

taken.

Figure 30—Proportion of 

landscape in whitebark 

pine in upper elevations 

(whitebark pine zone) of 

EFBR across all scenarios 

including: historic (green) 

and future (blue) climate; 

fire suppression at none, 

low and high levels (SN, SL, 

SH respectively); planting at 

none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). There is 

over twice the area occupied 

by whitebark pine in the 

future when restoration 

measures are taken
USDA Forest Service RMRS-GTR-361. 2017. 51

Table 4—Results of generalized linear mixed models for the effects of planting, restoration, suppression climate 

and year for the amount of whitebark pine basal area and the proportion landscape area in whitebark pine 

dominated communities in the whitebark pine zone of the East Fork of the Bitterroot River (EFBR) and Crown 

of the Continent (CROWN) landscapes for Future only, Historic only, and Future and Historic combined.

Asterisk indicates significant interaction between effects. Asterisk indicates significant interaction between 

effects.

Type III tests of fixed effects

EFBR CROWN

Effect F statistic

Degrees of 

freedom

Numerator; 

denominator

P-value F statistic

Degrees of 

freedom

Numerator; 

denominator

P-value

Whitebark pine basal 

area (m2 ha-1)

Future

 Restoration

 Planting

 Suppression

 Year

145.80

0.98

2.05

760.92

2; 263

2; 263

2; 263

9; 2421

<0.0001

0.38

0.13

<0.0001

0.79

1.46

8.06

11871.20

2; 263

2; 263

2; 263

9; 2421

0.45

0.23

0.0004

<0.0001

Historic

 Restoration

 Planting

 Suppression

 S * R

 Year

31.89

0.70

1.55

3.69

894.39

2; 259

2; 259

2; 259

4; 259

9; 2421

<0.0001

0.50

0.21

0.006

<0.0001

0.21

0.08

77.21

3254.78

2; 263

2; 263

2; 263

9; 2421

0.81

0.92

<0.0001

<0.0001

Future and Historic

 Restoration

 Planting

 Suppression

 Climate

 C * Year

 Year

130.97

0.03

2.29

234.11

356.83

1274.36

2; 532

2; 532

2; 532

2; 532

9; 4842

9; 4842

<0.0001

0.97

0.10

<0.0001

<0.0001

<0.0001

0.54

1.04

57.57

14512.15

3017.36

14110.61

2; 532

2; 532

2; 532

2; 532

9; 4842

9; 4842

0.58

0.35

<0.0001

<0.0001

<0.0001

<0.0001

Proportion of whitebark 

pine in landscape (%)

Future

 Restoration

 Planting

 Suppression

 Year

511.94

1.35

3.58

256.02

2; 156

2; 154

2; 154

9; 1961

<0.0001

0.26

0.03

<0.0001

1.19

1.04

27.55

1721.78

2; 276

2; 276

2; 276

9; 2233

0.30

0.36

<0.0001

<0.0001

Historic

 Restoration

 Planting

 Suppression

 Year

218.12

0.11

0.76

316.23

2; 256

2; 252

2; 252

9; 2166

<0.0001

0.90

0.47

<0.0001

0.63

0.72

63.96

328.32

2; 317

2; 317

2; 317

9; 2356

0.53

0.49

<0.0001

<0.0001

Future and Historic

 Restoration

 Planting

 Suppression

 Climate

 Year

459.27

0.71

3.23

950.84

261.53

2; 542

2; 536

2; 536

2; 533

9; 4452

<0.0001

0.49

0.04

<0.0001

<0.0001

0.62

0.44

46.72

789.15

934.17

2; 518

2; 518

2; 518

2; 518

9; 4578

0.54

0.65

<0.0001

<0.0001

<0.0001
52 USDA Forest Service RMRS-GTR-361. 2017.

Table 5—Multiple comparisons of the main treatment effect’s least square means (± standard error) on whitebark pine (PIAL) basal area and 

proportion landscape area of the EFBR and CROWN landscapes in the whitebark pine zone for planting, restoration, suppression and 

climate factors. Tukey’s honestly significant difference (HSD) test was used to evaluate whether means were significantly different from 

each other with corrections for unbalanced replication using the Tukey-Kramer. Future and historical climates evaluated separately and 

combined.

EFBR landscape

Restoration

Variables None Low High P-valuea

Basal area (Future)

Basal area (Historic)

Basal area (Future/Historic)

Prop. PIAL (Future)

Prop. PIAL (Historic)

Prop. PIAL (Future/Historic)

2.32 ± 1.03

1.34 ± 1.04

1.77 ± 1.03

0.1217 ± 0.006

0.0642 ± 0.003

0.0901 ± 0.004

4.77 ± 1.03

2.08 ± 1.04

3.13 ± 1.03

0.4558 ± 0.009

0.1271 ± 0.004

0.2772 ± 0.006

4.77 ± 1.03

1.72 ± 1.04

2.83 ± 1.03

0.5025 ± 0.009

0.1605 ± 0.004

0.3210 ± 0.007 

a, <0.0001

b, <0.0001

c, <0.0001

b, <0.0001

d, <0.0001

d, <0.0001

Planting

None Low High P-valuea

Basal area (Future)

Basal area (Historic)

Basal area (Future/Historic)

Prop. PIAL (Future)

Prop. PIAL (Historic)

Prop. PIAL (Future/Historic)

3.71 ± 1.03

1.72 ± 1.04

2.51 ± 1.03

0.3177 ± 0.009

0.1097 ± 0.003

0.2023 ± 0.006 

3.86 ± 1.03

1.62 ± 1.04

2.51 ± 1.03

0.3380 ± 0.009

0.1098 ± 0.003

0.2112 ± 0.006 

3.60 ± 1.03

1.72 ± 1.04

2.48 ± 1.03

0.3302 ± 0.009

0.1116 ± 0.004

0.2090 ± 0.006 

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Suppression

P-value None a Low High

Basal area (Future)

Basal area (Historic)

Basal area (Future/Historic)

Prop. PIAL (Future)

Prop. PIAL (Historic)

Prop. PIAL (Future/Historic)

3.53 ± 1.03

1.82 ± 1.04

2.44 ± 1.03

0.3133 ± 0.009

0.1071 ± 0.003

0.1976 ± 0.006

3.90 ± 1.03

1.77 ± 1.04

2.61 ± 1.03

0.3265 ± 0.009

0.1120 ± 0.004

0.2080 ± 0.006

3.74 ± 1.03

1.60 ± 1.04

2.46 ± 1.03

0.3463 ± 0.009

0.1120 ± 0.004

0.2173 ± 0.006

n.s.

n.s.

n.s.

e,<0.05

n.s.

e, <0.05

CROWN landscape

Restoration

None Low High P-valuea

Basal area (Future)

Basal area (Historic)

Basal area (Future/Historic)

Prop. PIAL (Future)

Prop. PIAL (Historic)

Prop. PIAL (Future/Historic)

3.53 ± 1.01

9.97 ± 1.01

5.81 ± 1.01

0.2193 ± 0.002

 0.3326 ± 0.003

0.2761 ± 0.002

3.53 ± 1.01

10.07 ± 1.01

5.81 ± 1.01

0.2187 ± 0.002

0.3326 ± 0.003

0.2760 ± 0.002

3.29 ± 1.01

9.97 ± 1.01

5.75 ± 1.01

0.2223 ± 0.002

0.3366 ± 0.003

0.2794 ± 0.002

n.s.

n.s.

n.s.

n.s.

n.s

 n.s.

Planting

None Low High P-valuea

Basal area (Future)

Basal area (Historic)

Basal area (Future/Historic)

Prop. PIAL (Future)

Prop. PIAL (Historic)

Prop. PIAL (Future/Historic)

3.32 ± 1.01

10.07 ± 1.01

5.81 ± 1.01

0.2189 ± 0.002

0.3314 ± 0.003

0.2753 ± 0.002

3.29 ± 1.01

9.97 ± 1.01

5.75 ± 1.01

0.2222 ± 0.002

0.3341 ± 0.003

0.2783 ± 0.002

3.39 ± 1.01

10.07 ± 1.01

5.81 ± 1.01

0.2192 ± 0.002

0.3363 ± 0.003

0.2779 ± 0.002

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Suppression

P-value None a Low High

Basal area (Future)

Basal area (Historic)

Basal area (Future/Historic)

Prop. PIAL (Future)

Prop. PIAL (Historic)

Prop. PIAL (Future/Historic)

3.22 ± 1.01

9.12 ± 1.01

5.41 ± 1.01

0.2107 ± 0.002

0.3119 ± 0.003

0.2552 ± 0.002

3.40 ± 1.01

10.07 ± 1.01

5.82 ± 1.01

0.2205 ± 0.002

0.3324 ± 0.003

0.2765 ± 0.002

3.38 ± 1.01

11.02 ± 1.01

6.11 ± 1.01

0.2294 ± 0.002

0.3584 ± 0.003

0.2945 ± 0.003

a, <0.05.

a, <0.0001

b, <0.0001

c, <0.0001

b, <0.0001

b, <0.0001

a Significant differences among factors indicated by a, RN v RL and RH are significantly different from each other; b, RN v RL and RH at <0.0001, RH v RL 

at 0.001; c, RN v RL and RH at <0.0001, RH v RL at <0.05; d, all treatments are significantly different from each other; e, SH v SN.
USDA Forest Service RMRS-GTR-361. 2017. 53

pine increased with increasing levels of suppression 

(each level significantly different from each other to 

varying degrees; table 4). When climate was included 

as a factor, suppression and climate were the most 

important influences. Finally neither restoration nor 

planting treatments had any notable influence on 

whitebark pine basal area in the CROWN landscape.

When WPBR resistance (two levels, 5 percent and 

30 percent resistance) was considered with the other 

factors, both rust resistance and restoration had the 

greatest effects on basal area in the EFBR under both 

future and historical climates (table 6). Rust resistance 

interacting with restoration and suppression were also 

highly significant, and to a lesser degree, planting. In 

general, basal area was higher when restoration measures were implemented at both low and high levels of 

rust resistance (table 7). Perhaps most striking is that 

under future climates at the 5 percent resistance level, 

both low and high restoration treatments had 7 to 8 

times more whitebark pine basal area than without 

restoration. Basal area was also significantly higher 

with low and high suppression compared to historical fire regimes—at the low rust resistance level—in 

every climate scenario. Similar results occurred at the 

Figure 31—Basal area (m2ha-1) 

of whitebark pine in upper 

elevations (whitebark pine 

zone) of CROWN landscape 

across all scenarios 

including: historic (green) 

and future (blue) climate; 

fire suppression at none, 

low and high levels (SN, SL, 

SH respectively); planting at 

none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). These 

results are dramatically 

different than those for the 

EFBR in that future whitebark 

pine stands will have lower 

basal areas regardless of 

restoration treatment.

Figure 32—Proportion of 

landscape in whitebark 

pine in upper elevations 

(whitebark pine zone) of 

the CROWN landscape 

across all scenarios 

including: historic (green) 

and future (blue) climate; 

fire suppression at none, 

low and high levels (SN, SL, 

SH respectively); planting at 

none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). These 

results are dramatically 

different than those for the 

EFBR in that whitebark pine 

will have lower occurrence 

in the future regardless of 

restoration treatment.
54 USDA Forest Service RMRS-GTR-361. 2017.

Table 6—Results of generalized linear mixed models for the effects of planting, restoration, suppression, climate and rust 

resistance for the amount of basal area (m2 ha-1) and the proportion of the landscape covered in whitebark pine (PIAL) in 

the upper elevation areas of EFBR landscape. Asterisk indicates significant interaction between effects.

Type III tests of fixed effects

Effect F statistic Degrees of freedom

numerator; denominator P-value

Whitebark pine basal area

Future

 Rust resistance

 Restoration

 Planting

 Suppression

 Year

 Rust * Rx

 Rust * Planting

 Rust * Suppression

10634.30

139.72

0.84

1.35

1681.82

307.90

4.01

11.92

1; 5114

2; 263

2; 263

2; 263

9; 5114

2; 5114

2; 5114

2; 5114

<0.0001

<0.0001

0.43

0.26

<0.0001

<0.0001

0.02

<0.0001

Historical

 Rust resistance

 Restoration

 Planting

 Suppression

 Year

 Suppression * Restoration

 Rust * Restoration

 Rust * Suppression

8631.48

54.36

0.30

0.62

2054.37

3.73

58.01

34.53

1; 5116

2; 259

2; 259

2; 259

9; 5116

4; 259

2; 5116

2; 5116

<0.0001

<0.0001

0.74

0.54

<0.0001

0.006

<0.0001

<0.0001

Future and Historical

 Rust resistance

 Restoration

 Planting

 Suppression

 Climate

 Climate * Year

 Year

 Rust * Rx

 Rust * Planting

 Rust * Suppression

18300.44

184.11

0.09

1.90

422.71

636.84

3134.90

269.73

3.00

39.89

1; 10246

2; 532

2; 532

2; 532

1; 532

9; 10235

9; 10235

2; 10235

2; 10235

2; 10235

<0.0001

<0.0001

0.91

0.15

<0.0001

<0.0001

<0.0001

<0.0001

0.05

<0.0001

Landscape proportion area (%)

Future

 Rust resistance

 Restoration

 Planting

 Suppression

 Year

 Restoration * Suppression

0.01

1064.68

1.27

2.91

539.37

2.75

1; 266

2; 270

2; 266

2; 285

9; 3750

2; 279

0.92

<0.0001

0.28

0.06

<0.0001

0.03

Historical

 Rust resistance

 Restoration

 Planting

 Suppression

 Year

2.06

404.64

0.07

5.02

592.71

1; 460

2; 467

2; 460

2; 460

9; 4337

0.15

<0.0001

0.93

0.007

<0.0001

Future and Historical

 Rust resistance

 Restoration

 Planting

 Suppression

 Climate

 Year

 Restoration * Suppression

0.24

952.60

0.68

4.33

1927.29

547.39

2.49

1; 1056

2; 1067

2; 1056

2; 1088

1; 1048

9; 8875

4; 1077

0.62

<0.0001

0.51

0.01

<0.0001

<0.0001

0.04
USDA Forest Service RMRS-GTR-361. 2017. 55

Table 7—Multiple comparisons of the main treatment effect’s least square means (± standard error) and interactions 

for planting, restoration, suppression, rust resistance and climate using Tukey’s honestly significant difference 

(HSD) test to evaluate whether means were significantly different from each other with corrections for unbalanced 

replication using the Tukey-Kramer adjustment for Basal Area in whitebark pine (PIAL) and the proportion of area 

dominated by whitebark pine in upper elevations of EFBR. Future and historic climates evaluated separately and 

combined. Only significant results are shown. Asterisk indicates significant interaction under noted variable.

Variables None Low High P-valuea

Basal area

Future

 Restoration

 Rust = 5% * Restoration

 Rust = 30% * Restoration

 Rust = 5% * Suppression

Historic

 Restoration

 Rust = 5% * Restoration

 Rust = 30% * Restoration

 Rust = 5% * Suppression

 Rust = 30% * Suppression

Future and Historic

 Restoration

 Rust = 5% * Restoration

 Rust = 30% * Restoration

 Rust = 5% * Suppression

2.34 ± 1.04

0.72 ± 1.04

3.18 ± 1.04

3.38 ± 1.04

1.33 ± 1.03

1.20 ± 1.04

1.48 ± 1.04

1.57 ± 1.04

1.54 ± 1.04

1.76 ± 1.03

1.43 ± 1.04

2.18 ± 1.04

2.29 ± 1.04

4.75 ± 1.04

5.78 ± 1.04

3.90 ± 1.04

3.94 ± 1.04

2.18 ± 1.03

2.53 ± 1.04

1.88 ± 1.04

1.73 ± 1.04

1.79 ± 1.04

3.22 ± 1.03

3.86 ± 1.04

3.69 ± 1.04

2.62 ± 1.04

4.73 ± 1.04

5.33 ± 1.04

4.19 ± 1.04

3.98 ± 1.04

1.77 ± 1.03

1.77 ± 1.04

1.78 ± 1.03

1.98 ± 1.04

1.79 ± 1.04

2.89 ± 1.03

3.07 ± 1.04

2.73 ± 1.04

2.81 ± 1.04

a, <0.0001

a, <0.0001

b, <0.0001

c, <0.05

d, <0.0001

e, <0.0001

f, <0.0001

g, <0.0001

c, <0.05

h, <0.0001

e, <0.0001

a, <0.0001

c, <0.05

Proportion landscape area

Future

 Restoration

 Restoration = Low * Suppression

Historic

 Restoration

 Suppression

Future and Historic

 Restoration

 Suppression

 Restoration = Low * Suppression

0.12 ± 0.004

0.43 ± 0.011

0.07 ± 0.002

0.10 ± 0.003

0.09 ± 0.002

0.20 ± 0.004

0.26 ± 0.007

0.46 ± 0.006

0.45 ± 0.011

0.13 ± 0.003

0.11 ± 0.003

0.28 ± 0.005

0.21 ± 0.004

0.28 ± 0.008

0.50 ± 0.006

0.50 ± 0.011

0.16 ± 0.003

0.12 ± 0.003

0.32 ± 0.005

0.22 ± 0.004

0.31 ± 0.008

e, <0.0001

i, <0.0001

e, <0.0001

i, <0.05

e, <0.0001

i, < 0.05

j, <0.0001

a Significant differences (repeated measures ANOVA with Tukey’s HSD) among factors indicated by a, RN v RL and RH significantly 

different, b, RN v RH at <0.0001, RN v RL at <0.05; c, SN v SL and SH; d, RN v RL and RH at <0.0001, RL v RH at <0.05; e, all 

treatments significantly different; f, RN v. RL at <0.0001, RN v RH at <0.05; g, SN v SH at <0.0001, SL v SH at <0.05; h, RN v RL 

and RH at <0.0001, RL v RH at 0.05; i, SN v SH; j, SN v SH at <0.0001, SL v SH at <0.05.

high rust resistance level in the historical climate only. 

In contrast, the proportion of canopy in whitebark 

pine was not evidently influenced by rust resistance 

under any of the climate scenarios (tables 6, 7).

In the CROWN landscape, rust resistance also 

had a significant effect on the amount of basal area, 

as did suppression and their interactions under both 

climate scenarios (tables 8, 9). Whitebark pine basal 

area was significantly higher with increasing levels 

of suppression at both low and high rust resistance 

levels, except under the future climate scenario, where 

this was only the case at low rust resistance (table 8). 

Similar relationships were observed for the proportion of landscape area in whitebark pine, where rust 

resistance and suppression had the greatest effects in 

each of the climate scenarios (table 9). Also, as with 

basal area, the proportion of landscape cover in whitebark was greater with each increasing suppression 

level (table 9).

4.4.2 Forest Species Community Response

Whitebark pine (PIAL in table 3) was the dominant 

species (46 to 53 percent on average) under a future 

climate in the upper elevations (whitebark pine zone) 

of the EFBR when either low or high levels of restoration treatments were implemented (fig. 33). Without 

any restoration, subalpine fir (ABLA) was dominant 

(76 percent), with whitebark pine as the second most 

dominant specie (9 percent). Under the historical 
56 USDA Forest Service RMRS-GTR-361. 2017.

Table 8—Results of generalized linear mixed models for the effects of planting, restoration, 

suppression, climate and rust resistance for the amount of basal area (m2 ha-1) and 

proportion of landscape cover in whitebark pine (PIAL) in the upper elevation areas of 

CROWN. Asterisk indicates significant interaction between effects

Type III tests of fixed effects

Effect CROWN

Effect F statistic Degrees of freedom

numerator; denominator P-value

Whitebark pine basal area

Future

 Rust

 Restoration

 Planting

 Suppression

 Year

 Rust * Suppression

58213.34

0.54

1.21

13.43 25156.16

13.36

1; 58213

2; 263

2; 263

2; 263

9; 518

2; 5118

<0.0001

0.59

0.30

<0.0001

<0.0001

<0.0001

Historic

 Rust

 Restoration

 Planting

 Suppression

 Year

 Rust * Suppression

12053.97

0.22

0.13

117.03

5905.59

38.45

1; 5118

2; 263

2; 263

2; 263

9; 5118

4; 5118

<0.0001

0.80

0.87

<0.0001

<0.0001

<0.0001

Future and Historic

 Rust

 Restoration

 Planting

 Suppression

 Climate

 Climate * Year

 Year

 Rust * Suppression

63599.78

0.50

0.87

88.95

24586.45

6485.98

34326.02

33.04

1; 10239

2; 532

2; 532

2; 532

1; 532

9; 10239

9; 10239

3; 10239

<0.0001

0.61

0.42

<0.0001

<0.0001

<0.0001

<0.0001

<0.0001

Proportion of landscape cover of whitebark pine

Future

 Rust

 Restoration

 Planting

 Suppression

 Year

 Rust * Suppression

8178.95

0.60

0.38

3.36

1440.62

4.61

1; 5381

2; 5381

2; 5381

2; 5381

9; 5381

2; 5381

<0.0001

0.55

0.68

0.04

<0.0001

0.01

Historic

 Rust

 Restoration

 Planting

 Suppression

 Year

 Rust * Suppression

15316.4

1.51

1.11

45.95

646.48

27.51

1; 576

2; 573

2; 573

2; 577

9; 4692

2; 577 

<0.0001

0.22

0.33

<0.0001

<0.0001

<0.0001

Future and Historic

 Rust

 Restoration

 Planting

 Suppression

 Climate

 Year

 Rust * Suppression

870.44

0.53

0.23

12.76

870.44

1170.36

9.4

1; 10780

2; 10780

2; 10780

2; 10780

1; 10780

9; 9862

2; 10780

<0.0001

0.59

0.79

<0.0001

<0.0001

<0.0001

<0.0001
USDA Forest Service RMRS-GTR-361. 2017. 57

Table 9—Multiple comparisons of the main treatment effect’s least square means (± standard error) and interactions for 

planting, restoration, suppression, rust resistance and climate using Tukey’s honestly significant difference (HSD) test 

to evaluate whether means were significantly different from each other with corrections for unbalanced replication 

using the Tukey-Kramer adjustment for basal area in whitebark pine (PIAL) and percent of whitebark pine in upper 

elevations of CROWN. Future and historic climates evaluated separately and combined. Only significant results 

shown. Asterisk indicates significant difference under the noted variable.

Variables None Low High P-valuea

Basal area

Future

 Rust

 Suppression

 Rust = 5% * Suppression

Historic

 Suppression

 Rust = 5% * Suppression

 Rust = 30% * Suppression

Future and Historic

 Rust

 Suppression

 Rust = 5% * Suppression

 Rust = 30% * Suppression

–

3.26 ± 1.01

3.25 ± 1.01

9.19 ± 1.01

8.89 ± 1.01

9.50 ± 1.01

–

5.47 ± 1.01

5.37 ± 1.01

5.58 ± 1.01

3.42 ± 1.01

3.42 ± 1.01

3.49 ± 1.01

9.95 ± 1.01

9.88 ± 1.01

10.03 ± 1.01

5.83 ± 1.00

5.84 ± 1.01

5.87 ± 1.01

5.82 ± 1.01

3.22 ± 1.01

3.42 ± 1.01

3.54 ± 1.01

10.88 ± 1.01

11.13 ± 1.01

10.62 ± 1.01

5.78 ± 1.00

6.10 ± 1.01

6.28 ± 1.01

5.94 ± 1.01

<0.0001

a, <0.0001

a, <0.0001

b, <0.0001

b, <0.0001

c, <0.0001

<0.05

b, <0.0001

b, <0.0001

a, <0.0001

Proportion PIAL

Future

 Rust

 Suppression

 Rust = 30% * Suppression

Historic

 Suppression

 Rust = 30% * Suppression

Future and Historic

 Suppression

 Rust = 30% * Suppression

–

0.11 ± 0.002

0.21 ± 0.003

0.20 ± 0.001

0.31 ± 0.002

0.15 ± 0.002

0.26 ± 0.003

0.06 ± 0.001

0.12 ± 0.002

0.22 ± 0.003

0.21 ± 0.002

0.33 ± 0.002

0.16 ± 0.002

0.28 ± 0.003

0.22 ± 0.002

0.12 ± 0.002

0.23 ± 0.003

0.22 ± 0.001

0.36 ± 0.002

0.17 ± 0.002

0.30 ± 0.003

<0.0001

e, <0.05

f, <0.0001

b, <0.0001

b, <0.0001

f, <0.0001

c, <0.0001

a Significant differences (repeated measures ANOVA with Tukey’s HSD) among factors indicated by a, SN v SL and SH significantly 

different, b, all treatments significantly different from each other; c, SH v SN and SL at <0.0001, SL v SN at <0.05; e, SN v SH; f, SN v 

SH at <00.0001, SL v SN and SH at <0.05.

climate, applying high restoration resulted in aspen 

(POTR) as the dominant species (59 percent), and 

comparable amounts of subalpine fir (21 percent) and 

whitebark pine (17 percent) (fig. 34). Both low and 

high levels of restoration kept whitebark pine on the 

landscape (>12 percent), but subalpine fir was still the 

dominant cover type (40 percent) followed by aspen 

(35 percent). Without restoration, subalpine fir dominated the high elevation landscape (76 percent) using 

historical weather data.

In contrast, there was little variation in community composition among treatments and climate in 

the CROWN landscape (figs. 35, 36). Subalpine fir 

dominated the upper elevation areas across all treatments in both the future (about 45 percent in fig. 35) 

and historical (about 35 percent in fig. 36) climates. 

Whitebark pine continues to remain on the high elevation landscape in both historical and future climates, 

but at low levels (10 percent) probably because 

restoration isn’t nearly as effective on this landscape 

compared to EFBR. The other species occurring under 

both future and historical climates were mainly a 

mixture of aspen, Douglas-fir (PSME), lodgepole pine 

(PICO) and Engelmann spruce (PIEN) at about 10 

percent each.

In terms of structural stage distributions, forest 

structure in the upper elevations of the EFBR were 

balanced in the low and high levels restoration treatments with a mixture of sapling (25 percent for low 

levels and 38 percent for high), pole (37 percent for 

low, 25 percent for high) and mature (23 percent for 

low, 18 percent for high) stages with low and high 

levels of restoration under the future climate (fig. 37). 

Without restoration measures, trees were mainly in the 

mature stage (48 percent), followed by the pole stage 

(40 percent). Under the historical climate scenario, 
58 USDA Forest Service RMRS-GTR-361. 2017.

there were similar distributions where low and high 

restoration treatments had stands with mixtures of 

sapling (17 percent for low, 40 percent for high), 

pole (51 percent for low, 30 percent for high), and 

mature (22 percent for low, 15 percent for high) trees 

(fig. 38). Without restoration, trees were mainly in 

Figure 33—Forest species composition for the upper elevations of the EFBR landscape under 

a future climate for fire suppression at none, low and high levels (SN, SL, SH respectively); 

planting at none, low and high levels (PN, PL, PH); and restoration at none, low and 

high levels (RN, RL and RH). See table 3 for species abbreviations. Whitebark pine 

will comprise more of the EFBR landscape in the future, especially with decreasing fire 

suppression and increasing restoration measures.

the mature (47 percent) and pole (39 percent) stages 

under the historical climate. In the CROWN landscape, trees were primarily mature (50 percent) across 

all treatments and climates, followed by the pole (20 

percent), sapling (15 percent) and large (10 percent) 

structural stages (figs. 39, 40).
USDA Forest Service RMRS-GTR-361. 2017. 59 Figure 34—Forest species composition for the upper elevations of the EFBR landscape under a historical climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). See table 3 for species abbreviations. Historically, whitebark pine dominated only 20 percent of the EFBR landscape with WPBR and that proportion is dramatically reduced with fire suppression and it is enhanced by restoration measures. Figure 35—Forest species composition for the upper elevations of the CROWN landscape under a future climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). See table 3 for species abbreviations. Whitebark pine dominates less than 10 percent of the landscape regardless of restoration activities or suppression efforts.
60 USDA Forest Service RMRS-GTR-361. 2017. Figure 36—Forest species composition for the upper elevations of the CROWN landscape under a historical climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). See table 3 for species abbreviations. Similar to future predictions, whitebark pine dominates less than 10 percent of the landscape regardless of restoration activities or suppression efforts. Figure 37—Structural stage distributions across the upper elevation areas of the EFBR landscape under a future climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). In the future, the EFBR landscape was dominated by early seral whitebark pine stands when fires are allowed to burn regardless of restoration measures.
USDA Forest Service RMRS-GTR-361. 2017. 61 Figure 38—Structural stage distributions across the upper elevation areas of the EFBR landscape under a historic climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). There was much less area in mature stands in the past than in the future (fig. 37) and late seral stands dominate when fires are suppressed. Figure 39—Structural stage distributions across the upper elevation areas of the CROWN landscape under a future climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). In the future, the CROWN landscape was dominated by mature whitebark pine stands regardless of suppression or restoration measures.
62 USDA Forest Service RMRS-GTR-361. 2017.

4.4.3 Fuel Characteristics

Canopy bulk densities in the EFBR landscape were 

slightly higher under the historical climate (13 percent 

on average) compared to the future (10 percent) 

(fig. 41). Among treatments, canopy bulk densities 

were slightly higher without restoration treatment (up 

to 0.03 kg m-3) compared to low or high restoration 

in the historical and future climates. In the CROWN 

landscape, canopy bulk densities were quite similar 

between climates (20 percent for historical, 21 percent 

for future; fig. 42). Across treatments, canopy bulk 

densities were also similar (about 20 percent), with 

the greatest differences in simulations without suppression (up to 0.02 kg m-3 lower) compared to low or 

no suppression.

4.4.4 Fire Regimes

On average, more area burned under a future 

climate than the historical climate in the EFBR (157 

percent increase). Without suppression, a median of 

550 ha burned annually under the future climate compared to 350 ha under the historical climate. With low 

suppression, 186 ha vs 155 ha burned annually, and 

52 ha vs 58 ha with high suppression in the future vs 

historical climates, respectively. The historical climate 

Figure 40—Structural stage distributions across the upper elevation areas of the CROWN landscape under 

a historic climate for fire suppression at none, low and high levels (SN, SL, SH respectively); planting at 

none, low and high levels (PN, PL, PH); and restoration at none, low and high levels (RN, RL and RH). 

The distribution of structural stages on the CROWN landscape in the past was similar to that predicted 

for the future regardless of suppression or restoration.
USDA Forest Service RMRS-GTR-361. 2017. 63

scenario generated few large fire years (years 11, 54 

and 67) that were not observed in the future climate 

(fig. 43), but these high burn years had little overall 

effect on the central tendency across simulations. 

We estimated about 1 to 2 fewer ha burned annually 

under the historical climate when those fire years were 

removed. The percent of the landscape burned each 

year was only slightly less (10 percent) in the historical climate scenario than in the future scenario with 

corresponding levels of fire suppression (fig. 44). The 

median percentage burned annually under the historical versus the future scenario was: 0.48 percent versus 

0.56 percent without suppression; 0.21 percent vs 

0.25 percent with low suppression; and 0.059 percent 

vs 0.062 percent with high suppression. Average fire 

sizes across all scenarios were relatively invariant 

with a median of about 50 ha (fig. 45).

In the CROWN landscape, about half as much area 

burned (median of 70 ha annually across all scenarios) 

compared to the EFBR (143 ha). Moreover, more area 

burned annually under the historical climate than the 

future without suppression (135 ha vs 79 ha respectively) and with low suppression (68 ha vs 62 ha, 

respectively). With high suppression, less area burned 

under the historical climate (53 ha annually) than 

the future (58 ha). Similar to the EFBR, there were 

Figure 41—Canopy bulk 

density in the EFBR across 

all scenarios including: 

historic and future climate; 

fire suppression at none, 

low and high levels (SN, SL, 

SH respectively); planting at 

none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). Canopies 

were less dense in the 

future, especially with high 

restoration, which results in 

less chance of crown fires.

Figure 42—Canopy bulk density 

in the CROWN across all 

scenarios including: historic 

and future climate; fire 

suppression at none, low 

and high levels (SN, SL, SH 

respectively); planting at 

none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). Canopies 

were roughly the same 

density across all scenarios 

for the crown, but maybe a 

bit less dense in the future.
64 USDA Forest Service RMRS-GTR-361. 2017.

Figure 43—Average annual area 

burned (ha) by simulation time 

in the EFBR under historic and 

future climate and with fire 

suppression at none, low and 

high levels. More land burns in 

the future especially at the end 

of the simulation, but there was 

more variability in the past. 

Figure 44—Percent of landscape 

burned per year in the EFBR 

across all scenarios including: 

historic and future climate; 

fire suppression at none, low 

and high levels (SN, SL, SH 

respectively); planting at none, 

low and high levels (PN, PL, 

PH); and restoration at none, 

low and high levels (RN, RL 

and RH). There was slightly 

more area burned in the 

future for the EFBR landscape. 

Suppression is the main factor 

that reduces landscape fire 

rotation. 

Figure 45—Fire size (ha) in the 

EFBR across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels (SN, 

SL, SH respectively); planting 

at none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). Fires were 

about the same size regardless 

of scenario on the EFBR 

landscape.
USDA Forest Service RMRS-GTR-361. 2017. 65

three very large fire years (11, 54, and 67) under the 

historical climate scenario to extents not observed in 

the future (fig. 46). When these fire years are removed 

from the historical dataset, the amount of area burned 

annually was relatively unaffected in the low and high 

suppression scenarios (67 ha and 52 ha respectively), 

but substantially lower when fires were allowed to 

burn (125 ha). The percent of the landscape burned 

each year was essentially the same between climates 

with low suppression (0.1 percent) and high (0.06 

percent) and marginally higher without suppression 

under the historical climate (0.19 percent) as compared to the future scenario (0.16 percent) (fig. 47). 

As in the EFBR, fire sizes in the CROWN landscape 

were very similar across all scenarios having a median 

size of 49 ha (fig. 48).

4.4.5 Fire Effects

The fire-caused tree mortality in the EFBR landscape was somewhat higher under the historical 

climate as compared to the future (fig. 49). We simulated average tree mortality to be 20 percent for the 

historical simulations versus 16 percent of trees killed 

annually in the future without suppression; 21 percent 

versus 17 percent with low suppression; and 22 percent versus 17 percent with high. Similar relationships 

Figure 46—Average annual area 

burned (ha) by simulation time 

in the CROWN landscape under 

historic and future climate and 

with fire suppression at none, low 

and high levels. Fires become 

more frequent in the future 

towards the end of the simulation. 

Note that fires increase in the 

future towards the end of the 

simulation time span and also 

there is less variability in the 

future. Even high suppression 

strategies can sometimes result in 

a large fire year (bottom right).

Figure 47—Percent of 

landscape burned per year 

in the CROWN landscape 

across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels 

(SN, SL, SH respectively); 

planting at none, low and 

high levels (PN, PL, PH); 

and restoration at none, low 

and high levels (RN, RL and 

RH). Burned area rate was 

about the same in the future 

as it was in the past for the 

CROWN. Suppression is 

the only factor that reduces 

landscape fire rotation.
66 USDA Forest Service RMRS-GTR-361. 2017.

for fire effects occurred in the CROWN landscape 

although with substantially greater magnitudes. Fire 

caused tree mortality was slightly higher under the 

historical versus future climate: 63 percent vs 61 percent without suppression; 62.5 percent vs 61.6 percent 

with low suppression; and 62 percent and 60 percent 

with high suppression (fig. 50).

Conversely, the amount of biomass consumed by 

fire annually in the EFBR landscape was slightly 

lower under the historical versus the future climate: 

0.0018 kg m-2 versus 0.0023 kg m-2 without suppression; 0.0018 kg m-2 versus 0.0025 kg m-2 with low 

suppression; 0.0019 kg m-2 versus 0.0026 kg m-2 with 

high suppression (fig. 51). Marginally less biomass 

was consumed by fire on an annual basis under the 

historical versus future climate: without suppression 

(0.0052 kg m-2 versus 0.0053 kg m-2) and with low 

suppression (0.0055 kg m-2 versus 0.0067 kg m-2). 

With suppression, slightly more biomass was 

consumed in the historical versus future climate 

(0.0063 kg m-2 versus 0.0059 kg m-2) (fig. 52).

4.4.6 Lynx and Grizzly Bear Habitat 

Suitability

Habitat suitability for lynx in the EFBR declined 

only slightly in the future scenario (46 percent of the 

landscape on average) as compared to the historical 

climate scenario (44 percent) (fig. 53). Most notably, 

more lynx habitat was available when no restoration 

Figure 48—Fire size (ha) in the 

CROWN landscape across 

all scenarios including: 

historic and future climate; 

fire suppression at none, low 

and high levels (SN, SL, SH 

respectively); planting at none, 

low and high levels (PN, PL, 

PH); and restoration at none, 

low and high levels (RN, 

RL and RH). There was little 

difference in fire size across 

suppression, restoration, and 

planting scenarios.

Figure 49—Percent tree mortality 

in the EFBR landscape across 

all scenarios including: 

historic and future climate; 

fire suppression at none, 

low and high levels (SN, SL, 

SH respectively); planting at 

none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). There was 

less tree mortality in the future 

regardless of suppression or 

restoration action.
USDA Forest Service RMRS-GTR-361. 2017. 67

Figure 50—Percent fire-caused 

tree mortality in the CROWN 

landscape across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels (SN, 

SL, SH respectively); planting 

at none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). There was 

little difference in fire mortality 

across suppression, restoration, 

and planting scenarios.

Figure 51—Biomass consumed 

by fire (kg m-2) in the EFBR 

landscape across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels (SN, 

SL, SH respectively); planting 

at none, low and high levels 

(PN, PL, PH); and restoration at 

none, low and high levels (RN, 

RL and RH). It appears more 

biomass will be consumed 

in the future but restoration 

actions may also reduce 

biomass consumed.

Figure 52—Biomass consumed 

by fire (kg m-2) in the CROWN 

landscape across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels (SN, 

SL, SH respectively); planting 

at none, low and high levels 

(PN, PL, PH); and restoration 

at none, low and high levels 

(RN, RL and RH). Amount of 

biomass consumed varied 

little over restoration actions, 

but it appears more biomass 

was consumed when fires are 

suppressed.
68 USDA Forest Service RMRS-GTR-361. 2017.

treatments were implemented in either the future 

or historic climates; restoration treatments reduced 

subalpine fir on the landscape. For grizzly bears, 

the amount of habitat available was essentially the 

same under both the historical and future climates 

(44 percent) (fig. 54). But restoration had the reverse 

effect with bear habitat increasing at low and high 

treatments. Planting and suppression had little effect 

on either lynx or bear habitat.

The response of lynx and grizzly habitat was quite 

different in the CROWN landscape with the dominant 

influence being climate. For lynx, more habitat was 

available in the future climate scenario (59 percent) 

compared to the historical climate (53 percent) 

(fig. 55). Grizzly bear habitat suitability declined 

slightly in the future climate scenario (52 percent) 

as compared to the historical climate scenario 

(55 percent; fig. 56). None of the other treatments 

(restoration, suppression, planting) had any discernable influence on either lynx or grizzly bear habitat.

Figure 53—Percent of the EFBR 

landscape with habitat suitable 

for lynx across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels (SN, 

SL, SH respectively); planting 

at none, low and high levels 

(PN, PL, PH); and restoration at 

none, low and high levels (RN, 

RL and RH). It appears lynx 

habitat will decrease because of 

restoration actions in both the 

future and past.

Figure 54—Percent of the EFBR 

landscape with habitat suitable 

for grizzly bear across all 

scenarios including: historic 

and future climate; fire 

suppression at none, low 

and high levels (SN, SL, SH 

respectively); planting at none, 

low and high levels (PN, PL, 

PH); and restoration at none, 

low and high levels (RN, RL 

and RH). It appears grizzly 

bear habitat may increase with 

restoration actions in both the 

future and past.
USDA Forest Service RMRS-GTR-361. 2017. 69

4.5 Simulation Summary

4.5.1 Findings

It appears from our simulations that there may 

be landscapes in whitebark pine’s range where the 

species will actually do well in abundance and extent 

under a future of warming climates (for example, 

the EFBR; figs. 29, 30). However, these future gains 

are only realized if there is some sort of restoration 

activity; even low levels of restoration will ensure 

potentially resilient whitebark pine forests in the 

future. Conversely, there are other landscapes, such 

as CROWN, where whitebark pine will not do well 

in the future (figs. 31, 32), even with restoration 

and increased planting of rust-resistant seedlings. 

Knowing which landscapes will support future whitebark pine forests will be difficult without the aid of 

models such as FireBGCv2 because the interactions 

of fire, climate, and vegetation will dictate future 

landscape dynamics.

While FireBGCv2 simulation results show that 

whitebark pine appears to be more productive in 

the next of 95 years of climate warming (figs. 29, 

30) for the EFBR landscape, the long-term future 

for whitebark pine for both simulation landscapes 

is (fig. 57). Our independent simulations show a 

downward trend in whitebark pine basal area over the 

next 100 years regardless of restoration level (fig. 57) 

Figure 55—Percent of the CROWN 

landscape with habitat suitable 

for lynx across all scenarios 

including: historic and future 

climate; fire suppression at 

none, low and high levels (SN, 

SL, SH respectively); planting 

at none, low and high levels 

(PN, PL, PH); and restoration at 

none, low and high levels (RN, 

RL and RH). It appears lynx 

habitat will increase by 3–4 

percent in the future regardless 

of restoration or suppression 

actions.

Figure 56—Percent of the 

CROWN landscape with 

habitat suitable for grizzly bear. 

across all scenarios including: 

historic and future climate; 

fire suppression at none, low 

and high levels (SN, SL, SH 

respectively); planting at none, 

low and high levels (PN, PL, 

PH); and restoration at none, 

low and high levels (RN, RL 

and RH). It appears grizzly bear 

habitat will decrease by about 4 

percent in the future regardless 

of restoration activities and 

suppression actions.
70 USDA Forest Service RMRS-GTR-361. 2017.

that is primarily a result of the damaging impacts 

of WPBR; climate plays a minor role. However, 

there is an increase in whitebark pine basal area and 

landscape proportion after 120 years of simulation as 

rust resistance increases on the landscape from both 

natural selection and planting. Without any restoration 

measures, whitebark pine abundance remains low 

because of poor seed dispersal by nutcrackers. It appears that whitebark pine can eventually overcome the 

damaging effects of WPBR in about 150 years under 

the historical climate. Increased fire and higher temperatures in the future, however, may keep whitebark 

pine populations low but trending upwards. Whitebark 

pine levels stay low without restoration measures, but 

trend upwards as whitebark pine populations become 

more rust-resistant and as restoration activities are 

implemented (fig. 57).

Planting rust-resistant seedlings doesn’t seem to 

enhance whitebark pine abundance in our simulations (figs. 29, 31; tables 5–8), but this is primarily 

an artifact of the short simulation period (95 years). 

Previous simulations by Loehman et al. (2011b) 

showed that it took at least 90 years for populations 

of whitebark pine to become sufficiently resistant to 

WPBR. Independent model runs of 500 years for the 

EFBR landscape shows that it takes about a century 

for the pine to rebound against WPBR and develop 

enough resistance to combat the disease (fig. 57). We 

attempted to rectify the short time period by simulating both 5 and 30 percent levels of resistance in the 

whitebark pine population and found that the higher 

resistance improved both whitebark pine basal area 

and proportion cover (tables 8, 9). The real payoff 

from planting rust-resistant seedlings probably won’t 

Figure 57—Independent simulations (Section 4.3) of the long-term dynamics (500 year simulations) of whitebark 

pine basal area (m2 ha-1) and landscape extent (proportion) for the two landscapes for historical (blue, green) 

and future climates (red, yellow) for no (RN-PN; yellow, blue) and high (RH-PH; green, red) restoration efforts 

(table 5): (A) CROWN landscape showing changes in whitebark pine (PIAL) basal area, (B) CROWN landscape 

showing changes in whitebark pine (PIAL) landscape extent as a proportion, (C) EFBR landscape showing 

changes in whitebark pine (PIAL) basal area, (D) EFBR landscape showing changes in whitebark pine (PIAL) 

landscape extent as a proportion. It appears whitebark pine will be reduced from both landscapes over the 

long-term but restoration treatments will ensure it will endure on the high mountain landscape.
USDA Forest Service RMRS-GTR-361. 2017. 71

be realized for a century and its main effect is to 

shorten the time it takes to create rust-resistant populations of seed-producing whitebark pine.

Interestingly, simulated future wildland fire activity increased by only 57 percent in area burned over 

historical EFBR simulations (fig. 44), and actually 

went down 10 percent for CROWN simulations (fig. 

47). Increases in wildland fire are one reason why 

whitebark pine has responded both well and poorly 

in future climates. Increased wildfires created ample 

competition-free places for nutcracker seed caching 

and rust-resistant seedling planting, but these same 

fires also killed many whitebark pine. Minor increases 

in burned area can result in major gains in fire-tolerant 

species over long time spans, but if fire is too frequent, it may kill the planted and natural whitebark 

pine regenerated stands. Decreases in fire activity 

in the CROWN are primarily due to the warmer but 

wetter climate projections; precipitation is projected 

to increase while temperatures actually start out lower 

and end up marginally higher in future projections 

(fig. 28). It is also interesting that the characteristics 

of the fire, primarily biomass consumed and tree 

mortality, remained the same under future climates 

for the CROWN landscape, but changed on the EFBR 

landscape—more biomass was consumed and fewer 

trees were killed. This could also explain why whitebark pine increased under future climates on the more 

xeric EFBR landscape. Future projections also have 

fire-caused tree mortality decreasing on the EFBR 

landscape, and this is primarily because the increases 

in whitebark pine basal area are creating more resilient forests in the future (fig. 49).

The large contrast in simulation results between 

EFBR and CROWN landscapes is probably a consequence of four interacting factors: (1) delineated 

whitebark pine zone, (2) climate, (3) whitebark pine 

abundance, and (4) landscape characteristics. The area 

of suitable whitebark pine was much larger on the 

CROWN landscape (>30,000 ha) than on the EFBR 

landscape (<10,000 ha) (fig. 26). The majority of the 

CROWN area could not support viable whitebark 

pine forests because of rock, glaciers, snowfields, 

and lodgepole pine dominance (>20 percent of the 

mapped whitebark pine zone). This meant that all 

the basal area and proportion of the landscape for 

whitebark pine was heavily biased towards areas that 

could never support the species and explains why it 

was difficult to see differences across the scenarios. 

When we restricted the zone to its current range in 

the CROWN landscape, we got results somewhat 

similar to those from the EFBR landscape (fig. 58). 

Moreover, past and future climates for the CROWN 

landscape were colder and wetter (figs. 26, 62) than 

those simulated for EFBR, resulting in significantly 

less wildland fire (fig. 47) and more subalpine fir 

(figs. 33 to 36). Previous simulations by Loehman et 

al. (2011b) showed significantly more fire and more 

whitebark pine on the CROWN landscape using the 

offset observed historical weather rather than the 

simulated dataset used in this study. In addition, the 

CROWN landscape consists of high elevation alpine 

tundra, rock, and barren areas with fuel loadings so 

low that wildland fire could not spread to many parts 

of the high elevation settings. As a result, fire return 

intervals are also low (>400 years; fig. 47) and shadetolerant species (subalpine fir) dominate and easily 

outcompete the shade-intolerant whitebark pine, 

even under future climates (fig. 35). If we had used a 

warmer, drier climate scenario for the CROWN, such 

as the A2 scenario used by Loehman et al. (2011b) 

or Holsinger et al. (2014), we would have simulated 

significantly more fires. And, as a result, would have 

seen more whitebark pine coverage but less basal 

area. This provides insight into the consequences 

regarding the high uncertainty of future climate 

projections. Small, subtle changes in high elevation 

climate can lead to a substantially different set of 

landscape dynamics, and it is nearly impossible to 

predict restoration success in the future without accurate climate projections.

The distributions of structural stages on the 

simulation landscape were used to evaluate two important issues: (1) the continued presence of mature, 

cone-producing whitebark pine (d.b.h. >20 cm) that 

provides food for wildlife and (2) high heterogeneity in the upper subalpine landscape. We found that 

all restoration treatments created landscapes that 

were heterogeneous with respect to stand development stages and these landscapes also contained an 

abundance of cone-producing whitebark pine trees 

(figs. 37 to 40). High levels of fire suppression nearly 
72 USDA Forest Service RMRS-GTR-361. 2017.

always created landscapes with lower heterogeneity 

where one or two structural stages comprised over 

70 percent of the landscape. We also found that both 

wildfires and restoration created landscapes that had 

significant portions of the landscape in seedling and 

pole stages thereby ensuring long-term coneproduction and resilient forests.

Another interesting finding is that the simulation 

results show that variations of whitebark pine basal 

area and proportion landscape are significantly greater 

under future climates for both landscapes (figs. 27 to 

37). It appears that whitebark pine can remain on the 

landscape over a wide range of fire, MPB, and WPBR 

conditions. This is in contrast to the lower variability 

of future weather predictions compared to observed 

weather (figs. 26, 27). This high variability provides 

for more resilient whitebark pine forests in the future 

because simulations show it can remain on the landscape under a wide range of conditions.

Last, we found it interesting that simulated habitat 

suitabilities for wildlife species are significantly 

different under future climates, but the trend in 

suitability depends on the wildlife species in question (Loehman et al. 2011b). Lynx habitat appears 

Figure 58—The large area of whitebark pine habitat in the CROWN whitebark pine zone 

influenced results. Shown are independent simulations (Section 4.3) of the long-term 

dynamics (500-year simulations) of whitebark pine landscape extent (proportion) for the 

CROWN landscape for historical (blue, green) and future climates (red, yellow) for no (RNPN; yellow, blue) and high (RH-PH; green, red) restoration efforts (table 5): (A) CROWN 

landscape showing changes in whitebark pine (PIAL) landscape extent as a proportion using 

the whitebark pine zone shown in figure 27, (B) CROWN landscape showing changes in 

whitebark pine (PIAL) landscape extent as a proportion using a whitebark pine zone that was 

reduced by over 30 percent. As climates warmed, whitebark pine was quickly lost from the 

lower habitats and there were few upper habitats that it could populate in the short time span.
USDA Forest Service RMRS-GTR-361. 2017. 73

to decline in the future while grizzly bear habitat 

increases. This demonstrates why management for 

a single species may be ineffective in the future. 

Complex ecological interactions could multiply over 

time to make a short-term plan for habitat restoration 

become ineffective under long-term climate change. 

An ecosystem approach that balances all ecological 

processes and characteristics is the only way to have a 

successful restoration program, especially for whitebark pine (Keane et al. 2012b).

4.5.2 Simulation Limitations

There are several limitations of this simulation 

experiment that must be addressed when interpreting 

the modeling results. First, there is a major drawback 

with the climate scenarios that demands future attention—the simulated historical weather record does not 

match the historical observed weather for both landscapes (figs. 27, 28). We used the simulated historical 

weather because it was recommended by GCM modelers, but our simulation results did not match results 

simulated from the real historical weather as found 

by Holsinger et al. (2014). This adds another level of 

uncertainty to interpretation and clouds the findings 

because the simulated historical climate does not 

produce the vegetation dynamics actually observed in 

the landscapes. Second, there appears to be the same 

or less variability in future projections, especially for 

precipitation and especially on the EFBR landscape 

(fig. 27, 28). This may be a result of the downscaling 

of the GCM climate data or it could be inadequate 

GCM simulations. This lack of variability probably 

resulted in unrealistic fire dynamics, especially for the 

EFBR landscape (fig. 43), where large fire years were 

rare in the future simulations. And last, there appears 

to be a problem with the transition of the historical 

simulation to the future. Many of the temperature, 

precipitation, and radiation projections at the end of 

the historical simulation do not match the start of the 

future projections (figs. 27, 28). This compromises the 

validity of the short 95 year future projection if it is 

starting at a different point than the end of the historical data.

The list of tree species used in the simulations of 

both landscapes (table 3) represents those species 

that exist on the landscape today and the species that 

could possibly inhabit the landscape in the future. It is 

impossible for species not on the list to immigrate into 

the simulation landscape in the 95-year simulation 

period. It may be that the new climates could support 

tree species that were not in our list, but we assumed 

that the slow rate of tree species migration would preclude any new species becoming established. Our tree 

species list might need to include other tree species if 

simulation runs are longer than 500 years so that other 

species have the opportunity to become established 

in the new climates. We also used the same species 

parameter set for both landscapes (see Keane et al. 

2012b) when in reality, there may be subtle differences in some species parameters between landscapes, 

such as cone crop frequency, adaptive strategies, and 

site index.
74 USDA Forest Service RMRS-GTR-361. 2017.

This section presents important strategies and 

tactics for incorporating the climate change information from the previous two sections into each of 

four guiding principles and, more appropriately, into 

each of the management actions of the whitebark 

pine rangewide restoration strategy (Keane et al. 

2012b) (fig. 1; Section 2). Generalized approaches 

for addressing climate change impacts in restoration 

designs are presented in the “Guiding Principles” 

subsections below to set the context for understanding and implementing the climate change tactics 

and strategies detailed for each management action. 

There may be redundancy across the guiding principles and management actions because we structured 

each subsection to stand on its own.

The information presented in this section was 

compiled from material collected from the comprehensive literature search summarized in Section 3 

and the FireBGCv2 simulation experiment presented 

in Section 4. Background material on whitebark 

pine ecology that may be needed to understand some 

climate change impacts can be found in Section 2 

and the rangewide strategy (Keane et al. (2012b). 

Limitations, caveats, and concerns regarding the 

use of these recommendations are presented in the 

“Discussion” below (Section 6.2).

Some may find that this section lacks sufficient 

detail for managers to implement the proposed 

considerations in restoration actions, and that may 

be true for some of the recommendations. However, 

this lack of detail is because of the difficultly in 

implementing any of these actions without knowledge of local conditions. An appropriate action 

on one landscape or stand might be ineffective on 

other landscapes and stands as evidenced from our 

simulation results (see “Simulation Effort,” Section 

4). The strategies, approaches, and tactics presented 

here must be further adjusted to account for local 

surroundings.

Several important findings from this study’s literature review (Section 3) and simulation experiment 

(Section 4) provide the framework of this section. 

The first is that whitebark pine forests may be the 

tree species most resilient and resistant to climate 

change as compared to forests comprised of any 

of its upper subalpine associates if it weren’t for 

WPBR. The second is that without proactive restoration, whitebark pine, on those sites where it is seral 

to subalpine fir, will continue to decline and become 

a minor, if not absent, component on high elevation 

landscapes in western North America. Even a low 

level of restoration activity may help keep these 

whitebark pine forests from vanishing on the high 

mountain landscape. It appears that if we do nothing, 

whitebark pine will cease to function as a keystone 

species, regardless of climate conditions. Third, 

we found that whitebark pine can continue to exist 

under future climates on many landscapes, especially 

with more frequent fire, but probably at lower than 

historical levels. Next, we found that the impacts of 

climate change on whitebark pine populations are 

highly governed by local conditions. As the simulated outputs presented in Section 4.4 show, whitebark 

pine actually increases in abundance and stature 

on one landscape with future warming while it 

decreases on another. This is a direct consequence of 

the complex interactions of climate with vegetation 

and disturbance. Thus, there is no “magic bullet” or 

“one-size-fits-all” solution for restoring this iconic 

and valuable ecosystem; managers must tailor broad 

strategies to local conditions for the most effective 

restoration treatments (Diggins et al. 2010). And last, 

we found that the best approach to restoring whitebark pine forests and for creating resilient landscapes 

in the face of climate change is to implement the recommendations proposed in the rangewide strategy 

(Keane et al. 2012b) (see Section 1.2 for details). 

However, modifications to the rangewide strategy 

are needed to account for future changes in climate. 

These changes are the subject of this section.

5. Climate Change Adaption Strategies and Tactics for 

Restoring Whitebark Pine Ecosystems
USDA Forest Service RMRS-GTR-361. 2017. 75

5.1 Climate Change Adaptation Primer

The real question now facing land managers is how 

to mitigate the adverse impacts of changing climates 

so that restoration actions will be effective in the long 

term. The climate change community often uses the 

word “adaptation” to describe any actions toward 

mitigating climate change effects (Aitken et al. 2008; 

Chmura et al. 2011). Adaptation actions range from 

the simple, such as planting rust-resistant seedlings, 

to the complex, such as implementing thinning treatments to increase whitebark pine vigor (Janowiak et 

al. 2014) and to reduce the risk of high severity fire 

to protect whitebark pine seed trees (Spittlehouse and 

Stewart 2004).

Most land managers already have the tools, 

knowledge, and resources to begin to address climate 

change impacts. But as Swanston and Janowiak 

(2012) argue, managers will need a new perspective to 

expand their thinking to include consideration of new 

climates, spatial scales, treatment timing, and prioritization of efforts (Diggins et al. 2010). Often, all that 

is needed is to integrate climate change concerns into 

land management plans is to account for the high variability and trend of climate in the design of alternative 

land management actions (Janowiak et al. 2014).

There are some fundamental principles that can 

serve as starting points for climate change adaptation 

design and approaches (Joyce et al. 2008; Millar et al. 

2007; West et al. 2009):

1. Prioritization. It will be increasingly important to 

prioritize management actions for adaptation based 

on both the vulnerability of species to climate 

change and the likelihood that adaptation actions 

to reduce vulnerability will be effective.

2. Adaptive Management. Adaptive management 

principles must provide a decisionmaking 

framework that maintains flexibility and 

incorporates new knowledge and experience over 

time

3. Low hanging fruit. Management actions that 

result in a wide variety of benefits under multiple 

scenarios, but have little or no risk, may be initial 

places to look for near-term implementation.

4. Triage. Where vulnerability to whitebark pine 

communities is high, precautionary actions to 

reduce risk in the near term, even with existing 

uncertainty, may be extremely important.

5. Variability. It is important to remember that 

climate change is much more than increasing 

temperatures. Increasing climate variability across 

all components of climate, such as precipitation, 

humidity, and radiation, may lead to equal or 

greater impacts that will need to be addressed.

6. Multiple Objectives. Many adaptation actions are 

often complementary with other land management 

and adaptation actions (e.g., thinning may increase 

vigor, resilience, and carbon sequestration 

rates); multiple actions to adapt forests to future 

conditions may also help restore these forests.

The concepts of resistance, resilience, and response 

serve as the fundamental framework for managers to 

consider when responding to climate change using 

adaptation actions (Millar et al. 2007; Swanston and 

Janowiak 2012) (fig. 59). Resistance options improve 

ecosystems’ defenses against anticipated climate 

change responses or directly defend ecosystems 

against disturbance to maintain current conditions. 

Resistance actions are often effective in the short 

term, but they may require greater effort over the 

long term as the climate shifts further from historical 

norms. Moreover, there is a risk that the ecosystem 

will undergo irreversible change because of large climatic shifts thereby rendering all resistance activities 

ineffective.

Resilience options allow some change, but emphasize a return to prior conditions after a disturbance. 

Resilience actions are also short term and should be 

used for high-value resources or areas that are buffered from climate change impacts.

Response options intentionally accommodate 

change and allow ecosystems to adaptively respond to 

changing and new conditions. A wide range of actions 

exists under this option, all working to influence ways 

in which ecosystems adapt to future conditions.

Resistance, resilience, and response options serve 

as the broadest and the most widely applicable level 

of a continuum or gradient of adaptation responses 

to climate change. They are most often called the 
76 USDA Forest Service RMRS-GTR-361. 2017.

adaptation options (fig. 59). Along this continuum, 

adaptation actions become increasingly specific from 

options (resilience, resistance, and response), to 

strategies (broad adaptation responses that consider 

ecological conditions and overarching management 

goals), to approaches (detailed adaptation responses 

with consideration of local site conditions and 

management objectives), and to tactics (prescriptive 

actions designed for specific site conditions and 

management objectives) (fig. 59).

Adaptation strategies describe how adaptation options could be employed, but they are still broad and 

general in their application across ecosystems. In this 

report, an adaptation strategy would be comparable 

to the guiding principles in the rangewide strategy 

(Keane et al. 2012b). Approaches provide greater detail in how managers may respond to climate change 

effects and major differences in application among 

ecosystems are now more evident. Ultimately, tactics 

are the most specific adaptation response and they 

provide prescriptive directions on how actions can 

be applied on the ground. Adaptation tactics are detailed actions using these strategies and approaches, 

but tactics are designed for regional to local situations and for specific objectives, species, cover 

types, or resource concerns.

This section is dedicated to the adaption strategies, approaches, and tactics that managers may 

use to design effective tactics to mitigate climate 

change effects in restoring whitebark pine forests. 

Adaptation strategies and approaches are linked 

to the rangewide strategy’s guiding principles and 

adaptation tactics are linked to the rangewide strategy’s management actions. A summary of all climate 

change adaptation options, approaches strategies and 

tactics is presented in table 10.

Options

Foundational

adaptation concepts,

i.e., resistance,

resilience, and

response (after Millar

et al.2007)

Strategies

Broad adaptation

responses that

consider ecological

conditions and

overarching

management goals

Approaches

More detailed

adaptation responses

with consideration of

site conditions and

management

objectives

Tactics

Prescriptive actions

designed for specific

site conditions and

management

objectives

Broad application Implementation

Resilence

Reduce impace

of stressors

Reduce risk of

high severity fire Reduce fuels

Remove

competition to

improve vigor

Regenerate fire

–tolerant

ecosystems

Thin shade

tolerant species

Plant fire–

tolerant species

Prescribe burn

dense forests

Modified from “Forest adaptation resources: Climate change tools and approaches for land managers.”

Source: https://www.treesearch.fs.fed.us/pubs/40543

Figure 59—Climate change adaptation action hierarchy taken from Swanston and Janowiak (2012) and illustrated by a set of 

specific actions at each scale of the hierarchy. 
USDA Forest Service RMRS-GTR-361. 2017. 77

Table 10—Summary of climate change tactics in developing and implementing effective whitebark pine restoration actions in 

the future of climate change. Abbreviations include MPB-mountain pine beetle, WPBR-white pine blister rust. See Section 5 

for descriptions of Adaptation option definitions.

Adaptation option Strategy Climate change impact Adaptation tactic and restoration recommendation in 

the face of climate change

Guiding principles—Adaptation strategies

Resistance Promote rust 

resistance

Rust mutation Collect seed and pollen from a wide variety of 

individuals. Replace plus-trees frequently. Rotate seed 

orchard stock.

Resistance Conserve genetic 

diversity

Loss of genetic variation Gather and archive seed and pollen from a wide 

variety of trees. Conduct extensive genetic surveys. 

Create spatial products depicting genetic diversity.

Resilience Save seed sources Increasing wildland fire 

frequency, intensity, size

Proactive fuel treatments around living individuals; 

allowing wildfires to burn in moderate years (wildland 

fire use); suppression tactics that include protecting 

living whitebark pine trees during a wildfire event

Resistance Increasing frequency of 

MPB outbreaks

Create heterogeneous landscapes within historical 

range of variation; prescribe fire and silvicultural 

treatments to improve tree vigor; individual tree 

protection using anti-aggregating pheromones or 

insecticides

Resistance Unwanted human 

activities

Modify suppression tactics in whitebark pine forests to 

save living trees; allow fires to burn, especially those 

in moderate years; avoid cutting rust-resistant trees in 

timber sales; do not plant competitors of whitebark 

pine in suitable habitat; 

Resistance Employ restoration 

treatments

Unanticipated loss of 

mature whitebark pine

Implement treatments to save rust-resistant seedproducing trees and plant rust-resistant seedlings in the 

best sites for survival and growth; design treatments 

to increase the likelihood trees will survive in future 

climates (details presented in each management 

action)

Resilience Create resilient 

landscapes

Use a landscape approach; create heterogeneous 

landscapes; use historical range and variation of 

composition and structure as a reference for landscape 

heterogeneity until better spatial models are available.

Management actions

Resistance, 

Resilience

Assess condition Warmer temperatures; 

longer growing season

Add biophysical descriptors to inventory and 

monitoring protocols; use potential vegetation site 

classifications to describe climate in context of 

vegetation; Include spatial data layers of climate 

change predictions; use climate change projections to 

identify those areas that will experience the greatest 

warming and drying. 

Resistance, 

Resilience

Plan activities All predicted climate 

change conditions

Prioritize areas for restoration that are in the upper 

elevational range of local seral whitebark pine types; 

prioritize areas on the cooler aspects from northwest to 

northeast; select landscapes that have abundant seral 

and climax whitebark pine stands; consider wilderness 

restoration 

continue Table 10 on next page
78 USDA Forest Service RMRS-GTR-361. 2017.

Table 10—Continued.

Adaptation option Strategy Climate change impact Adaptation tactic and restoration recommendation in 

the face of climate change

Management actions

Resilience Reduce 

disturbance 

impacts

All disturbances Create heterogeneous landscapes; take a landscape 

approach to planning and prioritizing; emphasize 

whitebark pine ecology and avoid treatments designed 

only to reduce disturbance agents, such fuel treatments

Resilience Increasing fire Embrace a more holistic wildland fire policy that 

balances wildfires, prescribed fires, and fire suppression; 

manage fire to balance losses in rust-resistant trees with 

gains in competition-free burned areas;

Resilience Increasing MPB Remove competition using fire, silvicultural cuttings; 

create landscape diversity of age classes

Resistance, 

Resilience

Increasing WPBR Promote rust resistance through planting or direct sowing; 

promote natural rust resistance by providing regeneration 

opportunities where seed sources are intact;

Resistance, 

Resilience

Gather seeds All predicted climate 

change conditions

Collect from many different seed sources; store as many 

seeds as possible; create seed libraries and central 

storage areas.

Resistance, 

Resilience

Grow seedlings All predicted climate 

change conditions

Grow as many seedlings as possible and outplant to 

burned areas; inoculate seedlings with mycorrhizae to 

facilitate establishment on harsh sites;

Resilience Protect seed 

sources

Increasing fire under 

warming climates

Proactive fuel treatments around living individuals; 

widening the treated area around protected trees in 

anticipation of future disturbances; allowing wildfires 

to burn in moderate years (wildland fire use); modifying 

suppression tactics that include protecting living 

whitebark pine trees during a wildfire event 

Resistance, 

Resilience

Increasing MPB Spray with insecticides (Carbaryl); use anti-aggregation 

pheromones; trap the beetles

Against increasing WPBR Spray with fungicides; remove Ribes spp. from 

surrounding area

Resistance, 

Resilience

Implement 

treatments

Against increasing 

disturbances; warming 

climates

Be more liberal with cutting and burning prescriptions; 

burn under hotter conditions; lower diameter limits on 

thinnings; cut or burn more seedling and sapling shadetolerant species; reduce fuels in treated stands to ensure 

seed source survival after wildfire; Augment fuelbed 

to widen prescribed burn window; create ground 

conditions that facilitate the planting of rust-resistant 

seedlings (e.g., do not leave slash)

Resistance, 

Resilience

Plant seedlings All predicted climate 

change conditions

Plant at the highest elevations of the treated areas first; 

plant in favorable microsites and create these microsites 

if missing from a planting site; make sure mycorrhizae 

are available; focus on areas within the current range of 

whitebark pine (i.e., do not attempt assisted migration as 

“restoration”)

Resistance, 

Resilience, 

Response

Monitor activities All predicted climate 

change conditions

Conduct monitoring over long time spans; always 

include a control unit; measure additional variables 

at the sample site to understand and mitigate climate 

warming effects in future treatments; increase sampling 

intensity; improve sampling design to accommodate 

increasing variabilities caused by climate change; create 

centralized databases and standardized protocols.

Resistance, 

Resilience, 

Response

Conduct research All predicted climate 

changes

Support research on the efficacy of these different 

treatment approaches.
USDA Forest Service RMRS-GTR-361. 2017. 79

5.2 Guiding Principles for Adaptation 

Strategies and Approaches

5.2.1 Promote Rust Resistance

The central principle in the rangewide strategy 

(Keane et al. 2012) is to enhance the level of rust 

resistance in whitebark pine populations at local to 

rangewide scales for restoration treatments to be 

effective. It is critical that future populations of the 

species have high frequencies of resistance to WPBR, 

otherwise all restoration treatments will be ineffective 

(Schultz 1989). This happens because WPBR occurs 

range-wide, and infection levels continue to increase 

when site and climatic conditions favor completion of 

the rust life cycle (Mahalovich 2013).

To increase rust resistance, the rangewide strategy 

emphasizes the importance of a three-pronged approach: (1) supporting existing and developing new 

selective breeding programs to produce blister rustresistant whitebark pine, (2) facilitating and accelerating natural selection for blister rust-resistant genotypes 

in stands by reducing competition to increase survival 

of healthy trees in high blister rust areas, and by providing openings for natural seed dispersal and seedling 

survival, and (3) planting seedlings from seed sources 

known to have some level of blister rust resistance 

based on blister rust screening trials.

There are several major caveats connected with the 

enhancement of blister rust resistance independent 

of climate change. The most important caveat is that 

all programs for collecting seed from plus-trees, 

breeding for rust-resistant pines, and planting rustresistant seedlings diversify the genetic mechanisms 

of resistance and the underlying genetic variation of 

resistance mechanisms. Cronartium ribicola has mutated and could mutate further and overcome a limited 

set of resistance mechanisms (Garrett et al. 2006).

The most important step towards assuring enduring 

rust resistance is to collect seed from a wide variety of 

individuals that survived in a wide range of high rustmortality areas. To improve blister rust resistance, a 

sufficient number of plus trees designated for selective breeding programs must include population sizes 

large enough to sustain gains in rust resistance while 

minimizing the negative consequences of inbreeding 

(Mahalovich and Dickerson 2004; Mahalovich and 

Foushee, submitted). In the absence of reproductively 

mature seed orchards, trees selected for operational 

cone collections in areas with high rust mortality 

should be rotated to safeguard against using the same 

rust-resistance genetics in future whitebark pine 

populations. All operational cone collections should 

be evaluated for rust resistance levels to further 

guide site-specific restoration prescriptions, as rust 

resistance levels vary by ecosystem (Mahalovich and 

Foushee 2015; Mahalovich et al. 2006; Sniezko et al. 

2007). We are hopeful that advances in genomics will 

result in genetic evaluations of rust-resistance traits of 

new trees more quickly and less expensively than the 

current common garden techniques, which take 5–7 

years.

A similar approach can be used in designing the 

whitebark pine seed orchards of the future. Scions 

should be taken from diverse collections of proven 

rust-resistant donors from more than one area. 

Recurrent selection programs for general combining 

ability—that portion of genetic variation that can be 

passed on to the next generation—can be augmented 

with new selections after they have been robustly 

tested to ensure that gains in rust resistance are not 

lost (reduction in selection intensity). Further, thorough analysis of pedigrees can ensure that related 

individuals are not added to the testing, breeding, or 

seed orchard populations.

5.2.2 Conserve Genetic Diversity

Genetic diversity across the entire range of whitebark pine must be conserved for the future. Climate 

change may facilitate more frequent and perhaps more 

severe disturbance processes that may compromise 

the genetic legacy of the species (Bower and Aubry 

2009). Preserving genetic diversity is accomplished 

by collecting and archiving seeds from known rustresistant parent trees throughout the range of whitebark pine, and then growing and planting genetically 

diverse, rust-resistant seedlings. We must be careful 

not to lose the broad genetic diversity inherent in the 

species while disturbances increase during the process 

of selecting rust-resistant lineages for growing seedlings for planting (Mahalovich and Hipkins 2011).
80 USDA Forest Service RMRS-GTR-361. 2017.

We should also consider other critical activities 

including archiving pollen and developing seed 

orchards to produce blister rust-resistant seed, and 

establishing clone banks. Clone banks would be 

used to archive the selections possessing desirable 

characteristics of blister rust resistance, drought 

tolerance, cold hardiness, and perhaps tolerance to 

mountain pine beetle (Bower et al. 2011). We should 

also strive to identify representative samples of live, 

putative rust-resistant whitebark pine trees to replenish those plus-tree selections that are lost to mortality. 

Similarly, even under optimum storage conditions 

(6–8 percent moisture content), seed and pollen do 

not stay viable indefinitely. Active genetic conservation efforts should include the periodic replenishing 

of seed and pollen stores. A bet-hedging restoration 

strategy that preserves genetic variation and enhances 

rust resistance is to plant rust-resistant seedlings in 

restoration treatments from the current seed transfer 

zone (fig. 60). It is hoped that this would diversify the 

genetic variation present for cold hardiness, growth, 

phenology, and resistance traits.

The climate change considerations detailed here 

mirror some of those presented in the discussion about 

promoting rust resistance (see above). We should 

collect seed and pollen from many individuals within 

each of the seed zones of whitebark pine to ensure 

broad genetic diversity. For example, Mahalovich et 

al. (2006) found that in parts of the Northern Rockies 

there was a decrease in cold hardiness in seedlings 

grown from seed with high rust resistance (r = –0.18, 

P < 0.01). We need to collect, store, and catalog the 

Figure 60—Genetic zone maps used as seed zones for whitebark pine from Mahalovich et al. (2011). When used in restoration 

treatments, rust-resistant seedlings from these seed transfer zones would preserve genetic variation and enhance rust 

resistance. At this time it is probably best to not transfer seed across seed zones under climate change. 
USDA Forest Service RMRS-GTR-361. 2017. 81

baseline levels of genetic diversity, genetic variation 

in adaptive traits, and levels of inbreeding throughout 

the range of whitebark pine. These samples would 

serve as a reference for future management and research projects (e.g., to reconstruct entire populations 

that may be lost to disturbance) (Bower et al. 2011). 

Seed collections from a wide variety of individual 

trees will be incredibly important if local whitebark 

pine populations go below thresholds where they are 

no longer viable (see McKinney et al. 2009). These 

seed collections should be stored using methods that 

will ensure their long-term viability (Bower 2011). 

The pedigrees of these collections should be documented and periodically reviewed so that new and 

innovative genetic approaches can be used to restore 

the species. Detailed genetic analysis of these collections using next generation sequencing can reveal 

both individual variation and geographic differences. 

New technologies may uncover population differences in the frequency of genes that contribute to rust 

resistance (Sniezko et al. 2011).

5.2.3 Save Seed Sources

Mature, seed-producing, putatively rust-resistant 

whitebark pine trees, especially in regions that are 

experiencing the most severe decline, must be protected from severe disturbances so that genetically 

diverse seed can be harvested well into the future. 

Major perturbations to protect against include unwanted wildland fire, MPB, and silvicultural cuttings. 

Localized disturbances include clearing forests for 

ski area runs, mining, road-building, and avalanches 

(Tomback et al. 2001b).

There is no effective protection of trees from 

WPBR, as most proactive treatments (such as pruning, 

fungicide application, or removing alternate hosts) are 

ineffective, Further, these practices do not promote 

rust resistance. Identification and prioritization of 

areas that contain rust-resistant and genetically diverse trees can be accomplished with comprehensive 

genetics profiles using data generated from regional 

genetics programs and collaborative research partnerships. Nevertheless, all whitebark pine trees still 

living after prolonged rust infection have great value 

to the restoration program because they have a high 

probability of carrying genes related to resistance.

Wildland fire may endanger many individual whitebark pine seed sources and are the biggest threat to 

rust-resistant individuals, but mitigation actions are 

possible. Since fires are predicted to be more frequent, 

more intense, and larger in the upper subalpine areas 

where whitebark pine is commonly found (Section 

3.4.1), precautions to protect known rust-resistant 

whitebark pine trees from fire damage are recommended. Policies designed to exclude fires from these 

high elevation forests are mostly ineffective, unrealistic, and costly; wildfires will eventually burn many 

whitebark pine stands regardless of the level of fire 

suppression (Loehman et al. 2011b).

We advocate a more holistic fire management 

policy for whitebark pine forests that balances wildfires, prescribed fires, and fire suppression (Black 

2004). This approach entails proactive fuel reduction 

treatments, perhaps using prescribed fire, around potential and known rust-resistant trees as the first line 

of defense against wildfires. Wildland fire use (WFU: 

letting fires burn under prescribed weather conditions) 

in moderate years may also be effective, but some 

highly valuable trees would likely be lost without 

prior fuel reduction treatments in WFU fires.

Suppression is warranted in seed orchards or stands 

containing known rust-resistant trees where whitebark 

pine populations are in severe decline, and where 

seed sources are rare. Suppression tactics that implement fuel treatments during a wildfire event may help 

preserve key whitebark pine stands such as backfires. 

This approach is only realistic if saving whitebark 

pine trees is considered as a resource priority in suppression decisionmaking.

On a related issue, the anthropogenic policy of 

fire exclusion often results in a disrupted fire regime, 

high accumulations of fuels, and more intense future 

fires (Keane et al. 2002c). Policies supporting full 

suppression actions under projected warmer and drier 

climates might be counter-productive and potentially 

damaging in the long run, especially for whitebark 

pine restoration. Suppression efforts may cause declines of whitebark pine on seral sites by advancing 

succession towards subalpine fir domination in many 

parts of the species range. But this was not always the 

case in our 95-year simulation results (Section 4.4.2). 

Moreover, fire suppression activities may be harder 
82 USDA Forest Service RMRS-GTR-361. 2017.

to successfully employ as canopy and surface fuels 

increase in the absence of fire.

Fire exclusion policies have resulted in excessive 

fuel accumulations that when burned generate fire 

intensities that tend to kill most of those trees that 

we want to remain on the landscape (Keane 2001b). 

Advocating for allowing fires to burn on high elevation landscapes, especially during moderate fire years, 

may appear contradictory as these fires may kill 

rust-resistant whitebark pine trees. But this is probably a more desirable alternative than protecting trees 

from the predominantly high severity fires resulting 

from prolonged fire exclusion. Genetic diversity can 

be ensured by archiving seed and tissue sources in 

secure, off-site locations. Allowing more fires to burn 

in moderate years will result in lower severity fires 

and create burn mosaics that satisfy many restoration 

objectives from creating fire breaks to reducing competition from subalpine fir.

Another disturbance to negatively impact 

whitebark pine is MPB (see Section 3.4.2). Beetle 

outbreaks are also predicted to increase in intensity 

and frequency in the future, so it is important to minimize losses of rust-resistant pine trees. This can be 

done using a multiple-scale design where coarse scale 

approaches create heterogeneous landscapes with 

patch structures within the historical range of variability (HRV) (Keane et al. 2002b, 2010a). This will limit 

excessive buildups of MPB populations by minimizing the number of host species that are of adequate 

size to sustain major MPB outbreaks (Schoettle and 

Sniezko 2007). Finer scale approaches include treatments implemented at both the stand and tree levels, 

such as thinning, verbenone, and daylighting (Keane 

et al. 2012b).

Silvicultural treatments to improve tree vigor and 

increase tree defenses, such as release thinnings and 

group selection cuts, might be effective in stands that 

are dominated by subalpine fir. Protecting individual 

trees from MPB attack using various proactive techniques, such as release cuttings (Keane and Parsons 

2010a), anti-aggregation pheromones (Bentz et al. 

2005), and insecticides (Gibson and Bennett 1985), 

may also be effective (Keane et al. 2012b).

Land management practices that may impact 

whitebark pine populations in the future are those that 

involve removing potential and known rust-resistant, 

cone-producing whitebark pines. This includes fire 

suppression activities as detailed above, but also commercial harvesting and tree cutting for ski area runs 

and power line construction. Valuable seed sources 

can be saved by training crews to identify whitebark 

pine, to avoid cutting potentially rust-resistant trees. 

Managing forests for carbon sequestration to mitigate 

climate change should never supersede whitebark pine 

restoration efforts. Cutting mature cone-producing 

whitebark pine trees to create more productive stands 

that sequester more carbon are counter-productive to 

the restoration of whitebark pine ecosystems.

5.2.4 Employ Restoration Treatments

Areas where whitebark pine seed sources are still 

in reasonable health but may be threatened by MPB, 

WPBR, or advanced succession should be considered 

for restoration treatments. Treating these seed sources 

will create sustainable whitebark pine populations. 

Restoration treatments include any combination of 

prescribed fire, silvicultural treatments, and regeneration plantings as described below.

Prescribed fire can be used to manage the spread 

of WPBR when used in successionally advanced 

communities. Treatments would encourage whitebark 

pine regeneration and increase the presence of rustresistant genotypes. Silvicultural treatments (cutting 

trees) at the local scale can be used to remove competing vegetation. This would increase the vigor of 

surviving trees and reduce the likelihood of mountain 

pine beetle attacks. Finally, planting rust-resistant 

seedlings would accelerate stocking of whitebark pine 

and rust resistance in the populations.

A landscape approach should be taken when 

implementing treatments at stand scales to ensure 

heterogeneity of stand compositions and structures 

across a landscape so that there are large populations 

for natural selection for rust resistance. A landscape 

approach would also ensure a diversity of age class 

structures to maintain ecosystem function and reduce 

MPB hazard. The landscape scale includes areas 

across elevation zones where whitebark pine occurs, 

including the uppermost subalpine zones and transition zones to treeline.
USDA Forest Service RMRS-GTR-361. 2017. 83

The first step towards employing restoration treatments under changing climates is to design restoration 

treatments with an understanding of the landscape 

context. Several restoration objectives can be met 

when treatments are designed within a landscape 

context. First, the severity of future MPB, WPBR, and 

wildfire events can be reduced. The overall efficacy of 

the restoration treatments is increased because standlevel treatments would not be overwhelmed by adverse 

stand conditions at the landscape-level. Finally, 

landscape-scale treatments would create corridors and 

improve the connectivity and transfer of genes and 

individuals between disjunct populations. Landscapes 

evaluated for treatments must include not only the upper subalpine forests, but all forests susceptible to fire, 

blister rust, and mountain pine beetle. Fuel reduction 

treatments, for example, may be best implemented 

in whitebark pine stands that are adjacent to more 

fire-prone, lower elevation areas. The foundation of 

a landscape approach is to ensure resilient whitebark 

pine forests in the future by creating heterogeneous 

landscapes composed of many age classes and successional stages of whitebark pine. This, in turn, will 

reduce the scale and intensity of fire, WPBR, and MPB 

events (Schoettle and Sniezko 2007) (see Section 

4.4.2). We can mitigate the potential climate warming 

effects on whitebark pine by being strategic in our restoration efforts to increase community heterogeneity in 

targeted upper subalpine areas. 

The dilemma facing most land managers is estimating the right mix of whitebark pine communities 

for these high elevation settings to ensure optimal 

landscape heterogeneity. This is best accomplished 

by using spatially explicit ecological modeling to 

simulate the range of landscape conditions that will 

occur under both past (HRV) and future climates 

(Keane 2013; Keane et al. 2008b) (see Section 4.1). 

Unfortunately, most managers do not have access to 

these types of models so other means must be used.

Therefore, we think most recommendations in the 

rangewide strategy (Keane et al. 2012b) still apply 

under climate change and we advocate that the concept of HRV (Landres et al. 1999) be used to estimate 

and design for landscape heterogeneity under future 

conditions. While past conditions might not be the 

best analog for future conditions, we think that HRV 

represents our best knowledge of the landscape conditions and disturbance regimes under which species 

have evolved. HRV is the reference conditions that 

provide least uncertainty that are the most ecologically appropriate (Keane et al. 2009; Loehman et al. 

2011b; Millar et al. 2007). However, there may be 

times when future climate will make it impossible 

to restore whitebark pine landscapes back into HRV. 

This is why a quantification of the future range and 

variation (FRV) of landscape conditions is also important. By comparing HRV with FRV we are able to use 

the range of overlap as a possible target. Judging from 

the results of our simulation efforts (see Section 4.4), 

it appears that landscape conditions of historical and 

future climates often have large overlaps and these 

may be our target zone for restoration treatment designs (figs. 29 to 55). Moreover, our modeling shows 

landscape conditions for the high elevation settings 

are quite different if WPBR is included, so we suggest 

that any FRV include WPBR.

Most of the other climate change considerations for 

this guiding principle often concern the locations and 

designs of restoration treatments. The most important 

treatment with respect to long-term benefits in the 

rangewide strategy is planting rust-resistant seedlings 

in burned areas. But other restoration actions may 

be necessary in support of planting, such as cutting 

or using prescribed fire or WFU to reduce whitebark 

pine competition and create optimal planting sites. For 

the last decade, sowing seeds (direct sowing) from 

potential or known rust-resistant trees, as opposed 

to planting seedlings, has been explored as a more 

cost-efficient option that would enable restoration in 

remote terrain (e.g., Schwandt et al. 2011). In some 

landscapes, germination rates and seedling survival 

may be high enough for direct sowing to be a viable 

and more economical alternative. The biggest climate 

change consideration when implementing planting or 

sowing treatments is deciding where exactly to apply 

these treatments so they will be most effective and 

last the longest. Modifying the design criteria, such 

as increasing prescribed fire intensity, decreasing 

minimum cutting diameters, and increasing planting 

densities to anticipate climate-mediated change could 

improve the chances of success (Diggins et al. 2010). 

All of these climate considerations are described in 
84 USDA Forest Service RMRS-GTR-361. 2017.

detail in several of the management actions presented 

in the following section.

5.3 Management Actions

5.3.1 Assess Condition

The first step in designing whitebark pine restoration projects is assessing the conditions of upper 

subalpine stands to determine if they are suitable for 

restoration. Assessments consist of inventory and 

monitoring projects, GIS mapping, spatial modeling, 

and remote sensing. These tools document the status 

and trend of whitebark pine across multiple spatial 

scales from the stand to landscape to regional levels. 

Assessments are used in nearly all other management 

actions as a reference for design and implementation 

of treatments so they most effectively restore whitebark pine landscapes (see Section 5.3.8 below).

An assessment should include a comprehensive 

description of the biophysical setting to appropriately 

evaluate and mitigate effects of climate change at all 

scales. Biophysical classifications such as seral vs. 

climax sites, habitat types, landforms, and elevation 

zones can also be used to stratify landscapes into 

areas that represent environmental gradients that 

provide the context in which to plan and evaluate 

climate change strategies. These classifications can 

also be used to fine-tune coarse scale climate change 

projections using various extrapolation techniques. 

Slope, aspect, and elevation, for example, can be used 

in weather extrapolator software to downscale coarse 

scale (e.g., 50 km pixel size) climate change predictions to the site level to account for local conditions 

into the future (Hungerford et al. 1989; Keane and 

Holsinger 2006; Thornton et al. 1997).

Inventory and monitoring protocols and methods 

should include comprehensive descriptions of site 

conditions so that climate change predictions and 

mitigation activities can be modified to account for 

local variation and scale. Regeneration success from 

stakerow surveys, for example, could be more useful 

in the future if microsite (e.g., planting site condition) 

and macrosite site conditions (e.g., habitat type) are 

recorded. This could provide a basis for deciding 

which site characteristics can be used to evaluate 

where to put restoration treatments. Possible site 

assessments include topography (e.g., slope, aspect, 

elevation), landscape position (e.g., valley bottom, 

frost pocket, bench), topographic shading, and georeferenced location (e.g., longitude-latitude, UTMs) 

(Bush and Kies 2012; Lutes et al. 2006, 2009). 

Collectively, biophysical stratifications can then be 

used to guide inventory, monitoring, prioritization, 

and implementation efforts in large scale adaptive 

management projects. For example, colder whitebark 

pine sites may warrant a higher restoration priority.

Maps of site classifications such as habitat types 

(Pfister et al. 1977) and plant associations (Henderson 

et al. 1989), are also important. They can be created for both current and future conditions based on 

climate inputs (Holsinger et al. 2006; Rollins et al. 

2004). Potential vegetation classifications are more 

suitable for bioclimatic envelop modeling (see Section 

3.1.3) than species distributions to predict where 

whitebark pine habitat will be in the future. Potential 

vegetation classes represent those unique biophysical 

conditions that may support late-seral or climax vegetation and therefore are more easily and accurately 

correlated to climate than individual species distributions (Keane et al. 2008b). These classifications 

also provide a more comprehensive description of a 

plant community and its possible movement across a 

landscape than the independent projections of species 

distribution models.

One drawback of predicting future potential vegetation communities using climate projections is the 

disparate rates of seed dispersal and migration among 

the species that comprise a potential type (Keane 

2000). Tree species move across landscapes at different rates so the movement of potential vegetation 

types and their community associations might not 

consist of the same species assemblages in the future 

as occurred in the past. The time spans used in most 

climate change evaluations (e.g., 50–100 years) may 

be too short to allow for the terminal migration of 

most vegetation species that comprise the subalpine 

landscape.

The acquisition of spatial data products that describe both historical and future weather and climate 

are also important in addressing climate change in 

spatial analyses of restoration assessments. Daily 

weather data captured at 1 to 10 km resolutions, 
USDA Forest Service RMRS-GTR-361. 2017. 85

can be used to directly represent climate changes in 

restoration assessments. This weather data is available 

through DAYMET (Thornton et al. 1997), PRISM (Di 

Luzio et al. 2008), and future gridded climate projections (Meehl et al. 2007; Taylor et al. 2012). The 

Northern Region Adaptation Partnership project, for 

example, used 4 km pixel resolution to describe potential evapotranspiration across the Northern Region 

of the National Forest System (fig. 61) (Keane et al., 

in press). Downscaled climate projections are available in many spatial data formats across whitebark 

pine’s range.

When conducting spatial assessments, it is 

important to emphasize trends in temperature and 

precipitation from historical to future conditions 

rather than absolute values because most global 

circulation model predictions have a high degree 

of uncertainty (Section 3.1.1). Using data from the 

weather stations nearest to the restoration site, landscape, or region may also be important. Extrapolating 

recorded weather to local conditions can more realistically describe local climate (Hungerford et al. 1989). 

Climate descriptions can then be used to assess where 

restoration activities are possible or most successful, such as conducting restoration treatments on the 

cooler, moister parts of the project area.

Lastly, it is important that assessments be done at 

multiple scales using landscape approaches. Given 

Historic Historic

A1B: 2020-2050 A1B: 2020-2050

A1B: 2070-2100 A1B: 2070-2100

A2: 2020-2050 A2: 2020-2050

A2: 2070-2100 A2: 2070-2100

No Suppression Suppression

$ 0 240 480 720 960

Km

Mean Annual

PET (mm)

High : 2800

Low : 330

Figure 61—Maps of potential 

evapotranspiration averaged 

across each of three time spans 

(historic, 2020–2050, 2070–

2100) and without and with 

fire suppression for two climate 

change senarios (A1B and 

A2) used in the NRAP project 

(Keane et al., in press). Note, the 

highest increases in potential 

evapotranspiration in the future 

are in the low elevation forest 

types, not in the high elevations 

where whitebark pine is found.
86 USDA Forest Service RMRS-GTR-361. 2017.

projected increases in the frequency of disturbance 

events, it makes little sense to analyze potential 

restoration areas at only the stand level when most 

of the future disturbances originate from outside 

stand boundaries. Conducting all assessments at the 

landscape scale ensures that spatial relationships are 

included in the evaluations, especially processes such 

as nutcracker-mediated seed dispersal (Barringer et 

al. 2012). For example, it would be futile to reduce 

fuels in a 10-acre stand when it is surrounded by 

vast expanses of dense subalpine fir stands with high 

fuel loadings. Restoration treatments on ridgetops 

with isolated whitebark pine populations, as another 

example, would require significantly different designs 

than treatments implemented in large, continuous 

landscapes of whitebark pine habitats. Assessment 

areas must be big enough to encompass those future 

disturbance regimes that might impact treatments, 

but also be small enough so that treatments can be 

properly designed and their effects monitored. This 

means that assessments should include lower elevation settings to evaluate if fires or other disturbances 

can spread into treatment areas from below.

5.3.2 Plan Activities

Every restoration project needs a plan. Planning 

includes prioritizing landscapes for restoration and 

identifying which stands to restore, designing possible 

treatment alternatives, evaluating alternatives for efficacy, and scheduling areas to treat. Accounting for 

climate change in restoration planning will increase 

the likelihood that the outcome of restoration will be 

effective over the long term and create those forests 

that will be resilient in the future.

The primary recommendation for planning is to 

prioritize stands for restoration based on site-specific 

considerations. Seral whitebark pine communities, which occur on more productive sites than 

climax sites, are often the most in need of restoration 

(Keane et al. 2012b). Elevation is one of the easiest 

biophysical characteristic to use to prioritize stands 

and landscapes. However, climate data layers, habitat 

types, and other land classifications can also be used 

to augment prioritization. Both the literature synthesis 

(Section 3) and our FireBGCv2 modeling effort 

(Section 4) indicate that restoring stands that are at 

the local upper elevations of whitebark pine seral 

sites might be more effective than restoration in lower 

elevations in the future for several reasons (Koteen 

1999; Warwell et al. 2007) (fig. 62). First, it could be 

that lower portions of whitebark pine seral sites may 

be too harsh (i.e., high radiation loads, drier, warmer) 

for survival of whitebark pine seedlings in the 

majority of years, thereby limiting effective natural 

regeneration (Larson and Kipfmueller 2010; Lonergan 

et al. 2013; McCaughey and Weaver 1990). Second, 

the lower portions of seral sites, especially those on 

southern slopes, may have warmer climates that facilitate rapid successional replacement of whitebark pine 

by subalpine fir, spruce, and perhaps a suite of lower 

elevation species, such as Douglas-fir, especially in 

areas that already experience abundant precipitation 

(Arno and Hoff 1990). Lodgepole pine might also 

increase in productivity on seral sites in the future 

and out-compete whitebark pine (Keane 2001a). Seral 

whitebark pine sites with little snow cover because of 

strong winter winds might also favor establishment 

of subalpine fir and spruce because of the longer 

growing seasons and longer snow-free periods. 

Conversely, warming climates in the lower elevations 

and south-facing aspects of seral sites, especially in 

southernmost latitudes of whitebark pine’s range in 

the Rocky Mountains and Sierra Nevada where summer moisture deficits are more common, may receive 

lower priority for restoration. This requires evaluating 

the lowest and highest elevations that encompass 

whitebark pine seral communities at local scales, and 

assigning a higher priority for restoration to the top 

half of this elevational range.

We believe that whitebark pine climax communities across the range are probably more resilient in the 

long run than seral communities—even those with 

lower mortality from WPBR or MPB and less climatic 

influences (Keane et al. 2012b). As a result, proactive 

restoration treatments may often be confined to those 

sites where whitebark pine will be lost through competitive interactions.

Disturbance responses to climate change might also 

favor upper elevational whitebark pine seral sites for 

restoration projects. Wildland fire, MPB, and WPBR 

are all projected to increase in the future and this 

increase will be especially prominent on the warmer, 
USDA Forest Service RMRS-GTR-361. 2017. 87

drier seral whitebark pine sites, which tend to be at 

lower elevations (see Section 3). Both MPB and rust 

will kill mature whitebark pine trees and thereby 

accelerate succession towards shade-tolerant fir and 

spruce. Fire, on the other hand, may kill whitebark 

pine trees, but it will also create competition-free 

growing space that may be suitable for planting rustresistant seedlings. Fire may also reduce fuels and 

lower the potential for future crown fires that could 

kill even more whitebark pine trees, especially those 

that are rust-resistant. However, if exposed burned 

sites become drier and experience higher insolation, 

whitebark pine regeneration may not survive and the 

fuels may become dry enough to foster more frequent 

fire regimes.

Restoration treatments implemented on isolated 

ridges that are distant from other whitebark pine 

seed sources are important because these are the settings in which whitebark pine may likely go locally 

extinct (Keane et al. 2012b). However, this may not 

Figure 62—Results from the FireBGCv2 independent simulation runs. Increases in whitebark pine 

basal area in the future would occur on the higher elevation sites of the whitebark pine zone 

in the (A) CROWN and (B) EFBR landscapes. And whitebark pine on the CROWN landscape 

decline rapidly because there is more whitebark pine habitat at the lower elevations.
88 USDA Forest Service RMRS-GTR-361. 2017.

be the most efficient use of restoration funding under 

rapidly warming climates because seed production of 

these isolated seral whitebark pine communities will 

continue to decline into the future, despite treatment. 

Locations with a diversity of seral and climax whitebark pine communities covering large areas might 

rate a higher priority for restoration because these are 

more likely to persist into the future. It is essential 

that the evaluation process include an assessment of 

the magnitude and condition of proximate whitebark 

pine seed sources to heighten the effectiveness of any 

treatment or disturbance.

Finally, we should start to think about restoring 

whitebark pine in designated wilderness areas as a 

hedge against adverse climate change impacts outside 

of wilderness. Over 48 percent of whitebark pine’s 

range occurs in designated wilderness (Keane 2000) 

and most of these wilderness locations have unique 

whitebark pine populations living in settings that are 

important to the long-term conservation of the species, especially as the climate warms. It is important 

that the entire range of whitebark pine be considered 

to account for the great variability in climate change, 

species response, and plant genetics. To exclude half 

the species range from restoration may compromise 

the effectiveness of local, non-wilderness efforts 

and fail to preserve the genetic diversity needed to 

maintain the species on the changing landscape. 

Wilderness restoration, however, is often considered 

an unacceptable action by some wilderness advocates 

(Landres 2010).

5.3.3 Reduce Disturbance Impacts

Various local treatments are implemented to 

mitigate and control WPBR, MPB, and fire effects, 

such as removal of Ribes spp., spraying fungicide or 

pesticide, use of anti-aggregation pheromones, thinning, and reducing fuels in stands with cone-bearing 

trees (Keane et al. 2012b). Most disturbances in 

whitebark pine forests are predicted to increase in 

frequency and perhaps severity under future climates 

(see Section 3.4), so management actions are needed 

to reduce their impacts on whitebark pine populations. Except for wildland fire, there are few viable 

proactive treatments that can mitigate expected 

climate-mediated changes in WPBR and MPB 

disturbances over the long term. We believe that the 

long-term solution is to manage non-fire disturbances 

indirectly through a holistic ecosystems management approach. The money saved from protective 

actions designed to directly reduce MPB or WPBR 

populations could be more effectively used for other 

restoration treatments that will in turn mitigate impacts of these disturbances.

Fire is the one disturbance we can manage at a 

multitude of scales. At the coarsest scale, fire exclusion policies, through the suppression of all high 

elevation fires, may seem like a viable approach for 

saving cone-bearing, rust-resistant whitebark pine 

trees. But we think that fire exclusion in a future of 

climate change may expedite the decline rather than 

conserve this foundation species over the long term. 

This is evidenced by our simulation results in Section 

4 and illustrated in figure 63. Long-term fire exclusion 

often results in two unwanted effects: (1) a buildup 

of surface and canopy fuels that, when burned, often 

kills all remaining rust-resistant pine trees, and 

(2) increased competition from fir and spruce that 

will reduce whitebark pine vigor, thus causing more 

infrequent and smaller cone crops and reductions in 

resistance to other potential disturbances.

A more effective way to reduce impacts from 

fire is to create landscapes where the impacts from 

future fire are mitigated using a mix of fire management approaches. Tree-level treatments (see Section 

5.3.6) and stand-level actions (see Section 5.3.7) 

are integrated with a landscape-level wildland fire 

use approach in which most fires are allowed to 

burn, especially those mixed severity fires that burn 

in moderate fire danger years (Keane et al. 2012b). 

Moderate or mixed severity fires promote several 

beneficial restoration objectives: (1) reduce competition and thereby increase whitebark pine vigor that 

will enhance resilience to other future disturbances, 

(2) produce burned patches that facilitate nutcracker 

seed caching, and (3) create burn perimeters that 

reduce fuel loads and act as fuelbreaks to protect 

surviving rust-resistant trees from future fires (Keane 

and Parsons 2010b). If known rust-resistant trees and 

stands are protected from wildfire using fuel treatments or if their genetic legacy is archived in clone 

banks, then perhaps fires can be allowed to burn, 
USDA Forest Service RMRS-GTR-361. 2017. 89

even in severe weather years. If high elevation forest 

fires must be suppressed, then special care must be 

taken to ensure that critical rust-resistant individuals 

are protected from future fire suppression activities, 

especially the damaging impacts of back burns, which 

often have the highest fire severities (Amiro et al. 

2001).

Many interacting factors could increase the future 

rate of spread of the WPBR pathogen, both geographically and within landscapes, making the reduction of 

WPBR impacts difficult, especially in the uncertain 

climate future (see Section 3.1, Climate Change). 

Ribes spp., the most important alternate hosts for 

Cronartium ribicola, regenerate well after disturbances, especially fire. And the prevalence of Ribes

spp. may well increase in the high country with more 

frequent fire events and less competition from other 

species (Zambino 2010). Therefore, any treatment 

to reduce Ribes spp. by mechanical removal, spraying herbicide, or burning might require prohibitive 

amounts of resources and time (Carlson 1978), and 

these treatments have been proven to be ineffective 

(Geils et al. 2010). Pruning rust-infected branches will 

also be ineffectual as the incidence of rust intensifies, 

especially if the tree has little resistance. This also 

applies to the repetitive use of fungicide for containing or eliminating the rust, which must be limited to 

a small number of trees. Not only is the application 

of fungicide labor-intensive and expensive, it forestalls the effects of natural selection: The key to the 

future survival of whitebark pine populations is the 

enhancement of resistance to Cronartium ribicola. 

Protecting highly susceptible trees is counter to this 

process and will often fail over time. Therefore, we 

believe that the best way to reduce WPBR impacts 

on future whitebark pine populations is by following 

the first guiding principle of promoting rust resistance 

(Section 1.2; Keane et al. 2012b).

Treatments at the stand or tree level for MPB are 

similarly problematic, with some exceptions. Use of 

pesticides or pheromones to protect high value trees 

against attack by MPB may be appropriate in small 

areas or at the individual tree level for short-term 

preservation and mitigation (see Section 5.3.6; Keane 

et al. 2012b). These applications, however, must be 

repeated and are highly impractical at the operational 

scales of multiple stands and landscapes, especially 

when future climates are predicted to heighten MPB 

population dynamics (Bentz et al. 2011). There is 

some indication that thinning stands to increase tree 

vigor may increase the amount of radiation and wind 

to the stand and create conditions that withstand 

MPB attacks. But these thinning treatments may be 

financially prohibitive in the long run because most 

whitebark pine stands do not contain commercial 

timber and most whitebark pine have few defenses 

against MPB, especially during outbreak years. It 

is better if thinning is part of a suite of proactive, 

multi-purpose restoration actions meant to improve 

whitebark pine vigor by reducing competition from 

Figure 63—The difference in 

whitebark pine basal area under 

a fire exclusion management 

approach (SH-high suppression) 

vs a more historically 

appropriate management policy 

of letting fires burn (SN-No 

suppression) in both a historical 

climate and a RCP8.5 scenario 

climate. Note that this is only 

95 years of simulation in areas 

with a 250-year fire return 

interval for the whitebark pine 

zone. Thus, major differences 

are clearly only manifest in the 

CROWN landscape, not the 

EFBR (East Fork Bitterroot River) 

landscape.
90 USDA Forest Service RMRS-GTR-361. 2017.

shade-tolerant conifers while also reducing canopy 

fuels to minimize fire-caused tree mortality (Section 

5.3.7).

As mentioned in the Guiding Principles, creation 

of heterogeneous upper subalpine landscapes that 

contain a wide variety of whitebark pine communities is potentially the best defense against large-scale 

MPB outbreaks, as well as protection against 

wildfire. Landscapes with multiple age classes of 

whitebark pine along with plantings of rust-resistant 

seedlings are the best hedge against all disturbance 

agents. Upper subalpine landscapes should contain 

a diversity of successional stages in proportions that 

anticipate the disturbance regimes of tomorrow’s 

climates (Schoettle and Sniezko 2007). Possibly the 

most economical and effective means to accomplish 

this is to allow wildfires that occur in moderate fire 

danger years to burn, as mentioned above (Keane 

and Arno 2001). Wildfires that burn under moderate 

drought conditions often stay small and burn in a 

mixed severity pattern (Lasko 1990). Granted, no 

mature whitebark pine tree is safe when MPB is in 

outbreak mode or when wildfires are severe (Millar et 

al. 2012), but as upper subalpine landscapes become 

heterogeneous, the severity, intensity, and frequency 

of MPB outbreaks and severe fire should also diminish. Declines in the size and abundance of host trees 

across the landscape may restrict MPB populations 

to an endemic level, and the greater extent of burned 

area and heterogeneous forest types may reduce future 

severe fires.

Other strategies to create heterogeneous landscapes 

include use of the restoration treatments incorporating prescribed burning and silvicultural cuttings 

detailed in Keane and Parsons (2010b), summarized 

in the rangewide strategy (Keane et al. 2012b), and 

discussed in the context of climate change in Section 

5.3.7 (Implement Treatments) here. Given the great 

uncertainty in the climate future, it is especially 

important that any tree and stand level treatment be 

designed in a landscape context. It makes little sense 

to treat a single tree or stand when it lies within a matrix of stands that are highly susceptible to increased 

disturbances predicted for the future. Thinning to 

remove competition from one stand without considering the entire landscape might be ineffective in the 

long run because fires or MPB outbreaks that occur 

on that landscape might be so severe that they may 

overwhelm the minor treatment area.

Treatments implemented for the landscape approach should reduce competition and open up the 

upper subalpine stands. Open-grown whitebark pine 

forests have lower canopy fuels and the tree crowns 

are more heterogeneously distributed across the stand 

than successionally advanced pine forests with fir 

in both the understory and overstory. This openness 

reduces the potential for crown fires, and, if there is 

a crown fire, it may be patchy and passive (Keane 

and Parsons 2010b). Mature, healthy and vigorous 

whitebark pine trees are better able to defend against 

endemic (low level) MPB outbreaks and, if some of 

these trees have WPBR-resistance, they have a better chance of surviving. The challenge facing land 

managers and research scientists is estimating how 

much of the landscape to treat so that it will be most 

resilient in the future. This depends on local conditions and the current state of the landscape (Keane et 

al. 2012b). Again, we suggest using the concept of 

HRV to determine the ranges of landscape compositions for the near term and then compare against some 

approximation of the FRV compiled from an ecological model that simulates future climate and ecosystem 

processes and the landscape compositions they create 

(Keane et al. 2009).

5.3.4 Gather Seed

Collecting seeds from trees that are proven or 

phenotypically rust-resistant (level of resistance is 

unknown) is important for many reasons. First, a 

collection of seeds throughout the range of whitebark 

pine is important for archiving genetic diversity and 

variation so that viable populations suited for future 

warmer climates are available for planting (Sections 

5.2.1, 5.2.2). Stored seed from source trees known 

to have rust resistance can be used for operational 

planting and direct seeding. Seeds can also be used by 

research to pioneer new methods for improving nursery techniques, planting guidelines, and direct seeding 

protocols.

Now more than ever, seed collections should be 

made throughout the geographical distribution of 

whitebark pine to capture the greatest range of genetic 
USDA Forest Service RMRS-GTR-361. 2017. 91

diversity, including locally and regionally rare alleles, before the species declines to very low levels 

as a result of disturbance and climate threats (Hoban 

and Schlarbaum 2014). Seed inventories should be 

managed and periodically assessed for seed viability 

and to ensure that effective population sizes are being 

maintained both in space and time. This will enable 

agencies to be proactive in current conservation 

efforts and to provide a buffer for large-scale disturbance and climate change. Agencies need to identify 

and test rust resistance in a sufficient number of plus 

trees to maintain genetic variation throughout the 

range of whitebark pine, since no region will be free 

of WPBR. Although costly, we suggest that more plus 

trees be selected and tested to provide a hedge against 

the anticipated mortality expected from increases 

in future abiotic and biotic disturbances caused by 

warming climates. Future plus trees should be identified as we lose current plus trees to disturbance.

We also suggest that cone collections from known 

rust-resistant trees in areas where genetic testing has 

occurred, vary spatially and temporally to further 

enhance genetic diversity and capture the spatial 

heterogeneity of different rust-resistant traits. This 

will minimize the chance that WPBR may overcome 

rust resistance gains from selective breeding programs 

(Mahalovich and Foushee, submitted). Collecting 

cones from the same collection areas and the same 

trees does not effectively capture the full range of 

genetic variation in key adaptive traits. Moreover, it 

increases the levels of related offspring and the subsequent consequences of inbreeding depression.

In anticipation of climate change, we also suggest 

that the proven rust-resistant trees from which most 

seeds are harvested within an area be replaced with 

other proven rust-resistant trees over time to increase 

genetic diversity and diversify tree resistance mechanisms. This will minimize the chance that rust will 

evolve and overcome these resistance mechanisms. 

Continued harvests of seed for operational planting 

from only one set of trees over time may not ensure 

that the full range of rust resistance be captured in 

the seedlings that will be planted on the landscape. 

With current technology, it is expensive to constantly 

test new plus trees for their level of rust resistance, 

but confining seed collection from a small plus tree 

population may reduce the genetic variability that is 

so important in responding to climate change fluctuations. We hope that the application of genomics will 

provide faster, cheaper tests to determine whether 

individual trees carry key genes contributing to polygenic resistance.

We also advocate that managers harvest additional 

seed in anticipation of future increases in planting 

treatments. Although it is difficult to store the seed 

for more than 14 years, we think that a large seed 

collection will provide the critical materials for implementing future restoration treatments. The seed may 

have a wide variety of uses such as (1) growing seedlings for operational planting and research studies, 

(2) cataloging genetic diversity, (3) supplying a source 

of seed for other land management agencies that have 

few seed resources, (4) use in research and management studies, and (4) sustaining gene conservation 

cone collections in seed stores where typical grow-out 

procedures for other annual and biannual species to 

replenish inviable seed is impractical for a long-lived 

conifer. It is acknowledged that rust resistance by 

these means is based solely on the performance of 

the maternal (cone-bearing) parent, as these cone collections are a result of open-pollination from random 

(both resistant and susceptible) pollen donors.

There are several other important activities that fall 

within this management action (Mahalovich 2000; 

Mahalovich and Dickerson 2004; Mahalovich and 

Foushee, submitted; Nelson 2014). We need to build 

up a comprehensive pollen bank to use in special 

breeding programs and genetic efforts. We also need 

to create a clone bank. The primary advantage of a 

clone bank is to archive valuable genetic material 

scattered across several areas into a well-protected 

location (White et al. 2007). Plus trees of interest 

(i.e., proven, rust resistance, cold hardiness, drought 

tolerance, etc.) are commonly propagated through 

grafting as a means of ex situ conservation. Protecting 

valuable genetic material in a centralized location 

serves as an added buffer if the original plus tree dies 

and also reduces the costs of protecting scattered plus 

trees in the field. Moreover, this form of gene conservation would afford more flexibility in designing 

treatments (mechanical cuttings and prescribed fire) 

in the field to promote whitebark pine regeneration if 
92 USDA Forest Service RMRS-GTR-361. 2017.

backup copies of plus trees are established in a clone 

bank (Mahalovich and Foushee 2015).

5.3.5 Grow Seedlings

Now, and increasingly over time, we need to grow 

thousands of whitebark pine seedlings from seeds 

gathered from trees that demonstrate rust resistance 

(Burr et al. 2001). Restoration activities are increasing, and as fire size and frequency increase, we will 

need to supply the seedlings to plant in the extensive 

areas that have been treated or burned, especially 

where seed sources are dead or damaged. Rustresistant seedlings are especially important for the 

future if wildland fire increases in areas with high 

damage and mortality from WPBR and MPB. This 

is because red squirrels, especially, will cut cones 

and nutcrackers will act as predators and eat most 

of the cached seed in highly damaged areas (Keane 

and Parsons 2010a; McKinney and Tomback 2007). 

Planting is the most important restoration activity in 

those areas with high whitebark pine mortality (>50 

percent) to ensure continued whitebark pine regeneration and increase the frequency of rust resistance on 

the landscape.

Mycorrhizae are critical for whitebark pine 

establishment so it may be beneficial, especially in 

this time of warming climates in the upper subalpine 

areas, to inoculate seedlings with mycorrhizae. This 

would improve their survival because of increased 

water, phosphorus, and nitrogen uptake (Cripps and 

Antibus 2010). At the time sowing requests are submitted, nurseries should be told that seedling stock is 

going to be planted in settings that might be devoid 

of mycorrhizae (e.g., after a severe fire, a re-burn, or 

a hot prescribed burn), to determine if mycorrhizal 

inoculation is appropriate in a nursery setting. Perhaps 

the best approach may be to inoculate all seedlings 

regardless of where they are planted, depending on 

the expense (Cripps and Grimme 2010). Continued 

research to determine the efficacy of nursery inoculations of mycorrhizae is critically needed.

When planting programs within a recognized 

ecosystem or seed zone exceed 40,000 seedlings 

annually, managers should consider the benefits of 

establishing grafted seed orchards. Seed orchards can 

be carefully designed to optimize genetic diversity, 

minimize the negative consequences of inbreeding 

depression, double the levels of blister rust resistance 

with both rust-resistant maternal (cone) and paternal 

(pollen) parents, manage correlated response with 

other adaptive traits (e.g., cold hardiness and drought 

tolerance), provide early and abundant flowering to 

overcome infrequent and variable cone crops, and 

provide a cost effective means of harvesting seed for 

planting programs. Establishing seed orchards should 

have been included among the management actions 

in the Keane et al. (2012b) rangewide strategy. And, 

because branch tips (scions) remember their age and 

position in the tree crown, 80 to 300-year old scions 

from the better performing plus trees grafted onto 

3–6 year old rootstock initiate cone production shortly 

thereafter. Abundant conelets and pollen catkins are 

routinely observed on greenhouse whitebark pine 

grafts. Cones in orchard crowns can be easily protected with wire cages so that tree climbing or traveling 

long distances to backcountry, high elevation sites is 

unnecessary. Developing cone crops in seed orchards 

can be readily monitored for maturity to ensure that 

only the well-developed cones and mature seed are 

harvested. Smaller sized orchards (one to five acres) 

are easier to finance, can be customized for site specific management objectives, and can reflect a broader 

genetic base by varying the genetic constitution of 

each seed orchard.

5.3.6 Protect Seed Sources

Protecting rust-resistant, seed-producing whitebark 

pine trees against mortality from future disturbance 

and climate regimes will be important for maintaining genetically diverse populations on the landscape. 

These mature trees are key to effective restoration in 

times of rapid climate change because they enhance 

resistance to WPBR and contain unique genetic 

legacies. The more rust-resistant screened plus trees 

that can be retained in the future, the more likely we 

can maintain genetically diverse populations on the 

landscape. Thus, it is important that these trees don’t 

die in wildfires, avalanches, and MPB outbreaks 

that are likely to be more frequent and severe in the 

future. These seed-source trees fall in two classes: 

(1) the high priority plus trees that have been selected 

for seed collection and tested for rust resistance, 
USDA Forest Service RMRS-GTR-361. 2017. 93

and (2) the lower priority future plus trees that are 

identified as potentially rust resistant, but have yet 

to be screened for resistance. The latter class of trees 

are all those currently growing in high rust-mortality 

areas (Hoff et al. 2001). All trees of both classes warrant protection. One last and important point is that 

any protection treatment should attempt to satisfy 

multiple objectives when implemented; for example, 

fuel treatments should also be thinnings that increase 

whitebark pine tree vigor and cone production and 

planting rust-resistant whitebark pine seedlings should 

be part of the treatment design.

The most common tree-level restoration activity 

is the protection of trees from disturbance agents, 

primarily fire, MPB, and WPBR. Protecting trees 

against wildland fire (prescribed, wildland fire use, 

or wildfire) is difficult and costly, but it can often be 

effective, especially in moderate fire danger years 

(Keane and Parsons 2010a; Murray 2007). Fire 

protection has been implemented, with mixed success, using any combination of the following actions: 

(1) mechanical or manual manipulation of surface 

fuel surrounding the plus trees by raking or blowing 

(via leaf blower) downed wood, litter, and duff away 

from tree bases; (2) cutting competing fir and spruce; 

(3) mechanical reduction of shrub, and herbaceous 

fuels using mowers or clippers; and (4) prescribed 

burning to kill competing vegetation and reduce surface fuels (Keane and Parsons 2010b).

The most important design criterion is deciding the 

amount of area to treat around each tree. A general 

rule of thumb is a circular area around the tree that 

has a radius equal to the average stand height (Keane 

and Parsons 2010b; Keane et al. 2012b). That area, 

however, can be increased or decreased depending on 

canopy and surface fuel conditions in adjacent stands, 

especially in the prevailing downwind stands. We 

believe that it may be more effective to increase the 

fuel treatment radii around selected trees and to treat 

more trees to anticipate future impacts. Tree-level 

protection treatments against fire are most effective 

when little woody surface fuel is present in the treated 

area and that there are few competing tree and shrub 

fuels surround the tree being protected. Fire crews 

have wrapped large whitebark pine with fire shelters 

to protect against fire mortality, but with limited 

success (Keane and Parsons 2010b). These tree-level 

fuel treatments are commonly done by people with 

chainsaws cutting trees around the target tree and then 

removing, piling, and burning the slash. The slash 

should be piled away from the target tree to minimize 

Ips spp. beetle damage and prolonged soil heating 

when piles are burned (Keane and Parsons 2010b).

Methods of protecting known or potential rustresistant trees from MPB and WPBR fall into two 

categories: indirect and direct protection. Indirect 

protection comes from increasing tree vigor by cutting competing vegetation and by eliminating the 

factors that favor the spread of MPB or WPBR. 

Treatments that remove competing vegetation around 

individual trees, often called “daylighting” (tree 

level) or thinning (stand level), provide the target tree 

competition-free growing space for a long time span 

(Keane et al. 2012b). Daylighting treatments can also 

function as fuel-removal treatments that reduce surface and canopy fuels surrounding the target tree (see 

previous paragraph). Daylighting has other benefits, 

many of which have yet to be extensively studied. 

Some think that the post-daylighting environment 

increases radiation to the stems of the target trees, 

thereby increasing temperature and perhaps limiting 

MPB attack. This open environment also increases 

wind speeds that may better disperse the aggregating 

pheromones produced by MPB in the surrounding attacked trees. Increased wind and radiation could also 

decrease humidity that may decrease the incidence of 

new WPBR infections on the target trees in moderate 

fire years. Again, the size of treatment area is critical 

in the design of daylighting treatments (see above). 

Many treatments have used a circle around the target 

tree with the radius that is equal to the height of the 

surrounding canopy or 1–3 times the height of the 

target tree. Daylighting treatments should also include 

fuel reduction to protect against multiple disturbances. 

Thinnings have the same benefits of daylighting if 

done correctly, but most thinnings will be more effective into the future if paired with a prescribed burn 

(see Section 5.3.7).

Direct protection treatments for MPB and WPBR 

are any action designed to reduce populations or 

prevent attack. These include tree-level treatments 

that kill the damaging pathogens or pests, such as 
94 USDA Forest Service RMRS-GTR-361. 2017.

fungicides for WPBR (Brown 1969) and insecticides 

for MPB, and treatments that change the behavior of 

the organism, such as the anti-aggregating pheromone 

Verbenone (Bentz et al. 2005; Kegley and Gibson 

2004). Pruning has also been suggested by some 

as a means to retard or eliminate WPBR infections 

(Hungerford et al. 1982; Hunt 1998). However, direct 

approaches are costly and may take funding away 

from projects that make more effective and longerterm contributions to restoration. Direct approaches 

could be valuable for protecting small numbers of 

whitebark pine in local situations that may have 

disproportionate impacts, such as within ski areas, 

for isolated stands of whitebark pine, and around a 

municipal watershed (Keane et al. 2012b).

All of these tree-level treatments demand a longterm commitment, especially as we progress into the 

uncertain future when we know restoration efforts 

might be compromised. It is hoped that protection 

methods for these important rust-resistant trees will be 

improved as other restoration actions create landscape 

heterogeneity, increase rust resistance, and allow natural landscape processes to occur. Ultimately, we need 

to ensure that there are other trees to take the place of 

plus trees lost from disturbance.

5.3.7 Implement Treatments

The rangewide strategy emphasizes the need to 

create conditions that encourage whitebark pine 

regeneration, conserve seed sources, and promote rust 

resistance (Shoal et al. 2008). Objectives for treatments include creating nutcracker caching habitat, 

reducing competing vegetation, decreasing surface 

and canopy fuels, manipulating forest structure and 

composition, and diversifying age class structure. 

These actions can be implemented using a host of 

passive and active treatments to create areas where 

whitebark pine can prosper. As mentioned in the 

guiding principles (Section 5.2.4), before stand- and 

tree-level treatments are implemented, managers need 

to take a landscape approach and manage for heterogeneous landscapes and plan treatments as part of a 

landscape level evaluation.

Many types of treatments can accomplish the primary restoration objectives of facilitating whitebark 

pine regeneration, increasing whitebark pine cone 

crops by increasing vigor, and reducing disturbance 

impacts. This usually involves some combination of 

silvicultural cuttings, prescribed burning, and planting 

rust-resistant seedlings. These treatments should attempt to improve landscape heterogeneity while also 

facilitating whitebark pine resilience, rust resistance, 

and sustainable cone crops. We discuss prescribed 

burning, cuttings, and plantings as the primary tools 

for implementing treatments in the context of the 

primary reason or objectives for the treatment—competition removal, fuel reduction, fuel augmentation, 

and regeneration facilitation. Any treatment should 

be designed to address multiple objectives. Fuel 

reduction treatments, for example, should also reduce competition and allow for natural and artificial 

regeneration.

5.3.7.1 Competition Removal

Eliminating vegetation that competes with whitebark pine trees is meant to improve tree vigor, which 

is increasingly important as the climate warms. 

Improved vigor often results in greater forest resilience because the trees are now about to allocate 

resources to defenses against increasing incidences of 

disturbance events. Improved vigor may also increase 

the frequency and quantity of cone crops because 

trees may allocate more resources to reproduction. 

And last, increased vigor will contribute to longevity 

and allow trees to remain on the landscape for longer 

times.

Mechanical thinning where chainsaws are used to 

cut competing subalpine fir, spruce, and mountain 

hemlock is the primary tool used for competition 

removal treatments. It is important that all competing shade-tolerant conifers be cut, including the 

regeneration; this is rarely done because of the cost. 

Any residual trees of competing species, even small 

fir seedlings, will compromise the efficiency of the 

mechanical treatment, especially when productivity increases projected for the future will accelerate 

successional advancement. Therefore, many cutting 

treatments can be improved by including prescribed 

burning after the cut because, hopefully, the fire will 

tend to kill most of the small and large shade-tolerant 

tree competitors and leave the more fire-tolerant 

whitebark pine individuals (Keane and Parsons 
USDA Forest Service RMRS-GTR-361. 2017. 95

2010b). Prescribed burning alone, however, is not as 

exacting as mechanical cuttings. Prescribed fires are 

highly variable across space and may miss parts of the 

stand thereby leaving many fir and spruce trees alive. 

These fires can also severely burn parts of the stand 

resulting in high mortality in mature whitebark pine 

trees (Keane and Parsons 2010b). If done correctly, 

prescribed fires can kill most of the shade-tolerant 

understory layer that otherwise would take significant 

effort to remove if mechanical cuttings were used. 

This is especially true when fuels are augmented prior 

to the fire treatment (see following sections).

Since climate change may result in significant 

increases in vegetative productivity in upper 

subalpine areas, it is important to remove as many 

shade-tolerant competitors as possible to make any 

restoration treatment last longer. Therefore, several 

modifications of these competition removal treatments 

are needed to account for potential climate change. 

First, mechanical cuttings and prescribed burning 

treatments should take a more liberal approach and 

remove more of the competitors than normal. Cutting 

non-merchantable trees or removing advanced 

regeneration is probably best. In prescribed burning, it 

is better to burn on the hotter side of the prescription 

while protecting those valuable plus trees. This may 

be difficult in most operational settings because the 

fuelbed may be quite dry; this might increase the risk 

of escape and spotting, and result in high whitebark 

pine mortality. Therefore, we suggest using a pre-fire 

mechanical treatment, here called fuel augmentation, 

to add more fuels (needles and small branches) to 

the normally sparse fuelbed. With fuel augmentation 

(discussed below), a greater fire intensity can be 

obtained while the weather conditions are still moist, 

thus protecting whitebark pines (Keane and Parsons 

2010b). It is vitally important that any mechanical 

thinning or cutting to improve whitebark pine growing 

conditions should also treat the fuels surrounding the 

apparent rust-resistant trees to minimize the chance 

that they are lost from fire.

5.3.7.2 Fuel Reduction

Fuel treatments will undoubtedly play an important 

role in reducing wildfire impacts on living rustresistant trees and are therefore considered a viable 

restoration action. Treating fuel through the use of 

controlled and uncontrolled wildfires will be important in the future as a protection in the role of creating 

heterogeneous landscapes. Wildfires, however, can’t 

be planned and can be difficult to manage, especially 

once they get above 50 ha in size. There is always 

a chance that they will adversely impact whitebark 

pine restoration efforts by killing rust-resistant trees 

or planted seedlings rather than providing benefits by 

creating competition-free growing space for future 

populations. Therefore, mechanical and prescribed fire 

fuel treatments may be more desirable and manageable than wildfires in the future.

Fuel treatments involve reducing canopy fuels 

by cutting, masticating, or burning living subalpine 

fir, spruce, and other shade-tolerant conifer trees 

and reducing surface fuels by burning or cutting. 

Reducing fuels in or near stands that contain valuable 

rust-resistant trees can be an important hedge against 

losing them to future wildfires. It is critically important that any fuel treatment be also designed in the 

context of a whitebark pine restoration treatment, and 

vice versa. This means that the reduction of canopy 

and surface fuels should be considered a secondary 

objective. Many contemporary fuel treatments, such 

as mastication, canopy thinning, and chipping, are not 

designed with ecological relationships in mind. It is 

entirely possible that live whitebark pine trees could 

be cut during fuel reduction treatments. And conversely, restoration treatments that do not also reduce fuels 

may result in unnecessary losses of seed sources from 

future wildfires.

5.3.7.3 Fuel Augmentation

Fuel augmentation is the process of changing the 

fuelbed to facilitate a wider prescribed burning window and a more comprehensive and consistent burn 

once the fire is ignited. Usually fuel augmentation 

involves felling the shade-tolerant, fire susceptible 

competing trees in areas where surface fuels are 

insufficient to achieve the prescribed burning objective. The red needles and small twigs of the felled 

trees create additional fine surface fuels that allow 

ignition of hotter fires under cooler and moister conditions. This creates a wider temporal burn window 

thereby allowing fire specialists the ability to ignite 
96 USDA Forest Service RMRS-GTR-361. 2017.

a prescribed burn when fuel moistures are higher, 

such as towards the end of the autumn burning season 

(Keane and Parsons 2010b). Many seral whitebark 

pine stands have discontinuous fuelbeds with highly 

variable fuel loadings that, when burned under typical 

prescriptions, do not generate enough heat to kill the 

shade tolerant competitors, so fuel augmentation is 

often a necessity (Keane and Parsons 2010b).

The successful melding of fuel augmentation and 

prescribed burning will be an important treatment in 

the near-term but it may become even more important 

as climate changes become manifest in high elevation 

landscapes. The treatment that accomplishes the most 

restoration objectives is often prescribed burning. To 

get the best results from prescribed fires, it is important to augment surface fuels when needed to provide 

additional control to fire managers. In anticipation of 

future increases in wildland fire, fuel augmentation 

and prescribed burning can be used together as fuel 

reduction treatments to protect rust-resistant pine 

trees and also as competition removal treatments to 

improve whitebark pine vigor. Keane and Parsons 

(2010a) found that those stands that were treated with 

prescribed fire after fuel augmentation acted as effective fuelbreaks against wildfires that occurred after 

the prescribed burn. Therefore, the importance of fuel 

augmentation coupled with prescribed burning will be 

to condition current stands against future increases in 

disturbances, primarily fire (through fuel reduction), 

but also insects and disease (through improved vigor).

5.3.7.4 Regeneration Facilitation

Some proactive, stand-level restoration treatments 

are designed to remove competition in order to improve natural regeneration of whitebark pine (Keane 

and Parsons 2010b). This involves creating stand 

conditions that facilitate seed caching by the Clark’s 

nutcracker on the treated site. If nutcrackers cache 

enough seeds, then they may not recover some caches, 

or snowmelt and spring and summer rains may trigger germination before nutcrackers retrieve the seeds 

(Tomback 2001). Seedlings from these caches become 

the whitebark pine forests of the future. Regeneration 

restoration treatments usually involve removing 

vegetation from the overstory and understory to create 

open ground conditions that are used by nutcracker 

for seed caching (Keane and Arno 2001; Tomback 

2001). A variety of mechanical cutting and prescribed 

burning treatments can be used to create conditions 

that facilitate regeneration. The most common approaches are group selection harvests and moderately 

severe prescribed burns (Keane and Parsons 2010a).

Facilitating natural regeneration using management 

treatments may not be dependable in the near-future, 

especially with changing climates and continued 

losses from MPB and WPBR. Relying on natural 

regeneration is a risky business considering that many 

areas may have insufficient populations of mature, 

cone-bearing whitebark pine to sustain viable regeneration because the Clark’s nutcracker eats most of the 

seed it caches. Keane and Parsons (2010a) found little 

natural whitebark pine regeneration in their treated 

areas probably because the nutcracker reclaimed most 

of the cached seed in areas of low seed-producing 

trees. Even if natural regeneration does occur, the 

majority of the nutcracker cached seeds may be from 

whitebark pine trees that are susceptible to rust. 

Therefore, most regeneration facilitation treatments 

should attempt to create suitable ground conditions 

to allow the successful planting of rust-resistant 

seedlings. This may be the best option under changing climates, especially in those stands decimated by 

MPB and WPBR.

5.3.8 Plant Seedlings

To mitigate the loss of whitebark pine due to 

climate-mediated changes in disturbance regimes, 

we must plant those disturbed or treated areas where 

whitebark pine seed sources have lost cone-producing 

capacity through MPB mortality or WPBR infection 

with rust-resistant whitebark pine seedlings (Fiedler 

and McKinney 2014). This is one of the main principles of the rangewide restoration strategy (Keane et 

al. 2012b). Reforestation with rust-resistant seedlings 

will increase the representation of blister rust-resistant 

genotypes in the next generation and eventually create resilient whitebark pine forests of diverse age 

structures that are more likely to withstand frequent 

fire, MPB outbreaks, and the spread of WPBR. 

Planting rust-resistant seedlings is recognized as the 

key management action in the rangewide strategy 

(Keane et al. 2012b). Sowing seeds from rust-resistant 
USDA Forest Service RMRS-GTR-361. 2017. 97

sources directly in treated or burned areas, if shown 

to be efficacious, may be a cost-efficient alternative 

to growing seedlings and planting them. Areas with 

declining whitebark pine seed sources are unlikely to 

produce enough seeds to attract and support nutcrackers, so natural seed dispersal is unlikely (Barrringer 

et al. 2012; McKinney et al. 2009). Because blister 

rust is at the northern limit of whitebark pine’s range, 

as well as its upper elevational limits, both important 

climate change fronts, seedlings from rust-resistant 

parent trees should be planted at both limits. Most 

other restoration actions will be ineffective without 

the planting of rust-resistant seedlings.

We have several suggestions for planting seedlings 

to mitigate the effects of climate change and ensure 

high seedling survival. First, planting probably should 

be prioritized first on the higher portions of whitebark 

pine seral sites based on results of our simulation experiment (Section 4). Given the high costs of growing 

rust-resistant seedlings and of planting them in remote 

settings, planting should start at the highest regions in 

burned areas below climax whitebark pine sites where 

they are most likely to survive in the future, and then 

progress downwards in elevation.

Second, seedlings should be planted in microsites 

that best mitigate harsh conditions and provide shade 

or wind protection (McCaughey et al. 2009). For 

example, they should be planted on the side of a rock, 

stump, or other object that provides some protection. 

Microsites may moderate seasonally arid conditions 

when the planted seedling is most susceptible to 

drought effects, or protect against hard frosts, deep 

snow packs, prolonged insolation, drought, and soil 

erosion during the critical time of seedling establishment (Scott and McCaughey 2006). Since snags 

eventually fall, planting next to snags should be 

avoided, but planting next to stumps often provides 

good protection. If no favorable microsites for planting exist, then we suggest that planting crews be 

instructed to create the microsite using a log, rock, or 

wood stake, or other protection device.

Next, selection of planting sites should be based 

on whether they might contain important mycorrhizae 

needed to ensure seedling survival (Lonergan et al. 

2013). Seedlings planted in proximity to sapling or 

mature whitebark pine regeneration have a chance 

to be colonized by the appropriate mycorrhizae 

(Mohatt et al. 2008; Perkins 2015). Moreover, it may 

be advantageous to wait for undergrowth vegetation, particularly shrubs, to develop before planting 

whitebark pine seedlings on burned sites, although 

this could require a number of years for extreme sites. 

There may be excessive erosion and soil movement 

during the years directly after a burn that may dislodge planted seedlings, and undergrowth shrubs may 

provide partial shade that is favorable to seedling survival (Tomback et al. 2011b). Waiting until shrub and 

herbaceous plants have reestablished before extensive 

planting is implemented may be more effective, 

except when beargrass (Xerophyllum tenax) is present 

(Izlar 2007; McCaughey et al. 2009).

In the future, special attention should be given to 

the planting guidelines of McCaughey et al. (2009) 

and Scott and McCaughey (2006). Large, hardy seedlings with well-developed root systems will survive 

best in the highly variable climates of the future. 

Seedlings should be planted in competition-free 

environments so that shading effects are minimized 

and the seedling can grow its best to be more resilient. 

However, some partial shade and physical protection may enhance survival by using shade cards and 

planning site mitigation, such as placing logs or rocks 

around the seedling. Moist soils will be critical for 

high survival after planting seedlings; managers are 

now waiting until the fall to plant whitebark pine 

seedlings to avoid summer droughts. So it might also 

be more effective to wait until autumn rains have 

wet the soils before planting, especially with future 

warmer and drier climates.

Our simulation efforts show that the gains in planting rust-resistant trees are not manifest in the short 

simulation time (90 years) (see Section 5). However, 

the real gains in planting may take centuries not 

decades to become manifest (fig. 57). By planting rustresistant seedlings, we may reduce by half the amount 

of time it would take natural selection to create viable, 

rust-resistant populations. We also avoid the possibility that the species might decline to such low levels 

that it is unable to create rust-resistant populations 

naturally without management intervention (Keane et 

al. 2012b). While some areas will regenerate naturally 

from surrounding seed sources, the surviving seedlings 
98 USDA Forest Service RMRS-GTR-361. 2017.

might have a low frequency of resistance to WPBR 

and most may eventually die after WPBR wave years, 

which could be more frequent in the future. One 

general way to restore whitebark pine and mitigate for 

climate change is to plant all burned or treated areas 

with rust-resistant seedlings, including those burned 

areas in somewhat healthy whitebark pine landscapes 

with abundant live, mature trees. However, in the interest of effectiveness and cost containment, the higher 

elevations in a treatment landscape or stand should be 

targeted for planting first.

Assisted migration is broadly defined as the translocation of a species into more habitable locations 

outside of their current range (McLachlan et al. 2007). 

This treatment option has been promoted as a means 

of saving species or vulnerable populations from 

extinction due to climate change (McLane and Aitken 

2012). While this may seem like a viable approach, 

we think it is fraught with uncertainty and pitfalls that 

may render most plantings ineffective. First of all, 

rust-resistant seedlings at this time are not available 

in sufficient quantities, and they are quite expensive. 

Planting expensive seedlings in a foreign environment 

is probably not a good use of funding.

Secondly, it will be nearly impossible to decide 

the best places to plant these expensive rust-resistant 

seedlings based on the highly uncertain simulated 

climate change predictions (see Section 3). The high 

uncertainty in climate predictions among global circulation models and scenarios, coupled with the high 

uncertainty in extrapolating predicted coarse scale 

weather to finer scales, make it challenging to identify 

which areas will be climatically suitable for whitebark 

pine planting beyond its current range, especially 

at the microsite scale. Bioclimatic envelope models 

(BEMs) are often used to locate potential assisted 

migration areas (see Section 3), but BEMs often are 

based on the distribution of mature trees and do not 

account for regeneration processes in their design.

Third, sown seeds and planted seedlings may not 

survive the near term climates in these new areas; 

cold, snow, frost, lack of mycorrhizae, and novel soil 

conditions in the new areas may kill the seedlings 

long before the climate changes sufficiently. However, 

a recent study of sown seeds at treeline has shown 

good seedling germination and survival in the Greater 

Yellowstone Area (Pansing 2014). McLane and 

Aitken (2012) found that whitebark pine in British 

Columbia could establish in model-predicted climate 

zones north of the current species range in a small 

assisted migration trial. Their BEM, however, could 

be enhanced by adding snow-duration variables to 

facilitate capturing the influence of snow on seedling 

germination and survival. We think it is best to use 

the limited resources available to restore whitebark 

pine within its historical range, with the goal of 

maintaining healthy, cone-producing populations that 

can naturally expand distribution (Breed et al. 2013). 

Nutcrackers cache seeds within and above treeline 

in alpine tundra—essentially providing “assisted 

migration” at the upper elevation climate change 

front (Tomback 1986, 2001). Little is known about 

nutcracker seed dispersal at the northern limits of 

whitebark pine, but nutcrackers may well occasionally 

cache seeds beyond the current distributional limits.

5.3.9 Monitor Activities

The success of future whitebark pine restoration efforts will be greatly dependent on the lessons learned 

in previous attempts (Keane and Parsons 2010b). 

Future implementation of restoration protocols and 

tools will benefit by the detailed documentation of 

the successes and failures of all previous restoration 

attempts, especially in this time of climate change 

(Logan et al. 2008; White et al. 1990). Therefore, the 

monitoring of restoration treatments with pre- and 

post-treatment sampling is vital for providing the 

critical information needed to fine-tune future treatment planning, designs, and implementation to adjust 

both for local conditions and for changing climates. 

Historically there has been little financial support for 

the comprehensive monitoring needed to evaluate the 

effectiveness of land management actions, and these 

monitoring efforts will be even more important with 

climate change.

Monitoring requires a system of uniform protocols, 

databases, and sampling methods; it also requires the 

free exchange of data across agencies and the public 

(see Lockman et al. 2007). Several field monitoring 

systems are available (Tomback et al. 2005), including 

FIREMON (Lutes et al. 2006), the FIREMON-FEAT 

Integration (FFI; Lutes et al. 2009), the U.S. Forest 
USDA Forest Service RMRS-GTR-361. 2017. 99

Service’s FSVEG (USFS 2016), and the National Park 

Service’s Fire Monitoring Handbook (NPS 2001).

All monitoring data should be entered into one 

interactive database for analysis by both research and 

management and analyzed at various spatial and temporal scales, from local to regional and from yearly 

to decadal, to comprehensively document ecosystem 

responses to restoration treatments. The results are 

best for feedback on restoration treatment efficacy. 

The time frame for monitoring is also a consideration: 

Should treated areas be assessed within a year after 

restoration is implemented, or should 3, 5, or 10 years 

elapse first? In the case of planted seedlings, information on first-year survival seems essential; similarly, 

soon after application of prescribed fire, effectiveness 

of treatment should be evaluated.

Modifying current monitoring project designs 

is an important way to accommodate the potential 

impacts of climate change. First and foremost, all 

monitoring should be done over long time periods to 

ensure that any ecological response from changing 

climates is manifest in quantifying treatment effects. 

This means that we need to monitor over decades 

to centuries. This will entail monitoring of legacy 

projects from previous generations of restoration 

initiatives, along with revised agency work priorities. It is also important that monitoring networks be 

continually revisited to detect if restoration activities 

are still effective under these new climates. Moreover, 

whitebark pine ecosystem response to disturbance, 

especially wildland fire, often takes a long time, up to 

50 years in some areas (Agee and Smith 1984; Keane 

and Parsons 2010b). Most importantly, all monitoring 

should always include a control unit to document 

those changes that results from climate alone so that 

the responses resulting from restoration actions can be 

interpreted in the appropriate context.

Monitoring sample designs might also include 

variables that might be important to describe climate 

impacts on restoration treatments. Most monitoring 

efforts are designed to evaluate some restoration 

objective. For example, if a restoration objective is 

to regenerate recent burned areas, then the monitoring design might only include methods to quantify 

seedling density using fixed-area plots. In times of 

changing climates, the count of seedlings by species 

and size in the plot could be augmented with a variable that indicates if seedlings are next to protected 

microsites or specific shrub or herb plant species. 

Variables that describe biophysical attributes of the 

fixed area plot could be assessed, such as aspect, soil 

type, percent cover by lifeform, and ground cover. 

The primary monitoring data can then be stratified by 

or analyzed by the secondary variables as covariates 

to detect differences and adjust restoration designs 

accordingly.

Because ecological processes are dynamic, they 

tend to be highly variable, and the predicted climates 

of the future will surely increase that variability. 

Therefore, another recommendation is that sampling 

intensities (number of plots) be increased to accommodate changing variabilities over time. Of course, it 

will be difficult to determine just exactly how many 

plots or sampling units will be needed to quantify 

future variabilities because of the high uncertainty in 

climate prediction and ecosystem response. There are, 

however, minor things that can be done to mitigate 

declining sampling power. First, tighter tolerances 

can be used to compute sample sizes from an estimate of contemporary variability. Instead of using 

a 20 percent threshold for estimating how close the 

sample should approximate the mean, for example, 

15 percent could be used. Next, a more conservative 

approach can be used to approximate the sample size 

and design. The parameters detailed in the methods 

presented in FIREMON (Lutes et al. 2006) or Keane 

(2015) for estimating sample size can be computed 

for a best case and worst case scenario; under climate 

change, the worst case scenario might be appropriate. 

Next, those variables being estimated in a sample plot 

must be measured using the most accurate techniques 

to minimize high uncertainty in final estimates. Tree 

height, for example, is more accurately estimated 

using laser rangefinders rather than clinometers, and 

plant cover could be more accurately estimated with 

digital photos rather than visual estimates. Last, sampling could be confined to those variables that best 

assess restoration objectives and have lower natural 

variabilities. Total fine woody surface fuel load, for 

example, has a lower variability than the variability 

of any of the three surface fuel components that comprise fine woody fuels (0 to 0.25 in diameter, 0.25 to 
100 USDA Forest Service RMRS-GTR-361. 2017.

1 inch diameter, and 1 to 3 inches in diameter) (Keane 

et al. 2012a, 2013).

5.3.10 Conduct Research

New research in all phases of whitebark pine restoration is needed to ensure that all management actions 

utilize the most scientifically credible methods to effectively return the species to high mountain settings. 

Unfortunately, there continues to be little interest in 

funding basic and applied whitebark pine ecological 

research, especially in the context of climate change, 

and this could impact the future success of restoration treatments. For example, many whitebark pine 

restoration projects are proposing or implementing 

treatments designed to release understory whitebark 

pine. However, there is little research to show that the 

suppressed seedlings and saplings of this moderate 

shade tolerant species will actually release to increase 

in growth and become cone-bearing mature trees. In a 

limited sample, Keane et al. (2007a) found that about 

a third of the trees released, but many released years 

later or didn’t release at all. The key then is to conduct 

the research that develops methods to identify which 

species will release and which will remain at the same 

size. This lack of comprehensive research is perhaps 

the most important barrier for efficient and successful restoration treatments. In general, few research 

dollars are being spent on this endangered foundation 

species and the forests it creates, and there are few 

scientists that have an extensive knowledge of this 

complex ecosystem. It is vital that research provide 

the information needed by managers to conduct successful treatments for the sustainable management of 

whitebark pine ecosystems.

Several high priority topics for guiding future 

research in whitebark pine restoration are presented 

in Keane et al. (2012b), but there are many others that 

should be included to account for climate change. We 

present these topics by major categories and also by 

the four guiding principles (Sections 5.2.1 to 5.2.4), 

but our examples are by no means exhaustive.

Basic Ecological Research

• Study basic ecophysiological relationships of whitebark pine to climate and environmental conditions.

• Study the recent and long-term past climates of 

whitebark pine sites.

• Carefully study the regeneration processes of whitebark pine as it is probably the most sensitive to 

climate change impacts.

• Improve current information on the survival of 

whitebark pine after fires, MPB, and WPBR and 

develop predictive models from this information 

(Perkins and Roberts 2003).

Silvicultural Investigations

• Evaluate those characteristics of whitebark pine 

seedlings and saplings that indicate if individuals 

will release from competition or remain suppressed 

(Keane et al. 2007a).

• Create a classification system that easily identifies 

those whitebark pine trees that will increase in 

growth after removal of competition.

• Improve planting guidelines to optimize planting 

success.

• Develop better ways to harvest cones, clean seeds, 

and grow seedlings (Ward et al. 2006).

• Develop better cutting prescriptions for ecosystem 

restoration in whitebark pine with rust (Waring and 

O’Hara 2005); 

• Determine optimal thinning designs for whitebark 

pine forests.

Nutcracker Interactions

• Continue to study the interacting dynamics of seed 

predation by birds, mammals, and insects.

• Estimate the density of cone production needed to 

sustain natural whitebark pine regeneration in most 

cone years and at what cone production density 

nutcrackers become seed predators rather than 

dispersal vectors.

• Determine whether regional populations of nutcrackers fluctuate over time and how population 

size may influence seed dispersal.

• Determine if whitebark pine is extirpated regionally 

and then restored, whether nutcrackers may return 

in future decades.

Modeling

• Design and develop spatially explicit, ecophysiological models that simulate whitebark pine landscape 
USDA Forest Service RMRS-GTR-361. 2017. 101

dynamics over the entire range of the species 

(Nelson 2014).

• Develop methods to simulate both the past and 

future ranges of variation of whitebark pine 

community dynamics to use as reference in management analyses (Keane et al. 2009).

• Obtain more accurate, realistic and comprehensive 

daily climate data for both the past and future.

• Improve methods to downscale climate predictions 

to local settings.

• Develop simulation protocols and modules to 

simulate alternative management treatments on 

whitebark pine stands in landscapes (Mladenoff 

and Baker 1999).

• Simulate how much of the landscape needs to be 

treated to ensure the optimal efficacy of whitebark 

pine restoration treatments (Loehman et al. 2011b).

Promote Rust Resistance

• Develop genomics-based cost-efficient methods to 

evaluate the presence of rust resistance in individual whitebark pine trees and at the population level.

• Determine what frequency of rust resistance in a 

whitebark pine population is needed to sustain 

that population in the future with different climate 

change and blister rust response scenarios.

• Add the impact of MPB and fire to these models.

• Invent new ways to accelerate natural selection for 

rust resistance in whitebark pine.

• Explore tree breeding and other ways to improve 

rust resistance in seedlings; 

• Monitor genetic variation in Cronartium ribicola

both geographically and over time to determine 

rates of evolution.

Conserve Genetic Diversity

• Create methods to assess the durability of rust 

resistance under operational conditions and natural 

inoculum.

• Determine the genetic diversity of outplanted 

rust-resistant seedlings in relation to natural 

populations.

• Periodically reassess seed transfer guidelines and 

seed source performance under changing climatic 

conditions to ensure the highest seedlings survival.

• Plant beyond seed transfer guidelines, using genotypes from more southerly locations that have 

climatic conditions more similar to future climates.

Save Seed Sources

• Develop new techniques to protect rust-resistant 

trees from fire, MPB, and future climates.

• Develop protocols for protecting cone-bearing 

whitebark pine trees from future severe fire during 

wildfire suppression efforts.

• Develop new techniques to accelerate cone production in seed orchards.

Employ Restoration Treatments.

• Evaluate efficacy of current and future restoration 

treatments; develop new techniques to harvest 

seeds from trees and grow seedlings in the nursery 

to reduce the high cost.

• Explore new techniques for planting the seeds of 

whitebark pine in burned and treated areas.

• Explore how to increase seed germination and seedling survival, and reduce rodent seed theft in direct 

sowing projects.

• Determine when is the best time to plant whitebark 

pine seedlings after a severe wildfire.
102 USDA Forest Service RMRS-GTR-361. 2017.

6.1 Restoring Whitebark Pine in the Face 

of Climate Change

Several important findings from this study’s literature review (Section 3) and simulation experiment 

(Section 4) may dictate how we approach whitebark 

pine restoration in the future. The first finding is 

that whitebark pine will continue to decline over 

the next several decades, mostly from WPBR and 

MPB mortality and only indirectly by direct climate 

change impacts (Section 4). This agrees with another 

modeling effort by Smith-McKenna et al. (2014). The 

second finding is that this decline can be reversed 

with proactive restoration actions; if no restoration 

activities are attempted, whitebark pine forests will 

continue to decline and become a minor, if not missing, component on the high elevation landscapes in 

western North America. Even a low level of restoration activity will help keep whitebark pine forests 

from vanishing on the high mountain landscape 

(Section 4). We also found that the effects of planting 

rust-resistant seedlings on whitebark pine ecosystem 

restoration take a long time to become manifest, probably more than a century. Third, we found that the 

recommendations proposed in the rangewide strategy 

(Keane et al. 2012b) (see Section 2 for details) are 

still valid. Only a few modifications to the rangewide 

strategy are needed to account for future changes in 

climate (Section 5). We also found that whitebark 

pine can do quite well under future climates on many 

landscapes, especially with more frequent fire, but 

there are some landscapes where whitebark pine will 

continue to decline (Sections 3, 4). In fact, whitebark 

pine appears to be able to create the most resilient and 

resistant upper subalpine forest under climate change 

compared to any other of its associates, primarily subalpine fir (Sections 3, 4). We also found that creating 

heterogeneous landscape structures will also mitigate 

the severity of disturbances. And last and most important, we found that the impacts of climate change on 

whitebark pine populations vary across its range based 

on local conditions. As the simulated output presented 

in Section 4 shows, whitebark pine actually increases 

in abundance and stature on one landscape with future 

warming while it decreases on another. This implies 

that there is no magic bullet or one-size-fits-all solution for restoring this enigmatic ecosystem; managers 

must tailor broad strategies to local conditions for the 

most effective restoration treatments.

There is great concern among managers that treating declining whitebark pine ecosystems during a 

time of widespread climate change, WPBR infections, 

and MPB outbreaks might destroy the important 

remaining whitebark pine seed sources. It is possible 

that allowing lightning fires to burn as controlled 

wildfires and proactively lighting prescribed fires 

might kill valuable rust-resistant, cone bearing 

whitebark pine trees. However, we should consider 

the alternative. Wildfires will happen regardless of 

our best suppression efforts, especially in this highelevation ecosystem where uncontrolled wildfires 

would have a greater chance of killing valuable 

rust-resistant individuals than managed fires because 

they burn under drier, hotter, and windier conditions. 

Even if uncontrolled wildfires don’t occur, vegetation succession will occur, and the result will put 

an even greater competitive stress on the remaining 

shade-intolerant whitebark pine trees. Seeds from 

these surviving trees would have less chance of being 

planted in favorable sites free of competition because 

there are fewer burned areas on the landscape due to 

fewer fires. Mountain pine beetle impacts on whitebark pine are devastating, but these impacts provide 

little reason to suspend restoration activities. In fact, 

the most important time to initiate restoration actions 

on the landscape might be right now while we still 

have remaining whitebark pine populations on the 

high mountain landscapes.

The key to successful restoration in the future is 

the planting of rust-resistant whitebark pine seedlings 

after wildland fires, whether these fires are controlled 

wildfires, uncontrolled wildfires, or prescribed fires 

(Keane et al. 2012b). It is also important that the 

genetic diversity of these seedlings is optimized to 

ensure whitebark pine remains on the landscape as 

6. Discussion
USDA Forest Service RMRS-GTR-361. 2017. 103

the climate changes. Maintaining a diversity of age 

classes that contain rust-resistant whitebark pine 

is critical to sustaining the species over long time 

periods because it provides the resilience to survive 

unwanted wildfires and the resistance to outbreaks, 

disease, and climate shifts.

Undoubtedly, land managers and researchers will 

need to assess the future of whitebark pine with and 

without restoration for various projects and we think 

that the primary vehicle that will be used to conduct 

these prognostications is simulation modeling. The 

problem, then, is deciding which modeling technique, 

model, or model output to use to develop the projections. We suggest that perhaps the best guide to use 

for evaluation is to determine if the model in question 

simulates the major interacting factors presented in 

figure 22. If a model doesn’t explicitly include these 

interactions, then certain assumptions and caveats 

must be made and used in the interpretations of the 

model projections. Bioclimatic enveloping models, 

for example, fail to account for several factors in 

figure 22. Therefore, if people want to use BEMs to 

decide the future of whitebark pine, then they must 

interpret the results in that context that the major 

disturbance effects are missing. Unfortunately, models 

that include explicit simulations of all the factors in 

figure 22 are rare and difficult to use at present. Thus, 

managers and researchers may often have to forgo 

modeling, and use the material in this report as context, or partner with modelers to simulate whitebark 

pine dynamics for their targeted landscapes. Models 

that integrate climate and management actions with 

ecosystem and landscape processes are rare, especially for whitebark pine, and there are no management 

oriented models as yet. This report represents perhaps 

the most comprehensive modeling effort for the species at landscape levels.

Given the high uncertainties in predicting climate, 

vegetation, and disturbance responses to increasing carbon dioxide levels, restoring whitebark pine 

ecosystems is much more likely to lead to a successful outcome than deciding not to restore based 

on the uncertain predictions for the future. It may 

be more prudent to wait until simulation technology 

has improved to include credible pattern and process 

interactions with regional climate dynamics coupled 

with significant model validation before we base decisions on the restoration of whitebark pine on uncertain 

future climate projections. Ecosystem models may 

take decades of further development before their simulations can be used to predict species and landscape 

response to climate change with reasonable accuracy. 

While we wait, we may lose valuable populations 

and rust-resistant trees, and our options for restoration diminish greatly. There is a small chance that the 

benefits of the proposed restoration efforts might be 

negated or compromised by climate change effects, 

but any restoration action implemented today might 

be better than the alternative of doing nothing. The no 

action alternative will almost certainly result in major, 

if not complete, losses of whitebark pine populations, 

even without climate change.

6.2 Caveats

The Keane et al. (2012b) rangewide strategy and 

this report provide critical information for planning 

and implementing whitebark pine restoration efforts 

at multiple scales in the face of climate change. 

Successful restoration, however, depends on the ability of managers to tailor restoration designs to fit local 

conditions. It is impossible to design a restoration 

approach that will work everywhere with the same 

level of effectiveness in the projected climates of the 

future because of the high variability in site conditions 

across the range of whitebark pine. The manager must 

craft restoration plans that will be successful for a 

specific area and effective under changing climates by 

addressing critical local conditions such as rust infection levels, MPB mortality, fuel loadings, successional 

status, public issues, and whitebark pine mortality. 

We hope that this report coupled, with the rangewide 

strategy, provides helpful direction on how to design 

restoration projects while detailing those efforts that 

need to be accomplished at the appropriate scale.

The climate change considerations for the rangewide restoration strategy presented here have their 

limitations for implementation. First, findings in this 

report are based on information, technology, models, 

and data that were available at the time of writing. 

The field of climate change science is moving quickly 

so there may be more current information now that 
104 USDA Forest Service RMRS-GTR-361. 2017.

can be included in any restoration evaluation (see 

Keane et al. 2011). The recommendations in Section 5 

may be improved as (1) new restoration technologies 

are developed, (2) additional research is completed, 

and (3) more data become available. The guidelines 

presented in this document are provided for reference 

and are not designed to be implemented in the same 

way across the range of whitebark pine. This report is 

meant to guide land managers as to which attributes 

to consider when evaluating the condition of their 

whitebark pine communities and to demonstrate the 

need for active restoration. Managers should use this 

information along with specific management direction 

established for lands to determine when, where, and if 

restoration and planting should occur.
USDA Forest Service RMRS-GTR-361. 2017. 105

Active restoration appears to be the only course 

of action for conserving whitebark pine ecosystems. 

Restoration must include the implementation of 

strategies that hedge the effects of climate warming 

on whitebark pine ecosystems, as well as mitigate the 

anthropogenic threats. However, the high uncertainty 

inherent in most current climate and ecosystem models and assessments may limit our ability to design 

restoration actions that are effective in the face of 

climate change. Uncertain climate change warming 

predictions are not justifications for inaction and 

allowing this important high mountain resource to 

continue to decline, especially considering the resiliency exhibited by whitebark pine forests over time 

and in response to wildland fire.

The best available information must be used to 

design effective restoration approaches and the toolkit 

of potential restoration techniques. Managers have 

the knowledge, skills, and experience to successfully 

restore whitebark pine forests across the entire species’ range, even though WPBR and climate change 

will make this task more difficult and complex. These 

factors only further underscore the need for immediate 

action to prevent the loss of this species and provide 

a solid rationale for strategic research and management planning for the conservation of this ecosystem. 

Sustaining these valued upper subalpine landscapes 

so that they will be resistant and resilient to climate 

change requires that we conserve as many parts of 

this ecosystem as we can. Losing whitebark pine 

ecosystems before the full range of climate change 

impacts are manifest could lead to a less resilient forest and dramatic shifts in high-elevation ecosystems 

of western North America. The set of management 

actions and adaptation strategies, approaches, and 

tactics presented in Section 5 will hopefully provide 

guidance in developing effective restoration actions 

using the rangewide strategy.

7. Summary
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