Forest 

Service

Pacifi c Northwest 

Research Station

General Technical Report

PNW-GTR-900 September 2014

United States Department of Agriculture

Climate Change Effects on 

Vegetation in the Pacifi c Northwest: 

A Review and Synthesis of the 

Scientifi c Literature and Simulation 

Model Projections

David W. Peterson, Becky K. Kerns, and Erich K. Dodson
Authors

David W. Peterson is a research forester, U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station, Forestry Sciences Laboratory, 1133 

N Western Avenue, Wenatchee, WA 98801; Becky K. Kerns is a research ecologist, U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, OR 

97331; and Erich K. Dodson is a faculty research assistant, Oregon State University, Forest Ecosystems and Society Department, 321 Richardson Hall, Corvallis, 

OR 97331-5752.

Cover art by Mari L. Schramm, USDA Forest Service.

Non-Discrimination Policy

The U.S. Department of Agriculture (USDA) prohibits discrimination against its customers, 

employees, and applicants for employment on the bases of race, color, national origin, age, 

disability, sex, gender identity, religion, reprisal, and where applicable, political beliefs, 

marital status, familial or parental status, sexual orientation, or all or part of an individual’s 

income is derived from any public assistance program, or protected genetic information in 

employment or in any program or activity conducted or funded by the Department. (Not all 

prohibited bases will apply to all programs and/or employment activities.) 

To File an Employment Complaint

If you wish to fi le an employment complaint, you must contact your agency’s EEO Counselor 

(PDF) within 45 days of the date of the alleged discriminatory act, event, or in the case of a 

personnel action. Additional information can be found online at http://www.ascr.usda.gov/

complaint_fi ling_fi le.html. 

To File a Program Complaint

If you wish to fi le a Civil Rights program complaint of discrimination, complete the USDA 

Program Discrimination Complaint Form (PDF), found online at http://www.ascr.usda.gov/

complaint_fi ling_cust.html, or at any USDA offi ce, or call (866) 632-9992 to request the 

form. You may also write a letter containing all of the information requested in the form. 

Send your completed complaint form or letter to us by mail at U.S. Department of Agriculture, Director, Offi ce of Adjudication, 1400 Independence Avenue, S.W., Washington, D.C. 

20250-9410, by fax (202) 690-7442 or email at program.intake@usda.gov.

Persons with Disabilities

Individuals who are deaf, hard of hearing or have speech disabilities and you wish to fi le 

either an EEO or program complaint please contact USDA through the Federal Relay 

Service at (800) 877-8339 or (800) 845-6136 (in Spanish). 

Persons with disabilities, who wish to fi le a program complaint, please see information 

above on how to contact us by mail directly or by email. If you require alternative means 

of communication for program information (e.g., Braille, large print, audiotape, etc.) please 

contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

Supplemental Nutrition Assistance Program

For any other information dealing with Supplemental Nutrition Assistance Program (SNAP) 

issues, persons should either contact the USDA SNAP Hotline Number at (800) 221-5689, 

which is also in Spanish or call the State Information/Hotline Numbers. 

All Other Inquiries

For any other information not pertaining to civil rights, please refer to the listing of the USDA 

Agencies and Offi ces for specifi c agency information.
Climate Change Effects on 

Vegetation in the Pacifi c 

Northwest: A Review and 

Synthesis of the Scientifi c 

Literature and Simulation 

Model Projections

David W. Peterson, Becky K. Kerns,

and Erich K. Dodson

U.S. Department of Agriculture, Forest Service

Pacifi c Northwest Research Station

Portland, Oregon 

General Technical Report, PNW-GTR-900

Published in cooperation with: 

U.S. Department of the Interior

Bureau of Land Management

Abstract

Peterson, David W.; Kerns, Becky K.; Dodson, Erich K. 2014. Climate change 

effects on vegetation in the Pacifi c Northwest: a review and synthesis of the 

scientifi c literature and simulation model projections. Gen. Tech. Rep. PNWGTR-900. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c 

Northwest Research Station. 183 p.

The purpose of this study was to review scientifi c knowledge and model projections on vegetation vulnerability to climatic and other environmental changes in 

the Pacifi c Northwest, with emphasis on fi ve major biome types: subalpine forests 

and alpine meadows, maritime coniferous forests, dry coniferous forests, savannas and woodlands (oak and juniper), and interior shrub-steppe. We started by 

reviewing and synthesizing the scientifi c literature on past and projected changes 

in atmospheric carbon dioxide concentrations and climate for the Pacifi c Northwest 

(and globally), and how these changes are likely to infl uence snowpack dynamics, 

soil water availability, and selected disturbance regimes. We also reviewed and 

synthesized the scientifi c literature on plant growth, reproduction, and mortality 

in response to changing climate and disturbance regimes, and on the ability of 

plants to adapt to these changes through phenotypic plasticity, local adaptation, 

and migration. We then reviewed the strengths and weaknesses of several types 

of simulation models commonly used to project vegetation responses to climate 

change and discussed recent model projections of vegetation responses to future 

climate change scenarios in the Pacifi c Northwest, as well as how these projections 

might best be used in developing management plans for forests and rangelands. 

We next reviewed the existing scientifi c literature on plant sensitivity and adaptation to changing climate and disturbance regimes for fi ve major vegetation biomes 

in the Pacifi c Northwest. We concluded with a discussion of current approaches and 

resources for developing climate change adaptation strategies, including restoring 

historical vegetation structure and composition, promoting resistance to change, 

promoting resilience to change, and facilitating anticipated responses to change.

Keywords: Climate change, Pacifi c Northwest, forests, rangelands, vulnerability.
F

Summary

Climate change is expected to profoundly alter vegetation structure and composition, terrestrial ecosystem processes, and the delivery of important ecosystem 

services over the next century. Since 1750, atmospheric carbon dioxide (CO2) 

concentrations have increased from 280 to over 390 parts per million (ppm) and are 

expected to continue rising, reaching 450 to 875 ppm by 2100. Global mean temperatures have increased by about 0.74 °C over the past century and could increase 

by another 2 to 4 °C by 2100 as a result of increasing atmospheric concentrations of 

CO2 and other greenhouse gases. Globally, mean annual precipitation is expected 

to increase somewhat, but there is great uncertainty about precipitation changes for 

any particular region, including the Pacifi c Northwest. Temperature and precipitation changes are expected to alter soil water availability by altering amounts and 

timing of precipitation, snowpack dynamics, and evapotranspiration rates.

Climatic variability and change can affect plant physiological processes, 

including altering growth and reproductive phenology, rates of photosynthesis and 

respiration, root and shoot growth, and seed production. Elevated CO2 can infl uence 

many of these same processes, either enhancing or offsetting climatic infl uences. 

Biotic interactions, population and community dynamics, and ecosystem processes 

can further modify vegetation responses to elevated CO2 and changing climate. 

Changing climate can also alter disturbance processes—including stress-related 

mortality, fi re, and insect outbreaks—and their impacts on vegetation.

Vegetation adapts to changing climate in various ways. Individual plants adjust 

to climatic changes through phenotypic plasticity via traits like growth phenology 

and biomass allocation. Populations adapt through natural selection of traits based 

on genetic variability within the population and through long-distance pollen or 

seed dispersal. Species also adapt to changing climate through migration, resulting 

in establishment of new populations in favorable habitats and the extirpation of 

populations from unfavorable habitats.

Computer simulation models offer a way to project vegetation responses to 

various scenarios of future atmospheric CO2 concentrations and climate based on 

current knowledge about species biogeography, physiological requirements, biotic 

interactions, and ecosystem processes. Unfortunately, we rarely know enough 

to fully inform model functions, and no single model can address all vegetation 

processes at all spatial scales, so judgment must be used in selecting models and 

interpreting their outputs.

The Pacifi c Northwest supports a diverse fl ora, ranging from grasslands to 

forests and from deserts to rain forests. Environmental controls differ considerably among the major biomes but mostly involve winter temperatures, snowpack 
G

duration, summer vapor pressure defi cits, and soil water defi cits. Climate can also 

infl uence vegetation structure and function indirectly through the effects of fi re 

and other climate-infl uenced disturbance agents. Climate change and elevated CO2

can increase or reduce the importance of these environmental controls, alter disturbance processes, and alter the importance of biotic interactions like facilitation 

and competition, with uncertain impacts on vegetation communities and ecosystem 

functioning. Experimental studies give insights into potential vegetation responses 

but cannot represent the range of complexity found in most ecosystems.

Modeling results suggest that subalpine forests may be the Pacifi c Northwest 

biome most vulnerable to climatic changes, but there are no projections for alpine 

meadows. Models suggest that maritime coniferous forests may be somewhat 

vulnerable to future climate change in the southern portion of the region. There 

is little model agreement about projected changes for dry coniferous forests, and 

only limited model output for the shrub-steppe biome. Models remain critical for 

alerting us to the potential magnitude of the effects of climate and, although they 

are imperfect, model projections complement projections made based on empirical studies and expert opinion. The role of models in this context is not to predict 

the future but rather to help (1) identify the more plausible future conditions, (2) 

highlight vulnerabilities with respect to specifi c resources and management objectives, (3) provide insight into the range and variability of potential climate change 

effects, (4) examine general ecological principles rather than predict the behavior of 

a specifi c ecosystem, and (5) inform strategic decisionmaking processes and policy 

development. 

Although climate change clearly could have profound effects on global and 

regional vegetation, it is less clear what those effects will be and what activities 

land managers could or should undertake to preserve values at risk and continue to 

achieve a variety of management objectives. Previous publications have endorsed 

a “toolbox approach” to managing ecosystem responses to climate change that 

features three basic adaptive strategies: (1) promote resistance to change, (2) promote resilience to change, and (3) facilitate response to change. We note that the 

“historic range of variability” concept has a limited application and that future 

conditions will strongly differ from those of the past. Applying our knowledge and 

understanding of processes, interactions, and conditions to the current biophysical, 

social, and economic environment may be the best way to manage for uncertainty 

and change.
Contents

 1 Chapter 1: Introduction

 5 Chapter 2: Observed and Projected Environmental Changes

 5 Mechanisms for Changes in Temperature and Precipitation

 10 Atmospheric CO2 Concentrations

 10 Observed Changes in Atmospheric CO2 Concentrations

 11 Projected Changes in Atmospheric CO2 Concentrations

 14 Temperature and Precipitation

 14 Observed Changes in Temperature and Precipitation

 18 Projections of Future Temperature and Precipitation

 23 Water Availability

 24 Snowfall and Snowpack Water Storage

 25 Evapotranspiration

 26 Soil Water Balance

 27 Chapter 3: Vegetation Sensitivity and Acclimation to Changing Climate 

 and CO2

 27 Physiological Processes, Plant Growth, and Ecosystem Productivity

 28 Photosynthesis and Respiration

 31 Phenology

 32 Plant Growth and Ecosystem Productivity

 35 Plant Reproduction

 36 Seed Production

 36 Dispersal (Including Long Distance)

 37 Seed Dormancy and Germination

 38 Establishment

 39 Biotic Interactions

 40 Mutualisms—Pollination and Mycorrhizal Associations

 41 Facilitation

 42 Competition

 44 Herbivory

 45 Community Responses

 45 Ecosystem Responses

 46 Productivity and Nutrient Cycling

 48 Carbon Storage

 49 Invasive Species

 51 Chapter 4: Vegetation Sensitivity to Changing Disturbance Regimes

 51 Physiological Stress—Drought
ix

 53 Fire

 55 Insects and Diseases

 58 Disturbance Interactions

 59 Chapter 5: Vegetation Adaptation to Changing Climate and CO2

 59 Phenotypic Plasticity

 62 Adaptive Value of Plasticity

 63 Adaptation Through Natural Selection

 64 Migration

 65 Velocity of Change

 65 Persistence in Refugia

 66 Invasion (Leading Edge)

 67 Extirpation (Trailing Edge)

 69 Chapter 6: Simulation Models Used to Project Vegetation Responses

 70 Statistical Species Distribution Models

 70 Introduction to Species Distribution Models

 71 How Species Distribution Models Work

 72 How Species Distribution Models Are Applied to Climate Change Issues

 75 Strengths and Weaknesses of Species Distribution Models

 78 Using Species Distribution Models

 79 Gap-Replacement Models

 79 Introduction to Gap-Replacement Models

 80 How Gap-Replacement Models Work

 81 How Gap-Replacement Models Are Applied to Climate Change Issues

 81 Strengths and Weaknesses of Gap-Replacement Models

 83 Using Gap-Replacement Models 

 84 Landscape Models

 84 Introduction to Landscape Models

 84 How Landscape Models Work

 85 How Landscape Models Are Applied to Climate Change Issues

 86 Strengths and Weaknesses of Landscape Models

 87 Using Landscape Models 

 87 Biogeochemical Models

 87 Introduction to Biogeochemical Models

 87 How Biogeochemical Models Work

 88 How Biogeochemical Models Are Applied to Climate Change Issues

 88 Strengths and Weaknesses of Biogeochemical Models

 89 Using Biogeochemical Models 

 89 Dynamic Global Vegetation Models
x

 89 Introduction to Dynamic Global Vegetation Models

 89 How Dynamic Global Vegetation Models Work

 90 How Dynamic Global Vegetation Models Are Applied to Climate 

 Change Issues

 90 Strengths and Weaknesses of Dynamic Global Vegetation Models

 91 Using Dynamic Global Vegetation Models 

 91 Growth and Yield Models

 91 Introduction to Growth and Yield Models

 92 How Growth and Yield Models Work

 92 How Growth and Yield Models Are Applied to Climate Change Issues

 93 Strengths and Weaknesses of Growth and Yield Models

 93 Using Growth and Yield Models

 95 Chapter 7: Using Model Projections

 96 Ensemble Approaches

 97 Using Model Projections Wisely

 99 Chapter 8: Climate Change and Major PNW Biomes

 99 Synthesis of Model Projections for the Pacifi c Northwest

103 Subalpine Forests and Alpine Meadows

103 General Description

104 Environmental Controls

106 Biotic Interactions

106 Disturbances

107 Responses to Elevated CO2 and Climate Change

108 Maritime Coniferous Forests

108 General Description

109 Environmental Controls

110 Biotic Interactions

111 Disturbances

111 Responses to Elevated CO2 and Climate Change

113 Dry Coniferous Forests

113 General Description

114 Environmental Controls

114 Biotic Interactions

114 Disturbances

115 Responses to Elevated CO2 and Climate Change

117 Savannas and Woodlands

117 General Description

118 Environmental Controls
xi

118 Biotic Interactions

118 Disturbances

120 Responses to Elevated CO2 and Climate Change

121 Shrub-Steppe

121 General Description

122 Environmental Controls

122 Biotic Interactions

123 Disturbances

124 Responses to Elevated CO2 and Climate Change

127 Chapter 9: Conclusions

131 Acknowledgments

131 English Equivalents

131 References
xii

GENERAL TECHNICAL REPORT PNW-GTR-900
1

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 1: Introduction

Climate change is expected to profoundly alter vegetation structure and composition, terrestrial ecosystem processes, and the delivery of important ecosystem 

services over the next century. Climate infl uences the spatial distribution of major 

vegetation biomes, the abundance of species and communities within biomes, 

biotic interactions, and the geographic ranges of individual species. Climate infl uences the rates at which terrestrial ecosystems process water, carbon, and nutrients 

and deliver ecosystem services like fresh water, food, and biomass. Climate also 

infl uences the disturbance processes that shape vegetation structure and composition and are often the catalysts for vegetation change. Climate-induced vegetation 

changes have important implications for wildlife habitat, biodiversity, hydrology, 

future disturbance regimes, ecosystem services, and the ability of ecosystems to 

absorb and sequester carbon from the atmosphere. 

Land managers are currently seeking scientifi c information about the potential 

effects of climate change on terrestrial vegetation and practical approaches to 

managing forests and rangelands to sustain key ecosystem functions, ecosystem 

services, and critical habitats, despite considerable uncertainty about the nature and 

magnitude of future climatic change. Scientists have learned much about historical rates and patterns of vegetation change during glacial and interglacial epochs; 

forest growth responses to past climatic variability; plant physiological responses to 

higher temperature and carbon dioxide levels; and how phenotypic plasticity, local 

adaptation, and migration can help individual plants, plant populations, and species 

adapt to changing environments. However, this information has not been adequately 

reviewed, synthesized, and compiled into a form that managers can use to establish 

short- and long-term management objectives and adopt appropriate management 

strategies. Research-management partnerships are needed to develop science-based 

management approaches, fi ll knowledge gaps in critical areas, and resolve apparent 

confl icts in the scientifi c record.

Federal land management agencies recognize the potential for climate change to 

disrupt terrestrial ecosystems and alter delivery of vital ecosystem services and are 

working to develop appropriate and effective approaches for facilitating ecosystem 

adaptations to climate change. For example, the 2010–2015 U.S. Department of 

Agriculture Strategic Plan established a strategic goal to “ensure our national 

forests and private working lands are conserved, restored, and made more resilient 

to climate change, while enhancing our water resources,” and set the “restoration of 

watershed and forest health as a core management objective of the National Forests 

and Grasslands” (USDA 2010). Building upon this and the Strategic Framework, 

the Forest Service then developed a National Roadmap for Responding to Climate 

Change (USDA FS 2010) that called for the Forest Service to respond to projected 

Climate-induced 

vegetation changes 

have important 

implications for wildlife 

habitat, biodiversity, 

hydrology, future 

disturbance regimes, 

ecosystem services, 

and the ability of 

ecosystems to absorb 

and sequester carbon 

from the atmosphere.
2

GENERAL TECHNICAL REPORT PNW-GTR-900

climatic changes by (1) assessing risks and vulnerability for ecosystems, (2) identifying knowledge gaps and policy shortcomings that limit our ability to respond 

appropriately, (3) establishing and supporting monitoring programs to detect 

ecosystem responses to climate change and assess the effectiveness of management 

activities, (4) educating the public and federal land managers about climate change 

science and its implications for natural resources, and (5) encouraging the development and use of research-management partnerships to address climate change 

issues. The U.S. Department of the Interior (USDI) developed a comparable climatechange-response strategy in 2009 (USDI 2009) and has created two national 

networks to provide an integrated approach to climate change science and adaptation. Eight regional USDI Climate Science Centers provide scientifi c information, 

tools, and techniques that land managers can apply to anticipate, monitor, and adapt 

to climatic changes at regional to local scales. The USDI Landscape Conservation 

Cooperatives represent a network of public-private partnerships that seek to address 

broad-scale conservation and adaptation issues (USDI 2009).

This study reviews scientifi c knowledge and model projections regarding 

vegetation vulnerability to climatic and other environmental changes in the Pacifi c 

Northwest, with emphasis on fi ve major biome types: subalpine forests and alpine 

meadows, maritime coniferous forests, dry coniferous forests, juniper savannas and 

woodlands, and interior shrub-steppe. We adopt the approach that vegetation vulnerability to environmental changes depends on three factors: exposure, sensitivity, 

and adaptability (Glick et al. 2011, IPCC 2007c). 

• Exposure depends on the type, magnitude, rate, and variability of observed 

 (or projected) changes in climate; climatic interactions with site factors like 

 topographic position and vegetation structure that alter plant biophysical 

 environments; and climate-induced changes in disturbance regimes. 

• Sensitivity refers to the degree to which the environmental changes alter 

 plant growth, reproduction, and mortality; plant population dynamics; biotic 

 interactions; plant community dynamics, and ecosystem functioning. 

• Species adaptability refers to physiological, evolutionary, and biogeo-

 graphical changes that can occur at the individual, population, and species 

 levels to mitigate negative impacts of environmental changes or allow a 

 species to better take advantage of environmental changes.

This report reviews knowledge about observed and projected environmental 

changes and how vegetation might respond, providing a foundation for the development of vulnerability assessments related to vegetation. We draw on experimental, 

observational, and modeling studies to summarize current scientifi c knowledge 

Vegetation vulnerability 

to environmental 

changes depends on 

three factors: exposure, 

sensitivity, and 

adaptability.
3

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

about vegetation responses to climate change in a broad sense, and specifi cally in 

the context of major vegetation types in the Pacifi c Northwest. Material in this 

document overlaps somewhat with other recent reviews of potential vegetation 

responses to climate change in the Pacifi c Northwest (Aubry et al. 2011, Bachelet 

et al. 2011, Chmura et al. 2011, Halofsky et al. 2011, Littell et al. 2010) but is 

intended to be more general and cover a wider range of vegetation types. We 

provide minimal guidance on developing adaptation plans or other management 

strategies related to climate change, as these topics are well covered in other recent 

publications (e.g., Glick et al. 2011, Halofsky et al. 2011, Littell et al. 2012, Peterson 

et al. 2011, Stephens et al. 2010, Swanston and Janowiak 2012).
4

GENERAL TECHNICAL REPORT PNW-GTR-900
5

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 2: Observed and Projected 

Environmental Changes

Earth’s atmosphere and climate system are changing. Long-term climate records 

show that global mean temperatures have increased over the past century, and scientists are now convinced that this warming is partly due to human activities (IPCC 

2007a). Atmospheric concentrations of greenhouse gases have risen signifi cantly 

over the past century or more and will continue to rise over the next century, likely 

causing further increases in mean global temperatures (IPCC 2007a). Climatic 

changes have, in turn, altered the seasonal timing and magnitude of snowpack 

accumulation and melt, stream peak and base fl ows, soil water recharge, evapotranspiration, and soil water defi cits (IPCC 2007b). Changing climatic conditions 

may also be driving changes in disturbance processes such as wildfi res and insect 

outbreaks. All of these environmental changes have the potential to affect Pacifi c 

Northwest vegetation, although the relative exposure of vegetation to these different 

environmental changes may vary through time and among biomes.

In this chapter, we describe historical observations and model projections 

of environmental changes at the global and regional (Pacifi c Northwest) scales. 

Environmental changes of interest include atmospheric carbon dioxide (CO2) 

concentrations, mean temperature and precipitation, snowpack hydrology, and soil 

water dynamics. Some observations of environmental changes are limited to the 

past century or so (the period of instrumental records), while others extend back in 

time for millennia or longer (e.g., CO2 concentrations in air bubbles from ice cores). 

Mechanisms for Changes in Temperature 

and Precipitation

Historically, climatic changes have been driven primarily by changes in the Earth’s 

radiation balance. This radiation balance is a function of the amount of incoming 

solar radiation (insolation), the fraction of incoming solar radiation that is refl ected, 

and the amount of longwave radiation (heat) that is radiated back to space (fi g. 2.1). 

Each of these elements has changed over time, either directly as a result of physical 

processes (e.g., variations in the Earth’s orbit) or indirectly through feedbacks in the 

system (e.g., refl ectance of incoming solar radiation by surface snow and ice). 

Evidence strongly suggests that Ice Age cycles are driven, in large part, by 

variations in Earth’s orbital characteristics (often called Milankovich cycles) that 

alter the tilt of the Earth on its rotational axis and the eccentricity of the Earth’s 

orbit (fi g. 2.2). The tilt angle infl uences the seasonality of incoming solar radiation 

and the amount of summer solar radiation reaching the higher latitudes. Eccentricity in the Earth’s orbit controls variation in the distance of the Earth from the 

sun throughout the year, and the season in which the Earth is closest to the sun. 

Historically, climatic 

changes have been 

driven primarily by 

changes in the Earth’s 

radiation balance.
6

GENERAL TECHNICAL REPORT PNW-GTR-900

Figure 2.2—Schematic of the Earth’s orbital changes (Milankovitch cycles) that drive ice age 

cycles. “T” denotes change in the tilt (or obliquity) of the Earth’s axis, “E” denotes changes in 

the eccentricity of the orbit (owing to variations in the minor axis of the ellipse), and “P” denotes 

precession, that is, changes in the direction of the axis tilt at a given point in the orbit. (Source: 

Rahmstorf and Schellnhuber 2006. From Jansen et al. 2007, FAQ 6.1, fi g. 1.)

Figure 2.1—Estimate of the Earth’s annual and global mean energy balance. Over the long term, 

the amount of incoming solar radiation absorbed by the Earth and its atmosphere is balanced by 

the Earth and atmosphere releasing the same amount of outgoing longwave radiation. About half 

of the incoming solar radiation is absorbed by the Earth’s surface. This energy is transferred to the 

atmosphere by warming the air in contact with the surface (thermals), by evapotranspiration, and by 

longwave radiation that is absorbed by clouds and greenhouse gases. The atmosphere in turn radiates 

longwave energy back to Earth as well as out to space. (Source: Kiehl and Trenberth 1997. From 

LeTreut et al. 2007, FAQ 1.1, fi g. 1.)
7

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Although these variations in the Earth’s orbit have little effect on total incoming 

solar radiation, they do infl uence the amount of solar radiation reaching different 

latitudes in different seasons. These seasonal variations in incoming solar radiation 

help drive glacial and interglacial cycles by interacting with seasonal variations in 

the amount of incoming radiation that is refl ected by the atmosphere (e.g., cloud 

cover) and land surface (e.g., snow cover), and by strengthening or weakening 

feedback between climate and the radiative properties of the Earth. Milankovitch 

cycles function over time periods of 23,000 to 100,000 years (Hays et al. 1976) but 

are not expected to trigger global cooling and another Ice Age for about 30,000 

years (Jansen et al. 2007).

There are additional factors that infl uence incoming solar radiation and operate 

on much shorter time scales. The best known is the 11-year solar cycle associated 

with sunspot activity, which creates small, systematic variations in incoming solar 

radiation (Gray et al. 2010). None of these factors produce large effects (the solar 

cycle produces changes of 0.1 percent), but they may still infl uence the climate 

system. 

Incoming solar radiation is reduced by the amount of radiation that is refl ected 

back into space, either by the Earth’s atmosphere or the land surface (fi g. 2.1). Cloud 

cover reduces net incoming radiation as water droplets refl ect incoming radiation. 

Because cloud cover frequency and extent varies seasonally and between oceans 

and land masses, variability in incoming solar radiation is believed to be most 

important for summer months in the Northern Hemisphere, when cloud cover is 

relatively low and where large land masses can absorb the incoming radiation. 

Volcanic activity is another major source of aerosols (small suspended particles 

in the atmosphere) that can refl ect incoming radiation and reduce net absorbed 

radiation at the Earth’s surface. A single volcanic eruption can alter global climate, 

but only for a short time as the residence time of ash particles in the atmosphere 

is limited to one to several years. However, periods of elevated volcanic activity 

(multiple events) can reduce global mean temperatures signifi cantly, as appears to 

have happened in the middle of the 19th century.

Surface albedo infl uences the fraction of net incoming solar radiation that is 

absorbed at the Earth’s surface (fi g. 2.1). Ice, snow, and concrete refl ect large fractions of incoming solar radiation, while green vegetation, wet soil, and water bodies 

absorb most of incoming solar radiation. Therefore, incoming solar radiation during 

summer in the Northern Hemisphere is most likely to contribute to global warming 

(or cooling) because the large land masses with large areas of green vegetation and 

little snow or ice tend to absorb a large fraction of incoming solar radiation.
8

GENERAL TECHNICAL REPORT PNW-GTR-900

Land surface albedo changes seasonally with vegetation and snow cover and 

can change over time owing to human land use changes and natural disturbances. 

Climate can also produce long-term changes in surface albedo via feedback mechanisms. A cooling climate allows snow cover to persist longer and allows glaciers to 

expand, thereby increasing refl ectance and reducing absorption of incoming solar 

radiation, which can lead to further cooling of the climate. Similarly, a warming 

climate can reduce the duration of snow cover and shrink glaciers while potentially 

increasing plant cover, thereby increasing absorption of solar radiation and promoting further warming. 

Transmission of longwave (heat) radiation from the Earth back into space is 

the third major factor infl uencing the Earth’s radiation balance and global mean 

temperatures (fi g. 2.1). Incoming solar radiation that is absorbed at the Earth’s 

surface heats the Earth’s surface and that heat is radiated back to the atmosphere. 

Some longwave radiation is refl ected back toward the Earth’s surface by atmospheric “greenhouse” gases, which act as a blanket of sorts, warming the lower 

atmosphere and allowing it to support a wide variety of biological organisms. This 

process forms the basis of the “greenhouse effect” and the atmospheric gases that 

contribute most to retention of long-wave radiation are called “greenhouse gases” 

(fi g. 2.3). Water vapor is by far the most common greenhouse gas (about 80 percent 

Figure 2.3—An idealized model of the natural greenhouse effect. (From Le Treut et al. 2007, 

FAQ 1.3, fi g. 1.)

Some longwave 

radiation is refl ected 

back toward the 

Earth’s surface 

by atmospheric 

“greenhouse” gases, 

which act as a blanket 

of sorts, warming the 

lower atmosphere and 

allowing it to support 

a wide variety of 

biological organisms. 
9

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

of greenhouse gases by mass, 90 percent by volume) and produces 66 to 85 percent 

of greenhouse warming. Effects of water vapor on air temperatures become clear 

if one compares summer day-night temperature differences in humid versus nonhumid climates. High humidity traps heat, reducing night-time cooling (e.g., a hot, 

muggy night).

In addition to water vapor, major natural greenhouse gases include CO2, nitrous 

oxide (N2O), and methane (CH4). These gases also produce a warming of the lower 

atmosphere but differ from water vapor in two important ways. First, they persist in 

the atmosphere much longer, on average, than water vapor. The mean atmospheric 

residence time for a molecule of water is estimated at about 10 days, while mean 

residence times are estimated at 12 years for CH4, 100 years for CO2, and over 

100 years for N2O. Second, humans have directly and indirectly contributed to 

increased atmospheric concentrations of CO2, N2O, and CH4 (fi g. 2.4).

Models strongly suggest that increasing concentrations of greenhouse gases 

in the atmosphere are producing (“forcing”) much of the warming that has been 

observed in the past century (IPCC 2007b). Increasing concentrations of these 

greenhouse gases may produce additional warming by increasing evaporation 

Figure 2.4—Atmospheric concentrations of important long-lived greenhouse gases over the past 

2,000 years. Increases since about 1750 are attributed to human activities in the industrial era. 

Concentration units are parts per million (ppm) or parts per billion (ppb), indicating the number 

of molecules of the greenhouse gas per million or billion air molecules, respectively, in an atmospheric sample. (From Forster et al. 2007, FAQ 2.1, fi g. 1.)

Models strongly 

suggest that increasing 

concentrations of 

greenhouse gases in 

the atmosphere are 

producing (“forcing”) 

much of the warming 

that has been observed 

in the past century.
10

GENERAL TECHNICAL REPORT PNW-GTR-900

of water and atmospheric water vapor concentrations. In addition, atmospheric 

warming forced by increasing concentrations of greenhouse gases may lead to 

further increases in greenhouse gases owing to release of CO2 from warming 

oceans and release of CO2 and CH4 from boreal soils following the melting of 

permafrost layers.

Atmospheric CO2 Concentrations

Observed Changes in Atmospheric CO2 Concentrations

Atmospheric CO2 concentrations have been steadily increasing since the start of 

modern industrialization, from about 280 parts per million (ppm) in 1750 (as measured from air bubbles in dated ice cores) to about 394 ppm in 2012 (Hofmann et al. 

2009, Keeling et al. 1976, Tans and Keeling 2013) (fi g. 2.4). Much of this increase 

(about 68 percent) has occurred in the last 50 years, with atmospheric CO2 concentrations rising by about 79 ppm (315 to 394 ppm) since 1960 (fi g. 2.5). Hofmann et 

al. (2009) analyzed atmospheric CO2 data from Mauna Loa in Hawaii and noted 

that (1) CO2 concentrations have been increasing exponentially since measurements 

began in 1958, (2) the doubling period for both anthropogenic CO2 concentrations 

and the rate of change in CO2 concentrations is about 31 years, and (3) changes in 

atmospheric CO2 concentrations have closely tracked human population growth 

over the past 50 years. 

Figure 2.5—Observed changes in atmospheric carbon dioxide (CO2) concentration 

(ppm [parts per million]), 1958–2012, at Mauna Loa Observatory in Hawaii. (Data from 

Tans and Keeling 2013).

Atmospheric CO2

concentrations 

have been steadily 

increasing since 

the start of modern 

industrialization.
11

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Over geological time scales, atmospheric CO2 concentrations have fl uctuated 

considerably between glacial and interglacial periods. Atmospheric CO2 concentrations have generally been low during cold glacial periods (Ice Ages), with values 

as low as 180 ppm being observed in ice core records from the past 650,000 years 

(Jansen et al. 2007). Atmospheric CO2 concentrations were higher during warm, 

interglacial periods. Although CO2 concentrations in the ice core records were limited to 300 ppm prior to the increases of the past century, proxy evidence suggests 

that CO2 concentrations exceeded current levels for extended periods over the past 

400 million years, particularly during warm periods of the Pliocene (3 to 5 million 

years ago) and the Paleocene-Eocene Thermal Maximum (about 55 million years 

ago) (Jansen et al. 2007). Historical records also indicate that, in the past, changes 

in global mean temperatures have preceded changes in atmospheric CO2 concentrations, suggesting that high CO2 concentrations were the result of, rather than the 

cause of, warming temperatures in the past (Jansen et al. 2007).

Projected Changes in Atmospheric CO2 Concentrations

Projecting future anthropogenic climatic changes fi rst requires projections or 

scenarios about future emissions and atmospheric concentrations of greenhouse 

gases. Projections of future greenhouse gas emissions require many assumptions 

about the future course of human events—including human population growth, 

economic development, technological innovation, energy systems, and political 

cooperation—that are often packaged as scenarios or “stories” (Nakićenović 2000). 

These scenarios not only consider conditions at some point in the distant future, but 

also intermediate (transient) conditions. Many scenarios have been developed by 

a variety of research groups, but the Intergovernmental Panel on Climate Change 

(IPCC) has organized the scenarios into four groups, in which scenarios share a 

common storyline (Nakićenović 2000).

The IPCC generated four storylines of the future, which included potential 

future variability in major socioeconomic factors that would infl uence greenhouse 

gas emissions (Nakićenović 2000, Nakićenović et al. 2000). The A1 storyline 

describes a future world with very rapid economic growth, low population growth, 

and rapid technological advancement, with convergence among regions, increased 

cultural and social interactions, and a substantial reduction in regional differences 

in per capita income. The A1 scenario family subdivides into four scenario groups 

that describe alternative directions of technological change in the energy system. 

The A2 storyline describes a very heterogeneous world in which self-reliance 

and preservation of local identities is a key theme, population growth is high, 

economic development is regionally oriented, and per capita economic growth and 

technological change are more fragmented and slower than in other storylines. 

In the past, changes 

in global mean 

temperatures have 

preceded changes 

in atmospheric CO2

concentrations.

High CO2

concentrations were 

the result of, rather 

than the cause of, 

warming temperatures.
12

GENERAL TECHNICAL REPORT PNW-GTR-900

The B1 storyline describes a convergent world with the same low population 

growth as in the A1 storyline, but with rapid economic changes toward a service 

and information economy, with reductions in material intensity, and the introduction 

of clean and resource-effi cient technologies. The emphasis is on global solutions 

to economic, social, and environmental sustainability, including improved equity. 

The B2 storyline describes a world of moderate population growth, intermediate economic development, and slower technological change (relative to A1 and B1), 

in which the emphasis is on local solutions to economic, social, and environmental 

sustainability. 

For each storyline of the future, research groups developed (modeled) specifi c 

future emissions scenarios, which include a quantitative interpretation of the future 

storyline and the resulting greenhouse gas emissions. A set of 40 future emissions 

scenarios were developed for the IPCC fourth assessment, and more have been 

developed in recent years. Taken as a group, these scenarios encompass a wide 

range of possible futures, each one representing a pertinent, plausible, alternative 

future. Unfortunately, there is no way to know which one will be the most accurate, 

and even assigning probabilities to scenarios would be a highly subjective process. 

Thus, in evaluating future risks and developing adaptation strategies, it is important 

to consider the whole range of possible emissions outcomes and the associated 

ranges of climatic changes and ecosystem impacts (Beaumont et al. 2008).

Biogeochemical models are used to convert projected emissions from the 

scenarios described above into the atmospheric greenhouse gas concentration used 

in climate models. Biogeochemical models simulate the uptake and release of CO2

through photosynthesis and respiration, and the storage of carbon in plant biomass, 

soils, and ocean sediments. As with any model, biogeochemical models generate 

additional sources of potential error and uncertainty owing to errors or biases in 

input datasets, imperfect understanding of biogeochemical processes, and potential 

errors in model representation of those processes (Beaumont et al. 2008). 

Scenarios developed for the IPCC Special Report on Emission Scenarios 

(SRES) (Nakićenović et al. 2000) all project increasing greenhouse gas emissions 

between 2000 and 2030, with individual scenarios projecting increases of 25 to 90 

percent over baseline (IPCC 2007a). Carbon dioxide emissions from energy use 

(mostly fossil fuels) are expected to increase by 40 to 110 percent over that period. 

Extending scenario projections to 2100 produces a wide range of possible changes 

in greenhouse gas emissions, from slight reductions to tenfold increases, relative to 

1990 baseline emissions, with corresponding variability in resulting atmospheric 

greenhouse gas concentrations and radiative forcing (fi g. 2.6). The lowest emission 

SRES scenario produces global mean CO2 concentrations of 450 ppm by 2100, 

Commonly used SRES 

emission scenarios 

project increases 

of 25 to 90 percent 

in greenhouse gas 

emissions between 

2000 and 2030.
13

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

while scenarios with higher emissions produce mean CO2 concentrations of 875 

ppm (or more) by 2100 (Clarke et al. 2007). 

Although the SRES (Nakićenović et al. 2000) states that the emission scenarios 

are pertinent, plausible, alternative futures that are equally possible, this claim has 

been disputed. Moreover, rates of increase in CO2 emissions have recently exceeded 

those in the highest of the SRES scenarios (Raupach et al. 2007). Because of 

this, Beaumont et al. (2008) suggested that the use of more conservative emission 

scenarios (B1 and B2) be replaced by the more extreme A1 and A2 scenarios. The 

A1FI scenario is even more extreme in terms of emissions but has not been widely 

used for making future climate projections. Scenarios developed since the SRES 

have projected an even wider range (higher and lower) of estimates of future emissions and atmospheric CO2 concentrations (IPCC 2007b) (fi g. 2.7).

The SRES scenarios are limited in some ways, which has led to further scenario development and in changes to the scenario development process. The SRES 

scenarios assume no effective mitigation policies for greenhouse gas emissions over 

the next century, so they may be biased toward higher emissions. Several new 

“intervention” scenarios have now been developed that include future mitigation 

efforts and generally produce lower future greenhouse gas concentrations and 

associated climatic effects. More recently, the IPCC developed a new parallel 

Figure 2.6—Comparison of the Special Report on Emission Scenarios (SRES) and 

pre-SRES energy-related and industrial carbon dioxide (CO2) emissions scenarios in 

the literature with the post-SRES scenarios. (Source: After Nakićenović et al. 2006. 

From Fisher et al. 2007, fi g. 3.8.)

Note: The two vertical bars on the right extend from the minimum to maximum of the 

distribution of scenarios and indicate the 5th, 25th, 50th, 75th, and the 95th percentiles 

of the distributions by 2100. 

Rates of increase in 

CO2 emissions have 

recently exceeded 

those in the highest of 

the SRES scenarios. 
14

GENERAL TECHNICAL REPORT PNW-GTR-900

process for scenario development that starts with four scenarios of future changes 

in atmospheric forcing levels. The process allows climate modelers to assess a range 

of potential future climate scenarios for each forcing scenario at the same time as 

researchers explore suites of future social, economic, and policy scenarios under 

which each of the forcing scenarios could occur (Moss et al. 2010).

Temperature and Precipitation

Observed Changes in Temperature and Precipitation

Global mean temperatures have been gradually increasing over the past 150 years, 

the period for which we have fairly reliable measurements with global coverage (fi g. 

2.8). Global mean temperatures have increased by about 0.65 °C (± 0.2 °C) over 

the past century (1900 to 2005), with much of the warming occurring in the last 50 

years (Trenberth et al. 2007) (fi gs. 2.8 and 2.9). Warming has been greater in the 

Northern than in the Southern Hemisphere, greater at high latitudes than near the 

equator, and greater over large land masses than over the oceans (Trenberth et al. 

Figure 2.7—Global greenhouse gas (GHG) emissions (in gigatons of carbon dioxide 

(GtCO2)-eq per year) in the absence of additional climate policies: six illustrative 

SRES marker scenarios (coloured lines) and 80th percentile range of recent scenarios 

published since SRES (post-SRES) (gray shaded area). Dashed lines show the full 

range of post-SRES scenarios. The emissions include carbon dioxide, methane, 

nitrous oxide and fl uorinated or F-gases. (From IPCC 2007a, fi g. 3.1.)

Global mean 

temperatures have 

been gradually 

increasing over the 

past 150 years.
15

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

2007) (fi g. 2.8). Nighttime low temperatures have increased more than daytime high 

temperatures, thereby reducing diurnal temperature fl uctuations somewhat 

(Trenberth et al. 2007).

Comparing current global mean temperatures and trends with historical 

climates prior to 150 years ago is diffi cult owing to a lack of global coverage by 

instrumental records and uncertainty in reconstructions from proxy data, such as 

tree rings. However, current global temperatures are probably warmer than any 

previous period in the last 2,000 years, being slightly warmer than the so-called 

Figure 2.8—Global and hemispheric annual combined land-surface air temperature and 

sea surface temperature (SST) anomalies (°C) (red) for 1850 to 2006 relative to the 1961 to 

1990 mean, along with 5 to 95 percent error bar ranges, from HadCRUT3 (adapted from 

Brohan et al. [2006]). The smooth blue curves show decadal variations (see app. 3.A). 

From Trenberth et al. 2007, fi g. 3.6.)
16

GENERAL TECHNICAL REPORT PNW-GTR-900

Figure 2.9—(Top) Annual global mean observed temperatures (black dots) along with simple fi ts to 

the data. The left hand axis shows anomalies relative to the 1961 to 1990 average, and the right hand 

axis shows the estimated actual temperature (°C). Linear trend fi ts to the last 25 (yellow), 50 (orange), 

100 (purple), and 150 (red) years are shown, and correspond to 1981 to 2005, 1956 to 2005, 1906 to 

2005, and 1856 to 2005, respectively. Note that for shorter recent periods, the slope is greater, indicating accelerated warming. The blue curve is a smoothed depiction to capture the decadal variations. To 

give an idea of whether the fl uctuations are meaningful, decadal 5 percent to 95 percent (light grey) 

error ranges about that line are given (accordingly, annual values do exceed those limits). Results 

from climate models driven by estimated radiative forcings for the 20th century suggest that there was 

little change prior to about 1915, and that a substantial fraction of the early 20th-century change was 

contributed by naturally occurring infl uences including solar radiation changes, volcanism, and natural 

variability. From about 1940 to 1970, the increasing industrialization following World War II increased 

pollution in the Northern Hemisphere, contributing to cooling, and increases in carbon dioxide (CO2)

and other greenhouse gases dominate the observed warming after the mid-1970s. (Bottom) Patterns 

of linear global temperature trends from 1979 to 2005 estimated at the surface (left), and for the 

troposphere (right) from the surface to about 10 km altitude, from satellite records. Grey areas indicate 

incomplete data. Note the more spatially uniform warming in the satellite tropospheric record, whereas 

the surface temperature changes more clearly relate to land and ocean. (From Trenberth et al. 2007, 

FAQ 3.1, fi g. 1.)
17

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

“Medieval Warm Period” (950–1200 CE) (Jansen et al. 2007). Global temperatures 

have probably been higher than today in the distant past, however, most recently in 

the last interglacial period, roughly 116,000 to 130,000 years ago. 

Climate change is not always a global phenomenon, however, and there are 

several examples of persistent temperature changes at regional to continental scales 

that have persisted long enough to produce vegetation responses. Analysis of pollen 

records suggests that temperatures in northern Europe and parts of North America 

were warmer than today between 5,000 and 7,000 years ago. Parts of Europe may 

have also been warmer than today during the Medieval Warm Period, based on 

reconstructions from tree rings and other climate proxies (Jansen et al. 2007). 

Climate reconstructions for the Pacifi c Northwest indicate that climate has 

varied considerably over the past 20,000 years, altering both the mean and seasonal 

variability in temperature and precipitation. Whitlock (1992) reviewed the vegetation and climatic history of the Pacifi c Northwest based on studies of fossil pollen 

and charcoal from different regions and climatic zones. She found evidence for both 

warmer and cooler temperatures and higher and lower levels of summer seasonal 

water defi cits, compared to modern climate. Summers appear to have been warmer 

and drier for extended periods between 5,000 and 10,000 years ago, although winter 

temperatures may have been colder than at present. Likewise, Pacifi c Northwest 

climate appears to have been cooler and wetter than at present during a period 

roughly 2,500 to 3,500 years ago. The modern regional climate (and associated 

vegetation) developed only recently, within the past 2,500 years (Whitlock 1992). 

During the 20th century, temperatures in the Pacifi c Northwest have risen by 

an average of 0.7 to 0.9 °C, but rates of warming have varied spatially and among 

seasons (Mote 2003). Warming rates have been highest in winter, at low elevations, 

and in the maritime climate zones, and lowest in autumn, at high elevations, and 

in the interior climate zones (Mote 2003). Similarly, signifi cant warming of winter 

temperatures was observed throughout the mountainous regions of the Western 

United States during the period 1950–1999 in excess of that which could be caused 

by cyclical variations in climate associated with the El Niño-Southern Oscillation 

and Pacifi c Decadal Oscillation (Bonfi ls et al. 2008).

Global annual precipitation over land has varied over the past century, but that 

variability displays no signifi cant long-term linear trends. Rather, global annual 

precipitation over land has been cyclical with precipitation increasing and decreasing at decadal time scales. For example, precipitation was above average during 

much of the period 1950–1979 and below average for much of the period 1980–1995 

(Trenberth et al. 2007). Although there is no signifi cant linear trend in annual 

precipitation at the global scale, many regions experienced signifi cant increases 

During the 20th century, 

temperatures in the 

Pacifi c Northwest have 

risen by an average of 

0.7 to 0.9 °C.
18

GENERAL TECHNICAL REPORT PNW-GTR-900

and decreases in annual precipitation over the period 1900–2005 (Trenberth et al. 

2007). There is a positive trend in precipitation for much of North America during 

this period, including the Pacifi c Northwest, but excluding the desert southwest 

(Trenberth et al. 2007).

In the Pacifi c Northwest, precipitation increases during the 20th century have 

averaged 13 to 38 percent, exceeding the global averages (Mote 2003). As with 

temperature, however, observed increases in precipitation have been spatially and 

seasonally variable. Proportional increases in precipitation were highest for spring 

and early summer and for dry areas of eastern Washington and northeastern Oregon 

(Mote 2003). Proportional increases in precipitation were smaller, though absolute 

increases in precipitation were generally highest, in the maritime region with the 

highest mean annual precipitation (Mote 2003).

Projections of Future Temperature and Precipitation

Climate models—

Climate models are the primary tools available for projecting future climatic 

changes in response to varying emissions scenarios. Climate models are mathematical representations of the Earth’s climate system that are based on equations of 

physical laws describing the Earth’s radiation budget and ocean and atmospheric 

dynamics (Beaumont et al. 2008, Randall et al. 2007). Climate is infl uenced by a 

Figure 2.10—Schematic view of the components of the climate system, their processes and interactions. (From Le Treut et al. 2007, FAQ 1.2, fi g. 1.) N2 = nitrogen gas, O2 = oxygen, Ar = argon, H2O = 

water, CO2 = carbon dioxide, CH4 = methane, N2O = nitrous oxide, O3 = ozone.

In the Pacifi c Northwest, 

precipitation increases 

during the 20th century 

have averaged 13 to 38 

percent.
19

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

wide range of factors (fi g. 2.10); consequently, climate models vary in complexity 

as model developers incorporate different numbers and types of processes into 

the models, depending on data availability, available computing power, and model 

application objectives. Climate models used for climate change projections range 

from complex atmosphere-ocean general circulation models—which explicitly represent a wide range of interacting entities and processes that infl uence climate (e.g., 

atmosphere, oceans, land surfaces, sea ice, and seasonal snow cover)—to simple 

climate models that are more typically used in weather forecasting (Meehl 

et al. 2007). 

Individual projections of future climate can be highly variable owing to differences in future emission scenarios, climate model structure and parameterization 

(Lynn et al. 2009), and data used. Currently, climate projections can be derived 

from over 20 available global climate models (GCMs). Global climate models 

differ in the physical processes represented, interactions among physical processes, 

initialization procedures, and assumptions about climate system sensitivity to 

changes in CO2 concentration. They can therefore produce different climate projections when given the same emission scenario and initial conditions. Similarly, 

climate projections produced by a single GCM can differ in response to varying 

emission scenarios and initial conditions. This combined variability owing to model 

selection, emissions scenarios, and initial conditions provides a measure of the 

uncertainty in climate projections. Beaumont et al. (2008) asserted that end users 

of climate model projections (and associated impact model projections) should be 

aware of how well the climate models simulate components of the climate that are 

applicable to the questions being asked at the scale of interest. 

When communicating future climate projections, the IPCC and others often 

present mean projected changes for each of a small group of emission scenarios. 

Emission scenarios are typically chosen to represent a wide range of future emissions, often using high, low, and intermediate CO2 conditions (fi g. 2.7). Each of 

these representative CO2 scenarios is given as input to a suite of climate models, 

and the resulting climate projections are averaged together to produce a mean 

projected response for the scenario. This multimodel ensemble approach has been 

useful in that it produces a small number of future climate scenarios that represent 

much of the range of possible future conditions. These scenarios are easily communicated to the public and can be used to develop ensembles of potential climate 

and ecosystem responses. Unfortunately, the multimodel ensemble approach also 

obscures much of the model-based variability and uncertainty in future climate 

projections and focuses attention on mean projected conditions rather than the 

extremes. Some have also begun to question the current convention of giving equal 
20

GENERAL TECHNICAL REPORT PNW-GTR-900

weight to each model (“one model, one vote”) in developing multimodel ensembles. 

Such a convention ignores potential differences in model skill; gives extra weight 

to projections from sibling models (whose projections may be similar because they 

share common assumptions and subroutines); and may obscure important spatial or 

temporal variability in model projections, including extreme behavior (Knutti 2010, 

Knutti et al. 2010).

Often the spatial scale of climate and other projections desired for management purposes is much fi ner than the scale at which most projections are available. 

Climate models produce projections at a spatial resolution (grain size) of 100 to 

300 km, while regional studies require a resolution of 10 to 50 km or fi ner (Salathé 

et al. 2007). Spatial downscaling methods are used to translate the climate projections from the coarse spatial resolution produced by GCMs to the fi ner resolutions 

deemed more useful for assessing impacts. Two methods are commonly used—

statistical downscaling and regional climate models—each of which has its 

strengths and weaknesses (Salathé et al. 2007). 

Climate model projections are currently being downscaled to relatively fi ne 

spatial scales (e.g., 800-m2

 grain size) (Rogers 2011), but the accuracy and benefi ts 

of such downscaling are not entirely clear. The creation of fi ne-scaled projections 

does not necessarily mean that the projections are more accurate or better represent 

underlying mechanisms (Littell et al. 2011, Maslin and Austin 2012). Weather 

station data are sparse throughout much of the world, limiting the degree to which 

spatial interpolation methods can capture climate anomalies at fi ner spatial scales. 

Indeed, downscaling can overresolve the information in both the observational 

record and global models and create a false sense of confi dence. There can also be 

signifi cant topographic infl uences on local climate that may require analysis at an 

even fi ner spatial scale (e.g., 100 to 200 m2

 grain size) to describe adequately (Daly 

et al. 2010). Similarly, GCMs typically produce climate projections at a monthly 

temporal resolution, while ecosystem process models typically require weather 

inputs at a daily (or even hourly) time step, thereby requiring temporal downscaling 

(disaggregation) using estimates of monthly variability drawn from historical datasets (Salathé et al. 2007). Spatial and temporal downscaling are therefore important 

for local projections of climate change impacts, but add potential sources of error 

and uncertainty.

Projected temperature changes—

All climate models used in the 2007 IPPC fourth assessment project future increases 

in global mean surface air temperatures over the next century, driven in large part 

by increased radiative forcing owing to anthropogenic CO2 emissions and resulting higher atmospheric CO2 concentrations (Meehl et al. 2007). The amount of 

Spatial downscaling 

methods are used to 

translate the climate 

projections from 

the coarse spatial 

resolution produced 

by GCMs to the fi ner 

resolutions deemed 

more useful for 

assessing impacts.

All climate models 

used in the 2007 IPPC 

fourth assessment 

project future increases 

in global mean surface 

air temperatures over 

the next century.
21

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

warming projected differs considerably, however, depending on the climate model 

and CO2 emission scenario used to make the projection and the time horizon being 

used (fi g. 2.11). During the fi rst part of the 21st century (2011–2030), models project 

a model-averaged increase in mean surface air temperature of 0.64 to 0.68 °C 

regardless of the emission scenario used, and much of this increase is considered a 

lagged response to past greenhouse gas emissions. By the middle of the 21st century 

(2046–2065), models project a model-averaged increase in surface air temperatures 

of 1.3 to 1.8 °C, depending on the emission scenario used. By the end of the 21st

century (2090–2099), models project a model-averaged increase in surface air 

temperatures of 1.8 to 4.0 °C, depending on the emission scenario used.

For any given CO2 emission scenario, there is considerable model-based uncertainty in projections of future warming, particularly by the end of the 21st century 

(Meehl et al. 2007). For example, the low-emissions B1 scenario produces the 

lowest model-averaged projected mean temperature increase of 1.8 °C by the end of 

the 21st century (fi g. 2.11); however, the uncertainty range is 1.1 to 2.9 °C. Similarly, 

the high-emissions A1F1 scenario produces the highest model-averaged projected 

mean temperature increase of 4.0 °C by the end of the 21st century; however, 

the uncertainty range is 2.6 to 6.4 °C. Therefore, although all model projections 

indicate signifi cant increases in global mean surface air temperatures over the next 

Figure 2.11—Left panel: Solid lines are multimodel global averages of surface warming relative to 1980–1999 for the 

Special Repost on Emission Scenarios (SRES) scenarios A2, A1B, and B1, shown as continuations of the 20th century 

simulations. The orange line is for the experiment where concentrations were held constant at year 2000 values. The bars 

in the middle of the fi gure indicate the best estimate (solid line within each bar) and the likely range assessed for the six 

SRES marker scenarios at 2090–2099 relative to 1980–1999. The assessment of the best estimate and likely ranges in 

the bars includes the Atmosphere-Ocean General Circulation Models (AOGCMs) in the left part of the fi gure, as well as 

results from a hierarchy of independent models and observational constraints. Right panels: Projected surface temperature changes for the early and late 21st century relative to the period 1980–1999. The panels show the multi-AOGCM 

average projections for the A2 (top), A1B (middle), and B1 (bottom) SRES scenarios averaged over decades 2020–2029 

(left) and 2090–2099 (right). (From IPCC [2007a], fi g. 3.2.)
22

GENERAL TECHNICAL REPORT PNW-GTR-900

century, the amount of warming expected remains uncertain, ranging from a little 

more warming than in the past century to almost eight times as much.

Warming is not expected to be uniform (spatially) or even (temporally). Warming is expected to be greatest over land areas and at higher latitudes, and least 

over oceans (Meehl et al. 2007) (fi g. 2.11). Heat waves (extreme temperatures) are 

expected to be more frequent and intense (Meehl and Tebaldi 2004, Meehl et al. 

2007), but periods of extreme cold may continue to occur in response to spatial and 

temporal variability in climate. Daily minimum temperatures are expected to rise 

faster than daily maximum temperatures, reducing diurnal temperature fl uctuations (Meehl et al. 2007). Drivers of interdecadal climatic variability (e.g., Pacifi c 

Decadal Oscillation) also will continue to operate, potentially creating extended 

periods of stable or cooling mean temperatures followed by periods of rapidly 

increasing mean temperatures (along with associated changes in precipitation).

For the Pacifi c Northwest, Mote and Salathé (2010) analyzed climate projections 

from 21 GCMs to summarize the projected changes in climate during the 21st

century under two emissions scenarios (B1 and A1B). They reported an average 

projected increase in mean surface air temperature by the late 21st century of 2.5 

°C for the low-emission B1 scenario and 3.4 °C for the high-emission A1B scenario 

(total range of 1.5 to 5.8 °C for both scenarios across all models). This is slightly 

higher than the projected increases in global mean temperatures when controlling 

for emission scenario. Some of the difference may be attributed to the weighted 

mean used by Mote and Salathé (2010), which gives higher weight to models with 

high skill in reproducing climatic conditions and changes in the 20th century. Model 

projections suggest that the greatest warming will occur in summer (June through 

August), increasing seasonal temperature fl uctuations (Mote and Salathé 2010).

Projected precipitation changes—

Globally, mean annual precipitation is expected to increase in conjunction with 

warming temperatures (Meehl et al. 2007). Although the model-averaged projections for global mean annual precipitation increases are relatively small (3 to 5 percent), the models project considerable regional and seasonal variability in precipitation changes (fi g. 2.12), projected precipitation changes vary somewhat by emission 

scenario, and there is considerable variability among individual model projections 

for the same emission scenario (Meehl et al. 2007). Decadal variability in sea surface temperatures (e.g., the Pacifi c Decadal Oscillation) is likely to interact with 

changing climate to infl uence precipitation patterns at a regional scale, but models 

suggest that climate change resulting from increasing greenhouse gases will become dominant within two to three decades (Meehl et al. 2010).

The amount of warming 

expected remains 

uncertain, ranging 

from a little more 

warming than in the 

past century to almost 

eight times as much.
23

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

For the Pacifi c Northwest, GCM projections for changes in mean annual precipitation are equivocal, with model-averaged projections for changes in mean annual 

precipitation of less than 5 percent through the end of the 21st century, but also with 

individual model projections of change ranging from -10 percent to +20 percent by 

the end of the century (Mote and Salathé 2010). Most of the models reviewed by 

Mote and Salathé (2010) project reductions in summer precipitation in the Pacifi c 

Northwest, with the model-averaged mean reaching -14 percent by the end of the 

21st century. Most of models also project increases in mean winter precipitation 

(December through February), with the model-averaged projection reaching as high 

as +8 percent by the end of the 21st century (Mote and Salathé 2010).

Water Availability

One of the most important mechanisms by which climate change can infl uence 

vegetation composition and dynamics is through its effects on soil water availability, water uptake by plants, and evapotranspiration. Soil water availability 

depends on precipitation inputs (form, amount, intensity, and seasonality), surface 

and subsurface water movement, evaporative demand, and vegetation structure and 

composition. Evapotranspiration is driven primarily by temperature and humidity 

but can be constrained by reductions in stomatal conductance or reduced water 

uptake owing to low soil water availability. In this section, we briefl y review the 

likely effects of projected changes in annual and seasonal mean temperature and 

precipitation on important components of the hydrologic cycle, including the fraction of winter precipitation received as snowfall, snowpack water storage, snowmelt 

rates, evapotranspiration, and soil water availability.

Figure 2.12—Relative changes in precipitation (in percent) for the period 2090–2099, relative to 1980–1999. 

Values are multimodel averages based on the Special Report on Emissions Scenarios A1B scenario for December to February (left) and June to August (right). White areas are where less than 66 percent of the models agree 

in the sign of the change and stippled areas are where more than 90 percent of the models agree 

in the sign of the change. (From IPCC [2007a], fi g. 3.3.)

One of the most 

important mechanisms 

by which climate 

change can 

infl uence vegetation 

composition and 

dynamics is through 

its effects on soil 

water availability, 

water uptake 

by plants, and 

evapotranspiration.
24

GENERAL TECHNICAL REPORT PNW-GTR-900

Snowfall and Snowpack Water Storage

Winter snowpack is an important regulator of vegetation structure and productivity in the Pacifi c Northwest and many other regions of the world. One of the most 

important functions of snowpack in western North America is storing winter precipitation for use in spring and summer months when demand for water by humans, 

fi sh and wildlife, and vegetation often exceeds supply. 

The effectiveness of snowpack as a water storage mechanism depends on the 

total amount of winter precipitation, the proportion of winter precipitation that falls 

as snow, and rates at which snow melts during the winter and spring. In regions 

with reliably cold winter temperatures, like high-elevation mountainous areas of the 

interior West, snowpack accumulation is typically limited by the amount of winter 

precipitation, as most winter precipitation falls as snow (Cayan 1996, Hamlet et 

al. 2005). In warmer, maritime climates (e.g., lower elevation maritime mountains 

of Washington and Oregon), snowpack accumulation depends more on winter 

temperatures, as winter precipitation is often abundant, but much of it can fall as 

rain during warmer periods (Cayan 1996, Hamlet et al. 2005, Mote 2006). Spring 

snowpack melt rates depend largely on spring temperatures, winds, and the occurrence of warm rainfall events. Given the importance of climate in accumulating 

and melting snowpack, it is no surprise that snowpack dynamics are sensitive to 

climatic variability and change. For the Pacifi c Northwest, changes in snowpack 

in the colder interior mountains will largely be driven by changes in precipitation, 

while changes in snowpack in the warmer maritime mountains (e.g., Olympics and 

western Cascades) will be driven largely by changes in temperature (Hamlet et al. 

2005). 

Despite high interannual variability, several studies have reported long-term 

negative trends in snowpack accumulation, spring snowpack water storage, 

snowpack duration over the past 50 to 100 years that are attributable to warming 

winter and spring temperatures. Knowles et al. (2006) found signifi cant reductions 

throughout the Western United States in the proportion of winter precipitation 

(November to March) that fell as snow from 1949 to 2004. Mote et al. (2005) found 

a corresponding reduction in spring (April 1) snowpack water equivalent for a 

similar region and time period. Finally, Stewart et al. (2004) analyzed streamfl ow 

patterns and found a trend toward earlier snowmelt in the Western United States 

from 1948 to 2000, with streams and rivers reaching the midway point in their 

annual water discharge up to 20 days earlier in the water year. Glacial ice has also 

declined dramatically in the Pacifi c Northwest over the past century (Pelto 2006). 

Analysis of spring snowpack sensitivity to winter temperatures suggests that 

spring snowpack water storage (April 1 snowpack water equivalent) should decline 

Changes in Vnowpack 

in the cedar interior 

mountains will largely 

be driven by changes 

in precipitation, while 

changes in snowpack 

in the warmer maritime 

mountains (e.g., 

Olympics and western 

Cascades) will largely 

be driven by changes 

in temperature.
25

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

by about 16 percent for every 1 °C of warming, assuming that warming is accompanied by increased precipitation (Casola et al. 2009). If this analysis is correct, mean 

spring snowpack water equivalent would have already declined by 8 to 16 percent 

over the past 30 years, and would be expected to decline by an additional 11 to 21 

percent by 2050, based on projected temperature increases (Casola et al. 2009).

Projected future increases in winter temperatures and precipitation are likely to 

reduce snowpack accumulation and duration in much of the Pacifi c Northwest by 

increasing the proportion of winter precipitation that falls as rain and causing the 

snowpack to melt earlier (Barnett et al. 2005, Brown and Mote 2009). Changes in 

snowpack water content and duration will be most obvious at middle elevations in 

the mountains, where warming will cause the transitory snow zone and permanent 

snowline to move upward (Elsner et al. 2010, Stewart 2009). High-elevation sites 

that maintain freezing winter temperatures may accumulate additional snowpack 

as additional winter precipitation falls as snow; however, snowpack water storage 

is still likely to decline at the regional scale as high-elevation sites comprise a low 

fraction of total land area (Stewart 2009).

Evapotranspiration

Warmer winter and spring temperatures may also increase evaporative demand and 

alter the timing of evaporative water losses and plant water use. Evapotranspiration 

is the sum of water returned to the atmosphere through evaporation from the soil 

and other surfaces and through transpiration from plants. Potential evapotranspiration is driven largely by temperature and solar radiation, so it is expected to 

increase as mean temperatures increase unless cloud cover also increases and 

reduces solar radiation inputs. Actual evapotranspiration also depends on water 

availability, so increases in actual evapotranspiration are likely only if precipitation increases along with temperatures. In some areas, however, the seasonality 

of evapotranspiration may be altered with projected climatic changes, as warming 

temperatures cause water to be evaporated earlier in the spring, leaving less available in the summer and fall months.

Hamlet et al. (2007) used gridded historical climate records for the Pacifi c 

Northwest along with a hydrological model to examine possible trends in evapotranspiration over the past century. Based on model output, they reported a positive trend in spring evapotranspiration over the past century, as earlier snowmelt 

promoted earlier soil water recharge, plant activity, and evaporative losses (Hamlet 

et al. 2007). Summer evapotranspiration became more dependent on summer rainfall, as soil water from melting snowpack became less available for use in summer 

months (Hamlet et al. 2007).
26

GENERAL TECHNICAL REPORT PNW-GTR-900

Soil Water Balance

Summer water defi cits occur annually in most regions and vegetation types of 

the Pacifi c Northwest, though the duration and intensity of water defi cits vary 

considerably throughout the region and along elevational gradients. Precipitation 

falls primarily during the cooler months (October through March), while potential 

evapotranspiration is highest in the warmer and drier months (April through 

September). This temporal separation between precipitation inputs and evaporative 

demand creates high potential for summer water defi cits where evaporative demand 

exceeds water storage capacity. At higher elevations, winter snowpack can store a 

signifi cant portion of winter precipitation and release it to the soil during spring and 

early summer, thereby reducing the duration and magnitude of summer soil water 

defi cits. The frequency and intensity of summer drought therefore depends on winter recharge of soil water, snowpack water storage, and spring/summer temperature 

and precipitation. The relative importance of these elements, and the mean summer 

drought duration and intensity, varies considerably within the Pacifi c Northwest and 

is an important factor determining the distribution of vegetation types, ecosystem 

productivity, and dominant disturbance regimes. 

Summer soil water availability is likely to decline modestly in much of the 

Pacifi c Northwest under future projected climatic changes, but the magnitude and 

mechanisms of projected changes differ within the region (Elsner et al. 2010). In 

the western mountains and coastal lowlands, reduced snowpack, earlier snowmelt, 

and higher evapotranspiration rates are likely to enhance summer soil drying and 

reduce soil water availability (Elsner et al. 2010). In the interior regions, increased 

winter precipitation could enhance deep soil water recharge, thereby increasing 

deep soil water availability in the summer (Elsner et al. 2010). A continuation of 

the recent trend toward increasing spring precipitation in the dry interior could 

also enhance spring and summer soil water availability if amounts are suffi cient to 

promote deep soil recharge; if not, it is likely to simply enhance spring evapotranspiration rates. 

The frequency and 

intensity of summer 

drought depends on 

winter recharge of soil 

water, snowpack water 

storage, and spring/

summer temperature 

and precipitation. 
27

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 3: Vegetation Sensitivity and 

Acclimation to Changing Climate and CO2

Assessing vegetation sensitivity to climate change requires assessing the degree to 

which climatic variability and change alter vegetation performance and survival. 

Individual plants are immobile, so their sensitivity to climate can be assessed 

primarily in terms of their growth (productivity), reproduction, and survival. Plant 

population responses integrate climatic effects on reproduction and survival at 

the individual plant level and can be measured as rates of population increase (or 

decline), including local extirpation. Plant community responses integrate climatic 

effects on constituent plant populations, as well as effects on biotic interactions, 

and are commonly measured as the number and relative abundances of interacting 

species. Finally, assessing vegetation sensitivity to climate at the ecosystem level 

typically involves measuring responses—such as net primary productivity, total 

carbon stocks, and nutrient cycling rates—that integrate individual plant performance across populations and communities.

In this chapter, we review common vegetation responses that represent sensitivity to climate and elevated carbon dioxide (CO2) at each of these levels, with 

emphasis on processes and responses commonly found in Pacifi c Northwest vegetation. We fi rst examine climate and CO2 effects on plant growth and reproduction, as 

these are basic measures of vegetation performance at the individual and population 

levels. We also examine the potential for changing climate to alter the intensity of 

and outcomes from biotic interactions and the implications for plant community 

responses to climate change. Finally, we examine ecosystem responses to changing 

climate and CO2, including potential changes in net primary productivity, ecosystem carbon storage, and nutrient cycling. Plant mortality responses to climaterelated stressors and disturbances are discussed in chapter 4, while plant adaptive 

responses are discussed in chapter 5.

Physiological Processes, Plant Growth, 

and Ecosystem Productivity

Climate infl uences plant growth through its effects on temperature, soil water, light, 

and nutrients. These factors are important for regulating rates of photosynthesis and 

respiration, water and nutrient uptake from soils, and biomass production. Temperature and soil water also infl uence soil biota and their contributions to organic 

matter decomposition, nutrient cycling, and exchange of resources with plants. Plant 

growth is also infl uenced by atmospheric CO2 concentrations, which can directly 

alter rates of photosynthesis, respiration, and transpiration, and can indirectly infl uence soil water and nutrient availability. 
28

GENERAL TECHNICAL REPORT PNW-GTR-900

Plant growth in temperate zones occurs within the context of a constantly 

changing climate. As sessile organisms, plants must have the ability to adjust to 

daily, seasonal, interannual, and longer term changes in solar radiation, temperature, and precipitation, and associated changes in soil temperature, soil water 

availability, and soil nutrient availability. In addition to these relatively predictable 

cycles in environmental conditions and resource availability, plants must also have 

mechanisms for coping with periodic episodes of extreme environmental conditions, such as heat waves, extreme cold temperatures, and drought. 

Increasing atmospheric concentrations of CO2 and associated changes in 

climate are expected to infl uence plant growth by altering the ranges of environmental conditions and resource availability plants experience throughout their 

annual growth cycle. In this section, we highlight some key experimental and 

observational research that provides insights to the likely effects of changing 

climate and atmospheric CO2 on plant carbon uptake (photosynthesis and respiration), plant growth, and allocation of carbon to mycorrhizae, plant defense 

compounds, and other nonstructural purposes. We also describe ways in which 

plants acclimate to seasonal and longer term climatic variability and to elevated 

CO2 concentrations. Experimental studies are particularly important sources for 

this information, as they provide information about potential responses of existing vegetation to future climate and CO2, in the absence of genetic adaptation in 

populations or species migrations (which are addressed in chapter 5).

Photosynthesis and Respiration

Photosynthesis and respiration regulate net uptake and fi xation of carbon. Photosynthesis allows plants to convert CO2 and water into organic compounds that 

store energy and can be used for plant growth and maintenance, seed production, 

conversion to plant defense compounds, or export to soil biota (among other things). 

Respiration allows plants to use stored energy for plant growth and maintenance. 

Both processes are sensitive to changing environmental conditions and resource 

availability, but they differ in both their initial responses and ability to acclimate.

Increasing mean annual temperatures are likely to increase photosynthetic rates 

for many temperate plant species, given suffi cient light and water (Saxe et al. 2001). 

Photosynthetic rates in temperate-zone plants generally increase with short-term 

increases in temperature up to a maximum at about 25 to 40 °C, while acclimation 

to persistent temperature increases can raise photosynthetic maxima by up to 10 

°C (Berry and Björkman 1980, Saxe et al. 2001). Warming nighttime temperatures 

can also increase photosynthetic rates by reducing the frequency of low-temperature 

photoinhibition (Germino and Smith 1999). Warming temperatures can increase 

Increasing mean 

annual temperatures 

are likely to increase 

photosynthetic rates 

for many temperate 

plant species, given 

suffi cient light and 

water.
29

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

plant photosynthetic rates indirectly by changing leaf nitrogen content and photosynthetic capacity (Lewis et al. 2004). In Douglas-fi r (Pseudotsuga menziesii (Mirb.) 

Franco) seedlings, for example, experimental warming increased chlorophyll and 

carotenoid concentrations (Ormrod et al. 1999) and increased seedling needle 

nitrogen (N) concentrations by 26 percent and light-saturated photosynthetic rates 

by 17 percent (Lewis et al. 2004). Warming temperatures are not uniformly benefi -

cial for photosynthesis, however, as temperatures in excess of the photosynthetic 

optimum can rapidly reduce photosynthesis by impairing protein functioning 

(Saxe et al. 2001) and high daytime temperatures can reduce net photosynthesis 

by increasing rates of photorespiration (Long et al. 1994). 

Soil water availability is another major control on photosynthetic rates of 

Pacifi c Northwest plants. Summer soil water defi cits occur annually in most vegetation types of the Pacifi c Northwest, producing some level of drought stress in 

plants. Plants typically respond to short-term drought stress by reducing stomatal 

apertures, which can reduce water loss through transpiration, but also reduces photosynthetic rates by reducing CO2 concentrations within plant leaves. By increasing 

evaporative demands, warmer temperatures can increase the frequency and severity 

of summer drought and associated effects on photosynthesis.

Autotrophic respiration can consume 30 to 80 percent of net photosynthesis 

in plants, as respiration generates the energy needed for plants to do work (Atkin 

and Tjoelker 2003, Saxe at al. 2001). Although growth respiration is relatively 

insensitive to temperature, maintenance respiration is quite sensitive to short-term 

temperature changes. Respiration rates typically rise by a factor of 1.8 to 2.5 

(roughly double) in response to a 10 °C increase in temperature (Atkin and Tjoelker 

2003, Saxe et al. 2001). Over longer periods, however, respiration can acclimate to 

elevated ambient temperatures (Atkin and Tjoelker 2003). There is evidence that 

respiration may become substrate-limited at higher temperatures, so that respiration 

rates become more tightly coupled to photosynthetic rates over longer time scales 

(Atkin and Tjoelker 2003, Atkin et al. 2005, Saxe et al. 2001).

Elevated CO2 may help to offset the effects of higher temperatures and reduced 

soil water availability on photosynthesis and net carbon uptake. Carbon dioxide is 

a critical resource for photosynthesis but is available at relatively low atmospheric 

concentrations. In acquiring CO2 from the atmosphere through stomatal diffusion, 

plants lose water to the atmosphere through transpiration, requiring mechanisms 

like stomatal aperture control to balance the effects of low water, CO2, and light 

availability on productivity (Hetherington and Woodward 2003, Lambers et al. 

1998). Theory suggests that increasing atmospheric concentrations of CO2 could 

increase individual plant growth and ecosystem productivity (Long et al. 2004), 

Elevated CO2 may help 

to offset the effects of 

higher temperatures 

and reduced soil 

water availability on 

photosynthesis and net 

carbon uptake. 
30

GENERAL TECHNICAL REPORT PNW-GTR-900

and there has been considerable research in recent decades to detect and better 

understand this potential CO2 “fertilization effect.” 

Experimental studies have shown that the primary effect of elevated CO2 on 

plants is to increase resource-use effi ciency—the amount of carbon fi xed per unit of 

resource obtained or used (Drake et al. 1997). Elevated CO2 allows plants to reduce 

stomatal conductance and leaf-level transpiration while maintaining adequate 

CO2 concentrations for photosynthesis within leaves, thereby increasing water-use 

effi ciency (Ainsworth and Long 2005, Drake et al. 1997, Leakey et al. 2009, Long 

et al. 2004, Medlyn et al. 2001). Improved water-use effi ciency under elevated CO2

is most prevalent in plants with the C3 photosynthetic pathway, which includes the 

majority of forest plants in the Pacifi c Northwest as well as many rangeland species. 

Elevated CO2 also stimulates photosynthesis and increases light-use effi ciency by 

increasing the carboxylation rate of Rubisco and competitively inhibiting oxygenation of Ribulose-1, 5-bisphosphate (RubP) (Ainsworth and Long 2005, Drake et al. 

1997). Plants appear to acclimate to prolonged exposure to elevated CO2 by reducing Rubisco concentrations and mass-based leaf nitrogen content while maintaining 

elevated photosynthetic rates, thereby increasing nitrogen-use effi ciency (Ainsworth 

and Long 2005, Drake et al. 1997, Leakey et al. 2009, Long et al. 2004). 

Greater resource-use effi ciencies can facilitate greater total carbon acquisition by plants, if plant demand for carbon (the “sink strength”) is suffi ciently high. 

Increased carbon acquisition rates, in turn, can produce a wide range of ecological 

impacts on plants and ecosystems, including increased plant growth and ecosystem 

productivity (Leakey et al. 2009), increased shade tolerance (Drake et al. 1997), 

reduced drought stress, increased soil water availability (Holtum and Winter 2010), 

reduced plant nutrient quality for insect and animal herbivores (Lincoln et al. 1993, 

Robinson et al. 2012, Zvereva and Kozlov 2006), and reduced rates of litter decomposition and soil nutrient cycling. 

In general, warmer temperatures produce a positive effect on photosynthesis, 

except where ambient temperatures are already close to (or beyond) the photosynthetic optimum or where soil water availability strongly limits stomatal conductance and photosynthesis (Saxe et al. 2001). Warmer temperatures signifi cantly 

increase respiration rates over short time periods, but respiration rates can acclimate to changes in mean temperatures over time. Elevated CO2 can help reduce the 

frequency and severity of drought-induced reductions in photosynthesis by helping 

plants maintain adequate internal concentrations of CO2 while reducing water 

losses by reducing stomatal aperture.
31

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Phenology

Warmer mean temperatures may also infl uence vegetation growth and productivity through their effects on plant phenology (the timing and duration of growth 

stages) and growing season length. Plant phenology in temperate woody plants is 

largely controlled by winter and spring temperatures and by photoperiod (Polgar 

and Primack 2011). Processes infl uenced by temperatures include the initiation of 

fl owering (Dunne et al. 2003), budburst (Guak et al. 1998, Harrington et al. 2010, 

Worrall 1983), root growth (Lopushinsky and Max 1990), and cold hardening (Guak 

et al. 1998, Saxe et al. 2001). For example, Worrall (1993) reported that budburst in 

subalpine larch (Larix lyallii Parl.) varied by up to 6 weeks and leaf-fall varied by 

up to 4 weeks from year to year and that both were correlated with spring and summer temperatures. Similarly, Lopushinsky and Max (1990) used a greenhouse study 

to show that root growth began when soil temperatures exceeded 5 °C. 

Winter temperatures can also infl uence spring budburst timing. Some species 

delay budburst if they have not experienced a suffi ciently long or intense period of 

cold temperatures, which is often referred to as a “chilling requirement” (Campbell 

and Sogano 1979, Cannell and Smith 1983, Harrington et al. 2010, Kozlowski and 

Pallardy 2002). In areas with mild winters, warmer winter temperatures could fail 

to satisfy the chilling requirements for some species (e.g., Douglas-fi r), thereby 

delaying the onset of spring budburst and shoot growth and offsetting some of the 

potential benefi ts of warmer spring temperatures (Harrington et al. 2010). However, 

early initiation of budburst or fl owering can also expose plants to increased risk of 

frost damage (Cannell and Smith 1986, Inouye 2008), so delays in budburst owing 

to unmet chilling requirements may also provide some benefi ts. 

Temperature triggers on growth phenology, if well-adapted, can help plants 

synchronize their growth activity with periods of favorable resource availability, 

including favorable temperatures. In subalpine zones, low heat requirements allow 

trees to initiate and complete their annual growth cycle (including seed production 

and cold hardening) before the return of cold temperatures in the fall (Tranquillini 

1979; Worrall 1983, 1993), thereby maximizing growing season length. In waterlimited systems, early growth initiation may also be advantageous, allowing plants 

to capture and use soil water that would not be available later in the summer and 

perhaps gaining a competitive advantage over species with later growth initiation 

(resource preemption). 

Plant phenological responses to warming of global temperatures in recent 

decades have been well documented by direct observation of plant species—some 

extending back hundreds of years—and, more recently, by satellite imagery and 

analysis of seasonal changes in atmospheric CO2 concentrations (Badeck et al. 
32

GENERAL TECHNICAL REPORT PNW-GTR-900

2004, Cleland et al. 2007, Parmesan 2006, Polgar and Primack 2011). A recent 

meta-analysis of phenological studies from multiple sources indicated that spring 

growth phenology has been advancing at an average rate of 2.8 days per decade 

(Parmesan 2007). Although phenological responses can differ considerably by species, a study of more than 500 European plant species found signifi cant advances in 

30 percent of leafi ng, fl owering, and fruiting records, compared to signifi cant delays 

in only 3 percent of records, in response to warming during 1971–2000 (Menzel 

et al. 2006). Unfortunately, records on the cessation of growth are less common, 

so it is diffi cult to conclusively link spring advancement of growth phenology with 

longer growing season lengths, except through remote sensing approaches.

Plant Growth and Ecosystem Productivity

Plant growth and biomass production are important measures of plant responses to 

changing climate and elevated CO2. Plant growth allows plants to forage for and 

capture critical resources (e.g., root growth and water uptake; height growth and 

light interception), compete for resources and growing space with other plants, 

replace tissues lost to disturbances (e.g., herbivory or wind damage), and increase 

resistance to disturbances (e.g., thicker bark to resist fi re; greater height to avoid 

herbivory). Plant growth is also a major sink for carbon fi xed through photosynthesis, so high growth rates can promote higher levels of carbon uptake (and vice 

versa). Finally, biomass production is a required precursor for long-term ecosystem 

carbon storage and serves as an important feedback of ecosystem productivity on 

disturbance processes such as fi re.

Plant growth and biomass production can be limited by one or more climatic 

limiting factors, including air and soil temperatures, soil water availability, and 

solar radiation. Ecosystem model simulations of global terrestrial biomes suggest 

that temperature and water availability are the dominant climatic factors limiting 

net primary productivity globally, with solar radiation being the dominant limiting 

factor for only about 5 percent of the biomes (Churkina and Running 1998). Given 

the diverse range of environments and biomes in the Pacifi c Northwest, all three 

climatic limiting factors are likely to operate in some regions and seasons, but winter temperatures and water limitations are considered the dominant controls on leaf 

area and net primary productivity in Pacifi c Northwest ecosystems (Gholz 1982, 

Grier and Running 1977). Soil nutrients (especially nitrogen) are another important 

factor limiting vegetation growth and productivity throughout much of the Pacifi c 

Northwest, which, though not directly climate-related, may modify vegetation 

structure and productivity (Gholz 1982). 

Tree ring analyses have demonstrated the effects of climatic limiting factors 

on growth and biomass production in a number of forest types and biophysical 

A recent meta-analysis 

of phenological studies 

from multiple sources 

indicated that spring 

growth phenology has 

been advancing at an 

average rate of 2.8 

days per decade.
33

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

settings. Tree growth in low-elevation juniper woodlands and ponderosa pine 

(Pinus ponderosa Lawson and C. Lawson) forests is positively correlated with 

precipitation, particularly outside the growing season, but relatively insensitive to 

temperature fl uctuations (Knutson 2006, Kusnierczyk and Ettl 2002), suggesting 

a strong water limitation on net primary production. In contrast, tree growth in 

subalpine forests can be negatively correlated with winter precipitation and spring 

snowpack depth and positively correlated with summer temperatures, suggesting that temperature and growing season length limit production (Peterson and 

Peterson 1994, 2001; Peterson et al. 2002). Within the continuous forest zone, tree 

growth is often positively correlated with soil water availability to varying degrees 

(Case and Peterson 2005, 2007; Littell et al. 2008; Nakawatase and Peterson 2006). 

In general, tree ring studies support ecosystem model projections regarding the 

dominant environmental factors limiting net primary productivity (NPP) in forests; 

however, recent work suggests that site and individual tree characteristics may generate signifi cant variability in growth responses to climatic variability and change 

(Carnwath et al. 2012; Ettl and Peterson 1995a,1995b; Knutson 2006).

Overall, research suggests that warming air and soil temperatures will enhance 

plant growth and ecosystem production, given suffi cient water availability (Boisvenue and Running 2006, 2010). Warmer temperatures are expected to increase net 

carbon gain as respiration acclimates to warmer temperatures more than photosynthesis, allowing plants to adjust and maintain carbon-use effi ciency (Maseyk et al. 

2008). Temperature effects on phenology will tend to extend the growing season 

and, potentially, plant growth. Increased availability of soil N could also increase 

growth and productivity. All of these potential gains, however, depend on adequate 

availability of soil water, which is likely to decline with increasing temperatures.

Temperature and moisture effects on vegetation growth and productivity are 

likely to be highly variable within and among biomes. Positive responses to warming temperatures are more likely to be observed in subalpine and alpine ecosystems 

(where temperature is limiting) than in deserts, shrublands, and savannas (where 

water is the dominant limiting factor) (Latta et al. 2010). Warming temperatures 

appear to increase growth and productivity in deciduous species more than in 

evergreen trees (Way and Oren 2010), suggesting that warming temperatures may 

benefi t early-seral and understory species (e.g., alders [Alnus spp.] and maples 

[Acer spp.]) more than evergreen conifers in the Pacifi c Northwest. 

Plant growth responses to elevated CO2 differ spatially and temporally with 

climate, soil water availability, and soil nutrient availability (Housman et al. 2006, 

McCarthy et al. 2010, McMurtrie et al. 2008, Norby et al. 2010, Reich et al. 2001) and 

also among species. In an experimental study with loblolly pine (Pinus taeda L.) 

Overall, research 

suggests that 

warming air and 

soil temperatures 

will enhance plant 

growth and ecosystem 

production, given 

suffi cient water 

availability.
34

GENERAL TECHNICAL REPORT PNW-GTR-900

at Duke Forest, 10 years of exposure to elevated CO2 produced sustained increases 

in plant biomass production; however, positive responses to elevated CO2 were 

greater on sites with high N availability than on sites with low N availability and 

were greater (on a relative basis) in dry years than in wet years (McCarthy et al. 

2010). Similarly, experimental N additions have produced increased plant biomass 

production responses to elevated CO2 on nutrient-limited sites (Oren et al. 2001, 

Reich et al. 2001). In the Mojave Desert, positive effects of elevated CO2 on photosynthesis and productivity in three desert shrub species–creosote bush (Larrea 

tridentatea (DC.) Coville), littleleaf ratany (Krameria erecta Willd. Ex Schult.), and 

burrobush (Ambrosia dumosa (A. Gray) Payne)—were largely limited to seasons 

and years of higher relative water availability, suggesting that future responses of 

desert vegetation to elevated CO2 may be dependent on concomitant changes in 

precipitation patterns and growing season length (Housman et al. 2006, Naumburg 

et al. 2003).

Species responses to elevated CO2 have been shown to differ among plant functional groups (grasses, forbs, shrubs, and trees), among photosynthetic pathways 

(C3 or C4), between N2-fi xing and non-N2-fi xing species, and with leaf structure 

and longevity. A meta-analysis of experimental studies showed that trees were 

more responsive to elevated CO2 than C3 grasses, with greater percentage increases 

in leaf area index, height, and aboveground dry matter production (Ainsworth 

and Long 2005). Similarly, in studies of four crop species, only the woody crop, 

cotton, showed a signifi cant yield enhancement under elevated CO2 (Ainsworth 

and Long 2005). Species employing the C3 photosynthetic pathway were more 

responsive than species employing the C4 pathway, with C3 grasses demonstrating 

greater mean increases in dry matter production than C4 grasses (Ainsworth and 

Long 2005). Lee et al. (2003) found that elevated CO2 increased production by 57 

percent in a leguminous forb with symbiotic N2 fi xation (sundial lupine, Lupinus 

perennis), while the response of a nonleguminous forb (yarrow, Achillea millefolium 

L.) was less (0 to 25 percent increase), with positive growth responses dependent 

on nitrogen additions. Niinemets et al. (2011) have also proposed that evergreen 

plants that feature leaves with more robust structures will experience more gains in 

water use effi ciency and productivity under elevated CO2 than deciduous plants, as 

their “tougher” leaves inhibit internal diffusion of CO2 and increase sensitivity to 

atmospheric CO2 concentrations. 

Plant responses to elevated CO2 levels also differ considerably among species 

within functional groups. Housman et al. (2006) reported differences in growth 

enhancement under elevated CO2 for three desert shrub species. Dawes et al. 

(2011) found that continued exposure (9 years) to elevated CO2 in a subalpine forest 
35

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

increased leaf canopy cover, stem basal area, and new shoot production in European 

larch (Larix decidua Mill.), but found no signifi cant growth response in mountain 

pine (Pinus mugo ssp. uncinata (Raymond) Domin. Biomass responses to elevated 

CO2 also differed considerably among tallgrass prairie species within four plant 

functional groups: C3 grasses, C4 grasses, forbs, and legumes (Craine et al. 2003, 

Reich et al. 2001). 

Interactions among elevated CO2, water and nutrient availability, and altered 

climate make it diffi cult to predict the net productivity response of plants (Angert 

et al. 2005, Huang et al. 2007). Although elevated CO2 has been shown to improve 

resource use effi ciency and increase productivity for some species and sites under 

current climate, warmer temperatures or increased drought intensity projected 

under future climate scenarios could more than offset those benefi ts, leading to no 

net gains or reduced productivity for some species. Similarly, the potential benefi ts 

of elevated CO2 on plant growth and productivity differ considerably with site 

conditions.

Future responses of Pacifi c Northwest vegetation to elevated CO2 are highly 

uncertain owing to limited experimental studies with elevated CO2 in the Pacifi c 

Northwest (or with Pacifi c Northwest species) and the wide range of biophysical 

settings and vegetation types. However, we can make a few projections based on 

general fi ndings from studies in other ecosystems. First, increases in water-use 

effi ciency produced by elevated atmospheric CO2 levels could partially offset 

temperature-induced increases in evapotranspiration and drought stress, particularly in ecosystems with mild to moderate seasonal drought. However, Reich et 

al. (2006b) hypothesized that elevated CO2 would provide little positive benefi t in 

ecosystems where water is the dominant limiting factor (e.g., rangelands and dry 

forests of the interior Pacifi c Northwest), as appears to be the case for desert shrubs 

(Naumburg et al. 2003). Second, low nitrogen availability (and low atmospheric 

N deposition rates) in many Pacifi c Northwest forests and rangelands could limit 

the benefi ts of elevated CO2 over time as progressive nitrogen limitation offsets 

increased nitrogen-use effi ciency. 

Plant Reproduction

Plant reproduction is another measure of vegetation sensitivity to changing climate 

and atmospheric CO2 concentrations and is critical for population growth and 

species colonization. Plant reproduction infl uences the ability of species to migrate 

to new areas, persist in or colonize disturbed sites, and compete with established 

vegetation to infl uence plant community composition and successional dynamics. 

In this section, we provide a brief review of the sensitivity of plant reproductive 

Future responses of 

Pacifi c Northwest 

vegetation to elevated 

CO2 are highly 

uncertain owing to 

limited experimental 

studies with elevated 

CO2 in the Pacifi c 

Northwest (or with 

Pacifi c Northwest 

species) and the wide 

range of biophysical 

settings and vegetation 

types.
36

GENERAL TECHNICAL REPORT PNW-GTR-900

processes—including fl owering and pollen production, fertilization, seed production, dispersal, germination, and establishment—to climatic variability and 

elevated CO2. 

Seed Production

Seed production in plants can be infl uenced by climate and elevated atmospheric 

CO2 (Jablonski et al. 2002, LaDeau and Clark 2006). Warmer air and soil temperatures can advance fl owering phenology in plants (Beaubien and Hamann 

2011, Dunne et al. 2003, Parmesan 2006). Although earlier fl owering can promote 

increased seed production and viability (Richardson et al. 2005, Walck et al. 2011), 

it can also expose plants to frost damage and reduced seed production, particularly at high elevations and latitudes (Beaubien and Hamann 2011, Inouye 2008). 

Climate-induced shifts in reproductive phenology can also alter plant reproductive 

capacity by altering phenological synchrony between plants and insect pollinators 

or herbivores (Hegland et al. 2009, Liu et al. 2011). Studies of seed masting in trees 

suggest that temperature and precipitation (water availability) may act at different 

stages of fl owering and fruiting to infl uence seed production (Selås et al. 2002, 

Woodward et al. 1994). In general, however, the climatic and other environmental 

factors infl uencing seed production and viability are still poorly understood for 

most species, making it diffi cult to project responses to future climatic changes.

Elevated CO2 can stimulate seed production and mass in both crop and wild 

species (Jablonski et al. 2002), as seed production provides a sink for surplus 

carbon. For example, loblolly pines growing under elevated CO2 matured earlier 

and produced more seeds and cones (adjusted for tree size) than those growing at 

ambient CO2 levels (LaDeau and Clark 2001, 2006), but there was no CO2 effect on 

seed mass, viability, or nutrient content (Way et al. 2010). However, CO2 effects on 

seed production differ among plant species and functional groups (Hikosaka et al. 

2011, Jablonski et al. 2002), and plant allocation to seed production can also decline 

under elevated CO2 for some species (HilleRisLambers et al. 2009). 

Dispersal (Including Long Distance)

Seed dispersal is an important process by which populations can expand into 

neighboring sites (e.g., colonizing disturbed sites) and by which species can expand 

their geographic ranges. Wind is the dominant seed dispersal agent in forests and 

rangelands of the Pacifi c Northwest, but animals (including humans) and water 

also transport seeds. Seed (and pollen) dispersal distances are typically skewed, 

with most seed falling close to the source and a very small number of seeds being 

transported great distances (Nathan et al. 2008). The mechanisms, frequency, and 

Elevated CO2 can 

stimulate seed 

production and mass 

in both crop and wild 

species.
37

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

impacts of long-distance seed transport have received considerable research attention, as pollen studies have suggested that long-distance dispersal played an important role in species migrations following the last glacial period and may be the only 

way by which species migrations can track future climate change in some regions 

(Loarie et al. 2009, Nathan et al. 2008). However, recent studies have indicated 

that some populations once believed to have established via long-distance dispersal 

actually derived from remnant populations that persisted through the glacial period 

in refugia (McLachlan et al. 2005, Petit et al. 2008). These latter studies suggest 

that estimates of historical migration rates for some species will have to be reduced 

signifi cantly and that true potential migration rates are substantially lower than 

what would be needed to track future climate changes (McLachlan et al. 2005).

Whether future climate changes will have a signifi cant infl uence on longdistance seed dispersal is unclear. Increased fecundity in populations on the leading 

edges of species ranges could increase chances for long-distance dispersal simply 

by providing more seed (Hampe 2011). Changing climate could alter dispersal 

pathways at landscape to regional scales by altering arrival site suitability for 

seed germination and establishment (Hampe 2011). Kuparinen et al. (2009) also 

suggested that warmer air temperatures in the future could increase atmospheric 

instability and increase distances for wind dispersal (including extreme events). 

It is also unclear if other mechanisms for long-distance dispersal (e.g., transport 

by humans, rivers, or migratory animals) would be signifi cantly infl uenced by a 

changing climate.

Seed Dormancy and Germination

Plant seeds often have some mechanism for maintaining dormancy and inhibiting 

germination until some external condition is met (Kozlowski and Pallardy 2002, 

Walck et al. 2011). Endodormancy is common in many woody species of the Pacifi c 

Northwest (e.g., Douglas-fi r), in which moist seeds must be chilled to some degree 

prior to germination (Kozlowski and Pallardy 2002). Other species produce seeds 

with hard coats or waxes that must be broken (or melted) prior to germination 

(Kozlowski and Pallardy 2002); common examples from the Pacifi c Northwest 

include lodgepole pine (Pinus contorta; some populations feature serotinous cones 

that require heating to open and release seeds) and snowbrush ceanothus (Ceanothus velutinus Douglas ex Hook.; seeds with hard coats remain dormant in soil until 

heated).

Climate change has the potential to alter seed dormancy period and subsequent 

germination rates through its effects on fi re regimes and winter temperatures. 

Changes in fi re-return intervals would alter restocking of canopy and soil seed 

banks between fi res, residence times for seeds in canopy and soil seed banks 
38

GENERAL TECHNICAL REPORT PNW-GTR-900

(potentially infl uencing viability), depths of seeds buried in soils (infl uencing 

responses to soil heating), and heights of canopy seed banks.

Warming winter temperatures in areas with mild winters could delay or prevent 

germination if they fail to meet the chilling requirements for releasing seeds from 

dormancy (Walck et al. 2011). On the other hand, warming winter temperatures 

could produce premature germination in cold environments, leading to seedling 

death (Walck et al. 2011). Chilling requirements are generally well studied for commercially important plant species in the Pacifi c Northwest, as they are important for 

processing seed for nursery stock. However, little is known about the potential for 

acclimation or adaptation of chilling requirements in altered climates. 

Establishment

Successful establishment is crucial for maintaining the viability of existing populations, population expansion into new sites, and species migrations. Establishment is 

also the stage in the life cycle of plants that best exemplifi es the infl uence of historical, physiological, and biotic fi lters on plant species presence and plant community 

composition (Lambers et al. 1998). Successful plant establishment requires the 

arrival of seed (historical fi lter) to a microsite with suitable environmental conditions and resource availability (physiological fi lter), where it can coexist with other 

biotic organisms (the biotic fi lter). Plants cannot become established and persist on 

a site unless each of these elements is in place.

Because most seed falls within relatively short distances of the parent plant, 

establishment tends to be dominated by species with established, reproducing 

populations at or near the site (Donato et al. 2009, Larson and Franklin 2005, Zald 

et al. 2008). Some local populations may be remnant or sink populations that are 

incapable of producing viable seed, whereas others may require favorable climatic 

conditions to produce seed. In the absence of an abundant local seed source, establishment becomes dependent on long-distance dispersal mechanisms, providing 

stability in established plant communities, but lengthening recovery times following severe disturbances.

Plants are usually the most sensitive to environmental stressors (the physiological fi lter) during the establishment phase when plants are small. Establishing plants 

initially have small root systems, making them dependent on soil water and nutrients from shallow soil depths in their immediate vicinity. Some species require or 

prefer specifi c types of seedbeds, like bare mineral soil. Carbohydrate and nutrient 

reserves needed for initial growth can also be very limiting early in the establishment process, depending on seed size and resource storage and local shading. 

High surface to volume ratios also make plant tissues more sensitive to extreme 

temperatures.

Plants are usually 

the most sensitive 

to environmental 

stressors (the 

physiological 

fi lter) during the 

establishment phase 

when plants are small.
39

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Because of these limitations, successful plant establishment often depends 

on favorable microsite conditions or benefi cial biotic interactions (Gray and Spies 

1997). Regeneration after wildfi res and other large disturbances may be facilitated 

by shading from snags, large rocks, or topographic features (e.g., on north-facing 

slopes) that limits radiation inputs and potential evapotranspiration. Similarly, shading from neighboring trees and shrubs can help reduce heat and water stress 

for some species at the expense of lower light levels. Other species require high 

light levels, while tolerating low water availability. Downed logs can help tree seedlings become established by reducing competition from understory plants (Harmon 

and Franklin 1989). Recent studies have suggested the importance of soil biota—

particularly ectomycorrhizal networks—for facilitating tree seedling establishment 

(Bingham and Simard 2012). 

Changes in atmospheric CO2 concentrations and climate are expected to have 

a wide range of impacts on tree seedling establishment. Higher CO2 concentrations 

can increase light- and water-use effi ciency in seedlings, potentially increasing 

shade tolerance, reducing drought stress, and increasing survival. However, Mohan 

et al. (2007) reported that the benefi ts of elevated CO2 on understory seedling 

growth was largely limited to shade-tolerant species. 

Changes in temperature and precipitation (and associated changes in drought 

stress) could reduce establishment rates by long-lived tree and shrub populations by 

creating establishment conditions that differ considerably from those under which 

the original population became established. These altered environmental conditions 

could exert selective pressure on future generations, leading to in situ adaptation 

of populations to changing climate. If changes exceed the adaptive capacity of the 

population, local regeneration could cease or be restricted to multiyear periods of 

favorable climate.

Biotic Interactions

Biotic interactions signifi cantly infl uence species distributions and community 

composition at local to regional scales (Araújo and Luoto 2007). Competition has 

long been recognized as an important process in structuring plant communities and 

vegetation dynamics (Connell and Slatyer 1977, Grime 1979, Tilman 1982), while 

the importance of facilitation (positive interactions) in community structure has 

received increasing recognition in recent decades (Brooker et al. 2008, Callaway 

1995, Callaway and Walker 1997). There is also a rich literature on herbivory and 

its effects on vegetation structure and dynamics (e.g., Bakker et al. 2006, Ritchie et 

al. 1998, Schreiner et al. 1996). Plants often rely on benefi cial biotic interactions for 

initial establishment (e.g., nurse plants or logs, mycorrhizal infection), pollination, 
40

GENERAL TECHNICAL REPORT PNW-GTR-900

seed dispersal, and protection from herbivores. They must also respond to or defend 

against detrimental biotic interactions such as competition for limiting resources, 

herbivory, and seed predation.

Projecting the potential effects of elevated CO2 and changing climate on biotic 

interactions and resulting plant community dynamics has become a topic of great 

interest in recent years, but much work remains to be done in this area. Rising CO2 

and changing climate could alter the outcomes of biotic interactions if competing individuals or species respond differently to environmental changes, thereby 

increasing or reducing relative rates of growth and reproduction. Changing climate 

and CO2 could also alter the intensity of biotic interactions by increasing or reducing overall resource availability (physiological stress). In this section, we review 

some examples of biotic interactions that are important for structuring vegetation 

communities and how they might be infl uenced by elevated CO2 and changing 

climate. We also describe potential ways that biotic interactions may mediate 

vegetation responses to changing climate and CO2.

Mutualisms—Pollination and Mycorrhizal Associations

Although many tree and shrub species in the Pacifi c Northwest are wind pollinated, 

other plants rely on insects or birds for pollination, exchanging nectar or pollen for 

pollen transport. This mutually benefi cial arrangement requires close synchronization of phenology between plants and pollinators, so that pollinators are present 

and actively seeking rewards while the plants are fl owering (Hegland et al. 2009, 

Rathcke and Lacey 1985). It can also promote synchrony or asynchrony in fl owering among plant species; synchronous fl owering can be benefi cial for attracting 

pollinators, while asynchronous fl owering can reduce competition for pollinator 

service and cross-pollination problems (Hegland et al. 2009, Rathcke and Lacey 

1985). 

Phenological synchrony between plants and their pollinators could be disrupted 

if plants and pollinators respond differently to changing environmental cues 

(Hegland et al. 2009, Memmott et al. 2007, Parmesan 2007). This could alter plant 

species fi tness by increasing or reducing cofl owering and competition for pollinators (Forrest et al. 2010, Forrest and Thomson 2011). Pollination mutualisms could 

also be altered by species migrations, either the arrival of a new potential pollinator, 

or the loss of a current pollinator. On the other hand, pollinators may be suffi ciently 

fl exible in their responses to environmental cues that they could modify their 

behavior to track changes in plant phenology; if so, the plant-pollinator mutualism 

would be modifying species responses to climate change.

Many plants also rely on mutualistic relationships with insects, birds, and 

animals for seed dispersal. As with fl owering phenology, the success and timing 

Phenological 

synchrony between 

plants and their 

pollinators could be 

disrupted if plants and 

pollinators respond 

differently to changing 

environmental cues.
41

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

of seed and fruit ripening is often related to climate (Rathcke and Lacey 1985). 

Synchrony of seed and fruit ripening could benefi t co-occurring species by attracting animal dispersers, while competition for animal dispersers could promote 

asynchrony in seed and fruit ripening (Rathcke and Lacey 1985). Elevated CO2

and changing climate could alter plant resource allocation to fruit and seed production, the timing of ripening and dispersal, and levels of synchrony in fruit and seed 

production within plant communities. As with pollination, potential dispersers 

might be suffi ciently dependent on fruit rewards to track changes in the timing of 

fruit availability, thereby reducing climate change impacts on vegetation. 

Mycorrhizal associations are another common and important mutualism that 

could be infl uenced by elevated CO2 and changing climate in ways that alter plant 

performance and ecosystem functioning. Plants provide carbon compounds to 

mycorrhizal fungi in return for enhanced water and nutrient uptake (McCormack 

et al. 2010). Soil fungi provide a sink for carbon fi xed through photosynthesis and 

could therefore be important for helping plants maintain high carbon uptake rates 

under elevated CO2 (McCormack et al. 2010). Stimulation of plant carbon uptake 

and productivity could also be enhanced if the additional carbon provided to mycorrhizal fungi increases nutrient and water uptake and delivery to the plant.

Facilitation

Positive interactions (facilitation) among plant species have received increased 

attention in recent years (Callaway 1995, Brooker et al. 2008). Positive interactions 

are particularly important for initial plant establishment, after which competition for 

limiting resources often (but not always) becomes more dominant among mature 

plants. Positive interactions can occur in all vegetation types but appear to be most 

important in stressful environments, like alpine, semiarid, and arid ecosystems 

(Callaway 1995, Callaway et al. 2002). Elevated CO2 and changing climate are 

likely to alter positive interactions among plants primarily by increasing or reducing their importance (making them more or less benefi cial). In some cases, however, 

disturbances, species migrations, or local extinctions, could add or remove benefactor or benefi ciary species, with potentially large impacts on vegetation structure and 

ecosystem functioning.

Large trees and shrubs can facilitate establishment and persistence of understory plants (and tree seedlings) by modifying understory microclimate and soil 

resource availability (McPherson 1997, Scholes and Archer 1997). Shading from 

overstory trees can reduce temperatures and evaporative demand in understory 

plants (McPherson 1997), while soils under tree canopies can accumulate nutrients 
42

GENERAL TECHNICAL REPORT PNW-GTR-900

and provide greater nutrient availability to plants through more rapid litter decomposition and nutrient cycling (Reich et al. 2001, Scholes and Archer 1997). Subalpine conifers can reduce snowpack duration and increase growing season length, 

thereby facilitating establishment of understory plants and tree seedlings (Brooke 

et al. 1970), or can increase local soil moisture by altering windfl ow patterns and 

enhancing local snow deposition (Holtmeier and Broll 1992). 

In the Great Basin of North America, sagebrush (Artemisia tridentatea (Nutt.) 

can increase near-surface soil water availability by transporting water from deeper, 

moister soils at night via “hydraulic lift,” thereby increasing water (and perhaps 

nutrient) availability to neighboring plants (Caldwell and Richards 1989, Caldwell 

et al. 1998, Richards and Caldwell 1987). Griffi th (2010) found that higher soil 

fertility and less extreme microclimates under two Great Basin shrubs, big sagebrush (A. tridentatea) and bitterbrush (Purshia tridentatea (Pursh) DC.), promoted 

higher densities and growth rates of an invasive annual grass, cheatgrass (Bromus 

tectorum L.). 

Trees and shrubs can also facilitate woody plant invasions of grasslands 

indirectly by suppressing or eliminating potential competitors (Rice et al. 2012). 

Although overstory tree and shrub canopies can facilitate initial understory 

establishment, relationships between understory and overstory plants can turn 

competitive if light becomes the dominant factor limiting growth and survival. In 

a changing climate, however, overstory modifi cation of understory microclimate 

could become increasingly important for facilitating establishment and maintaining populations.

Plants can also facilitate establishment and persistence of other plants through 

their effects on soil nutrient availability and acquisition (Callaway 1995). Nitrogen 

fi xation by legumes and nonleguminous shrubs can increase soil nutrient availability, which can facilitate establishment and persistence of species that might 

otherwise be excluded by low nutrient availability. Because nitrogen-fi xing species 

benefi t from elevated CO2, such interactions could become more important in 

the future. Plants may facilitate the establishment of new individuals of the same 

species or of other species by maintaining local populations of compatible mycorrhizal fungi, thereby promoting rapid colonization of seedling roots and associated 

benefi ts for water and nutrient uptake (Amaranthus and Perry 1989, Dickie et al. 

2005).

Competition

Competition for light, water, and nutrients is an important process driving vegetation structure and composition (Connell and Slatyer 1977, Tilman 1982). Competition occurs within and among species and is often density dependent. Plants can 
43

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

avoid direct competition for limiting resources by partitioning resource usage 

in time (e.g., different growth phenologies) or space (“fi rst come, fi rst served”). 

Theory suggests that many different species can coexist, despite competition, by 

varying their relative resource usage or if disturbances are frequent enough to 

prevent competitive exclusion (Grubb 1977, Tilman 1982). 

Changing climate and atmospheric CO2 concentrations could alter the intensity 

of competitive interactions among plants and the relative advantage of competing 

species by altering plant resource availability, rates of resource consumption, or 

relative demand for resources. Increasing atmospheric CO2 levels make CO2 more 

available to plants, alter demand for soil water resources, and potentially alter soil 

nutrient availability (see “Ecosystem Responses” section below); such changes 

could be more benefi cial for some species than for others, thereby altering the 

competitive balance. Changes in temperature and precipitation alter soil water 

availability and potential evapotranspiration (demand for soil water), thereby altering water-related stress and potentially altering competition intensity (Maestre et al. 

2009, and references therein). Changes in plant density could also alter competition 

intensity, while climate-mediated invasions of new species or extirpation of existing 

species could alter plant community composition and the relative demand for different resources (similar to changes in resource availability). 

Demonstrating the effects of changing climate and CO2 on vegetation composition and structure via competitive interactions is diffi cult. Many factors contribute 

to changes in vegetation structure and composition in natural vegetation (e.g., 

disturbances and factors regulating successful regeneration), making it diffi cult to 

determine proximate causes for change. Experimental approaches are better able to 

isolate causes but typically require limiting the number of species and environmental conditions. In a synthesis study of recent vegetation changes in arctic and alpine 

tundra, Elmendorf et al. (2012) reported increases in plant height, litter abundance, 

and evergreen shrub abundance, and reductions in bare soil, apparently in response 

to recent warming. Such changes are expected to increase the future importance of 

competition in these systems, particularly during plant establishment (Klanderud 

2010). In experimental studies, Suttle et al. (2007) found that supplementing natural 

precipitation in winter and spring in California grasslands produced signifi cant 

changes in plant community composition, while Niu and Wan (2008) found that 

warming temperatures altered the competitive hierarchies of grasses and forbs in 

temperate steppe vegetation of northern China. A comparison of warming and CO2

enrichment effects on short-grass steppe vegetation found that warming favored C4

grasses while elevated CO2 favored C3 grasses (Morgan et al. 2011); elevated CO2

had little effect on total species richness but increased shrub cover 20-fold (Morgan 

et al. 2007).
44

GENERAL TECHNICAL REPORT PNW-GTR-900

Herbivory

Elevated CO2 and changing climate could affect herbivore interactions and their 

impacts on vegetation by increasing or reducing synchrony in herbivore activity and 

plant availability, altering herbivore abundance or feeding behavior, and altering 

plant nutritional value. Plants and insect herbivores can both alter the timing of their 

growth stages based on environmental cues, but the cues used are not necessarily 

identical (Rathcke and Lacey 1985). Differing phenological responses to changing 

temperature and precipitation patterns could therefore alter plant-herbivore and 

insect-host interactions by increasing or reducing synchrony in life stage development (Harrington et al. 1999, Liu et al. 2011, Parmesan 2007). Inouye et al. (2000) 

observed a changing relationship over 25 years between snowmelt and fl owering in alpine meadows (which was variable, but stable) and progressively earlier 

emergence of yellow-bellied marmots (Marmota fl aviventris), apparently caused 

by warming air temperatures, which produced progressively longer periods of food 

shortages for the marmots prior to snowmelt. 

Changing climatic conditions could alter herbivore communities through 

migrations or changes in predator activity (Tylianakis et al. 2008). Organisms at 

higher trophic levels are expected to respond more readily than vegetation to changing environmental conditions (mobility being the key), so migration responses could 

alter the species composition and abundance of herbivores and their predators, with 

uncertain effects on herbivory. Changes in winter snowpack depth and duration 

could alter migration patterns and winter feeding behavior by deer and other large 

herbivores, with potential impacts on vegetation structure and composition and 

herbivore performance.

Elevated CO2 could also alter herbivore communities and herbivore performance through its effects on plant productivity and tissue chemistry (Robinson 

et al. 2012, Zvereva and Kozlov 2006). Numerous studies have reported effects of 

elevated CO2 on plant tissue chemistry that could reduce palatability for herbivores, 

including increased concentrations of nonstructural carbohydrates and phenolics, 

reduced nitrogen concentrations, higher carbon/nitrogen ratios, and increased 

tissue toughness (Lincoln et al. 1993, Lindroth 2010, Robinson et al. 2012, Zvereva 

and Kozlov 2006). These changes in plant tissue chemistry under elevated CO2

can reduce insect herbivore performance (Peñuelas and Estiarte 1998, Stiling and 

Cornelissen 2007, Zvereva and Kozlov 2006) and alter arthropod community composition (Hamilton et al. 2012). However, the detrimental effects of elevated CO2

on herbivores via changes in plant tissue chemistry may be partially or fully offset 

when combined with warming temperatures, as warmer temperatures may alter net 

plant production of and demand for carbohydrates (Zvereva and Kozlov 2006). 

Numerous studies 

have reported 

effects of elevated 

CO2 on plant tissue 

chemistry that could 

reduce palatability for 

herbivores.
45

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Community Responses

Plant community responses to elevated CO2 and climate change refl ect the sum of 

individual species responses, including changes in seed production and dispersal, 

establishment success, physiological tolerances of altered environmental conditions, and biotic interactions. Paleoecological studies suggest that species will 

respond individually to changing environmental conditions (Davis 1981, Jackson 

and Overpeck 2000). Some common plant associations may persist, whereas others 

disappear and are replaced by novel associations (Jackson and Overpeck 2000). As 

previous sections in this chapter have shown, interactions among plant physiological responses to changing CO2 and temperature and biotic interactions can create a 

wide range of outcomes for plant communities.

Most examples of plant community responses to changing climate involve 

invasion of communities by new species and associated changes owing to altered 

resource availability and biotic interactions (Breshears et al. 2008, Elmendorf et al. 

2012, Kelly and Goulden 2008, Walther et al. 2005). Community changes have also 

been documented in response to increased dominance of woody species in response 

to elevated CO2 (Morgan et al. 2007) or changing disturbance regimes (Briggs et al. 

2005). Extirpation of a dominant species can also produce signifi cant community 

changes (Allen and Breshears 1998, Mueller et al. 2005), though stabilizing processes within communities may limit long-term community responses to extreme 

events (Kreyling et al. 2008, Lloret et al. 2012). 

Community responses will also refl ect responses of organisms at higher trophic 

levels (Walther 2010). These organisms may respond directly to changing climate 

and associated environmental stressors, or they may respond to climate- or CO2-

mediated changes in plant communities. Changes in pollinator, herbivore, and 

predator communities can also produce changes in plant communities through 

changes in reproduction, establishment, biomass loss, and mortality. 

Ecosystem Responses

Changing climate and increasing CO2 concentrations have important implications 

for ecosystem structure and function. Climate change and elevated CO2 have the 

potential to alter ecosystem carbon uptake and storage, nutrient cycling rates, and 

feedbacks among carbon uptake, nutrient cycling, and vegetation structure and 

composition. In this section, we highlight some key fi ndings regarding ecosystem 

responses to elevated CO2 and changing climate.

Carbon uptake and storage is an important issue, as terrestrial ecosystems may 

be able to partially mitigate anthropogenic carbon emissions and their effects on 

Paleoecological 

studies suggest 

that species will 

respond individually 

to changing 

environmental 

conditions.
46

GENERAL TECHNICAL REPORT PNW-GTR-900

climate by fi xing atmospheric carbon and storing it in aboveground or belowground 

biomass, in coarse woody debris, or in soil organic matter. These different carbon 

pools provide different mean residence times for carbon and interact differently 

with environmental changes and disturbances (Norby and Zak 2011). Carbon uptake 

also provides energy for biological processes, including decomposition of organic 

matter and release of mineral nutrients, which leads to feedbacks between carbon 

uptake and nutrient availability.

Productivity and Nutrient Cycling

Experimental studies have shown that elevated CO2, warming temperatures, and 

increasing precipitation can all increase plant growth and net primary production 

across a wide range of terrestrial ecosystems (Dieleman et al. 2012, Hyvönen et 

al. 2007, Norby and Zak 2011, Wu et al. 2011, Zak et al. 2011), although combined 

effects are not always additive (Dieleman et al. 2012, Wu et al. 2011). On average, 

elevated CO2 and warming temperatures increase biomass production alone and in 

combination, but elevated CO2 stimulated fi ne root biomass production more than 

aboveground biomass production (Dieleman et al. 2012, Norby and Zak 2011, Wu 

et al. 2011). In a young aspen (Populus tremuloides Michx.) forest, elevated CO2

increased net primary productivity by 26 percent (Zak et al. 2011). The mechanisms 

by which productivity is enhanced (and sustained) are still unclear, however, and 

appear to involve not only the direct effects of CO2, temperature, and water availability (and their interactions) on photosynthesis, respiration, and growth, but also 

indirect effects on nutrient cycling and nitrogen availability (Felzer et al. 2011).

Productivity gains under elevated CO2 can decline over time on nutrient-limited 

sites as sequestration of nitrogen in long-lived plant biomass and soil organic matter 

progressively limits nitrogen availability (de Graaff et al. 2006, Luo et al. 2004, 

Reich et al. 2006a). Evidence for this progressive nitrogen limitation comes from 

long-term free-air CO2 enrichment (FACE) studies with fertilization treatments in 

tallgrass prairie (Reich et al. 2006a) and sweetgum (Liquidambar styracifl ua L.) 

forest (Norby et al. 2010), but not loblolly pine forest (McCarthy et al. 2010). Norby 

et al. (2010) noted that reduced forest productivity over time owing to progressive 

nitrogen limitation is common as forest biomass increases with stand age, but that 

elevated CO2 appears to accelerate the process. Progressive nitrogen limitation on 

productivity can be delayed or avoided only if nitrogen inputs through fi xation or 

atmospheric deposition equal or exceed rates of nitrogen sequestration in biomass, 

litter, and soils. 

Recent results from long-term FACE studies suggest that several feedback processes may work to reduce or reverse the effects of progressive nitrogen limitation 

on productivity. Schleppi et al. (2012) attributed elevated nitrifi cation rates and high 
47

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

levels of soil nitrate availability under trees subjected to elevated CO2 to higher 

soil moisture levels produced by reduced evapotranspiration. Recent studies have 

concluded that observed reductions in soil carbon and increased nitrogen availability could be the result of root “priming,” in which trees export carbon to soil biota 

through the root system, thereby stimulating soil organic matter decomposition by 

fungi and bacteria, and subsequent release of mineral nutrients bound up in that 

organic matter (Drake et al. 2011, Phillips et al. 2012, Schleppi et al. 2012, Zak et al. 

2011). 

Soil temperatures infl uence organic matter decomposition processes and the 

cycling of nitrogen and other important nutrients, which can feed back on plant 

growth and productivity (Berbeco et al. 2012, Felzer et al. 2011, Harmon et al. 1986, 

Melillo et al. 2011, Rustad et al. 2001). Warming temperatures typically stimulate 

biological activity and may signifi cantly increase rates of soil organic matter 

decomposition in temperate forests (Berbeco et al. 2012, Saxe et al. 2001). Warming 

soils can also increase rates of nitrogen mineralization and nitrifi cation, making 

more nitrogen available for uptake by plants (Butler et al. 2012, Melillo et al. 2011, 

Rustad et al. 2001, Van Cleve et al. 1990). Understanding the potential for increased 

nitrogen availability is important, as nitrogen is an important plant nutrient that 

often limits growth in forested ecosystems (Vitousek et al. 1997). Temperature 

effects on soil organic matter decomposition and nutrient cycling are likely to 

vary considerably, however, with soil fertility and vegetation composition, as these 

infl uence litter quality and the potential for immobilization of nutrients in microbial 

biomass.

The stimulatory effect of warming temperatures on organic matter decomposition and nutrient cycling is likely to be limited by precipitation and soil water 

availability, however (Felzer et al. 2011, Harmon et al. 1986, Saxe et al. 2001). 

Decomposition of coarse woody debris varies with wood moisture content, as water 

becomes unavailable to microbes below 30 percent water content, while very high 

moisture contents create anaerobic conditions (Harmon et al. 1986). Similarly, 

spatial and temporal variability in precipitation and soil moisture infl uence soil 

respiration rates in seasonally dry biomes like savannas and shrublands (Norton et 

al. 2011, Raich et al. 2002). Low soil moisture can also limit nitrogen mineralization 

and availability (Felzer et al. 2011, McFarlane et al. 2010).

Finally, changes in plant communities could also alter soil environments, soil 

biota, nutrient cycling processes, and soil nutrient availability, with potential effects 

on net primary productivity. Tree invasions into shrub-steppe of the Great Basin 

have altered the spatial distribution of soil carbon and nutrients (Klemmedson 

and Tiedemann 2000, Rau et al. 2011b), while tree removal in Texas caused a slow 
48

GENERAL TECHNICAL REPORT PNW-GTR-900

reversal of tree effects on soils (Tiedemann and Klemmedson 1986a, 1986b). Similarly, experimental fi eld studies have shown that grassland species can signifi cantly 

alter nitrogen cycling rates (Craine et al. 2002, Wedin and Tilman 1990) and the 

amount and vertical distribution of soil carbon and nitrogen (Rau et al. 2011a).

Carbon Storage

Storing carbon in forests has been proposed as a strategy for mitigating increasing 

atmospheric CO2 concentrations and their effects on current and future climate 

(Malmsheimer et al. 2008, McKinley et al. 2011). Through increased net primary 

production and ecosystem carbon exchange, anthropogenic carbon emissions could 

be partially offset by carbon uptake and long-term storage in terrestrial ecosystems, 

particularly in trees and shrubs (perennial woody tissue) and soils. It is estimated 

that extensive afforestation efforts and active management following disturbances 

could allow increased forest carbon storage to offset 10 to 20 percent of anthropogenic carbon emissions in the United States (McKinley et al. 2011). 

Disturbance regimes (and their responses to changing climate) will be important for determining whether woody plant biomass can store atmospheric carbon 

effectively (McKinley et al. 2011), particularly in aboveground biomass. Large-scale 

insect outbreaks (Kurz et al. 2008), fi res (Kashian et al. 2006, Westerling et al. 

2006), or drought-mediated mortality (van Mantgem et al. 2009) could convert 

forests from carbon sinks to carbon sources, at least in the short term. If regeneration and growth eventually returns forests to their former condition (e.g., biomass, 

coarse woody debris, and soil carbon), the long-term effect on carbon storage 

is neutral (Hyvönen et al. 2007, Kashian et al. 2006). However, if disturbances 

convert forests to stable savannas, grasslands, or shrublands, the long-term effect 

on carbon storage is likely to be negative (Kashian et al. 2006).

Soils store signifi cant amounts of carbon for long periods of time and hold the 

majority of terrestrial carbon stocks. Carbon enters the soil in the form of woody 

debris (e.g., branches and roots), plant litter (e.g., leaves and fi ne roots), dead algal 

or bacterial cells, and root exudates (Kilham 1994). Residence times are short 

for many types of soil carbon, as physical processes and soil biota cause rapid 

decomposition, but other soil organic carbon stocks are much more stable and could 

provide long-term storage of carbon (Norby and Zak 2011). Soil warming has been 

shown to increase biological activity; soil respiration; and decomposition rates of 

litter, fi ne woody debris, and other (unspecifi ed) forms of soil carbon (Berbeco et 

al. 2012, Dieleman et al. 2012, Hyvönen et al. 2007, Melillo et al. 2011, Rustad et al. 

2001, Zak et al. 2011), thereby offsetting or reversing increased carbon inputs from 

increased net primary production.
49

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Until recently, there was general belief that effects of elevated CO2 on litter 

chemistry would offset or reverse warming effects on decomposition and soil 

carbon storage in forests by increasing carbon inputs, reducing nitrogen concentrations (increasing carbon:nitrogen ratios) and increasing concentrations of secondary 

compounds in litter. Recently, however, results from long-term experiments with 

elevated CO2 suggest that elevated CO2 could actually accelerate decomposition 

of soil organic matter and reduce soil carbon storage (Cheng et al. 2012, Drake 

et al. 2011, Kowalchuk 2012, Phillips et al. 2012, Schleppi et al. 2012, Zak et al. 

2011). Unfortunately, it is unclear how these results, derived primarily from experiments in temperate deciduous forests, translate to western coniferous forests and 

rangelands with signifi cant seasonal water limitations that can infl uence soil carbon 

turnover (Norton et al. 2011). 

Invasive Species

Invasive plants are introduced nonnative, exotic, or nonindigenous species that 

potentially can become successfully established or naturalized, and that spread 

into new localized natural habitats or ecoregions and potentially cause economic or environmental harm (Lodge et al. 2006). Familiar examples include 

the shrub scotch broom (Cytisus scoparius L. Link) and the exotic annual grass 

cheatgrass (Bromus tectorum). Some native species like juniper (Juniperus spp.) 

have expanded their range in recent decades and are treated as invasive in certain 

ecosystems (Miller and Wigand 1994, Miller et al. 2005). This document only 

considers nonnative invasive plants, but we note that climate change could cause 

additional native plants to exhibit invasive behavior; indeed, many would have to 

do so to migrate at currently projected rates. 

There is considerable evidence that future climate change will further increase 

the likelihood of invasion of forests and rangelands and the consequences of those 

invasions, largely because of the potential for increased ecosystem disturbance (e.g., 

wildfi re, landslides), the impact of warming on species distributions, the enhanced 

competitiveness of invasive plants as a result of elevated CO2, and increased stress 

to native species and ecosystems (Dukes and Mooney 1999, Pauchard et al. 2009, 

Ziska and Dukes 2011). Pauchard et al. (2009) noted that high levels of natural 

disturbance in alpine systems (e.g., landslides) are forecast to increase in frequency 

and extent owing to climate change. Numerous studies have documented the positive relationship between fi re and the spread of invasive plants (D’Antonio et al. 

2000, Kerns et al. 2006, Keeley and McGinnis 2007). Postdisturbance invasion may 

be particularly problematic in areas adjacent to seed sources.

Climate change could 

cause some native 

plants to exhibit 

invasive behavior; 

indeed, many would 

have to do so to 

migrate at currently 

projected rates.
50

GENERAL TECHNICAL REPORT PNW-GTR-900

With warming temperatures, the upper elevational and latitudinal limit of many 

invasive plants may expand. For example, the current distribution of cheatgrass 

is limited by soil temperature at the higher elevations and soil moisture at lower 

elevations (Chambers 2007). Theoretically, as the soil temperature regime warms 

at higher elevation and the soil moisture regime dries in the lower elevation, 

cheatgrass could shift upward in elevation assuming no signifi cant change in the 

precipitation regime. Recent evidence suggests that invasive plants may be better 

able to adjust to rapid changes in abiotic conditions by tracking seasonal temperature trends and shifting their phenologies (e.g., earlier spring warming) (Willis et al. 

2010). Invasives might also be able to migrate more rapidly than most native species 

owing to the rapid dispersal ability and genetic fl exibility that allowed them to 

become invasives in the fi rst place. 

Increases in productivity in response to elevated CO2 have also been documented for many invasive plant species including cheatgrass, Canada thistle 

(Cirsium vulgare (Savi) Ten.), spotted knapweed (Centaurea stoebe L.), yellow 

star-thistle (Centraurea solstitialis L.), and kudzu (Pueraria montana (Lour.) Merr.) 

(Dukes et al. 2011, Ziska and Dukes 2011). However, plant species response to CO2

enrichment is less predictable when they are grown in diverse communities and 

in the fi eld (Dukes and Mooney 1999, Ziska and Dukes 2011) and actual response 

to CO2 enrichment may be limited by other nutrient constraints and water availability. The expansion of cheatgrass into salt desert shrub areas, which generally 

occur in areas of lower precipitation than sagebrush-steppe ecosystems (Jessop and 

Anderson 2007), may suggest that improved water-use effi ciency (e.g., as a result of 

elevated CO2 concentrations) is allowing cheatgrass to expand into areas previously 

considered too dry.

Species performance such as growth, phenology, and productivity may also 

change in novel conditions (Willis et al. 2010). The ability of the native plant community to resist an invasion may also change in the future. For example, invasive 

plants may be exposed to above- and belowground biotic interactions different from 

those in their current range, and “enemy-release” may occur (Engelkes et al. 2008). 

Climate change will also alter numerous aspects of propagule supply. Most invasive 

species reach new regions by purposeful or accidental human-aided transport 

(tourism, commerce). Human population (urban, visitation) is positively correlated 

to plant invasions (Eschtruth and Battles 2009, Lonsdale 1999). Atmospheric patterns (hurricanes, wind patterns) that transfer seeds will also change in the future. 

Climate change may also result in increased management actions that cause new 

disturbances (e.g., biofuel production, thinning).
51

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 4: Vegetation Sensitivity 

to Changing Disturbance Regimes

Disturbances are a major driver of vegetation change, as they kill or damage mature 

vegetation and release resources that can be used by newly established individuals. Disturbances typically alter vegetation structure, which can alter understory 

environments, particularly in forests. Disturbances produce compositional changes 

in vegetation if new species replace those killed by disturbance. They can also 

facilitate genetic shifts within existing populations or alter the relative abundance 

of existing species. Some of the most common and infl uential disturbance types in 

the Pacifi c Northwest are also infl uenced by climate and climatic changes in some 

way. We therefore also discuss the effects of past and future climatic changes on 

stress-related mortality, fi re, and insect and disease outbreaks as indirect conduits 

for exposing vegetation to environmental changes. 

Intact plant communities are often diffi cult for new species to invade. Resource 

niches are partially or completely fi lled, limiting potential invaders to those that can 

tolerate lower resource availability (for at least one resource) than established species (Tilman 1982, 2004) or to periods of additional resource availability (Davis et 

al. 2000). Seed deposition from local populations is typically much more abundant 

than that from distant populations, giving local species an advantage in claiming 

open growing space and available resources. Mature trees and shrubs are able to 

buffer themselves (and sometimes their neighbors) against climatic variability and 

resource limitations, thereby reducing climate-related stress. Long-lived woody 

species can persist as relict populations for hundreds of years and maintain the 

potential to regenerate during occasional periods of favorable climate (Hampe and 

Jump 2011). 

Climate-driven vegetation mortality is expected to be an important driver of 

vegetation changes in response to changing climate. In severe cases, climate-driven 

vegetation mortality may directly alter vegetation by causing local extinction of one 

or more species (on the “trailing edge”). Plant mortality can also facilitate new species colonization and changes in relative abundance of previously present species by 

opening up physical growing space, reducing competition for critical soil resources, 

reducing local seed abundance, and altering site microclimate. In this chapter, we 

briefl y review three major sources of climate-driven vegetation mortality—physiological stress, fi re, and insects and diseases—and their interactions, and how each 

may be affected by a changing climate.

Physiological Stress—Drought 

Climatic variability and change can kill plants by inducing stress beyond the 

tolerances of individual plants, either in terms of intensity or duration (Allen et al. 

Disturbances are 

a major driver of 

vegetation change, 

as they kill or damage 

mature vegetation 

and release resources 

that can be used by 

newly established 

individuals.
52

GENERAL TECHNICAL REPORT PNW-GTR-900

2010). The most common physiological stress for Pacifi c Northwest vegetation is 

probably low soil water availability (drought), though low temperatures and late 

snowmelt can also produce signifi cant physiological stress in subalpine, alpine, and 

boreal ecosystems. Future warming is likely to increase the frequency and severity 

of plant drought stress, while potentially reducing stress from extreme cold or short 

growing seasons at high elevations. 

Drought is a common physiological stress that limits establishment and productivity at lower and middle elevations throughout much of western North America. 

Physiological stress from drought can be amplifi ed by high temperatures, reducing 

the time required for drought-induced mortality (Adams et al. 2009). Episodes 

of widespread drought-induced mortality have been reported for ponderosa pine 

(Pinus ponderosa Lawson and C. Lawson) and pinyon pine (P. edulis Engelm.) 

in the Southwestern United States (Adams et al. 2009; Allen and Breshears 1998; 

Breshears et al. 2005, 2009; Koepke et al. 2010) and for aspen (Populus tremuloides 

Michx.) in western Canada (Hogg et al. 2008). Increased drought stress due to 

warmer temperatures has also been implicated in observed increases in mortality 

throughout forests of western North America (Ganey and Vojta 2011, 

van Mantgem and Stephenson 2007, van Mantgem et al. 2009).

The proximate mechanisms causing tree mortality during drought can be 

diffi cult to determine, largely because there are often multiple contributing factors 

(Anderegg et al. 2012). Plants typically reduce stomatal conductance in response 

to drought—which can reduce net carbon uptake—and reduce growth (Anderegg 

2012). Droughts can produce extended periods of net carbon loss, particularly 

if maintenance respiration rates are high, and this can reduce resistance to bark 

beetles (Breshears et al. 2009, Ganey and Vojta 2011). Severe droughts can cause 

widespread tree mortality through reductions in nonstructural carbon reserves, 

cavitation and loss of hydraulic conductance, and increased susceptibility to insect 

outbreaks (Adams et al. 2009; Anderegg et al. 2012; Breshears et al. 2009; Frey et 

al. 2004; Hogg et al. 2008; Leuzinger et al. 2009; McDowell et al. 2008, 2011; Plaut 

et al. 2012).

Species differ in their tolerances of heat and drought stress, so stress-related 

mortality tends to favor some species over others and can produce signifi cant 

changes in vegetation structure and composition (Koepke et al. 2010, Mueller et al. 

2005). For example, mortality rates were considerably higher for pinyon pine than 

for its common associate, one-seeded juniper (Juniperus monosperma), during the 

2002–2003 drought in the Southwestern United States, leading to lower tree densities and increased overstory dominance by junipers (Koepke et al. 2010, Mueller et 

al. 2005). Furthermore, high rates of mortality and dieback for associated shrubs 
53

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

during the drought produced dramatic reductions in total woody canopy cover, 

allowing herbaceous understory populations to expand rapidly (Rich et al. 2008). 

Drought-related mortality could also alter vegetation structure by reducing plant 

density, perhaps reducing future demands on limited soil water (but see Ganey and 

Vojta 2011), and produce persistent shifts in biome boundaries (Allen and Breshears 

1998).

Warming temperatures under future climate change are likely to increase the 

frequency and severity of droughts throughout the Pacifi c Northwest, regardless 

of small changes in mean annual precipitation. We are not aware of any modern 

accounts of extensive tree mortality caused by drought stress in the Pacifi c Northwest (comparable to those recently reported for the Southwestern United States). If 

drought frequency and intensity increases in the future, however, we would expect 

to start seeing widespread regeneration failures and dieback or mortality episodes, 

along with insect outbreaks and fi re (see below). Although future droughts may be 

most obvious in the water-limited ecosystems of the interior Pacifi c Northwest and 

Siskiyou Mountains, species in those regions are also the best adapted to drought, 

so we should not expect drought-related mortality to be limited to those regions. 

Fire

Climate is an important factor infl uencing the spatial distribution of fi re regimes 

from landscape to global scales, including fi re frequency, intensity, seasonality, and 

size (Krawchuk et al. 2009). At a very basic level, climate controls the distribution 

and productivity of vegetation that provides fuel for wildfi res, excluding wildfi res 

from desert regions with little or no vegetation (Flannigan et al. 2009). Within 

vegetated regions, climatic controls on plant productivity partially infl uence fi re 

frequency and intensity by infl uencing rates of fuel accumulation. Climate also 

determines the timing and duration of the fi re season during which fi re can occur 

through its effects on fuel moisture, fi re weather, and ignitions. Finally, climate 

interacts with topography, soils, and land use to infl uence fi re extent. 

Within any given vegetation type and fi re regime, climatic variability infl uences the occurrence and characteristics of individual fi re events and fi re-year 

characteristics (Taylor et al. 2008). Climatic variability at annual to decadal time 

scales can alter fi re season length, fuel characteristics (e.g., mean moisture levels), 

fi re weather, and ignition frequencies, thereby infl uencing fi re occurrence, fi re 

intensity, and area burned. Drought years are associated with large area burned 

and regionally synchronous fi re occurrences in forests of western North America 

(Brown 2006, Gedalof et al. 2005, Hessl et al. 2004, Littell et al. 2009, McKenzie 

et al. 2004, Trouet et al. 2010), as drought increases fuel fl ammability and enhances 
54

GENERAL TECHNICAL REPORT PNW-GTR-900

fi re spread potential across diverse landscapes. Warm spring and summer temperatures are also positively associated with fi re frequency, fi re duration, and area 

burned in these forests, as warm spring temperatures extend the fi re season length 

(providing more opportunity for ignitions) and warm summer temperatures favor 

more extreme fi re behavior and hinder fi re suppression efforts (Heyerdahl et al. 

2008, Taylor et al. 2008, Westerling et al. 2006). Climate infl uences fi re differently 

in dry western grasslands, however, where large fi res are commonly associated 

with drought years that follow multiple wet years during which plant productivity 

and fuel production are elevated (Littell et al. 2009). In both forests and grasslands, 

however, fi re frequency and area burned are related to fuel availability (amount and 

moisture content), fi re season length, and fi re weather, all of which are infl uenced 

by climate.

Wildfi re area burned in the Western United States varied considerably through 

the course of the 20th century, with decadal trends as well as considerable interannual variability. Average area burned by wildfi res was generally high early in the 

century (around 1916–1930) before declining to low levels during the middle of the 

century (about 1950 to 1975) and then increasing again with a series of large fi re 

years in the last two to three decades (Littell et al. 2009). Although technological 

advances improved fi refi ghting effectiveness in the mid-20th century, several studies have concluded that climate played an important role in controlling area burned 

throughout the 20th century (Littell et al. 2009, McKenzie et al. 2004). An analysis 

of wildfi re area burned since 1970 in forests of the Western United States concluded 

that a trend toward increasing mean annual area burned was caused by higher 

spring and summer temperatures and earlier snowmelt that increased the duration 

of the fi re season, particularly in mid-elevation forests (Westerling et al. 2006).

Future climatic changes are likely to alter wildfi re regimes and wildfi re effects 

by altering fi re season length, fi re weather, fuel production, and vegetation structure 

and composition. Warmer spring and summer temperatures would lengthen the 

fi re season and increase fi re frequency in forests, particularly in temperate and 

high-elevation forests that currently support high- and mixed-severity fi re regimes 

(Westerling et al. 2006). Warming temperatures would also alter mean fi re weather 

conditions (and fuel moisture), producing higher potential fi re severity and larger 

fi re sizes in many areas where fi re weather is currently suboptimal for fi re spread. 

Over longer periods, climatic changes (and disturbances) could also modify vegetation structure and composition, providing a feedback on fi re frequency and severity 

(Westerling et al. 2006). 

In the short term, warming temperatures could increase the frequency and 

extent of large, high-severity wildfi res in Pacifi c Northwest forests (Krawchuk et 

A recent trend toward 

increasing mean 

annual area burned 

is attributed to higher 

spring and summer 

temperatures and 

earlier snowmelt that 

increased the duration 

of the fi re season, 

particularly in midelevation forests.
55

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

al. 2009). Mesic and dry-mesic forests that support high productivity and surface 

fuels could be a particular concern, but these forest types burn infrequently owing 

to high mean fuel moisture content and short fi re seasons. A warmer climate, with 

longer fi re seasons and lower fuel moisture could allow wildfi res to “mine” these 

fuels over one to several fi res, some of which are likely to be of high severity. 

Fire regime changes in semiarid shrublands and grasslands are less certain 

and could, in some cases, be quite different from those of forests. These vegetation types typically have long fi re seasons; however, fi re frequency and extent are 

limited by ignitions and fuel continuity. It is not clear what infl uence climate change 

might have on ignition frequency (e.g., lightning strikes, human activity). Climate 

change could alter vegetation productivity, fuel production, and fuel continuity by 

changing the amount and seasonality of precipitation inputs to soil moisture and 

rates of evapotranspiration. It is not clear if the net result would be increased or 

reduced fi re frequency, and results may be spatially variable. Cheatgrass (Bromus 

tectorum L.) and other annual exotic grasses have progressively invaded dry shrublands throughout the Western United States in recent decades. These grasses have 

altered the fi re regime by increasing vegetation fl ammability and horizontal fuel 

continuity, which greatly increases the potential for fi re spread and larger wildfi res 

(Link et al. 2006). 

Insects and Diseases

Like fi re, insects and diseases facilitate changes in vegetation structure and composition by damaging or killing plants (especially dominant trees and shrubs), releasing suppressed individuals and species, and creating opportunities for colonization 

by new species. Insects and plant diseases in western North American forests 

may kill individual trees within stands or more than 50 percent of the trees across 

large regions (Ganey and Vojta 2011, Raffa et al. 2008). Their long-term effects on 

vegetation structure and composition depend not only on spatial extent and percentage mortality within the disturbed area, but also on whether disturbance agents are 

affecting canopy dominants, older or younger individuals, suppressed individuals, 

or some combination of these (e.g., Klenner and Arsenault 2009). 

Climate is one of several factors infl uencing insect and disease disturbance 

regimes and the incidence of large disturbance events. Climate infl uences the geographic distribution, population dynamics, and disturbance effects of insects and 

diseases through (1) direct environmental infl uences on the development and survival of insects and disease organisms, (2) altered host susceptibility and defenses, 

and (3) indirect effects caused by environmental infl uences on biotic interactions 
56

GENERAL TECHNICAL REPORT PNW-GTR-900

with predators (Ayres and Lombardero 2000, Garrett et al. 2006, Kliejunas et al. 

2009, Sturrock et al. 2011). 

Temperature is the dominant abiotic factor directly affecting herbivorous 

insects as it directly affects development, survival, range, and abundance (Bale 

et al. 2002). In particular, winter temperatures infl uence survival of herbivorous 

insects in temperate zones, while spring/summer temperatures infl uence insect life 

cycle duration, synchrony of insect life cycle stages with host plant phenology, and 

insect generation time (Bale et al. 2002). Climatic variability can also alter biotic 

interactions involving insects and diseases by altering the abundance of predators, 

parasites, mutualists, and competitors (Ayres and Lobardero 2000). Because of 

these effects, geographic distributions of insect species have changed in the past 

in response to climate change (Bale et al. 2002, and references therein).

Climate also infl uences insect and disease impacts indirectly by modifying 

vigor and defenses in host plants (Bentz et al. 2010, Raffa et al. 2008). Climatic 

variability can alter physiological stress levels and carbon fi xation in host plants, 

increasing or reducing their susceptibility to insect attacks and plant diseases. 

Woody plant mortality in response to heat and drought stress is often mediated by 

insects and diseases; trees weakened by prolonged drought stress have reduced 

defenses against the insect and disease attacks that eventually kill the tree (Allen 

et al. 2010, McDowell et al. 2011). For example, Ganey and Vojta (2011) reported 

that increased mortality in ponderosa pine and mixed-conifer forests during a 

period of severe drought was attributable to a suite of insects, mediated by drought. 

Similarly, extensive, rapid, mortality of aspen (Populus tremuloides Michx.) in 

Colorado during the same drought was attributed to insects and diseases that 

normally would have had limited impacts if drought had not predisposed the trees 

to mortality (Worrall et al. 2008).

Insect- and disease-caused mortality tends to be species specifi c, so large 

outbreaks can signifi cantly alter vegetation structure and composition in mixedspecies stands (Astrup et al. 2008). These outbreaks also open up growing space 

that could be colonized by new species, but seed source limitations and competition 

from residual vegetation can make colonization diffi cult (Astrup et al. 2008). Future 

vegetation is more likely to develop from residual vegetation and release of advance 

regeneration (trees and shrubs).

Future climatic changes—particularly temperature changes—are expected to 

signifi cantly alter insect and disease disturbance regimes in terrestrial ecosystems 

(Kliejunas et al. 2009). Projecting changes to insect and disease activity is diffi cult 

owing to the many potential interacting factors, but some effects of increasing 
57

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

temperatures can be inferred with some certainty. For forests, insect and disease 

disturbances will probably increase, as the ability of the pathogens to adapt to new 

climatic conditions will be greater than that of their long-lived hosts (Kliejunas et 

al. 2009, Sturrock 2010). They will also be able to migrate to locations where climate is suitable for their survival and reproduction at a faster rate than tree species 

(Logan et al. 2003). 

Warming temperatures could increase the potential for insect and disease 

outbreaks, particularly as a transient response in temperate and boreal zones where 

pathogen activity has been limited by suboptimal temperatures (Bentz et al. 2010). 

For example, widespread dieback and mortality of thinleaf alder (Alnus incana (L.) 

Moench ssp. tenuifolia (Nutt.) Breitung) in the southern Rocky Mountains has been 

attributed to an epidemic of cytospora canker (Valsa melanodiscus), which may 

be related to increasing summer temperatures (Worrall et al. 2010). Higher warmseason temperatures should also increase growth rates for most temperate insect 

herbivores, though the rate of increase will vary by species (Bale et al. 2002). For 

some species, faster growth rates and reduced development time could enhance 

juvenile survivorship by reducing predation rates during the larval and nymphal 

feeding stages (Bernays 1997, cited by Bale et al. 2002). Increased growth rates 

could reduce generation times for some species, which could signifi cantly increase 

population growth rates (Bale et al. 2002, Mitton and Ferrenberg 2012). Increasing 

population success increases the potential for insect outbreaks to develop, though 

outbreaks can be limited by host and predator constraints as well (Bale et al. 2002, 

Boone et al. 2011). Warmer temperatures could also increase host stress levels and 

the ability to defend against insect attacks (Boone et al. 2011).

Increasing temperatures could also facilitate migration of insects and diseases 

toward higher elevations and latitudes (Bale et al. 2002, Bentz et al. 2010). Higher 

cold-season temperatures could reduce overwinter mortality in bark beetle populations (Bentz et al. 2010). Similarly, species ranges could contract at lower elevations 

and latitudes if warm-season temperatures exceed tolerance levels during the 

juvenile (or other) growth stages (Bale et al. 2002).

Although much attention has been paid to insect and disease responses to 

increasing temperatures, those responses may be altered by the indirect effects of 

increasing atmospheric carbon dioxide (CO2) concentrations. Higher CO2 levels 

could reduce insect impacts by increasing carbon availability for defenses and 

reducing substrate quality in host plants (Stiling and Cornelissen 2007); however, 

the inhibitory effects of increasing CO2 concentrations may be offset by the stimulatory effects of warmer temperatures on insect activity (Zvereva and Kozlov 2006).

For forests, insect and 

disease disturbances 

will probably increase, 

as the ability of the 

pathogens to adapt to 

new climatic conditions 

will be greater than 

that of their long-lived 

hosts.
58

GENERAL TECHNICAL REPORT PNW-GTR-900

Disturbance Interactions

In addition to their primary effects on vegetation, disturbance agents can interact, 

thus increasing or reducing vegetation susceptibility to other disturbance agents. 

The links from drought, to insects, to fi re is a clear example of interacting disturbances. Windthrow and diseases can also increase risks from insects or fi re, and 

vice versa.

Perhaps the most common interaction among disturbance agents in western 

forests involves drought stress and insects, particularly bark beetles. Episodes 

of widespread mortality of ponderosa pine and pinyon pine in the Southwestern 

United States has been attributed to beetles killing trees weakened by extended 

regional droughts (Allen and Breshears 1998; Breshears et al. 2005, 2009; Ganey 

and Vojta 2011; Plaut et al. 2012). The proposed mechanism for this interaction is 

that prolonged drought reduces photosynthesis and shifts carbon utilization from 

growth and plant defenses to support basic plant functions (respiration), reducing 

the ability of trees to fend off insect attacks (Breshears et al. 2009). Similarly, 

extensive mortality of aspen in southwestern Colorado has been attributed to high 

temperatures and drought that allowed secondary insect and disease agents to kill 

trees (Worrall et al. 2008).

Fire can interact with a variety of other disturbance agents, including drought, 

insects, diseases, and windthrow. Mortality from drought, windthrow, avalanches, 

or insects can increase fi re risks (potential fi re intensity, severity, and size) by initiating a process of fuel succession that increases surface fuels over time (Bigler et al. 

2005, Hicke et al. 2012, Simard et al. 2011). Crown fi re risks may decline, however, 

if tree mortality reduces overstory canopy densities (Simard et al. 2011). Fire can 

also set the stage for further overstory tree mortality by attracting bark beetles that 

attack trees weakened by crown scorch or basal heating (Dale et al. 2001).

Climate change effects on disturbances and their interactions are important not 

only for their immediate effects on vegetation, but also for their potential feedbacks 

on atmospheric CO2 concentrations and climate. Widespread disturbances can convert forests from net carbon sinks to carbon sources, further increasing atmospheric 

CO2 concentrations and potential for climatic changes (Ayers and Lombardero 

2000, Edburg et al. 2012, Kurz et al. 2008). 
59

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 5: Vegetation Adaptation 

to Changing Climate and CO2

Vegetation can adapt to changing climate and local environmental conditions 

in various ways, thereby avoiding mortality (individual level), local extirpation 

(population level), or extinction (species level). 

 Individual plants can respond to persistent changes in their environment 

through phenotypic plasticity (the ability to alter their physiology), morphology, and reproduction to improve their performance (growth and reproduction) 

and survival.

 Plant populations can adapt to changing climate by altering their genetic 

makeup through reproduction and natural selection, improving their ability 

to grow and persist at a particular location. 

 Species can adapt to changing climate through migration, which can allow 

new populations to establish in areas with suitable environmental conditions 

while other populations are extirpated from areas where environmental conditions are no longer suitable for establishment and persistence.

In this chapter, we review three broad types of vegetation adaptation to changing environmental conditions: (1) phenotypic plasticity, (2) local adaptation through 

natural selection, and (3) migration. All are likely to be important in infl uencing 

the rate and magnitude of vegetation responses to changing carbon dioxide (CO2) 

and climate. The relative importance of these adaptation strategies is likely to differ 

among species and sites and with the rate and magnitude of climate change. 

Phenotypic Plasticity

Plants experience a great deal of environmental variability during their lifetimes at 

varying temporal scales. Incoming radiation changes over short periods within a 

day owing to changing cloud cover or sunfl eck movement, daily and seasonally in 

response to Earth’s rotation and orbital position, and from year to year in response 

to climatic variability and its effects on cloud cover. Temperatures, precipitation, 

and soil moisture availability all vary daily, seasonally, and from year to year. As 

sessile organisms, plants must tolerate or acclimate to these changing conditions if 

they are to persist and reproduce. Longer term changes in climate and atmospheric 

CO2 concentrations add yet another source of environmental variability for plants.

Phenotypic plasticity refers to the range of functional traits that can be 

expressed by a particular genotype (or individual plant) in response to environmental cues. Categories of functional traits that can differ in response to environmental cues include plant physiological processes (e.g., respiration rates, growth 

phenology), morphology (e.g., height, allocation to roots), and reproduction 
60

GENERAL TECHNICAL REPORT PNW-GTR-900

(e.g., method, timing). Sultan (2000) reviewed the literature on phenotypic plasticity 

and described four types of plasticity: (1) functional traits plasticity, (2) developmental plasticity, (3) life-history plasticity, and (4) cross-generational plasticity. 

Trait variation can be expressed over monthly, annual, decadal, or generational 

timespans. Phenotypic plasticity for specifi c traits may be adaptive (and selected for 

in a natural selection sense) if it serves to increase plant performance and survival 

(Matesanz et al. 2010, Nicotra et al. 2010).

Physiological plasticity allows plants to partially compensate for resource limitations by altering physiological processes and helps plants acclimate to changing 

environmental conditions at varying time scales, from minutes to months (Sultan 

2000). Plants can vary stomatal apertures over short time periods to regulate the 

exchange of water and CO2 in response to changing evaporative demand (temperature and humidity) and internal plant water balance (Hetherington and Woodward 

2003). Plants can adjust their temperature optima for photosynthesis and respiration 

processes to improve plant performance under seasonally varying temperatures 

(Atkin and Tjoelker 2003; Atkin et al. 2005, 2006; Grulke 2010; Gunderson et al. 

2010, Ryan 1991).

Functional trait or morphological plasticity can also help plants compensate 

for resource limitations by altering leaf and root structure and by altering carbon 

allocation to roots, leaf area, and stemwood. Changes in leaf structure and function 

can allow plants to adapt to persistent changes in CO2, light, temperature, and soil 

water (Apple et al. 2000, Atkin et al. 2006, Fraser et al. 2009, Sprugel et al. 1996, 

Sultan 2000). Temperatures during leaf development can infl uence specifi c leaf 

area (the ratio of leaf area to leaf mass), with subsequent effects on carbon uptake 

and plant growth (Atkin et al. 2006, Sultan 2000). Fraser et al. (2009) showed that 

experimental manipulations of temperature and water availability produced changes 

in leaf area and stomatal density in bluebunch wheatgrass (Pseudoroegneria spicata 

(Pursh) Á. Löve). Similarly, Apple et al. (2000) found that elevated growth temperatures increased needle length but did not affect stomatal density in Douglas-fi r 

(Pseudotsuga menziesii (Mirb.) Franco) saplings, while elevated CO2 did not signifi -

cantly alter needle morphology at all. In Pacifi c silver fi r (Abies amabilis (Douglas 

ex Loudon) Douglas ex Forbes), shading by overstory trees strongly infl uenced 

needle thickness and specifi c leaf area (Sprugel et al. 1996). In most cases, plasticity 

in leaf traits is limited to the period of leaf development.

Changes in biomass allocations—particularly among roots, shoots, and leaf 

area—also allow plants to acclimate to changing environmental conditions and 

biotic interactions (Atkin et al. 2006, Lapenis et al. 2005, Niinemets 2010, Sprugel 

et al. 1996). Varying biomass allocation between roots and shoots can allow trees 

Physiological 

plasticity allows 

plants to partially 

compensate for 

resource limitations by 

altering physiological 

processes.
61

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

and other plants to compensate for light or soil resource limitations on growth 

(Markesteijn and Poorter 2009). Pines have been shown to alter their ratio of leaf 

area to sapwood conducting area in response to site evaporative demand, apparently 

to improve water relations and reduce risks of hydraulic failure (DeLucia et al. 

2000; Maherali and DeLucia 2000, 2001; Maherali et al. 2002). Biomass allocation 

responses to environmental variation differ among species and environmental drivers, however (DeLucia et al. 2000).

Another form of morphological plasticity allows plants to alter their growth 

form in response to environmental conditions and biotic interactions. In subalpine 

ecosystems, some conifers adopt a stunted “krummholz” growth form that allows 

them to better tolerate cold temperatures and high winds; these trees retain the 

ability, however, to develop into upright trees if climatic conditions improve or 

through facilitative interactions (Hadley and Smith 1987, Smith et al. 2003). In 

maritime coniferous forests, understory seedlings and saplings often alter their 

crown and needle morphologies to maximize light capture (Niinemets 2010, 

Sprugel et al. 1996).

Growth and reproductive phenology is another form of phenotypic plasticity (Sultan 2000). Phenological adjustments help plants adjust their growth and 

reproduction to periods of favorable resource availability (or reduced herbivory), 

and potentially lengthen or shorten the growing season (Cleland et al. 2007, Körner 

and Basler 2010, Kramer et al. 2000). The timing of fl owering and leaf-out in plants 

is often controlled by environmental cues such as air and soil temperatures, but 

can include both chilling and warming requirements (Campbell and Sugano 1979, 

Harrington et al. 2010, Menzel et al. 2006, Polgar and Primack 2011). Chuine (2010) 

has argued that growth and reproductive phenology is an important determinant of 

species ranges, as failure to complete annual growth cycles limits species expansion 

into cooler environments, while failure to meet winter chilling requirements limits 

expansion into warmer environments (or persistence in a warming climate). Phenological responses to climatic variability and change are discussed in more detail in 

chapter 3.

Finally, cross-generational plasticity allows plants to confer plastic responses on 

their offspring by varying seed size (and stored resources), seed coat thickness, and 

seed dormancy, or by infl uencing initial biomass allocation (Sultan 2000). Seed size 

can infl uence initial growth rates and biomass of seedlings, while dormancy can 

infl uence the timing of seedling emergence. In some cases, parent plants experiencing resource limitations (e.g., light or nutrients) have been found to produce offspring that allocate biomass preferentially to roots or shoots in a way that promotes 

acquisition of the dominant limiting resource (Sultan 2000).
62

GENERAL TECHNICAL REPORT PNW-GTR-900

Adaptive Value of Plasticity

Phenotypic plasticity is important in considering potential vegetation responses to 

changing climate because it allows plant individuals and populations to persist on a 

site in a changing environment (Matesanz et al. 2010). It allows plants to adjust to 

seasonal changes in climate as well as interannual and interdecadal climatic variability, and helps avoid radical vegetation responses to most climatic variability. 

Phenotypic plasticity can also help plants become established and persist under low 

resource availability caused by decadal climatic variability or biotic interactions 

(e.g., during forest stand development). The ability to persist as a juvenile (advance 

regeneration) provides the opportunity to respond rapidly to increased resource 

availability following disturbance (or a favorable period of climate), while the 

ability to persist as an adult provides a continuing opportunity to reproduce during 

favorable periods of climate. 

Phenotypic plasticity can also contribute to local adaptation of populations to 

changing climate and to species migration processes. Phenotypic plasticity may 

reduce selective pressure on plant genotypes, thereby allowing plants to maintain 

higher genetic diversity and future adaptation potential within populations. It 

may also facilitate local adaptation of populations by allowing new individuals 

to establish under altered environmental conditions (Matesanz et al. 2010). By 

these mechanisms, phenotypic plasticity can delay or prevent range retraction on 

the “trailing edge” of a species’ range. High plasticity may also be benefi cial for 

facilitating migration through colonization of new sites on the “leading edge” of the 

species’ range, as high plasticity allows a single genotype to occupy varying environments (Matesanz et al. 2010). This is probably why high phenotypic plasticity is 

associated with ecological generalists and many invasive plant species (Matesanz 

et al. 2010, Sultan 2000).

Phenotypic plasticity is not without its costs, however, and is less common 

than one might expect, given the apparent benefi ts to individuals and populations. 

Valladares et al. (2007) reviewed a wide range of potential internal and external 

limitations that could prevent plants from fully responding to environmental cues. 

Ecological limitations include the reliability of environmental cues, abiotic stresses, 

biotic interactions, and multiple stresses. Internal limitations include genetic costs, 

carbon (maintenance) costs, developmental constraints, and lag time of responses. 

When plasticity cannot allow plants to acclimate suffi ciently to changing environmental conditions, genetic adaptation or migration responses may be required to 

maintain species viability.

Phenotypic plasticity 

allows plant individuals 

and populations to 

persist on a site in a 

changing environment.
63

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Adaptation Through Natural Selection

Local genetic adaptation (or in situ evolution) is another way by which plant populations can adapt to changing environmental conditions (Davis et al. 2005). As part 

of sexual reproduction, plants recombine genetic material and may develop new 

genotypes (combinations of genes) that are better suited to current conditions than 

the genotypes of earlier generations. They are also likely to produce genotypes that 

are less well suited to current environmental conditions. The better genotypes will 

be more likely to become established and survive, thereby increasing the frequency 

of favorable genes in the population and increasing the ability of the population to 

persist. 

The effectiveness of local population adaptation depends on several factors, 

including genetic variability, fecundity, dispersal, and generation time (Aitken et 

al. 2008, Brubaker 1986, Jump and Peñuelas 2005). Populations with high genetic 

variability have a higher potential for adaptation if the genetic variability includes 

traits that affect performance and reproduction responses to climate (Davis and 

Shaw 2001, Davis et al. 2005). High levels of seed production and dispersal enhance 

adaptation potential by producing a large number of new genotypes and distributing them into a wide range of habitats, thereby increasing the chances for fi nding 

a favorable combination. Short generation times enhance adaptation potential by 

increasing selection opportunities over time.

Local genetic adaptation might be most important and effective for annual 

plants, as they replace their entire population each year (short generation time), 

typically produce large amounts of seed, and often have good seed dispersal (Jump 

and Peñuelas 2005). Population adaptation can be rapid in small, annual plant species, with signifi cant changes in traits observed after one generation. For example, 

Franks et al. (2007) found that fl owering time by an annual plant population 

advanced by 2 to 8 days in response to selective pressure from a summer drought.

Local population adaptation is likely to occur much more slowly for tree 

species but will still play an important role in determining the effects of changing 

climate and elevated CO2 on tree species. In general, tree species have more genetic 

diversity within their populations than herbaceous plant and shrubs (Hamrick 

2004, Petit and Hampe 2006). They also produce large numbers of seeds over their 

lifespans, and typically have developed effective mechanisms for seed dispersal 

(Petit and Hampe 2006; Nathan et al. 2008, 2011). Although longer generation 

times (juvenile periods) reduce their potential for rapid population adaptation, long 

lifespans supported by phenotypic plasticity may provide time for populations to 

adapt suffi ciently to changing climate to avoid local extinction.
64

GENERAL TECHNICAL REPORT PNW-GTR-900

Trees often display high local differentiation for adaptive traits (Petit and 

Hampe 2006), with the traits involved in local adaptation being the product of the 

small effects of many genes (Aitken et al. 2008). Common garden studies with 

seedlings have found local differentiation in species traits like phenology, height, 

and cold hardiness for ponderosa pine (Pinus ponderosa Lawson & C. Lawson), 

(Keller et al. 2004), Douglas-fi r (Monserud and Rehfeldt 1990, Rehfeldt 1989), 

lodgepole pine (Pinus contorta) (Rehfeldt 1988), Engelmann spruce (Picea engelmannii Parry ex Engelm.), (Rehfeldt 1994a), western redcedar (Thuja plicata Donn 

ex D. Don), (Rehfeldt 1994b), western larch (Larix occidentalis Nutt.) (Rehfeldt 

1995), and Sitka spruce (Picea sitchensis (Bong.) Carrière) (Mimura and Aitken 

2007, 2010). Such responses are not universal, however, and some species are only 

weakly differentiated genetically to local environmental conditions (e.g., western 

white pine [Pinus monticola] Douglas ex D. Don]), perhaps as a result of strong 

acclimation through phenotypic plasticity (Chuine et al. 2006, Rehfeldt et al. 1984).

Gene fl ow via seeds or pollen is common in trees and can accelerate or impede 

population adaptation to altered environmental conditions (Aitken et al. 2008, 

Kremer et al. 2012, Petit and Hampe 2006). Long-distance dispersal of pollen and 

seeds can help introduce new genes and genotypes into an existing population, and 

it is often assumed that the dominant fl ow of genes is from the center of a species 

range toward the periphery. Long-distance gene fl ow could facilitate adaptation 

in “leading-edge” populations during species migration by introducing genes that 

are preadapted for warmer temperatures (assuming the migration pattern is toward 

higher latitudes or elevations), and can generally increase genetic diversity and 

adaptation potential (Aitken et al. 2008, Kremer et al. 2012). On the other hand, 

gene fl ow toward trailing edge populations may slow or prevent population adaptation by introducing less suitable genes (Aitken et al. 2008, Kremer et al. 2012).

Migration

Migration is perhaps the most widely anticipated and discussed adaptation response 

of plant species to changing climate. Migration, in theory, allows plant species to 

maintain their current bioclimatic niches by tracking changes in climate across 

landscapes and regions. Migration responses of trees and other species in response 

to postglacial warming during the Quaternary are documented in fossil pollen 

sequences and macrofossils (Cole 1982, Davis and Shaw 2001, Gugger and Sugita 

2010, Petit et al. 2008). Biogeographical models suggest that most plant species 

will have to migrate in response to changing climate in the next century or face 

dramatic reductions in species ranges and possible extinction. In this section, we 
65

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

briefl y describe existing information about the need for plant species migration and 

some aspects of migration as an adaptive response to changing climate. 

Velocity of Change

Biogeographers have used geographical information systems, regional climate 

and vegetation databases, and bioclimatic envelope models to translate projected 

changes in mean annual temperature and precipitation onto real landscapes. This 

has helped them describe spatial shifts in bioclimatic zones and the rates at which 

plant and animal species would have to migrate in the future to track their current 

environmental conditions (Loarie et al. 2009). The rate of migration of bioclimatic 

conditions across landscapes has been aptly referred to as the “velocity of change,” 

and is a useful concept for evaluating the potential for species migrations to keep 

up with changing climate. These biogeographical approaches have also proved 

useful as a fi rst approximation for evaluating the long-term potential for conserving 

biodiversity in current reserve systems (Ackerly et al. 2010, Loarie et al. 2009).

Studies of spatial shifts in bioclimatic zones under climate change have noted 

that the velocity of change varies considerably with topography. The velocity 

(meters/year) needed to track projected changes in climate is much greater in fl at 

terrain like the central Great Plains than in complex topography like the mountainous regions of western North America (Loarie et al. 2009). This is largely because 

temperature changes much more quickly along elevational gradients than along 

latitudinal gradients.

Persistence in Refugia

There may be some regions within a species’ range that retain climatic suitability 

despite large changes in climate (Ashcroft 2010). Such refugia may exist because 

the species has a broad ecological niche or high levels of phenotypic plasticity, 

because elevated CO2 or local topography buffer the impact of changing climate 

on critical resources, or because antagonistic biotic interactions are too weak to 

cause signifi cant mortality despite poor plant performance. Populations occupying 

these refugia may be able to persist without adaptation or, more likely, can persist 

through local adaptation, perhaps with the benefi t of gene fl ow from other parts of 

the species range. 

Recent studies have shown the importance of refugia for supporting relict 

populations of trees during the last glacial advance. Those populations preserved 

important components of genetic diversity and contributed signifi cantly to species migration following glacial retreat (Gugger and Sugita 2010, Hu et al. 2009, 

McLachlan et al. 2005, Petit et al. 2008) and current distribution patterns (Norris 

et al. 2006). There is now great interest in identifying future refugia for maintaining 

Populations occupying 

these refugia may 

be able to persist 

without adaptation 

or, more likely, can 

persist through local 

adaptation, perhaps 

with the benefi t of gene 

fl ow from other parts of 

the species range.
66

GENERAL TECHNICAL REPORT PNW-GTR-900

plant biodiversity in general as well as currently threatened species or species of 

particular conservation value (Ashcroft 2010, Ashcroft et al. 2012, Dobrowski 2011). 

Areas outside the current distribution of a species could serve as refugia if species migration rates and future landscape permeability would allow the species to 

colonize and persist in those areas (Ashcroft 2010).

Invasion (Leading Edge)

Invasion or colonization of previously unoccupied or newly suitable habitat is the 

“leading edge” of a species migration. Invasion success and rates of “leading edge” 

movement depend on the fecundity of leading edge populations, seed dispersal 

patterns and distances, landscape barriers, community invasibility, biotic interactions, and generation times (Hampe 2011). Because of differences in fecundity, seed 

size, dispersal mechanisms, and competitive ability, potential migration rates vary 

among species, and species have migrated at different rates following the last major 

glacial retreat (Davis et al. 1986, Nathan et al. 2011). Historically, species migrations have lagged signifi cantly behind climatic changes (Davis 1989, Gavin 2009, 

Gavin and Hu 2006).

Perhaps the most important factor controlling migration potential is the ability 

of a plant species to produce suffi cient quantities of viable seeds, disperse seeds into 

new habitats, and have the seeds germinate successfully in the new habitat (Burton 

et al. 2010, Hampe 2011). Wind and animal (including human) dispersal are the 

most likely mechanisms for producing successful long-distance dispersal (Nathan 

et al. 2008) but may not be able to match the rate of changing climate in areas with 

a high “velocity of change.” Maximizing the migration benefi ts of seed dispersal 

mechanisms requires leading edge populations to serve as the primary seed source 

for colonization of new habitat, which requires leading edge populations to be old 

enough to reproduce and for the climate to support investment in reproduction. 

Elevated CO2 and changing climate could alter seed production and dispersal 

for some species, thereby altering their migration potential. Elevated CO2 could 

increase seed production by making more carbon resources available for reproduction; however, nutrient availability could limit increases in seed production, 

and increased seed size (owing to greater carbon reserves) could limit dispersal 

for wind-dispersed seed. Warming temperatures could increase wind speeds and 

thereby increase potential dispersal distances for wind-dispersed seeds (Kuparinen 

et al. 2009), while changes in prevailing wind directions could alter spatial patterns 

of seed dispersal (Ackerly et al. 2010). Similarly, climate change effects on animal 

behavior could alter the distance and direction of dispersal for animal-dispersed 

species (Hampe 2011, Nathan et al. 2008). Finally, changing climate and disturbance 
67

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

regimes could alter seed dispersal patterns by altering landscape permeability 

(Nathan et al. 2008).

Invasion success for migrating species also depends on the invasibility of the 

plant community being colonized (Pauchard et al. 2009). Intact plant communities 

with one or more dominant competitors may be diffi cult to invade unless the invading species is preadapted to the low resource availability produced by the dominant 

competitor. Disturbances can increase community invasibility by reducing or 

extirpating established populations and increasing resource availability (Pauchard 

et al. 2009). Fluctuating resources (e.g., soil water availability) may also facilitate 

invasion (Davis et al. 2000). Some have argued that highly diverse plant communities will be less invasible because of more complete resource utilization, but empirical evidence suggests otherwise (Lonsdale 1999). 

Biotic interactions can also limit invasion rates. The presence or absence of 

facilitators, mutualists, or antagonists can infl uence invasion success. Release 

from pathogens or herbivores may increase establishment success and persistence 

(Hampe 2011). The presence or absence of mutualist species, like benefi cial soil 

biota or pollinators, may also determine invasion success (Hampe 2011). Competition intensity may also be important, as the species traits that promote invasiveness 

(high seed production and dispersal) can come at the expense of traits that promote 

competitive ability (Best et al. 2007, Burton et al. 2010). 

Extirpation (Trailing Edge)

Systematic extirpation of populations in areas that can no longer support viable 

populations of a species defi nes the “trailing edge” of a species migration. Population extirpation is typically associated with disturbances that kill plants, after which 

recolonization cannot succeed. In the absence of such disturbances, phenotypic 

plasticity or local population adaptation may allow populations to persist at reduced 

fi tness levels. Regeneration may be limited to years or decades with favorable 

climate, or may cease altogether, leaving populations to rely on phenotypic plasticity, vegetative reproduction, and long lifespans of individuals to persist (Chevin et 

al. 2010, Hampe and Jump 2011). This latter condition has been called an “extinction debt,” in which local extinction is expected but not yet realized (Dullinger et al. 

2012, Jackson and Sax 2010). 

Although there are examples of rapid and persistent range retractions driven 

by extreme climatic events and related disturbances, well-documented examples 

of systematic population extirpation of trailing edge plant populations are still not 

common (Allen and Breshears 1998, Allen et al. 2010, Breshears et al. 2009, Jump 

et al. 2009). In some cases, populations may persist until the arrival (and subsequent 

population growth) of a superior competitor (Dullinger et al. 2012, Hampe and 

Disturbances can 

increase community 

invasibility by 

reducing or extirpating 

established 

populations and 

increasing resource 

availability.

Population extirpation 

is typically associated 

with disturbances that 

kill plants, after which 

recolonization cannot 

succeed.
68

GENERAL TECHNICAL REPORT PNW-GTR-900

Jump 2011). While they persist, these populations preserve the potential for the 

species to respond to future shifts in climate, while also maintaining genotypes 

adapted to warmer conditions (Hampe and Petit 2005, Jump et al. 2009).
69

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 6: Simulation Models Used 

to Project Vegetation Responses

The complexity of climatic infl uences on vegetation and potential adaptations 

of plant populations and species to changing climate makes conducting fi eld 

experiments to assess potential species and community responses to increasing 

atmospheric carbon dioxide (CO2) concentrations and climatic changes diffi cult, 

expensive, and generally impractical for more than a few species and environments. 

Instead, models are often used to project vegetation responses to changing CO2

and climate, including changes in species distributions, biodiversity, community 

composition, vegetation structure (e.g., biomass, leaf area), and productivity. Model 

projections may be used to assess vegetation sensitivity or vulnerability to climatic 

changes, potential rates and magnitudes of change, or the adequacy of conservation 

reserve systems.

The ideal vegetation model for supporting the development of climate change 

adaptation strategies would describe the range of climatic and other environmental 

conditions under which plant species could establish, grow, reproduce, and persist. 

It would also account for the effects of biotic interactions and disturbances on plant 

species distributions. If used to project changes in species ranges over time, the 

model would not only describe the end result, but also indicate the rates of change, 

intermediate conditions, and the mechanisms producing change. Such ideal vegetation models do not currently exist. Instead, current vegetation models focus on one 

or more aspects of the overall problem—describing environmental niches, simulating competition, or examining spatial processes—while greatly simplifying other 

aspects of the problem through assumptions, theory-based approximations, 

 or simply ignoring them altogether. 

Several types of vegetation models can and have been used to assess potential 

impacts of climate change on terrestrial vegetation. Each type has its strengths and 

weaknesses and addresses vegetation responses at different temporal and spatial 

resolution. For example, gap models are used to examine species interactions and 

vegetation change at very fi ne spatial scales (plots the size of an individual canopy 

gap or small stand of trees) over daily to annual time steps, while dynamic global 

vegetation models project changes in vegetation properties (e.g., leaf area and 

phenology) at very typically broad spatial scales (thousands of square kilometers, 

although more fi ne-scale projections are now available, e.g., Rogers et al. 2011) over 

annual to decadal time steps. In applying vegetation models to inform management 

adaptations to climate change, it is important to match the spatial and temporal 

scale of the model with that of the question being asked. It is also important to note 

that models are developed based on assumptions that are usually reasonable for the 

In applying vegetation 

models to inform 

management 

adaptations to climate 

change, it is important 

to match the spatial 

and temporal scale of 

the model with that 

of the question being 

asked.
70

GENERAL TECHNICAL REPORT PNW-GTR-900

original scale and purpose of the model, but may be completely unreasonable when 

models are applied to a different problem or at a different spatial scale. 

A frequent quote about models is that they will always be wrong to some 

degree, but may be useful anyway. In the context of climate change, models may 

be more useful for illustrating the magnitude of potential vegetation impacts and 

identifying areas of vulnerability than for making projections about outcomes 

(Littell et al. 2011, Jackson et al 2009). Many different types of models have been 

developed, and continue to be developed, to examine how vegetation may change 

with future climate change, but their strengths, limitations, and best uses are often 

not made clear to decisionmakers. Vegetation model development often requires 

considerable investments of time and resources, and there is an understandable 

tendency for model developers to try to fund continued model development by 

applying it to new and varied applications, even when another tool (new or existing) 

might be better suited to the task. Similarly, decisionmakers are often pressed to act 

quickly and may accept model output as an authoritative basis for decisions without 

fully understanding the uncertainty in such projections. 

This section reviews the major classes of vegetation models; describes their 

basic function, strengths, and weaknesses; and discusses the contribution each 

could make toward understanding and projecting vegetation responses to future 

climatic changes. 

Statistical Species Distribution Models

Introduction to Species Distribution Models

Statistical species distribution models (SDM), also known as niche models or 

bioclimatic envelope models, are used to describe the range of environmental 

conditions (niche) under which species occur by quantifying relationships between 

species occurrence data and corresponding environmental descriptors. The SDMs 

have arisen out of decades of ecological inquiry and analyses related to speciesenvironment relationships (e.g., MacArthur 1972) and share a common philosophy 

and theoretical foundation with indirect gradient analysis, in that they attempt to 

relate observed spatial variations in species frequency to associated underlying 

environmental gradients. Although correlations between species occurrence and 

environmental conditions can suggest causal mechanisms, the models themselves 

are purely descriptive and do not directly include processes or mechanisms, 

although an approach to doing so has been proposed recently (Kearney and Porter 

2009).

Species distribution models have been used to address a variety of applied and 

theoretical questions (Elith and Graham 2009, Guisan and Thuiller 2005, Jeschke 
71

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

and Strayer 2008), including the planning and establishment of conservation reserve 

networks and identifying potential habitat for species recovery efforts (Araújo et al. 

2004). They require little or no knowledge about the ecological requirements of species, which can be an advantage for poorly studied taxa (Kearney and Porter 2009), 

and are relatively easy to develop and use (Robinson et al. 2008). Their use has 

grown considerably in recent decades as geographic information system (GIS) technologies have improved and become widely used and as spatial datasets describing 

interpolated climatic conditions have become available (Elith and Leathwick 2009). 

Because these models are widely used and there are numerous discussions and 

projections in the literature using these models, we devote a considerable amount 

of space to this class of models. 

How Species Distribution Models Work

Species distribution models are empirical models describing relationships between 

observed species occurrences and associated environmental variables based on 

statistical or other quantitative models (Guisan and Zimmerman 2000). Species data 

can be presence/absence, presence only, or some measure of species abundance, and 

are typically obtained from species range maps or extensive networks of vegetation 

survey plots (e.g., the U.S. Forest Service’s Forest Inventory and Analysis monitoring network). Environmental predictor variables typically include summary 

climatic descriptors (e.g., mean summer temperature, mean annual precipitation, 

or mean January temperature), but can also include climate-related biophysical 

descriptors (e.g., potential or actual evapotranspiration), topographic descriptors (e.g., slope aspect or potential solar radiation), disturbance properties, or soil 

properties (Guisan and Thuiller 2005). 

A wide range of quantitative methods have been developed for describing these 

relationships between species and their environment, including generalized linear 

models, generalized additive models, and regression trees (Austin 2007). In selecting a quantitative method for an SDM, model developers should ideally consider 

the relevant ecological theory, whether species responses to modeled environmental 

gradients are linear or nonlinear, the likely importance of interactions among environmental predictor variables, and properties of the species and environment data 

(including scales of observation, correlations among environmental predictors, and 

spatial autocorrelation in species data), though this is often not done (Austin 2007). 

Similarly, environmental predictor variables should be chosen to represent (directly 

or indirectly) factors that contribute to establishment, growth, and mortality (e.g., 

temperature extremes, growing degree-days, water balance). 

Depending on the species data and quantitative method used, SDMs will 

produce estimates of species abundance or the probability of species presence for 
72

GENERAL TECHNICAL REPORT PNW-GTR-900

locations within the study region, given the environmental conditions. In many 

cases, the most useful way to examine SDM projections, however, is to map those 

projected abundances or probabilities of species occurrence onto the study region 

(geographic space) and display those projections using GIS. This can be done by 

displaying continuous abundance or probability values (using levels of shading) or 

by establishing threshold abundance or probability values and mapping all areas 

with values exceeding the threshold as potential species range. Although SDM 

projections are often interpreted as species range maps, they actually represent a 

spatial mapping of the environmental conditions within which the model projects 

the likelihood of species occurrence to be above some minimum threshold level. 

Not all areas identifi ed as potential suitable climate will be occupied by the target 

species. Observed species absences within areas modeled as potential suitable 

climate could to be due to chance alone, barriers to migration, disturbances, or 

some additional factor that infl uences the species’ distribution but was not included 

in the model. 

For more details about SDM history, development, function, and usage, see 

reviews by Guisan and Thuiller (2005), Austin (2007), and Elith and Leathwick 

(2009). For the remainder of this review, we focus on key elements of SDMs that 

pertain specifi cally to their use in projecting vegetation vulnerability and responses 

to climatic changes.

As with most statistical methods, developers of SDMs make assumptions about 

how the world works and the empirical data from which the models are developed. 

Some of these assumptions are explicitly acknowledged, while others are less clear, 

but all have important implications for how the models can be used and their suitability for projecting species responses to climatic changes (Austin 2007, Thuiller et 

al. 2008). Major assumptions associated with SDMs are summarized in box 6.1.

How Species Distribution Models Are Applied 

to Climate Change Issues

A carefully developed SDM can characterize the current environmental distribution of a species well and provide evolutionary and ecological insights (Elith and 

Leathwick 2009, Rehfeldt et al. 2006). For example, examination of the environmental predictors in the model can generate hypotheses about the ecological factors 

limiting current species ranges that can be subjected to further study. Comparison 

of SDMs developed for different regions within a species’ range can also provide 

insights about evolutionary changes during prolonged periods of isolation in glacial 

refugia (Norris et al. 2006). 

More recently, SDMs have been increasingly used to project suitable species 

habitat through interpolation and extrapolation. Projection through interpolation 
73

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

 Box 6.1. Major assumptions associated with widely used species 

 distribution models (SDMs):

• Models assume that the description of the realized niche is suitable for 

meeting study objectives (Guisan and Thuiller 2005). The SDMs attempt 

to describe the environmental conditions under which a species actually

occurs, not the range of conditions under which it could occur. The 

realized niche could be further limited to only those environmental 

conditions under which a species can reproduce successfully, eliminating relict populations that may be committed to extinction (Guisan and 

Thuiller 2005); this is not common practice, however, in part because 

assessments of species reproductive success are often unavailable. 

• The target species is in equilibrium with its current environment. If 

the species is still responding to recent widespread disturbances or past 

climate changes, or still actively invading, species occurrence data may 

not adequately represent the species’ realized niche.

• Biotic interactions are either of minor importance as determinants of 

the species’ distribution, or are correlated with and adequately represented by environmental variables (Araújo and Luoto 2007).

• The target species does not have unique subpopulations—for which the 

fundamental/realized niche varies signifi cantly—within the region of 

interest.

• The environmental predictor variables represent all of the critical elements affecting plant establishment, growth, and survival for a species 

and have been measured accurately.

• Species and environmental data are being observed and analyzed at 

appropriate and comparable spatial scales to meet study objectives (the 

scale at which model predictions are required). 

• Relevant environmental gradients have been adequately sampled (Elith 

and Leathwick 2009, Guisan and Thuiller 2005).

• The statistical methods used are capable of describing changes in 

species occurrences across the selected environmental gradients and of 

capturing important interactive effects among the environmental 

predictor variables.
74

GENERAL TECHNICAL REPORT PNW-GTR-900

involves projecting model occurrence probabilities (or projected abundance) onto 

the current landscape under current environmental conditions; such an approach 

can be useful for identifying suitable, but unoccupied, habitat for inclusion in 

reserve planning or species recovery efforts. Projection through extrapolation 

involves applying model projections to geographic regions or environmental conditions beyond those for which the model was developed. Extrapolating SDM projections in time or space has become increasingly popular, but is much riskier and 

carries higher levels of uncertainty (Elith and Leathwick 2009). Although SDMs 

are typically run using data and projections for individual species, they can also be 

applied to plant functional types (e.g., shrubs) (Kerns and Ohmann 2004). 

Species distribution models have been used extensively in climate change 

research to project potential changes in species geographic ranges in response to 

changing climate. In a typical application, SDMs are developed for a suite of species occupying a target region using available species and environmental datasets, 

where the candidate environmental variables include a full range of climatic 

descriptors thought to represent various limitations on species ranges. After acceptable models have been developed, their projections are projected onto geographic 

space using both current climatic conditions (interpolation) and one or more scenarios of future climatic conditions (extrapolation) as derived from climate models. 

The resulting maps (or the underlying projections) can then be compared to address 

questions about potential changes in species ranges, potential regions for expansion 

or contraction of species boundaries, and stable habitat areas. 

In extrapolating SDMs from current to future climatic conditions, additional 

assumptions are being made that infl uence levels of confi dence and uncertainty in 

SDMs, including the following: 

 Current fundamental and realized niches are conserved under a new 

climatic regime (Dormann 2007). This further implies that:

o Species will not adapt signifi cantly to climatic changes 

(stability in the fundamental niche).

o Dominant environmental limiting factors are preserved under 

future climate.

o Biotic interactions (e.g., competition or facilitation) and their 

outcomes are preserved under future climate (stability in the 

realized niche). This implies that currently interacting species 

migrate together and that the relative effects of their interactions 

do not change signifi cantly.
75

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

o Changing CO2 concentrations or altered disturbance regimes 

will not alter species-environment relationships or biotic 

interactions.

 Model projections can reasonably be extrapolated to project species 

responses to novel climates. Novel climates include ranges and combinations of environmental conditions not considered during model 

development because they do not occur on the modern landscape 

(no current analog) or because data were not available for representative sites. 

Strengths and Weaknesses of Species Distribution Models

The major strength of statistical species distribution models is their fl exibility and 

ease of use. Species distribution models have become very popular because they 

are relatively fast and easy to create and apply. Model development requires little 

knowledge about species ecophysiology, autecology, or synecology. The necessary 

species and environmental data are increasingly available in usable forms, allowing 

species models to be generated for many areas and spatial scales. Many of the quantitative methods employed are readily accessible in free software packages (e.g., 

as applications libraries for the free R statistical software) that can facilitate rapid 

model development and assessment. The SDMs can often produce a good statistical 

fi t between available species and environmental data, lending confi dence to their 

application (though see Chapman 2010 and Beale and Lennon 2012 for cautionary 

notes on this topic). Finally, the ability to project model output onto geographic 

space and create maps adds to the popularity of the models, as maps are familiar 

and appear to be easy to understand and interpret (though this can produce a false 

sense of confi dence). 

A major weakness of SDMs is that they achieve their fl exibility and ease of 

use through adoption of a suite of simplifying assumptions that allows models to 

be created without prior knowledge about species ecophysiology, autecology, or 

synecology. Unfortunately, many of these assumptions are likely to be violated, 

depending on the species involved and the study question. Many plant species 

are unlikely to be in equilibrium with current climate, as climate has changed at 

centennial to millennial scales, creating a moving target (Svenning and Skov 2004). 

For example, western juniper (Juniperus occidentalis Hook.) is still rapidly expanding its range and infi lling in less dense areas with almost 90 percent of the western 

juniper woodland having developed in the past 150 years (Miller et al. 2000). 

Similarly, biotic interactions are poorly studied for most species, but there is suffi cient research demonstrating the importance of overstory-understory relationships, 

pathogens, herbivory, and mycorrhizal associations on the abundance patterns of 

The major strength 

of statistical species 

distribution models 

is their fl exibility and 

ease of use. 
76

GENERAL TECHNICAL REPORT PNW-GTR-900

selected species to demonstrate the potential importance of biotic interactions for 

shaping species realized niches. Although the effects of some biotic interactions 

may be correlated with climate, allowing them to be captured by a statistical model, 

this is not universally true (Araújo and Luoto 2007).

Climate data availability is another source of weakness for many SDMs, 

particularly as applied to climate change questions. The SDM methodology 

assumes that climate variables are accurately measured and can be matched up 

with associated vegetation data. Weather stations producing high-quality, long-term 

climate data are distributed sparsely in most regions, particularly in remote areas 

and mountainous terrain. They are often skewed toward low-elevation sites and 

clustered near population centers (where they are easiest to monitor and repair). As 

a result, climatic conditions associated with vegetation sampling plots are usually 

estimated from spatial interpolations of climate station data (e.g., PRISM) (Daly 

et al. 2008). The accuracy of such estimates may be adequate for studies at broad 

spatial scales (coarse spatial resolution), but they are probably not suitable at fi ner 

spatial resolutions where topoedaphic infl uences are strong.

Model development methodologies have also been a source of uncertainty 

and criticism for SDMs. Many different SDM methodologies are available, each 

employing different assumptions, algorithms, and parameterizations (Buisson et 

al. 2010). Unfortunately, models developed from the same data—but with different 

methods—can produce signifi cantly different results (Buisson et al. 2010, DinizFilho et al. 2009). In an ensemble forecasting framework with 8,400 projections of 

species responses to climate change, Buisson et al. (2010) found that SDM methodology accounted for more variability in model projections than climate model, 

emission scenario, or initial (training) data used. Clearly, there remains uncertainty 

about the reliability of SDM projections of species responses to climate change, and 

it is unlikely that any single statistical approach will be best for all applications and 

species (Jeschke and Strayer 2008). 

For the SDM approach to be effective for projecting species responses to future 

climate change, the current correlation of species with the environment must be a 

good predictor of future suitable habitat. Therefore, SDMs implicitly assume niche 

conservation—that species maintain their current ecological requirements—and 

constancy in the genetic and phenotypic composition of a species over space and 

time (Broennimann et al. 2007, Jeschke and Stayer 2008). These assumptions may 

be ecologically untenable, however, at least for some species. Invasive species 

provide examples of lack of niche conservation, changing niches from their native 

to introduced ranges (Broennimann et al. 2007). Recent evidence also suggests 

that detectable evolution in vegetation occurs at time scales that are comparable to 
77

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

climate change—within years for some annuals, decades for herbaceous species, 

and centuries for longer lived trees (Franks and Weis 2008, Hairston et al. 2005, 

Skelly et al. 2007). 

Species distribution models also assume that certain processes, such as biotic 

interactions, are either unimportant or will remain stable with climate change. 

However, biotic and abiotic factors both constrain species ranges, the relative 

effects of which may vary in different parts of the species range (Barry and Elith 

2006). Changes in climate can alter interactions between plants, as well as plant 

interactions with herbivores, seed predators, and pathogens (Ayres and Lombardero 

2000, Ibanez et al. 2006; see also chapter 4). Higher atmospheric CO2 levels may 

also alter biotic interactions through effects on plant growth, carbon allocation, and 

defenses. A recent review of over 600 studies showed that climate change infl uenced nearly every aspect of species interactions (Tylianakis et al. 2008). Finally, 

biotic interactions are likely to change because species have been shown to respond 

individually to climate change, producing new species assemblages in new environments. Species whose ranges are infl uenced by biotic interactions are therefore 

likely to respond in complex ways to novel environments (Elith et al. 2010). 

Species distribution model quality also differs by species (Guisan et al. 2007). 

Species with very specifi c environmental requirements are modeled more easily 

than generalists (Elith et al. 2006, Guisan et al. 2007, Hernandez et al. 2006, Luoto 

et al. 2005, Segurado and Araújo 2004, Thuiller et al. 2004). Early-successional 

species are often dependent on disturbances to become established and may be 

excluded from large portions of their potential habitat by competition from latesuccessional species; it is diffi cult to determine the environmental requirements 

for such species (Guisan et al. 2007). 

Finally, climate change is expected to result in substantial areas that have no 

modern analog in terms of climate (“novel climates”) (Williams and Jackson 2007, 

Williams et al. 2007). This may be especially true of the Western United States, 

where almost half the land area may have novel (or extramural) climatic conditions 

by the end of the century (Rehfeldt et al. 2006). The more the future state differs 

from the present (novel climates), the less adequately models will be able to project 

ecological phenomena such as species distributions (Williams and Jackson 2007) 

and projections made by correlative models for novel climates may not be defensible 

(Rehfeldt et al. 2006). In fact, SDMs may produce projections indicating dramatic 

reductions in suitable habitat for a species simply because future novel climates do 

not correspond to modern conditions under which the species occurs. Furthermore, 

as models of modern realized niches increase in complexity (more climatic predictors and interactions), the less likely they are to exist under future climate scenarios. 

SDMs may produce 

projections indicating 

dramatic reductions 

in suitable habitat 

for a species simply 

because future novel 

climates do not 

correspond to modern 

conditions under which 

the species occurs.
78

GENERAL TECHNICAL REPORT PNW-GTR-900

In some cases, a novel climate could be quite favorable for a species. As a result, 

novel climates often create a bias in SDM projections toward reduced area for most 

species under future climate, without identifying which species would replace them. 

Using Species Distribution Models

Species distribution models have a number of potentially useful applications in the 

fi elds of ecology and conservation, including describing species’ environmental 

niches, suggesting unsurveyed sites with high potential for occurrence of rare species, supporting appropriate management plans for species recovery and mapping 

reintroduction sites, and supporting conservation planning and reserve selection 

(Guisan and Thuiller 2005). These applications focus on describing the current 

environmental niche for a species and using interpolation to identify potential 

species habitat on the current landscape.

With respect to climate change, SDMs have proven highly valuable for raising 

awareness about the potential magnitude of climate change impacts on terrestrial 

biota. At their most basic level, SDM procedures can be used to show how the “bioclimatic envelope” or climatic conditions currently associated with the geographic 

range of a particular species or functional group would move across a landscape or 

region in response to projected climatic changes. By comparing current and future 

geographic distributions, one can estimate the distances and migration rates needed 

for species to maintain their current environmental niches in the face of climate 

change (Shafer et al. 2001). These rates will differ among species and can be highly 

infl uenced by topographic complexity, with mountainous terrain requiring shorter 

migration distances and slower migration rates, in general, than fl at terrain (Loarie 

et al. 2009). 

Species distribution models may also be useful for identifying core habitat for 

species that is likely to remain suitable (within that of the current species range) 

under future climate scenarios. For example, SDMs could be used to quantify 

sustainability of current reserve systems for maintaining key species or communities. Such an application limits SDM use to current climatic conditions and 

avoids problems with extrapolating species responses to novel climates (a negative 

response indicates higher vulnerability, not guaranteed commitment to extinction).

Species distribution models could also be valuable in a research context by 

generating empirically based hypotheses about what environmental factors limit 

the distribution of a species in different parts of its range. Depending on the SDM 

methodology used, it may be possible to examine which predictor in a model is 

most limiting (or producing a projection of “no occurrence”) and use that knowledge to infer a causal factor. For example, if total annual precipitation is a signifi -

cant statistical predictor for a species, it should be possible to identify locations 
79

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

within the study area where the model indicates that annual precipitation is low 

enough to signifi cantly reduce the likelihood of occurrence (or prevent occurrence 

altogether). Such a projection would be useful to researchers, who could design 

studies to test that projection and either confi rm the validity of the model and the 

associated inference, provide information to change the inference, or identify a fl aw 

in the model (incorrect predictor variable, missing interaction, or incorrect model 

structure).

Although SDMs can be used in an illustrative or precautionary sense to identify 

potential species vulnerabilities, Elith and Graham (2009) noted that projecting species distributions into the future is the most uncertain usage of SDMs. Projections 

of species migrations should be interpreted and used with great caution. Such projections may be useful for identifying target zones for future species colonization or 

extirpation that could be subject to more intensive monitoring as an early warning 

of change. The uncertainty levels associated with those projections—including 

uncertainty in emissions scenarios, climate responses, species environmental 

requirements and adaptation potential, the effects of biotic interactions, and SDM 

methodology—are probably too great to allow these models to motivate proactive 

management activities (e.g., assisted migration). However, they may lend additional 

support to conducting management activities that would be justifi ed for other 

purposes (“no-regrets” activities).

Gap-Replacement Models

Introduction to Gap-Replacement Models

Gap models are a class of forest succession models developed to simulate the effects 

of gap-phase disturbances on forest structure and composition (Bugmann 2001). 

Originally developed primarily for eastern deciduous forests and later applied to 

western coniferous forests, these models simulate the establishment, growth, and 

mortality of individual trees within small forest stands (Bugmann 2001). They have 

been used to project forest responses to chestnut blight (Shugart and West 1977), 

air pollution (Kercher and Axelrod 1984a, West et al. 1980), fi re (Keane et al. 1990, 

Kercher and Axelrod 1984b), and climate change (Dale and Franklin 1989, Pastor 

and Post 1988, Solomon 1986, Zolbrod and Peterson 1999), though their application to climate change has raised questions about model assumptions and the way 

climate was incorporated into the establishment, growth, and mortality processes 

(Bugmann 2001). Gap models have largely been replaced by SDMs and other 

model types in recent efforts to project vegetation responses to climate change, but 

offer an approach that largely complements those taken by SDMs. Whereas SDMs 

Projecting species 

distributions into the 

future is the most 

uncertain usage of 

SDMs.
80

GENERAL TECHNICAL REPORT PNW-GTR-900

project future vegetation condition without specifying the “how” or “when,” gap 

models simulate the process of change in forest stands. 

How Gap-Replacement Models Work

Gap models simulate the establishment, growth, and mortality of individual trees 

in small forest stands as stochastic processes that are driven primarily by resource 

availability, which is itself a function of climate and competition from neighboring 

trees (Bugmann 2001). Gap models are process models that simulate processes 

using mathematical functions that can be parameterized from a basic knowledge 

of the physiology and allometry of the species of interest (Perry and Enright 2006), 

though biogeographical knowledge can also assist in developing parameters. 

Functional relationships and parameters used in major gap models were originally 

developed from a combination of theory and fi eld data (Botkin et al. 1972, Pacala et 

al. 1993, Ribbens et al. 1994). 

Changes in structure and composition are typically modeled on an annual time 

step, based on stochastic simulation of establishment, growth, and mortality of 

individual trees within small stands. The establishment (“birth”) process typically 

includes the effects of seed production/availability, understory light availability, 

soil moisture, temperature, and herbivory on tree establishment. Growth of trees is 

regulated by shading (light), crowding (soil nutrients), soil water availability, and 

temperature. Mortality is a random process, with a low background probability of 

mortality that increases if the tree is under stress (low growth rate). Mortality can 

also result from discrete natural disturbances like fi re or logging. At each annual 

time step, tree inventories are updated and summarized to produce new descriptors 

of stand-level structure and composition.

Gap models typically employ an ensemble approach for generating projections. 

One simulation produces a single potential outcome, given the starting conditions. 

Repeating the simulation many times for the same starting conditions provides an 

assessment of the range and variability of outcomes arising from random chance. 

Repeating the simulation many times with varying starting conditions (e.g., representing spatial variability in structure and composition within a stand) produces a 

more general assessment of overall variability in future structure and composition. 

Gap models have been modifi ed (adapted) to project forest responses to a 

variety of disturbance types, including chestnut blight in eastern deciduous forests 

(Shugart and West 1977), air pollution in coniferous forests of California (Kercher 

and Axelrod 1984a,1984b), and fi re in the inland Pacifi c Northwest (Keane et al. 

1990). In most cases, this has involved modifying the mortality (“kill”) procedure 

to produce periodic pulses of mortality on a fi xed interval (e.g., silvicultural treatment) or as a random process (e.g., fi re), and examining the resulting effects. 

Gap models simulate 

the establishment, 

growth, and mortality 

of individual trees in 

small forest stands as 

stochastic processes 

that are driven 

primarily by resource 

availability.
81

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

How Gap-Replacement Models Are Applied 

to Climate Change Issues

Gap models were among the fi rst models used to project the potential impacts 

of rising atmospheric CO2 levels and changing climate on forest vegetation. The 

original gap models (e.g., JABOWA and FORET) were created to simulate climatic 

infl uences on establishment, growth, and mortality, largely through temperature 

and precipitation infl uences on growing degree-days and soil moisture. These 

process-based models can therefore simulate both transient and equilibrium forest 

responses to climate change if given a suitably modifi ed climatic time series as 

input to the simulations. For transient responses, gap models can be either set to an 

initial structural state or run for a long period under current climate conditions to 

establish equilibrium baseline conditions, after which climatic inputs are altered 

and simulated structural and compositional changes are observed. For equilibrium 

responses, gap models can simply be run for a long period under projected future 

climate conditions. By simulating vegetation responses for a network of plots within 

a region (with suitable initial vegetation states and climatic inputs), gap models can 

also project changes in species ranges and regional vegetation dynamics.

Strengths and Weaknesses of Gap-Replacement Models

The major advantage of gap models is that they explicitly model the basic processes 

that drive forest structural and community dynamics, including tree establishment, 

growth, competition for resources, and mortality. In addition to simulating forest 

succession, they provide information about the ecological processes driving succession. The projected outcomes and mechanisms can be subjected to validation 

through well-crafted fi eld studies, providing a way to improve model performance 

and increase confi dence in model projections. Their performance can also be 

assessed by comparing model projections to forest succession data from long-term 

monitoring plots (Pabst et al. 2008).

Another advantage of gap models is their fl exibility. Gap models can be adapted 

to new regions and forest types through modifi cation of species pools, defi ning or 

modifying species descriptors, and modifying process parameters. The establishment, growth, and mortality functions can be modifi ed to include locally important 

disturbances (e.g., fi re, insects) or to better represent factors infl uencing establishment, growth, and mortality. Additional processes can be added to accommodate 

new applications (e.g., new disturbance types or stressors) and forest management 

scenarios (Pabst et al. 2008). If necessary, functional relationships can be updated 

as they become better understood. 

The emphasis of gap models on simulating processes that drive forest succession has also limited their utility for many applications. Properly simulating tree 
82

GENERAL TECHNICAL REPORT PNW-GTR-900

responses to disturbances, resource limitations, and climate requires a theoretical 

or empirical understanding of vegetation-environment relationships that has not 

yet been developed for many species and vegetation types. Although much effort 

has been invested in modeling tree growth, there has been less in formulating 

tree establishment and mortality, although these will certainly be important under 

climate change scenarios (Bugmann et al. 2001). Little or no effort has gone into 

modeling growth, establishment, and mortality for other life forms, such as shrubs 

and herbaceous species. Even climate-growth relationships are not well understood 

for sites where there is no obvious single limiting factor (e.g., upper and lower 

treeline). Relationships also vary among regions and climatic settings. Species 

and process parameters developed for eastern deciduous forests are unlikely to 

work well for simulating forest succession in western coniferous forests. Although 

parameters and response curves can be developed for new species and regions, such 

an effort requires time and resources that are often not available. 

Most gap models are not spatially explicit and assume no interactions among 

simulated plots. This has limited their ability to simulate neighborhood effects on 

seed inputs and other spatial processes within forest stands. This problem has been 

partially resolved in more recent gap models like SORTIE (Pacala et al. 1993), in 

which simulated plots are larger, tree locations are explicitly “mapped,” crown 

shapes conform to available gap sizes and locations, and processes such as competition and dispersal are modeled as continuous functions (Perry and Enright 2006). 

This increased functionality comes with increased parameterization and computation costs, however (Perry and Enright 2006), and still does not resolve problems 

with spatial interactions at broader spatial scales. For example, topographic infl uences, sharp climatic gradients, large wildfi res, and seed dispersal processes can 

all produce spatially autocorrelated effects on vegetation dynamics that exceed 

the spatial extent of gap models. Capturing these processes typically requires a 

spatially explicit landscape model (see below). However, gap models can be linked 

with landscape models (e.g., Johnson et al. 2007), and a new class of individual tree 

models can operate at landscape scales (Seidl et al. 2012). 

A major weakness of typical gap models with respect to climate change 

applications has been the lack of information about climatic infl uences on forest tree establishment, growth, and mortality, particularly in the juvenile stages 

(seedlings and saplings). In the absence of such information, assumptions must 

be made regarding climate response curves and climatic thresholds associated 

with mortality at different life stages. When gap models were initially applied 

to studies of forest successional responses to disturbance, climatic infl uences on 

tree growth were parameterized based on species geographic distributions, with 
83

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

minimal growth (and mortality thresholds) corresponding to climatic conditions at 

the margins (typically northern and southern limits) and optimal growth associated 

with intermediate climatic conditions. This approach was reasonable for projecting 

successional responses to disturbances in eastern deciduous forests, as it limited 

the pool of potential species to locally adapted species and allowed gap sizes and 

relative shade tolerances among species to drive gap replacement processes. However, this approach implicitly assumes either a stable climate (which is a problem for 

climate change applications) or the same sort of equilibrium conditions assumed by 

SDMs. The parameterization method was of questionable utility for climate change 

studies, as warming temperatures automatically produced mortality of existing 

forest trees and replacement with species from warmer climates, much the same as 

with statistical species distribution models. Although such a vegetation response 

is certainly possible, a better understanding and modeling of climate-growth and 

growth-mortality relationships would likely produce much less formulaic results 

and be more informative. 

Finally, differences in gap model structure and parameterization can produce 

a broad range of simulated outcomes among models for the same simulation data 

(initial conditions, climate, and species traits), generating considerable uncertainty 

about the reliability of model projections. A review of six gap models in several 

undisturbed European forests found that tree species composition was correctly 

projected under the current climate about half of the time (Badeck et al. 2001). Tree 

species with very wide geographical distributions may differ signifi cantly across 

their range in autecological properties (local adaptation), so local parameterization may be required (Badeck et al. 2001). Gap models and input data need to be 

improved to provide a more robust assessment of the effects of climate change 

(Badeck et al. 2001). For example, CO2 interactions with vegetation are not explicitly represented in most existing models. Lastly, gap models have largely been only 

applied to forested environments, although the structure of the models, with appropriate parameterization, could be applicable to any ecosystem type. 

Using Gap-Replacement Models

Well-designed and locally calibrated gap models could be very useful in stand-to 

landscape-level management planning (tactical). Their focus on individual trees 

and stands allows them to provide estimates of rates and trajectories of change in 

response to disturbances, management treatments, species invasions, or changes in 

climate and atmospheric CO2 concentrations. Their ability to simulate future forest 

responses to a wide variety of disturbances (including management treatments) 

could be useful in evaluating and choosing among alternative treatment options. 
84

GENERAL TECHNICAL REPORT PNW-GTR-900

They could also be useful for improving our understanding of forest species invasion processes, as species with known (or hypothetical) characteristics can be 

introduced to stands with varying initial structure and composition. 

With respect to climate change, gap models appear to be best suited to evaluating “what-if” scenarios generated by theory, other models, or proposed management plans. If the climate at a site became suitable for a new, highly competitive 

species (based on an SDM projection), how long might it take the species to establish itself and become dominant, and how long might the current species persist? 

How might thinning dry forest stands for restoration and fi re management purposes 

infl uence the sensitivity of those stands to changing climate? If intense summer 

droughts caused dieback of a species (or promoted insect outbreaks), how would 

this alter future stand structure and composition? In some cases, resource managers 

may be comfortable using model simulations as reasonable projections of the range 

of potential outcomes. In other cases, the management questions or simulations may 

raise questions about the adequacy of our knowledge about key processes.

Finally, relationships or outcomes from gap models may be useful for calibrating landscape models (see below). 

Landscape Models

Introduction to Landscape Models

Landscape models are a class of models that simulate spatial dynamics of vegetation in response to disturbance and succession (Keane et al. 2004). Some have the 

ability to characterize transient changes in vegetation in response to climate as well. 

They simulate the interactions of vegetation, disturbances, and climate across broad 

spatial scales and through time (Xi et al. 2009). Like gap models, landscape models 

can model successional processes within stands (grid cells), but typically do so with 

less detail than in gap models. Unlike gap models, landscape models are designed 

to allow distance-based spatial interactions in abiotic and biotic processes, such as 

fi res and seed dispersal, among stands or grid cells. Like gap models, landscape 

models were originally developed to investigate the infl uences of disturbances and 

spatial variation in environmental conditions on vegetation (vegetation structure, 

species composition, and landscape spatial patterns) and have subsequently been 

adapted for use in climate change research and planning.

How Landscape Models Work

Landscape models attempt to simulate dynamic interactions among vegetation, 

climate, and disturbances (including management activities). Landscape models 

typically model vegetation changes within a grid of cells (or a cluster of polygons), 

where each cell (polygon) represents a “stand” of some size and the entire grid 
85

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

(cluster) represents a landscape. Individual cells can represent stands of varying 

sizes (Yang et al. 2011), from hundreds of square meters (e.g., 900 m2

 in LANDIS 

and LANDSUM) (Keane et al. 2008, Yang et al. 2011) to one or more square 

kilometers (e.g., 1 km2

 in LANDIS-2) (Scheller and Mladenoff 2008). Modern landscape models can simulate up to 1 million grid cells on a single landscape, thereby 

representing landscape areas of thousands to millions of hectares (Yang et al. 2011).

Vegetation succession is modeled within each grid cell, with the level of 

complexity and the driving processes varying from model to model. At the high end 

of the complexity continuum, a landscape model could include a fully functional 

individual-based gap model or biogeochemical model to simulate vegetation growth 

and compositional changes as a function of climate, disturbances, and site factors 

(e.g., FIRE-BGC, Keane et al. 1996; iLand, Seidl et al. 2012). Other landscape 

models reduce within-cell computing requirements by modeling vegetation structure and composition simply as a function of time since disturbance or by modeling 

growth of species cohorts rather than individuals (Yang et al. 2011). At the low end 

of the complexity spectrum are landscape models that represent vegetation as a 

collection of predefi ned, static communities that can migrate on the landscape (e.g., 

MAPSS and TELSA) (Kurz et al. 2000, Neilson 1995).

Most landscape models allow explicit representation of multiple spatial processes and operate in a spatially explicit environment. A key feature of landscape 

models is the ability to simulate the effects of spatial processes on vegetation 

dynamics within and among stands, and to allow analysis of the resulting landscape 

patterns. Important spatial processes can include disturbances such as fi res, windstorms, and insect outbreaks, as well as biological processes such as pollination and 

seed dispersal (Scheller and Mladenoff 2007). Landscape models could also use 

spatial variability in site characteristics like topographic setting, soil properties, or 

biophysical environment to simulate spatial variability in vegetation composition, 

successional patterns, or responses to disturbances or climate.

How Landscape Models Are Applied to Climate Change Issues

As with gap models, some landscape models can address climate change questions by simulating vegetation responses to various scenarios. If the within-cell 

vegetation succession model allows direct input of climate data, climate data can 

be altered to match projected future conditions under the proposed scenario, with 

multiple simulations used to defi ne a range of outcomes for that scenario. Many 

different types of outcomes could be assessed, including individual species frequencies on the landscape, landscape-level changes in species abundance, patch 

size distributions, or overall landscape heterogeneity or species diversity. Even 
86

GENERAL TECHNICAL REPORT PNW-GTR-900

if climate is not an explicit driver of vegetation succession in a landscape model, 

climate change effects could still be evaluated indirectly by simulating the effects 

of altered processes (e.g., disturbance regimes, seed availability) on vegetation, and 

landscape patterns.

Strengths and Weaknesses of Landscape Models

The primary strength of landscape models is the ability to include spatial processes 

as drivers of vegetation change on landscapes, including disturbances like fi re and 

human activities and seed dispersal and species migration processes. Large disturbances like wildfi res, insect outbreaks, and human management activities often 

initiate vegetation change by killing dominant species or individuals, releasing 

growing space and available resources, and altering microclimate. Seed-dispersal 

processes infl uence migration rates and pathways for species and, in turn, regulate 

rates of potential change in species composition. These spatial processes can signifi -

cantly infl uence vegetation change and landscape patterns, but are largely absent 

from most other types of models. They are also important for modeling transient 

responses to climate change.

Landscape models also benefi t from being, at varying levels of detail, processbased models. Landscape models can be developed to include simulation of stand 

development or biogeochemical processes, as needed to meet model objectives. 

They can therefore share many of the strengths of gap models and biogeochemical 

models for addressing climate change questions. 

One limitation with landscape models is that they are computationally demanding, forcing users to make choices about the complexity of processes simulated 

within each cell (e.g., stand dynamics, biogeochemical cycling), the number and 

types of spatial processes to be simulated, and the spatial extent of the simulation 

(cell size and number of cells). Although advances in software design and computer 

hardware have improved landscape model capabilities, they still cannot do everything over large areas.

 An even more basic limitation that landscape models share with gap models 

is the lack of basic understanding of the effects of climate on vegetation dynamics, 

disturbances, and their interactions, and on nonforested ecosystems. Simulating 

processes adequately requires basic knowledge about those processes. Parameterizing process-rich landscape models for use over large spatial and temporal scales 

requires a great deal of information about species responses to climate, disturbances, drivers of landscape pattern and process, and the spatial and temporal 

scales at which processes are expressed (Perry and Enright 2006). This information 

typically does not exist and must be estimated or omitted. Even fully parameterized 

The primary strength 

of landscape models 

is the ability to include 

spatial processes as 

drivers of vegetation 

change on landscapes.
87

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

landscape models may fail to produce realistic results, particularly if key processes 

are not included in the model. Parameterization problems can be useful for revealing data and knowledge gaps, but they limit the immediate utility of landscape 

models for addressing climate change questions.

Using Landscape Models

The best use of landscape models may be as tools for designing and testing 

strategies for managing disturbances and landscape patterns, including potential 

interactions with specifi c climate change scenarios. However, as with all models, 

they should not be used to make specifi c projections about future conditions or as 

the basis for making specifi c conservation predictions (Perry and Enright 2006, 

Wennergren et al. 1995). For example, a landscape model might be used to examine 

the effects of projected changes in fi re frequency or extent on landscape patterns. 

Or, it might be used to examine the potential effects of restoration treatments on 

landscape fi re resilience under current and future climate scenarios. It could also 

estimate potential species migration rates across a landscape once climate becomes 

suitable (native or exotic species). Such applications make good use of the ability 

of landscape models to model spatial processes, but do not require the models to 

predict the nature or timing of direct climate change effects on vegetation.

Biogeochemical Models

Introduction to Biogeochemical Models

Biogeochemical models are process-based models that simulate carbon, water, and 

mineral (nutrient) cycles in terrestrial ecosystems, including forests and rangelands 

(Aber and Federer 1992, Running and Nemani 1991, Waring and Running 1998). 

Biogeochemical models are commonly used to simulate ecosystem net primary 

productivity and carbon storage, and this is their primary usage in climate change 

research. Biogeochemical models may also simulate site water balances and nutrient 

availability at varying levels of detail associated with varying spatial and temporal 

scales (Schimel et al. 1997a, 1997b). 

How Biogeochemical Models Work

Biogeochemical models represent sites by describing one or more soil layers (e.g., 

depth, water-holding capacity, fertility), climate (input time series at daily to 

monthly time steps), and any topographic factors that might infl uence waterfl ows 

or solar radiation inputs. Vegetation is generally described in terms of foliage (e.g., 

leaf area index, leaf nitrogen concentration), structural wood (e.g., stems, branches, 

and coarse roots), and fi ne roots. Some biogeochemical models (e.g., Biome-BGC 
88

GENERAL TECHNICAL REPORT PNW-GTR-900

or pNET) treat vegetation on the landscape as a single leaf layer (Aber and Federer 

1992, Boisvenue and Running 2010), while others may simulate multiple canopy 

layers and light attenuation through the canopy. Such models can be parameterized to capture the broad ecophysiological characteristics of plant functional types 

and are generally not species specifi c, or to represent the observed characteristics 

of particular species (e.g., Pietsch et al. 2005). Biogeochemical models “grow” 

vegetation by determining net carbon uptake through photosynthesis and respiration, which is regulated by temperature and availability of light, water, and soil 

nutrients. Growth (carbon) is then allocated to different biomass pools, where it 

may contribute to future resource acquisition (fi ne roots), photosynthesis (foliage), 

or structure (wood). Climate data are input as drivers of ecosystem changes through 

time (Boisvenue and Running 2010), interacting with site environmental conditions 

and vegetation ecophysiological traits. 

How Biogeochemical Models Are Applied 

to Climate Change Issues

Biogeochemical models can be applied to climate change issues by simulating 

transient and equilibrium responses of vegetation and, in some cases, ecosystem 

services to changing climate (Aber et al. 1995). Responses of particular interest 

may include net carbon uptake and storage, site water balances, and changes in sustainable vegetation characteristic (e.g., leaf area index). As with gap and landscape 

models, transient and equilibrium responses to climate change can be studied by 

altering the climate data input stream to refl ect projected future changes in climate 

(with or without a transition period). 

Strengths and Weaknesses of Biogeochemical Models

Biogeochemical models directly address ecosystem responses to climate change 

that are important in natural resources management. Carbon uptake and storage 

directly address questions about the role of forests and rangelands as sources or 

sinks for atmospheric CO2. Estimates of soil water balances and nutrient cycling 

can help inform assessments of climate change effects on water quality and yields. 

Describing vegetation changes in terms of changes in foliage leaf area and nitrogen 

content can also be useful for suggesting ways that vegetation structure and composition might be managed to increase resilience to climate change.

Biogeochemical models are relatively amenable to validation, increasing 

confi dence in model projections. For example, model projections of ecosystem 

productivity responses to past climatic variability may be tested against data from 

long-term ecosystem studies, or compared to growth indices derived from tree-ring 
89

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

records. Simulated ecophysiological processes such as photosynthesis and respiration can be measured under fi eld conditions. Soil moisture and runoff can also be 

measured in the fi eld and compared to model projections. As model projections 

improve for past and current conditions, confi dence in future projections improves.

Biogeochemical models that do not consider individual species or biotic interactions make it diffi cult to forecast vegetation responses to climatic changes in terms 

of future species composition or biodiversity. They also do not explicitly model 

spatial processes, but can be incorporated into spatially explicit landscape models 

(e.g., Fire-GBC; Keane et al. 1996) or into dynamic global vegetation models (e.g., 

MC1; Bachelet et al. 2001a). However, unlike other model types, these models can 

be used across a range of ecosystem types (grasslands, woodlands, forests). 

Using Biogeochemical Models

Biogeochemical models are probably best used to simulate climate and management effects on site carbon and water balances, and to identify key climatic factors 

limiting ecosystem productivity (environmental stressors). Biogeochemical models 

could also be used effectively in tandem with gap or landscape models to achieve 

additional objectives. 

Dynamic Global Vegetation Models

Introduction to Dynamic Global Vegetation Models

Dynamic global vegetation models (DGVMs) are a class of process models developed to model ecosystem processes and the distribution of major vegetation biomes 

at continental to global scales. They have been developed primarily for addressing 

questions related to global ecology, biogeography, and climate change, including 

interactions among atmospheric CO2 concentrations, climate, and terrestrial plant 

productivity. Dynamic Global Vegetation models can be run independently using 

historical or future projected climate data, or they can be coupled with GCMs for 

use in generating future climate scenarios. 

How Dynamic Global Vegetation Models Work

Dynamic global vegetation models are mechanistic models that integrate many 

processes—plant biogeography, ecophysiology, biophysics, biogeochemistry, and 

sometimes disturbances and vegetation dynamics—to simulate land-atmosphere 

interactions (Cramer et al. 2001). They simulate changes through time for vegetation types, not species, where vegetation types are described by properties like 

vegetation height, growth phenology, rooting depth, leaf area index, photosynthetic 

pathway, and stomatal resistance (not species). Climate is one of the main drivers 
90

GENERAL TECHNICAL REPORT PNW-GTR-900

of DGVMs, and climate variables and CO2 are incorporated directly into process 

simulations. 

Dynamic global vegetation models share characteristics with modern biogeochemical models and statistical species distribution models. Like biogeochemical 

models, DGVMs seek to simulate climate and vegetation effects on carbon and 

water cycling. In fact, some DGVMs have directly incorporated biogeochemical 

models into their model structure (e.g., the CENTURY biogeochemical model was 

incorporated into the MAPSS biogeographical model to produce the MC1 DGVM). 

Like species distribution models, some DGVMs simulate shifts in the spatial 

distribution of broad plant functional types (rather than species) by using statistical 

relationships between vegetation types and either climatic conditions or modeled 

vegetation characteristics. 

How Dynamic Global Vegetation Models Are Applied 

to Climate Change Issues

Dynamic global vegetation models have been used to project vegetation responses 

to increasing atmospheric CO2 concentrations and changing climate at global, 

national, and regional scales (Bachelet et al. 2004, Cramer et al. 2001, Pan et al. 

1998, Rogers et al. 2011). As noted above, climate is one of the main drivers of 

DGVMs, and climate variables and CO2 are incorporated directly into process 

simulations.

Strengths and Weaknesses of Dynamic Global 

Vegetation Models

Because processes are specifi ed and constraints on new responses are limited, these 

models may respond well to novel climates. However, many important processes 

are also faster than the minimum model time steps, and simplifi ed equations 

that roughly represent the process must be used instead of detailed mechanisms 

(Neilson et al. 2005). The models are typically evaluated through observation and 

experimentation. 

Dynamic global vegetation models were designed to operate at continental to 

global extents, with individual grid cells representing thousands of square kilometers (Neilson et al. 2005). At such broad scales, climate is often a dominant driver 

of spatial variability in vegetation structure and ecosystem processes. The models 

have also been applied at landscape to regional spatial scales (Rogers 2009, 2011). 

Their use is not limited to one particular ecosystem type or plant functional type, 

so they are broadly applicable. However, their use at fi ner spatial scales could 

greatly increase the uncertainty of model output, as the models often do not simulate the infl uence of local drivers on ecosystem processes (e.g., topographic effects). 
91

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Differences between models are noteworthy and not easily linked to differences in 

model process formulations or model complexity (Cramer et al. 2001). Cramer et al. 

(2001) also noted that regional details of DGMV simulations are not to be taken as 

predictions. Although these models do include some disturbance processes (notably 

fi re), other important processes, such as insect and disease dynamics, are lacking. 

Although all models require some sort of future climate data for parameterization, 

these models are spatially and temporally dynamically coupled to output from 

GCMs, and highly sensitive to GCM output. 

Using Dynamic Global Vegetation Models

These models are best used at the broad global scales for which they were developed. They can be very useful in combination with GCMs for projecting carbon 

and water fl uxes. They can also be used to make broad-scale projections about the 

capacity of terrestrial ecosystems to take up and sequester (or release) atmospheric 

carbon. They may also be useful for examining possible changes in terrestrial productivity and environmental limiting factors in response to future climate change 

scenarios (Neilson et al. 2005).

Dynamic global vegetation models do not appear to be suitable for regional 

planning efforts, although they are being incorporated into other models used for 

planning (e.g., Kerns et al. 2012). Their best contribution would likely be estimates 

of potential sustainable vegetation characteristics (e.g., maximum leaf area), but 

these could be obtained from biogeochemical models as well. The DGVMs do 

not try to model vegetation at the species level, so they are not particularly useful 

for species-level conservation planning. However, some applications have shown 

that DGVMs can be applied to simulate vegetation dynamics on the species level 

(Hickler et al. 2004). As noted earlier, DGVMs generally ignore important local 

drivers of vegetation structure, composition, and productivity (as they should for 

global-scale applications) making results from their application at fi ne spatial scales 

problematic. Adding such drivers would likely convert the model to some form of 

gap-biogeochemical or SDM-biogeochemical hybrid. Indeed, some hybrid models 

have recently been developed to address these concerns (e.g., LPJ-GUESS, Smith 

et al. 2001; HYBRID, Friend et al. 1997). 

Growth and Yield Models

Introduction to Growth and Yield Models

Growth and yield models are used in forest management to estimate future growth 

and yield of forest stands based on site characteristics (e.g., site index) and mathematical or statistical representations of tree growth through time. They typically 

Dynamic global 

vegetation models 

do not appear to be 

suitable for regional 

planning efforts, 

although they are 

being incorporated into 

other models used for 

planning.
92

GENERAL TECHNICAL REPORT PNW-GTR-900

model forest growth and biomass accumulation for entire stands or species cohorts, 

but some simulate growth at the tree level. A stand growth model simulates the 

growth, mortality, and other changes in stand composition and structure, and mathematically describes the growth and yield of trees and stands. Growth and yield 

models have also been coupled with stand visualization systems to provide visual 

representation of the stand structural conditions being simulated by the models.

How Growth and Yield Models Work

Growth and yield models begin with site information and an initial stand inventory. 

They then simulate forest stand development by growing existing trees, establishing 

new cohorts (given growing space availability), and killing trees based on established rules and probabilities. Model parameters can be modifi ed to better suit local 

conditions or to conduct “what-if” investigations.

The Forest Vegetation Simulator (FVS) is widely used in forest management 

applications and can serve as a good example of how modern growth and yield 

models work. The FVS is an individual-tree, semidistance-independent growth and 

yield model (Crookston et al. 2010, Dixon 2002). Inputs include an inventory of site 

conditions and a set of measurements on a sample of trees (e.g., tree size, species, 

crown ratio, recent growth and mortality rates). Outputs include summaries of tree 

volume, species distributions, and growth and mortality rates that are often customized for specifi c user needs. Model “variants” have been developed for specifi c 

geographic areas of the United States by calibrating model equations to local fi eld 

data. Because it simulates individual trees, FVS can simulate growth and yield 

responses to a wide range of silvicultural treatments across a wide range of forest 

types and initial stand conditions.

How Growth and Yield Models Are Applied 

to Climate Change Issues

Growth and yield models have traditionally assumed static climatic conditions on 

a site and have not incorporated temporal climatic variability into establishment, 

growth, and mortality functions. As such, they have had limited utility for climate 

change applications.

Recently, climate considerations have been integrated into FVS so that standlevel impacts of climate change can be simulated (Crookston et al. 2010). This was 

accomplished by (1) adding functions that link mortality and regeneration of species to climate variables expressing climatic suitability, (2) constructing a function 

linking site index to climate and using it to modify growth rates, and (3) adding 

functions accounting for changing growth rates resulting from climate-induced 

genetic responses. This model incorporates climate by using information and data 
93

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

from the species distribution modeling approach that closely parallels Rehfeldt et 

al. (2006, 2009) (Crookston et al. 2010). It can be argued, however, that in adding 

these climate considerations to a model that was already simulating individual 

trees, FVS is becoming more of a gap or hybrid model. The newest model version 

(2.1) also includes “genetic” variation in the mortality model, largely for species that 

have very large ranges. For example, the modifi cation in version 2.1 will likely kill 

off Douglas-fi r (Pseudotsuga menziesii (Mirb.) Franco) at a given location even if 

Douglas-fi r from elsewhere in the range of the species would grow well. The model 

may regenerate Douglas-fi r in that case under the assumption that the new trees will 

be genetically adapted to the site.

Strengths and Weaknesses of Growth and Yield Models

Growth and yield models have the benefi t of being relatively simple to parameterize and run. To the extent that parameters are derived from local growth and yield 

data, the models can produce reasonably accurate projections for the local area. 

These models have proved to be very useful in forest planning efforts and designing 

silvicultural strategies.

The primary weakness of growth and yield models is the basic assumption of a 

static climate, such that climatic variability is not a driver of establishment, growth, 

and survival. Climate functions simply as an invariant site descriptor. Growth and 

yield models are also most reliable when calibrated against local fi eld data; however, uncertainties about future climate make it diffi cult to fi nd appropriate model 

parameters to use to simulate future stand growth. Growth and yield models have 

also been developed only for forested stands and species. 

Using Growth and Yield Models

We do not recommend the use of traditional growth and yield models for projecting forest responses to climate change. Hybrid models like FVS-Climate may have 

some utility as pseudo-gap models, but this new modeling approach has not yet 

been widely applied and assessed in the literature.
94

GENERAL TECHNICAL REPORT PNW-GTR-900
95

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 7: Using Model Projections

Chapter 6 highlights the many different types of models that have been developed, 

and continue to be developed, to examine how vegetation may change with future 

climate change. We reviewed the major classes of vegetation models; described 

their basic function, strengths, and weaknesses; and discussed the contribution each 

could make toward understanding and projecting vegetation responses to future 

climatic changes. It is clear from this review that the literature is not mature enough 

at this time to suggest which modeling approach is “best,” or which models within 

a particular approach are “best” although we know some things that are “better” 

(e.g., using direct gradients not proxies). Our assessment revealed numerous caveats 

associated with commonly applied empirical species distribution approaches, and 

suggests that process-based approaches may be more robust to prediction. Yet all 

process models rely on parameter estimates from experimentation or the literature, 

which are themselves estimates. Each equation or parameter input into a model has 

associated uncertainty (Beale and Lennon 2012), which may propagate through the 

model. The more complex the model is, the better it may deal with novel conditions, but the more uncertainty in results there may be with so many parameters 

estimated (Sklar and Hunsaker 2001). Overfi tting a model with lots of parameters 

limits extrapolation into novel condition as well. Despite the potential complexity 

of process models, critical processes may still be omitted (Verboom and Wamelink 

2005). In addition, scale issues, lack of data for parameterization of more species, 

model complexity, lack of management-relevant model output information, and lack 

of species-specifi c output from dynamic global vegetation models (DGVMs) like 

MC1, has often been a barrier to use by the management community. 

Despite the specifi c strengths and weaknesses of the different modeling 

approaches, there are certain caveats specifi c to all the models. For example, most 

of the models do not deal with inertia in vegetation, although some landscape 

models have the capacity to deal with inertia if they are parameterized to include 

climate change. None of the models we examined deal with dispersal or genetic 

adaptation, although some models are starting to do so (Gibson 2011). A new 

generation of models incorporating climate variability along with dispersal and 

establishment are clearly needed (Gray et al. 2006). Most of the models also do 

not incorporate disturbance processes, or may only address fi re. The inclusion of 

disturbance and extreme events in most models is still early in the process (Keane 

et al. 2004, Lenihan et al. 1998, Thonicke et al. 2001). Many models are also often 

used without being calibrated and analyzed for sensitivity and uncertainty (Verboom and Wamelink 2005). And for all models, the more future conditions differ 

from current conditions, the more our understanding of ecological patterns and 

processes will be incomplete and the less accurate our models will be at predicting 

ecological phenomena (Williams and Jackson 2007).

The more future 

conditions differ from 

current conditions, 

the more our 

understanding of 

ecological patterns 

and processes will 

be incomplete and 

the less accurate 

our models will be at 

predicting ecological 

phenomena.
96

GENERAL TECHNICAL REPORT PNW-GTR-900

Ensemble Approaches

Because there is no consensus on which modeling approach is “best,” ensemble 

approaches, such as used in chapter 8, are important. Ensemble approaches are 

commonly used when looking at climate projections with global climate models 

(GCMs). Such ensemble forecasting using multiple GCMs and multiple vegetation 

models can be used to fi nd consistent projections, which may be more likely to be 

correct (Araújo and New 2007, Bradley 2010). Use of different models in combination also provides information at multiple taxonomic levels (e.g., species vs. vegetation type) about how climate might affect vegetation change, and the difference 

between projections and their sources are themselves useful tools with which to 

assess uncertainty (Littell et al. 2011). 

In general, agreement among ecological models with different underlying 

principles and different conceptual approaches when climate inputs are similar (e.g., 

same scenarios), or across multiple climate models and multiple ecological models, 

suggests scientifi c agreement (if not consensus) on sensitivity of the vegetation 

response to climate (Littell et al. 2011). However, model development, lineages, 

and model input data are sometimes shared (e.g., sibling models), so it may be 

important to note that if there is agreement, the models need to be independent 

in their assumptions for potential reductions in uncertainty to hold. In particular, 

species distribution models share a similar conceptual approach and many tend to 

use similar data for training (e.g., Forest Inventory and Analysis data sources for 

tree presence/absence). Thus it is unclear how independent these types of models 

are from each other and not surprising that these models often produce quite similar 

results, despite very different analytical approaches. Agreement among several 

species distribution models may not be as meaningful in terms of uncertainty as 

agreement among species distribution and process models. Moreover, process 

models tend to differ considerably in their conceptual approaches and development 

(e.g., gap models, DGVMs) and to be truly independent of each other. Therefore, 

agreements among several process models may be potentially more meaningful 

than agreement among several species distribution models. 

Frequent disagreement among ecological models when climate is similar 

suggests different assumptions and sensitivities of ecosystem response to climate 

among models. Frequent disagreement in output from a single ecological model 

with variable climate inputs suggests high sensitivity of vegetation to climate model 

input. When models disagree and uncertainty is large, local knowledge, monitoring, 

observations of historical patterns, and knowledge from other sources may help 

decrease uncertainty. Even when models agree, there is a need for local knowledge, 

especially when translating the results into management actions.
97

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

When interpreting and using model output, it is key to try to understand why 

the output may differ so dramatically. Chapter 6 provides some background information that can help inform managers about why there might be differences in 

model output. For example, the differences in model output shown may be due to 

the fact that some models contain the disturbance fi re and include processes such as 

nitrogen fertilization (e.g., MC1). However, more than the modeling approaches can 

differ. For example, modelers often develop or downscale their historical climate 

data and future climate data differently. Assessing the exact mechanism of why the 

models differ is a complicated task, which could only realistically be undertaken by 

running the models using similarly generated input data and extensive sensitivity 

analyses. 

Using Model Projections Wisely

Models have been critical for alerting us to the potential magnitude of the effects of 

climate change (Sinclair et al. 2010). Furthermore, while the broad scales and long 

time periods in model simulations make them hard to validate, they also highlight 

the fact that empirical studies are limited in time and space and that models are 

needed to fi ll this gap. Although imperfect, models are necessary for projecting 

future conditions and likely perform better than expert opinion (Verboom and 

Wamelink 2005). The role of models in this context is not to predict the future, 

but rather to help manage uncertainty by narrowing the possible range of futures 

to a subset of plausible futures that pertain directly to vulnerabilities for specifi c 

resources and management objectives (Littell et al. 2011). This is why we often use 

the term “projection” in reference to simulated model output about the future. There 

is always the fundamental problem that extremely simplifying assumptions are 

necessary in modeling exercises (Green and Sadedin 2005). In these cases, simulations are best used to examine general ecological principles rather than predict the 

behavior of a specifi c ecosystem (Green and Sadedin 2005).

Models may be insuffi cient to answer specifi c management questions at a 

local scale, but can provide insight into the effects of climate change (Guisan 

and Thuiller 2005). Therefore, use of model output is better suited for informing 

strategic decisionmaking or land use planning, rather than tactical stand management decisions (Robinson et al. 2008). A hierarchical approach may be useful where 

fi rst the climatic suitability is evaluated, then more specifi c relationships based on 

soils, land use, and other factors can be used to fi ne tune the assessment (Bradley 

2009, Larson et al. 2001, Pearson and Dawson 2003). For example, potential habitat 

suitability can be projected for an invasive exotic species under climate change 

scenarios (Kerns et al. 2009). Combining this information with local information on 

The role of models 

is not to predict the 

future, but rather 

to help manage 

uncertainty by 

narrowing the 

possible range of 

futures to a subset 

of plausible futures 

that pertain directly 

to vulnerabilities for 

specifi c resources 

and management 

objectives.
98

GENERAL TECHNICAL REPORT PNW-GTR-900

the species distribution could alert managers to areas that have a higher probability 

of being invaded and help focus monitoring and early eradication efforts. Modeling 

is also a very good way to clarify policy concerns and begin to identify processes 

that are likely to be important (Perry and Enright 2006). 

Littell et al. (2011) suggested asking the following questions when choosing 

which models to select:

1. Are multiple scenarios and multiple GCMs needed, or is an ensemble mean 

suffi cient? Because it is likely that the mean may not be relevant for impact 

modeling, and that extremes are important, we suggest using multiple GCMs, 

emission scenarios, and multiple vegetation models. 

2. Do the models and emission scenarios selected match the risk framework 

(risk tolerant vs. risk averse)? 

3. Do the models chosen have good fi delity to 20th century observations for the 

region of focus? 

4. Is the spatial and temporal scale of the climate information appropriate to 

planning or decisionmaking? 

5. If downscaled information is being used, is the extra detail both necessary 

and realistic? 

6. How does the scale of information match the detail of the ecosystem impact 

model being used? 

After model selection is complete, keep in mind the best uses of model 

 output (box 7.1).

 Box 7.1. After vegetation model selection is complete, the best uses of 

 simulation model output include (based on Littell et al. 2011):

• Examining general ecological principles and identifying processes that 

are likely to be important.

• Providing insight into the range of potential effects of climate 

change.

• Informing strategic, not tactical decisionmaking.

• Helping to narrow the possible range of futures to a subset relevant 

to vulnerabilities for specifi c resources and management objectives. 

• Clarifying policy concerns.
99

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 8: Climate Change 

and Major PNW Biomes

The Pacifi c Northwest supports a diverse array of environmental settings, from dry 

deserts of the interior Columbia Basin to the wet Pacifi c Coast and from warm, 

low-elevation areas to high-elevation alpine and subalpine areas of the Cascade and 

Olympic Mountains. These diverse environments support an equally diverse suite 

of vegetation types, including subalpine forests and meadows, maritime coniferous 

forests, dry coniferous forests, juniper savannahs and woodlands, and shrub-steppe. 

Because each of these vegetation types is associated with distinct climatic conditions, environmental stressors, and disturbance regimes, they are likely to respond 

differently to future climate change. In the following sections, we provide a general description of fi ve major vegetation types in the Pacifi c Northwest, highlighting their unique features with respect to climate, vegetation, and disturbance, and 

describe past vegetation changes in response to climate change. We then review 

the relevant fi eld- and model-based research for each vegetation type (key species 

and communities) and highlight key fi ndings, projections, and uncertainties about 

vegetation responses to future climatic changes.

In particular, we seek to answer the following questions:

1. What are the major climate-related factors limiting vegetation growth, 

reproduction, and survival and how are they likely to change?

2. What are the dominant climate-related disturbance regimes and how are 

they likely to change?

3. What do we know about vegetation sensitivity and adaptation to elevated 

carbon dioxide (CO2) and climatic variability?

4. What are the likely vegetation responses to changing CO2 and climate?

Synthesis of Model Projections for 

the Pacifi c Northwest

We synthesized model projections by biome from relatively recent and peerreviewed literature (although level of peer review differs). Table 8.1 summarizes 

the data sources we used with brief information about the global climate models 

(GCMs) and vegetation models. We combined projections of vegetation change 

from these different types of models, essentially creating a qualitative ensemble 

of projections. Table 8.2 provides a summary ensemble of projected responses for 

some key Pacifi c Northwest species to climate change by the end of the 21st century 

based on multiple model output. This summary is based on a qualitative assessment using classes: some loss, moderate loss, major loss, no habitat remaining, 

some gain, moderate gain, major gain, or little to no change, or shift in habitat. We 
100

GENERAL TECHNICAL REPORT PNW-GTR-900 Table 8.1—Description of data sources for model output Species Species occurrence Model name Model output source modeled GCM(s) Model type data Time period

1. Random Plant Species and Climate Many trees GCM3, Species Forest 2090 forests Pro

fi le Predictions. http: of North HADCM3 distribution inventory

 \\forest.moscowfsl.wsu. America model and edu/climate/species/index.php research (Crookston et al. 2010, plot data Rehfeldt et al. 2006)2. ANUCLM Plant Hardiness of Canada website, Many trees HADCM3, Species Forest 2071–2100 Natural Resources Canada, and shrubs CGM31, distribution inventory http://planthardiness.gc.ca/ of western CSIRO mk35, model plot data (McKenney et al. 2007, 2011) North MIROC 32mr additional  America sources3. MSTC The ForeCASTS Project Many trees HADCM3, Species Forest 2100 http:\\ww.geobabble.org/~hnw/ and shrubs with A1F1 distribution inventory global/treeranges3/climate_ of North emission model plot data; change/atlas.html America scenario additional (Hargrove and Hoffman 2005) sources for  rare species4. Bradley Publication 

fi gures and Bromus Summary of Species Remote 2090–2100

 descriptions (Bradley 2009) tectorum 10 GCMs, distribution sensing

 (cheatgrass) with A1B model (AVHRR) emission map for the scenario Great Basin5. MC1 The Conservation Biology Institute Broad HADCM3, Dynamic None 2070–2099 website Data Basin potential SCIROmk3, global http:\\databasin.org/ vegetation MIROC32r vegetation (Rogers et al. 2011) type model
101

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Table 8.1—Description of data sources for model output (continued) Species Species occurrenceModel name Model output source modeled GCM(s) Model type data Time period6. FORCLIM Publication 

fi gures and descriptions Maritime Summary of Gap model Forest 2050

 (Busing et ea. 2007) forest trees seven inventory of western GCMs plot data North America (USDA and  USDI)7. 3-PG Publication 

fi gures and descriptions Pinus HadCM2, Biogeochemical Species 2080–2090

 (Coops et al. 2005) ponderosa with IS92a model range map

 (ponderosa emission  pine) scenario8. Hybrid Paci

fi c Northwest Species Change Pinus contorta CGCM2 Hybrid of Forest 2071–2100

 3-PG website, (lodgepole biogeochemical inventory Model http://www.pnwspecieschange. pine) and species plot data info/index.html distribution  (Coops and Waring 2011a) models 9. Hybrid Paci

fi c Northwest Species Change Trees of CGCM3 Hybrid of Forest 2080

 3-PG website, Paci

fi c biogeochemical inventory

 Model http://www.pnwspecieschange. Northwest and species plot data info/index.html distribution (U.S. and (Coops and Waring 2011b) models Canada)

Model number are referenced in table 8.2. Most global climate model (GCM) data used for model input were generated from the Intergovernmental Panel on Climate 

Change 4th assessment report (IPCC 2007b) and only the A2 emission scenario was examined, except as noted.
102

GENERAL TECHNICAL REPORT PNW-GTR-900

examined only a handful of representative species within each biome, and only 

included models with suffi cient spatial information to examine output by biome 

(see table 8.2). Background information about the vegetation models used to project 

Table 8.2—Projected response of some key Pacifi c Northwest species to climate 

change by the end of the 21st century based on multiple model output 

 List of 

 model 

 output 

Biome Key species Projected response (table 8.1)

Subalpine forests

 Unspecifi ed Major to total loss 5

Abies lasiocarpa Moderate to total loss 1, 2, 3, 9

Tsuga mertensiana Increase to total loss, 

 some shifts 1, 2, 3, 9

Pinus albicaulis Increase to total loss, 

 some shifts 1, 2, 3, 9

Maritime coniferous

 forests Unspecifi ed Moderate to major loss 5

Pseudotsuga menziesii No change to total loss 

Tsuga heterophylla Increase to total loss, 

 some shifts 1, 2, 3, 6a

, 9

Abies grandis Moderate to total loss 1, 2, 3, 6a, 9

Thuja plicata No change to total loss, 

 some shifts 1, 2, 3, 9

Picea sitchensis Some to total loss 1, 2, 3, 9

Dry coniferous forests 

 Unspecifi ed Moderate to major gain 5

Pinus ponderosa Moderate to total 

 loss; gains 1, 2, 3, 7, 9

Pinus contorta Moderate to total loss 1, 2, 3, 8, 9

Pseudotsuga menziesii No change to total loss 1, 2, 3, 9

Abies grandis Moderate to total loss 1, 2, 3, 9

Populus tremuloides Total loss 3b

Savannas and wood-

 lands

 Unspecifi ed Moderate to major gain 5

Juniperus occidentalis Moderate to total loss 1, 2

Quercus crysolepsis Highly variable 1, 2, 3

Quercus garryana Highly variable 1, 2, 3

Quercus kelloggii All show shifts, 

 mostly gains 1, 2, 3

Note: Model output refers to climate habitat, rather than actual species range. Loss of habitat is based on a 

qualitative assessment and ranked into classes: some loss, moderate loss, major loss, no habitat remaining, 

some gain, moderate gain, major gain, little to no change, or shift in habitat. Details on models and sources are 

provided in table 8.1. Some of the models listed in table 8.1 are not summarized in this table because of the lack 

of spatial information associated with the maps. 

a Note that the Busing et al. (2007) projections only go to the middle of the century. 

b Only one model was available for assessment. Random Forests and ANUCLIM (models 1, 2 in table 8.1) have 

data available for this species but do not project suitable climate in the Cascades for this species under historical 

(current) conditions. 
103

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

future response of vegetation to climate is summarized in chapter 6. We tried to 

assess only output across common GCMs and emission scenarios, but that was not 

practical given the wide variety of scenarios that researchers have used. We also 

limited our assessment to recent models and scenarios associated with the Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (IPCC 2007), 

although there was an exception. It is important to note that differences in projected 

changes in temperature and especially precipitation in the Pacifi c Northwest differ 

more among GCMs than among emissions scenarios. The emissions scenarios 

and climate models used in the next IPCC assessment will refl ect this. We also 

limited our assessment to the A2 or A1FI emission scenarios as these scenarios 

have higher emissions and demonstrate potential “worst case scenarios.” If the A2 

emission scenario was not used for a particular effort (e.g., ForCASTS), then we 

selected the scenario with the highest future CO2 emissions. Although the A2 and 

A1FI emission scenarios may represent the worst case scenario compared to other 

scenarios, current trajectories of greenhouse gas emissions are either at the upper 

end or surpass these scenarios (Raupach et al. 2007). Therefore, it is unclear if these 

scenarios really represent a “worst case.” Only the MC1 model output from Rogers 

et al. (2011) included any disturbance processes, and only fi re was modeled. 

Subalpine Forests and Alpine Meadows

General Description

Within the Pacifi c Northwest, subalpine forests and alpine meadow vegetation 

occupy high-elevation sites in the Cascade, Olympic, and Rocky Mountains and the 

Okanogan Highlands. We defi ne subalpine forests as high-elevation forests with 

short growing seasons produced by deep, persistent winter snowpack (5 months or 

longer, on average) and relatively cool summer temperatures. This includes primarily the mountain hemlock (Tsuga mertensiana (Bong.) Carriere) forest zone in the 

western Washington and Oregon Cascades, Olympic Mountains, and northern 

Rocky Mountains, and the subalpine fi r (Abies lasiocarpa (Hook.) Nutt.) forest zone 

of the eastern Washington and Oregon Cascades, Rocky Mountains, and Okanogan 

Highlands. Commonly associated tree species include Pacifi c silver fi r (Abies amabilis (Douglas ex Loudon) Douglas ex Forbes), Alaska yellow cedar (Chamaecyparis nootkatensis (D. Don) Spach), Engelmann spruce (Picea engelmannii Parry ex 

Engelm.), lodgepole pine (Pinus contorta Douglas ex Loudon), whitebark pine (P. 

albicaulis Engelm.), and subalpine larch (Larix lyallii Parl.) (Franklin and Dyrness 

1973). Alpine meadow vegetation includes the heath shrub (Phyllodoce-CassiopeVaccinium), lush herbaceous (Valeriana sitchensis Bong.-Carex spectabilis Dewey), 

dwarf sedge (Carex nigricans C.A. Mey), rawmark and low herbaceous, and grass 

(Festuca viridula Vasey) communities (Franklin and Dyrness 1973). 
104

GENERAL TECHNICAL REPORT PNW-GTR-900

The climate in the subalpine and alpine zones is characterized by cold, wet winters, and mild, relatively dry summers. Mean annual precipitation in the subalpine 

and alpine zones varies considerably across the west-to-east gradient from highest 

amounts in the Olympic Mountains and western Cascades, to lesser amounts in the 

eastern Cascades, Okanogan Highlands, and Rocky Mountains. In all cases, however, much of the cool season precipitation falls as snow and is stored in a persistent 

snowpack that typically forms in November and December and persists until June 

or later. 

Environmental Controls

Subalpine forests and alpine meadows are characterized by relatively short growing seasons, low air and soil temperatures, and slow nutrient cycling rates. Short 

growing seasons are primarily the result of late-melting snowpack throughout much 

of the region, while cool air and soil temperatures become more important from 

west to east. Cool soil temperatures limit nutrient cycling rates, which could lead to 

nitrogen limitations on growth and reproduction.

Deep, late-melting snowpack and cold soils have a strong infl uence on plant 

fl owering and growth phenology in subalpine forests and alpine meadows (Canaday 

and Fonda 1974; Dunne et al. 2003; Inouye 2008; Worrall 1983, 1993). Worrall 

(1983) found that budburst in subalpine fi r and Pacifi c silver fi r plantations varied 

considerably from year to year, apparently in response to differences in the timing of snowmelt and soil heating. Subalpine fi r budburst preceded that of Pacifi c 

silver fi r, indicating a lower temperature threshold for accumulating heat sums that 

trigger budburst (Worrall 1983). Budburst phenology in subalpine larch appears to 

be controlled primarily by air temperatures, as leaves commonly emerge while the 

snowpack is still present (Worrall 1993). Cessation of growth and cold hardening in 

true fi rs (Abies spp.) and other subalpine tree species has generally been attributed 

to a combination of day length (photoperiod) and nighttime air temperatures, so 

that snowpack duration is also correlated with growing season length for these 

species (Worrall 1983, but see also Worrall 1993 for a possible exception). 

Snowpack melting and subsequent soil heating have been shown to infl uence 

fl owering, growth phenology, and vegetation community patterns in alpine meadows. Snow removal and soil heating manipulations have shown that both factors 

infl uence the timing of fl owering in alpine meadows (Dunne et al. 2003, Inouye 

2008). Spatial variability in snowpack persistence infl uences fl owering and growth 

phenology within species and, to the extent that spatial patterns of snowmelt are 

consistent among years, also infl uences species composition and community types 

in alpine meadows (Canaday and Fonda 1974, Evans and Fonda 1990).

Subalpine forests 

and alpine meadows 

are characterized by 

relatively short growing 

seasons, low air and 

soil temperatures, and 

slow nutrient cycling 

rates.
105

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Interannual variability in subalpine tree growth has often been linked to 

effective growing season length. Tree ring studies of subalpine conifers often fi nd 

that annual diameter increment is negatively correlated with winter precipitation or 

spring snowpack depth (Ettl and Peterson 1995b; Graumlich and Brubaker 1986; 

Heikkinen 1985; Peterson and Peterson 1994, 2001; Peterson et al. 2002), particularly in the western subalpine zones that receive more winter precipitation and 

develop deeper snowpacks. Summer drought may also be a common factor limiting 

growth in some of the drier, interior subalpine forests (Ettl and Peterson 1995b, 

Peterson and Peterson 2001, Peterson et al. 2002). 

Growing season length and summer drought also infl uence subalpine tree 

reproduction, establishment, and mortality particularly at upper treeline. Woodward 

et al. (1994) found that the production of large cone crops in subalpine fi r and 

mountain hemlock were infl uenced by multiyear patterns of climatic variability, 

including factors associated with prior-year growing season length. Seedling 

demographic studies have shown that regeneration is often episodic in subalpine 

forests and alpine meadows and associated with decadal climatic variability. Tree 

invasions of subalpine meadows in the western Cascades and Olympics during the 

20th century have been attributed to extended periods (up to a decade or more) of 

warm temperatures, earlier snowmelt, and extended growing seasons (Fonda and 

Bliss 1969, Franklin et al. 1971, Heikinnen 1984, Taylor 1995, Zald et al. 2012). Late 

summer soil moisture can also infl uence regeneration, as studies have associated 

successful seedling establishment on drier subalpine sites with a combination of 

early snowmelt, mild summer temperatures, and above-average summer precipitation that produces a longer growing season while limiting late summer soil water 

defi cits (Little et al. 2004, Taylor 1995, Woodward et al. 1995). Bigler et al. (2007) 

investigated possible linkages between regional drought and subalpine tree mortality and found that mortality rates for subalpine fi r and Engelmann spruce increased 

for up to a decade following drought, but lodgepole pine appeared to be more 

drought-resistant.

Air and soil temperatures also infl uence physiological processes and growth 

in trees, organic matter decomposition, and soil nutrient cycling. Cold soil and air 

temperatures limit photosynthesis outside of the growing season (Runyon et al. 

1994). Low nighttime temperatures during the growing season can inhibit photosynthesis in subalpine conifer seedlings for up to several days, thereby limiting 

carbon uptake (Germino and Smith 1999, Johnson et al. 2004). Cold soils can limit 

leaf water conductance and photosynthesis (Day et al. 1989, Teskey et al. 1984). 

However, because temperature limitations on growth are stronger than temperature 

limitations on photosynthesis, growth may be considered temperature rather than 
106

GENERAL TECHNICAL REPORT PNW-GTR-900

carbon limited in subalpine forests (Grace et al. 2002, Harsch and Bader 2011). Cold 

soils also limit belowground biological processes associated with organic matter 

decomposition and nutrient cycling, thereby limiting nutrient availability to support 

plant growth (Hobbie 1996). 

Biotic Interactions

Facilitation plays an important role in plant establishment and survival in subalpine 

and alpine communities, with its importance increasing from sites with lower to 

higher stress levels (Callaway et al. 2002, Smith et al. 2003). Highly stress-tolerant 

conifers in subalpine and alpine meadows can facilitate establishment by less 

tolerant (but potentially more competitive) tree species by ameliorating stress from 

deep snowpack, cold temperatures, or high light levels, or by reducing competition 

from herbaceous vegetation (Bansal et al. 2011, Little et al. 1994, Smith et al. 2003, 

Stueve et al. 2009, Zald et al. 2012). For example, subalpine fi r trees are often able 

to establish under the canopies of large subalpine larch or whitebark pine trees and, 

being more shade tolerant, may later be able to grow up through the canopy and 

overtop the pioneer tree (Arno and Habeck 1972, Tomback et al. 2001). 

Mutualistic relationships can also be important in subalpine and alpine vegetation communities. Presence of mycorrhizal fungi can help determine plant 

community composition and primary succession following glacial retreat in alpine 

ecosystems (Cázares et al. 2005, Jumpponen et al. 2012). The Clark’s nutcracker 

(Nucifraga columbiana) depends on seeds from whitebark pine trees as a major 

part of its diet. In collecting and storing seed for later use, the bird disperses and 

“plants” the seeds, with an apparent preference for sites with early snowmelt (and 

hence greater accessibility).

Disturbances

Large-scale disturbances are infrequent in the subalpine and alpine zones but 

can still play an important role in shaping vegetation. Wildfi res are infrequent in 

subalpine forests, as deep snowpacks produce a very short fi re season, fuels are 

often wet, and spatial discontinuities can inhibit fi re spread (Agee 1993). However, 

forest vegetation can be dramatically altered by rare wildfi re events, as recovery 

from stand-replacing wildfi res can be very slow (Little et al. 1994). In fact, it can 

take decades to centuries for tree reestablishment in burned subalpine forests that 

do not include lodgepole pine (Agee and Smith 1984, Little et al. 1994). Wildfi re has 

probably played an important historical role in creating or maintaining subalpine 

and alpine meadows (Griggs 1938, Kuramoto and Bliss 1970). Recovery of subalpine 

forests following wildfi re probably requires nearby seed sources, an extended 
107

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

period of favorable climate, and favorable biotic and abiotic microsite conditions 

(Bansal et al. 2011, Stueve et al. 2009, Zald et al. 2012). 

Responses to Elevated CO2 and Climate Change

Across all scenarios, species distribution models (models 1 through 3, table 8.1) 

project that the suitable climate available for most key subalpine species will be 

moderately reduced to nonexistent by the end of the century (table 8.2). Suitable 

climate is either nonexistent or only available in the far northern Cascade Mountains at high elevations. Similarly, output from MC1 (model 5, table 8.1) shows that 

the subalpine forest type will be severely reduced in the future, with only a very 

minor amount of this type remaining in the northern Cascade Mountains for a 

relatively cool and wet scenario (CSIRO A2). Interpretation of model output from 

the species distribution models and MC1 are quite similar. For the 3-PG hybrid 

model (model 9, table 8.1), projections were in agreement for subalpine fi r (Abies 

lasiocarpa (Hook.) Nutt.). However, the model projected that mountain hemlock 

habitat may actually increase to the west in Oregon, and whitebark pine may have 

more suitable habitat in interior Washington and southern Oregon. It is apparent 

that there is some model agreement on the sensitivity of the subalpine forest ecosystem responses to similar future climate scenarios, that this ecosystem might be 

highly vulnerable to climate change, and that the northern high-elevation Cascade 

Mountains may serve as refugia for subalpine species. Although there is some 

disagreement among the models, there is more model agreement for this biome than 

any other biome examined. Note that information regarding the nontree (meadow) 

alpine component of alpine systems is lacking. 

Projected model results suggest that this forest type may be severely reduced 

in the future. However, results from experimental and observational studies are 

not as clear and even suggest potential contrary responses. Warming temperatures, 

reduced snowpack accumulations, and earlier snowmelt are likely to produce signifi cant changes in alpine and subalpine vegetation over time, including changes in 

plant growth phenology, establishment and persistence of trees in current meadow 

communities, and establishment of alpine meadow vegetation in new habitats 

exposed by melting snow and ice. Earlier snowmelt will facilitate earlier soil warming and likely shift plant fl owering and growth phenology earlier in the growing 

season. This will increase mean growing season length but could also expose plants 

to early season frost damage (Martin et al. 2010). Longer growing seasons should 

also promote tree seedling establishment in subalpine meadows on snow-dominated 

sites, as occurred during warmer periods in the last century (Franklin et al. 1971, 

Taylor 1995, Woodward et al. 1995). 
108

GENERAL TECHNICAL REPORT PNW-GTR-900

The effects of warming and earlier snowmelt on treeline and alpine meadow 

advance are less clear. Migration of tree species or alpine meadow communities 

to higher elevations or into previously hostile microtopographic positions may be 

limited by the simultaneous development of the soil fungal communities needed 

for mycorrhizal associations. Facilitation may also be limited if benefactor trees 

and shrubs are not yet established. On the other hand, release of previously suppressed tree seedlings and saplings could allow rapid development of new tree 

clumps within subalpine meadows as well as rapid advance of the forest line. Strong 

treeline advances have been found in some areas, such as Mount Rainier National 

Park (Stueve et al. 2009), but treeline responses to recent warming have been 

inconsistent (Harsch et al. 2009). The infl uence of broad-scale climate on treeline 

is modifi ed by the local environment, orographic and edaphic factors, and disturbances (Holtmeier and Broll 2005). 

Warming temperatures and elevated CO2 could also increase carbon uptake and 

growth by subalpine forest trees. Warmer summer nighttime temperatures should 

reduce the frequency of cold-temperature photoinhibition, thereby increasing 

photosynthetic carbon fi xation. Warmer summer temperatures, elevated CO2, and 

increased nutrient availability could also increase subalpine tree growth. Tree ring 

studies have linked decadal trends in past tree growth rates to decadal fl uctuations 

in summer temperatures (Graumlich et al. 1989, Peterson and Peterson 2001), but it 

is unclear how much summer temperatures could increase before growth became 

limited by other factors (e.g., soil water). Experimental tests of warming and CO2

enrichment effects suggest that responses differ among species, which could also 

infl uence community structure (Dawes et al. 2011, Lambrecht et al. 2007). 

Maritime Coniferous Forests

General Description

Evergreen coniferous forests currently occupy much of western Oregon and Washington, occupying low to middle elevations between the Pacifi c Ocean and the 

Cascade Mountains. Deciduous broadleaf forests are also common in the region but 

are mostly limited to recently disturbed forests (e.g., red alder [Alnus rubra Bong.] 

and bigleaf maple [Acer macrophyllum Pursh]) or stressful sites (e.g., Oregon white 

oak [Quercus garryana Douglas ex Hook.]) (Franklin and Dyrness 1973). For this 

discussion, we consider forests of the Sitka spruce (Picea sitchensis Bong.) Zone, 

western hemlock (Tsuga heterophylla (Raf.) Sarg.) Zone, and lower portions of the 

Pacifi c silver fi r Zone (see Franklin and Dyrness (1973) for indepth descriptions 

of these forest types). We do not explicitly include the moist coniferous forests of 

the Selkirk Mountains in northeastern Washington, but note that they have similar 
109

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

characteristics to maritime coniferous forests of the western Cascade Mountains in 

terms of environmental conditions, disturbance regimes, and forest species composition.

Maritime coniferous forests are characterized by strong conifer dominance, 

great size and longevity for dominant trees, and high forest productivity and 

biomass accumulation. The dominant species in this region (e.g., Douglas-fi r [Pseudotsuga menziesii (Mirb.) Franco], western hemlock, and Sitka spruce) can achieve 

great heights (up to 50 to 75 m) and large diameters (1 to 2 m) and are generally 

long lived with lifespans of 400 to 800 years (Franklin and Dyrness 1973). These 

forests can be highly productive and retain high productivity for centuries, resulting 

in high biomass accumulations (both above- and belowground). These forests also 

accumulate large amounts of coarse woody debris, making them especially wellsuited for sequestering large amounts of carbon per unit area.

Maritime coniferous forests are supported by a maritime climate that features 

high annual precipitation, most of which falls as rain during the winter and spring 

(November through April), mild winter temperatures, and relatively mild, dry 

summers. Although maritime coniferous forests all receive considerable precipitation, there is considerable variability within the region; for example, precipitation 

at selected sites in the western hemlock zone ranges from about 145 to 321 cm per 

year (Franklin and Dyrness 1973). Mean annual temperatures also vary considerably within this region. This shows that although maritime coniferous forests are 

favored by certain climatic features, they are able to tolerate a fairly wide range of 

environmental settings. Snowpack water storage does not play a signifi cant role in 

most of these forests.

Environmental Controls

Photosynthesis, net primary productivity, and tree growth are limited primarily by 

vapor-pressure defi cits, low soil water availability, and freezing temperatures in the 

winter (Runyon et al. 1994). Mild, wet winters favor evergreen conifers by allowing them to assimilate much of their annual carbon requirements during the cool 

months (fall, winter, and spring) when deciduous trees have no leaves (Waring and 

Franklin 1979). Cold winter temperatures reduce photosynthesis, which can also 

impact growth and productivity by reducing carbon reserves (Gholz 1982, Runyon 

et al. 1994).

Dry summers with high vapor pressure defi cits and low precipitation limit 

summer photosynthesis and productivity in maritime coniferous forests but also 

favor conifers over temperate deciduous tree species owing to differences in 

hydraulic architecture, stomatal conductance, water storage capacity, and rooting 
110

GENERAL TECHNICAL REPORT PNW-GTR-900

depths (Waring and Franklin 1979). Warm temperatures coupled with low humidity during the summer growing season can create high vapor pressure defi cits, 

which can reduce photosynthesis by inducing partial or complete stomatal closure. 

Severe soil water defi cits are uncommon, but evaporative demand can often exceed 

rates of water uptake, requiring trees to reduce leaf water conductance through 

stomatal closure or make use of internal water stores to buffer diurnal fl uctuations 

in demand. Tree ring studies have shown that interannual variability in tree growth 

is positively correlated with summer soil moisture (Case and Peterson 2005; Chen 

et al. 2010; Little et al. 1995, 2008). Site water balance also appears to control the 

distribution of some important species, like western hemlock (Gavin and Hu 2006).

Photosynthesis and productivity could also be considered to be light-limited 

in much of the maritime coniferous forest zone, at least during certain seasons. 

Studies of maritime coniferous forests in western Oregon suggest that undisturbed 

forests develop high leaf area index values that allow them to intercept more than 

90 percent of incident photosynthetically active radiation (IPAR) and that leaf area 

is positively correlated with mean site water balance (Grier and Running 1977, 

Runyon et al. 1994). Standing biomass and productivity increase with increasing 

leaf area, except at very high levels (Gholz 1982). It appears that maritime coniferous forests develop toward canopy structures and leaf area indices that maximize 

light capture subject to the constraints imposed by site water availability (Grier 

and Running 1977). Interannual variability in IPAR could therefore infl uence net 

primary productivity (indicating a light limitation), unless changes in IPAR are 

strongly correlated with changes in vapor pressure defi cits, soil water availability, 

or winter temperatures.

Finally, there is evidence for nutrient limitations on growth in Douglas-fi r forests of western Washington and Oregon. Studies have shown that fertilization treatments increase forest productivity in Douglas-fi r stands and that fertilization can 

increase productivity more than supplemental water (Gessel et al. 1990). Although 

nitrogen enhancement could increase leaf area somewhat, the primary benefi t of 

fertilization might be to increase photosynthetic rates and light-use effi ciency. Such 

an effect could be important if warming temperatures or elevated CO2 increase 

nutrient cycling rates and nutrient uptake rates.

Biotic Interactions

Competition for light is the dominant biotic interaction in maritime coniferous 

forests, though facilitation may be important as forests recover from severe disturbances. Early successional species like Sitka spruce and Douglas-fi r maintain 

access to light by growing tall and persisting for up to hundreds of years. Forest 
111

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

succession favors species that can establish and persist under low light levels, 

eventually reaching the overstory canopy through slow growth (given a less dense 

canopy) or through a canopy gap replacement process. 

Disturbances

The major disturbances in maritime coniferous forests include timber harvesting, 

windstorms, land use changes, and high-severity wildfi res. High forest productivity 

and high timber values have led to extensive harvesting of old- and second-growth 

forests and replacement of mixed-species forests with single-species plantations. 

Human population growth and economic development have led to replacement of 

large areas of forest by agriculture, housing, and urban infrastructure (e.g., roads, 

stores, factories, and offi ce buildings). 

Wind and wildfi re are the primary natural disturbances. Wind can cause 

signifi cant tree mortality, particularly in late fall and winter, when windstorms 

occur in conjunction with heavy rains or wet snow, saturated soils, and ice storms. 

Although most wind disturbances involve individual trees or small groups of trees 

(gap processes), large blow-down events also occur periodically. Wildfi res are relatively rare in these forests owing to mild temperatures, high fuel moisture levels, 

and very low levels of natural ignitions (e.g., lightning ignitions are uncommon). 

However, high levels of coarse woody debris, litter, and live biomass can produce 

occasional large, high-severity wildfi res when ignitions coincide with favorable fi re 

weather and dry fuel conditions (Agee 1993). 

Responses to Elevated CO2 and Climate Change

Species distribution models (models 1–3, table 8.1) projected that most species 

would have less suitable climate habitat by the end of the century with changes 

occurring by the midcentury, even for models that project less warming such as 

CSIRO. Climate habitat losses are primarily in the south and west, while climate 

habitat remains in the north and for the Cascade Mountains. Model output from 

MC1 (model 5, table 8.1) projected moderate to more extreme loss of this vegetation 

type, particularly for the HADCM3 model, which is considered a comparatively 

warm (especially summer) and dry GCM (Rogers et al. 2011). Losses are largely 

in the southern and eastern part of the existing climate habitat for MC1. The 3-PG 

hybrid model (model 9, table 8.1) projects little change for Douglas-fi r, western 

redcedar (Thuja plicata Donn ex D. Don), and Sitka spruce and only a slight 

decrease for western hemlock. Model output from FORCLIM (Busing et al. 2007, 

model 6, table 8.1) indicated no change in forest dominance as of 2050 for either 

future scenario for Douglas-fi r or western hemlock. It is apparent that there is some 
112

GENERAL TECHNICAL REPORT PNW-GTR-900

model agreement that this ecosystem is somewhat vulnerable to climate change, 

particularly the southwestern part of the region. 

Projected model results suggest that this forest type may be reduced in the 

future, particularly in the southern part of the region. However, there was limited 

model agreement, and some models predicted little to no change for this forest type. 

Important aspects of future climate change for maritime coniferous forests probably include the effects on winter temperatures, seasonal precipitation patterns and 

amounts, vapor pressure defi cits, and perhaps elevated CO2. Winter temperatures 

are already mild in this zone. Warmer fall, winter, and spring temperatures would 

further enhance the mildness of Pacifi c Northwest winters, potentially enhancing 

cool-season photosynthesis and nutrient cycling. The Pacifi c Ocean would likely 

continue to ameliorate temperature extremes, generate coastal fog, and promote 

high levels of annual precipitation in coastal areas, maintaining suitable habitat for 

Sitka spruce forests, but this is not certain. Reduced snowfall and winter snowpack 

accumulation could also allow slow expansion of western hemlock into the Pacifi c 

silver fi r zone.

Warmer winter temperatures could also alter the phenology of Douglas-fi r 

shoot growth, with implications for tree growth and forest productivity. Although 

warmer temperatures are typically associated with earlier budburst and longer 

growing seasons, fi eld studies and subsequent phenological models suggest that 

substantially warmer winter temperatures could delay spring budburst in Douglasfi rs because chilling requirements that serve to prevent premature budburst would 

not be met (Bailey and Harrington 2006, Campbell and Sugano 1979, Harrington et 

al. 2010). Predicted temperatures are still within a suitable range to allow Douglasfi r to maintain dominance in these wet coniferous forests (Dale and Franklin 1989, 

Franklin and Dryness 1973), but populations may need to adapt their phenological 

cues to match changing climatic conditions.

Warmer summer temperatures could affect maritime coniferous forests by 

increasing vapor pressure defi cits, stomatal conductance, and transpiration (Apple 

et al. 2000); reducing soil water availability; and increasing the length or intensity 

of the summer dry period. Douglas-fi r productivity could decline with warming 

temperatures at severely moisture-limited sites, while increasing at high-elevation 

sites (Case and Peterson 2005; Littell et al. 2008, 2010). Elevated CO2 levels and 

changes in nutrient availability could alter these simple responses to temperature 

and moisture, however, by altering water- and light-use effi ciency. For example, 

Lewis et al. (2004) reported that elevated temperatures increased needle nitrogen 

concentrations and net photosynthesis in Douglas-fi r seedlings, while elevated CO2

increased photosynthetic rates.
113

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

The effects of changing climate on precipitation patterns are likely to signifi -

cantly infl uence vegetation responses in these forests. Increases in precipitation (and 

humidity) outside of the winter months could help to offset the effects of increasing 

temperatures on vapor pressure defi cits and soil water availability, with subsequent 

effects on productivity. Unfortunately, this remains an area of great uncertainty.

Extending the summer dry period could also increase wildfi re frequencies in 

the maritime coniferous forests (Littell et al. 2010). However, even with warmer 

temperatures and shorter fi re-return intervals, most maritime coniferous forests 

would continue to support low-frequency, high-severity fi re regimes. In addition, 

the primary effect of severe fi re is to reduce total standing biomass rather than 

change forest composition, as forests usually recover well after individual wildfi res 

(Franklin and Dryness 1973).

Dry Coniferous Forests

General Description

The dry coniferous forest biome occupies the lower to middle elevations of the 

eastern slopes of the Cascade Mountains; the Okanogan Highlands and Rocky 

Mountains in eastern Washington; and the Blue, Ochoco, and Wallowa Mountains 

in eastern Oregon (Franklin and Dyrness 1973). Dry coniferous forests in this 

region typically feature ponderosa pine (Pinus ponderosa Lawson & C. Lawson) 

and Douglas-fi r as early seral dominant species, but may also include signifi cant 

components of true fi rs (Abies grandis [Douglas ex D. Don] Lindl., A. concolor 

[Gord. & Glend.] Lindl. Ex Hildebr., A. magnifi ca A. Murray bis, and A. lasiocarpa), western larch (Larix occidentalis), western white pine (Pinus monticola), 

lodgepole pine, and western redcedar (Thuja plicata) (Franklin and Dyrness 1973). 

In eastern Oregon, western juniper (Juniperus occidentalis Hook.) often occupies 

the ecotone between the dry coniferous forest and interior steppe biomes (Franklin 

and Dyrness 1973). Dry coniferous forests also occupy much of the forested area in 

the Klamath Mountains of southwestern Oregon, though they often also contain a 

signifi cant component of deciduous trees and shrubs. 

The climate of the dry coniferous forest biome varies with elevation, but common features include warm, dry summers, with warm to hot daytime temperatures 

and cool nighttime temperatures, and cold, moist winters. Much of the annual 

precipitation falls as snow in winter or during spring rainstorms. Mean annual 

precipitation can range from about 35 cm per year in ponderosa pine forests to over 

100 cm per year in mixed-conifer forests. Most dry coniferous forests in the region 

support a persistent winter snowpack, but the mean duration of the snowpack varies 

from about 1 to 5 months, depending on elevation and winter temperatures. The 
114

GENERAL TECHNICAL REPORT PNW-GTR-900

snowpack serves to store winter precipitation onsite and transfer it to soil water 

storage during spring snowmelt. 

Environmental Controls

Dry coniferous forests experience a signifi cant period of summer drought every 

year during which high vapor pressure defi cits and low soil water availability limit 

tree establishment and growth (Barrett 1979, Boisvenue and Running 2010, Runyon 

et al. 1994). In some ponderosa pine forests, regeneration pulses are associated with 

one or more consecutive wet years (Barrett 1979, Brown 2006). Drought stress is 

prevalent, but most species are adapted to dry summers. Interannual variation in 

tree growth is positively correlated with precipitation or negatively correlated with 

summer temperatures for ponderosa pine (Carnwath et al. 2012, Knutson and Pyke 

2008, Kusnierczyk and Ettl 2002), Douglas-fi r (Carnwath et al. 2012, Case and 

Peterson 2005, Chen et al. 2010, Griesbauer and Green 2010, Littell et al. 2008), 

and lodgepole pine (Case and Peterson 2007). There is little evidence that summer 

drought is the direct cause of signifi cant tree mortality; rather, drought may indirectly cause mortality through its effects on wildfi res and forest health.

Cold winter temperatures also limit forest productivity by limiting winter 

photosynthesis and increasing the importance of cold hardening and frost tolerance 

(Runyon et al. 1994). Growth is largely confi ned to spring and early summer, when 

mild temperatures and high soil moisture availability produce favorable conditions.

Biotic Interactions

Facilitation and competition both infl uence vegetation dynamics in dry coniferous 

forests. Competition for soil water is undoubtedly the primary form of competition, 

as annual soil water defi cits force plants to either compete directly or partition soil 

water use in time and space. For example, many forbs and grasses complete their 

annual growth and reproduction in the spring and early summer, when soil water 

is relatively abundant. Trees (and some shrubs) can extend the growing season by 

tapping into deep soil water resources. 

Facilitation is probably most important during seedling establishment. Shading 

from overstory trees, tall shrubs, or neighboring saplings could facilitate establishment and persistence by reducing evaporative demands and improving plant water 

balance, more than offsetting negative effects of reduced light on plant performance 

(Fajardo et al. 2006, Holmgren et al. 1997).

Disturbances

Summer drought creates a long fi re season during which surface and live fuels are 

dry enough to carry wildfi res. Most dry coniferous forests in this region historically 
115

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

supported low- or mixed-severity fi re regimes (Agee 1993, Everett et al. 2000, 

Hessburg and Agee 2003, Perry et al. 2011, Wright and Agee 2004). Low-severity 

fi re regimes were concentrated in dry, low-elevation ponderosa pine forests, while 

mixed-severity fi re regimes were concentrated in mesic Douglas-fi r and mixedconifer forests. Logging, livestock grazing, and fi re suppression have reduced fi re 

frequencies in most dry coniferous forests over the past century, producing changes 

in forest structure, fuel profi les, and fi re behavior (Hessburg and Agee 2003). 

Higher tree densities, higher fuel loadings, and greater fuel continuity (horizontal 

and vertical) now predispose these forests to larger and more severe wildfi res than 

the historical norms, despite efforts to restore historical structure and fi re behavior 

in these dry forests.

Insects and diseases are also common disturbance agents in dry coniferous 

forests (Fettig et al. 2007). Mortality from bark beetles and defoliators is generally 

low, but can rise dramatically during periodic outbreaks (Fettig et al. 2007). For 

example, a mountain pine beetle (Dendroctonus ponderosae) outbreak in southern 

British Columbia killed more than 75 percent of ponderosa pines with diameter 

greater than 7.5 cm and almost 95 percent of large ponderosa pines (diameter > 30 

cm) (Klenner and Arsenault 2009). 

Responses to Elevated CO2 and Climate Change

The species distribution models (models 1 through 3, table 8.1) indicate that suitable climate available for interior forest tree species by the end of the century will 

be severely reduced, particularly for Oregon and the Blue Mountains. However, 

MC1 (model 5, table 8.1) projects a dramatic increase in the temperate needleleaf 

forest type in eastern Oregon and Washington, which is representative of this biome 

(Rogers et al. 2011). The 3-PG model for ponderosa pine also suggested an increase 

in species habitat (model 7, table 8.1), which is consistent with the MC1 projections 

(Coops et al. 2005). However, the more recent 3-PG hybrid model (model 9, tables 

8.1 and 8.2) projected loss of suitable climate for ponderosa pine in the Pacifi c 

Northwest (Coops and Waring 2011b). Variability in model projections for ponderosa pine, and the analogous MC1 vegetation type temperate needleleaf forest for the 

HADCM2 models (except as noted) are shown in fi gure 8.1. There seems to be little 

model agreement on the sensitivity of the dry coniferous forest species to similar 

future climate scenarios based on the model output. If we assume that the process 

models are more robust to prediction, then the vulnerability of this forest type may 

not be high. However, the dramatic disagreement in model output suggests that 

caution should be used in any interpretation. 

Projected model results showed no model agreement regarding potential 

changes for this forest type and major species such as ponderosa pine (fi g. 8.1). 
116

GENERAL TECHNICAL REPORT PNW-GTR-900

Yet, the dry coniferous forest biome is potentially vulnerable to future warming temperatures and their effects on winter snowpack (water storage), summer 

drought, and disturbance regimes. Warming winter temperatures would reduce the 

fraction of winter precipitation that falls as snow as well as snowpack accumulation and snowpack duration. Earlier snowmelt and warmer spring and summer temperatures would promote earlier onset of summer drought and increase the mean 

drought duration and intensity. Future increases in spring and summer precipitation and higher CO2 concentrations could partially offset the effects of reduced 

snowpack and higher temperatures on evapotranspiration and summer drought, 

but the net result is still likely to be longer and more intense summer drought. 

Longer and more intense summer droughts may reduce productivity in dry 

coniferous forests at lower elevations, and especially near the lower treeline. Kusnierczyk and Ettl (2002) found that ponderosa pine growth was positively correlated 

with precipitation in the fall and winter prior to the growing season but was not 

signifi cantly correlated with temperatures, suggesting that ponderosa pine growth is 

more sensitive to changes in site water balance than to temperature itself. Similarly, 

Littell et al. (2008) found that water availability limited Douglas-fi r growth across 

much of its range in the Pacifi c Northwest. Increasing atmospheric CO2 levels could 

limit drought-related losses in productivity by reducing stomatal conductance and 

delaying soil water depletion but are unlikely to fully offset the effects of warmer 

temperatures on evapotranspiration. 

Some evidence indicates that ponderosa pine could adapt to increased summer drought stress by altering biomass allocation (Callaway et al. 1994). Desert 

Figure 8.1—Projections of Pinus ponderosa (ponderosa pine) climate habitat based on modeled current (top) and end-ofcentury (bottom) climates. (A) Rehfeldt et al. (2006) HADCM3 A2, (B) McKenney et al. (2007) HADCM3 A2, (C) Hargrove 

and Hoffman (2005) HADCM3 A1F1, (D) Coops and Waring (2011b) CGCM3 A2, and (E) temperate needleleaf forest 

type, Rogers et al. (2011) HADCM3 A2. End of century is defi ned as 2090 for Rehfeldt, 2071–2100 for McKenney, 2100 for 

Hargrove, 2080 for Coops, and 2070–2099 for Rogers.

The dry coniferous 

forest biome is 

potentially vulnerable 

to future warming 

temperatures and 

their effects on 

winter snowpack 

(water storage), 

summer drought, and 

disturbance regimes.
117

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

ponderosa pine allocated more biomass to sapwood conducting area, reducing 

the ratio of leaf area to sapwood area and presumably reducing risks of hydraulic 

failure (Callaway et al. 1994). Maherali et al. (2002) concluded that this was likely 

an example of phenotypic plasticity and not genetic adaptation, suggesting that this 

could be a future response to changing climate. 

Longer mean summer drought would likely alter disturbance regimes in dry 

coniferous forests. Increased summer drought stress could further increase susceptibility to insects and diseases. Dale and Franklin (1989) noted that climate change 

may change the intensities of disturbances such as bark beetle outbreaks that may 

result in greater changes than gap models predict. 

Longer summer droughts would also lengthen the fi re season for most dry 

coniferous forest types and may increase the risk of large wildfi res, particularly 

in areas that historically supported mixed-severity fi re regimes (Westerling et al. 

2006). Increased frequency and duration of summer drought would allow wildfi res 

to burn wetter and cooler sites, where high fuel loads become more available owing 

to reduced fuel moisture (effectively mining fuels that accumulated in a wetter 

climate). Stand-replacing wildfi res could present problems for forest managers as 

warmer temperatures and reduced water availability could lead to reduced postfi re 

tree establishment or regeneration failures (on planted sites).

Savannas and Woodlands

General Description

Western juniper savannas and woodlands occupy the biome transition zone between 

the Interior Steppe and Dry Coniferous Forest biomes in eastern Oregon but are 

scarce in eastern Washington (Gedney et al. 1999, Miller et al. 2005). It is a Pacifi c 

Northwest version of the more widespread pinyon-juniper woodlands of the Great 

Basin and other dry regions of western interior North America (Franklin and Dyrness 1973). Western juniper is the dominant tree species, with occasional ponderosa 

pines found on mesic microsites (Miller et al. 2005). Common shrubs include big 

sagebrush (Artemisia tridentatea Nutt.) and bitterbrush (Purshia tridentatea Pursh 

DC.) (Miller et al. 2005). Western juniper savannas occupy much of the transition 

zone, but denser juniper woodlands can also develop.

Western juniper savannas and woodlands occupy the driest of the treedominated vegetation zones in eastern Oregon. Summers are hot and very dry, 

while winters are cold and relatively wet. Annual precipitation in western juniper 

savannas and woodlands ranges from 13 to 75 cm per year, but most sites fall 

within the range of 25 to 50 cm per year (Gedney et al. 1999). Much of this precipitation falls during the winter as rain or snow (Franklin and Dyrness 1973). 

Longer summer 

droughts would also 

lengthen the fi re 

season for most dry 

coniferous forest types 

and may increase the 

risk of large wildfi res, 

particularly in areas 

that historically 

supported mixedseverity fi re regimes.
118

GENERAL TECHNICAL REPORT PNW-GTR-900

Environmental Controls

Drought, high vapor pressure defi cits, and cold winter temperatures all serve as 

environmental controls on vegetation in western juniper savannas and woodlands 

(Runyon et al. 1994). High summer temperatures coupled with low relative humidity produce high evaporative demand and require vegetation to exercise strong 

controls on water loss or tolerate high levels of moisture stress. Much of the annual 

precipitation falls during the winter and is stored in a shallow snowpack or soil 

layers. With relatively little recharge during the active growing season, soil water 

becomes severely limiting during the growing season, forcing plants to concentrate 

their growth and reproduction during the spring and early summer.

Tree ring analyses suggest that western juniper growth is driven primarily by 

soil moisture availability and drought (Knapp et al. 2004, Knutson and Pyke 2008). 

Growth was positively correlated with winter and spring precipitation (October 

through June) and negatively correlated with spring and summer temperatures 

(Knutson and Pyke 2008). Growth sensitivity to drought was greatest at lower 

elevations and on steep, rocky sites.

Cold winter temperatures also exert infl uence on vegetation communities and 

performance. Runyon et al. (1994) reported cold winter temperatures to be almost 

as important as drought for limiting photosynthesis in juniper woodlands. Extreme 

cold temperatures can also function as a disturbance agent (Knapp and Soulé 2005).

Biotic Interactions

Facilitation and competition are both important in juniper savannas and woodlands. 

Western juniper often established initially in shrub understories, apparently benefi ting from altered microclimate and soil fertility during the establishment phase. As 

junipers grow, however, they compete strongly with their neighbors for soil water, 

relying on their evergreen nature and cold tolerance to take up and use (preempt) 

soil water early in the growing season. Large junipers can fully utilize soil water 

under and near their canopies, limiting tree densities and creating patches with few 

or no understory plants.

Disturbances

Historical fi re regimes are not well described for juniper savannas and woodlands 

in Oregon (Agee 1993, Young and Evans 1981). Young junipers have thin bark 

and are readily killed by fi res. Junipers that avoid fi res early in their lifespan can 

subsequently escape injury and death from fi re by having thicker bark and suppressing understory herbaceous fi ne fuels through competition for water (Agee 

1993). Consequently, fi re-scarred junipers are limited to microsites with limited fi ne 

fuel production. Fire scars in scattered or adjacent ponderosa pine forests suggest 
119

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

a mixed-severity fi re regime, with mean fi re-return intervals of 15 years to more 

than a century and occasional large fi res (Agee 1993, Miller and Rose 1999, Miller 

et al. 2005). In this predominantly fuel-limited biome, climate change effects on fi re 

frequency and severity will likely depend on changes in soil water availability and 

its effect on understory plant productivity (fuel generation).

Juniper has expanded its range in the interior Pacifi c Northwest during the past 

130 years, invading and creating savannas and woodlands in semiarid ecosystems 

that were formerly shrub-steppe and grassland communities (Miller et al. 2000). 

More than 90 percent of the 3.2 million hectares of current juniper savannas and 

woodlands developed in the past 100 years (Miller et al. 2000). The area of juniper 

forest and woodland is estimated to have increased fi vefold between 1936 and 1988 

(Gedney et al. 1999). Much of this expansion is attributable to heavy livestock grazing and reduced fi re frequencies from fuel reductions and active fi re-suppression 

efforts (Miller et al. 2000), but there is some indication that woodland expansion 

was initiated between 1850 and 1870 in some areas owing to wet and mild climatic 

conditions (Miller et al. 2005). The combination of reduced fi re occurrences as a 

result of European settlement and optimal climatic conditions for establishment 

at the turn of the century were probably the two dominant factors that initiated 

postsettlement western juniper expansion (Miller et al. 2005). Junipers can live for 

up to a thousand years (Miller et al. 2000); therefore, once established, they may 

persist for a very long time in the absence of disturbance. 

Climate infl uences the dynamics of juniper woodlands much like the shrubsteppe biome, with winter temperatures and soil water availability being the most 

important infl uences. Wet, mild conditions promote vigorous growth in western 

juniper and are associated with prior periods of expansion in the Pacifi c Northwest 

(Miller et al. 2005). Junipers are generally cold tolerant, but an extreme October 

freeze produced signifi cant damage and mortality in Oregon juniper woodlands, as 

the freeze occurred before trees were fully cold hardened (Soulé and Knapp 2007). 

Mortality was highest in high-density patches, on recently colonized sites, and on 

warmer microsites, suggesting some degree of self-regulation in the system (Soulé 

and Knapp 2007). 

Tree growth and productivity in juniper woodlands are sensitive to soil water 

availability and drought. A tree ring analysis of interannual growth variability 

found that growth was positively correlated with fall, winter, and spring precipitation (October to June), suggesting the importance of deep soil water storage for 

supplying water during the growing season (Knutson and Pyke 2008). Drought has 

produced dramatic changes in pinyon-juniper woodlands of southwestern North 

America (pinyon pine [Pinus edulis Engelm.] and oneseed juniper [Juniperus 
120

GENERAL TECHNICAL REPORT PNW-GTR-900

monosperma (Engelm.) Sarg.]) in the past century. A severe drought in the 1950s 

caused extensive ponderosa pine mortality and favored advance of pinyon-juniper 

woodlands into former ponderosa pine forest (Allen and Breshears 1998). A more 

recent drought (2002 to 2003) produced high mortality of pinyon pine, leading to 

increased dominance by oneseed juniper (Breshears et al. 2005, 2009). We are not 

aware of any similar episodes of widespread tree mortality in Pacifi c Northwest 

juniper woodlands, however.

As temperatures warmed during the early Holocene, western juniper began 

migrating north into its present range in the Pacifi c Northwest. Since the arrival of 

western juniper in central and eastern Oregon (circa 6600–4800 BP), northeastern 

California, and southeastern Idaho, its abundance and distribution have fl uctuated 

(see Miller et al. 2005 for an excellent presentation of juniper paleohistory). Dry 

climatic periods tend to result in regional declines of juniper, with wet (particularly 

summer precipitation) and mild (mild winters) periods resulting in expansion. 

Responses to Elevated CO2 and Climate Change

Model projections for the savannas and woodlands and shrub-steppe vegetation 

types were too limited to synthesize in table 8.2, but we do discuss the limited 

available model output. For western juniper, the species distribution models (models 

1 through 3, table 8.1) projected that this species would have substantially less or no 

suitable climate habitat by the end of the century. MC1 (model 5, table 8.1) projects 

minor changes in the temperate evergreen needleleaf woodland vegetation type; 

although a minor reduction for the MIROC model is noted, as well as some redistribution. It is apparent that there is little model agreement on the sensitivity of juniper 

savannas and woodlands to similar future climate scenarios. 

For oak species (Quercus spp.), some species distribution models (models 1 

through 3, table 8.1) project increases and shifts in distribution, although there is no 

consensus among the models. Many of the models also performed poorly in predicting the historical climate habitat for oak species. Unlike most of the other species’ 

distribution model output, agreement for the sign of change differed between the 

models. Projections vary widely by species and generalizations are diffi cult. MC1 

(model 5, table 8.1) projects an increase for temperate warm and cool mixed forests 

across all GCM scenarios, with signifi cantly more of this vegetation type emerging for the MIROC scenario (Rogers et al. 2011). There appears to be little model 

agreement on the sensitivity of oak and mixed-woodlands species to similar future 

climate scenarios. Potential response of oak species to future climate change is 

highly uncertain based on this analysis. 

Little or no experimental work has been done on the response of junipers to 

elevated CO2 or warming temperatures. As noted above, modeling studies are also 
121

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

somewhat limited for this biome, and provide little agreement about potential future 

change. Junipers might be expected to benefi t from increasing atmospheric CO2 if 

it reduces stomatal conductance and delays depletion of deep soil water; however, it 

is unclear if improved water-use effi ciency would signifi cantly increase growth, or 

simply reduce drought stress. Increased growth of western juniper in recent decades 

suggests that elevated CO2 may be increasing growth through increased water-use 

effi ciency (Knapp et al. 2001), but such evidence is not conclusive. 

Shrub-Steppe

General Description

Steppe (grassland) and shrub-steppe vegetation covers much of eastern Washington 

and Oregon, occupying the basins between the Cascade Mountains and the Rocky 

Mountain foothills (or Blue Mountains). The steppe biome encompasses numerous 

grass- and shrub-dominated vegetation community types that differ according to 

the dominant grasses and shrubs, and whose distribution largely refl ects underlying 

gradients in annual mean precipitation and soil properties (Franklin and Dyrness 

1973). Characteristic species include cool season (C3) bunchgrasses (e.g., bluebunch 

wheatgrass [Pseudoroegneria spicata (Pursh A. Love)], Idaho fescue [Festuca idahoensis Elmer], and Sandberg bluegrass [Poa sandbergii J. Presl.]) and sagebrushes 

(e.g., big sagebrush, little sagebrush [A. arbuscula (Nutt.) A. Gray], and scabland 

sagebrush [A. rigida]). The climate is classifi ed as arid to semiarid with low precipitation, hot dry summers, and relatively cold winters, and the region has often been 

described as cold desert or high desert (Franklin and Dyrness 1973). 

Vegetation in the steppe biome often forms complex mosaics at ecotones. In 

eastern Washington, it forms an ecotone with the dry coniferous forest biome 

(Franklin and Dyrness 1973). In eastern Oregon, the steppe biome more often 

forms an ecotone with juniper (Juniperus spp.) woodlands, which occupy a narrow 

ecological niche between the steppe and dry coniferous forest biomes (Franklin and 

Dyrness 1973). 

Steppe communities have long occupied parts of the interior Pacifi c Northwest, 

but their abundance and distribution has changed considerably through time. At 

Waits Lake in northeastern Washington state, steppe and sage-steppe vegetation 

alternated with dry coniferous (pine) forest for thousands of years following the 

last glacial period before being replaced by the current Douglas-fi r (Pseudotsuga 

menziesii Mirb. Franco)-dominated forest vegetation around 2,300 years ago (Mack 

et al. 1978). At Carp Lake in southern Washington state, a 125,000-year pollen 

record shows that the site has alternated between periods of montane coniferous 

forest, pine forest, and steppe vegetation; it currently supports dry coniferous forest 
122

GENERAL TECHNICAL REPORT PNW-GTR-900

(Whitlock et al. 2000). In southeastern Washington, a 100,000-year record of silica 

phytoliths (fossil records from grasses) indicated large shifts in vegetation over 

time, with a low-elevation site alternating between different grassland communities, 

and two higher elevation sites transitioning from cold sage-steppe and subalpine 

parkland vegetation to dry forest and grassland vegetation, before transitioning to 

modern dry mixed-conifer and subalpine forests (Blinnikov et al. 2002).

Environmental Controls

Soil moisture and winter temperatures are the major environmental limiting factors 

infl uencing vegetation composition and productivity in Pacifi c Northwest steppe 

communities, with precipitation, temperature, soil texture, and soil depth being 

the primary abiotic determinants of soil moisture (Bates et al. 2006, Comstock and 

Ehleringer 1992, Schlaepfer et al. 2011). Winter precipitation (snow and rain) is 

important for recharging water storage in deep soil layers (Schlaepfer et al. 2011, 

Schwinning et al. 2003), and most of the Great Basin and Columbia Basin in 

Oregon and Washington receive the greatest amount of precipitation in the winter 

and spring (Bates et al. 2006). However, high temperatures and low summer 

precipitation combine to produce extended periods of soil moisture defi cits each 

summer. Although summers are warm and dry, winters can be quite cold throughout much of the steppe region in the Pacifi c Northwest (Comstock and Ehleringer 

1992).

Steppe vegetation is generally well adapted to both cold winter temperatures 

and summer drought. Grasses and forbs can avoid summer drought stress by 

concentrating growth in the spring and early summer, when soil water is still 

available and cooler temperatures promote high water-use effi ciency (Comstock and 

Ehleringer 1992). Some shrubs are able to tolerate drought and remain photosynthetically active during periods of water and heat stress (DePuit and Caldwell 1975) 

or avoid severe drought stress by developing deep root systems that allow them 

to access deep soil water reserves throughout the summer (Franklin and Dyrness 

1973). 

Biotic Interactions

As with other stressful environments, facilitation plays an important role in the 

shrub-steppe biome. Established shrubs can facilitate establishment and growth of 

other species by enhancing soil water and nutrient availability while reducing light 

levels and evaporative demand. Some deep-rooted shrubs employ “hydraulic lift” 

mechanisms to transfer soil water between deep and shallow soil layers (Caldwell 

and Richards 1989, Caldwell et al. 1998). Hydraulic lift can provide soil water to 
123

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

neighboring plants but may also promote nutrient cycling and fi ne root growth during the dry summer season and deep soil water recharge in the winter and spring 

months (Caldwell and Richards 1989, Caldwell et al. 1998). Griffi th (2010) found 

that modifi cation of microclimate and soil fertility by two shrubs, big sagebrush and 

bitterbrush (Purshia tridentatea), increased understory establishment and reproduction of cheatgrass (Bromus tectorum L.) compared to areas between shrubs.

Disturbances

Steppe ecosystems have been strongly affected by human activities, including 

livestock grazing, introduction of exotic species, and cultivation for agricultural 

crops (Franklin and Dyrness 1973). Grazing fi rst became a major factor in Pacifi c 

Northwest steppe communities with the introduction of cattle grazing in 1834 and 

sheep grazing in 1860. Settlers also introduced numerous alien grasses, including 

cheatgrass, which was well adapted to climate within parts of the steppe region 

(Mack 1981). Heavy grazing allowed alien grasses to invade native communities, 

where they became highly persistent (Mack 1981). In addition to grazing, much of 

the steppe region in the Columbia River Basin has been cultivated for dryland agricultural crops like winter wheat or irrigated to produce summer fruits, vegetables, 

and grains.

Few studies have documented fi re regimes across shrub-steppe communities 

and woodlands throughout this region (Miller et al 2005), in part because it is 

diffi cult to fi nd the equivalent of fi re-scarred trees that provide a biological record 

of past fi re activity. In productive mountain big sagebrush (Artemisia tridentatea

ssp. vaseyana (Rydb.) Beetle) plant associations, such as those characterized by 

Idaho fescue, mean fi re-return intervals ranged between 10 to 25 years, with large 

fi res every 38 years (Miller et al. 2005). However, fi re was much less frequent in the 

more arid plant associations such as Wyoming big sagebrush/Thurber needlegrass 

(A. tridentatea ssp. wyomingensis/Achnatherum thurberianum J. Presl.) (50 to 70 

years) and low sagebrush/Sandberg bluegrass (A. arbuscula/Poa sandbergii) (Miller 

and Rose 1999, Young and Evans 1981), where fi re-free periods of 90 (Young 

and Evans 1981) and 138 years (Miller and Rose 1999) were reported in northern 

California and south-central Oregon, and fi re-free periods probably exceeded 150 

years for some sites. Baker (2006) argues that historical fi re rotations were 70 to 

200 years in mountain big sagebrush and longer in other types. An examination of 

long-term charcoal records suggests that fi re regimes in these types are climate and 

fuel driven; sagebrush densities and fi re frequencies increased during wet periods 

(decades to centuries) and declined during dry periods (Mensing et al. 2006).

Most of the dominant bunch grasses and some of the shrubs recover well from 

fi res by resprouting from belowground organs. However, two of the major shrub 
124

GENERAL TECHNICAL REPORT PNW-GTR-900

species in the region, big sagebrush and bitterbrush are fi re sensitive and can be 

temporarily eliminated from a site by burning. Recovery of shrub canopy cover to 

predisturbance levels can require 10 to 50 years or more, with recruitment of new 

shrubs from soil seed banks being an important factor controlling recovery time 

(Ziegenhagen and Miller 2009). Short fi re-return intervals can cause signifi cant 

vegetation change if shrub communities have not fully recovered between disturbances (Davies et al. 2012). In some areas, introductions of invasive plant species 

such as cheatgrass and medusahead (Taeniatherum caput-medusae (L.) Nevski) 

have signifi cantly altered fi re regimes by producing suffi cient fi ne fuels to carry 

wildfi res (D’Antonio and Vitousek 1992).

Responses to Elevated CO2 and Climate Change

Only one species distribution model had projections available for shrub-steppe 

species. The ANUCLIM species distribution model (model 2, table 8.1) projected 

severe reductions in suitable climate for bitterbrush, big sagebrush, mountain 

mahogany (Cercocarpus ledifolius S. Watson), and gray rabbitbrush (Ericameria 

nauseosa (Pall. Ex Pursh) G.L. Nesom & Baird) (McKenney et al. 2007, 2011). 

The MC1 model (model 5, table 8.1) projects some decrease in this vegetation type, 

with the largest decreases for the CSIRO model (Rogers et al. 2011). There appears 

to be some model agreement on the general sign of a potential response for this 

biome, indicating some vulnerability to climate change. However, the difference in 

the magnitude of response among the models is striking, and only two models are 

available for comparison. 

Bradley (2009) reported that for a “worst case future climate scenario,” cheatgrass (model 4, table 8.1) climatic habitat will either be maintained or decrease. The 

worst case future climate scenario was characterized by the maximum decrease 

in summer precipitation out of all the climate models assessed. However, it is 

important to note that the current distribution of cheatgrass in eastern Oregon and 

Washington only covered a fraction of the modeled current climate habitat, indicating that the species has not yet fully invaded all suitable climate habitats. Therefore, 

although changes in future climate habitat may not be great according to this 

analysis, there still could be considerable risk for the expansion of cheatgrass. 

The only model available with relevant output for grassland or steppe vegetation was MC1 (model 5, table 8.1) (Rogers et al. 2011). MC1 projected that for the 

warmer scenarios, grasslands will experience some reductions in spatial extent. 

The less warm and wetter MIROC model projected little to no change. However, 

only one impact model is available for assessment, and there are no species-specifi c 

model outputs for grass species.
125

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

There is little model information regarding relevant grassland and steppe 

species; however, predicted climatic changes could alter environmental conditions 

in the steppe region in important ways that ultimately affect soil water availability. 

Grasslands are largely structured by climate and soils (Fay et al. 2012). Higher 

winter temperatures could reduce the percentage of winter precipitation that falls 

as snow, thereby reducing winter snowpack water storage and possibly increasing 

winter runoff (Schlaepfer et al. 2011, 2012). Warming spring temperatures would 

likely melt winter snowpack, warm soils, and begin evaporating soil water earlier 

in the spring, causing earlier onset of seasonal drought (Schlaepfer et al. 2012). 

Warmer summer temperatures would further increase potential evapotranspiration 

and increase drought stress.

Vegetation responses to climatic change in the steppe region of the Pacifi c 

Northwest are likely to be strongly infl uenced by changes in precipitation patterns, 

particularly total annual precipitation and the timing, frequency, and intensity of 

precipitation events. Cool season precipitation (late fall to early spring) is important 

for recharge of shallow and deep soil water reserves, so changes in net cool season 

water infi ltration—a function of both precipitation and runoff—could infl uence the 

intensity and duration of warm season drought, particularly for deep-rooted shrubs. 

Winter precipitation and deep soil water recharge are also important for grass 

productivity (Bowling et al. 2010). Changes in the frequency and intensity of dry 

season precipitation events could also alter plant water and nutrient availability, but 

are unlikely to have signifi cant effects on vegetation productivity and composition 

until summer precipitation becomes abundant and regular enough to reduce summer drought stress signifi cantly. Unfortunately, predictions of future precipitation 

patterns from global climate models carry a high degree of uncertainty, and this 

uncertainty may be magnifi ed by regional topographic effects on the spatial and 

seasonal distribution of precipitation.

Increasing atmospheric CO2 concentrations may also infl uence vegetation 

patterns by increasing water-use effi ciency for some species. Higher CO2 concentrations are expected to be particularly benefi cial for cool-season plant species 

that employ the C3 photosynthetic pathway. Morgan et al. (2011) reported that a 

CO2 enrichment treatment (600 parts per million by volume) increased water-use 

effi ciency suffi ciently to offset increased desiccation from a warming treatment 

(1.5/3.0 °C day night warming) and maintain soil water content equivalent to that 

of a control treatment in a semiarid grassland. Dukes et al. (2005) found no effect 

of modest warming or CO2 enrichment on root and shoot production, however, 

in California grassland. McMurtrie et al. (2008) suggested that CO2 enrichment 

will have the greatest benefi ts for plants on sites where water is highly limiting, 
126

GENERAL TECHNICAL REPORT PNW-GTR-900

but nitrogen is not. Fay et al. (2012) demonstrated that an increase in aboveground 

net primary productivity associated with CO2 enrichment in grasslands depends 

strongly on soil type.

It is not clear whether predicted climatic changes would alter disturbance 

regimes in the steppe region. Wildfi re occurrences are generally limited by lack of 

ignitions during the fi re season or by lack of continuous fuels. Elevated CO2 concentrations have been shown to increase biomass production of cheatgrass and other 

exotic annual grasses, which could lead to increased buildup of matrix fuels that 

could promote the spread of fi res (Lucash et al. 2005, Smith et al. 2000. Increased 

drought stress could reduce fi re frequencies as in the past (Ziegenhagen and Miller 

2009), unless increased evaporative demand is offset by increased precipitation or 

CO2-induced reductions in stomatal conductance (Lucash et al. 2005, Naumburg 

et al. 2003). It is also possible that climatic changes could alter the frequency of 

lightning ignitions during summer thunderstorms, but we are not aware of any 

research in this area.

Several studies also show that grasslands may be resistant to climate change 

effects (Dukes et al. 2005, Grime et al. 2008). Short-term changes in the interannual precipitation regime may not result in large changes in semiarid vegetation 

communities (Jankju 2008). Shrub-steppe communities in Idaho have considerable 

interannual variability in climate and productivity (West and Yorks 2006). Semiarid 

ecosystems may be able to buffer against the effects of climate change by shifting 

the type or intensity of plant-plant interactions (Bates et al. 2006, Brooker 2006, 

Jankju 2008). Sagebrush is tolerant of summer drought and is unresponsive to 

shifts in the seasonality of precipitation in regard to cover and density (Bates et al. 

2006). High spatial and temporal variability in precipitation in semiarid ecosystems 

(Augustine 2010) may further isolate these systems against climate change effects. 

However, sagebrush ecosystems may be vulnerable to encroachment from woodlands owing to increasing precipitation (Bachelet et al. 2001b). 

Altered climate could have mixed infl uences on cheatgrass and efforts to 

manage it. The exotic annual is likely to benefi t (and perhaps has already benefi ted) 

from the positive effects of elevated atmospheric CO2 on productivity (Ziska et al. 

2005). On the other hand, model projections suggest that the response of cheatgrass 

to future climate may depend greatly on changes in precipitation, with cheatgrass 

expected to benefi t from reduced precipitation (Bradley 2009, see also chapter 7). 
127

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chapter 9: Conclusions

The goal of this document was to provide insight into the range of potential effects 

of climate change on vegetation in the Pacifi c Northwest using information from 

the literature and modeling studies. Although it is clear that climate change could 

have profound effects on global and regional vegetation, it is less clear what those 

effects will be and what activities land managers could or should undertake to 

preserve values at risk and to continue to achieve a variety of management objectives. Recently, there has been much information published regarding managing for 

climate change and developing adaptation approaches in the Pacifi c Northwest (e.g., 

Aubry et. al. 2011, Halofsky et al. 2011, Littell et al. 2012, Peterson et al. 2011), other 

forested ecosystems in the United States (Rice et al. 2012, Swanston and Janowiak 

2012), and the United States in general (Vose et al. 2012). Much literature focuses 

on generalizable “toolbox approaches” (Joyce et al. 2009, Millar et al. 2007, Peterson et al. 2011, Spies et al. 2010, Stephens et al. 2010). 

Although a complete review of the climate and natural resource adaptation 

literature is beyond the scope of this document, we note that several of the “toolbox 

approaches” to managing ecosystem responses to climate change feature three basic 

adaptive strategies: (1) promoting resistance to change, (2) promoting resilience to 

change, and (3) facilitating response to change (Joyce et al. 2009, Millar et al. 2007, 

Spies et al. 2010, Stephens et al. 2010). Resistance strategies seek to delay responses 

to climate change by reducing climate-related stresses and preventing disturbances; 

they are used primarily to protect high-value resources from rapid change (Millar et 

al. 2007). Resistance strategies have long been used in the restoration and management of such fi re-prone ecosystems as dry coniferous forests in the Pacifi c Northwest. Resilience strategies seek to manage ecosystems so that the ecosystem can 

return to desired conditions following disturbance (Millar et al. 2007). Landscapescale resilience strategies are currently being developed to manage spotted owl 

(Strix occidentalis caurina) habitat in the dry mixed-conifer forests of the eastern 

Cascades in Washington and Oregon. Resilience strategies would logically be used 

in combination with resistance strategies but recognize uncertainty in the success 

of resistance strategies. Response strategies seek to manage the process of change, 

allowing for a more orderly transition from current to future conditions; they recognize the inevitability (and potential benefi ts) of change but seek to manage the rate 

of change and avoid potentially undesirable outcomes. Managing for high species 

and genetic diversity is one example of a response strategy across all biomes of the 

Pacifi c Northwest.

The contemporary management focus on climate change and uncertainty 

contrasts sharply with heavily relied on paradigms of ecosystem management such 

as historical range of variability (HRV). This concept has been widely embraced by 

Although it is clear 

that climate change 

could have profound 

effects on global and 

regional vegetation, 

it is less clear what 

those effects will be 

and what activities 

land managers could 

or should undertake to 

preserve values at risk.
128

GENERAL TECHNICAL REPORT PNW-GTR-900

natural resource managers, planners, and policymakers over the past two decades. 

The concept suggests that past conditions and processes provide context and guidance for managing ecological systems today (Franklin et al. 2002, Landres et al. 

1999, Moore et al 1999). However, many forest managers, planners, and policymakers now question whether or not the HRV concept is even applicable. Application of 

the concept has strayed from the original intent, and some question whether or not 

the concept is “quaint” (Jackson and Hobbs 2009). Often temporally and spatially 

static or narrow defi nitions of past conditions are used as benchmarks for desired 

future conditions and the focus is largely on structural rather than process-based 

management. Although this structural focus may be necessary for agencies and 

landowners in terms of recordkeeping and accountability, process-based approaches 

do not have to be abandoned. This issue is problematic even now, and becomes 

increasingly so as both natural and human-induced climatic variability and the 

potential for a “no analog” future are considered (Williams and Jackson 2007, 

Williams et al. 2007). If “natural” states are diffi cult to quantify, the environment 

is always changing and ecosystems are always coming and going, and if multiple 

realizations are normal, then the principles underlying ecological restoration, 

potential natural vegetation, and HRV come under question (Jackson and Hobbs 

2009, Jackson 2012). 

Millar et al. (2007) noted that attempts to maintain past conditions could 

result in forests and ecosystems that are not well adapted to current and future 

conditions and may be more vulnerable to future change. However, understanding the past still is important for thinking about the future and HRV can provide 

important information about possible future trajectories and ways that species and 

communities respond to changes in climate and disturbance regimes. Thus a more 

nuanced view is needed, one that is better aligned with the original focus of the 

original HRV concept, which emphasized an evolutionary (long-term) context, a 

focus on process structure, and on ranges rather than means (box 9.1). Moreover, 

rare and uncommon events need to be incorporated into any HRV framework. A 

more nuanced HRV framework will provide better guidance for land management, 

but uncertain future climatic shifts and no analog situations will still arise. Given 

the certainty of unpredictable changing conditions, forest managers may need 

to rely more on understanding, accepting, and working with current and future 

conditions and processes and less on using the past as a benchmark. We provide 

several suggestions for incorporating HRV into management (box 9.1), and several 

frameworks are provided in the literature regarding restoration decisionmaking in 

the context of novel conditions (Belnap et al. 2012, Hobbs et al. 2009). Hobbs et al. 
129

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

(2009) suggested that developing and evaluating hybrid and novel ecosystems may 

be inevitable (Hobbs et al. 2009). 

Natural resource agencies may want to critically gauge the potential challenges 

that climate change will have on the success of traditional restoration activities 

based on HRV and better incorporate climate change in management and planning, including the development of resistance, resilience, and response options that 

facilitate adaptation of natural resources to potentially adverse effects of climate 

change. Peterson et al. (2011) outlined the following steps, based on sciencemanagement partnerships, that can be used to facilitate adaptation on national 

forests or any natural resource land base: (1) become aware of basic climate change 

science and integrate that understanding with knowledge of local resource conditions and issues (review), (2) evaluate sensitivity of natural resources to climate 

change (rank), (3) develop and implement options for adapting resources to climate 

change (resolve), and (4) monitor the effectiveness of on-the-ground management 

(observe) and adjust as needed. Before developing adaptation strategies or a management response to climate change, mangers and others require information to 

“review” and “rank.” This document could serve as a potentially important source 

of information about climate change science in relation to vegetation in the Pacifi c 

Northwest, and a useful source for evaluating the potential sensitivity of vegetation 

to climate change. We highlight that there are uncertainties about exactly what the 

effects of climate change will be; however, information does exists and can be used 

 Box 9.1. Suggestions for incorporating historical range of variability 

 (HRV) concepts into contemporary management.

 1) Focus on variability, considering past and potential future varia-

 bility, and do not discount rare events, and/or anomalies. The mean of a 

 particular attribute from the past should not be used alone because rare and 

 extreme events will not be included and boundaries using extreme events 

 need to be considered. Use of spike descriptors, trend descriptors, and proba- 

 bilistic descriptions can be used to examine rare, uncommon, and extreme or 

 severe events and consistency with past system behavior (Landres et al. 1999). 

 Assess the potential “future range of variability,” the estimated range of some 

 ecological condition that may occur in the future (Duncan et al. 2010). Taking 

 a longer view (e.g., paleoecological studies) can also help inform understand-

 ing of existing and historical ecosystems, determine the circumstances under 

 which they arose, gauge the range of environmental variability they have 

 experienced, and potentially identify environmental thresholds at which they 

 will require different levels of management. Paleoecological insights, together 
130

GENERAL TECHNICAL REPORT PNW-GTR-900

 with modeling, experimentation, and observation, will advance our capacity 

 to successfully manage ecosystems (Jackson and Hobbs 2009). 

 2) Avoid specifi c and static benchmarks based on structure and con-

 centrate on process and ecosystem services. Several authors note that use of

 historical information for identifying desired future conditions does not imply 

 management for static conditions (Moore et al. 1999, Swetnam et al. 1999). 

 However, application of the HRV concept has often resulted in using static 

 benchmarks, often based on forest structure (e.g., tree density). Jackson (2012) 

 noted that vegetation is a dynamic entity, and we need to focus attention on the 

 processes that govern it in order to understand it, manage it and forecast its 

 future states. Management could focus on targeting the maintenance of desir-

 able ecological goods and services, including aesthetic values, rather than 

 focusing on static benchmarks (Jackson and Hobbs 2009). In many cases, this 

 approach will lead to ecosystems unlike those of the past, but more resilient to 

 the future. 

 3) Apply our understanding of processes, interactions, and conditions 

 to the current biophysical, social, and economic environment: try to 

 manage for change. Without consideration of current conditions and multiple 

 disturbances and stresses, or the realized stress complex, unintended conse-

 quences or management surprises are likely (McKenzie et al. 2009, Paine et 

 al. 1998). Livestock grazing, fi re suppression, drought, reduced biological 

 legacies, and exotic species introductions are just a few factors currently infl u-

 encing forest systems and the outcome of management actions. Today, disturb- 

 ance regimes and ecosystem processes are now interacting with present and 

 future environment conditions that are very different than historical condi-

 tions. State-and-transition models can be used to examine a range of future 

 conditions and multiple realizations, and these models are now taking climate 

 change transitions into account (Kerns et al. 2012). 

 4) Use information from HRV to develop and evaluate hybrid or novel 

 ecosystem models that may provide key ecosystem services but be quite 

 different than past ecosystems. Hybrid ecosystems retain some original 

 ecosystem characteristics as well as novel elements, whereas novel ecosys-

 tems comprise different species, interactions, and functions than past ecosys-

 tems (Hobbs et al. 2009). In certain ecosystems, novel mixes of species, 

 structure, or processes (e.g., less desirable native species, nonnative species) 

 might be used to maintain valuable hybrid or novel ecosystems and their 

 services (high carbon storage, resistance to fi re or invasive species, wildlife 

 habitat). 
131

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

to begin the adaptation and risk management process and respond to the range of 

potential effects of climate change on vegetation in the Pacifi c Northwest.

Acknowledgments

Funding for this work was provided the Bureau of Land Management (BLM), 

Oregon State Offi ce, and the U.S. Forest Service (USFS), Pacifi c Northwest 

Research Station. We thank staff members from the Region 6 Climate Change 

Working Group (BLM and USFS) for their input and guidance on the project. We 

especially thank Jim Alegria, Louisa Evers, David Chmura, Crystal Raymond, and 

Tom Spies for their input and manuscript reviews. Finally, we thank Michelle Day 

for providing geographic information system assistance for examining model output 

and editorial reviews. 

English Equivalents

When you know: Multiply by: To fi nd:

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Meters (m) 1.094 Yards

Kilometers (km) 0.621 Miles

Hectares (ha) 2.47 Acres

Square meters (m2

) 10.76 Square feet

Square kilometers (km2

) 0.386 Square miles

Degrees Celsius (°C) 1.8 °C + 32 Degrees Fahrenheit

References

Aber, J.D.; Federer, C.A. 1992. A generalized lumped-parameter model of 

photosynthesis, evapotranspiration and net primary production in temperate 

and boreal forest ecosystems. Oecologia. 92: 463–474.

Aber, J.D.; Ollinger, S.V.; Federer, C.A.; Reich, P.B.; Goulden, M.L.; 

Kicklighter, D.W.; Melillo, J.M.; Lathrop, R.G., Jr. 1995. Predicting 

the effects of climate change on water yield and forest production in the 

Northeastern U.S. Climate Research. 5: 207–222.

Ackerly, D.D.; Loarie, S.R.; Cornwell, W.K.; Weiss, S.B.; Hamilton, H.; 

Branciforte, R.; Kraft, N.J.B. 2010. The geography of climate change: 

implications for conservation biogeography. Diversity and Distributions. 

16: 476–487.
132

GENERAL TECHNICAL REPORT PNW-GTR-900

Adams, H.D.; Guardiola-Claramonte, M.; Barron-Gafford, G.A.; Villegas, 

J.C.; Breshears, D.D.; Zou, C.B.; Troch, P.A.; Huxman, T.E. 2009.

Temperature sensitivity of drought-induced tree mortality portends increased 

regional die-off under global-change-type drought. Proceedings of the National 

Academy of Sciences of the United States of America. 106: 7063–7066.

Agee, J.K. 1993. Fire ecology of Pacifi c Northwest forests. Washington, DC: 

Island Press. 493 p.

Agee, J.K.; Smith, L. 1984. Subalpine tree reestablishment after fi re in the 

Olympic Mountains, Washington. Ecology. 65: 810–819.

Ainsworth, E.A.; Long, S.P. 2005. What have we learned from 15 years of 

free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of 

photosynthesis, canopy properties and plant production to rising CO2. New 

Phytologist. 165: 351–372.

Aitken, S.N.; Yeaman, S.; Holliday, J.A.; Wang, T.L.; Curtis-McLane, S. 

2008. Adaptation, migration or extirpation: climate change outcomes for tree 

populations. Evolutionary Applications. 1: 95–111.

Allen, C.D.; Breshears, D.D. 1998. Drought-induced shift of a forest-woodland 

ecotone: Rapid landscape response to climate variation. Proceedings of the 

National Academy of Sciences of the United States of America. 95: 14839–14842.

Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; 

Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; 

Gonzalez, P.; Fensham, R.; Zhang, Z.; Castro, J.; Demidova, N.; Lim, J.H.; 

Allard, G.; Running, S.W.; Semerci, A.; Cobb, N. 2010. A global overview of 

drought and heat-induced tree mortality reveals emerging climate change risks 

for forests. Forest Ecology and Management. 259: 660–684.

Amaranthus, M.P.; Perry, D.A. 1989. Interaction effects of vegetation type and 

Pacifi c madrone soil inocula on survival, growth, and mycorrhiza formation in 

Douglas-fi r. Canadian Journal of Forest Research. 19: 550–556.

Anderegg, W.R.L. 2012. Complex aspen forest carbon and root dynamics. 

Climatic Change. 111: 983–991.

Anderegg, W.R.L.; Berry, J.A.; Field, C.B. 2012. Linking defi nitions, 

mechanisms, and modeling of drought-induced tree death. Trends in Plant 

Science. 17: 693–700.
133

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Angert, A.; Biraud, S.; Bonfi ls, C.; Henning, C.C.; Buermann, W.; Pinzon, 

J.; Tucker, C.J.; Fung, I. 2005. Drier summers cancel out the CO2 uptake 

enhancement induced by warmer springs. Proceedings of the National Academy 

of Sciences of the United States of America. 102: 10823–10827.

Apple, M.E.; Olszyk, D.M.; Ormrod, D.P.; Lewis, A.; Southworth, D.; Tingey, 

D.T. 2000. Morphology and stomatal function of Douglas fi r needles exposed 

to climate change: elevated CO2 and temperature. International Journal of Plant 

Sciences. 161: 127–132.

Araújo, M.B.; Cabeza, M.; Thuiller, W.; Hannah, L.; Williams, P.H. 2004. 

Would climate change drive species out of reserves? An assessment of existing 

reserve-selection methods. Global Change Biology. 10: 1618–1626.

Araújo, M.B.; Luoto, M. 2007. The importance of biotic interactions for modelling 

species distributions under climate change. Global Ecology and Biogeography. 

16: 743–753.

Araújo, M.B.; New, M. 2007. Ensemble forecasting of species distributions. 

Trends in Ecology and Evolution. 22: 42–47.

Arno, S.F.; Habeck, J.R. 1972. Ecology of alpine larch (Larix lyallii Parl.) in the 

Pacifi c Northwest. Ecological Monographs. 42: 417–450.

Ashcroft, M.B. 2010. Identifying refugia from climate change. Journal of 

Biogeography. 37: 1407–1413.

Ashcroft, M.B.; Gollan, J.R.; Warton, D.I.; Ramp, D. 2012. A novel approach to 

quantify and locate potential microrefugia using topoclimate, climate stability, 

and isolation from the matrix. Global Change Biology. 18: 1866–1879.

Astrup, R.; Coates, K.D.; Hall, E. 2008. Recruitment limitation in forests: lessons 

from an unprecedented mountain pine beetle epidemic. Forest Ecology and 

Management. 256: 1743–1750.

Atkin, O.K.; Bruhn, D.; Hurry, V.M.; Tjoelker, M.G. 2005. The hot and the 

cold: unraveling the variable response of plant respiration to temperature. 

Functional Plant Ecology. 32: 87–105.

Atkin, O.K.; Loveys, B.R.; Atkinson, L.J.; Pons, T.L. 2006. Phenotypic 

plasticity and growth temperature: understanding interspecifi c variability. 

Journal of Experimental Botany. 57: 267–281.

Atkin, O.K.; Tjoelker, M.G. 2003. Thermal acclimation and the dynamic response 

of plant respiration to temperature. Trends in Plant Science. 8: 343–351.
134

GENERAL TECHNICAL REPORT PNW-GTR-900

Aubry, C.; Devine, W.; Shoal, R.; Bower, A.; Miller, J.; Maggiulli, N. 2011. 

Climate change and forest biodiversity: a vulnerability assessment and action 

plan for National Forests in western Washington. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacifi c Northwest Region. 299 p.

Augustine, D.J. 2010. Spatial versus temporal variation in precipitation in a 

semiarid ecosystem. Landscape Ecology. 25: 913–925.

Austin, M. 2007. Species distribution models and ecological theory: a critical 

assessment and some possible new approaches. Ecological Modelling. 200: 1–19.

Ayres, M.P.; Lombardero, M.J. 2000. Assessing the consequences of global 

change for forest disturbance from herbivores and pathogens. Science of the 

Total Environment. 262: 263–286.

Bachelet, D.; Johnson, B.R.; Bridgeman, S.D.; Dunn, P.V.; Anderson, H.E.; 

Rogers, B.M. 2011. Climate change impacts on western Pacifi c Northwest 

prairies and savannas. Northwest Science. 85: 411–429.

Bachelet, D.; Lenihan, J.M.; Daly, C.; Neilson, R.P.; Ojima, D.S.; Parton, 

W.J. 2001a. MC1: a dynamic vegetation model for estimating the distribution 

of vegetation and associated ecosystem fl uxes of carbon, nutrients, and water—

technical documentation. Version 1. Gen. Tech. Rep. PNW-GTR-508. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station. 95 p.

Bachelet, D.; Neilson, R.P.; Lenihan, J.M.; Drapek, R.J. 2001b. Climate change 

effects on vegetation distribution and carbon budget in the United States. 

Ecosystems. 4: 164–185.

Bachelet, D.; Neilson, R.P.; Lenihan, J.M.; Drapek, R.J. 2004. Regional 

differences in the carbon source-sink potential of natural vegetation in the U.S.A. 

Environmental Management. 33(S1): S23–S43. doi:10.1007/s00267-003-9115-4.

Badeck, F.W.; Bondeau, A.; Böttcher, K.; Doktor, D.; Lucht, W.; Schaber, 

J.; Sitch, S. 2004. Responses of spring phenology to climate change. New 

Phytologist. 162: 295–309.

Badeck, F.W.; Lischke, H.; Bugmann, H.; Hickler, T.; Honninger, K.; Lasch, 

P.; Lexer, M.J.; Mouillot, F.; Schaber, J.; Smith, B. 2001. Tree species 

composition in European pristine forests: comparison of stand data to model 

predictions. Climatic Change. 51: 307–347.
135

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Bailey, J.D.; Harrington, C.A. 2006. Temperature regulation of bud-burst 

phenology within and among years in a young Douglas-fi r (Pseudotsuga 

menziesii) plantation in western Washington, USA. Tree Physiology. 

26: 421–430.

Baker, W.L. 2006. Fire and restoration of sagebrush ecosystems. Wildlife Society 

Bulletin. 34: 177–185.

Bakker, E.S.; Ritchie, M.E.; Olff, H.; Milchunas, D.G.; Knops, J.M.H. 2006. 

Herbivore impact on grassland plant diversity depends on habitat productivity 

and herbivore size. Ecology Letters. 9: 780–788.

Bale, J.S.; Masters, G.J.; Hodkinson, I.D.; Awmack, C.; Bezemer, T.M.; 

Brown, V.K.; Butterfi eld, J.; Buse, A.; Coulson, J.C.; Farrar, J.; Good, 

J.E.G.; Harrington, R.; Hartley, S.; Jones, T.H.; Lindroth, R.L.; Press, 

M.C.; Symrnioudis, I.; Watt, A.D.; Whittaker, J.B. 2002. Herbivory in global 

climate change research: direct effects of rising temperature on insect herbivores. 

Global Change Biology. 8: 1–16.

Bansal, S.; Reinhardt, K.; Germino, M.J. 2011. Linking carbon balance to 

establishment patterns: comparison of whitebark pine and Engelmann spruce 

seedlings along an herb cover exposure gradient at treeline. Plant Ecology. 

212: 219–228.

Barnett, T.P.; Adam, J.C.; Lettenmaier, D.P. 2005. Potential impacts of a 

warming climate on water availability in snow-dominated regions. Nature. 

438: 303–309.

Barrett, J.W. 1979. Silviculture of ponderosa pine in the Pacifi c Northwest: the 

state of our knowledge. Gen Tech. Rep. PNW-GTR-79. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station. 

106 p.

Barry, S.; Elith, J. 2006. Error and uncertainty in habitat models. Journal of 

Applied Ecology. 43: 413–423.

Bates, J.D.; Svejcar, T.; Miller, R.F.; Angell, R.A. 2006. The effects of 

precipitation timing on sagebrush steppe vegetation. Journal of Arid 

Environments. 64: 670–697.

Beale, C.M.; Lennon, J.J. 2012. Incorporating uncertainty in predictive species 

distribution modelling. Philosophical Transactions of the Royal Society Biology. 

367: 247–258.
136

GENERAL TECHNICAL REPORT PNW-GTR-900

Beaubien, E.; Hamann, A. 2011. Spring fl owering response to climate change 

between 1936 and 2006 in Alberta, Canada. BioScience. 61: 514–524.

Beaumont, L.J.; Hughes, L.; Pitman, A.J. 2008. Why is the choice of future 

climate scenarios for species distribution modelling important? Ecology Letters. 

11: 1135–1146.

Belnap, J.; Ludwig, J.A.; Wilcox, B.P.; Betancourt, J.L.; Dean, W.R.J.; 

Hoffmann, B.D.; Milton, S.J. 2012. Introduced and invasive species in novel 

rangeland ecosystems: friends or foes? Rangeland Ecology and Management. 

65: 569–578.

Bentz, B.J.; Regniere, J.; Fettig, C.J.; Hansen, E.M.; Hayes, J.L.; Hicke, J.A.; 

Kelsey, R.G.; Negron, J.F.; Seybold, S.J. 2010. Climate change and bark 

beetles of the western United States and Canada: direct and indirect effects. 

Bioscience. 60: 602–613.

Berbeco, M.R.; Melillo, J.M.; Orians, C.M. 2012. Soil warming accelerates 

decomposition of fi ne woody debris. Plant Soil. 356: 405–417.

Berry, J.; Björkman, O. 1980. Photosynthetic response and adaptation to 

temperature in higher plants. Annual Review of Plant Physiology. 31: 491–543.

Best, A.S.; Johst, K.; Munkemuller, T.; Travis, J.M.J. 2007. Which species will 

successfully track climate change? The infl uence of intraspecifi c competition and 

density dependent dispersal on range shifting dynamics. Oikos. 116: 1531–1539.

Bigler, C.; Gavin, D.G.; Gunning, C.; Veblen, T.T. 2007. Drought induces 

lagged tree mortality in a subalpine forest in the Rocky Mountains. Oikos. 

116: 1983–1994.

Bigler, C.; Kulakowski, D.; Veblen, T.T. 2005. Multiple disturbance interactions 

and drought infl uence fi re severity in rocky mountain subalpine forests. Ecology. 

86: 3018–3029.

Bingham, M.A.; Simard, S. 2012. Ectomycorrhizal networks of Pseudotsuga 

menziesii var. glauca trees facilitate establishment of conspecifi c seedlings 

under drought. Ecosystems. 15: 188–199.

Blinnikov, M.; Busacca, A.; Whitlock, C. 2002. Reconstruction of the late 

Pleistocene grassland of the Columbia basin, Washington, USA, based on 

phytolith records in loess. Palaeogeography Palaeoclimatology Palaeoecology. 

177: 77–101.
137

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Boisvenue, C.; Running, S.W. 2006. Impacts of climate change on natural forest 

productivity-evidence since the middle of the 20th century. Global Change 

Biology. 12: 862–882.

Boisvenue, C.; Running, S.W. 2010. Simulations show decreasing carbon stocks 

and potential for carbon emissions in Rocky Mountain forests over the next 

century. Ecological Applications. 20: 1302–1319.

Bonfi ls, C.; Santer, B.D.; Pierce, D.W.; Hidalgo, H.G.; Bala, G.; Das, T.; 

Barnett, T.P.; Cayan, D.R.; Doutriaux, C.; Wood, A.W.; Mirin, A.; Nozawa, 

T. 2008. Detection and attribution of temperature changes in the mountainous 

western United States. Journal of Climate. 21: 6404–6424.

Boone, C.K.; Aukema, B.H.; Bohlmann, J.; Carroll, A.L.; Raffa, K.F. 2011. 

Effi cacy of tree defense physiology varies with bark beetle population density: 

a basis for positive feedback in eruptive species. Canadian Journal of Forest 

Research. 41: 1174–1188.

Botkin, D.B.; Janak, J.F.; Wallis, J.R. 1972. Some ecological consequences 

of a computer model of forest growth. Journal of Ecology. 60: 849–852.

Bowling, D.R.; Bethers-Marchetti, S.; Lunch, C.K.; Grote, E.E.; Belnap, 

J. 2010. Carbon, water, and energy fl uxes in a semiarid cold desert grassland 

during and following multiyear drought. Journal of Geophysical Research. 115, 

G04026. doi:10.1029/2010JG001322.

Bradley, B.A. 2009. Regional analysis of the impacts of climate change on 

cheatgrass invasion shows potential risk and opportunity. Global Change 

Biology. 15: 196–208.

Bradley, B.A. 2010. Assessing ecosystem threats from global and regional change: 

hierarchical modeling of risk to sagebrush ecosystems from climate change, land 

use and invasive species in Nevada, USA. Ecography. 33: 198–208.

Breshears, D.D.; Cobb, N.S.; Rich, P.M.; Price, K.P.; Allen, C.D.; Balice, R.G.; 

Romme, W.H.; Kastens, J.H.; Floyd, M.L.; Belnap, J.; Anderson, J.J.; 

Myers, O.B.; Meyer, C.W. 2005. Regional vegetation die-off in response to 

global-change-type drought. Proceedings of the National Academy of Sciences 

of the United States of America. 102: 15144–15148.

Breshears, D.D.; Huxman, T.E.; Adams, H.D.; Zou, C.B.; Davison, J.E. 2008.

Vegetation synchronously leans upslope as climate warms. Proceedings of the 

National Academy of Sciences of the United States of America. 

105: 11591–11592.
138

GENERAL TECHNICAL REPORT PNW-GTR-900

Breshears, D.D.; Myers, O.B.; Meyer, C.W.; Barnes, F.J.; Zou, C.B.; Allen, 

C.D.; McDowell, N.G.; Pockman, W.T. 2009. Tree die-off in response to global 

change-type drought: mortality insights from a decade of plant water potential 

measurements. Frontiers in Ecology and the Environment. 7: 185–189.

Briggs, J.M.; Knapp, A.K.; Blair, J.M.; Heisler, J.L.; Hoch, G.A.; Lett, M.S.; 

McCarron, J.K. 2005. An ecosystem in transition: causes and consequences of 

the conversion of mesic grassland to shrubland. BioScience. 55: 243–254.

Broennimann, O.; Treier, U.A.; Muller-Scharer, H.; Thuiller, W.; Peterson, 

A.T.; Guisan, A. 2007. Evidence of climatic niche shift during biological 

invasion. Ecology Letters. 10: 701–709.

Brohan, P.; Kennedy, J.J.; Harris, I.; Tett, S.F.B.; Jones, P.D. 2006. 

Uncertainty estimates in regional and global observed temperature changes: 

a new dataset from 1850. Journal of Geophysical Research. Vol. 111, D12106. 

doi:10.1029/2005JD006548.

Brooke, R.C.; Peterson, E.B.; Krajina, V.J. 1970. The subalpine mountain 

hemlock zone. Ecology of Western North America. 2: 148–239.

Brooker, R.W. 2006. Plant-plant interactions and environmental change. New 

Phytologist. 171: 271–284.

Brooker, R.W.; Maestre, F.T.; Callaway, R.M.; Lortie, C.L.; Cavieres, L.A.; 

Kunstler, G.; Liancourt, P.; Tielborger, K.; Travis, J.M.J.; Anthelme, 

F.; Armas, C.; Coll, L.; Corcket, E.; Delzon, S.; Forey, E.; Kikvidze, Z.; 

Olofsson, J.; Pugnaire, F.; Quiroz, C.L.; Saccone, P.; Schiffers, K.; Seifan, 

M.; Touzard, B.; Michalet, R. 2008. Facilitation in plant communities: the past, 

the present, and the future. Journal of Ecology. 96: 18–34.

Brown, P.M. 2006. Climate effects on fi re regimes and tree recruitment in Black 

Hills ponderosa pine forests. Ecology. 87: 2500–2510.

Brown, R.D.; Mote, P.W. 2009. The response of Northern Hemisphere snow cover 

to a changing climate. Journal of Climate. 22: 2124–2145.

Brubaker, L.B. 1986. Responses of tree populations to climatic change. Vegetatio. 

67: 119–130.

Bugmann, H. 2001. A review of forest gap models. Climatic Change. 51: 259–305.

Bugmann, H.K.M.; Wullschleger, S.D.; Price, D.T.; Ogle, K.; Clark, D.F.; 

Solomon, A.M. 2001. Comparing the performance of forest gap models in 

North America. Climatic Change. 51: 349–388.
139

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Buisson, L.; Thuiller, W.; Casajus, N.; Lek, S.; Grenouillet, G. 2010.

Uncertainty in ensemble forecasting of species distribution. Global Change 

Biology. 16: 1145–1157.

Burton, O.J.; Phillips, B.L.; Travis, J.M.J. 2010. Trade-offs and the evolution 

of life-histories during range expansion. Ecology Letters. 13: 1210–1220.

Busing, R.T.; Solomon, A.M.; McKane, R.B.; Burdick, C.A. 2007. Forest 

dynamics in Oregon landscapes: evaluation and application of an individualbased model. Ecological Applications. 17: 1967–1981.

Butler, S.M.; Melillo, J.M.; Johnson, J.E.; Mohan, J.; Steudler, P.A.; Lux, H.; 

Burrows, E.; Smith, R.M.; Vario, C.L.; Scott, L.; Hill, T.D.; Aponte, N.; 

Bowles, F. 2012. Soil warming alters nitrogen cycling in a New England forest: 

implications for ecosystem function and structure. Oecologia. 168: 819–828.

Caldwell, M.M.; Dawson, T.E.; Richards, J.H. 1998. Hydraulic lift: 

consequences of water effl ux from the roots of plants. Oecologia. 113: 151–161.

Caldwell, M.M.; Richards, J.H. 1989. Hydraulic lift: water effl ux from upper 

roots improves effectiveness of water uptake by deep roots. Oecologia. 79: 1–5.

Callaway, R.M. 1995. Positive interactions among plants. Botanical Review. 

61: 306–349.

Callaway, R.M.; Brooker, R.W.; Choler, P.; Kikvidze, Z.; Lortie, C.J.; 

Michalet, R.; Paolini, L.; Pugnaire, F.I.; Newingham, B.; Aschehoug, E.T.; 

Armas, C.; Kikodze, D.; Cook, B.J. 2002. Positive interactions among alpine 

plants increase with stress. Nature. 417: 844–848.

Callaway, R.M.; Delucia, E.H.; Schlesinger, W.H. 1994. Biomass allocation of 

montane and desert ponderosa pine: an analog for response to climate change. 

Ecology. 75: 1474–1481.

Callaway, R.M.; Walker, L.R. 1997. Competition and facilitation: a synthetic 

approach to interactions in plant communities. Ecology. 78: 1958–1965.

Campbell, R.K.; Sugano, A.I. 1979. Genecology of bud-burst phenology in 

Douglas-fi r: response to fl ushing temperature and chilling. Botanical Gazette. 

140: 223–231.

Canaday, B.B.; Fonda, R.W. 1974. Infl uence of subalpine snowbanks on 

vegetation pattern, production, and phenology. Bulletin of the Torrey Botanical 

Club. 101: 340–350.
140

GENERAL TECHNICAL REPORT PNW-GTR-900

Cannell, M.G.R.; Smith, R.I. 1983. Thermal time, chill days and prediction of 

budburst in Picea sitchensis. Journal of Applied Ecology. 20: 951–963.

Cannell, M.G.R.; Smith, R.I. 1986. Climatic warming, spring budburst and forest 

damage on trees. Journal of Applied Ecology. 23: 177–191.

Carnwath, G.C.; Peterson, D.W.; Nelson, C.R. 2012. Effect of crown class and 

habitat type on climate-growth relationships of ponderosa pine and Douglas-fi r. 

Forest Ecology and Management. 285: 44–52.

Case, M.J.; Peterson, D.L. 2005. Fine-scale variability in growth-climate 

relationships of Douglas-fi r, North Cascade Range, Washington. Canadian 

Journal of Forest Research. 35: 2743–2755.

Case, M.J.; Peterson, D.L. 2007. Growth-climate relations of lodgepole pine in the 

North Cascades National Park, Washington. Northwest Science. 81: 62–75.

Casola, J.H.; Cuo, L.; Livneh, B.; Lettenmaier, D.P.; Stoelinga, M.T.; Mote, 

P.W.; Wallace, J.M. 2009. Assessing the impacts of global warming on 

snowpack in the Washington Cascades. Journal of Climate. 22: 2758–2772.

Cayan, D.R. 1996. Interannual climate variability and snowpack in the western 

United States. Journal of Climate. 9: 928–948.

Cázares, E.; Trappe, J.M.; Jumpponen, A. 2005. Mycorrhiza-plant colonization 

patterns on a subalpine glacier forefront as a model system of primary 

succession. Mycorrhiza. 15: 405–416.

Chambers, J.C.; Roundy, B.A.; Blank, R.R.; Meyer, S.E.; Whittaker, A. 2007.

What makes Great Basin sagebrush ecosystems invasible by Bromus tectorum? 

Ecological Monographs. 77: 117–145.

Chapman, D.S. 2010. Weak climatic associations among British plant distributions. 

Global Ecology and Biogeography. 19: 831–841.

Chen, P.Y.; Welsh, C.; Hamann, A. 2010. Geographic variation in growth 

response of Douglas-fi r to interannual climate variability and projected climate 

change. Global Change Biology. 16: 3374–3385.

Cheng, L.; Booker, F.L.; Tu, C.; Burkey, K.O.; Zhou, L.; Shew, H.D.; Rufty, 

T.W.; Hu, S. 2012. Arbuscular mycorrhizal fungi increase organic carbon 

decomposition under elevated CO2. Science. 337: 1084–1087.

Chevin, L.M.; Lande, R.; Mace, G.M. 2010. Adaptation, plasticity, and 

extinction in a changing environment: toward a predictive theory. PLoS Biology. 

8(4): e1000357. doi:10.1371/hournal.pbio.1000357.
141

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Chmura, D.J.; Anderson, P.D.; Howe, G.T.; Harrington, C.A.; Halofsky, J.E.; 

Peterson, D.L.; Shaw, D.C.; St. Clair, J.B. 2011. Forest responses to climate 

change in the northwestern United States: ecophysiological foundations for 

adaptive management. Forest Ecology and Management. 261: 1121–1142.

Chuine, I. 2010. Why does phenology drive species distribution? Philosophical 

Transactions of the Royal Society, Biological Sciences. 365: 3149–3160.

Chuine, I.; Rehfeldt, G.E.; Aitken, S.N. 2006. Height growth determinants and 

adaptation to temperature in pines: a case study of Pinus contorta and Pinus 

monticola. Canadian Journal of Forest Research. 36: 1059–1066.

Churkina, G.; Running, S.W. 1998. Contrasting climatic controls on the estimated 

productivity of global terrestrial biomes. Ecosystems. 1: 206–215.

Clarke, L.; Edmonds, J.; Jacoby, H.; Pitcher, H.; Reilly, J.; Richels, R. 2007.

Scenarios of greenhouse gas emissions and atmospheric concentrations. Subreport 2.1. A of synthesis and assessment product 2.1 by the U.S. Climate 

Change Science Program and the Subcommittee on Global Change Research. 

Washington, DC: Department of Energy, Offi ce of Biological and Environmental 

Research. 154 p.

Cleland, E.E.; Chuine, I.; Menzel, A.; Mooney, H.A.; Schwartz, M.D. 2007.

Shifting plant phenology in response to global change. Trends in Ecology and 

Evolution. 22: 357–365.

Cole, K. 1982. Late Quaternary zonation of vegetation in the eastern Grand 

Canyon. Science. 217: 1142–1145.

Comstock, J.P.; Ehleringer, J.R. 1992. Plant adaptation in the Great Basin and 

Colorado Plateau. Great Basin Naturalist. 52: 195–215.

Connell, J.H.; Slatyer, R.O. 1977. Mechanisms of succession in natural 

communities and their role in community stability and organization. American 

Naturalist. 111: 1119–1144.

Coops, N.C.; Waring, R.H. 2011a. A process-based approach to estimate 

lodgepole pine (Pinus contorta Dougl.) distribution in the Pacifi c Northwest 

under climate change. Climatic Change. 105: 313–328.

Coops, N.C.; Waring, R.H. 2011b. Estimating the vulnerability of fi fteen tree 

species under changing climate in Northwest North America. Ecological 

Modelling. 222: 2119–2129.
142

GENERAL TECHNICAL REPORT PNW-GTR-900

Coops, N.C.; Waring, R.H.; Law, B.E. 2005. Assessing the past and future 

distribution and productivity of ponderosa pine in the Pacifi c Northwest using 

a process model, 3-PG. Ecological Modelling. 183: 107–124.

Craine, J.M.; Reich, P.B.; Tilman, G.D.; Ellsworth, D.; Fargione, J.; Knops, J.; 

Naeem, S. 2003. The role of plant species in biomass production and response to 

elevated CO2 and N. Ecology Letters. 6: 623–630.

Craine, J.M.; Tilman, D.; Wedin, D.; Reich, P.; Knops, J. 2002. Functional 

traits, productivity and effects on nutrient cycling of 33 grassland species. 

Functional Ecology. 16: 563–574.

Cramer, W.; Bondeau, A.; Woodward, F.I.; Prentice, I.C.; Betts, R.A.; 

Brovkin, V.; Cox, P.M.; Fisher, V.; Foley, J.A.; Friend, A.D.; Kucharik, 

C.; Lomas, M.R.; Ramankutty, N.; Sitch, S.; Smith, B.; White, A.; YoungMolling, C. 2001. Global response of terrestrial ecosystem structure and 

function to CO2 and climate change: results from six dynamic global vegetation 

models. Global Change Biology. 7: 357–373.

Crookston, N.L.; Rehfeldt, G.E.; Dixon, G.E.; Weiskittel, A.R. 2010.

Addressing climate change in the forest vegetation simulator to assess impacts on 

landscape forest dynamics. Forest Ecology and Management. 260: 1198–1211.

Dale, V.H.; Franklin, J.F. 1989. Potential effects of climate change on stand 

development in the Pacifi c Northwest. Canadian Journal of Forest Research. 

19: 1581–1590.

Dale, V.H.; Joyce, L.A.; McNulty, S.; Neilson, R.P.; Ayres, M.P.; Flannigan, 

M.D.; Hanson, P.J.; Irland, L.C.; Lugo, A.E.; Peterson, C.J.; Simberloff, 

D.; Swanson, F.J.; Stocks, B.J.; Wotton, B.M. 2001. Climate change and forest 

disturbances. Bioscience. 51: 723–734.

Daly, C.; Conklin, D.R.; Unsworth, M.H. 2010. Local atmospheric decoupling 

in complex topography alters climate change impacts. International Journal of 

Climatology. 30: 1857–1864.

Daly, C.; Halbleib, M.; Smith, J.I.; Gibson, W.P.; Doggett, M.K.; Taylor, 

G.H.; Curtis, J.; Pasteris, P.P. 2008. Physiographically sensitive mapping of 

climatological temperature and precipitation across the conterminous United 

States. International Journal of Climatology. 28: 2031–2064.
143

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

D’Antonio, C.M.; Tunison, J.T.; Loh, R.K. 2000. Variation in the impact of exotic 

grasses on native plant composition in relation to fi re across an elevation gradient 

in Hawaii. Austral Ecology. 25: 507–522.

D’Antonio, C.M.; Vitousek, P.M. 1992. Biological invasions by exotic grasses, the 

grass/fi re cycle, and global change. Annual Review of Ecology and Systematics. 

23: 63–87.

Davies, G.M.; Bakker, J.D.; Dettweiler-Robinson, E.; Dunwiddie, P.W.; Hall, 

S.A.; Downs, J.; Evans, J. 2012. Trajectories of change in sagebrush steppe 

vegetation communities in relation to multiple wildfi res. Ecological Applications. 

22: 1562–1577.

Davis, M.A.; Grime, J.P.; Thompson, K. 2000. Fluctuating resources in plant 

communities: a general theory of invasibility. Journal of Ecology. 88: 528–534.

Davis, M.B. 1981. Quaternary history and the stability of forest communities. 

In: West, D.C.; Shugart, H.H., eds. Forest succession: concepts and application. 

New York: Springer-Verlag: 132–153.

Davis, M.B. 1989. Lags in vegetation response to greenhouse warming. Climatic 

Change. 15: 75–82.

Davis, M.B.; Shaw, R.G. 2001. Range shifts and adaptive responses to Quaternary 

climate change. Science. 292: 673–679.

Davis, M.B.; Shaw, R.G.; Etterson, J.R. 2005. Evolutionary responses to 

changing climate. Ecology. 86: 1704–1714.

Davis, M.B.; Woods, K.D.; Webb, S.L.; Futyma, R.P. 1986. Dispersal versus 

climate: expansion of Fagus and Tsuga into the Upper Great Lakes Region. 

Vegetatio. 67: 93–103.

Dawes, M.A.; Hättenschwiler, S.; Bebi, P.; Hagedorn, F.; Handa, I.T.; Körner, 

C.; Rixen, C. 2011. Species-specifi c tree growth responses to 9 years of CO2

enrichment at the alpine treeline. Journal of Ecology. 99: 383–394.

Day, T.A.; DeLucia, E.H.; Smith, W.K. 1989. Infl uence of cold soil and 

snowcover on photosynthesis and leaf conductance in two Rocky Mountain 

conifers. Oecologia. 80: 546–552.

de Graaff, M.-A.; van Groenigen, K.-J.; Six, J.; Hungate, B.; van Kessel, C. 

2006. Interactions between plant growth and soil nutrient cycling under elevated 

CO2: a meta-analysis. Global Change Biology. 12: 2077–2091.
144

GENERAL TECHNICAL REPORT PNW-GTR-900

DeLucia, E.H.; Maherali, H.; Carey, E.V. 2000. Climate-driven changes in 

biomass allocation in pines. Global Change Biology. 6: 587–593.

DePuit, E.J.; Caldwell, M.M. 1975. Gas exchange of three cool semi-desert 

species in relation to temperature and water stress. Journal of Ecology. 63: 

835–858.

Dickie, I.A.; Schnitzer, S.A.; Reich, P.B.; Hobbie, S.E. 2005. Spatially disjunct 

effects of co-occurring competition and facilitation. Ecology Letters. 

8: 1191–1200.

Dieleman, W.I.J.; Vicca, S.; Dijkstra, F.A.; Hagedorn, F.; Hovenden, M.J.; 

Larsen, K.S.; Morgan, J.A.; Volder, A.; Beier, C.; Dukes, J.S.; King, J.; 

Leuzinger, S.; Linder, S.; Luo, Y.; Oren, R.; de Angelis, P.; Tingey, D.; 

Hoosbeek, M.R.; Janssens, I.A. 2012. Simple additive effects are rare: a 

quantitative review of plant biomass and soil process responses to combined 

manipulations of CO2 and temperature. Global Change Biology. 18: 2681–2693.

Diniz-Filho, J.A.F.; Bini, L.M.; Rangel, T.F.; Loyola, R.D.; Hof, C.; NoguésBravo, D.; Araújo, M.B. 2009. Partitioning and mapping uncertainties in 

ensembles of forecasts of species turnover under climate change. Ecography. 

32: 897–906.

Dixon, G.E., comp. 2002. Essential FVS: a user’s guide to the Forest Vegetation 

Simulator. Internal Rep. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Forest Management Service Center. 226 p. (Revised: January 

17, 2013).

Dobrowski, S.Z. 2011. A climatic basis for microrefugia: the infl uence of terrain 

on climate. Global Change Biology. 17: 1022–1035.

Donato, D.C.; Fontaine, J.B.; Campbell, J.L.; Robinson, W.D.; Kauffman, 

J.B.; Law, B.E. 2009. Conifer regeneration in stand-replacement portions of 

a large mixed-severity wildfi re in the Klamath-Siskiyou Mountains. Canadian 

Journal of Forest Research. 39: 823–838.

Dormann, C.F. 2007. Promising the future? Global change projections of species 

distributions. Basic and Applied Ecology. 8: 387–397.

Drake, B.G.; Gonzàlez-Meler, M.A.; Long, S.P. 1997. More effi cient plants: a 

consequence of rising atmospheric CO2? Annual Review of Plant Physiology 

and Plant Molecular Biology. 48: 609–639.
145

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Drake, J.E.; Gallet-Budynek, A.; Hofmockel, K.S.; Bernhardt, E.S.; 

Billings, S.A.; Jackson, R.B.; Johnsen, K.S.; Lichter, J.; McCarthy, H.R.; 

McCormack, M.L.; Moore, D.J.P.; Oren, R.; Palmroth, S.; Phillips, R.P.; 

Pippen, J.S.; Pritchard, S.G.; Treseder, K.K.; Schlesinger, W.H.; DeLucia, 

E.H.; Finzi, A.C. 2011. Increases in the fl ux of carbon belowground stimulate 

nitrogen uptake and sustain the long-term enhancement of forest productivity 

under elevated CO2. Ecology Letters. 14: 349–357.

Dukes, J.S.; Chiariello, N.R.; Cleland, E.E.; Moore, L.A.; Shaw, M.R.; Thayer, 

S.; Tobeck, T.; Mooney, H.A.; Field, C.B. 2005. Responses of grassland 

production to single and multiple global environmental changes. PLoS Biology. 

3: 1829–1837.

Dukes, J.S.; Chiariello, N.R.; Loarie, S.R.; Field, C.B. 2011. Strong response 

of an invasive plant species (Centaurea solstitialis L.) to global environmental 

changes. Ecological Applications. 21: 1887–1894.

Dukes, J.S.; Mooney, H.A. 1999. Does global change increase the success of 

biological invaders? Trends in Ecology and Evolution. 14: 135–139.

Dullinger, S.; Gattringer, A.; Thuiller, W.; Moser, D.; Zimmerman, N.; 

Guisan, A.; Willner, W.; Plutzar, C.; Leitner, M.; Mang, T.; Caccianiga, 

M.; Dirnböck, T.; Ertl, S.; Fischer, A.; Lenoir, J.; Svenning, J.C.; Psomas, 

A.; Schmatz, D.R.; Silc, U.; Vittoz, P.; Hülber, K. 2012. Extinction debt of 

high-mountain plants under twenty-fi rst-century climate change. Nature Climate 

Change. 2: 619–622.

Duncan, S.L.; McComb, B.C.; Johnson, K.N. 2010. Integrating ecological and 

social ranges of variability in conservation of biodiversity: past, present, and 

future. Ecology and Society. 15(1): article 5. http://hdl.handle.net/10535/6002.

Dunne, J.A.; Harte, J.; Taylor, K.J. 2003. Subalpine meadow fl owering 

phenology responses to climate change: integrating experimental and gradient 

methods. Ecological Monographs. 73: 69–86.

Edburg, S.L.; Hicke, J.A.; Brooks, P.D.; Pendall, E.G.; Ewers, B.E.; Norton, 

U.; Gochis, D.; Gutmann, E.D.; Meddens, A.J.H. 2012. Cascading impacts of 

bark beetle-caused tree mortality on coupled biogeophysical and biogeochemical 

processes. Frontiers in Ecology and the Environment. 8: 416–424.

Elith, J.; Graham, C.H. 2009. Do they? How do they? WHY do they differ? 

On fi nding reasons for differing performances of species distribution models. 

Ecography. 32: 66–77.
146

GENERAL TECHNICAL REPORT PNW-GTR-900

Elith, J.; Graham, C.H.; Anderson, R.P.; Dudik, M.; Ferrier, S.; Guisan, 

A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; Li, J.; 

Lohmann, L.G.; Loiselle, B.A.; Manion, G.; Moritz, C.; Nakamura, M.; 

Nakazawa, Y.; Overton, J.M.; Peterson, A.T.; Phillips, S.J.; Richardson, 

K.; Scachetti-Pereira, R.; Schapire, R.E.; Soberon, J.; Williams, S.; Wisz, 

M.S.; Zimmermann, N.E. 2006. Novel methods improve prediction of species’ 

distributions from occurrence data. Ecography. 29: 129–151.

Elith, J.; Kearney, M.; Phillips, S. 2010. The art of modelling range-shifting 

species. Methods in Ecology and Evolution. 1: 330–342.

Elith, J.; Leathwick, J.R. 2009. Species distribution models: ecological 

explanation and prediction across space and time. Annual Review of Ecology 

Evolution and Systematics. 40: 677–697.

Elmendorf, S.C.; Henry, G.H.R.; Hollister, R.D.; Björk, R.G.; BoulangerLapointe, N.; Cooper, E.J.; Cornelissen, J.H.C.; Day, T.A.; Dorrepaal, E.; 

Elumeeva, T.G.; Gill, M.; Gould, W.A.; Harte, J.; Hik, D.S.; Hofgaard, A.; 

Johnson, D.R.; Johnstone, J.F.; Jónsdóttir, I.S.; Jorgenson, J.C.; Klanderud, 

K.; Klein, J.A.; Koh, S.; Kudo, G.; Lara, M.; Lévesque, E. 2012. Plot-scale 

evidence of tundra vegetation change and links to recent summer warming. 

Nature Climate Change. 2: 453–457.

Elsner, M.M.; Co, L.; Voisin, N.; Deems, J.S.; Hamlet, A.F.; Vano, J.A.; 

Mickelson, K.E.B.; Lee, S.-Y.; Lettenmaier, D.P. 2010. Implications of 21st

century climate change for the hydrology of Washington State. Climatic Change. 

102: 225–260.

Engelkes, T.; Morriën, E.; Verhoeven, K.J.F.; Bezemer, T.M.; Biere, A.; 

Harvey, J.A.; McIntyre, L.M.; Tamis, W.L.M.; van der Putten, W.H. 2008.

Successful range-expanding plants experience less above-ground and belowground enemy impact. Nature. 456: 946–948.

Eschtruth, A.K.; Battles, J.J. 2009. Assessing the relative importance of 

disturbance, herbivory, diversity, and propagule pressure in exotic plant invasion. 

Ecological Monographs. 79: 265–280. 

Ettl, G.J.; Peterson, D.L. 1995a. Extreme climate and variation in tree growthindividualistic response in subalpine fi r (Abies lasiocarpa). Global Change 

Biology. 1: 231–241.
147

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Ettl, G.J.; Peterson, D.L. 1995b. Growth response of subalpine fi r (Abies 

lasiocarpa) to climate in the Olympic Mountains, Washington, USA. Global 

Change Biology. 1: 213–230.

Evans, R.D.; Fonda, R.W. 1990. The infl uence of snow on subalpine meadow 

community pattern, North Cascades, Washington. Canadian Journal of Botany. 

68: 212–220.

Everett, R.L.; Schellhaas, R.; Keenum, D.; Spurbeck, D.; Ohlson, P. 2000.

Fire history in the ponderosa pine/Douglas-fi r forests on the east slope of the 

Washington Cascades. Forest Ecology and Management. 129: 207–225.

Fajardo, A.; Goodburn, J.M.; Graham, J. 2006. Spatial patterns of regeneration 

in managed uneven-aged ponderosa pine/Douglas-fi r forests of Western 

Montana, USA. Forest Ecology and Management. 223: 255–266.

Fay, P.A.; Jin, V.L.; Way, D.A.; Potter, K.N.; Gill, R.A.; Jackson, R.B.; Polley, 

H.W. 2012. Soil-mediated effects of subambient to increased carbon dioxide on 

grassland productivity. Nature Climate Change. 2: 742–746. 

Felzer, B.S.; Cronin, T.W.; Melillo, J.M.; Kicklighter, D.W.; Schlosser, C.A.; 

Dangal, S.R.S. 2011. Nitrogen effects on carbon-water coupling in forests, 

grasslands, and shrublands in the arid western United States. Journal of 

Geophysical Research. 116: G03023. doi:10.1029/2010JG001621, 2011.

Fettig, C.J.; Klepzig, K.D.; Billings, R.F.; Munson, A.S.; Nebeker, T.E.; 

Negrón, J.F.; Nowak, J.T. 2007. The effectiveness of vegetation management 

practices for prevention and control of bark beetle infestations in coniferous 

forests of the western and southern United States. Forest Ecology and 

Management. 238: 24–53.

Fisher, B.S.; Nakićenović, N.; Alfsen, K.; Corfee Morlot, J.; de la Chesnaye, 

F.; Hourcade, J.-Ch.; Jiang, K.; Kainuma, M.; La Rovere, E.; Matysek, A.; 

Rana, A.; Riahi, K.; Richels, R.; Rose, S.; van Vuuren, D.; Warren, R. 2007. 

Issues related to mitigation in the long term context. In: Metz, B.; Davidson, 

O.R.; Bosch, P.R.; Dave, R.; Meyer, L.A., eds. Climate Change 2007: Mitigation. 

Contribution of working group III to the fourth assessment report of the 

Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and 

New York: Cambridge University Press: 170–250. Chapter 3.

Flannigan, M.D.; Krawchuk, M.A.; de Groot, W.J.; Wotton, B.M.; Growman, 

L.M. 2009. Implications of changing climate for global wildland fi re. 

International Journal of Wildland Fire. 18: 483–507.
148

GENERAL TECHNICAL REPORT PNW-GTR-900

Fonda, R.W.; Bliss, L.C. 1969. Forest vegetation of the montane and subalpine 

zones, Olympic Mountains, Washington. Ecological Monographs. 39: 271–301.

Forrest, J.; Inouye, D.W.; Thomson, J.D. 2010. Flowering phenology in subalpine 

meadows: Does climate variation infl uence community co-fl owering patterns? 

Ecology. 91: 431–440.

Forrest, J.R.K.; Thomson, J.D. 2011. An examination of synchrony between 

insect emergence and fl owering in Rocky Mountain meadows. Ecological 

Monographs. 81: 469–491.

Forster, P.; Ramaswamy, V.; Artaxo, P.; Berntsen, T.; Betts, R.; Fahey, 

D.W.; Haywood, J.; Lean, J.; Lowe, D.C.; Myhre, G.; Nganga, J.; Prinn, 

R.; Raga, G.; Schulz, M.; Van Dorland, R. 2007. Changes in atmospheric 

constituents and in radiative forcing. In: Solomon, S.; Qin, D.; Manning, M.; 

Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, H.L., eds. Climate 

change 2007: the physical science basis. Contribution of working group I to the 

fourth assessment report of the Intergovernmental Panel on Climate Change. 

Cambridge, United Kingdom and New York: Cambridge University Press: 

129–234. Chapter 2.

Franklin, J.F.; Dyrness, C.T. 1973. Natural vegetation of Oregon and Washington. 

Gen. Tech. Rep. PNW-GTR-008. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacifi c Northwest Forest and Range Experiment Station. 427 p.

Franklin, J.F.; Moir, W.H.; Douglas, G.W.; Wiberg, C. 1971. Invasion of 

subalpine meadows by trees in the Cascade Range, Washington and Oregon. 

Arctic and Alpine Research. 3: 215–224.

Franklin, J.F.; Spies, T.A.; Van Pelt, R.; Carey, A.B.; Thornburgh, D.A.; 

Berg, D.R.; Lindenmayer, D.B.; Harmon, M.E.; Keeton, W.S.; Shaw, D.C.; 

Bible, K.; Chen, J. 2002. Disturbances and structural development of natural 

forest ecosystems with silvicultural implications, using Douglas-fi r forests as 

an example. Forest Ecology and Management. 155: 399–423.

Franks, S.J.; Sim, S.; Weis, A.E. 2007. Rapid evolution of fl owering time by an 

annual plant in response to a climate fl uctuation. Proceedings of the National 

Academy of Sciences of the United States of America. 104: 1278–1282.

Franks, S.J.; Weis, A.E. 2008. A change in climate causes rapid evolution of 

multiple life-history traits and their interactions in an annual plant. Journal of 

Evolutionary Biology. 21: 1321–1334.
149

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Fraser, L.H.; Greenall, A.; Carlyle, C.; Turkington, R.; Friedman, C.R. 

2009. Adaptive phenotypic plasticity of Pseudoroegneria spicata: response of 

stomatal density, leaf area and biomass to changes in water supply and increased 

temperature. Annals of Botany. 103: 769–775.

Frey, B.R.; Lieffers, V.J.; Hogg, E.H.; Landhäusser, S.M. 2004. Predicting 

landscape patterns of aspen dieback: mechanisms and knowledge gaps. Canadian 

Journal of Forest Research. 34: 1379–1390.

Friend, A.D.; Stevens, A.K.; Knox, R.G.; Cannell, M.G.R. 1997. A processbased terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). 

Ecological Modelling. 95: 249–287.

Ganey, J.L.; Vojta, S.C. 2011. Tree mortality in drought-stressed mixed-conifer 

and ponderosa pine forests, Arizona, USA. Forest Ecology and Management. 

261: 162–168.

Garrett, K.A.; Dendy, S.P.; Frank, E.E.; Rouse, M.N.; Travers, S.E. 2006.

Climate change effects on plant disease: genomes to ecosystems. Annual Review 

of Phytopathology. 44: 489–509.

Gavin, D.G. 2009. The coastal-disjunct mesic fl ora in the inland Pacifi c Northwest 

of USA and Canada: refugia, dispersal and disequilibrium. Diversity and 

Distributions. 15: 972–982.

Gavin, D.G.; Hu, F.S. 2006. Spatial variation of climatic and non-climatic 

controls on species distribution: the range limit of Tsuga heterophylla. Journal 

of Biogeography. 33: 1384–1396.

Gedalof, Z.; Peterson, D.L.; Mantua, N.J. 2005. Atmospheric, climatic, and 

ecological controls on extreme wildfi re years in the northwestern United States. 

Ecological Applications. 15: 154–174.

Gedney, D.R.; Azuma, D.L.; Bolsinger, C.L.; McKay, N. 1999. Western 

juniper in eastern Oregon. Gen. Tech. Rep. PNW-GTR-464. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station. 

53 p.

Germino, M.J.; Smith, W.K. 1999. Sky exposure, crown architecture, and lowtemperature photoinhibition in conifer seedlings at alpine treeline. Plant Cell and 

Environment. 22: 407–415.

Gessel, S.P.; Miller, R.E.; Cole, D.W. 1990. Relative importance of water and 

nutrients on the growth of coast Douglas fi r in the Pacifi c Northwest. Forest 

Ecology and Management. 30: 327–340.
150

GENERAL TECHNICAL REPORT PNW-GTR-900

Gholz, H.L. 1982. Environmental limits on above-ground net primary production, 

leaf-area, and biomass in vegetation zones of the Pacifi c Northwest. Ecology. 

63: 469–481.

Gibson, J. 2011. Individualistic response of piñon and juniper tree species 

distributions to climate change in North America’s arid interior west. Logan, UT: 

Utah State University. 95 p. Ph.D. dissertation. 

Glick, P.; Stein, B.A.; Edelson, N.A., eds. 2011. Scanning the conservation 

horizon: a guide to climate change vulnerability assessment. Washington, DC: 

National Wildlife Federation. 168 p.

Grace, J.; Berninger, F.; Nagy, L. 2002. Impacts of climate change on the tree 

line. Annals of Botany. 90: 537–544.

Graumlich, L.J.; Brubaker, L.B. 1986. Reconstruction of annual temperature 

(1590-1979) for Longmire, Washington, derived from tree rings. Quaternary 

Research. 25: 223–234.

Graumlich, L.J.; Brubaker, L.B.; Grier, C.C. 1989. Long-term trends in 

forest net primary productivity: Cascade Mountains, Washington. Ecology. 

70: 405–410.

Gray, A.N.; Spies, T.A. 1997. Microsite controls on tree seedling establishment 

in conifer forest canopy gaps. Ecology. 78: 2458–2473.

Gray, L.J.; Beer, J.; Geller, M.; Haigh, J.D.; Lockwood, M.; Matthes, K.; 

Cubasch, U.; Fleitmann, D.; Harrison, G.; Hood, L.; Luterbacher, J.; Meehl, 

G.A.; Shindell, D.; van Geel, B.; White, W. 2010. Solar infl uences on climate. 

Reviews of Geophysics. 48, RG4001. doi:10.1029/2009RG000282.

Gray, S.T.; Betancourt, J.L.; Jackson, S.T.; Eddy, R.G. 2006. Role of 

multidecadal climate variability in a range extension of pinyon pine. Ecology. 

87: 1124–1130.

Green, D.G.; Sadedin, S. 2005. Interactions matter—complexity in landscapes 

and ecosystems. Ecological Complexity. 2: 117–130.

Grier, C.C.; Running, S.W. 1977. Leaf area of mature northwestern coniferous 

forests—relation to site water-balance. Ecology. 58: 893–899.

Griesbauer, H.P.; Green, D.S. 2010. Regional and ecological patterns in interior 

Douglas-fi r climate-growth relationships in British Columbia, Canada. Canadian 

Journal of Forest Research. 40: 308–321.
151

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Griffi th, A.B. 2010. Positive effects of native shrubs on Bromus tectorum

demography. Ecology. 91: 141–154.

Griggs, R.F. 1938. Timberlines in the northern Rocky Mountains. Ecology. 

19: 548–564.

Grime, J.P. 1979. Plant strategies and vegetation processes. Chichester, United 

Kingdom: Wiley. ISBN 0-471-99692-0. 

Grime, J.P.; Fridley, J.D.; Askew, A.P.; Thompson, K.; Hodgson, J.G.; Bennett, 

C.R. 2008. Long-term resistance to simulated climate change in an infertile 

grassland. Proceedings of the National Academy of Sciences of the United States 

of America. 105: 10028–10032.

Grubb, P.J. 1977. The maintenance of species-richness in plant communities: the 

importance of the regeneration niche. Biological Reviews. 52: 107–145.

Grulke, N.E. 2010. Plasticity in physiological traits in conifers: implications for 

response to climate change in the western U.S. Environmental Pollution. 158: 

2032–2042.

Guak, S.; Olsyzk, D.M.; Fuchigami, L.H.; Tingey, D.T. 1998. Effects of elevated 

CO2 and temperature on cold hardiness and spring bud burst and growth in 

Douglas-fi r (Pseudotsuga menziesii). Tree Physiology. 18: 671–679.

Gugger, P.F.; Sugita, S. 2010. Glacial populations and postglacial migration of 

Douglas-fi r based on fossil pollen and macrofossil evidence. Quaternary Science 

Reviews. 29: 2052–2070.

Guisan, A.; Thuiller, W. 2005. Predicting species distribution: offering more than 

simple habitat models. Ecology Letters. 8: 993–1009.

Guisan, A.; Zimmermann, N.E. 2000. Predictive habitat distribution models in 

ecology. Ecological Modelling. 135: 147–186.

Guisan, A.; Zimmermann, N.E.; Elith, J.; Graham, C.H.; Phillips, S.; 

Peterson, A.T. 2007. What matters for predicting the occurrences of trees: 

techniques, data, or species’ characteristics? Ecological Monographs. 

77: 615–630.

Gunderson, C.A.; O’Hara, K.H.; Campion, C.M.; Walker, A.V.; Edwards, 

N.T. 2010. Thermal plasticity of photosynthesis: the role of acclimation in forest 

responses to a warming climate. Global Change Biology. 16: 2272–2286.

Hadley, J.L.; Smith, W.K. 1987. Infl uence of krummholz mat microclimate on 

needle physiology and survival. Oecologia. 73: 82–90.
152

GENERAL TECHNICAL REPORT PNW-GTR-900

Hairston, N.G.; Ellner, S.P.; Geber, M.A.; Yoshida, T.; Fox, J.A. 2005. Rapid 

evolution and the convergence of ecological and evolutionary time. Ecology 

Letters. 8: 1114–1127.

Halofsky, J.E.; Peterson, D.L.; O’Halloran, K.A.; Hawkins Hoffman, C., eds. 

2011. Adapting to climate change at Olympic National Forest and Olympic 

National Park. Gen. Tech. Rep. PNW-GTR-844. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacifi c Northwest Research Station. 130 p.

Hamilton, J.; Zangerl, A.R.; Berenbaum, M.R.; Sparks, J.P.; Elich, L.; 

Eisenstein, A.; DeLucia, E.H. 2012. Elevated atmospheric CO2 alters the 

arthropod community in a forest understory. Acta Oecologica. 43: 80–85.

Hamlet, A.F.; Mote, P.W.; Clark, M.P.; Lettenmaier, D.P. 2005. Effects of 

temperature and precipitation variability on snowpack trends in the western 

United States. Journal of Climate. 18: 4545–4561.

Hamlet, A.F.; Mote, P.W.; Clark, M.P.; Lettenmaier, D.P. 2007. Twentiethcentury trends in runoff, evapotranspiration, and soil moisture in the western 

United States. Journal of Climate. 20: 1468–1486.

Hampe, A. 2011. Plants on the move: the role of seed dispersal and initial 

population establishment for climate-driven range expansions. Acta Oecologia. 

37: 666–673.

Hampe, A.; Jump, A.S. 2011. Climate relicts: past, present, future. Annual Review 

of Ecology, Evolution, and Systematics. 42: 313–333.

Hampe, A.; Petit, R.J. 2005. Conserving biodiversity under climate change: the 

rear edge matters. Ecology Letters. 8: 461–467.

Hamrick, J.L. 2004. Response of forest trees to global environmental changes. 

Forest Ecology and Management. 197: 323–335.

Hargrove, W.W.; Hoffman, F.M. 2005. The potential of multivariate quantitative 

methods for delineation and visualization of ecoregions. Environmental 

Management. 34: S39–S60.

Harmon, M.E.; Franklin, J.F. 1989. Tree seedlings on logs in Picea-Tsuga forests 

of Oregon and Washington. Ecology. 70: 48–59.

Harmon, M.E.; Franklin, J.F.; Swanson, F.J.; Sollins, P.; Gregory, S.V.; Lattin, 

J.D.; Anderson, N.H.; Cline, S.P.; Aumen, N.G.; Sedell, J.R.; Lienkaemper, 

G.W.; Cromack, K., Jr.; Cummins, K.W. 1986. Ecology of coarse woody 

debris in temperate ecosystems. Advances in Ecological Research. 15: 133–302.
153

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Harrington, C.A.; Gould, P.J.; St. Clair, J.B. 2010. Modeling the effects of 

winter environment on dormancy release of Douglas-fi r. Forest Ecology and 

Management. 259: 798–808.

Harrington, R.; Woiwod, I.; Sparks, T. 1999. Climate change and trophic 

interactions. Trends in Ecology and Evolution. 14: 146–150.

Harsch, M.A.; Bader, M.Y. 2011. Treeline form—a potential key to understanding 

treeline dynamics. Global Ecology and Biogeography. 20: 582–596.

Harsch, M.A.; Hulme, P.E.; McGlone, M.S.; Duncan, R.P. 2009. Are treelines 

advancing? A global meta-analysis of treeline response to climate warming. 

Ecology Letters. 12: 1040–1049.

Hays, J.D.; Imbrie, J.; Shackleton, N.J. 1976. Variations in Earth’s orbit: 

pacemaker of the Ice Ages. Science. 194: 1121–1132.

Hegland, S.J.; Nielsen, A.; Lazaro, A.; Bjerknes, A.L.; Totland, O. 2009. How 

does climate warming affect plant-pollinator interactions? Ecology Letters. 

12: 184–195.

Heikkinen, O. 1984. Forest expansion in the subalpine zone during the past 

hundred years, Mount Baker, Washington, USA. Erdkunde. 38: 194–202.

Heikinnen, O. 1985. Relationships between tree growth and climate in the 

subalpine Cascade Range of Washington, U.S.A. Annales Botanici Fennici. 

22: 1–14.

Hernandez, P.A.; Graham, C.H.; Master, L.L.; Albert, D.L. 2006. The effect 

of sample size and species characteristics on performance of different species 

distribution modeling methods. Ecography. 29: 773–785.

Hessburg, P.F.; Agee, J.K. 2003. An environmental narrative of Inland Northwest 

United States forests, 1800-2000. Forest Ecology and Management. 178: 23–59.

Hessl, A.E.; McKenzie, D.; Schellhaas, R. 2004. Drought and Pacifi c Decadal 

Oscillation linked to fi re occurrence in the inland Pacifi c Northwest. Ecological 

Applications. 14: 425–442.

Hetherington, A.M.; Woodward, F.I. 2003. The role of stomata in sensing and 

driving environmental change. Nature. 424: 901–908.

Heyerdahl, E.K.; McKenzie, D.; Daniels, L.D.; Hessl, A.E.; Littell, J.S.; 

Mantua, N.J. 2008. Climate drivers of regionally synchronous fi res in the inland 

Northwest (1651–1900). International Journal of Wildland Fire. 17: 40–49.
154

GENERAL TECHNICAL REPORT PNW-GTR-900

Hicke, J.A.; Johnson, M.C.; Hayes, J.L.; Preisler, H.K. 2012. Effects of bark 

beetle-caused tree mortality on wildfi re. Forest Ecology and Management. 

271: 81–90.

Hickler, T.; Smith, B.; Sykes, M.T.; Davis, M.B.; Sugita, S.; Walker, K. 2004. 

Using a generalized vegetation model to simulate vegetation dynamics in 

northeastern USA. Ecology. 85: 519–530.

Hikosaka, K.; Kinugasa, T.; Oikawa, S.; Onoda, Y.; Hirose, T. 2011. Effects of 

elevated CO2 concentration on seed production in C3 annual plants. Journal of 

Experimental Biology. 62: 1523–1530.

HilleRisLambers, J.; Harpole, W.S.; Schnitzer, S.; Tilman, D.; Reich, P.B. 

2009. CO2, nitrogen, and diversity differentially affect seed production of prairie 

plants. Ecology. 90: 1810–1820.

Hobbie, S.E. 1996. Temperature and plant species control over litter decomposition 

in Alaskan tundra. Ecological Monographs. 66: 503–522.

Hobbs, R.J.; Higgs, E.; Harris, J.A. 2009. Novel ecosystems: implications for 

conservation and restoration. Trends in Ecology and Evolution. 24: 599–605.

Hofmann, D.J.; Butler, J.H.; Tans, P.P. 2009. A new look at atmospheric carbon 

dioxide. Atmospheric Environment. 43: 2084–2086.

Hogg, E.H.; Brandt, J.P.; Michaelian, M. 2008. Impacts of a regional drought 

on the productivity, dieback, and biomass of western Canadian aspen forests. 

Canadian Journal of Forest Research. 38: 1373–1384.

Holmgren, M.; Scheffer, M.; Huston, M.A. 1997. The interplay of facilitation and 

competition in plant communities. Ecology. 1966–1975.

Holtmeier, F.K.; Broll, G. 2005. Sensitivity and response of Northern Hemisphere 

altitudinal and polar treelines to environmental change at landscape and local 

scales. Global Ecology and Biogeography. 14: 395–410.

Holtum, J.A.M.; Winter, K. 2010. Elevated [CO2] and forest vegetation: More a 

water issue than a carbon issue? Functional Plant Biology. 37: 694–702.

Housman, D.C.; Naumburg, E.; Huxman, T.E.; Charlet, T.N.; Nowak, R.S.; 

Smith, S.D. 2006. Increases in desert shrub productivity under elevated carbon 

dioxide vary with water availability. Ecosystems. 9: 374–385.

Hu, F.S.; Hampe, A.; Petit, R.J. 2009. Paleoecology meets genetics: deciphering 

past vegetational dynamics. Frontiers in Ecology and the Environment. 

7: 371–379.
155

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Huang, J.G.; Bergeron, Y.; Denneler, B.; Berninger, F.; Tardif, J. 2007.

Response of forest trees to increased atmospheric CO2. Critical Reviews 

in Plant Sciences. 26: 265–283.

Hyvönen, R.; Ágren, G.I.; Linder, S.; Persson, T.; Cotrufo, M.F.; Ekblad, 

A.; Freeman, M.; Grelle, A.; Janssens, I.A.; Jarvis, P.G.; Kellomaki, S.; 

Lindroth, A.; Loustau, D.; Lundmark, T.; Norby, R.J.; Oren, R.; Pilegaard, 

K.; Ryan, M.G.; Sigurdsson, B.D.; Strömgren, M.; van Oijen, M.; Wallin, 

G. 2007. The likely impact of elevated [CO2], nitrogen deposition, increased 

temperature and management on carbon sequestration in temperate and boreal 

forest ecosystems: a literature review. New Phytologist. 173: 463–480.

Ibanez, I.; Clark, J.S.; Dietze, M.C.; Feeley, K.; Hersh, M.; LaDeau, S.; 

McBride, A.; Welch, N.E.; Wolosin, M.S. 2006. Predicting biodiversity 

change: outside the climate envelope, beyond the species-area curve. Ecology. 

87: 1896–1906.

Inouye, D.W. 2008. Effects of climate change on phenology, frost damage, and 

fl oral abundance of montane wildfl owers. Ecology. 89: 353–362.

Inouye, D.W.; Barr, B.; Armitage, K.B.; Inouye, B.D. 2000. Climate change 

is affecting altitudinal migrants and hibernating species. Proceedings of the 

National Academy of Sciences of the United States of America. 97: 1630–1633.

Intergovernmental Panel on Climate Change [IPCC]. 2007a. Climate change 

2007: impacts, adaptation and vulnerability. Contribution of working group II the 

fourth assessment report of the Intergovernmental Panel on Climate Change. In: 

Parry, M.L.; Canziana, O.F.; Palutikof, J.P.; van der Linden, P.J.; Hanson, C.E., 

eds. Cambridge, United Kingdom: Cambridge University Press.

Intergovernmental Panel on Climate Change [IPCC]. 2007b. Climate change 

2007: synthesis report. Contribution of working groups I, II and III to the fourth 

assessment report of the Intergovernmental Panel on Climate Change. In: 

Pachauri, R.K.; Reisinger, A., eds. Geneva, Switzerland. 104 p. http://www.ipcc.

ch/publications_and_data/publications_and_data_reports.shtml. (April 2012).

Intergovernmental Panel on Climate Change [IPCC]. 2007c. Climate change 

2007: the physical science basis. Contribution of working group I the fourth 

assessment report of the Intergovernmental Panel on Climate Change. In: 

Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, 

M.; Miller, H.L., eds. Cambridge, United Kingdom: Cambridge University Press.
156

GENERAL TECHNICAL REPORT PNW-GTR-900

Jablonski, L.M.; Wang, X.; Curtis, P.S. 2002. Plant reproduction under elevated 

CO2 conditions: a meta-analysis of reports on 79 crop and wild species. New 

Phytologist. 156: 9–26.

Jackson, S.T. 2012. Natural, potential and actual vegetation in North America. 

Journal of Vegetation Science. doi: 10.1111/jvs.12004.

Jackson, S.T.; Betancourt, J.L.; Booth, R.K.; Gray, S.T. 2009. Ecology and the 

ratchet of events: climate variability, niche dimensions, and species distributions. 

Proceedings of the National Academy of Sciences of the United States of 

America. 106: 19685–19692.

Jackson, S.T.; Hobbs, R.J. 2009. Ecological restoration in the light of ecological 

history. Science. 325: 567–569.

Jackson, S.T.; Overpeck, J.T. 2000. Responses of plant populations and 

communities to environmental changes of the late Quaternary. Paleobiology. 

26: 194–220.

Jackson, S.T.; Sax, D.F. 2010. Balancing biodiversity in a changing environment: 

extinction debt, immigration credit and species turnover. Trends in Ecology and 

Evolution. 25: 153–160.

Jankju, M. 2008. Individual performances and the interaction between arid land 

plants affected by the growth season water pulses. Arid Land Research and 

Management. 22: 123–133.

Jansen, E.; Overpeck, J.; Briffa, K.R.; Duplessy, J.-C.; Joos, F.; MassonDelmotte, V.; Olago, D.; Otto-Bliesner, B.; Peltier, W.R.; Rahmstorf, S.; 

Ramesh, R.; Raynaud, D.; Rind, D.; Solomina, O.; Villalba, R.; Zhang, D. 

2007. Paleoclimate. In: Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, 

M.; Averyt, K.B.; Tignor, M.; Miller, H.L., eds. Climate change 2007: the 

physical science basis. Contribution of working group I to the fourth assessment 

report of the Intergovernmental Panel on Climate Change. Cambridge, United 

Kingdom and New York: Cambridge University Press: 433–497. 

Jeschke, J.M.; Strayer, D.L. 2008. Usefulness of bioclimatic models for studying 

climate change and invasive species. Annals of the New York Academy of 

Sciences. 1134: 1–24.

Jessop, B.D.; Anderson, V.J. 2007. Cheatgrass invasion in salt desert shrublands: 

benefi ts of postfi re reclamation. Rangeland Ecology and Management.

 60: 235–243.
157

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Johnson, D.M.; Germino, M.J.; Smith, W.K. 2004. Abiotic factors limiting 

photosynthesis in Abies lasiocarpa and Picea engelmannii seedlings below and 

above alpine timberline. Tree Physiology. 24: 377–386.

Johnson, K.N.; Bettinger, P.; Kline, J.D.; Spies, T.A.; Lennette, M.; Lettman, 

G.; Garber-Yonts, B.; Larsen, T. 2007. Simulating forest structure, timber 

production, and socioeconomic effects in a multi-owner province. Ecological 

Applications. 17: 34–47.

Joyce, L.A.; Blate, G.M.; McNulty, S.G.; Millar, C.I.; Moser, S.; Neilson, R.P.; 

Peterson, D.L. 2009. Managing for multiple resources under climate change: 

National Forests. Environmental Management. 44: 1022–1032.

Jump, A.S.; Matyas, C.; Peñuelas, J. 2009. The altitude-for-latitude disparity in 

the range retractions of woody species. Trends in Ecology and Evolution. 

24: 694–701.

Jump, A.S.; Peñuelas, J. 2005. Running to stand still: adaptation and the response 

of plants to rapid climate change. Ecology Letters. 8: 1010–1020.

Jumpponen, A.; Brown, S.P.; Trappe, J.M.; Cázares, E.; Strömmer, R. 2012.

Twenty years of research on fungal-plant interactions on Lyman Glacier forefront 

—lessons learned and questions yet unanswered. Fungal Ecology. 5: 430–442.

Kashian, D.M.; Romme, W.H.; Tinker, D.B.; Turner, M.G.; Ryan, M.G. 2006.

Carbon storage on landscapes with stand-replacing fi res. Bioscience. 

56: 598–606.

Keane, R.E.; Arno, S.F.; Brown, J.K. 1990. Simulating cumulative fi re effects 

in ponderosa pine/Douglas-fi r forests. Ecology. 71: 189–203.

Keane, R.E.; Cary, G.J.; Davies, I.D.; Flannigan, M.D.; Gardner, R.H.; 

Lavorel, S.; Lenihan, J.M.; Li, C.; Rupp, T.S. 2004. A classifi cation of 

landscape fi re succession models: spatial simulations of fi re and vegetation 

dynamics. Ecological Modelling. 179: 3–27.

Keane, R.E.; Holsinger, L.M.; Parsons, R.A.; Gray, K. 2008. Climate change 

effects on historical range and variability of two large landscapes in western 

Montana, USA. Forest Ecology and Management. 254: 375–389.

Keane, R.E.; Morgan, P.; Running, S.W. 1996. FIRE-BGC—A mechanistic 

ecological process model for simulating fi re succession on coniferous forest 

landscapes of the northern Rocky Mountains. Res. Pap. INT-RP-484. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station. 122 p.
158

GENERAL TECHNICAL REPORT PNW-GTR-900

Kearney, M.; Porter, W. 2009. Mechanistic niche modelling: combining 

physiological and spatial data to predict species ranges. Ecology Letters. 

12: 334–350.

Keeley, J.E.; McGinnis, T.W. 2007. Impact of prescribed fi re and other factors on 

cheatgrass persistence in a Sierra Nevada ponderosa pine forest. International 

Journal of Wildland Fire. 16: 96–106.

Keeling, C.D.; Bacastow, R.B.; Bainbridge, A.E.; Ekdahl, C.A.; Guenther, 

P.R.; Waterman, L.S.; Chin, J.F.S. 1976. Atmospheric carbon dioxide 

variations at Mauna Loa Observatory, Hawaii. Tellus. 28: 538–551.

Keller, E.A.; Anekonda, T.S.; Smith, B.N.; Hansen, L.D.; St. Clair, 

J.B.; Criddle, R.S. 2004. Stress and respiration traits differ among four 

geographically distinct Pinus ponderosa seed sources. Thermochimica Acta. 

422: 69–74.

Kelly, A.E.; Goulden, M.L. 2008. Rapid shifts in plant distribution with recent 

climate change. Proceedings of the National Academy of Sciences of the United 

States of America. 105: 11823–11826.

Kercher, J.R.; Axelrod, M.C. 1984a. Analysis of SILVA: a model for forecasting 

the effects of SO2 pollution and fi re on western coniferous forests. Ecological 

Modelling. 23: 165–184.

Kercher, J.R.; Axelrod, M.C. 1984b. A process model of fi re ecology and 

succession in a mixed-conifer forest. Ecology. 65: 1725–1742.

Kerns, B.K.; Hemstrom, M.A.; Conklin, D.; Yospin, G.; Johnson, B.; Bachelet, 

D.; Bridgham, S. 2012. Approaches to incorporating climate change effects in 

state and transition simulation models of vegetation. In: Kerns, B.K.; Shlisky, 

A.J.; Daniel, C.J., tech. eds. Proceedings of the First Landscape State-andTransition Simulation Modeling conference. Gen. Tech. Rep. PNW-GTR-869. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest 

Research Station: 161–171.

Kerns, B.K.; Naylor, B.J.; Buonopane, M.; Parks, C.G.; Rogers, B. 2009.

Modeling tamarisk (Tamarisk spp.) habitat and climate change effects in the 

northwestern United States. Invasive Plant Science and Management. 2: 200–215. 

Kerns, B.K.; Ohmann, J.L. 2004. Evaluation and prediction of shrub cover in 

coastal Oregon forests (USA). Ecological Indicators. 4: 83–98.
159

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Kerns, B.K.; Thies, W.G.; Niwa, C.G. 2006. Season and severity of prescribed 

burn in ponderosa pine forests: implications for understory native and exotic 

plants. Ecoscience. 13: 44–55. 

Kiehl, J.T.; Trenberth, K.E. 1997. Earth’s annual global mean energy budget. 

Bulletin of the American Meteorological Society. 78: 197–208.

Kilham, K. 1994. Soil ecology. Cambridge, United Kingdom: Cambridge 

University Press. 242 p.

Klanderud, K. 2010. Species recruitment in alpine plant communities: the role of 

species interactions and productivity. Journal of Ecology. 98: 1128–1133.

Klemmedson, J.O.; Tiedemann, A.R. 2000. Infl uence of western juniper 

development on distribution of soil and organic layer nutrients. Northwest 

Science. 74: 1–11.

Klenner, W.; Arsenault, A. 2009. Ponderosa pine mortality during a severe bark 

beetle (Coleoptera: Curculionidae, Scolytinae) outbreak in southern British 

Columbia and implications for wildlife habitat management. Forest Ecology and 

Management. 258S: S5–S14.

Kliejunas, J.T.; Geils, B.W.; Glaeser, J.M.; Goheen, E.M.; Hennon, P.; Kim, 

M.-S.; Kope, H.; Stone, J.; Sturrock, R.; Frankel, S.J. 2009. Review of 

literature on climate change and forest diseases of western North America. Gen. 

Tech. Rep. PSW-GTR-225. Albany, CA: U.S. Department of Agriculture, Forest 

Service, Pacifi c Southwest Research Station. 54 p.

Knapp, P.A.; Soulé, P.T. 2005. Impacts of an extreme early-season freeze event in 

the interior Pacifi c Northwest (30 October–3 November 2002) on western juniper 

woodlands. Journal of Applied Meteorology. 44: 1152–1158.

Knapp, P.A.; Soulé, P.T.; Grissino-Mayer, H.D. 2001. Detecting potential 

regional effects of increased atmospheric CO2 on growth rates of western 

juniper. Global Change Biology. 7: 903–917.

Knapp, P.A.; Soulé, P.T.; Grissino-Mayer, H.D. 2004. Occurrence of sustained 

droughts in the interior Pacifi c Northwest (AD 1733–1980) inferred from treering data. Journal of Climate. 17: 140–150.

Knowles, N.; Dettinger, M.D.; Cayan, D.R. 2006. Trends in snowfall versus 

rainfall in the western United States. Journal of Climate. 19: 4545–4559.
160

GENERAL TECHNICAL REPORT PNW-GTR-900

Knutson, K.C. 2006. Climate-growth relationships of western juniper and 

ponderosa pine at the pine-woodland ecotone in southern Oregon. Corvallis, 

OR: Oregon State University. 55 p. M.S. thesis.

Knutson, K.C.; Pyke, D.A. 2008. Western juniper and ponderosa pine ecotonal 

climate-growth relationships across landscape gradients in southern Oregon. 

Canadian Journal of Forest Research. 38: 3021–3032.

Knutti, R. 2010. The end of model democracy? An editorial comment. Climatic 

Change. 102: 395–404.

Knutti, R.; Furrer, R.; Tebaldi, C.; Cermak, J.; Meehl, G.A. 2010. Challenges 

in combining projections from multiple climate models. Journal of Climate. 

23: 2739–2758.

Koepke, D.F.; Kolb, T.E.; Adams, H.D. 2010. Variation in woody plant mortality 

and dieback from severe drought among soils, plant groups, and species within 

a northern Arizona ecotone. Oecologia. 163: 1079–1090.

Körner, C.; Basler, D. 2010. Phenology under global warming. Science. 

327: 1461–1462.

Kowalchuk, G.A. 2012. Bad news for soil carbon sequestration? Science. 

337: 1049–1050.

Kozlowski, T.T.; Pallardy, S.G. 2002. Acclimation and adaptive responses of 

woody plants to environmental stresses. Botanical Review. 68: 270–334.

Kramer, K.; Leinonen, I.; Loustau, D. 2000. The importance of phenology 

for the evaluation of impact of climate change on growth of boreal, temperate 

and Mediterranean forests ecosystems: an overview. International Journal of 

Biometeorology. 44: 67–75.

Krawchuk, M.A.; Moritz, M.A.; Parisien, M.A.; Van Dorn, J.; Hayhoe, K. 

2009. Global pyrogeography: the current and future distribution of wildfi re. 

PLoS ONE 4(4): e5102. doi:10.1371/journal.pone.0005102.

Kremer, A.; Ronce, O.; Robledo-Arnuncio, J.J.; Guillaume, F.; Bohrer, G.; 

Nathan, R.; Bridle, J.R.; Gomulkiewicz, R.; Klein, E.K.; Ritland, K.; 

Kuparinen, A.; Gerber, S.; Schueler, S. 2012. Long-distance gene fl ow and 

adaptation of forest trees to rapid climate change. Ecology Letters. 15: 378–392.

Kreyling, J.; Wenigmann, M.; Beierkuhnlein, C.; Jentsch, A. 2008. Effects of 

extreme weather events on plant productivity and tissue die-back are modifi ed 

by community composition. Ecosystems. 11: 752–763.
161

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Kuparinen, A.; Katul, G.; Nathan, R.; Schurr, F.M. 2009. Increases in 

air temperature can promote wind-driven dispersal and spread of plants. 

Proceedings of the Royal Society—Biological Sciences. 276: 3081–3087.

Kuramoto, R.T.; Bliss, L.C. 1970. Ecology of subalpine meadows in the Olympic 

Mountains, Washington. Ecological Monographs. 40: 317–347.

Kurz, W.A.; Beukema, S.J.; Klenner, W.; Greenough. J.A.; Robinson D.C.E.; 

Sharpe, A.D.; Webb, T.M. 2000. TELSA: the Tool for Exploratory Landscape 

Scenario Analyses. Computers and Electronics in Agriculture. 27: 227–242.

Kurz, W.A.; Dymond, C.C.; Stinson, G.; Rampley, G.J.; Neilson, E.T.; Carroll, 

A.L.; Ebata, T.; Safranyik, L. 2008. Mountain pine beetle and forest carbon 

feedback to climate change. Nature. 452: 987–990.

Kusnierczyk, E.R.; Ettl, G.J. 2002. Growth response of ponderosa pine (Pinus 

ponderosa) to climate in the eastern Cascade Mountains, Washington, USA: 

implications for climatic change. Ecoscience. 9: 544–551.

LaDeau, S.L.; Clark, J.S. 2001. Rising CO2 levels and the fecundity of forest 

trees. Science. 292: 95–98.

LaDeau, S.L.; Clark, J.S. 2006. Elevated CO2 and tree fecundity: the role of 

tree size, interannual variability, and population heterogeneity. Global Change 

Biology. 12: 822–833.

Lambers, H.; Chapin, F.S.; Pons, T.L. 1998. Plant physiological ecology. New 

York: Springer-Verlag. 540 p.

Lambrecht, S.C.; Loik, M.E.; Inouye, D.W.; Harte, J. 2007. Reproductive and 

physiological responses to simulated climate warming for four subalpine species. 

New Phytologist. 173: 121–134.

Landres, P.B.; Morgan, P.; Swanson, F.J. 1999. Overview of the use of natural 

variability concepts in managing ecological systems. Ecological Applications. 

9: 1179–1188.

Lapenis, A.; Shvidenko, A.; Shepaschenko, D.; Nilsson, S.; Aiyyer, A. 2005.

Acclimation of Russian forests to recent changes in climate. Global Change 

Biology. 11: 2090–2102.

Larson, A.J.; Franklin, J.F. 2005. Patterns of conifer tree regeneration following 

an autumn wildfi re event in the western Oregon Cascade Range, USA. Forest 

Ecology and Management. 218: 25–36.
162

GENERAL TECHNICAL REPORT PNW-GTR-900

Larson, D.L.; Anderson, P.J.; Newton, W. 2001. Alien plant invasion in 

mixed-grass prairie: effects of vegetation type and anthropogenic disturbance. 

Ecological Applications. 11: 128–141.

Latta, G.; Temesgen, H.; Adams, D.; Barrett, T. 2010. Analysis of potential 

impacts of climate change on forests of the United States Pacifi c Northwest. 

Forest Ecology and Management. 259: 720–729.

League, K.; Veblen, T. 2006. Climatic variability and episodic Pinus ponderosa

establishment along the forest-grassland ecotones of Colorado. Forest Ecology 

and Management. 228: 98–107.

Leakey, A.D.; Ainsworth, E.A.; Bernacchi, C.J.; Rogers, A.; Long, S.P.; 

Ort, D.R. 2009. Elevated CO2 effects on plant carbon, nitrogen, and water 

relations: six important lessons from FACE. Journal of Experimental Botany. 

60: 2859–2876.

Lee, T.D.; Tjoelker, M.G.; Reich, P.B.; Russelle, M.P. 2003. Contrasting growth 

response of an N2-fi xing and non-fi xing forb to elevated CO2: dependence on 

soil N supply. Plant and Soil. 255: 475–486.

Lenihan, J.M.; Daly, C.; Bachelet, D.; Neilson, R.P. 1998. Simulating broadscale fi re severity in a dynamic global vegetation model. Northwest Science. 

72 (Special Issue): 91–103.

Le Treut, H.; Somerville, R.; Cubasch, U.; Ding, Y.; Mauritzen, C.; Mokssit, 

A.; Peterson, T.; Prather, M. 2007. Historical overview of climate change 

science. In: Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, 

K.B.; Tignor, M.; Miller, H.L., eds. Climate change 2007: the physical science 

basis. Contribution of working group I to the fourth assessment report of the 

Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and 

New York: Cambridge University Press: 93–127. Chapter 1.

Leuzinger, S.; Bigler, C.; Wolf, A.; Körner, C. 2009. Poor methodology for 

predicting large-scale tree die-off. Proceedings of the National Academy 

of Sciences of the United States of America. 106(38): E106. doi:10.1073/

pnas.0908053106.

Lewis, J.D.; Lucash, M.; Olszyk, D.M.; Tingey, D.T. 2004. Relationships 

between needle nitrogen concentration and photosynthetic responses of Douglasfi r seedlings to elevated CO2 and temperature. New Phytologist. 162: 355–364.
163

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Lincoln, D.E.; Fajer, E.D.; Johnson, R.H. 1993. Plant-insect herbivore 

interactions in elevated CO2 environments. Trends in Ecology and Evolution. 

8: 64–68.

Lindroth, R.L. 2010. Impacts of elevated atmospheric CO2 and O3 on forests: 

phytochemistry, trophic interactions, and ecosystem dynamics. Journal of 

Chemical Ecology. 36: 2–21.

Link, S.O.; Keeler, C.W.; Hill, R.W.; Hagen, E. 2006. Bromus tectorum cover 

mapping and fi re risk. International Journal of Wildland Fire. 15: 113–119.

Littell, J.S.; McKenzie, D.; Kerns, B.K.; Cushman, S.; Shaw, C.G. 2011.

Managing uncertainty in climate-driven ecological models to inform adaptation 

to climate change. Ecosphere. 2(9): 102. doi:10.1890/ES11-00114.1.

Littell, J.S.; McKenzie, D.; Peterson, D.L.; Westerling, A.L. 2009. Climate 

and wildfi re area burned in western U. S. ecoprovinces, 1916-2003. Ecological 

Applications. 19: 1003–1021.

Littell, J.S.; Oneil, E.E.; McKenzie, D.; Hicke, J.A.; Lutz, J.A.; Norheim, R.A.; 

Elsner, M.M. 2010. Forest ecosystems, disturbance, and climatic change in 

Washington State, USA. Climatic Change. 102: 129–158.

Littell, J.S.; Peterson, D.L.; Millar, C.I.; O’Halloran, K.A. 2012. U.S. National 

Forests adapt to climate change through science-management partnerships. 

Climatic Change. 110: 269–296.

Littell, J.S.; Peterson, D.L.; Tjoelker, M. 2008. Douglas-fi r growth in mountain 

ecosystems: water limits tree growth from stand to region. Ecological 

Monographs. 78: 349–368.

Little, R.L.; Peterson, D.L.; Conquest, L.L. 1994. Regeneration of subalpine fi r 

(Abies lasiocarpa) following fi re: effects of climate and other factors. Canadian 

Journal of Forest Research. 24: 934–944.

Little, R.L.; Peterson, D.L.; Silsbee, D.G.; Shainsky, L.J.; Bednar, L.F. 1995. 

Radial growth patterns and the effects of climate on second-growth Douglas-fi r 

(Pseudotsuga menziesii) in the Siskiyou Mountains, Oregon. Canadian Journal 

of Forest Research. 25: 724–735.

Liu, Y.; Reich, P.B.; Li, G.; Sun, S. 2011. Shifting phenology and abundance 

under experimental warming alters trophic relationships and plant reproductive 

capacity. Ecology. 92: 1201–1207.
164

GENERAL TECHNICAL REPORT PNW-GTR-900

Lloret, F.; Escudero, A.; Iriondo, J.M.; Martínez-Vilalta, J.; Valladares, F. 

2012. Extreme climatic events and vegetation: the role of stabilizing processes. 

Global Change Biology. 18: 797–805.

Loarie, S.R.; Duffy, P.B.; Hamilton, H.; Asner, G.P.; Field, C.B.; Ackerly, D.D. 

2009. The velocity of climate change. Nature. 462: 1052–1055.

Lodge, D.M.; Williams, S.; MacIsaac, H.J.; Hayes, K.R.; Leung, B.; Reichard, 

S.; Mack, R.N.; Moyle, P.B.; Smith, M.; Andow, D.A.; Carlton, J.T.; 

McMichael, A. 2006. Biological invasions: recommendations for U.S. policy 

and management. Ecological Applications. 16: 2035–2054.

Logan, J.A.; Regniere, J.; Powell, J.A. 2003. Assessing the impacts of global 

warming on forest pest dynamics. Frontiers in Ecology and the Environment. 

1: 130–137.

Long, S.P. 1991. Modifi cation of the response of photosynthetic productivity to 

rising temperature by atmospheric CO2 concentrations. Has its importance been 

underestimated? Plant Cell Environment. 14: 729–739.

Long, S.P.; Ainsworth, E.A.; Rogers, A.; Ort, D.R. 2004. Rising atmospheric 

carbon dioxide: plants FACE the future. Annual Review of Plant Biology. 

55: 591–628.

Long, S.P.; Humphries, S.; Falkowski, P.G. 1994. Photoinhibition of 

photosynthesis in nature. Annual Review of Plant Physiology and Plant 

Molecular Biology. 45: 633–662.

Lonsdale, W.M. 1999. Global patterns of plant invasions and the concept of 

invasibility. Ecology. 80: 1522–1536.

Lopushinsky, W.; Max, T.A. 1990. Effect of soil temperature on root and shoot 

growth and on budburst timing in conifer seedling transplants. New Forests. 

4: 107–124.

Lucash, M.S.; Farnsworth, B.; Winner, W.E. 2005. Response of sagebrush 

steppe species to elevated CO2 and soil temperature. Western North American 

Naturalist. 65: 80–86.

Luo, Y.; Su, B.; Currie, W.S.; Dukes, J.S.; Finzi, A.C.; Hartwig, U.; Hungate, 

B.; McMurtrie, R.E.; Oren, R.; Parton, W.J.; Pataki, D.E.; Shaw, M.R.; 

Zak, D.R.; Field, C.B. 2004. Progressive nitrogen limitation of ecosystem 

responses to rising atmospheric carbon dioxide. Bioscience. 54: 731–739.
165

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Luoto, M.; Pöyry, J.; Heikkinen, R.K.; Saarinen, K. 2005. Uncertainty of 

bioclimatic envelope models based on the geographical distribution of species. 

Global Ecology and Biogeography. 14: 575–584.

Lynn, B.H.; Healy, R.; Druyan, L.M. 2009. Quantifying the sensitivity of 

simulated climate change to model confi guration. Climatic Change. 92: 275–298.

MacArthur, R.H. 1972. Geographical ecology: patterns in the distribution of 

species. Princeton, NJ: Princeton University Press. 269 p. 

Mack, R.N. 1981. Invasion of Bromus tectorum L. into western North America: an 

ecological chronicle. Agro-Ecosystems. 7: 145–165.

Mack, R.N.; Rutter, N.W.; Valastro, S.; Bryant, V.M. 1978. Late Quaternary 

vegetation history at Waits Lake, Colville River Valley, Washington. Botanical 

Gazette. 139: 499–506.

Maestre, F.T.; Callaway, R.M.; Valladares, F.; Lortie, C.J. 2009. Refi ning the 

stress-gradient hypothesis for competition and facilitation in plant communities. 

Journal of Ecology. 97: 199–205.

Maherali, H.; DeLucia, E.H. 2000. Interactive effects of elevated CO2 and 

temperature on water transport in ponderosa pine. American Journal of Botany. 

87: 243–249.

Maherali, H.; DeLucia, E.H. 2001. Infl uence of climate-driven shifts in biomass 

allocation on water transport and storage in ponderosa pine. Oecologia. 

129: 481–491.

Maherali, H.; Williams, B.L.; Paige, K.N.; Delucia, E.H. 2002. Hydraulic 

differentiation of ponderosa pine populations along a climate gradient is not 

associated with ecotypic divergence. Functional Ecology. 16: 510–521.

Malmsheimer, R.W.; Heffernan, P.; Brink, S.; Crandall, D.; Deneke, F.; Galik, 

C.; Gee, E.; Helms, J.A.; McClure, N.; Mortimer, M.; Ruddell, S.; Smith, 

M.; Stewart, J. 2008. Forest management solutions for mitigating climate 

change in the United States. Journal of Forestry. 106: 115–117.

Markesteijn, L.; Poorter, L. 2009. Seedling root morphology and biomass 

allocation of 62 tropical tree species in relation to drought- and shade-tolerance. 

Journal of Ecology. 97: 311–325.

Martin, M.; Gavazov, K.; Körner, C.; Hättenschwiler, S.; Rixen, C. 2010. 

Reduced early growing season freezing resistance in alpine treeline plants under 

elevated atmospheric CO2. Global Change Biology. 16: 1057–1070.
166

GENERAL TECHNICAL REPORT PNW-GTR-900

Maseyk, K.; Grünzweig, J.M.; Rotenberg, E.; Yakir, D. 2008. Respiration 

acclimation contributes to high carbon-use effi ciency in a seasonally dry pine 

forest. Global Change Biology. 14: 1553–1567.

Maslin, M.; Austin, P. 2012. Climate models at their limit? Nature. 486: 183–184.

Matesanz, S.; Gianoli, E.; Valladares, F. 2010. Global change and the evolution 

of phenotypic plasticity in plants. Annals of the New York Academy of Sciences. 

1206: 35–55.

McCarthy, H.R.; Oren, R.; Johnsen, K.H.; Gallet-Budynek, A.; Pritchard, 

S.G.; Cook, C.W.; Ladeau, S.L.; Jackson, R.B.; Finzi, A.C. 2010. Reassessment of plant carbon dynamics at the Duke free-air CO2 enrichment site: 

interactions of atmospheric [CO2] with nitrogen and water availability over stand 

development. New Phytologist. 185: 514–528.

McCormack, M.L.; Pritchard, S.G.; Breland, S.; Davis, M.A.; Prior, S.A.; 

Runion, G.B.; Mitchell, R.J.; Rogers, H.H. 2010. Soil fungi respond more 

strongly than fi ne roots to elevated CO2 in a model regenerating longleaf pinewiregrass ecosystem. Ecosystems. 13: 901–916.

McDowell, N.G.; Beerling, D.J.; Breshears, D.D.; Fisher, R.A.; Raffa, K.F.; 

Stitt, M. 2011. The interdependence of mechanisms underlying climate-driven 

vegetation mortality. Trends in Ecology and Evolution. 26: 523–532.

McDowell, N.; Pockman, W.T.; Allen, C.D.; Breshears, D.D.; Cobb, N.; 

Kolb, T.; Plaut, J.; Sperry, J.; West, A.; Williams, D.G.; Yepez, E.A. 2008.

Mechanisms of plant survival and mortality during drought: Why do some plants 

survive while others succumb to drought? New Phytologist. 178: 719–739.

McFarlane, K.J.; Schoenholtz, S.H.; Powers, R.F.; Perakis, S.S. 2010. Soil 

organic matter stability in intensively managed ponderosa pine stands in 

California. Soil Science Society of America Journal. 74: 979–992.

McKenney, D.W.; Pedlar, J.H.; Lawrence, K.; Campbell, K.; Hutchinson, M.F. 

2007. Potential impacts of climate change on the distribution of North American 

trees. Bioscience. 57: 939–948.

McKenney, D.W.; Pedlar, J.H.; Rood, R.B.; Price, D. 2011. Revisiting projected 

shifts in the climate envelopes of North American trees using updated general 

circulation models. Global Change Biology. 17: 2720–2730.

McKenzie, D.; Gedalof, Z.; Peterson, D.L.; Mote, P. 2004. Climatic change, 

wildfi re, and conservation. Conservation Biology. 18: 890–902.
167

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

McKenzie, D.; Peterson, D.L.; Littell, J.J. 2009. Chapter 15. Global warming 

and stress complexes in forests of western North America. In: Bytnerowicz, A.; 

Arbaugh, M.; Riebau, A.; Andersen, C., eds. Developments in Environmental 

Science. 8: 319–337.

McKinley, D.C.; Ryan, M.G.; Birdsey, R.A.; Giardina, C.P.; Harmon, M.E.; 

Heath, L.S.; Houghton, R.A.; Jackson, R.B.; Morrison, J.F.; Murray, B.C.; 

Pataki, D.E.; Skog, K.E. 2011. A synthesis of current knowledge on forests and 

carbon storage in the United States. Ecological Applications. 21: 1902–1924.

McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005. Molecular indicators of tree 

migration capacity under rapid climate change. Ecology. 86: 2088–2098.

McMurtrie, R.E.; Norby, R.J.; Medlyn, B.E.; Dewar, R.C.; Pepper, D.A.; 

Reich, P.B.; Barton, C.V.M. 2008. Why is plant-growth response to elevated 

CO2 amplifi ed when water is limiting, but reduced when nitrogen is limiting? 

A growth-optimisation hypothesis. Functional Plant Biology. 35: 521–534.

McPherson, G.R. 1997. Ecology and management of North American savannas. 

Tucson, AZ: University of Arizona Press. 208 p.

Medlyn, B.E.; Barton, C.V.M.; Broadmeadow, M.S.J.; Ceulemans, R.; De 

Angelis, P.; Forstreuter, M.; Freeman, M.; Jackson, S.B.; Kellomäki, S.; 

Laitat, E.; Rey, A.; Roberntz, P.; Sigurdsson, B.D.; Strassemeyer, J.; Wang, 

K.; Curtis, P.S.; Jarvis, P.G. 2001. Stomatal conductance of forest species after 

long-term exposure to elevated CO2 concentration: a synthesis. New Phytologist. 

149: 247–264.

Meehl, G.A.; Hu, A.X.; Tebaldi, C. 2010. Decadal prediction in the Pacifi c 

Region. Journal of Climate. 23: 2959–2973.

Meehl, G.A.; Stocker, T.F.; Collins, W.D.; Friedlingstein, F.; Gaye, A.T.; 

Gregory, J.M.; Kitoh, A.; Knutti, R.; Murphy, J.M.; Noda, A.; Raper, 

S.C.B.; Watterson, I.G.; Weaver, A.J.; Zhao, Z.-C. 2007. Global climate 

projections. In: Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; 

Averyt, K.B.; Tignor, M.; Miller, H.L., eds. Climate change 2007: the physical 

science basis. Contribution of working group I to the fourth assessment report of 

the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom 

and New York: Cambridge University Press: 747–845. Chapter 10.

Meehl, G.A.; Tebaldi, C. 2004. More intense, more frequent, and longer lasting 

heat waves in the 21st century. Science. 305: 994–997.
168

GENERAL TECHNICAL REPORT PNW-GTR-900

Melillo, J.M.; Butler, S.; Johnson, J.; Mohan, J.; Steudler, P.; Lux, H.; 

Burrows, E.; Bowles, F.; Smith, R.; Scott, L.; Vario, C.; Hill, T.; Burton, 

A.; Zhou, Y.M.; Tang, J. 2011. Soil warming, carbon-nitrogen interactions, and 

forest carbon budgets. Proceedings of the National Academy of Sciences of the 

United States of America. 108: 9508–9512.

Memmott, J.; Craze, P.G.; Waser, N.M.; Price, M.V. 2007. Global warming and 

the disruption of plant-pollinator interactions. Ecology Letters. 10: 710–717.

Mensing, S.; Livingston, S.; Barker, P. 2006. Long-term fi re history in Great 

Basin sagebrush reconstructed from macroscopic charcoal in spring sediments, 

Newark Valley, Nevada. Western North American Naturalist. 66: 64–77.

Menzel, A.; Sparks, T.H.; Estrella, N.; Koch, E.; Aasa, A.; Ahas, R.; AlmKübler, K.; Bissolli, P.; Braslavská, O.G.; Briede, A.; Chmielewski, F.M.; 

Crepinsek, Z.; Curnel, Y.; Dahl, Å.; Defi la, C.; Donnelly, A.; Filella, Y.; 

Jatczak, K.; Máge, F.; Mestre, A.; Nordli, Ø.; Peñuelas, J.; Pirinen, P.; 

Remišová, V.; Scheifi nger, H.; Striz, M.; Susnik, A.; Van Vliet, A.J.H.; 

Wielgolaski, F.-E.; Zach, S.; Zust, A. 2006. European phenological response 

to climate change matches the warming pattern. Global Change Biology. 

12: 1969–1976.

Millar, C.I.; Stephenson, N.L.; Stephens, S.L. 2007. Climate change and forests 

of the future: managing in the face of uncertainty. Ecological Applications. 

17: 2145–2151.

Miller, J.D.; Safford, H.D.; Crimmins, M.; Thode, A.E. 2009. Quantitative 

evidence for increasing forest fi re severity in the Sierra Nevada and southern 

Cascade Mountains, California and Nevada, USA. Ecosystems. 12: 16–32.

Miller, R.F.; Bates, J.D.; Svejcar, T.J.; Pierson, F.B.; Eddleman, L.E. 2005.

Biology, ecology, and management of western juniper (Juniperus occidentalis). 

Technical Bulletin 152. Corvallis, OR: Oregon State University, Agricultural 

Experiment Station. 82 p.

Miller, R.F.; Rose, J.A. 1999. Fire history and western juniper encroachment in 

sagebrush steppe. Journal of Range Management. 52: 550–559.

Miller, R.F.; Svejcar, T.J.; Rose, J.A. 2000. Impacts of western juniper on plant 

community composition and structure. Journal of Range Management. 

53: 574–585.

Miller, R.F.; Wigand, P.E. 1994. Holocene changes in semi-arid pinyon-juniper 

woodlands. BioScience. 44: 465–474.
169

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Mimura, M.; Aitken, S.N. 2007. Adaptive gradients and isolation-by-distance with 

postglacial migration in Picea sitchensis. Heredity. 99: 224–232.

Mimura, M.; Aitken, S.N. 2010. Local adaptation at the range peripheries of Sitka 

spruce. Journal of Evolutionary Biology. 23: 249–258.

Mitton, J.B.; Ferrenberg, S.M. 2012. Mountain pine beetle develops an 

unprecedented summer generation in response to climate warming. American 

Naturalist. 179: E163–E171. doi:10.1086/665007.

Mohan, J.E.; Clark, J.S.; Schlessinger, W.H. 2007. Long-term CO2 enrichment 

of a forest ecosystem: implications for forest regeneration and succession. 

Ecological Applications. 17: 1198–1212.

Monserud, R.A.; Rehfeldt, G.E. 1990. Genetic and environmental components 

of variation of site index in inland Douglas-fi r. Forest Science. 36: 1–9.

Moore, M.M.; Covington, W.W.; Fulé, P.Z. 1999. Reference conditions and 

ecological restoration: a southwestern ponderosa pine perspective. Ecological 

Applications. 9: 1266–1277.

Morgan, J.A.; LeCain, D.R.; Pendall, E.; Blumenthall, D.M.; Kimball, B.A.; 

Carrillo, Y.; Williams, D.G.; Heisler-White, J.; Dijkstra, F.A.; West, M. 

2011. C4 grasses prosper as carbon dioxide eliminates desiccation in warmed 

semi-arid grassland. Nature. 476: 202–206.

Morgan, J.A.; Milchunas, D.G.; LeCain, D.R.; West, M.; Mosier, A.R. 2007. 

Carbon dioxide enrichment alters plant community structure and accelerates 

shrub growth in the shortgrass steppe. Proceedings of the National Academy 

of Sciences of the United States of America. 104: 14724–14729.

Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; 

Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; Meehl, 

G.A.; Mitchell, J.F.B.; Nakićenović, N.; Riahi, K.; Smith, S.J.; Stouffer, 

R.J.; Thomson, A.M.; Weyant, J.P.; Wilbanks, T.J. 2010. The next generation 

of scenarios for climate change research and assessment. Nature. 463: 747–756.

Mote, P.W. 2003. Trends in temperature and precipitation in the Pacifi c Northwest 

during the twentieth century. Northwest Science. 77: 271–282.

Mote, P.W. 2006. Climate-driven variability and trends in mountain snowpack 

in western North America. Journal of Climate. 19: 6209–6220.
170

GENERAL TECHNICAL REPORT PNW-GTR-900

Mote, P.W.; Hamlet, A.F.; Clark, M.P.; Lettenmaier, D.P. 2005. Declining 

mountain snowpack in western North America. Bulletin of the American 

Meteorological Society. 86: 39–49.

Mote, P.W.; Salathé, E.P. 2010. Future climate in the Pacifi c Northwest. Climatic 

Change. 102: 29–50.

Mueller, R.C.; Scudder, C.M.; Porter, M.E.; Trotter, R.T.; Gehring, C.A.; 

Whitham, T.G. 2005. Differential tree mortality in response to severe drought: 

evidence for long-term vegetation shifts. Journal of Ecology. 93: 1085–1093.

Nakawatase, J.M.; Peterson, D.L. 2006. Spatial variability in forest growth— 

climate relationships in the Olympic Mountains, Washington. Canadian Journal 

of Forest Research. 36: 77–91.

Nakićenović, N. 2000. Greenhouse gas emissions scenarios. Technological 

Forecasting and Social Change. 65: 149–166.

Nakićenović, N.; Alcamo, J.; Davis, G.; de Vries, B.; Fenhann, J.; Gaffi n, S.; 

Gregory, K.; Grübler, A.; Jung, T.Y.; Kram, T.; La Rovere, E.L.; Michaelis, 

L.; Mori, S.; Morita, T.; Papper, W.; Pitcher, H.; Price, L.; Riahi, K.; 

Roehrl, A.; Rogner, H-H.; Sankovski, A.; Schlesinger, M.; Shukla, P.; Smith, 

S.; Swart, R.; van Rooijen, S.; Victor, N.; Dadi, Z. 2000. Special report on 

emissions scenarios, working group III, Intergovernmental Panel on Climate 

Change. Cambridge, United Kingdom: Cambridge University Press. 595 p. http://

www.grida.no/climate/ipcc/emission/index.htm. (April 2012).

Nathan, R.; Horvitz, N.; He, Y.; Kuparinen, A.; Schurr, F.M.; Katul, G.G. 

2011. Spread of North American wind-dispersed trees in future environments. 

Ecology Letters. 14: 211–219.

Nathan, R.; Schurr, F.M.; Spiegel, O.; Steinitz, O.; Trakhtenbrot, A.; Tsoar, 

A. 2008. Mechanisms of long-distance seed dispersal. Trends in Ecology and 

Evolution. 23: 638–647.

Naumburg, E.; Housman, D.C.; Huxman, T.E.; Charlet, T.N.; Loik, M.E.; 

Smith, S.D. 2003. Photosynthetic responses of Mojave Desert shrubs to free air 

CO2 enrichment are greatest during wet years. Global Change Biology. 

9: 276–285.

Neilson, R.P. 1995. A model for predicting continental-scale vegetation distribution 

and water balance. Ecological Applications. 5: 362–385.
171

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Neilson, R.P.; Pitelka, L.F.; Solomon, A.M.; Nathan, R.; Midgley, G.F.; 

Fragoso, J.M.V.; Lischke, H.; Thompson, K. 2005. Forecasting regional to 

global plant migration in response to climate change. Bioscience. 55: 749–759.

Nicotra, A.B.; Atkin, O.K.; Bonser, S.P.; Davidson, A.M.; Finnegan, E.J.; 

Mathesius, U.; Poot, P.; Purugganan, M.D.; Richards, C.L.; Valladares, F.; 

van Kleunen, M. 2010. Plant phenotypic plasticity in a changing climate. Trends 

in Plant Science. 15: 684–692.

Niinemets, Ü. 2010. A review of light interception in plant stands from leaf to 

canopy in different plant functional types and in species with varying shade 

tolerance. Ecological Research. 25: 693–714.

Niinemets, Ü.; Flexas, J.; Peñuelas, J. 2011. Evergreens favored by higher 

responsiveness to increased CO2. Trends in Ecology and Evolution. 26: 136–142.

Niu, S.; Wan, S. 2008. Warming changes plant competitive hierarchy in a 

temperate steppe in northern China. Journal of Plant Ecology. 1: 103–110.

Norby, R.J.; Warren, J.M.; Iversen, C.M.; Medlyn, B.E.; McMurtrie, R.E. 

2010. CO2 enhancement of forest productivity constrained by limited nitrogen 

availability. Proceedings of the National Academy of Sciences of the United 

States of America. 107: 19368–19373.

Norby, R.J.; Zak, D.R. 2011. Ecological lessons from free-air CO2 enrichment 

(FACE) experiments. Annual Review of Ecology Evolution and Systematics. 

42: 181–203.

Norris, J.R.; Jackson, S.T.; Betancourt, J.L. 2006. Classifi cation tree and 

minimum-volume ellipsoid analyses of the distribution of ponderosa pine in the 

western USA. Journal of Biogeography. 33: 342–360.

Norton, U.; Saetre, P.; Hooker, T.D.; Stark, J.M. 2011. Vegetation and moisture 

controls on soil carbon mineralization in semi-arid environments. Soil Science 

Society of America Journal. 76: 1038–1047.

Oren, R.; Ellsworth, D.S.; Johnsen, K.H.; Phillips, N.; Ewers, B.E.; Maier, C.; 

Schafer, K.V.R.; McCarthy, H.; Hendrey, G.; McNulty, S.G.; Katul, G.G. 

2001. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-

enriched atmosphere. Nature. 411: 469–472.

Ormrod, D.P.; Lesser, V.M.; Olszyk, D.M.; Tingey, D.T. 1999. Elevated 

temperature and carbon dioxide affect chlorophylls and carotenoids in Douglasfi r seedlings. International Journal of Plant Science. 160: 529–534.
172

GENERAL TECHNICAL REPORT PNW-GTR-900

Pabst , R.J.; Goslin, M.N.; Garman, S.L.; Spies, T.A. 2008. Calibrating and 

testing a gap model for simulating forest management in the Oregon Coast 

Range. Forest Ecology and Management. 256: 958–972.

Pacala, S.W.; Canham, C.D.; Silander, J.A. 1993. Forest models defi ned by fi eld 

measurements: I. The design of a northeastern forest simulator. Canadian Journal 

of Forest Research. 23: 1980–1988. 

Paine, R.T.; Tegner, M.J.; Johnson, E.A. 1998. Compounded perturbations yield 

ecological surprises. Ecosystems. 1: 535–545.

Pan, Y.; Melillo, J.M.; McGuire, A.D.; Kicklighter, D.W.; Pitelka, L.F.; 

Hibbard, K.; Pierce, L.L.; Running, S.W.; Ojima, D.S.; Parton, W.J.; 

Schimel, D.S.; other VEMAP members. 1998. Modeled responses of terrestrial 

ecosystems to elevated atmospheric CO2: a comparison of simulations by the 

biogeochemistry models of the Vegetation/Ecosystem Modeling and Analysis 

Project (VEMAP). Oecologia. 114: 389–404.

Parmesan, C. 2006. Ecological and evolutionary responses to recent climate 

change. Annual Review of Ecology Evolution and Systematics. 37: 637–669.

Parmesan, C. 2007. Infl uences of species, latitudes and methodologies on 

estimates of phenological response to global warming. Global Change Biology. 

13: 1860–1872.

Pastor, J.; Post, W.M. 1988. Response of northern forests to CO2-induced climate 

change. Nature. 334: 55–58.

Pauchard, A.; Kueffer, C.; Dietz, H.; Daehler, C.C.; Alexander, J.; Edwards, 

P.J.; Arevalo, J.R.; Cavieres, L.A.; Guisan, A.; Haider, S.; Jakobs, G.; 

McDougall, K.; Millar, C.I.; Naylor, B.J.; Parks, C.G.; Rew, L.J.; Seipel, T. 

2009. Ain’t no mountain high enough: plant invasions reaching new elevations. 

Frontiers in Ecology and the Environment. 7: 479–486.

Pearson, R.G.; Dawson, T.P. 2003. Predicting the impacts of climate change 

on the distribution of species: are bioclimate envelope models useful? Global 

Ecology and Biogeography. 12: 361–371.

Pelto, M.S. 2006. The current disequilibrium of North Cascades glaciers. 

Hydrological Processes. 20: 769–779.

Peñuelas, J.; Estiarte, M. 1998. Can elevated CO2 affect secondary metabolism 

and ecosystem function? Trends in Ecology and Evolution. 13: 20–24.
173

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Perry, G.L.W.; Enright, N.J. 2006. Spatial modelling of vegetation change in 

dynamic landscapes: a review of methods and applications. Progress in Physical 

Geography. 30: 47–72.

Perry, D.A.; Hessburg, P.F.; Skinner, C.N.; Spies, T.A.; Stephens, S.L.; Taylor, 

A.H.; Franklin, J.F.; McComb, B.; Riegel, G. 2011. The ecology of mixed 

severity fi re regimes in Washington, Oregon, and northern California. Forest 

Ecology and Management. 262: 703–717.

Peterson, D.L.; Millar, C.I.; Joyce, L.A.; Furniss, M.J.; Halofsky, J.E.; Neilson, 

R.P.; Morelli, T.L. 2011. Responding to climate change in national forests: a 

guidebook for developing adaptation options. Gen. Tech. Rep. PNW-GTR-855. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest 

Research Station. 109 p.

Peterson, D.W.; Peterson, D.L. 1994. Effects of climate on radial growth of 

subalpine conifers in the North Cascade Mountains. Canadian Journal of Forest 

Research. 24: 1921–1932.

Peterson, D.W.; Peterson, D.L. 2001. Mountain hemlock growth responds to 

climatic variability at annual and decadal time scales. Ecology. 82: 3330–3345.

Peterson, D.W.; Peterson, D.L.; Ettl, G.J. 2002. Growth responses of subalpine 

fi r to climatic variability in the Pacifi c Northwest. Canadian Journal of Forest 

Research. 32: 1503–1517.

Petit, R.J.; Hampe, A. 2006. Some evolutionary consequences of being a tree. 

Annual Review of Ecology Evolution and Systematics. 37: 187–214.

Petit, R.J.; Hu, F.S.; Dick, C.W. 2008. Forests of the past: a window to future 

changes. Science. 320: 1450–1452.

Phillips, R.P.; Meier, I.C.; Bernhardt, E.S.; Grandy, A.S.; Wickings, K.; Finzi, 

A.C. 2012. Roots and fungi accelerate carbon and nitrogen cycling in forests 

exposed to elevated CO2. Ecology Letters. 15: 1042–1049.

Pietsch, S.A.; Hasenauer, H.; Thornton, P.E. 2005. BGC-model parameters 

for tree species growing in central European forests. Forest Ecology and 

Management. 211: 264–295.

Plaut, J.A.; Yepez, E.A.; Hill, J.; Pangle, R.; Sperry, J.S.; Pockman, W.T.; 

McDowell, N.G. 2012. Hydraulic limits preceding mortality in a piñon-juniper 

woodland under experimental drought. Plant, Cell and Environment. 

35: 1601–1617.
174

GENERAL TECHNICAL REPORT PNW-GTR-900

Polgar, C.A.; Primack, R.B. 2011. Leaf-out phenology of temperate woody plants: 

from trees to ecosystems. New Phytologist. 191: 926–941.

Raffa, K.F.; Aukema, B.H.; Bentz, B.J.; Carroll, A.L.; Hicke, J.A.; Turner, 

M.G.; Romme, W.H. 2008. Cross-scale drivers of natural disturbances prone to 

anthropogenic amplifi cation: the dynamics of bark beetle eruptions. Bioscience. 

58: 501–517.

Rahmstorf, S.; Schellnhuber, H.J. 2006. Der Klimawandel. Munich: Beck Verlag. 

144 p.

Raich, J.W.; Potter, C.S.; Bhagwati, D. 2002. Interannual variability in global 

respiration, 1980–94. Global Change Biology. 8: 800–812.

Randall, D.A.; Wood, R.A.; Bony, S.; Colman, R.; Fichefet, T.; Fyfe, J.; 

Kattsov, V.; Pitman, A.; Shukla, J.; Srinivasan, J.; Stouffer, R.J.; Sumi, 

A.; Taylor, K.E. 2007. Climate models and their evaluation. In: Solomon, S.; 

Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, 

H.L., eds. Climate change 2007: the physical science basis. Contribution of 

working group I to the fourth assessment report of the Intergovernmental Panel 

on Climate Change. Cambridge, United Kingdom and New York: Cambridge 

University Press. 589–662. Chapter 8.

Rathcke, B.; Lacey, E.P. 1985. Phenological patterns of terrestrial plants. Annual 

Review of Ecology and Systematics 16: 179–214.

Rau, B.M.; Johnson, D.W.; Blank, R.R.; Lucchesi, A.; Caldwell, T.G.; Schupp, 

E.W. 2011a. Transition from sagebrush steppe to annual grass (Bromus 

tectorum): infl uence on belowground carbon and nitrogen. Rangeland Ecology 

and Management. 64: 139–147.

Rau, B.M.; Johnson, D.W.; Blank, R.R.; Tausch, R.J.; Roundy, B.A.; Miller, 

R.F.; Caldwell, T.G.; Lucchesi, A. 2011b. Woodland expansion’s infl uence 

on belowground carbon and nitrogen in the Great Basin U.S. Journal of Arid 

Environments. 75: 827–835.

Raupach, M.R.; Marland, G.; Ciais, P.; Le Quéré, C.; Canadell, J.G.; Klepper, 

G.; Field, C.B. 2007. Global and regional drivers of accelerating CO2 emissions. 

Proceedings of the National Academy of Sciences of the United States of 

America. 104: 10288–10293.

Rehfeldt, G.E. 1988. Ecological genetics of Pinus contorta from the Rocky 

Mountains (USA)—a synthesis. Silvae Genetica. 37: 131–135.
175

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Rehfeldt, G.E. 1989. Ecological adaptations in Douglas-fi r (Pseudotsuga menziesii

var. glauca)—a synthesis. Forest Ecology and Management. 28: 203–215.

Rehfeldt, G.E. 1994a. Adaptation of Picea engelmannii populations to the 

heterogeneous environments of the intermountain West. Canadian Journal of 

Botany. 72: 1197–1208.

Rehfeldt, G.E. 1994b. Genetic structure of western red cedar populations in the 

Interior West. Canadian Journal of Forest Research. 24: 670–680.

Rehfeldt, G.E. 1995. Genetic variation, climate models and the ecological genetics 

of Larix occidentalis. Forest Ecology and Management. 78: 21–37.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; Evans, J.S. 2006. Empirical 

analyses of plant-climate relationships for the western United States. 

International Journal of Plant Sciences. 167: 1123–1150.

Rehfeldt, G.E.; Ferguson, D.E.; Crookston, N.L. 2009. Aspen, climate, and 

sudden decline in western USA. Forest Ecology and Management. 

258: 2353–2364.

Rehfeldt, G.E.; Hoff, R.J.; Steinhoff, R.J. 1984. Geographic patterns of genetic 

variation in Pinus monticola. Botanical Gazette. 145: 229–239.

Rehfeldt, G.E.; Wykoff, W.R.; Ying, C.C. 2001. Physiologic plasticity, evolution, 

and impacts of a changing climate on Pinus contorta. Climatic Change. 

50: 355–376.

Reich, P.B.; Hobbie, S.E.; Lee, T.; Ellsworth, D.S.; West, J.B.; Tilman, D.; 

Knops, J.M.H.; Naeem, S.; Trost, J. 2006a. Nitrogen limitation constrains 

sustainability of ecosystem response to CO2. Nature. 440: 922–925.

Reich, P.B.; Hungate, B.A.; Luo, Y. 2006b. Carbon-nitrogen interactions in 

terrestrial ecosystems in response to rising atmospheric carbon dioxide. Annual 

Review of Ecology Evolution and Systematics. 37: 611–636.

Reich, P.B.; Tilman, D.; Craine, J.; Ellsworth, D.; Tjoelker, M.G.; Knops, J.; 

Wedin, D.; Naeem, S.; Bahaudden, D.; Goth, J.; Bengtson, W.; Lee, T.D. 

2001. Do species and functional groups differ in acquisition and use of C, N and 

water under varying atmospheric CO2 and N availability regimes? A fi eld test 

with 16 grassland species. New Phytologist. 150: 435–448.

Ribbens, E.; Silander, J.A.; Pacala, S.W. 1994. Seedling recruitment in forests: 

calibrating models to predict patterns of tree seedling dispersion. Ecology. 

75: 1794–1806.
176

GENERAL TECHNICAL REPORT PNW-GTR-900

Rice, J.; Tredennick, A.; Joyce, L.A. 2012. Climate change on the Shoshone 

National Forest, Wyoming: a synthesis of past climate, climate projections, and 

ecosystem implications. Gen. Tech. Rep. RMRS-GTR-264. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 60 p.

Rich, P.M.; Breshears, D.D.; White, A.B. 2008. Phenology of mixed woodherbaceous ecosystems following extreme events: net and differential responses. 

Ecology. 89: 342–352.

Richards, J.H.; Caldwell, M.M. 1987. Hydraulic lift: substantial nocturnal water 

transport between soil layers by Artemisia tridentatea roots. Oecologia. 

73: 486–489.

Richardson, S.J.; Allen, R.B.; Whitehead, D.; Carswell, F.E.; Ruscoe, W.A.; 

Platt, K.H. 2005. Climate and net carbon availability determine temporal 

patterns of seed production by Nathofagus. Ecology. 86: 972–981.

Ritchie, M.E.; Tilman, D.; Knops, J.M.H. 1998. Herbivore effects on plant and 

nitrogen dynamics in oak savanna. Ecology. 79: 165–177.

Robinson, D.C.E.; Beukema, S.J.; Greig, L.A. 2008. Vegetation models 

and climate change: workshop results. 50 p. Unpublished report by ESSA 

Technologies Ltd. On fi le with: U.S. Department of Agriculture, Forest Service, 

Western Wildland Environmental Threat Center, 3160 NE Third St., Prineville, 

OR 97754. 

Robinson, E.A.; Ryan, G.D.; Newman, J.A. 2012. A meta-analytical review 

of the effects of elevated CO2 on plant-arthropod interactions highlights 

the importance of interacting environmental and biological variables. New 

Phytologist. 194: 321–336.

Rogers, B.M. 2009. Potential impacts of climate change on vegetation distributions, 

carbon stocks and fi re regimes in the U.S. Pacifi c Northwest. Corvallis, OR: 

Oregon State University. 89 p. M.S. thesis.

Rogers, B.M.; Neilson, R.P.; Drapek, R.; Lenihan, J.M.; Wells, J.R.; Bachelet, 

D.; Law, B.E. 2011. Impacts of climate change on fi re regimes and carbon stocks 

of the U.S. Pacifi c Northwest. Journal of Geophysical Research. 116: G03037. 

doi:10.1029/2011JG001695.

Running, S.W.; Nemani, R.R. 1991. Regional hydrologic and carbon balance 

responses of forests resulting from potential climate change. Climatic Change. 

19: 349–368.
177

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Runyon, J.; Waring, R.H.; Goward, S.N.; Welles, J.M. 1994. Environmental 

limits on net primary production and light-use effi ciency across the Oregon 

transect. Ecological Applications. 4: 226–237.

Rustad, L.E.; Campbell, J.L.; Marion, G.M.; Norby, R.J.; Mitchell, M.J.; 

Hartley, A.E.; Cornelissen, J.H.C.; Gurevitch, J. 2001. A meta-analysis of the 

response of soil respiration, net nitrogen mineralization, and aboveground plant 

growth to experimental ecosystem warming. Oecologia. 126: 543–562.

Ryan, M.G. 1991. Effects of climate change on plant respiration. Ecological 

Applications. 1: 157–167.

Salathé, E.P.; Mote, P.W.; Wiley, M.W. 2007. Review of scenario selection and 

downscaling methods for the assessment of climate change impacts on hydrology 

in the United States pacifi c northwest. International Journal of Climatology. 

27: 1611–1621.

Saxe, H.; Cannell, M.G.R.; Johnsen, B.; Ryan, M.G.; Vourlitis, G. 2001. Tree 

and forest functioning in response to global warming. New Phytologist. 

149: 369–399.

Scheller, R.M.; Mladenoff, D.J. 2007. An ecological classifi cation of forest 

landscape simulation models: tools and strategies for understanding broad-scale 

forested ecosystems. Landscape Ecology. 22: 491–505.

Scheller, R.M.; Mladenoff, D.J. 2008. Simulated effects of climate change, 

fragmentation, and inter-specifi c competition on tree species migration in 

northern Wisconsin, USA. Climate Research. 36: 191–202.

Schimel, D.S.; Braswell, B.H.; Parton, W.J. 1997a. Equilibration of the terrestrial 

water, nitrogen, and carbon cycles. Proceedings of the National Academy of 

Sciences of the United States of America. 94: 8280–8283.

Schimel, D.S.; VEMAP participants; Braswell, B.H. 1997b. Continental scale 

variability in ecosystem processes: models, data, and the role of disturbance. 

Ecological Monographs. 67: 251–271.

Schlaepfer, D.R.; Lauenroth, W.K.; Bradford, J.B. 2011. Ecohydrological niche 

of sagebrush ecosystems. Ecohydrology. 5: 453–466.

Schlaepfer, D.R.; Lauenroth, W.K.; Bradford, J.B. 2012. Consequences of 

declining snow accumulation for water balance of mid-latitude dry regions. 

Global Change Biology. 18: 1988–1997.
178

GENERAL TECHNICAL REPORT PNW-GTR-900

Schleppi, P.; Bucher-Wallin, I.; Hagedorn, F.; Körner, C. 2012. Increased nitrate 

availability in the soil of a mixed mature temperate forest subjected to elevated 

CO2 concentration (canopy FACE). Global Change Biology. 18: 757–768.

Scholes, R.J.; Archer, S.R. 1997. Tree-grass interactions in savannas. Annual 

Review of Ecology and Systematics. 28: 517–544.

Schreiner, E.G.; Krueger, K.A.; Houston, D.B.; Happe, P.J. 1996. Understory 

patch dynamics and ungulate herbivory in old-growth forests of Olympic 

National Park, Washington. Canadian Journal of Forest Research. 26: 255–265.

Schwinning, S.; Starr, B.I.; Ehleringer, J.R. 2003. Dominant cold desert plants 

do not partition warm season precipitation by event size. Oecologia. 

136: 252–260.

Segurado, P.; Araújo, M.B. 2004. An evaluation of methods for modelling species 

distributions. Journal of Biogeography. 31: 1555–1568.

Seidl, R.; Spies, T.A.; Rammer, W.E.; Steel, A.; Pabst, R.J.; Olsen, K. 2012. 

Multi-scale drivers of spatial variation in old-growth forest carbon density 

disentangled with lidar and an individual-based landscape model. Ecosystems. 

15: 1321–1335.

Selås, V.; Piovesan, G.; Adams, J.M.; Bernabei, M. 2002. Climatic factors 

controlling reproduction and growth of Norway spruce in southern Norway. 

Canadian Journal of Forest Research. 32: 217–225.

Shafer, S.L.; Bartlein, P.J.; Thompson, R.S. 2001. Potential changes in the 

distributions of western North America tree and shrub taxa under future climate 

scenarios. Ecosystems. 4: 200–215.

Shugart, H.H.; West, D.C. 1977. Development of an Appalachian deciduous forest 

succession model and its application to assessment of the impact of the chestnut 

blight. Journal of Environmental Management. 5: 161–179.

Simard, M.; Romme, W.H.; Griffi n, J.M.; Turner, M.G. 2011. Do mountain pine 

beetle outbreaks change the probability of active crown fi re in lodgepole pine 

forests? Ecological Monographs. 81: 3–24.

Sinclair, S.J.; White, M.D.; Newell, G.R. 2010. How useful are species 

distribution models for managing biodiversity under future climates? Ecology 

and Society. 15(1): 8. http://www.ecology andsociety.org/vol15/iss1/art8/.
179

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Skelly, D.K.; Joseph, L.N.; Possingham, H.P.; Freidenburg, L.K.; Farrugia, 

T.J.; Kinnison, M.T.; Hendry, A.P. 2007. Evolutionary responses to climate 

change. Conservation Biology. 21: 1353–1355.

Sklar, F.H.; Hunsaker, C.T. 2001. The use and uncertainties of spatial data for 

landscape models: an overview with examples from the Florida Everglades. 

In: Hunsaker, C.T.; Goodchild, M.F.; Friedl, M.A.; Case, T.J., eds. Spatial 

uncertainty in ecology: implications for remote sensing and GIS applications. 

New York, NY: Springer-Verlag: 15–46. Chapter 2.

Smith, B.; Prentice, I.C.; Sykes, M.T. 2001. Representation of vegetation 

dynamics in the modelling of terrestrial ecosystems: comparing two contrasting 

approaches within European climate space. Global Ecology and Biogeography. 

10: 621–637.

Smith, S.D.; Huxman, T.E.; Zitzer, S.F.; Charlet, T.N.; Houseman, D.C.; 

Coleman, J.S.; Fenstermaker, L.K.; Seeman, J.R.; Nowak, R.S. 2000. 

Elevated CO2 increases productivity and invasive species success in an arid 

ecosystem. Nature. 408: 79–82.

Smith, W.K.; Germino, M.J.; Hancock, T.E.; Johnson, D.M. 2003. Another 

perspective on altitudinal limits of alpine timberlines. Tree Physiology. 

23: 1101–1112.

Solomon, A.M. 1986. Transient response of forests to CO2-induced climatic 

change: simulation modeling experiments in eastern North America. Oecologia. 

68: 567–580.

Soulé, P.T.; Knapp, P.A. 2007. Topoedaphic and morphological complexity of 

foliar damage and mortality within western juniper (Juniperus occidentalis var. 

occidentalis) woodlands following an extreme meteorological event. Journal of 

Biogeography. 34: 1927–1937.

Spies, T.A.; Giesen, T.W.; Swanson, F.J.; Franklin, J.F.; Lach, D.; Johnson, 

K.N. 2010. Climate change adaptation strategies for federal forests of the Pacifi c 

Northwest, USA: ecological, policy, and socio-economic perspectives. Landscape 

Ecology. 25: 1185–1199.

Sprugel, D.G.; Brooks, J.R.; Hinckley, T.M. 1996. Effects of light on shoot 

geometry and needle morphology in Abies amabilis. Tree Physiology. 16: 91–98.
180

GENERAL TECHNICAL REPORT PNW-GTR-900

Stephens, S.L.; Millar, C.I.; Collins, B.M. 2010. Operational approaches to 

managing forests of the future in Mediterranean regions within a context 

of changing climates. Environmental Research Letters. 5: 024003. 

doi:10.1088/1748-9326/5/2/024003.

Stewart, I.T. 2009. Changes in snowpack and snowmelt runoff for key mountain 

regions. Hydrological Processes. 23: 78–94.

Stewart, I.T.; Cayan, D.R.; Dettinger, M.D. 2004. Changes in snowmelt runoff 

timing in western North America under a ‘business as usual’ climate change 

scenario. Climatic Change. 62: 217–232.

Stiling, P.; Cornelissen, T. 2007. How does elevated carbon dioxide (CO2) affect 

plant-herbivore interactions? A fi eld experiment and meta-analysis of CO2-

mediated changes on plant chemistry and herbivore performance. Global Change 

Biology. 13: 1823–1842.

Stueve, K.M.; Cerney, D.L.; Rochefort, R.M.; Kurth, L.L. 2009. Post-fi re tree 

establishment patterns at the alpine treeline ecotone: Mount Rainier National 

Park, Washington, USA. Journal of Vegetation Science. 20: 107–120.

Sturrock, R.N.; Frankel, S.J.; Brown, A.V.; Hennon, P.E.; Kliejunas, J.T.; 

Lewis, K.J.; Worrall, J.J.; Woods, A.J. 2011. Climate change and forest 

diseases. Plant Pathology. 60: 133–149.

Sultan, S.E. 2000. Phenotypic plasticity for plant development, function and life 

history. Trends in Plant Science. 5: 537–542.

Suttle, K.B.; Thomsen, M.A.; Power, M.E. 2007. Species interactions reverse 

grassland responses to changing climate. Science. 315: 640–642.

Svenning, J.C.; Skov, F. 2004. Limited fi lling of the potential range in European 

tree species. Ecology Letters. 7: 565–573.

Swanston, C.; Janowiak, M., eds. 2012. Forest adaptation resources: climate 

change tools and approaches for land managers. Gen. Tech. Rep. NRS-GTR-87. 

Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 121 p.

Swetnam, T.W.; Allen, C.D.; Betancourt, J.L. 1999. Applied historical ecology: 

using the past to manage for the future. Ecological Applications. 9: 1189–1206.

Tans, P.; Keeling, R. 2013. Data from U.S. Department of Commerce, National 

Oceanic and Atmospheric Administration. http://www.esrl.noaa.gov/gmd/ccgg/

trends/. (February 2013).
181

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

Taylor, A.H. 1995. Forest expansion and climate change in the mountain hemlock 

(Tsuga mertensiana) zone, Lassen Volcanic National Park, California, U.S.A. 

Arctic and Alpine Research. 27: 207–216.

Taylor, A.H.; Trouet, V.; Skinner, C.N. 2008. Climatic infl uences on fi re regimes 

in montane forests of the southern Cascades, California, USA. International 

Journal of Wildland Fire. 17: 60–71.

Teskey, R.O.; Hinckley, T.M.; Grier, C.C. 1984. Temperature-induced change 

in the water relations of Abies amabilis (Dougl.) Forbes. Plant Physiology. 

74: 77–80.

Thonicke, K.; Venevsky, S.; Sitch, S.; Cramer, W. 2001. The role of fi re 

disturbance for global vegetation dynamics: coupling fi re into a Dynamic Global 

Vegetation Model. Global Ecology and Biogeography. 10: 661–677.

Thuiller, W.; Albert, C.; Araújo, M.B.; Berry, P.M.; Cabeza, M.; Guisan, 

A.; Hickler, T.; Midgely, G.F.; Paterson, J.; Schurr, F.M.; Sykes, M.T.; 

Zimmermann, N.E. 2008. Predicting global change impacts on plant species’ 

distributions: future challenges. Perspectives in Plant Ecology Evolution and 

Systematics. 9: 137–152.

Thuiller, W.; Brotons, L.; Araújo, M.B.; Lavorel, S. 2004. Effects of restricting 

environmental range of data to project current and future species distributions. 

Ecography. 27: 165–172.

Tiedemann, A.R.; Klemmedson, J.O. 1986a. Long-term effects of mesquite 

removal on soil characteristics: I. Nutrients and bulk density. Soil Science 

Society of America Journal. 50: 472–475.

Tiedemann, A.R.; Klemmedson, J.O. 1986b. Long-term effects of mesquite 

removal on soil characteristics: II. Nutrient availability. Soil Science Society of 

America Journal. 50: 476–480.

Tilman, D. 1982. Resource competition and community structure. Monographs in 

Population Biology 17. Princeton, NJ: Princeton University Press. 296 p.

Tilman, D. 2004. Niche tradeoffs, neutrality, and community structure: a stochastic 

theory of resource competition, invasion, and community assembly. Proceedings 

of the National Academy of Sciences of the United States of America. 

101: 10854–10861.

Tomback, D.F.; Arno, S.F.; Keane, R.E. 2001. Whitebark pine communities: 

ecology and communities. Washington, DC: Island Press. 328 p.
182

GENERAL TECHNICAL REPORT PNW-GTR-900

Tranquillini, W. 1979. Physiological ecology of the alpine timberline: tree 

existence at high altitudes with species reference to the European Alps 

(Ecological Studies 31). Berlin: Springer-Verlag. 137 p. 

Trenberth, K.E.; Jones, P.D.; Ambenje, P.; Bojariu, R.; Easterling, D.; Klein 

Tank, A.; Parker, D.; Rahimzadeh, F.; Renwick, J.A.; Rusticucci, M.; 

Soden, B.; Zhai, P. 2007. Observations: surface and atmospheric climatic 

change. In: Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, 

K.B.; Tignor, M.; Miller, H.L., eds. Climate change 2007: the physical science 

basis. Contribution of working group I to the fourth assessment report of the 

Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and 

New York: Cambridge University Press: SM. 3–1–3–11.

Trouet, V.; Taylor, A.H.; Wahl, E.R.; Skinner, C.N.; Stephens, S.L. 2010. Fireclimate interactions in the American West since 1400 CE. Geophysical Research 

Letters. 37: L04702. doi:10.1029/2009GL041695.

Tylianakis, J.M.; Didham, R.K.; Bascompte, J.; Wardle, D.A. 2008. Global 

change and species interactions in terrestrial ecosystems. Ecology Letters. 

11: 1351–1363.

U.S. Department of Agriculture. 2010. Strategic plan, FY 2010–2015. 21 p. http://

www.ocfo.usda.gov/usdasp/sp2010/sp2010.pdf. (April 2012).

U.S. Department of Agriculture, Forest Service [USDA FS]. 2010. National 

roadmap for responding to climate change. http://www.fs.fed.us/climatechange/

pdf/roadmap.pdf. (April 2012).

U.S. Department of the Interior [USDI]. 2009. Addressing the impacts of climate 

change on America’s water, land, and other natural and cultural resources. 

Secretarial Order No. 3289. September 19, 2009.

Valladares, F.; Gianoli, E.; Gomez, J.M. 2007. Ecological limits to plant 

phenotypic plasticity. New Phytologist. 176: 749–763.

Van Cleve, K.; Oechel, W.C.; Hom, J.L. 1990. Response of black spruce (Picea 

mariana) ecosystems to soil temperature modifi cation in interior Alaska. 

Canadian Journal of Forest Research. 20: 1530–1535.

van Mantgem, P.J.; Stephenson, N.L. 2007. Apparent climatically induced 

increase of tree mortality rates in a temperate forest. Ecology Letters. 

10: 909–916.
183

Climate Change Effects on Vegetation in the Pacifi c Northwest: A Review and Synthesis of the Scientifi c Literature

van Mantgem, P.J.; Stephenson, N.L.; Byrne, J.C.; Daniels, L.D.; Franklin, 

J.F.; Fulé, P.Z.; Harmon, M.E.; Larson, A.J.; Smith, J.M.; Taylor, A.H.; 

Veblen, T.T. 2009. Widespread increase of tree mortality rates in the western 

United States. Science. 323: 521–524.

Verboom, J.; Wamelink, G.W.W. 2005. Spatial modelling in landscape ecology. 

In: Wiens, J.A.; Moss, M.R., eds. Issues and perspectives in landscape ecology. 

Greeley, CO: International Association for Landscape Ecology, Pioneer Press: 

79–89.

Vitousek, P.M.; Aber, J.D.; Howarth, R.W.; Likens, G.E.; Matson, P.A.; 

Schindler, D.W.; Schlesinger, W.H.; Tilman, D.G. 1997. Human alteration of 

the global nitrogen cycle: sources and consequences. Ecological Applications. 

7: 737–750.

Vose, J.M.; Peterson, D.L.; Patel-Weynand, T., eds. 2012. Effects of climatic 

variability and change on forest ecosystems: a comprehensive science synthesis 

for the U.S. forest sector. Gen. Tech. Rep. PNW-GTR-870. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station. 

265 p.

Walck, J.L.; Hidayati, S.N.; Dixon, K.W.; Thompson, K.; Poschlod, P. 2011.

Climate change and plant regeneration from seed. Global Change Biology. 

17: 2145–2161.

Walther, G.R. 2010. Community and ecosystem responses to recent climate 

change. Philosophical Transactions of the Royal Society B Biological Sciences. 

365: 2019–2024.

Walther, G.R.; Beissner, S.; Burga, C.A. 2005. Trends in the upward shift of 

alpine plants. Journal of Vegetation Science. 16: 541–548.

Waring, R.H.; Franklin, J.F. 1979. Evergreen coniferous forests of the Pacifi c 

Northwest. Science. 204: 1380–1386.

Waring, R.H.; Running, S.W. 1998. Forest ecosystems: analysis at multiple scales. 

2nd edition. San Diego, CA: Academic Press. 370 p.

Way, D.A.; LaDeau, S.L.; McCarthy, H.R.; Clark, J.S.; Oren, R.; Finzi, 

A.C.; Jackson, R.B. 2010. Greater seed production in elevated CO2 is not 

accompanied by reduced seed quality in Pinus taeda L. Global Change Biology. 
184

GENERAL TECHNICAL REPORT PNW-GTR-900
Pacifi c Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

 Pacifi c Northwest Research Station

 P.O. Box 3890

 Portland, OR 97208-3890
U.S. Department of Agriculture

Pacifi c Northwest Research Station

1220 SW 3rd Avenue.

P.O. Box 3890

Portland, OR 97204

Offi cial Business

Penalty for Private Use, $300