United States Department of Agriculture

Forest Service

Northern Research Station

General Technical Report NRS-168

April 2017

CHICAGO WILDERNESS REGION 

URBAN FOREST VULNERABILITY 

ASSESSMENT AND SYNTHESIS: 

A Report from the Urban Forestry 

Climate Change Response Framework 

Chicago Wilderness Pilot Project
ABSTRACT

The urban forest of the Chicago Wilderness region, a 7-million-acre area covering portions of Illinois, 

Indiana, Michigan, and Wisconsin, will face direct and indirect impacts from a changing climate over 

the 21st century. This assessment evaluates the vulnerability of urban trees and natural and developed 

landscapes within the Chicago Wilderness region to a range of future climates. We synthesized and 

summarized information on the contemporary landscape, provided information on past climate trends, 

and illustrated a range of projected future climates. We used this information to inform models of habitat 

suitability for trees native to the area. Projected shifts in plant hardiness and heat zones were used 

to understand how nonnative species and cultivars may tolerate future conditions. We also assessed 

the adaptability of planted and naturally occurring trees to stressors that may not be accounted for in 

habitat suitability models such as drought, flooding, wind damage, and air pollution. 

The summary of the contemporary landscape identifies major stressors currently threatening the urban 

forest of the Chicago Wilderness region. Major current threats to the region’s urban forest include 

invasive species, pests and disease, land-use change, development, and fragmentation. Observed 

trends in climate over the historical record from 1901 through 2011 show a temperature increase of 1 °F 

in the Chicago Wilderness region. Precipitation increased as well, especially during the summer. 

Mean annual temperature is projected to increase by 2.3 to 8.2 °F by the end of the century, with 

temperature increases across all seasons. Projections for precipitation show an increase in winter and 

spring precipitation, and summer and fall precipitation projections vary by model. Species distribution 

modeling for native species suggests that suitable habitat may decrease for 11 primarily northern 

species and increase or become newly suitable for 40 species. An analysis of tree species vulnerability 

that combines model projections, shifts in hardiness and heat zones, and adaptive capacity showed 

that 15 percent of the trees currently present in the region have either moderate-high or high 

vulnerability to climate change, and many of those trees with low vulnerability are invasive species. 

We developed a process for self-assessment of urban forest vulnerability that was tested by urban 

forestry professionals from four municipalities, three park districts, and three forest preserve districts 

in the region. The professionals generally rated the impacts of climate change on the places they 

managed as moderately negative, mostly driven by the potential effects of extreme storms and heavy 

precipitation on trees in the area. The capacity of forests to adapt to climate change ranged widely 

based on economic, social, and organizational factors, as well as on the diversity of species and 

genotypes of trees in the area. These projected changes in climate and their associated impacts and 

vulnerabilities will have important implications for urban forest management, including the planting and 

maintenance of street and park trees, management of natural areas, and long-term planning.

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance Implementation Plan 

which requires technical and policy review for all scientific publications produced or funded by the 

Station. The process included a blind technical review by at least two reviewers, who were selected by the 

Assistant Director for Research and unknown to the author. This review policy promotes the Forest Service 

guiding principles of using the best scientific knowledge, striving for quality and excellence, maintaining 

high ethical and professional standards, and being responsible and accountable for what we do.

Cover Photo

Residential street trees, Chicago, IL. Photo by L. Darling, The Morton Arboretum, used with permission. 

Manuscript received for publication August 2016 

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CHICAGO WILDERNESS REGION 

URBAN FOREST VULNERABILITY

ASSESSMENT AND SYNTHESIS: 

A Report from the Urban Forestry 

Climate Change Response Framework 

Chicago Wilderness Pilot Project

Leslie A. Brandt, Abigail Derby Lewis, Lydia Scott, Lindsay Darling, 

Robert T. Fahey, Louis Iverson, David J. Nowak, Allison R. Bodine, Andrew Bell, 

Shannon Still, Patricia R. Butler, Andrea Dierich, Stephen D. Handler, 

Maria K. Janowiak, Stephen N. Matthews, Jason W. Miesbauer, Matthew Peters, 

Anantha Prasad, P. Danielle Shannon, Douglas Stotz, and Christopher W. Swanston
AUTHORS

LESLIE A. BRANDT is a climate change specialist with 

the Northern Institute of Applied Climate Science, U.S. 

Forest Service, 1992 Folwell Avenue, St. Paul, MN 55108, 

lbrandt@fs.fed.us.

ABIGAIL DERBY LEWIS is a senior conservation 

ecologist at Keller Science Action Center, the Field 

Museum, 1400 S. Lake Shore Drive, Chicago, IL 60605, 

aderby@fieldmuseum.org.

LYDIA SCOTT is director of the Chicago Region Trees 

Initiative at The Morton Arboretum, 4100 Illinois Route 

53, Lisle, IL 60532, lscott@mortonarb.org.

LINDSAY DARLING is a geographic information 

systems analyst at The Morton Arboretum, 4100 Illinois 

Route 53, Lisle, IL 60532, ldarling@mortonarb.org.

ROBERT T. FAHEY is an assistant professor at the 

University of Connecticut, Department of Natural 

Resources and the Environment, and the Center for 

Environmental Sciences and Engineering, 1376 Storrs 

Road, Storrs, CT 06269, robert.fahey@uconn.edu.

LOUIS IVERSON is a landscape ecologist with the U.S. 

Forest Service, Northern Research Station, 359 Main 

Road, Delaware, OH 43015, liverson@fs.fed.us.

DAVID J. NOWAK is team leader and a research forester 

with the U.S. Forest Service, Northern Research Station, 

SUNY-ESF, 1 Forestry Road, 5 Moon Library, Syracuse, 

NY 13210, dnowak@fs.fed.us.

ALLISON R. BODINE is a planner with the SyracuseOnondaga County Planning Agency, 421 Montgomery 

St., 11th Floor, Syracuse, NY 13202, arbodine@gmail.

com. She was a research forester with the Davey Institute, 

Syracuse, NY at the time of writing.

ANDREW BELL is a visiting scientist at the United 

States National Arboretum, 3501 New York Ave. NE, 

Washington, DC 20002, acbellnc@gmail.com or Andrew.

Bell@ars.usda.gov. He was curator of woody plants at the 

Chicago Botanic Garden at the time of writing.

SHANNON STILL is director of plant conservation and 

curator at the University of California, Davis Arboretum 

and Public Garden, 1 Shields Avenue, Davis, CA 95616, 

still@ucdavis.edu.

PATRICIA R. BUTLER is a climate change outreach 

specialist with the Northern Institute of Applied Climate 

Science, Michigan Technological University, School 

of Forest Resources and Environmental Science, 1400 

Townsend Drive, Houghton, MI 49931, prbutler@mtu.edu.

ANDREA DIERICH is an environmental coordinator for 

DuPage County, 421 N. County Farm Road, Wheaton, 

IL 60187, Andi.Dierich@dupageco.org.

STEPHEN D. HANDLER is a climate change specialist 

with the Northern Institute of Applied Climate Science, 

U.S. Forest Service, 410 MacInnes Drive, Houghton, 

MI 49931, sdhandler@fs.fed.us. 

MARIA K. JANOWIAK is the deputy director of the 

Northern Institute of Applied Climate Science, U.S. Forest 

Service, 410 MacInnes Drive, Houghton, MI 49931, 

mjanowiak02@fs.fed.us.

STEPHEN N. MATTHEWS is an assistant professor at 

Ohio State University, 2021 Coffey Road, Columbus, 

OH 43210, matthews.204@osu.edu. 

JASON W. MIESBAUER is an arboriculture scientist 

with The Morton Arboretum, 4100 Illinois Route 53, 

Lisle, IL 60532, jmiesbauer@mortonarb.org.

MATTHEW PETERS is a geographic information 

systems technician with the U.S. Forest Service, Northern 

Research Station, 359 Main Road, Delaware, OH 43015, 

matthewpeters@fs.fed.us.

ANANTHA PRASAD is an ecologist with the U.S. 

Forest Service, Northern Research Station, 359 Main 

Road, Delaware, OH 43015, aprasad@fs.fed.us.

P. DANIELLE SHANNON is the coordinator of the 

USDA Northern Forests Climate Hub, Northern Institute 

of Applied Climate Science, Michigan Technological 

University, School of Forest Resources and Environmental 

Science, 1400 Townsend Drive, Houghton, MI 49931, 

dshannon@mtu.edu.

DOUGLAS STOTZ is a senior conservation ecologist 

at Keller Science Action Center, the Field Museum, 

1400 S. Lake Shore Drive, Chicago, IL 60605, 

dstotz@fieldmuseum.org.

CHRISTOPHER W. SWANSTON is the director of 

the Northern Institute of Applied Climate Science and a 

research ecologist with the U.S. Forest Service, Northern 

Research Station, 410 MacInnes Drive, Houghton, MI 

49931, cswanston@fs.fed.us.
PREFACE

Context and Scope

This assessment is a fundamental component of the 

Urban Forestry Climate Change Response Framework 

project. This project builds on lessons learned from the 

Climate Change Response Framework: a collaborative, 

cross-boundary approach among scientists, managers, 

and landowners to incorporate climate change 

considerations into natural resource management. 

Other Framework projects are currently underway, 

covering millions of acres of natural ecosystems or 

large-scale forestry plantations in the northeastern 

quarter of the United States. Each project interweaves 

four components: science and management partnerships, 

vulnerability assessments, adaptation resources, and 

demonstration projects. The scope of the Chicago 

Wilderness region assessment is the urban forest, 

broadly defined to include both developed and natural 

settings within the urban landscape.

We designed this assessment to be a synthesis of the 

best available scientific information. Its primary goal 

is to inform those who work, study, recreate in, and 

care about the urban forests in the Chicago Wilderness 

region. As new scientific information arises, we 

expect that new efforts will need to be undertaken to 

reflect that acquired knowledge and understanding. 

Most important, this assessment does not make 

recommendations about how this information should 

be used. 

Author Contributions 

and Acknowledgments

Leslie Brandt developed the document structure 

and was the primary author of the document, with 

substantial input and writing from Abigail Derby 

Lewis, Lydia Scott, Lindsay Darling, and Robert Fahey. 

Danielle Shannon and Lindsay Darling conducted much 

of the data analysis and developed maps for Chapters 

1, 2, and 4. Louis Iverson, Steve Matthews, Matthew 

Peters, and Anantha Prasad provided and interpreted 

the hardiness and heat zone projections for Chapter 2, 

and assisted with the data processing for the climate 

data presented in Chapter 4. Allison Bodine and 

Dave Nowak collaborated with Louis Iverson, Steve 

Matthews, Matthew Peters, and Anantha Prasad to 

provide Tree Atlas results for Chapter 3. Andrew Bell 

and Shannon Still provided the information on modeled 

cultivated species. Andrew Bell, Lydia Scott, Robert 

Fahey, Jason Miesbauer, and Lindsay Darling provided 

expert review of the modifying factor scores in Chapter 

3. All authors contributed to the content and structure 

of the report. 

We wish to thank the municipal foresters, park district 

representatives, and forest managers who participated in 

the vulnerability case studies. We also thank Elizabeth 

Larry, Lara Roman, Elizabeth Gibbons, and two 

anonymous reviewers, who provided technical reviews of 

the manuscript. 
CONTENTS

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xi

CHAPTER 1: The Contemporary Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

CHAPTER 2: Climate Trends and Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

CHAPTER 3: Climate Change Impacts and the Adaptive Capacity of the Urban Forest . . . . . . . . . . . . . . . . . . . . . . 30

CHAPTER 4: Urban Forest Vulnerability Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

CHAPTER 5: Management Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

LITERATURE CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

APPENDIX 1: Species Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

APPENDIX 2: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

APPENDIX 3: Downscaled Climate Models Used in this Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

APPENDIX 4: Plant Hardiness Zone and Heat Zone Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

APPENDIX 5: Additional Tree Atlas Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

APPENDIX 6: Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas . . . . . . . . 112

APPENDIX 7: Tree Species Vulnerability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

APPENDIX 8: Urban Forest Vulnerability Worksheet and Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
EXECUTIVE SUMMARY | ix

EXECUTIVE SUMMARY

This assessment evaluates the vulnerability of urban 

trees and forests in the Chicago Wilderness region 

(whose 7 million acres cover portions of Illinois, 

Indiana, Michigan, and Wisconsin) to a range of future 

climate scenarios. This assessment is part of the Urban 

Forestry Climate Change Response Framework project, 

a collaborative approach among researchers, managers, 

and landowners to incorporate climate change 

considerations into urban forest management. 

The assessment summarizes current conditions and 

key stressors and identifies past and projected trends 

in climate. This information is then incorporated 

into model projections of future forest change. 

These projections, along with local knowledge and 

expertise, are used to develop case studies of urban 

forest vulnerability for municipalities, park districts, 

and forest preserve districts across the region. A final 

chapter summarizes the implications of these impacts 

and vulnerabilities for urban forest management across 

the region. 

CHAPTER 1

The Contemporary Landscape

Summary

This chapter discusses the structure and function of 

the Chicago Wilderness regional forest, the forces that 

have shaped it, and stressors that currently threaten it. 

This information lays the foundation for understanding 

how shifts in climate may contribute to changes in this 

forest, and how climate may interact with other stressors 

present on the landscape.

Key Points

• Before Euro-American settlement, open oak 

ecosystems such as savannas and woodlands 

dominated the Chicago Wilderness region.

• Today, most of the natural areas have been lost 

to urbanization and suburban sprawl. The only 

remaining natural areas in the region are fragmented 

and are a small fraction of their original size.

• Key stressors to forests in the region include 

development, soil alteration, alteration of historical 

fire regimes, air pollution, the urban heat island effect, 

extreme weather events, invasive plants, insect pests, 

and diseases. 

• Current management of natural areas in the region 

focuses on restoring native ecosystems and simulating 

past disturbance regimes.

• Selection of trees for urban plantings weighs other 

factors besides native status, such as tolerance of site 

conditions, diversity of community trees, tree height, 

required maintenance, and ornamental appeal.

CHAPTER 2

Climate Trends and Projections 

Summary

This chapter discusses our current understanding of 

past and projected future changes in climate in the 

Chicago Wilderness region. This chapter examines 

how climate may change over the next century using 

two models representing a range of possible futures 

that are downscaled in order to be relevant to local 

decisionmaking. A review of the most recent scientific 

literature on local trends and projections is also included.

Key Points

• Over the past century, the Chicago Wilderness region 

has warmed by about 1 °F on average and has had a 

significant increase in precipitation, especially during 

the summer (3-inch increase). 

• Mean annual temperature is projected to increase 

by 2.3 to 8.2 °F by the end of the 21st century, with 

temperature increases across all seasons.

• Precipitation is projected to increase in winter and 

spring over the 21st century, but projections for 

summer and fall precipitation are less clear. 

• Heavy precipitation events have been increasing in 

number and intensity and are projected to continue to 

increase further, which could increase runoff and local 

flooding from stormwater. 
EXECUTIVE SUMMARY x |

• Extreme and exceptional droughts may increase in 

duration, frequency, and spatial extent compared to 

the end of the 20th century. 

• Rises in temperature may lead to a shift of one to two 

hardiness zones and two to four heat zones. 

CHAPTER 3

Climate Change Impacts and 

the Adaptive Capacity of the 

Urban Forest 

Summary

This chapter synthesizes the potential impacts of climate 

change on urban forests in the Chicago Wilderness 

region, with an emphasis on changes in habitat 

suitability and the adaptive capacity of different species. 

Key Points

• Species distribution modeling for native species 

suggests that suitable habitat may decrease for 11 

primarily northern species, and increase or become 

newly suitable for 40 species. 

• For species for which no model information is 

available (rare, nonnative, or cultivars), shifts in heat 

and hardiness zones could have a positive effect on 

about 23 percent of species that are either present in 

the area or considered for planting, and a negative 

effect on about 19 percent. 

• Adaptive capacity of 179 species was evaluated 

by using scoring systems for planted and natural 

environments, with invasive species among those with 

the highest capacity to adapt to a range of stressors. 

• An analysis of vulnerability that combines model 

projections, shifts in hardiness and heat zones, and 

adaptive capacity showed that 17 percent of the tree 

species currently present in the region have either 

moderate-high or high vulnerability to climate 

change, and 77 percent of individual trees with low 

vulnerability are invasive species. 

• Key impacts to trees in the Chicago Wilderness 

region projected over the next century include 

increased drought and heat stress, increased 

stormwater runoff and flooding, increases in wind 

damage, and increases in tree pests and pathogens. 

CHAPTER 4

Urban Forest Vulnerability 

Case Studies

Summary

This chapter focuses on the vulnerability of urban forests 

in the Chicago Wilderness region to climate change, 

describing case study examples from municipalities, park 

districts, and forest preserve districts. 

Key Points

• We developed a process for municipalities, park 

districts, and forest preserve districts to assess their 

vulnerability to climate change based on impacts and 

adaptive capacity.

• Ten case studies were developed in the Chicago 

Wilderness region using this approach. 

• Most of the variation in vulnerability among case 

studies was in adaptive capacity, driven by differences 

in biological, organizational, economic, and social 

factors among communities. 

CHAPTER 5

Management Considerations

Summary

This chapter provides an overview of climate change 

impacts on decisionmaking, management practices, and 

other issues related to urban and community forestry 

in the Chicago Wilderness region. The management 

of natural areas, street trees, and landscaped parks may 

become more challenging due to more-severe storms and 

changes in habitat suitability for dominant trees. Greater 

financial investments may be required in the short term 

to maintain the urban forest so it can continue to provide 

benefits to the community, such as clean air, reduced 

heat island effects, and stormwater management, in 

the long term. At the same time, confronting the 

challenge of climate change also presents opportunities 

for managers and other decisionmakers to protect 

their investments by planning ahead, building resilient 

landscapes, expanding their volunteer base, and 

engaging with their communities to adapt to future 

change.
INTRODUCTION | xi

INTRODUCTION

Context

This assessment is a fundamental component of the 

Urban Forestry Climate Change Response Framework 

project (www.forestadaptation.org/urban). This project 

builds on lessons learned from the Climate Change 

Response Framework: a collaborative, cross-boundary 

approach among scientists, managers, and landowners to 

incorporate climate change considerations into natural 

resource management. Five broader projects are currently 

underway, covering millions of acres in the northeastern 

quarter of the United States: Northwoods, Central 

Appalachians, Central Hardwoods, Mid-Atlantic, and 

New England. Each project interweaves four components: 

science and management partnerships, vulnerability 

assessments, adaptation resources, and demonstration 

projects (Fig. 1). The Chicago Wilderness region 

assessment is the first to focus on urban trees in a 

developed setting. Vulnerability assessments for the other 

five project areas focus on natural ecosystems or largescale forestry plantations within a specific ecoregion. 

The overarching goal of all Framework projects is to 

incorporate climate change considerations into forest 

management. The overall goal of the Urban project is 

to ensure that urban forests will continue to provide 

benefits to the people who live in urban communities 

as the climate changes. We define the urban forest 

as all publicly and privately owned trees within an 

urban area—including individual trees along streets 

and in backyards—as well as stands of remnant forest. 

The Urban project works across public and private 

organizations toward this goal by accomplishing the 

following objectives:

• Engage with communities across the Northeast, MidAtlantic, and Midwest that are interested in adapting 

their urban forest management to climate change

• Work with these communities to assess the 

vulnerability of their urban forests to climate change

• Identify and develop tools to aid adaptation of urban 

forests to climate change

• Develop real-world examples of climate-informed 

management of urban forests.

The tools and approaches developed in the Urban 

project are designed to be applied to any urban, 

suburban, or other developed area in the Midwest and 

Northeast. The Chicago Wilderness region was chosen 

as a pilot area to test these ideas, with the intention of 

applying lessons learned to other metropolitan regions 

and municipalities in the future. 

The Chicago Wilderness region pilot of the Urban 

project is a collaborative effort among the U.S. Forest 

Service and other organizations. Current partners in the 

effort include:

• Northern Institute of Applied Climate Science

• U.S. Forest Service 

Eastern Region

Northern Research Station

Northeastern Area (State & Private Forestry)

• Chicago Region Trees Initiative 

• The Morton Arboretum

• Chicago Wilderness

• The Field Museum

• The Chicago Botanic Garden

Figure 1.—Climate Change Response Framework components.
INTRODUCTION xii |

Scope and Goals

The primary goal of this assessment is to summarize 

potential changes to the urban forest of the Chicago 

Wilderness region under a range of future climates, and 

determine the vulnerability of trees and developed and 

natural landscapes to those changes. The assessment 

also includes a synthesis of information about the 

current landscape as well as projections of climate and 

vegetation changes used to assess these vulnerabilities. 

Uncertainties and gaps in understanding are discussed 

throughout the document. 

This assessment covers the urban forest of the Chicago 

Wilderness region, an area of about 7 million acres that 

stretches from southwestern Michigan to southern 

Wisconsin, reaching through northwestern Indiana and 

northern Illinois (Fig. 2). Chicago Wilderness is a 

regional alliance of more than 200 organizations that 

work together to improve the quality of life of the humans 

and the many other species living in the Chicago area. 

This region includes 38 counties, over 500 municipalities, 

and a population of more than 10 million people. 

The boundaries of the Chicago Wilderness region 

encompass several rare ecosystem types that support a 

high diversity of species. Boundaries of the watersheds 

containing the natural communities helped to define the 

region, as did the large concentration of natural preserves 

in the metropolitan area (Chicago Wilderness 1999). 

Assessment Chapters

This assessment comprises the following chapters: 

Chapter 1: The Contemporary Landscape describes 

existing conditions, providing background on the 

physical environment, ecological character, and 

current management of urban forests of the Chicago 

Wilderness region.

Chapter 2: Climate Trends and Projections discusses 

our current understanding of past and projected future 

changes in climate in the Chicago Wilderness region.

Chapter 3: Climate Change Impacts and the Adaptive 

Capacity of the Urban Forest synthesizes the potential 

impacts of climate change on urban forests in the 

Chicago Wilderness region, with an emphasis on 

changes in habitat suitability and the adaptive capacity 

of different species.

Chapter 4: Urban Forest Vulnerability Case Studies 

presents case study vulnerability assessments for 

municipalities, park districts, and forest preserve 

districts in the Chicago Wilderness region.

Chapter 5: Management Considerations provides 

an overview of climate change impacts on 

decisionmaking, management practices, and other 

issues related to urban and community forestry in the 

Chicago Wilderness region. Figure 2.—Chicago Wilderness region.
The Contemporary Landscape—CHAPTER 1 | 1

CHAPTER 1

THE CONTEMPORARY LANDSCAPE 

The urban forest is defined as all publicly and privately 

owned trees within an urban area—including individual 

trees along streets and in backyards—as well as stands 

of remnant forest (Nowak et al. 2001). The urban 

forest of the Chicago Wilderness region, a 7-millionacre area in portions of Illinois, Indiana, Michigan, 

and Wisconsin, can be viewed as two separate but 

interconnected entities: natural areas and developed 

sites. These areas are managed and maintained in 

vastly different ways and by different stakeholder 

groups. The urban forest is shaped by current land 

use and development imposed upon the ecosystems, 

landforms, and environmental gradients that existed 

before Euro-American settlement. Historically, the 

region was a mosaic of prairies, dunes, wetlands, and 

wooded ecosystems, but most of these natural areas 

have been developed for agricultural, urban, and 

suburban land uses. Even though much of the region 

has been developed, its natural history still influences 

current forest composition. In this chapter we describe 

the structure and function of the urban forest of the 

Chicago Wilderness region, the forces that have 

shaped it, and stressors that currently threaten it. This 

information lays the foundation for understanding how 

shifts in climate may contribute to changes in the urban 

forest of the Chicago Wilderness region, and how 

climate may interact with other stressors present on the 

landscape. 

Landscape Setting

This assessment covers the urban forest of the Chicago 

Wilderness region, which lies within parts of Ecological 

Provinces 221 and 251 (Midwest Broadleaf Forest and 

Prairie Parkland; Cleland et al. 2007) (Fig. 3). Provinces, 

and sections within them, are distinguished by 

differences in geologic parent material, elevation, plant 

distribution, and regional climate and are based on the 

U.S. Forest Service National Hierarchical Framework of 

Ecological Units (McNab and Avers 1994, McNab et al. 

2007). Following is a brief overview of the landscape 

setting. Additional descriptions of the landscape setting 

can be found in the resources listed in the sidebar.

More Information on 

Forests in the Chicago

Wilderness Region

Origins of the Chicago Urban Forest: 

Composition and Structure in Relation to Presettlement Vegetation and Modern Land-use 

(Fahey et al. 2012)—Analyzes the effect of preurban ecosystem conditions on modern urban 

forest composition and structure.

Sustaining our Oaks: a Vision for the Future 

of Oak Ecosystems in the Chicago Wilderness 

Region (Fahey et al. 2015)—Provides detailed 

information on the distribution of remnant wooded 

ecosystems across the Chicago Wilderness 

region and outlines a strategy for promoting 

continued oak dominance in the region across 

land-use categories and stakeholder groups.

Vegetation of the Chicago Region as Mapped 

by the Public Land Survey 1821–1845. 

Interactive Maps and Reports (McBride and 

Bowles 2007b)—Provides data, summary reports, 

and interactive maps focused on the distribution, 

structure, and composition of ecosystems in the 

pre-urban Chicago region. 

Chicago’s Urban Forest Ecosystem: Results 

of the Chicago Urban Forest Climate Project 

(McPherson et al. 1994)—Summarizes a 3-year 

study to quantify the effects of urban vegetation 

on the local environment and help city planning 

and management organizations increase the net 

environmental benefits from Chicago’s urban forest.

Urban Trees and Forests of the Chicago 

Region (Nowak et al. 2013)—Provides 

a quantitative assessment of the current 

composition of trees in the region along with their 

ecosystem service values.

Chicago Wilderness: an Atlas of Biodiversity 

(Sullivan 2011)—Describes the geologic history 

and natural communities in the area. 

(see the Literature Cited section starting on p. 76 

for complete citations)
2 CHAPTER 1—The Contemporary Landscape |

Landform, Soils, and Hydrology

The topography and soils of the Chicago Wilderness 

region were shaped by glaciers that advanced and 

retreated across the region from 85,000 to 10,000 years 

before present. These ice sheets were massive: some were 

more than a quarter of a mile thick (Sullivan 2011). 

The glaciers flattened most of the land, but they also 

carved the rivers and lakes that dot the region (Bowles 

and Jones 2007). Additionally they created a series of 

swells (low-relief undulations in the landscape) that run 

through the area, leading to occasional glacial features 

such as eskers, kames, and moraines (Pielou 2008).

Glaciers formed the foundation for soils in the Chicago 

Wilderness region. As the glaciers advanced across the 

region’s limestone bedrock, they gathered and carried 

large amounts of earth with them, leaving behind large 

rocks, finer pebbles, sand, clay, and silt as they receded. 

This material makes up the mineral components of the 

soils in the region (Bretz 1955). Topsoil tends to be quite 

sandy near Lake Michigan, and was primarily formed 

on beach deposits from the lake. Areas farther from the 

lake are formed primarily on glacial outwash and till, and 

have more silt and clay. Much of the region has a heavy 

clay layer a few feet below the topsoil. In the thousands 

of years since the final retreat of the glaciers, this soil has 

changed into its current state. Erosion moved some of the 

soil and deposited it in new places, and organic matter 

has been added to the soils by generations of plants and 

animals (Sullivan 2011). More recently, agriculture and 

development substantially altered the soils in the area from 

their original state, leading to heavy compaction, altered 

nutrient levels, and the presence of non-soil materials in 

the profile (Gregory and Dukes 2006) (see subsection on 

Major Stressors and Threats to the Urban Forest). 

Agricultural and urban development has changed the 

hydrology of the area (Fitzpatrick et al. 2005). By the 

late 1970s, peak flood levels in northeastern Illinois had 

tripled over the historical record, an increase believed to 

be driven by urbanization (Allen and Bejcek 1979). The 

relative effect of urbanization depends in part on factors 

such as watershed slope, percentage of clayey surficial 

deposits, reach (the length of a river), glacial landforms, 

hydrologic alterations (e.g., stormwater management 

practices and point sources), and historical and present 

channel alterations (Fitzpatrick et al. 2005). 

Natural Communities 

Before Euro-American settlement, the Chicago 

Wilderness region had many natural community types, 

though few remnants persist. Community types include 

dunes, wetlands, prairies, shrublands/barrens, 

woodlands, and forested ecosystems (Fig. 4). Landscape 

features such as soil texture, depth, and drainage; 

rainfall; slope and aspect; and hydrology determine what 

Figure 3.—Ecological provinces in the Chicago Wilderness 

region. Source: Cleland et al. (2007).

Figure 4.—Distribution of natural community types across the 

Chicago Wilderness region prior to Euro-American settlement. 

Source: Chicago Metropolitan Agency for Planning (2015).
The Contemporary Landscape—CHAPTER 1 | 3

groups of species are suited to a given site (Leitner et al. 

1991). Prairies formed in many sites, including those 

with poor or shallow soils, those with south-facing 

aspects, and areas that experienced frequent fire 

disturbance. Woodlands and forests formed where the 

soil was deeper and where firebreaks reduced 

disturbance frequency and severity (Sullivan 2011). The 

formation of fens, marshes, bogs, and swamps depends 

on the hydrology and pH of the waters. 

Hydrology, landscape features, and soils played only a 

part in determining ecosystem distributions; disturbance 

heavily influenced the ecology of the Chicago 

Wilderness region for thousands of years (Abrams 

1992, Leitner et al. 1991, Nowacki and Abrams 2008). 

Much of the region has suitable soil and hydrologic 

characteristics to support forests, but disturbance from 

fire and grazing limited their distribution. Prior to 

Euro-American settlement, fires were frequent and 

widespread across much of the area because of the 

region’s flat topography and few firebreaks (Anderson 

1991, Bowles et al. 1994). The fires affected the 

distribution, structure, and species composition of the 

area’s ecosystems. Forests were present only in sites that 

were protected from fires: primarily along the east side 

of rivers (Bowles et al. 1994). 

Disturbance not only limited the extent of woody 

ecosystems, but also influenced their structure and 

function. Woodlands were relatively open, with a low 

number of trees per acre, and they were dominated by 

fire-tolerant trees such as oaks and hickories (Fig. 5).

Before Euro-American settlement, oaks were the most 

abundant species in the Chicago Wilderness region, 

making up more than 65 percent of the regional forest 

by basal area (Fahey et al. 2012). Less fire-tolerant 

species, such as maples and American basswood, were 

not abundant, and existed only in areas with firebreaks 

(McBride and Bowles 2007a). For lists of the common 

and scientific names of species mentioned in this report, 

see Appendix 1. 

The variety of ecosystem types in the region has resulted 

in a diverse assemblage of plant and animal species. 

The region is home to approximately 1,650 native plant 

species. In the Indiana Dunes National Lake Shore, for 

example, there are 1,300 native plant species, the third 

highest number of species in the U.S. National Park 

Figure 5.—This diagram arranges the pre-settlement wooded communities of this region on two 

axes. One separates them according to soil moisture from wet to dry and the other according to 

the density of the tree canopy. This density gradient is also a fire gradient. Fires burned hotter and 

more often in the communities to the left of the diagram. Communities to the right saw fewer fires. 

Reproduced with permission from Sullivan (2011). 
4 CHAPTER 1—The Contemporary Landscape |

System (Sullivan 2011). Hundreds of species of birds 

use forests in the Chicago Wilderness region to nest, 

overwinter, or rest during migration. Nearly 100 species 

of reptiles and amphibians are also present. More detail 

on the biodiversity of the Chicago region can be found 

in Sullivan (2011).

Human Influences

Humans have strongly influenced the area for thousands 

of years for different purposes. Native Americans 

colonized the Chicago Wilderness region soon after 

the final glacial retreat (Hicks 2000). More recently, 

Euro-American settlement dramatically transformed the 

landscape through agriculture, development, hunting, 

and horticulture. 

Native American Influence

Native Americans were present in the area for thousands 

of years and primarily used fire to manipulate the 

landscape (Lesser 1993, MacCleery 2011). They lit 

fires to clear land for dwellings and fields, to control 

vegetation, and to flush wildlife from forested areas 

while hunting (Pyne 1982). The long-term and wideranging use of fire by Native Americans and the 

presence of grazing animals created ecosystems that 

were adapted to live with these disturbances (Curtis 

and McIntosh 1951). Native American practices created 

open woodlands and savannas. These activities tended 

to favor oaks and other fire-tolerant species and disfavor 

more fire-sensitive species.

Early Euro-American Settlement

Extensive Euro-American settlement began in the early 

to mid-19th century. Soon thereafter, settlers plowed 

many prairies and savannas to establish vast agricultural 

lands (Hicks 2000). Many of the region’s natural areas 

and numerous large trees were lost. Euro-Americans 

also suppressed fire in the remaining natural areas. Fireintolerant species became established in areas where they 

had previously been precluded. 

Euro-American Influence Through Present Day

As Euro-Americans continued to settle in the region, 

agricultural use intensified; farmers plowed most of the 

prairies and savannas in the outer counties of the Chicago 

Wilderness region. Closer to the City of Chicago, most of 

the natural areas have been lost to urbanization and 

suburban sprawl. The only remaining natural areas in the 

region are fragmented and are a small fraction of their 

original size (Mankin and Warner 1997) (Fig. 6). 

Many of the natural areas that still exist bear little 

resemblance to pre-Euro-American settlement 

landscapes. Although there have been recent efforts to 

reintroduce fire to these sites, it had largely been 

suppressed for the previous 100 years. Wooded 

ecosystems have a much higher tree density under fire 

suppression than was present historically. Canopy 

closure changed the composition of the forest: species 

such as oaks and hickories that were abundant before 

Euro-American settlement have become less common, 

as their seedlings require ample light to develop (Fig. 7). 

Shade-tolerant species have increased, including 

American basswood, elms, maples, and invasive species 

such as European buckthorn. 

Figure 6.—Current distribution of natural areas in the Chicago 

Wilderness region. Source: Chicago Metropolitan Agency for 

Planning (2015).
The Contemporary Landscape—CHAPTER 1 | 5

Current Conditions in the 

Chicago Wilderness Area

Land Use

Like its historical predecessor, the current landscape is 

heterogeneous; but instead of containing many natural 

ecosystem types, it varies by land use and ownership 

and across a gradient in density from urban to suburban 

to rural. Trees and forests are arrayed across land use 

types, from privately owned residential, commercial, 

and agricultural sites, and publicly owned parks, transit 

ways, and forest preserves to golf courses, cemeteries, 

and university campuses. Patterns of land use vary 

greatly from the urban core in the City of Chicago to 

the seven exurban and rural collar counties (Fig. 8). 

In the City of Chicago, the vast majority of land is 

residential and commercial. In Cook, DuPage, and 

Lake Counties residential, transit, and open space are 

the most abundant land-use types. Across the Chicago 

Wilderness region, open spaces are more common. 

Open spaces such as parks, forest preserves, and vacant 

lots make up almost one-quarter of the land. Residential 

areas account for nearly one-third. Although agriculture 

is virtually nonexistent in the City of Chicago, it is 

abundant in the outer counties, and makes up one-third 

of the land in the region. 

Species Composition Patterns

The forest of the Chicago Wilderness region is a 

mixture of remnant (pre-Euro-American settlement) 

trees, planted trees, and spontaneous recruitment from 

both sources. The proportion of remnant to planted 

trees varies from site to site. Some parts of the region 

still have a greater proportion of remnant trees and 

their offspring; other areas are dominated by newly 

planted and often nonnative species (Fahey et al. 2012, 

Nowak 2012). The ratio depends on the natural history 

and ecology, development history, and the land use 

and ownership of a particular site. Areas that were 

historically wooded often retain that legacy. Individual 

remnant trees can be seen not only in natural areas 

across the region, but even in some developed areas 

such as the Villages of Oak Park and Riverside. In these 

communities, 200-year-old trees stand in the parks and 

boulevards alongside newly planted trees. Parts of the 

region that were historically prairies lack these remnant 

trees, and a greater proportion of the trees at these sites 

were planted or spontaneously regenerated more recently 

(Fahey et al. 2012).

The current composition at a site also depends on 

ownership and management. In most areas there is a 

mix of both native and planted trees. For example, in 

golf courses and cemeteries, many remnant trees and 

their offspring grow next to recently planted trees that 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

1839 

2010 

Dominance (percent) 

White oak 

Bur oak 

American elm 

Red oak 

Black walnut 

Sugar maple 

White ash 

Eastern cottonwood 

Silver maple 

Boxelder 

Black cherry 

European buckthorn 

Siberian elm 

Honeylocust 

Norway maple 

Black locust 

Green ash 

Eastern white pine 

Figure 7.—Comparison of species dominance (percentage of estimated total basal area) in 

1839 (based on Government Land Offi ce Public Land Survey data) and 2010 (based on the 

urban tree census data) in the Chicago Wilderness region across all land-use types. Source: 

Fahey et al. (2012).
6 CHAPTER 1—The Contemporary Landscape |

are often nonnative. Parks, natural areas, and other 

open spaces tend to have a higher proportion of remnant 

vegetation. In areas where the original forest was cleared 

for agriculture or urban development, the current forest 

has a much higher proportion of planted and nonnative 

trees (Fahey et al. 2012). In residential and commercial 

sites many new trees have been planted, and these trees 

often bear little resemblance to what originally existed 

in the Chicago Wilderness region. Most trees have been 

removed from agricultural sites, but those that remain 

are generally not planted, and are more likely to be 

native, to have sprouted spontaneously, or both. 

Major Stressors and Threats 

to the Urban Forest

Land-use change, development, 

and fragmentation

Development is the major driver of forest change in 

the Chicago Wilderness region; it has altered species 

composition, as described earlier, and increased 

fragmentation among native populations. Fragmentation 

of natural landscapes leads to the creation of isolated 

populations that are unable to migrate easily and 

exchange genetic material. Consequently, biological and 

genetic diversity may be reduced (Fahrig 2003, Harrison 

and Bruna 1999, Robinson et al. 1995). In the Chicago 

Wilderness region, the number of parcels of remnant 

oak ecosystems greater than 500 acres has been reduced 

from 26 in 1939 to just 6 in 2010 (Fahey et al. 2015). 

Fragmentation has resulted in the near extinction of the 

lakeside daisy (DeMauro 1993) and in severe population 

losses for many reptiles and amphibians (Cushman 

2006). Fragmentation not only decreases connectivity 

among natural areas but also changes the structure 

of existing sites. As sites become fragmented and the 

amount of core ecosystem space is reduced, many plants 

and animals that rely on core habitat may be extirpated 

from the region (Saunders et al. 1991). Additionally, 

habitat edges are more likely to be affected by pollution 

runoff from nearby roads and industry, and are more 

likely to contain invasive species. As a result, they tend 

to be less biologically diverse than core areas, and offer 

less useful habitat for wildlife (Saunders et al. 1991). 

Alteration of soil

Changes in land use have altered soils in the region. 

Levels of nitrogen and phosphorus tend to be 

higher across the Midwest than historical levels due 

to application of fertilizers and heavy planting of 

leguminous, nitrogen-fixing crops (Vitousek et al. 

1997). Although little research is available specific to 

the Chicago Wilderness region, studies from other 

urban areas can shed light on likely impacts. In other 

areas, atmospheric deposition of nitrate, ammonium, 

Figure 8.—Land-use type by county. Source: Chicago Metropolitan Agency for Planning (2010).
The Contemporary Landscape—CHAPTER 1 | 7

calcium, and sulfate ions has been detected in areas 

nearly 30 miles from the urban core (Lovett et al. 2000). 

In heavily urbanized sites, soils tend to be compacted, 

which can decrease the rate at which water enters the 

soil, increasing rainwater runoff and making it more 

difficult for trees to grow (Gregory and Dukes 2006). 

Development and industrialization have caused the 

deposition of heavy metals such as lead, copper, and 

nickel (Pouyat et al. 1995). Heavy metals are more 

abundant in dense urban cores and are associated with 

industrial areas, but are also deposited near roadways 

(Helmreich et al. 2010). Salt loads (from both mineral 

salt and salt spray) are also heavy along roads and in 

parking lots. Runoff from limestone and concrete causes 

many urban soils to be more alkaline than is found in 

most natural areas (Ware 1990). The most severely 

altered soil conditions occur in tree pits: cutouts in 

sidewalks or along roads where trees are planted. Tree 

pits are often nutrient-deficient and heavily compacted, 

and have some of the highest salt inputs (Craul 1999). 

Shifts in fire regime

Fire suppression since Euro-American settlement has 

altered the structure and composition of the vegetation 

in the region. The number of mesic species, such as 

American basswood and maple, has increased, and the 

number of oak and hickory species has decreased (Fahey 

et al. 2012). More recently, managers of natural areas 

in the region are reintroducing fire through prescribed 

burning. However, the fragmentation of the landscape 

along with the high density of homes limits the use 

of fire in the region. In addition, the changes to the 

structure and composition of the forest (sometimes 

termed “mesophication”) have made the landscape less 

prone to fire and harder to manage effectively by using 

prescribed fire alone (Nowacki and Abrams 2008).

Invasive plant species

Invasive plant species such as European buckthorn and 

Amur honeysuckle strongly influence the structure, 

composition, and functioning of forests in the area. In 

fact, European buckthorn is the most abundant tree 

species currently in the Chicago Wilderness region, 

accounting for 28 percent of the total urban tree 

population (Nowak et al. 2013). Invasive plants create 

a dense shrub layer in woody ecosystems under which 

native forbs and trees are unable to grow (Heneghan 

et al. 2006, Knight et al. 2007, Olden 2006). They 

can also affect soil conditions such as nutrient cycling 

(Heneghan et al. 2006). Other problematic woody 

species include multiflora rose and Oriental bittersweet. 

Herbaceous invaders include garlic mustard, Japanese 

stiltgrass, and common reed. All of these invasive 

species can outcompete native species and reduce the 

biodiversity of the region’s natural areas.

A work in progress: shoreline of Jackson Park, on Chicago’s South Side. Photo by E. Johnson, Erika Hildegard Photography, used 

with permission. 
8 CHAPTER 1—The Contemporary Landscape |

Insect pests and diseases 

Some native and nonnative insect pests and diseases 

are dramatically affecting trees and forests, especially 

in developed areas. In the past, Dutch elm disease 

devastated the American elms that once lined city 

streets. A current major issue in the Chicago Wilderness 

region is emerald ash borer, which has the potential to 

eliminate all ash species in the region (MacFarlane and 

Meyer 2005). The emerald ash borer has killed tens 

of millions of ash trees in the Northeast and Midwest. 

This has cost municipalities, property owners, nursery 

operators, and forest products industries tens of millions 

of dollars, and the total cost of treatment across the 

United States is estimated to be in the tens of billions 

of dollars (Kovacs et al. 2010). Although currently not 

present, Asian longhorned beetle is another species that 

poses a great risk to the area as it attacks many of the 

most common natural and planted species. 

Extreme weather events 

Current climate- and weather-related events include 

wind disturbance, winter storms, droughts, and 

floods. Tornadoes, derechos, and downbursts are 

frequent features on the landscape, damaging trees and 

subsequently property and powerlines. Ice storms occur 

occasionally in the Chicago Wilderness region, and can 

cause damage to a variety of species (Hauer et al. 2006). 

Drought can lead to reduced growth rates as well as the 

secondary effects of pest and disease infestations (Fahey 

et al. 2013). Current and future projected impacts of 

extreme weather events on forests in the region are 

discussed in Chapter 3. 

Urban heat island effect and ozone pollution

The urban heat island effect occurs when developed 

areas are hotter than nearby rural areas. The urban 

heat island effect in the Chicago Wilderness region is 

noticeable despite the cooling effects of Lake Michigan. 

Summer temperatures in Chicago are 2.2 °F (1.2 °C) 

warmer than the surrounding area, with an even greater 

effect at night: 3.4 °F (1.9 °C) warmer (Kenward et 

al. 2014). Elevated temperatures can lead to direct 

heat-related stress on urban trees and increase the rate 

of ground-level ozone formation (Jacob and Winner 

2009). Ozone pollution can have negative effects 

on photosynthesis and cause damage to leaves, and 

some species such as black cherry and red maple are 

particularly sensitive (Chappelka and Samuelson 1998, 

Pye 1988, Reich and Amundson 1985). In addition, 

ozone damage can make trees more susceptible to 

disease, damage from insects, effects of other pollutants, 

competition, and harm from severe weather.

Current Management 

Management of Natural Systems in the Region

Residents of the Chicago Wilderness region recognized 

the rapid destruction of natural areas soon after EuroAmerican colonization began. In response, they created 

forest preserves and park districts that protected the 

remaining natural areas. The Chicago Park District 

was created in 1869, and aimed to provide ample green 

space for Chicago residents to enjoy (Bachrach 2001). 

Chicago architect Jens Jensen recognized the beauty of 

the Chicago region’s natural areas, and foresaw the need 

to protect sites that were even quite distant from the 

City of Chicago. In 1914, the Forest Preserves of Cook 

County were created. The forest preserves went beyond 

the goal of preserving parks within the city to protecting 

large swaths of natural areas throughout Cook County. 

In the following decades other counties created forest 

preserve districts of their own. These preserves contain 

much of the region’s existing natural areas, but are 

strongly biased toward protection of forested areas in 

fire-protected positions.

Management of these preserves has changed over 

the past century. When preserves were first created, 

it was considered sufficient to just preserve the land. 

More recently, managers have become aware of the 

widespread changes in structure and species composition 

of the natural areas relative to historical conditions. In 

response, management with prescribed fire and removal 

of invasive species have increased.

The threats posed by invasive species, lack of 

disturbance, overabundance of white-tailed deer, and 

fragmentation are an ongoing concern in the region’s 

natural areas. Management plans such as the Biodiversity 

Recovery Plan (Chicago Wilderness 1999), Green 

Infrastructure Vision (Chicago Wilderness 2004), and 

the Oak Recovery Plan (Fahey et al. 2015) have been 

developed to address these problems. These projects 

outline broad strategies to improve the management of 

the region’s natural areas so that they will continue to 

provide habitat for wildlife, recreation for people, and 

ecosystem services for generations to come. 
The Contemporary Landscape—CHAPTER 1 | 9

Selection and Management of 

Trees in Developed Sites 

Trees that are planted in developed areas undergo 

much different stressors than trees in natural areas, and 

consequently their species composition and management 

differ as well. Trees selected for planting on streets 

and other developed areas need to be able to withstand 

challenging environmental conditions such as the urban 

heat island effect, air pollution, and soils with compaction, 

high pH, and heavy salt loads (Nowak 2012). 

A survey of municipal foresters in the Chicago 

Wilderness region showed that although tolerance of 

site conditions was the most important factor that they 

considered when selecting a tree, they considered a 

variety of other factors as well (Table 1). The second 

most important factor that municipal foresters consider 

is maintaining or enhancing species diversity in their 

forest. Many aim to plant no more than 30 percent of 

a given family, 20 percent of a genus, and 10 percent 

of a species (Santamour 1990). However, other recent 

studies suggest a more nuanced approach to managing 

for enhanced diversity (La ́can and McBride 2008). 

Trees that flower and have striking fall color are often 

preferred, and mature height of trees is important if 

the trees are to be planted under powerlines or close to 

buildings. Many foresters are reluctant to plant trees 

that require regular pruning to encourage good shape 

or to prevent breakage; instead, they prefer trees that 

can withstand storms with minimal maintenance. 

Based on the survey, municipal foresters in the Chicago 

Wilderness region are less concerned about planting 

trees that are native to the region or trees that offer 

food and shelter to wildlife. Therefore, planted species 

composition differs greatly from the native species 

assemblage.

Table 1.—Number of municipal foresters who consider 

each characteristic “most important” or “not important” 

when selecting a tree species (Source: Unpublished 

survey of 104 municipal foresters as part of the Chicago 

Region Trees Initiative, The Morton Arboretum, Lisle, IL)

Characteristic Most important Not important 

Tolerance of site conditions 91 3

Diversity of community trees 84 9

Mature height 60 11

Required maintenance 43 8

Ornamental appeal 36 10

Native status 28 22

Wildlife services 11 53

Chicago Region Trees Initiative

Efforts are ongoing to improve the health and resilience 

of the natural and planted component of the Chicago 

regional forest. Pests such as the emerald ash borer have 

caused massive destruction in the region, and there 

is broad recognition that invasive species, pests, and 

climate change could further deteriorate the regional 

Winnemac Park, Chicago. Photo by L. Darling, The Morton Arboretum, used with permission. 
10 CHAPTER 1—The Contemporary Landscape |

forest. In response, the Chicago Region Trees Initiative 

has been developed to improve management of the 

region’s trees (The Morton Arboretum 2015). This 

initiative aims to improve management of individual 

trees by promoting proper species selection, planting, 

watering, and pruning of trees. It also strives to increase 

managers’ capacity to care for their trees by offering 

training. Ultimately, the Chicago Region Trees Initiative 

aims to increase canopy cover in the region, and to 

increase the resilience of the regional forest by planting 

an appropriate diversity of tree species. 

Summary

The Chicago Wilderness region has been heavily shaped 

by agriculture and urban development for more than 

150 years, which has resulted in dramatic changes in 

the landscape from its previous condition. The area was 

once dominated by open, fire-adapted systems such 

as savannas, prairies, and oak woodlands. Now it is a 

mosaic of land uses with a mix of native and nonnative 

species that are less fire-tolerant. Human land use has 

dramatically altered the soils in the region, and created 

an environment where many nonnative invasive species 

and forest pests and pathogens can thrive. Foresters 

and other natural resource managers in the area are 

balancing many needs as they work to select tree species 

that are able to cope with the realities of the harsh urban 

environment.

Key Points

• Before Euro-American settlement, open oak 

ecosystems such as savannas and woodlands 

dominated the Chicago Wilderness region.

• Today, most of the natural areas have been lost 

to urbanization and suburban sprawl. The only 

remaining natural areas in the region are fragmented 

and are a small fraction of their original size.

• Key stressors to forests in the region include 

development, soil alteration, alteration of historical 

fire regimes, air pollution, the urban heat island effect, 

extreme weather events, invasive plants, insect pests, 

and diseases. 

• Current management of natural areas in the region 

focuses on restoring native ecosystems and simulating 

past disturbance regimes.

• Selection of trees for urban plantings weighs other 

factors besides native status, such as tolerance of site 

conditions, diversity of community trees, tree height, 

required maintenance, and ornamental appeal.

A municipal park in Glencoe, IL. Photo by T. McDonald, Glencoe Park District, used with permission.
Climate Trends and Projections—CHAPTER 2 | 11

CHAPTER 2

CLIMATE TRENDS AND PROJECTIONS 

Climate, the long-term average weather for a particular 

place, can change dramatically on the scale of thousands 

of years. After the last ice age about 10,000 years ago, 

the climate in the Chicago Wilderness region shifted 

from cool and moist to hot and dry, before eventually 

becoming what we are familiar with today. Although 

the climate of the area has changed in the past, it is 

the rate of change that is of primary concern today. 

Temperature and precipitation are changing rapidly at 

a global scale and are projected to change at an even 

faster rate in the coming decades (Intergovernmental 

Panel on Climate Change [IPCC] 2014). These 

changes will manifest themselves differently in different 

areas and need to be summarized at a local level to 

be relevant for decisionmaking. To aid in evaluating 

these local changes, this chapter discusses our current 

understanding of past and projected future changes in 

climate in the Chicago Wilderness region.

Temperature and 

Precipitation Trends

Measurements of temperature and precipitation at 

weather stations in the area have been recorded for a little 

over 100 years. We used the ClimateWizard Custom 

Analysis tool to present the changes in temperature 

and precipitation across the Chicago Wilderness region 

(ClimateWizard 2011, Girvetz et al. 2009). Data for 

the tool are derived from PRISM (Parameter-elevation 

Regressions on Independent Slopes Model; Gibson et 

al. 2002), which models historical, measured point data 

onto a continuous 2.5-mile grid over the entire United 

States. We examined long-term (1901 through 2011) 

trends for annual, seasonal, and monthly temperature 

(mean, mean minimum, and mean maximum) and total 

precipitation within the Chicago Wilderness region. 

Accompanying tables and figures present the change 

over the 111-year period estimated from the slope of 

the linear trend. In the following text, we highlight 

increasing or decreasing trends for which we have 

high confidence that they did not occur by chance. For 

more precise information about how these trends were 

calculated, levels of confidence, and caveats related to the 

data presented, refer to Appendix 2. Please note that the 

information presented here is meant to give the reader a 

general overview of regional trends in climate and is not 

intended for interpretation at a particular location. More 

information on historical trends in climate for specific 

weather stations can be found online (see the “More 

Climate Information” box on the next page).

Temperature

The Chicago Wilderness region has gotten warmer over 

the past century. From 1901 through 2011, mean annual 

temperatures increased by about 1 °F (0.6 °C) across 

the region (Fig. 9). Increases were larger around Lake 

Michigan and more pronounced in spring and summer. 

Increases were especially pronounced in nighttime 

minimum temperatures across all seasons. Summer 

minimum temperatures increased by 2.5 °F (1.4 °C) 

on average across the region. Changes in summer 

maximum temperatures differed geographically, with 

increases around the City of Chicago and several other 

areas along the shore of Lake Michigan and decreases 

throughout the southern part of the region, away from 

the lake. Winters have also become slightly milder, 

especially minimum temperatures in the area directly 

adjacent to Lake Michigan. 

Precipitation

Precipitation has also increased across the region on 

average (Fig. 10). The greatest increases were around 

the City of Chicago and around southwestern McHenry 

County, IL. Across the entire area, increases were 

greatest in summer (3 inches). Spatial patterns in 

precipitation varied by season. Spring increases were 

primarily in southeastern Wisconsin and Lake and 

McHenry Counties, IL. Fall increases were primarily in 

Indiana along the Lake Michigan shore. The “Changes 

in Climate” sidebar on page 14 discusses changes in 

regional temperature and precipitation in the early 21st

century.
CHAPTER 2—Climate Trends and Projections 12 |

More Climate Information

Chicago Climate Action Plan 

To assess the impacts of climate change and to develop a plan for the future, the City of Chicago consulted 

leading scientists to describe various scenarios for Chicago’s climate future and how the scenarios would 

affect life in the city. This effort resulted in the Chicago Climate Action Plan (www.chicagoclimateaction.org) 

and the report Climate Change and Chicago: Projections and Potential Impacts (Hayhoe et al. 2008). 

Chapter 2 of the report summarizes past and projected changes in climate: www.chicagoclimateaction.org/

filebin/pdf/report/Chicago_climate_impacts_report_Chapter_Two_Climate.pdf

Chapter 3 summarizes past and projected changes in water cycling: www.chicagoclimateaction.org/filebin/

pdf/report/Chicago_climate_impacts_report_Chapter_Three_Water.pdf

Climate Adaptation Toolkit

The Chicago Metropolitan Agency for Planning developed the Climate Adaptation Toolkit for communities 

interested in adapting their planning and investment decisions to a changing climate. Appendix A of this 

toolkit, “Primary Impacts of Climate Change in the Chicago Region,” summarizes past and projected 

changes in temperature and precipitation: www.cmap.illinois.gov/livability/sustainability-climate-change/

climate-adaptation-toolkit

State Climatologists

State climatologists provide information about current and historical trends in climate throughout their states. 

The state climatologists for each of the four states within the Chicago Wilderness region have a wealth of 

information on trends in climate for their state:

•Illinois: www.isws.illinois.edu/atmos/statecli/index.htm

•Indiana: climate.agry.purdue.edu/climate/index.asp

• Michigan: climate.geo.msu.edu

• Wisconsin: www.aos.wisc.edu/~sco

Midwestern Regional Climate Center

The Midwestern Regional Climate Center (MRCC) is a cooperative program between the National Centers for 

Environmental Information (formerly the National Climatic Data Center) and the Illinois State Water Survey. 

The MRCC serves the nine-state Midwest region (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, 

Missouri, Ohio, and Wisconsin). It provides high-quality climate data, derived information, and data 

summaries for the Midwest: mrcc.isws.illinois.edu

Great Lakes Integrated Sciences and Assessments 

Great Lakes Integrated Sciences and Assessments (GLISA) integrates information from a wide array of 

scientific fields, develops collaborations between entities with similar goals, and helps inform decisionmakers 

throughout the region with sound science. GLISA offers a unique approach to building climate literacy and 

long-term sustainability, and facilitating smart decisionmaking across the eight Great Lakes states (Illinois, 

Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin) and the province of Ontario: 

glisa.umich.edu

Scenarios for Climate Assessment and Adaptation

This Web site contains a suite of climate and other scenarios produced as input to the U.S. National Climate 

Assessment. There are documents, graphics, references to datasets, and other resources that have been 

prepared to depict a range of plausible future conditions against which risks, vulnerability, and opportunities 

can be assessed at regional and national scales. For Midwest reports and graphics: scenarios.globalchange.

gov/regions/Midwest
Climate Trends and Projections—CHAPTER 2 | 13

Figure 9.—Change in annual and seasonal mean daily mean, daily minimum, and daily maximum 

temperature (°F) in the Chicago Wilderness region from 1901 through 2011. Stippling indicates there 

is less than 10-percent probability that the trend could have occurred by chance alone. Source: 

ClimateWizard (2011).
CHAPTER 2—Climate Trends and Projections 14 |

Figure 10.—Change in annual and seasonal precipitation 

(inches) in the Chicago Wilderness region from 1901 through 

2011. Stippling indicates there is less than 10-percent 

probability that the trend could have occurred by chance alone. 

Source: ClimateWizard (2011).

Early 21st-Century

Changes in Climate

Although weather fluctuates from year to year, 

temperatures in the early 21st century have 

continued to rise. The decade from 2001 

through 2010 was the warmest on record both 

globally and averaged across North America 

(World Meteorological Organization 2012). In the 

Chicago Wilderness region, temperatures were 

also generally higher than average between 2001 

and 2012. In fact, the year 2012 was the warmest 

on record for Illinois and Wisconsin, and the 

second warmest for Indiana (National Oceanic 

and Atmospheric Administration [NOAA] National 

Climatic Data Center [NCDC] 2013). 

The winter of 2013–2014 was a newsworthy 

event that introduced many people to the 

concept of the polar vortex, a large pocket of 

very cold air that typically sits over the polar 

regions during the winter season. That winter, 

however, the polar vortex shifted over the 

eastern United States, sending temperatures 

plummeting. Snowfall in Chicago was the third 

highest and temperatures were the third coldest 

on record for the December to February period 

(NOAA National Weather Service 2015b, 2015c). 

Similar cold periods were experienced during the 

winter of 2014–2015. Despite these cold winters, 

the overall trend has been toward increasing 

winter temperatures in the area. 

Trends in precipitation from 2000 to the present 

across the assessment area indicate a continuing 

pattern toward wetter conditions. The year 2012 

was an exception, with the area experiencing 

drought conditions that had not occurred in the 

region for many decades (NOAA NCDC 2013). 

However, the trend of rising precipitation could 

be seen again in spring 2013, when precipitation 

was 6 inches higher than the 1971 through 2000 

seasonal average (NOAA National Weather 

Service 2015a). 

These recent climatic events point to the fact that 

seasonal and year-to-year variability will continue 

to be important. Thus, examining a range of 

potential futures will be key when assessing 

future vulnerability to climate change. 
Climate Trends and Projections—CHAPTER 2 | 15

Temperature and Precipitation 

Projections 

Models that are used to simulate climate projections are 

called general circulation models (GCMs). These 

models simulate physical processes in the Earth’s 

surface, oceans, and atmosphere through time using 

mathematical equations in three-dimensional space. 

GCMs require information about changes in 

greenhouse gas concentrations to project future climates, 

and these changes must be estimated. The IPCC set of 

standard emissions scenarios has been widely used to 

estimate future greenhouse gas emissions under a range 

of potential futures (IPCC 2007). Throughout this 

document, we report climate projections for two modelemissions scenario combinations—GFDL A1FI and 

PCM B1 (unless otherwise noted)—that were used in 

the IPCC Fourth Assessment (IPCC 2007). The 

GFDL A1FI model-scenario combination represents a 

higher-end projection for future temperature increases, 

and the PCM B1 represents a lower end (see the 

sidebar). It is possible that actual emissions and 

temperature increases could be lower or higher than 

either of these projections. Based on current trends, 

however, the GFDL A1FI scenario represents a more 

likely projection of future greenhouse gas emissions and 

temperature increases. The future is likely to be different 

from any of the developed scenarios, and therefore we 

encourage readers to consider the range of possible 

climate conditions over the coming decades rather than 

one particular scenario. In addition, model projections 

change as new information becomes available and new 

greenhouse gas emissions scenarios are developed. 

General circulation models simulate climate conditions 

at relatively coarse resolutions. One pixel in a GCM can 

cover a large portion of a state, which is not very useful 

for making local decisions. One method of projecting 

climate at finer resolutions is statistical downscaling, 

a technique by which statistical relationships between 

GCM model outputs and on-the-ground measurements 

are derived for the past and used to adjust large-scale 

GCM simulations of the future for much finer spatial 

scales. Downscaling can help with visualizing local 

variability in climate projections due to differences 

in topography or proximity to large bodies of water. 

Although downscaling can be a useful tool, it is 

important to keep in mind that this additional layer 

of analysis can add uncertainty and error to climate 

projections and give a false impression that the accuracy 

Models and Scenarios 

Used in this Report

This assessment uses two model-scenario 

combinations from the IPCC Fourth Assessment 

Report (AR4; IPCC 2007). We chose to use 

these models and scenarios instead of the newer 

model results from the IPCC Fifth Assessment 

Report (AR5; IPCC 2013) because (1) the 

habitat suitability and hardiness and heat zone 

projections were available only for the older 

dataset and (2) the downscaled projections 

were not widely available when we started 

this assessment. We also chose to use this 

dataset for consistency with other vulnerability 

assessments that we performed using similar 

methods (e.g., Brandt et al. 2014).

There are some minor differences in the 

projections between AR4 and AR5. AR4 used 

emissions scenarios, and AR5 used resource 

concentration pathways (RCPs). Although the 

assumptions behind AR4 and AR5 are different, 

projections of greenhouse gas concentrations 

and global temperature are similar between 

the A1FI emissions scenario and RCP 8.5. 

Greenhouse gas concentrations are also 

similar between the B1 emissions scenario 

and RCP 4.5 at the end of the century, though 

the concentrations at mid-century are higher 

for RCP 4.5. Besides changes in emissions 

scenarios, models were also updated to reflect 

better understanding of the Earth’s physical 

processes and often perform at a finer spatial 

resolution than AR4. The overall magnitude 

and direction of change are generally similar 

in the Chicago Wilderness region between the 

two IPCC datasets, especially when comparing 

within models and analogous scenarios (Sun 

et al. 2015). Thus, we have high confidence 

that the changes projected using the earlier 

scenario-model combinations are representative 

of our current understanding of projected climate 

changes in the Midwest. 
CHAPTER 2—Climate Trends and Projections 16 |

of climate projections is greater than it actually is 

(Daniels et al. 2012). 

In this assessment, daily mean, minimum, and 

maximum temperature and total daily precipitation 

data were downscaled to an approximately 7.5-mile 

grid scale across the United States and then visualized 

and summarized for the Chicago Wilderness region 

(Hayhoe 2010, Stoner et al. 2013). To visualize changes, 

we calculated the average daily mean, minimum, and 

maximum temperature for each season and the entire 

year for three 30-year time periods (2010 through 

2039, 2040 through 2069, 2070 through 2099). Daily 

precipitation values were summed by year and season, 

and 30-year means were calculated. We subtracted 

temperature and precipitation values from the mean 

values for 1971 through 2000 as a baseline to determine 

the departure from current climate conditions. 

Historical climate data used for the departure analysis 

were taken from ClimateWizard based on the PRISM 

dataset (Girvetz et al. 2009) (see Appendix 2). Results 

for the end of the century (2070 through 2099) are 

reported in this chapter; results for early and midcentury are available in Appendix 3. 

Temperature 

There is general agreement among models that 

temperatures will increase, and at a faster rate than what 

has been experienced over the 20th and early 21st century. 

Both models project increases in mean, minimum, and 

maximum temperatures across all time periods and for 

all seasons. Mean annual daily temperature across the 

region is projected to increase by 8.2 °F (4.5 °C) under 

the GFDL A1FI scenario and 2.3 °F (1.2 °C) under 

PCM B1 for the final 30 years of the 21st century 

(Fig. 11) (see also Appendix 3) compared to the 1971 

through 2000 baseline. The most dramatic increase 

in temperature is projected to be in summer for the 

GFDL A1FI scenario and in winter for the PCM B1 

scenario. No spatial variation in temperature changes is 

discernable. Increases are projected to be slightly greater 

in minimum temperatures than maximum temperatures, 

with the exception of summer for the GFDL A1FI 

scenario (Figs. 11–13). 

Precipitation

There is less certainty about how precipitation patterns 

may change in the future. The magnitude and seasonal 

direction of projected changes in precipitation are not 

consistent between the two models presented. Mean 

annual precipitation is projected to increase by only 0.7 

inches under the GFDL A1FI scenario for the final 30 

years of the 21st century (Fig. 14) (see also Appendix 3) 

compared to the 1971 through 2000 baseline. In 

contrast, annual precipitation is projected to increase by 

an average of 3.8 inches for the PCM B1 scenario. 

Changes in precipitation are projected to vary greatly by 

season. Both models project an increase in precipitation 

in winter and spring. They differ in projections for 

summer and fall. PCM projects an increase of 1.8 

inches, whereas GFDL projects a decrease of 5.8 inches 

Downed trees being harvested for reuse after a storm in a Chicago park. Photo by J. Scott, Chicago Park District, used with 

permission.
Climate Trends and Projections—CHAPTER 2 | 17

Figure 11.—Projected difference in mean daily mean temperature (°F) in the Chicago Wilderness 

region at the end of the century (2070 through 2099) compared to baseline (1971 through 2000) for 

two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
CHAPTER 2—Climate Trends and Projections 18 |

Figure 12.—Projected difference in mean daily minimum temperature (°F) in the Chicago Wilderness 

region at the end of the century (2070 through 2099) compared to baseline (1971 through 2000) for 

two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
Climate Trends and Projections—CHAPTER 2 | 19

Figure 13.—Projected difference in mean daily maximum temperature (°F) in the Chicago Wilderness 

region at the end of the century (2070 through 2099) compared to baseline (1971 through 2000) for 

two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
CHAPTER 2—Climate Trends and Projections 20 |

in summer. In the fall, PCM projects a decrease of 0.9 

inches, but GFDL projects an increase of 1.4 inches. 

Trends and Projections in 

Extreme Weather Events

Extreme weather events, such as extreme temperatures, 

high winds (e.g., derechos), tornadoes, thunderstorms, 

and winter ice storms and snowstorms are important 

disturbance agents to trees in both urban and natural 

areas. Some evidence suggests that extreme events have 

been increasing across the United States and globally 

over recent decades, and this increase is consistent with 

global climate change (Coumou and Rahmstorf 2012, 

Kunkel et al. 2008, Peterson et al. 2013). The next 

subsections summarize how these extreme events have 

changed during the historical record and are projected to 

change over the next century based on models. 

Temperature Extremes

In addition to changes in means, temperature extremes 

are also likely to shift across the region. Increases in 

consecutive days with extremely high temperatures (heat 

waves) are a major concern in urban areas, where the 

urban heat island effect can make temperatures even 

more severe. The heat wave in July 1995 in Chicago, 

for example, was one of the worst weather-related 

disasters in Illinois history with more than 700 deaths 

over a 5-day period (Changnon et al. 1996, Kunkel et 

al. 1996). Extreme heat can also have negative effects 

on trees, leading to leaf scorch, root damage, and 

mortality. Today, Chicago experiences on average one 

more 3-day or greater heat wave per year than in the 

mid-20th century, leading to devastating effects on 

people and the environment (Perera et al. 2012). Despite 

the rising trend, extreme heat was more prominent in 

the early part of the 20th century (especially during the 

Dust Bowl era of the 1930s) than it has been in the 

past several decades (Kunkel et al. 2013). Although 

daytime maximum temperatures have not increased 

as much, there has been a significant increase in the 

nighttime minimum temperatures during heat wave 

events (Kunkel et al. 2013, Perera et al. 2012). High 

nighttime temperature can lead to water loss in trees and 

other vegetation, which could be especially harmful if 

summers are also drier (Zeppel et al. 2012).

Studies from across the Midwest indicate that there will 

be more days per year that are warmer than 95 °F 

(35 °C) and a greater frequency of multi-day heat 

waves over the 21st century (Diffenbaugh et al. 2005, 

Kunkel et al. 2013, Winkler et al. 2012). Under a high 

emissions scenario, the Chicago region could experience 

more than 30 heat waves like the one in 1995 (which 

was characterized as more than 7 consecutive days with 

a maximum daily temperature of greater than 90 °F, 

or 32 °C, and nighttime minimum of greater than 

70 °F, or 21 °C) in the last 10 years of the 21st century 

(Hayhoe et al. 2008). In addition, there could be more 

than 70 days per year warmer than 90 °F by the end 

of the century. The high humidity in the area is also 

projected to worsen, leading to a higher heat index and 

making temperatures feel even hotter (Vavrus and Van 

Dorn 2010). This could have negative effects on human 

health and mortality, and heighten the need for cooling 

measures such as increased tree canopy.

Extreme cold can be damaging to trees, and is the 

foundation for northern range limits of many species. It 

can also be a limiting range factor for tree pests such as 

the emerald ash borer (DeSantis et al. 2013). Despite 

the extreme cold during the winter of 2013–2014, the 

frequency and intensity of periods of extremely cold 

days (cold waves) in the Midwest have been decreasing, 

although there is no statistically significant trend 

(Kunkel et al. 2013). Extremely cold days that are less 

than 10 °F (-12 °C) are projected to decrease in the 

area by 15 to 20 days by mid-century compared to the 

average for 1980 through 2000 (Kunkel et al. 2013). 

One study for the Chicago area suggests that the lowest 

temperature of the year may increase at nearly double the 

rate of average winter temperatures and the frequency of 

nights less than 0 °F (-18 °C) may decline by 50 to 

90 percent (Vavrus and Van Dorn 2010). Despite an 

overall decreasing trend, extremely cold winters, like the 

one experienced in 2013–2014, may potentially continue 

to occur in the future. Some research indicates that 

the 2013–2014 cold wave was due to a weakened polar 

vortex that may have been driven by human-induced 

warming (Wang et al. 2014). This is still an active 

area of research, and more research is needed before 

we can understand the long-term implications of this 

phenomenon for the Midwest. 

Heavy Precipitation Events

Heavy precipitation events are a particular issue in 

urban areas, where stormwater runoff can overwhelm 

sewer systems and lead to local flooding. Studies suggest 

that heavy precipitation has become more frequent and 

intense in the United States over the past several decades 

(Groisman et al. 2012, Kunkel et al. 2008). Across the 

central United States, very heavy (greater than 3 inches 
Climate Trends and Projections—CHAPTER 2 | 21

Figure 14.—Projected difference in mean annual and seasonal precipitation (inches) in the Chicago 

Wilderness region at the end of the century (2070 through 2099) compared to baseline (1971 through 

2000) for two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
CHAPTER 2—Climate Trends and Projections 22 |

per day) and extreme (greater than 6 inches per day) 

precipitation events increased in the period from 1979 to 

2009 compared to the 1948 to 1978 period (Groisman 

et al. 2012). A recent report examined trends in heavy 

precipitation events for every state in the Midwest from 

1961 to 2011 (Saunders et al. 2012). The authors found 

that precipitation events of 3 inches or more increased 

by 26 percent in Illinois, and increases were even greater 

in Indiana (77 percent), Michigan (54 percent), and 

Wisconsin (92 percent) (Saunders et al. 2012). Another 

report shows that there has been an increase in the 

number of “100-year” (7.58 inches or greater) and 

“10-year” (4.47 inches or greater) storm events in 

Chicago in recent decades (Chicago Metropolitan 

Agency for Planning 2013).

Climate models project a global increase in the number 

of heavy precipitation events by the end of the century 

(IPCC 2012, 2014). There is greater agreement among 

models at high latitudes and in the tropics, but model 

projections for the central United States suggest a 

potential increase in these events, especially during 

winter months (IPCC 2012). Other future climate 

projections indicate that the Midwest may experience 

2 to 4 more days of extreme precipitation by the end 

of the century (Diffenbaugh et al. 2005). However, 

downscaled projections for the Midwest indicate 

less projected change in heavy precipitation events 

(greater than 1 inch) in the southern half of the region 

(including the Chicago area) than in the Midwest as 

a whole (Kunkel et al. 2013). A study that specifically 

focused on the Chicago area suggests that the frequency 

of extremely wet days (days with greater than 1.57 

inches of precipitation) will increase by 25 to 60 percent 

by the end of the century (Vavrus and Van Dorn 2010). 

These increases are greater than the increase in total 

precipitation, indicating there will also be a decrease 

in the number of days with light precipitation. Similar 

patterns of increased heavy precipitation were also 

projected by Hayhoe et al. (2010). 

Thunderstorms, Tornadoes, and Wind Storms

Thunderstorms, tornadoes, and wind storms are 

common phenomena in the Chicago area, and can 

lead to tree damage from lightning strikes and strong 

winds. There is no evidence of a change in the severity 

or frequency of thunderstorms across the United 

States over the past 100 years (Kunkel et al. 2008). 

Thunderstorms are reported as days when thunder 

audibly occurs; therefore, there is a propensity toward 

human error and inconsistency in record-keeping for 

these measurements (Changnon 2003). At first glance, 

the historical record seems to indicate an increase in the 

total number of tornadoes in the United States over the 

past century (Diffenbaugh et al. 2008). However, this 

trend is largely the result of an increase in the detection 

of tornadoes through technological enhancements and 

improved monitoring networks (Kunkel et al. 2008). 

It also appears that the number of severe tornadoes in 

Flooded open land, Hazel Crest, IL. Photo by K. Persons, Village of Hazel Crest Open Lands Commission, used with permission.
Climate Trends and Projections—CHAPTER 2 | 23

the United States has decreased over the past century 

(Diffenbaugh et al. 2008). However, the severity of 

a tornado is determined not by its wind speed but 

by the level of damage done to structures. Building 

construction has also changed over the past century, so 

it is difficult to discern whether we are observing weaker 

storms or simply less damage because of changes in 

construction practices. There are also many issues with 

collecting reliable wind data in order to detect trends. 

Based on available data, one study found no increasing 

or decreasing trend in wind speed between 1979 and 

2006 (Pryor and Ledolter 2010).

Although there are no clear trends for the past, 

modeling research suggests severe storms may become 

more frequent in the future. GCMs do not operate 

at a scale small enough to model thunderstorms 

explicitly, but a few studies have found a slight increase 

in the frequency of conditions favorable for intense 

thunderstorms in the Midwest by the end of the century 

(Trapp et al. 2007, 2009). Tornadoes are a result of 

both convective available potential energy (CAPE) 

and wind shear. In general, current global climate 

models suggest that CAPE may increase, while wind 

shear may decrease (Diffenbaugh et al. 2008, 2013). 

The balance of these two forces, as well as potential 

seasonal and geographic shifts in that balance, has been 

a key question. A recent study using the most recent 

set of climate models for the IPCC Fifth Assessment 

Report sheds new light on this question (Diffenbaugh 

et al. 2013). That study suggests that the timing of 

the decrease in wind shear is offset from the days with 

increased CAPE, indicating that conditions for severe 

storms, including tornadoes, may increase. For the 

Chicago area, this increase appears to be especially 

concentrated in the spring, and secondarily in the fall. 

As temperatures increase, this could mean a longer 

season for thunderstorms and tornadoes. Some research 

has also examined projected changes in wind speed, but 

currently there is insufficient information to tell whether 

wind speeds are likely to increase or decrease in the 

Midwest (Winkler et al. 2012). 

Winter Storms 

Winter storms are common in the Chicago area, 

occurring about one to two times per year on average 

(Changnon 2006). From 1949 through 2003, there 

appeared to be neither a negative nor a positive trend 

in the number of winter storms in the central United 

States (including the Chicago area). However, there was 

a trend toward an increasing amount of damage from 

those storms due to both an increase in infrastructure 

and an increase in storm intensity, which was 

interpreted as a trend consistent with increased warming 

(Changnon 2007). 

Ice storms can be particularly damaging to trees in the 

region, leading to stem and branch breakage and crown 

loss (Hauer et al. 2006). Ice storms are a severe form of 

a freezing rain event. The Chicago area has on average 

3 to 4 days of freezing rain events per year, which can 

occur between November and April, with a peak in 

January (Changnon and Karl 2003). A study examining 

changes in freezing rain across the United States from 

1949 through 1999 showed a “U-shaped” pattern in the 

number of freezing rain events in the Chicago area; the 

number decreased between 1949 and 1989 and then 

increased during the 1990s, without an overall upward 

or downward trend (Changnon and Bigley 2005). 

Warming temperatures may lead to a decrease in the 

overall frequency of ice storms and snowstorms due to 

a reduction in the number of days that are cold enough 

for those events to occur. However, there is also some 

evidence to suggest that these events could be more 

intense when they do happen (Vavrus and Van Dorn 

2010). Wang and Zhang (2008) examined changes in 

risk of extreme precipitation during the winter months 

under the A2 emissions scenario using statistically 

downscaled climate projections. They found an 

increased risk for extreme winter events at the end of the 

century for the Chicago Wilderness region. Whether 

these events occur as rain, snow, or ice will depend on 

the exact timing of these events and their interaction 

with projected changes in temperature. In general, more 

research is needed before we can determine the most 

likely effects of future climate change on winter storms. 

Drought

Droughts place great stress on trees in both urban and 

natural areas, and can often lead to secondary effects 

of insect and disease outbreaks on stressed trees (Fahey 

et al. 2013). No studies on droughts are available 

specifically for the Chicago Wilderness region, but 

information is available for the states of Illinois and 

Indiana. Over the past century (1916 to 2007), the 

frequency of extreme and exceptional droughts in 

Illinois and Indiana decreased (Mishra et al. 2010). 

Exceptional droughts are the most severe form of 

drought in the region, and extreme droughts are the 

second most severe. Until the recent drought of 2012, 

all of the exceptional droughts were prior to 1970, and 

the majority of them occurred during the Dust Bowl 
CHAPTER 2—Climate Trends and Projections 24 |

era of the 1930s. In general, more-recent drought events 

have been less intense in their severity, duration, and 

spatial extent than in the 20th century. However, the 

1988 drought was the fifth-driest year on record in 

Illinois and led to severe water shortages throughout 

the state (Lamb 1992). In addition, the 2012 drought 

was the most extensive drought on record across the 

United States since 1956 (NCDC 2012). Changes in 

precipitation coupled with warmer temperatures are also 

likely to lead to changes in drought characteristics, such 

as intensity, duration, frequency, and spatial extent. One 

study suggests that extreme and exceptional droughts in 

Illinois and Indiana may increase in duration, frequency, 

and spatial extent compared to the last 30 years of 

the 20th century (Mishra et al. 2010). These findings 

are consistent with a global-scale study that found a 

projected increase in drought frequency, duration, and 

severity across the central United States (Sheffield 

and Wood 2008). However, there is a lot of model 

uncertainty in summer precipitation, and thus we cannot 

say with high certainty the magnitude and direction of 

changes in droughts in the Chicago region over the next 

century (Kunkel et al. 2013). 

Changes in Soils and Hydrology

Information about how temperature and precipitation 

patterns may change in the Chicago area can further 

be used to examine how these changes may affect the 

cycling of water in urban and natural environments. 

Across the globe, increases in temperature are projected 

to intensify the hydrologic cycle, leading to greater 

evaporative losses and more heavy precipitation events 

(IPCC 2007). This can lead to changes in streamflow, 

soil moisture, and drought conditions. 

Soil Moisture 

Adequate soil moisture is important for supporting tree 

growth. From 1916 to 2007, annual soil moisture in 

the top 4 inches increased in the area around Chicago 

by about 0.02 inches, which is small but enough to be 

statistically distinguishable from chance (Mishra et al. 

2010). Total soil moisture (down to 6.6 feet) apparently 

did not change, however (Mishra et al. 2010). 

Changes in soil moisture are largely driven by the 

balance of precipitation and evapotranspiration (the sum 

of evaporation and transpiration by plants), and thus 

there is some uncertainty about future changes. Based 

on projected decreases in precipitation during summer 

and fall and increases in temperature throughout the 

year, one study found that surface soil moisture was 

projected to decrease in Illinois and Indiana over 

the next century (2009 to 2099) by a small amount, 

primarily during late summer, fall, and winter (1.2 to 

1.6 percent, depending on scenario; Mishra et al. 2010). 

Total soil moisture was also projected to decrease in the 

late summer and fall and increase in winter and spring. 

Another study in the region suggests a decrease in soil 

moisture during winter and early spring and increases 

in soil moisture during the growing season (Winter and 

Eltahir 2012). The difference between the two studies 

suggests that model assumptions made and scenarios 

chosen can have a large impact on projections of future 

soil moisture in the Midwest. 

Soil Frost and Freeze-Thaw

The duration and depth of soil frost and the number 

of freeze-thaw cycles can also affect winter and spring 

hydrologic cycles in the Midwest. An increase in frozen 

soil can lead to increases in spring peak flows due to a 

reduction of water infiltration into the soil. Soil frost can 

also increase water storage in the soil over the winter 

months. Soil temperatures in winter, and thus soil frost, 

can be influenced by changes in air temperature and 

the amount and duration of snowpack. In the Indiana 

portion of the Chicago Wilderness region, the duration 

of seasonal soil frost decreased from 1991 to 2006, but 

did not change for the rest of the region (Sinha et al. 

2010). Temperatures hovering around the freezing point 

can increase the frequency of freeze-thaw events, which 

can increase susceptibility to soil erosion. Historical 

data indicate that the frequency of freeze-thaw events in 

Chicago averages around 6.5 events per year. There are 

no clear upward or downward trends in the number of 

freeze-thaw cycles in the region.

As temperatures increase, some changes in soil frost are 

expected. One study suggests that over the next century, 

the Chicago Wilderness region may have a dramatic 

reduction in soil frost, up to 50 fewer days by the end 

of the century, from both an earlier spring and a later 

fall (Sinha and Cherkauer 2010). With a shorter frost 

season, a reduction in frost depth is also projected to 

occur. The number of freeze-thaw cycles in the region is 

projected to stay near the current average. 

Snowfall and Snow Cover

Snow plays an important role in the hydrology of the 

Chicago Wilderness region. The amount of snow 

influences annual runoff, recharge, and water supplies 

and can have local effects on temperature through its 
Climate Trends and Projections—CHAPTER 2 | 25

reflectivity (albedo). In addition, rapid melting after 

a large snowfall event can lead to flooding. Snow can 

also be an insulator to tree roots, protecting them from 

temperature extremes. Between 1981 and 2010, the 

region received an average of about 30 to 72 inches 

per year, with the greatest snowfall on the eastern side 

of Lake Michigan (Kunkel et al. 2013). According to 

the Illinois state climatology office, statewide snowfall 

has decreased in the most recent 20 years and is below 

the long-term average (Angel 2012b). However, some 

areas around Lake Michigan have had an increase in 

precipitation, particularly on the eastern side (Kunkel et 

al. 2013). Also, as mentioned earlier, snowfall intensity 

is increasing, meaning that more snow is falling in heavy 

storms when it does occur (Changnon 2007). There 

is also a trend toward earlier snowmelt and decreasing 

snow depth in the area, consistent with a warming trend 

(Dyer and Mote 2006). 

Future projected changes in snow are complex. The 

total amount of snow may not change substantially 

because, despite a shorter cold season, increased winter 

precipitation may create a greater probability of snow 

on any cold days that do occur (Hayhoe et al. 2008). 

However, another regional study suggests that the 

increase in temperature would be sufficient to reduce 

snowfall, with decreases in snow water equivalent 

up to 80 percent by the end of the century (Sinha 

and Cherkauer 2010). Projections for southeastern 

Wisconsin also indicate a similar reduction in 

snowfall (Notaro et al. 2011). Neither of these studies 

accounts for “lake effect” snow, which may increase 

on the eastern side of Lake Michigan (including 

northwestern Indiana and southwestern Michigan) 

as lake temperatures warm and ice cover decreases 

(see the “Lake Effect” box on the next page) (Wright 

et al. 2013). On both the west and east sides of Lake 

Michigan, any snow that does fall is more likely to fall 

in heavy storm events (Vavrus and Van Dorn 2010, 

Wang and Zhang 2008). Regardless of changes in the 

amount of snowfall, snow cover duration and extent 

may decrease, and at a faster rate than it did during the 

20th century (Brown and Mote 2009, Frei and Gong 

2005). By the end of the century, the number of days 

with snow on the ground is projected to decrease from 

an annual average of 40 days (between 1961 and 1990) 

to between 13 and 23 days per year (Hayhoe et al. 

2008).

Streamflow, Runoff, and Flooding

Streamflow is an important indicator of flood risk 

and affects important decisions about water use and 

management. It can be difficult to attribute any trends in 

streamflow specifically to climate change, as there have 

been large-scale land-use changes in the area that can 

obscure any climate-related signal (Tomer and Schilling 

2009, Zhang and Schilling 2006). A study examining 

trends in streamflow in the Mississippi River Basin 

from 1940 to 2003 showed a trend toward increasing 

streamflow across the Midwest, mostly due to an 

increase in baseflow attributed to agricultural land-use 

Winter in Hazel Crest, IL. Photo by K. Persons, Village of Hazel Crest Open Lands Commission, used with permission.
CHAPTER 2—Climate Trends and Projections 26 |

Lake Effect

Lake Michigan exhibits a strong influence on the climate and hydrology of the Chicago Wilderness region. 

The presence of Lake Michigan moderates temperature, reducing the annual difference between summer 

highs and winter lows and the daily difference between nighttime and daytime temperatures (Notaro et al. 

2013). The lake effect also increases precipitation during both winter (especially in the areas east of the lake) 

and summer in the Chicago Wilderness region compared to what it would be if the lake were not present 

(Notaro et al. 2013). Other major influences of Lake Michigan on the area include greater cloud cover, higher 

winter winds, and higher humidity in winter months compared to the surrounding area (Notaro et al. 2013). 

Lake Michigan can also cause an increase in winter storms, especially on the east side in December and 

January, before much of the lake freezes over (Notaro et al. 2013). 

Lake Michigan will continue to influence the climate in its vicinity over the next century, and large-scale 

changes in climate may alter its influence. As temperatures increase, the length of time that the lake remains 

frozen and the total area that freezes are likely to decrease, or the lake may stop freezing altogether. In fact, 

ice cover on Lake Michigan decreased by 77 percent from 1973 to 2010 (Wang et al. 2012). This reduction 

in ice cover and an increase in lake temperatures could cause a decrease in lake levels, and thus changes 

in the coastline and water availability, due to an earlier start to the evaporation season (Lenters et al. 2013). 

Lake levels have already been declining in Lake Michigan: the lake reached its lowest recorded level in 

2013 after a particularly hot, dry year (Gronewold and Stow 2014). However, there is much uncertainty in 

the projections of future lake levels; some models project an increase and others a decrease (Angel and 

Kunkel 2010, Hayhoe et al. 2010, Lofgren et al. 2011, MacKay and Seglenieks 2013). A loss of ice cover can 

also result in an increase in lake-effect snow: a recent modeling study found that a warmer, ice-free Lake 

Michigan could lead to a 50-percent increase in lake-effect snow downwind of the lake (i.e., on its east side), 

and the area with the greatest lake-effect precipitation could shift inland (Wright et al. 2013). 

As the climate changes at global and continental scales, it will be important to keep in mind how these 

changes may affect the influence of Lake Michigan on local conditions. Most models fail to fully account for 

the influence of Lake Michigan and other Great Lakes on local climates. Future advances in modeling to 

incorporate these influences will help us gain a richer understanding of what exactly these changes may be. 

Storm surge at Burnham Park, Chicago. Photo by J. Scott, Chicago Park District, used with permission.
Climate Trends and Projections—CHAPTER 2 | 27

changes (Zhang and Schilling 2006). One study in 

Iowa, Missouri, and Illinois showed that when changes 

in land use are accounted for, an increase in discharge 

consistent with local climate changes could be observed 

(Tomer and Schilling 2009). These changes were largely 

observed since the 1970s, and are due to an increase in 

the ratio of precipitation to potential evapotranspiration 

(i.e., evaporative demand). Streamflow is also projected 

to change in the near future, with changes varying by 

season. Winter and spring, which have historically had 

the greatest number of high-flow days, are projected to 

have further increases during these seasons (Cherkauer 

and Sinha 2010). Projected changes in high-flow days in 

the summer and fall are more mixed and vary based on 

geographic location. Changes in low-flow days also will 

vary by season: the number of low-flow days is projected 

to increase in summer and fall and decrease in the 

winter and spring. 

Runoff can be a major management issue in cities 

due to the large amount of impervious cover. Runoff 

is typically greatest in the spring, when there is 

high precipitation coupled with snowmelt. Runoff 

increased in the Chicago area from 1950 to 2009, and 

this increase was attributed to human influence (e.g., 

increases in impervious cover) rather than increases 

in precipitation (Velpuri and Senay 2013). Although 

no climate-driven changes have yet been observed, 

changes in climate will influence runoff in the Chicago 

Wilderness region in the future. Runoff is projected 

to increase slightly in winter and spring and decrease 

in summer and fall, roughly in line with projected 

changes in precipitation (Cherkauer and Sinha 2010). 

This is independent of additional likely increases in 

runoff from changes in land use. An increase in runoff, 

coupled with heavy precipitation in the area, is likely 

to lead to an increased risk of soil erosion in the region 

(Segura et al. 2014). 

Floods in the area typically peak in the spring (Villarini 

et al. 2011). Across the Midwest, economic losses from 

flooding have been increasing at a greater rate than 

elsewhere in the nation. Over a 45-year period (1955 

through 1999), Illinois sustained more than $5 billion 

in flood losses, and 74 percent of these losses occurred 

after 1985 (Angel 2012a). Although there are signs 

that flooding has increased in recent years, the link to 

changes in climate is less clear. Flooding in the region 

is partially linked to climate factors such as snowmelt 

and heavy precipitation events, but is more strongly 

influenced by non-climate factors such as land-use 

change and the construction of dams and other water 

infrastructure (Changnon and Demissie 1996). One 

study examined trends in peak floods in the Chicago 

metropolitan area. The researchers found that both an 

increase in heavy precipitation events and changes in 

land use contributed to increased peak floods across the 

region, but land-use change contributed a larger amount 

(Hejazi and Markus 2009). 

Change in flood risk under future climate change 

is difficult to determine because there are currently 

insufficient records to even determine flood risk at a 

particular location, irrespective of climate (Stedinger 

and Griffis 2011). Studies examining future projections 

for hydrology suggest that the magnitude of flooding 

could potentially increase in winter and spring due to 

increases in total runoff and peak streamflow during 

those time periods (Cherkauer and Sinha 2010). 

During summer and fall, there could be an increase 

in “flashiness,” with periods of very low flow followed 

by rapid flooding in response to heavy rain events 

(Cherkauer and Sinha 2010). One study specifically 

examined the potential effects of future climate change 

on heavy storms and flooding in six watersheds in the 

Chicago metropolitan area using a regional climate 

model (Markus et al. 2012). The study found that 

watersheds in the northern parts of the region could 

sustain 16- to 20-percent increases in heavy storms and 

corresponding floods by the 2050s compared to the last 

decade of the 20th century under a range of emissions 

scenarios. However, they found that the southeastern 

Chicago area may not experience any significant 

changes under a high emissions scenario, but it may have 

significant decreases in heavy storms (and flooding) 

under PCM B1. More research is needed to understand 

whether, in fact, these regional differences exist, and if 

so, what the underlying causes are. 

Growing Season Length

A large body of research indicates that the growing 

season has been getting longer on a global scale, 

largely from an earlier onset of spring (Christidis et 

al. 2007, Parmesan and Yohe 2003, Root et al. 2003, 

Schwartz et al. 2006). Growing season length is often 

determined biologically, through the study of the timing 

of leaf out and senescence, but can also be estimated 

climatologically. Growing season length can be defined 

as the period between the date of the last spring 

freeze and first fall freeze, as determined by minimum 

temperatures of 32 °F (0 °C). Using this definition, one 
CHAPTER 2—Climate Trends and Projections 28 |

study determined the climatological growing season 

lengthened by about 1 week on average between 1906 

and 1997 across Illinois, mostly due to an earlier date for 

the last spring freeze (Robeson 2002). Another study 

examined changes in growing season length from 1911 

through 2000 across the Corn Belt, including Illinois 

and Indiana, and found no discernible trend in the data, 

which were largely driven by a cool period in the 1920s 

and a warm period in the 1990s (Miller et al. 2005). 

Since these studies were conducted, a number of years 

have had last freezes that occurred very early in the 

spring, such as spring 2012, which may be indicative of 

things to come. 

Information for future projections of growing season 

length is primarily limited to length of time between 

the last day below 32 °F in the spring and the first day 

below 32 °F in the fall. A study covering the entire 

Midwest region examined the changes in dates for 

the last spring frost and first fall frost under a range of 

climate scenarios (Wuebbles and Hayhoe 2004). This 

study projected that growing season would be extended 

by 30 to 70 days by the end of the century, from both 

an earlier last spring freeze and a later first fall freeze. 

A more recent study suggests an increase in the frostfree season at mid-century of 20 to 26 days across the 

Chicago Wilderness region, with the largest increase 

in the Indiana portion (Kunkel et al. 2013). How this 

translates into the actual length of the growing season, 

as determined by leaf out and senescence, has not yet 

been examined for the region. 

Hardiness and Heat Zones

Hardiness zones and, more recently, heat zones are used 

to determine suitability for planting. As temperatures 

increase, it is expected that hardiness and heat zones will 

shift. Hardiness zones are determined by the average 

coldest temperature in a year. Chicago and areas around 

Lake Michigan in Indiana are in zone 6a (mean annual 

lowest temperature -10 to -5 °F, or -21 to -23 °C), and 

most of the surrounding area is currently in hardiness 

zone 5b (mean annual lowest temperature -15 to -10 °F, 

or -26 to -23 °C). The combination of the urban heat 

island effect and the moderating effects of Lake Michigan 

contributes to warmer temperatures in Chicago and 

northwestern Indiana. By the end of the century, 

hardiness zones are expected to shift to between 6a and 

6b under a low emissions scenario up to potentially 7b 

under a high emissions scenario (Table 2) (see also maps 

in Appendix 4). However, it is important to keep in mind 

that hardiness zones are based on a 30-year average and 

some very cold winters could still be possible. 

Heat zones are determined by the average number of 

days greater than 86 °F (30 °C). Much of the Chicago 

Wilderness region is in heat zone 5 (30 to 45 days 

exceeding 86 °F). Lake Michigan helps to moderate 

temperatures in areas along the coast, so the greatest 

effects of the Chicago area’s urban heat island during 

the day are actually observed in the western suburbs in 

DuPage and Will Counties (which are in heat zone 6; 

45 to 60 days). Lake and Porter Counties in Indiana are 

Chicago-area community garden. Photo by E. Johnson, Erika Hildegard Photography, used with permission.
Climate Trends and Projections—CHAPTER 2 | 29

also in heat zone 6. Lake County, IL and southeastern 

Wisconsin have cooler summer maximum temperatures 

and are in heat zone 4 (14 to 30 days). If summer 

maximum temperatures continue to rise, heat zones will 

also shift in the area, and some areas will be too hot to 

be suitable for some species. For the PCM B1 scenario, 

heat zones are not expected to change much from 

current conditions, but for the GFDL A1FI scenario 

they could shift up to zone 8 (90 to 120 days exceeding 

86 °F) (Table 2). 

Summary

Temperature and precipitation increased in the Chicago 

Wilderness region during the historical record, but 

future projections suggest that precipitation patterns 

may become more erratic. Across a range of potential 

futures, temperatures will almost certainly increase 

across all seasons over the 21st century, reaching annual 

temperatures that are 2 to 8 °F higher than the last 

30 years of the 20th century. This will lead to longer 

growing seasons and shifts in plant hardiness and heat 

zones. However, extreme cold events may still occur and 

potentially even become more frequent. Precipitation 

is projected to increase in winter and spring, leading to 

increased runoff, streamflow, and possibly flooding in 

some areas, especially when combined with continued 

urban development in the area. Climate models disagree 

about how precipitation may change in summer and 

fall. However, it appears likely that more rain will fall 

as heavy storm events with intermittent dry periods. 

Warmer winters will reduce the period when snowfall 

occurs, but snow will still be a key fixture in the area, 

especially in areas that are prone to “lake effect” snow. 

Key Points 

• Over the past century, the Chicago Wilderness region 

has warmed by about 1 °F on average and has had a 

significant increase in precipitation, especially during 

the summer (3-inch increase). 

• Mean annual temperature is projected to increase 

by 2.3 to 8.2 °F by the end of the 21st century, with 

temperature increases across all seasons.

• Precipitation is projected to increase in winter and 

spring over the 21st century, but projections for 

summer and fall precipitation are less clear. 

• Heavy precipitation events have been increasing in 

number and intensity and are projected to increase 

further, which could increase runoff and local 

flooding from stormwater. 

• Extreme and exceptional droughts may increase in 

duration, frequency, and spatial extent compared to 

the end of the 20th century. 

• Rises in temperature may lead to a shift of one to two 

hardiness zones and two to four heat zones. 

Table 2.—Projected shifts in plant hardiness and heat zones for the Chicago Wilderness region for two climate 

model-emissions scenario combinations1

PCM B1 GFDL A1FI

1980-2009 2010-2039 2040-2069 2070-2099 1980-2009 2010-2039 2040-2069 2070-2099

Hardiness zone 5b-6a 5b-6a 6a-6b 6a-6b 5b-6a 6a-6b 6b-7a 7a-7b

Heat zone 4 - 5 5 - 6 5 - 6 5 - 6 4 - 5 6 - 7 7 - 8 8

1 For maps and a description of methods, see Appendix 4. 
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 30 |

CHAPTER 3

CLIMATE CHANGE IMPACTS AND THE 

ADAPTIVE CAPACITY OF THE URBAN FOREST

A changing climate could have potentially profound 

effects on forests of the Chicago Wilderness region. 

Many tree species that are currently present may 

suffer declines in habitat suitability under warmer 

temperatures and altered precipitation patterns. Other 

species may gain improved habitat suitability under these 

conditions. Some species not currently present could be 

planted in the area, as long as they are able to withstand 

the periods of extreme cold that could still occur during 

some winters. In addition, climate change can have 

indirect effects on the urban forests in the region by 

influencing insect pests, pathogens, and invasive species, 

and changing the probability, severity, and extent of 

severe storms. Planted and naturally occurring trees will 

differ in their capacity to adapt to stressors. This chapter 

synthesizes the potential impacts of climate change on 

urban forests in the Chicago Wilderness region, with 

an emphasis on changes in habitat suitability and the 

adaptive capacity of different species. 

Modeled Projections of 

Habitat Suitability 

Climate change has the potential to alter the habitat 

suitability for tree species. Scientists can project future 

habitat suitability using species distribution models 

(SDMs). These models establish a statistical relationship 

between the current distribution of a species or ecosystem 

and key attributes of its habitat. This relationship is used 

to make projections about how the range of the species 

will shift as climate change affects those attributes. 

SDMs are much less computationally expensive than 

process models, which model ecosystem and tree 

species dynamics based on interactive mathematical 

representations of physical and biological processes. 

Because of their relative computational ease, SDMs can 

typically provide projections for the suitable habitat of 

many species over a large area. There are some caveats 

to be aware of when using SDMs, however (Wiens et al. 

2009). SDMs use the realized niche for a species instead 

of the fundamental niche. The realized niche is the actual 

habitat a species occupies given predation, disease, and 

competition with other species. The fundamental niche, 

in contrast, is the habitat it could potentially occupy in 

the absence of competitors, diseases, or predators. Given 

that the fundamental niche may be greater than the 

realized niche for a species, SDMs may underestimate 

current niche size and future suitable habitat. In addition, 

species distributions in the future might be constrained 

by competition, disease, and predation in ways that do 

not currently occur. If so, SDMs could overestimate 

the amount of suitable habitat in the future. If some 

constraints are removed due to future change, the 

opposite could also occur. Furthermore, fragmentation or 

other physical barriers to migration may create obstacles 

for species otherwise poised to occupy new habitat. With 

these caveats in mind, SDMs can still be a useful tool 

to understand general projections of changes in habitat 

suitability across species. 

Modeling Native Trees 

Suitable habitats for tree species native to the eastern 

United States were modeled in the Chicago Wilderness 

region by using the DISTRIB model, an SDM that 

is a component of the Tree Atlas toolset (Iverson et al. 

2008; U.S. Forest Service, n.d.). DISTRIB measures 

relative abundance, referred to as “importance values,” 

for 134 eastern tree species. Inputs include tree 

species distribution data from the U.S. Forest Service 

Forest Inventory and Analysis (FIA) program and 

environmental variables (pertaining to climate, soil 

properties, elevation, land use, and fragmentation), 

which are used to statistically model the current 

abundance of species with respect to current habitat 

distributions. DISTRIB then projects future importance 

values and suitable habitat for individual tree species 

using projections of future climate conditions on a 12-

mile grid (U.S. Forest Service, n.d.). For this assessment, 

the DISTRIB model uses the GFDL A1FI and PCM 

B1 model-scenario combinations. The results provided 

here differ slightly from those of the online Climate 

Change Tree Atlas because they are specific to the 
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 31

assessment area and use a slightly different statistically 

downscaled climate dataset than shown in the online 

tool (see Chapter 2 for climate model descriptions). 

For lists of the common and scientific names of species 

mentioned in this report, see Appendix 1.

Of the 134 tree species modeled in DISTRIB, 70 were of 

interest to the Chicago Wilderness region because they 

are currently present or expected to gain suitable habitat 

in the area. Projected changes in potential suitable habitat 

for these 70 species are compared to present values for 

the years 2070 through 2099 in Table 3. Species were 

categorized based on whether the results from the 

two climate-emissions scenarios projected an increase, 

decrease, or no change in suitable habitat compared 

to current conditions, or if the model results were 

mixed. Further, some tree species that are currently not 

present in the assessment area were identified as having 

potential suitable habitat in the future under one or both 

scenarios. See Appendix 5 for projections of importance 

values under each model-scenario combination for three 

different time periods (2010 through 2039, 2040 through 

2069, and 2070 through 2099). When examining these 

results, it is important to keep in mind that model 

reliability was generally higher for more-common species 

than for rare species. Appendix 5 shows the ranking of 

model reliability for each species. 

Suitable habitat for 11 of the 70 species examined was 

projected to decline under both climate scenarios. 

One species projected to decline in habitat suitability, 

black cherry, is one of the most common species in the 

Chicago region, according to the Regional Tree Census 

(Nowak et al. 2013). Other common species projected 

to decline are quaking aspen, red pine, black ash, and 

northern pin oak. 

Suitable habitat for only two species, American 

basswood (American linden) and black oak, was 

projected to remain relatively stable under projected 

climate change. The Chicago Wilderness region is near 

the center of the range for both species. 

Climate scenarios disagreed on the direction of change 

for 17 of the species examined. For some of these species, 

such as eastern hophornbeam (ironwood), northern red 

oak, and sugar maple, differences between projections 

were small and indicated little change in either direction. 

Other species, such as American beech and jack pine, are 

not common in the region, so low model reliability for 

these species may explain the mixed results. 

Twenty-five species were projected to have an increase 

in suitable habitat in the Chicago Wilderness region. 

Southern species, such as mockernut hickory, pignut 

hickory, and American sycamore, are projected to 

have large gains in suitable habitat. Many common 

bottomland species in the area, such as eastern 

cottonwood, silver maple, and hackberry are also 

projected to have increases in suitable habitat. 

Habitat was projected to become newly suitable under 

one or both scenarios for 15 species. A few species 

Mature trees in a municipal park in Glencoe, IL. Photo by T. McDonald, Glencoe Park District, used with permission.
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 32 |

Decrease under Both Scenarios

Common name FIA IV† PCM B1 GFDL A1FI

Bigtooth aspen 12 large decrease large decrease

Black ash 69 large decrease small decrease

Black cherry 752 small 

decrease large decrease

Eastern white pine 18 large decrease large decrease 

Northern pin oak 66 large decrease large decrease

Paper birch 25 large decrease large decrease

Quaking aspen 80 large decrease large decrease

Red pine 70 large decrease small decrease

Scarlet oak 13 small decrease large decrease

Shortleaf pine 4 large decrease large decrease

White spruce 5 large decrease large decrease

No Change under Both Scenarios

Common name FIA IV PCM B1 GFDL A1FI

American basswood 130 no change no change

Black oak 206 no change no change

Mixed Results Between Scenarios

Common name FIA IV PCM B1 GFDL A1FI

American beech 27 large increase small decrease

American plum 18 small 

decrease large increase

Boxelder 452 no change small increase

Bur oak 403 small decrease no change

Common chokecherry 9 no change large decrease

Eastern hophornbeam 66 small increase small decrease

Jack pine 5 large decrease large increase

Northern red oak 274 small increase small decrease

Red maple 148 no change small increase

Rock elm 4 large decrease no change

Shagbark hickory 214 small increase no change

Shellbark hickory 1 no change small increase

Sugar maple 213 small increase small decrease

Sweetgum 1 small increase no change

White ash 138 small increase no change

White oak 431 no change large decrease

Yellow-poplar 

(tuliptree) 18 large increase no change

Increase under Both Scenarios

Common name FIA IV PCM B1 GFDL A1FI

American elm 372 small increase small increase

American hornbeam 

(ironwood) 14 large increase large increase

American sycamore 5 large increase large increase

Bitternut hickory 68 large increase large increase

Black locust 145 small increase large increase

Black walnut 42 large increase small increase

Black willow 90 small increase small increase

Increase under Both Scenarios (continued)

Common name FIA IV PCM B1 GFDL A1FI

Blackgum 22 small increase small increase

Common hackberry 57 large increase small increase

Eastern cottonwood 101 small increase large increase

Eastern redcedar 3 large increase large increase

Flowering dogwood 15 large increase large increase

Green ash 166 small increase small increase

Honeylocust 107 large increase large increase

Mockernut hickory 1 large increase large increase

Osage-orange 67 large increase small increase

Pignut hickory 42 large increase large increase

Pin oak 63 large increase large increase

Red mulberry 129 large increase large increase

River birch 20 small increase small increase

Sassafras 28 large increase small increase

Shingle oak 1 small increase small increase

Silver maple 70 small increase large increase

Slippery elm 107 small increase small increase

Swamp white oak 26 large increase large increase

Species Gaining New Habitat

Common name FIA IV PCM B1 GFDL A1FI

Black hickory 0 new habitat new habitat

Blackjack oak 0 new habitat new habitat

Cedar elm 0 NA new habitat

Chinkapin oak 0 new habitat new habitat

Common persimmon 0 new habitat new habitat

Eastern redbud 0 new habitat new habitat

Kentucky coffeetree 0 new habitat new habitat

Longleaf pine 0 new habitat NA

Ohio buckeye 0 new habitat new habitat

Pawpaw 0 new habitat new habitat

Pecan 0 new habitat new habitat

Post oak 0 new habitat new habitat

Sugarberry 0 NA new habitat

Turkey oak 0 new habitat NA

Winged elm 0 NA new habitat

†FIA IV is the importance value of each species based on Forest 

Inventory and Analysis data. See Appendix 5 for more information. 

NA: no suitable habitat under current or projected climate conditions.

Table 3.—Projected changes in habitat suitability for trees native to the Chicago Wilderness region
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 33

are already planted in the area, including Kentucky 

coffeetree, Ohio buckeye, eastern redbud, and chinkapin 

oak. However, none of the species is commonly found. 

Note that these projections are available only for native 

species and are based on data collected in natural 

areas. Because they were developed for natural areas, 

projections are not directly applicable to native species 

planted in highly developed cultivated settings. 

Modeling Species in Cultivated Settings

Additional efforts are underway to model future habitat 

suitability for trees in cultivated settings. Researchers 

at the Chicago Botanic Garden are using a species 

distribution model to examine future habitat suitability 

of cultivated trees in the Chicago region (A. Bell and 

S. Still, Chicago Botanic Garden, pers. comm., 2015). 

They are using the species distribution modeling 

approach Maxent (Phillips et al. 2006) to model changes 

in habitat suitability between now and three future 

time points (2020, 2050, and 2080) for 54 species and 

cultivars. Survey data about the relative success of trees 

growing in botanic gardens and arboretums across the 

eastern United States are entered into the model for 

habitat suitability. Only cultivated settings are used as 

inputs because (1) the growing conditions from cultivated 

trees will better predict success in cultivated settings, and 

(2) many of the trees are not present in natural settings. 

To examine future climate, a multi-model average is used 

with a high emissions scenario (A2). 

Results of this study have not yet been published, but 

some preliminary results are available. These results 

suggest that habitat suitability may remain particularly 

favorable for pecan, American smoketree, ‘Autumn 

Gold’ ginkgo, ‘Village Green’ Japanese zelkova, 

sweetgum, yellow-poplar (tuliptree), American 

sycamore, sweetbay magnolia, and baldcypress. Some 

species and cultivars that may undergo a reduction in 

habitat suitability according to this model are American 

basswood; ‘Legacy’ sugar maple; Sargent cherry; eastern 

hophornbeam; shagbark hickory; Amur maackia; and 

Norway, Black Hills, and Serbian spruce. 

Projected changes in habitat suitability for native species 

in this study do not always align with those published 

in the Tree Atlas (discussed earlier), which could be 

for a number of reasons. First, these modeling efforts 

rely on different downscaled climate datasets as inputs 

to the models. Second, they use different modeling 

approaches with different underlying model structure 

and climate parameters. Third, the tree data are from 

a different source for each model: Tree Atlas relies on 

FIA data from natural environments, whereas this study 

relied on survey data from cultivated settings. This 

discrepancy does not mean that either or both of these 

model projections are wrong, but rather that modeling 

habitat suitability in urban environments is complex and 

may depend on site-specific situations and the degree to 

which a site is developed. 

Projected Changes from Hardiness 

and Heat Zone Shifts

Model information is not available for all species and 

cultivars that are found in the Chicago Wilderness 

region or many of the species being considered for future 

planting. These species are usually either too rare in 

the region to be modeled reliably, not native to North 

America, or cultivars. To understand how a changing 

climate may affect these species, one approach is to 

examine hardiness and heat zone ranges of the species 

to see how they compare to projected future zones in 

the region. Species that are currently present in the 

area or in consideration for city or regional planting 

lists were evaluated (Table 4). Species that are currently 

marginal for hardiness zone (lowest zone is 5 or higher) 

may benefit from milder winters. Species that are 

marginal for heat zone (highest zone is 7 or lower) may 

be negatively affected by hotter summers. See Chapter 

2 for projections of hardiness and heat zones in the area. 

Note that using hardiness and heat zones to estimate 

which species will benefit or fare worse in a changing 

climate is not as informative as the species distribution 

modeling described previously, because the models take 

into account changes in precipitation, seasonal climate 

changes, and other habitat requirements such as soil 

texture. This analysis is meant to provide only a coarse 

estimate of potential changes in habitat suitability. 

Based on this method, 28 species (23 percent of the 121 

species evaluated) may show a positive effect from an 

increase in hardiness zone over the next century (Table 

4). Twelve of these species are currently found in the 

region according to the Regional Tree Census (Nowak 

et al. 2013), although some are invasive species such as 

tree of heaven and privet. 

Twenty-three species (19 percent) may show a 

negative effect from an increase in heat zone. Four of 

these species are relatively common in the Chicago 

Wilderness region: Norway maple, Amur maple, 

European alder, and cockspur hawthorn.
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 34 |

Table 4.—Potential effects of shifts in hardiness and heat zones for tree species 

currently found in the Chicago Wilderness region or being considered for planting 

lists in the area that do not have any species distribution model information

Common name

Estimated 

number 

of trees 

Species 

hardiness 

zone range1

Species 

heat zone 

range1

Potential effect of 

zone change on 

habitat suitability2

‘Accolade’ elm 0 5 to 8 8 to 1 positive

‘Accolade’ flowering cherry 0 5 to 8 8 to 1 positive

Allegheny serviceberry 0 3 to 9 9 to 3 no effect

American smoketree 13,070 5 to 9 9 to 3 positive

American witchhazel 206,360 4 to 8 8 to 1 no effect

Amur cherry 0 2 to 6 n/a insufficient information

Amur corktree3 66,490 3 to 7 8 to 1 no effect

Amur honeysuckle3 3,370,400 3 to 8 9 to 1 no effect

Amur maackia 0 3 to 7 7 to 4 negative

Amur maple3 744,480 3 to 8 7 to 1 negative

Apple/crabapple species 1,724,980 4 to 8 (varies) 9 to 1 (varies) no effect

Apple serviceberry 0 3 to 7 7 to 1 negative

Austrian pine 983,160 5 to 8a 8 to 1 positive

Autumn-olive3 228,040 3 to 8 8 to 1 no effect

Baldcypress 26,030 5 to 11 12 to 5 positive

Balsam fir 205,390 3 to 6 6 to 1 negative

Blackhaw 68,650 3 to 9 9 to 1 no effect

Black Hills spruce 0 2 to 8 7 to 1 negative

Black maple 69,910 4 to 8 8 to 3 no effect

Blue spruce 1,107,240 4 to 7 8 to 1 no effect

Callery pear3 257,690 5 to 9a 8 to 3 positive

Cherry plum 157,440 4b/5a to 8a 9 to 1 positive

Chestnut oak 0 4 to 8 8 to 1 no effect 

Chinese catalpa 0 n/a n/a insufficient information

Chinese chestnut 11,090 4 to 8 8 to 1 no effect

Chinese fringetree 0 5 to 9 9 to 3 positive

Chinese juniper 0 3 to 9 9 to 1 no effect

Cockspur hawthorn 320,200 3 to 7 7 to 1 negative

Common elderberry 197,340 4 to 10 9 to 1 no effect

Common lilac 109,050 4 to 8 8 to 1 no effect

Common pear 266,140 4 to 8 9 to 5 no effect

Common persimmon 0 4 to 9 9 to 1 no effect

Cornelian cherry dogwood 11,090 4 to 8 8 to 5 no effect

Crimean linden 0 3 to 7 7 to 1 negative

Cucumbertree 0 4 to 8 8 to 2 no effect

Dawn redwood 0 5 to 9 10 to 5 positive 

‘Discovery’ elm 0 n/a n/a insufficient information

Douglas-fir 108,410 4 to 7 7 to 1 negative

Downy serviceberry 57,460 4 to 9 9 to 1 no effect

Eastern hemlock 268,660 4 to 8 8 to 1 no effect

Eastern wahoo 46,320 n/a n/a insufficient information

European alder3 382,610 3 to 7 7 to 1 negative

European beech 20,240 5 to 8 8 to 1 positive

European buckthorn3 44,281,470 3 to 8 n/a no effect

(Continued)
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 35

Table 4.—(Continued) Potential effects of shifts in hardiness and heat zones 

for tree species currently found in the Chicago Wilderness region or being 

considered for planting lists in the area that do not have any species distribution 

model information

Common name

Estimated 

number 

of trees 

Species 

hardiness 

zone range1

Species 

heat zone 

range1

Potential effect of 

zone change on 

habitat suitability2

European filbert 17,440 4 to 8 8 to 1 no effect

European hornbeam 99,760 4 to 7 8 to 1 no effect

European larch 0 2 to 6 6 to 1 negative

European mountain-ash 0 3 to 7 7 to 1 negative

Freeman maple 280,470 4 to 7 8 to 1 no effect

‘Frontier’ elm 0 n/a n/a insufficient information

Glossy buckthorn3 500,900 4 to 6 n/a no effect 

Gray alder 0 2 to 6 6 to 1 negative

Gray birch 145,590 3 to 6 6 to 1 negative

Gray dogwood 68,010 4 to 8 8 to 3 no effect

‘Harvest Gold’ linden 0 3 to 7 7 to 1 negative

Hedge maple 0 5 to 8 8 to 4 positive

Heritage® oak 0 n/a n/a insufficient information

Higan cherry 0 5 to 8 8 to 6 positive

Horse chestnut 40,250 3 to 8 8 to 1 no effect

Japanese maple 36,060 6 to 8 8 to 2 positive

Japanese red pine 11,090 4 to 7 7 to 1 negative

Japanese tree lilac 19,020 4 to 7a 7 to 1 negative

Japanese zelkova 11,090 5 to 8 9 to 5 positive

Katsura tree 11,090 4 to 8 8 to 1 no effect

Korean mountain-ash 0 5 to 8 10 to 1 positive

Korean SunTM pear 0 n/a n/a insufficient information

Kousa dogwood 0 5 to 8 8 to 5 positive

Leatherleaf viburnum 17,440 5 to 8 8 to 6 positive

Littleleaf linden 243,320 3 to 7 8 to 1 no effect

London planetree 0 4 to 8 n/a insufficient information

Maidenhair tree 199,650 4 to 9 9 to 3 no effect

Miyabe maple 0 4 to 8 8 to 1 no effect

Morden hawthorn 0 4 to 7 n/a no effect

Nannyberry 69,310 2 to 8 8 to 1 no effect

Northern catalpa 59,440 4 to 8 8 to 1 no effect

Northern white-cedar 2,457,220 3 to 7 7 to 1 negative

Norway maple3 1,858,800 4 to 7a 7 to 1 negative

Norway spruce 377,510 3 to 8 8 to 1 no effect

Oriental spruce 0 4 to 9 9 to 1 no effect

Pacific Sunset® maple 0 4 to 8 8 to 1 no effect

Pagoda dogwood 34,590 4 to 8 8 to 1 no effect

Peach 107,320 5 to 9 9 to 1 positive

Peachleaf willow 77,720 1 to 10 11 to 1 no effect

Peking lilac 0 3 to 7 6 to 1 negative

Pin cherry 40,550 2 to 8 n/a insufficient information 

‘Prairie Gem’ Ussurian pear 0 n/a n/a insufficient information 

Prickly ash 207,940 3 to 9 8 to 1 no effect

Privet3 28,830 7 to 10 10 to 7 positive

(Continued)
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 36 |

Table 4.—(Continued) Potential effects of shifts in hardiness and heat zones 

for tree species currently found in the Chicago Wilderness region or being 

considered for planting lists in the area that do not have any species distribution 

model information

Common name

Estimated 

number 

of trees 

Species 

hardiness 

zone range1

Species 

heat zone 

range1

Potential effect of 

zone change on 

habitat suitability2

‘Prospector’ Wilson Elm 0 n/a n/a insufficient information

Pussy willow 55,420 4 to 8 8 to 2 no effect

Robusta poplar 0 n/a n/a insufficient information

Rose-of-Sharon 77,240 5 to 9 9 to 1 positive

Russian-olive3 54,970 2 to 8 8 to 1 no effect

Sargent cherry 80,070 5 to 8a 9 to 4 no effect

Saucer magnolia 26,030 5 to 9 9 to 5 positive

Scholar tree 0 5 to 8a 9 to 5 positive

Scotch pine 23,500 3 to 7 7 to 1 negative

Serbian spruce 78,160 4 to 7 8 to 1 no effect

Shantung maple 0 4 to 8 8 to 1 no effect

Shumard oak 0 5 to 10 9 to 1 positive

Siberian elm3 2,240,590 2 to 9 9 to 1 no effect

Silver linden 0 6 to 9 n/a insufficient information 

Smoothleaf elm 0 5 to 8 n/a positive 

‘Snow Goose’ cherry 0 5 to 8 8 to 5 positive

Staghorn sumac 0 3 to 8 8 to 1 no effect

Star magnolia 4 4 to 9 9 to 5 no effect

Sycamore maple 0 5 to 8 8 to 1 positive

Tree of heaven 1,830,940 5 to 8a n/a positive

TriumphTM elm 0 4 to 9 9 to 1 no effect

Turkish hazelnut 0 5 to 7 7 to 4 negative

Washington hawthorn 23,100 4 to 8a 8 to 1 no effect

Weeping willow 11,090 4 to 9 9 to 1 no effect

White fir 0 3 to 7 7 to 1 negative

White fringetree 0 4 to 9 9 to 1 no effect

White mulberry3 1,584,250 3b to 9 8 to 1 no effect

White poplar 95,600 4 to 9 9 to 1 no effect

Willow oak 0 6 to 9 9 to 3 positive

Winged burningbush3 148,650 4 to 9 9 to 1 no effect

‘Winter King’ green hawthorn 0 4 to 7 7 to 5 negative

Yellow buckeye 0 3 to 8 8 to 1 no effect 

Yellowwood 0 4 to 8 9 to 1 no effect

1 Species hardiness/heat zone range is the range of zones for which the species is considered 

suitable for planting. See Chapter 2 for projected shifts in hardiness and heat zones.

2 Climate change was considered to have a positive effect on habitat suitability if the lowest zone for 

which the species was hardy was 5 or higher. Climate change was considered to have a negative 

effect on habitat suitability if the highest heat zone that the species can tolerate was 7 or lower.

3 Invasive species. 

The abbreviation “n/a” indicates information not available.

A value of “0” means no individuals were detected in the most recent tree census (Nowak et al. 2013). 

These species are either currently rarely planted, have been planted since the last census, or are 

being considered for planting in the area.

. 
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 37

For about half of the species evaluated (57 species), 

shifts in hardiness or heat zone may not have an effect 

on habitat suitability. However, these species could 

be affected by other climate-related changes, such as 

shifts in precipitation or insect or disease outbreaks. 

These include many of the most common species in the 

area, but several of them, such as European buckthorn, 

Siberian elm, and white mulberry, are invasive species. 

There was insufficient information on current hardiness 

and heat zones for 13 species, so a determination could 

not be made. 

Adaptive Capacity of 

Urban Trees

The results just presented provide information on 

potential changes in tree species habitat suitability across 

a range of projected future temperature and precipitation 

regimes, but do not account for all factors that may 

influence tree species under a changing climate. For 

the most part, models such as the ones described earlier 

consider only the direct effects of temperature and 

precipitation, and do not account for other changes, such 

as changes in flood regime, extreme weather events, 

insects and disease, and invasive species. 

To understand the capacity of tree species and cultivars 

in the area to adapt to these other effects of climate 

change, we relied on a scoring system developed by 

Matthews et al. (2011) called modifying factors. Other 

scoring systems have been developed (e.g., Roloff et al. 

2009), but we found the system developed by Matthews 

and colleagues to be the most comprehensive for all 

potential climate change-related stressors. Modifying 

factors can include life history traits or environmental 

factors that make a species more or less likely to persist 

on the landscape (Matthews et al. 2011). Examples of 

modifying factors are fire or drought tolerance, dispersal 

ability, shade tolerance, site specificity, and susceptibility 

to insect pests and diseases. These factors can then be 

weighted by their intensity, level of uncertainty about 

their impacts, and relative importance to future changes 

to arrive at a numerical score (Matthews et al. 2011) 

(see Appendix 6). Modifying factors are highly related 

to the adaptive capacity of a species: the ability to adjust 

to a changing climate (including climate variability 

and extremes) to moderate potential damages, to 

take advantage of opportunities, or to cope with the 

consequences (Intergovernmental Panel on Climate 

Change [IPCC] 2014). A species with a large number 

of positive modifying factors would have a high adaptive 

capacity, and a species with a large number of negative 

modifying factors would have a low adaptive capacity. 

We revised the modifying factors developed previously 

to better capture the unique environment of urban areas. 

Planted trees on developed sites (such as street trees, 

residences, city parks, and campuses) and naturally 

occurring trees (native or naturalized species that 

regenerate on their own) have very different growing 

environments. Thus, we developed two separate 

adaptive capacity scores. For naturally occurring trees, 

we kept factors developed by Matthews and colleagues, 

but added a few factors (salt tolerance, soil and water 

pollution) that we thought were particularly important 

in urban environments (Table 5). The weighting of some 

scores was also adjusted to account for regional climate 

changes and the additional stresses of urban areas. For 

planted trees, we eliminated factors related to seed 

dispersal, establishment, and reproduction because they 

would not apply. We also eliminated fire-related factors 

because we assume wildfire and prescribed fire will not 

be a common occurrence for planted trees on developed 

sites. We added factors that were important to nursery 

cultivation and maintenance of cultivated trees. We also 

added an “invasive potential” factor to take into account 

the expectation that species which are likely to be 

invasive will probably not be selected for future planting. 

We developed modifying factor scores for all 120 species 

listed as occurring in the Chicago area according to 

the recent Regional Tree Census (Nowak et al. 2013). 

In addition, we included other species that are being 

considered on planting lists for the City of Chicago and 

the Chicago Region Trees Initiative, bringing the total 

number of species evaluated to 179. For species present 

only in a cultivated setting, we generated only one score. 

Native species, even if not currently used in cultivated 

settings, were given scores for both planted and naturally 

occurring settings. Scores were then converted to 

categories of high, medium, and low adaptive capacity 

(Tables 6–11). See Appendix 6 for specific modifying 

factor scores and subscores for each species and a 

description of the two numerical scoring systems.

For planted conditions, 66 species received a high 

adaptability score, 24 received a low adaptability score, 

and the remaining 89 received a medium adaptability 

score. Common native species with high adaptability 

scores in planted environments include northern 

red oak, eastern hophornbeam, and hackberry. 

Many invasive species such as European buckthorn, 
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 38 |

Table 5.—Codes and descriptions for modifying factors1

Factor Code Type Description (if positive) Description (if negative)

Air pollution AIP N, P Tolerant of air pollution Intolerant of air pollution

Browse BRO N,P N/A Susceptible to browsing

Competition-light COL N, P Tolerant of shade or limited light conditions Intolerant of shade or limited light conditions

Disease DISE N, P N/A Has a high number and/or severity of known 

pathogens that attack the species

Dispersal DISP N High ability to effectively produce and 

distribute seeds

N/A

Drought DRO N,P Drought-tolerant Susceptible to drought

Edaphic specificity ESP N, P Wide range of soil tolerance Narrow range of soil requirements 

Environmental 

habitat specificity

EHS N Wide range of slopes/aspects/topographic 

positions

Small range of slopes/aspects/topographic 

positions

Fire regeneration FRG N Regenerates well after fire N/A

Fire topkill FTK N Resistant to fire topkill Susceptible to fire topkill

Flood FLO N, P Flood-tolerant Flood-intolerant 

Ice ICE N,P N/A Susceptible to breakage from ice storms

Insect pests INS N, P N/A Has a high number and/or severity of insects that 

may attack the species

Invasive plants INPL N,P N/A Strong negative effects of invasive plants on the 

species, either through competition for nutrients 

or as a pathogen

Invasive potential INPO P N/A Has the potential to become invasive

Land-use/Planting 

site specificity

LPS P Can be planted on a wide variety of sites Can be planted only in a narrow range of sites or 

as a specimen

Maintenance 

required

MAR P Requires little pruning, watering, or cleanup Requires considerable pruning, watering, or 

cleanup of debris

Nursery propagation NUP P Easily propagated in nursery and widely 

available

Not easily propagated or not usually available

Planting 

establishment

PLE P Easily transplanted and requires little care to 

establish

Difficult to transplant or requires considerable 

care to establish

Restricted rooting 

conditions

RRC P Can tolerate restricted rooting conditions Intolerant of restricted rooting conditions 

Salt tolerance SAL N,P Tolerant of road salt/salt spray Intolerant of road salt/salt spray

Seedling 

establishment

SES N High ability to regenerate with seeds to 

maintain future populations

Low ability to regenerate with seeds to maintain 

future populations

Soil and water 

pollution

SWP N,P Tolerant of soil or water pollution Intolerant of soil or water pollution

Temperature 

gradients

TEM N,P Tolerant of a wide range of temperatures Narrow range of temperature requirements

Vegetative 

reproduction

VRE N Capable of vegetative reproduction through 

stump sprouts or cloning

N/A

Wind WIN N,P N/A Susceptible to breakage from wind storms

1 Traits are listed if they were among the main contributors to the overall adaptability score. See Appendix 6 for more information.

N: Applies to naturally occurring trees

P: Applies to planted trees.

N/A: not applicable.
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 39

Common name Positive traits1

Negative 

traits1

Accolade® elm TEM LPS NUP --

Allegheny 

serviceberry

LPS DRO AIP 

American hornbeam FLO TEM NUP DRO AIP 

Amur cherry LPS RRC TEM AIP 

Amur honeysuckle2 TEM AIP ESP LPS PLE 

MAR

INPO 

Amur maackia DRO TEM LPS RRC NUP FLO

Apple serviceberry LPS RRC NUP DRO FLO AIP

Baldcypress FLO RRC NUP AIP 

Blackgum RRC AIP 

Blackhaw NUP TEM INS

Bur oak DRO TEM AIP LPS NUP DISE INSE FLO 

Chestnut oak LPS INS AIP 

Chinese fringetree LPS RRC --

Chinese juniper DRO LPS NUP FLO 

Common hackberry DRO TEM LPS NUP --

Cockspur hawthorn DRO TEM LPS RRC NUP INS AIP 

Common persimmon DRO FLO TEM ESP RRC 

NUP

--

Crimean linden DRO TEM LPS RRC NUP --

‘Discovery’ elm TEM LPS NUP INS

Downy serviceberry TEM NUP AIP

Eastern hophornbeam 

(ironwood)

DRO TEM LPS RRC NUP FLO AIP

Eastern redcedar DRO TEM LPS RRC AIP 

European buckthorn2 DRO TEM ESP NUP INPO 

European hornbeam TEM --

European mountainashLPS RRC NUP --

European smoketree DRO LPS RRC NUP FLO 

Freeman maple TEM ESP LPS NUP --

‘Frontier’ elm DRO TEM LPS RRC --

Glossy buckthorn2 FLO TEM ESP NUP INPO 

Hedge maple TEM LPS NUP INS

Heritage® oak DRO TEM LPS NUP DISE INS

Japanese tree lilac RRC LPS NUP AIP 

Japanese zelkova TEM LPS RRC NUP --

Common name Positive traits1

Negative 

traits1

Kentucky coffeetree DRO LPS NUP AIP 

Korean SunTM pear TEM LPS RRC NUP --

Kousa dogwood NUP DRO AIP

Leatherleaf viburnum NUP AIP

Littleleaf linden LPS NUP INS SAL 

Maidenhair tree DRO TEM LPS RRC NUP FLO 

Miyabe maple AIP 

Mockernut hickory TEM AIP 

Nannyberry TEM RRC NUP --

Northern red oak TEM LPS NUP DISE INS INPL 

Norway maple2 DRO FLO ESP RRC NUP INS INPO 

Osage-orange DRO TEM ESP RRC NUP AIP 

Pacific Sunset® maple DRO TEM LPS RRC INS

Peking lilac LPS NUP FLO TEM 

‘Prairie Gem’ Ussurian 

pear

TEM LPS NUP AIP

Rose-of-Sharon NUP AIP

Russian-olive2 DRO TEM NUP INPO 

Saucer magnolia TEM NUP DRO FLO

Scarlet oak ESP LPS AIP 

Scholar tree DRO TEM LPS RRC NUP FLO 

Shantung maple DRO TEM LPS RRC NUP INS

Shingle oak DRO NUP DISE INS AIP 

Shumard oak DRO FLO TEM LPS RRC 

NUP 

--

‘Snow Goose’ cherry NUP --

Swamp white oak TEM RRC NUP AIP 

Tree of heaven2 DRO TEM AIP ESP LPS NUP INPO

‘Triumph’ elm DRO TEM LPS RRC NUP --

Turkish hazelnut DRO TEM LPS RRC --

White fringetree LPS RRC --

Willow oak FLO LPS RRC NUP --

Winged burningbush2 TEM NUP INPO 

‘Winter King’ green 

hawthorn

LPS RRC NUP --

Yellowwood TEM RRC AIP 

1 See Table 5 for trait codes.

2

 Invasive species.

Table 6.—Tree species with high adaptability in planted environments, based on modifying factor scores
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 40 |

Common name Positive traits1 Negative traits1

‘Accolade’ flowering cherry TEM NUP FLO

American basswood 

(American linden)

FLO TEM NUP AIR RRC

American beech -- FLO AIP LPS 

RRC

American elm NUP DISE DRO

American plum NUP AIP

American sycamore TEM NUP --

American witchhazel TEM INS AIP 

Amur corktree2 TEM NUP RRC INPO 

Amur maple2 TEM LPS INPO 

Austrian pine TEM RRC DISE INS 

Autumn-olive2 LPS AIP NUP INPO

Balsam fir NUP DRO TEM AIP 

Bitternut hickory DRO AIP 

Black Hills spruce -- TEM AIP 

Black locust DRO TEM FLO AIP LPS 

RRC INPO 

Black maple TEM AIP 

Black oak DRO TEM DISE INS

Black walnut -- DRO AIP LPS 

RRC 

Blue spruce NUP INS FLO AIP

Boxelder DRO FLO TEM AIP INPO

Callery pear2 DRO TEM RRC 

NUP 

INS INPO 

Cherry plum NUP INS AIP 

Chinese catalpa FLO TEM AIP

Chinese chestnut TEM --

Chinkapin oak DRO TEM LPS DISE INS AIP 

Common chokecherry NUP DISE FLO AIP 

Common elderberry FLO TEM AIP INPO 

Common lilac RRC NUP FLO AIP

Cornelian cherry dogwood TEM AIP 

Crabapple species TEM LPS RRC 

NUP 

DISE INS AIP 

Cucumbertree NUP DRO AIP 

Dawn redwood TEM AIP 

Eastern redbud FLO TEM NUP AIP 

Eastern wahoo -- AIP

European alder2 FLO DISE AIP INPO 

European beech NUP DRO LPS RRC

European filbert NUP AIP RRC

European larch -- DRO TEM AIP 

Flowering dogwood TEM LPS DRO FLO AIP 

RRC

Gray alder FLO DRO AIP 

Gray dogwood TEM --

Green ash FLO NUP INS MAR

‘Harvest Gold’ linden RRC AIP 

Higan cherry -- FLO AIP RRC

Honeylocust DRO TEM RRC 

NUP 

--

Common name Positive traits1 Negative traits1

Horse chestnut TEM AIP 

Japanese maple NUP DRO AIP

Korean mountain-ash NUP AIP

London planetree DRO FLO TEM 

NUP

DISE AIP 

Morden hawthorn NUP AIP 

Northern pin oak (Hill’s 

oak)

DRO LPS TEM AIP 

Northern white-cedar 

(arborvitae)

NUP DRO AIP 

Norway spruce NUP INS FLO AIP 

Ohio buckeye -- AIP 

Oriental spruce -- AIP

Pagoda dogwood NUP AIP 

Paper birch NUP DRO TEM AIP 

Peach NUP DRO FLO

Pignut hickory TEM --

Pin oak FLO TEM RRC 

NUP 

DISE INS AIP 

Privet2 TEM NUP AIP INPO 

‘Prospector’ Wilson elm DRO LPS --

Red maple FLO TEM ESP LPS 

NUP 

INS DRO AIP

Red mulberry TEM NUP AIP

River birch TEM LPS NUP DISE INS DRO 

Robusta poplar DRO NUP --

Sargent cherry TEM LPS RRC WIN AIP 

Sassafras DRO NUP --

Scots pine NUP INS

Serbian spruce NUP INS 

Shagbark hickory TEM AIP 

Siberian elm DRO WIN INPO

Silver linden TEM NUP INS AIP 

Silver maple FLO TEM NUP RRC

Slippery elm TEM LPS DISE 

Staghorn sumac DRO TEM AIP RRC

Star magnolia NUP DRO AIP

Sugar maple NUP INS FLO AIP 

Sugarberry DRO FLO TEM WIN AIP NUP 

Sweetgum -- INS DRO RRC 

Sycamore maple NUP INS AIP

Washington hawthorn DRO TEM RRC 

NUP 

DISE INS 

Weeping willow FLO TEM NUP INS AIP LPS RRC

White fir -- DLO AIP 

White mulberry2 NUP LPS INPO 

White oak TEM ESP LPS FLO AIP 

White poplar DRO TEM ESP 

NUP 

INS WIN LPS 

RRC

White spruce INS --

Yellow buckeye DRO AIP --

1 See Table 5 for trait codes.

2 Invasive species.

Table 7.—Tree species with moderate adaptability in planted environments, based on modifying factor scores
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 41

Table 8.—Tree species with low adaptability in planted 

environments, based on modifying factor scores

Common name

Positive 

traits1 Negative traits1

Bigtooth aspen -- --

Black cherry DRO TEM AIP LPS RRC

Black willow FLO INS DRO AIP RRC

Common pear TEM NUP DRO AIP 

Douglas-fir NUP DRO FLO TEM LPS

Eastern cottonwood TEM NUP DISE INS AIP LPS RRC 

Eastern hemlock NUP DRO AIP LPS 

Eastern white pine NUP DISE INS DRO TEM AIP LPS RRC 

Gray birch -- DISE INS AIP LPS 

Jack pine DRO AIP LPS RRC

Japanese red pine -- AIP

Katsura tree DISE INS 

NUP 

DRO WIN AIP RRC 

Northern catalpa -- AIP 

Peachleaf willow FLO AIP 

Pecan NUP AIP LPS RRC

Pin cherry -- DRO AIP LPS

Prickly ash -- AIP LPS INPO

Pussy willow FLO TEM 

NUP

DRO AIP LPS RRC 

Quaking aspen TEM NUP INS DRO AIP RRC INPO 

Red pine -- INS DRO AIP RRC

Shellbark hickory -- DRO AIP

Smoothleaf elm -- DISE AIP 

White ash TEM NUP INS AIP RRC

Yellow-poplar 

(tuliptree)

NUP DRO AIP RRC 

1 See Table 5 for trait codes.

Table 9.—Tree species with high adaptability in natural areas and 

other locations where they are allowed to regenerate on their own, 

based on modifying factor scores

Common name Positive traits1 Negative traits1

Allegheny serviceberry COL INPL DRO AIP

American elm EHS DISE DRO AIP 

American hornbeam FLO COL SES INPL DRO FTK 

American witchhazel COL EHS INS INPL AIP 

Amur corktree2 SES COL

Amur honeysuckle2 TEM AIP COL ESP EHS 

DISP 

SES VRE 

--

Autumn-olive2 EHS DISP AIP 

Bigtooth aspen VRE FRG INPL DRO FTK AIP 

COL 

Blackgum COL INPL

Blackhaw EHS INS INPL

Black locust DRO INPL DRO AIP 

Black maple COL EHS INPL AIP INS

Black oak DRO DISE INS INPL

Boxelder DRO FLO TEM COL DISP 

SES 

AIP 

Bur oak DRO TEM AIP DISE INS FLO 

Chestnut oak EHS SES VRE INS INPL 

Common elderberry FLO EHS DISP SES VRE AIP COL 

Common hackberry DRO INPL FTK 

Downy serviceberry COL EHS SES FRG INPL FTK

Eastern hophornbeam 

(ironwood)

DRO TEM COL EHS INPL FLO AIP 

Eastern redbud FLO INPL AIP 

European buckthorn2 DRO TEM AIP COL ESP 

DISP SES VRE 

--

Flowering dogwood COL INPL DRO FLO AIP

Glossy buckthorn2 FLO TEM AIP COL DIP SES --

Gray dogwood SES INPL FTK

Honeylocust DRO INPL 

Nannyberry COL INPL

Northern red oak -- DISE INS INPL 

Norway maple2 DRO FLO COL ESP EHS 

SES 

INS 

Osage-orange DRO ESP EHS INPL

Pignut hickory EHS --

Prickly ash EHS VRE INPL AIP 

Privet2 COL EHS DISP SES VRE AIP

Red maple FLO COL ESP EHS SES VRE INS INPL DRO

Russian-olive2 DRO TEM DISP EHS SES COL

Scarlet oak ESP EHS VRE INPL AIP 

Shagbark hickory INPL AIP --

Siberian elm DRO EHS SES WIN COL 

Silver maple FLO COL DISP SES INS INPL 

Staghorn sumac DRO TEM SES VRE FRG INPL FLO FTK COL 

Sugar maple COL INS INPL FLO AIP 

Tree of heaven2 DRO AIP ESP EHS DISP 

SES 

VRE FRG

--

White mulberry2 EHS SES --

White oak TEM ESP EHS INPL AIP 

Winged burningbush2 COL EHS FTK

Yellow-poplar (tuliptree) EHS SES FRG INPL DRO AIP 

1 See Table 5 for trait codes.

2 Invasive species.
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 42 |

Table 10.—Tree species with moderate adaptability in 

natural areas and other locations where they are allowed 

to regenerate on their own, based on modifying factor 

scores

Common name

Positive 

traits1 Negative traits1

American basswood 

(American linden)

FLO COL INPL AIP SES 

American beech TEM FLO FTK AIP COL

American plum -- INPL AIP

American sycamore -- INPL 

Bitternut hickory DRO INPL COL 

Black cherry DRO EHS SES INPL FLO FTK AIP COL 

Black walnut SES INPL DRO COL 

Chinkapin oak DRO TEM DISE INS INPL 

Common chokecherry -- DISE INPL FLO AIP 

COL 

Eastern cottonwood TEM DIS INS INPL AIP COL 

Eastern redcedar DRO INPL FTK COL 

Eastern wahoo COL INPL AIP

Eastern white pine -- DISE INS INPL DRO 

FTK AIP 

Gray alder FLO INPL DRO FTK AIP

Gray birch -- DISE INS AIP LPS

Green ash FLO INS INPL COL 

Kentucky coffeetree DRO LPS NUP AIP

Mockernut hickory -- INPL FTK COL 

Northern pin oak (Hill’s 

oak)

DRO INPL 

Northern white-cedar COL INPL DRO SIP 

Pagoda dogwood COL INPL FTK

Paper birch NUP DRO TEM AIP

Pin cherry SES FRG INLP DRO AIP COL 

Pin oak FLO DISE INS INPL FTK 

COL 

Pussy willow FLO DISP SES 

FRG

INPL DRO AIP COL 

EHS 

Quaking aspen TEM EHS VRE INS INPL DRO FTK AIP 

COL 

Red mulberry COL INPL AIP FTK 

River birch -- DISE INS INPL DRO 

FTK COL 

Shellbark hickory COL INPL DRO AIP EHS 

Shingle oak DRO NUP DISE INS AIP

Shumard oak DRO FLO TEM 

LPS RRC NUP

--

Slippery elm -- DISE INPL FTK AIP COL 

Swamp white oak -- INPL AIP 

White ash -- INS INPL AIP COL 

1 See Table 5 for trait codes.

Table 11.—Tree species with low adaptability in natural 

areas and other locations where they are allowed to 

regenerate on their own, based on modifying factor 

scores

Common name

Positive 

traits1 Negative traits1

Black willow FLO INS INPL DRO COL FTK AIP

Northern catalpa -- INPL AIP COL EHS

Ohio buckeye COL NPL SES

Peachleaf willow FLO INPL AIP COL SES

1 See Table 5 for trait codes.
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 43

Amur honeysuckle, and tree of heaven also had high 

adaptability scores. Some very common native species 

had low adaptability scores for planted environments, 

including black cherry, eastern cottonwood, and white 

ash. Common pear was the most common nonnative 

species to receive a low adaptability score. 

For naturally occurring species (both native and 

naturalized), 46 received a high adaptability score, 4 

received a low adaptability score, and 34 received a 

medium adaptability score. Native species that received 

a high adaptability score for natural areas but not in 

planted environments include boxelder, American elm, 

sugar maple, silver maple, and white oak. All invasive 

species received a high adaptability score.

Overall Vulnerability of the 

Chicago Region’s Trees

Vulnerability is the susceptibility of a system to the 

adverse effects of climate change (IPCC 2007). It is a 

function of potential climate change impacts and the 

adaptive capacity of the system. Overall vulnerability 

of trees in the Chicago region can be estimated by 

considering the impacts on individual trees using 

model projections or changes in hardiness or heat zone, 

together with the adaptive capacity of trees as described 

in the previous section. This approach is meant to give a 

coarse picture of vulnerability, and readers should weigh 

the relative confidence in vulnerability estimates based 

on the level of information available (see Appendix 7). 

Of the 179 species and cultivars evaluated, 15 (8 percent) 

are considered to have high vulnerability (Table 12). 

These species account for 9 percent of the individual 

trees in the Chicago region counted in the Regional 

Tree Census. Thirteen of these species are native to 

North America, and two are nonnative (Japanese red 

pine and katsura tree). The highly vulnerable species 

tended to be native to mountainous or northern areas. 

Examples are black cherry, red and white pine, white 

spruce, gray and paper birch, quaking and bigtooth 

aspen, balsam fir, and Douglas-fir. 

Sixteen species of those evaluated (9 percent) are 

considered to have moderate-high vulnerability. These 

account for 6 percent of trees in the Chicago region. 

Two of these species are considered to be invasive: Amur 

maple and European alder. Common native species 

considered to have moderate-high vulnerability are 

white ash and northern white-cedar (arborvitae). 

Fifty-four species of those evaluated (30 percent) 

are considered to have moderate vulnerability, and 

account for 17 percent of the trees in the region. These 

include many native species such as green ash (which 

is vulnerable for other reasons besides climate change), 

sugar maple, black walnut, eastern cottonwood, and 

white oak. Commonly planted species in this category 

include Norway maple, apple/crabapple species, blue 

spruce, and Morden and cockspur hawthorn.

Forty-two of the species evaluated (23 percent) are 

considered to have low-moderate vulnerability. They 

make up 18 percent of the total trees in the region. They 

include many common native species such as American 

elm, silver maple, shagbark and bitternut hickory, 

honeylocust, American basswood, eastern hophornbeam, 

and river birch. The invasive species Siberian elm, white 

mulberry, and Callery pear fall into this category. 

Fifty-two species (29 percent) of those evaluated are 

considered to have low vulnerability. Of these, 19 are 

native and 6 are invasive, with the remainder being 

nonnative planted species or cultivars. Based on the total 

number of trees surveyed in the Regional Tree Census, 

half would be considered to have low vulnerability. 

However, European buckthorn accounts for 70 percent 

of those individual trees, and collectively 77 percent of 

individuals with low vulnerability ratings are invasive. 

Other common invasive species considered to have low 

vulnerability are Amur honeysuckle, tree of heaven, 

and glossy buckthorn. However, several common 

native trees, such as boxelder, black locust, bur oak, and 

hackberry, are also considered to have low vulnerability. 

Species that are often used in cultivated settings which 

had low vulnerability were littleleaf linden, Freeman 

maple, maidenhair tree, and European hornbeam. 

Vulnerability of species and cultivars recommended 

for planting in The Morton Arboretum’s Northern 

Illinois Tree Species List (The Morton Arboretum 

2015) was also evaluated. This list includes many species 

and cultivars that are not widely planted and were not 

included in our assessment. Thus, many species and 

cultivars on the list have no vulnerability rating. 

Of the species recommended for planting in parks and 

residential areas, 10 percent were considered to have 

moderate-high or high vulnerability. These include 

many northern species such as black cherry, white and 

jack pine, paper birch, eastern hemlock, and aspen 

species. Sixteen percent were considered to have low 

vulnerability. These included native and nonnative 

species along with some cultivars. Thirty-seven percent 

had either low-moderate or moderate vulnerability. 

Thirty-six percent had no rating as this list included 

many uncommon species and cultivars.
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 44 |

Table 12.—Tree species in each vulnerability class

High Moderate-High Moderate Low-Moderate Low

Balsam fir Amur maple1 American beech ‘Accolade’ flowering cherry Accolade® elm

Bigtooth aspen Black Hills spruce American plum American basswood Allegheny serviceberry

Black cherry Common chokecherry Amur maackia American elm American hornbeam

Douglas-fir Common pear Apple serviceberry American sycamore Amur cherry

Eastern hemlock European alder1 Apple/crabapple species American witchhazel Amur honeysuckle1

Eastern white pine European larch Black maple Amur corktree Baldcypress

Gray birch Gray alder Black walnut Austrian pine Black gum

Jack pine ‘Harvest Gold’ linden Black willow Autumn-olive1 Blackhaw

Japanese red pine Northern catalpa Blue spruce bitternut hickory Black locust

Katsura tree Northern white-cedar Chinese catalpa Callery pear1 Black oak

Northern pin oak (Hill’s oak) Peachleaf willow Chinese chestnut Cherry plum Boxelder

Paper birch Pin cherry Chinkapin oak Common elderberry Bur oak

Quaking aspen Scotch pine Cockspur hawthorn Eastern hophornbeam (ironwood) Chestnut oak

Red pine White ash Common lilac Eastern redbud Chinese fringetree

White spruce White fir Cornelian cherry dogwood Eastern redcedar Chinese juniper

White oak Crimean linden European beech Common hackberry

Cucumbertree Flowering dogwood Common persimmon 

Dawn redwood Higan cherry ‘Discovery’ elm

Eastern cottonwood Honeylocust Downy serviceberry

Eastern wahoo Korean mountain-ash European buckthorn

European filbert Mockernut hickory European hornbeam

European mountain-ash Northern red oak European smoketree

Gray dogwood Peach Freeman maple

Green ash Pignut hickory ‘Frontier’ elm

Horse chestnut Pin oak Glossy buckthorn1

Japanese maple Privet1 Hedge maple

Japanese tree lilac Red maple Heritage® oak

London planetree Red mulberry Japanese zelkova

Morden hawthorn River birch Kentucky coffeetree

Norway maple1 Sassafras Korean SunTM pear

Norway spruce Shagbark hickory Kousa dogwood

Ohio buckeye Shingle oak Leatherleaf viburnum

Oriental spruce Shumard oak Littleleaf linden

Pagoda dogwood Siberian elm1 Maidenhair tree

Pecan Silver maple Miyabe maple

Peking lilac Slippery elm Nannyberry

‘Prospector’ Wilson elm Staghorn sumac Osage-orange

Pussy willow Sugarberry ‘Prairie Gem’ Ussurian pear

Robusta poplar Swamp white oak Prickly ash

Sargent cherry Sweetgum Rose-of-Sharon

Scarlet oak Sycamore maple Russian-olive1

Serbian spruce White mulberry1 Saucer magnolia

Shellbark hickory Scholar tree

Silver linden Shantung maple

Smoothleaf elm ‘Snow Goose’ cherry

Star magnolia Pacific Sunset® maple

Sugar maple Tree of heaven1

Turkish hazelnut TriumphTM elm

Washington hawthorn White fringetree

Weeping willow Willow oak

White poplar Winged burningbush1

‘Winter King’ green hawthorn Yellowwood

Yellow buckeye

Yellow-poplar (tuliptree)

1 Invasive species. 
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 45

Only 8 percent of trees recommended for planting on 

city parkways (boulevards) were considered to have 

high or moderate-high vulnerability. Twenty-three 

percent had low vulnerability, and 43 percent had lowmoderate or moderate vulnerability. The low number 

of vulnerable tree species is likely due to the need for 

street trees to withstand a wide variety of stressors; 

thus, recommended species tend to have high adaptive 

capacity. Twenty-seven percent were not rated. 

Impacts of Climate Change 

on the Urban Forest

The impacts of climate change on individual trees will 

have important implications for the urban forest of the 

Chicago Wilderness region as a whole. There is 

increasing empirical evidence of the impacts of climate 

change on urban forests at both the global and local level 

(Gunderson et al. 2012, Hayhoe et al. 2008, Hellman et 

al. 2010, IPCC 2007, Staudinger et al. 2012). Urban 

sites are often more extreme (e.g., in terms of extreme 

heat, temperature fluctuations, flooding) than nonurban sites (Wilby 2008). Global climate change is 

projected to increase the frequency and duration of these 

extreme climatic events (Breshears et al. 2005). 

Therefore, urban trees and forests that are already under 

stress due to the intense urban environment are likely to 

have interactive and additional stress from the effects of 

global climate change (Gill et al. 2007, Kirshen et al. 

2008). Many urban trees may be adaptable to these 

changes either because they were selected for planting 

based on their tolerance of stress or because they have 

propagated and thrived naturally as a result of their wide 

adaptability. Following is a summary of some of the key 

impacts for the urban forest in the Chicago Wilderness 

region, recognizing that the urban forest is only a 

component of a broader urban ecosystem (see the 

sidebar).

Heat Stress

Increases in temperature from climate change can be 

exacerbated in urban areas (Wilby 2008). Urban areas 

with 1 million or more people can be 1.8 to 5.4 °F (1 

to 3 °C) warmer than their surrounding rural areas 

due to the urban heat island effect from heat-absorbing 

infrastructure such as pavement and buildings (Akbari 

2005). The heat island effect can make urban areas 

one hardiness zone warmer than the surrounding area, 

allowing some more-southern species to be planted 

(U.S. Department of Agriculture 2012). In addition 

Other Resources on Climate 

Change Impacts on the Chicago 

Wilderness Region

In this chapter, we focus specifically on climate 

change impacts on trees and the urban forest. 

We recognize that trees are only one component 

of a broader urban ecosystem that includes 

nonforested terrestrial and aquatic systems, 

grasses and herbaceous plants, insects, 

vertebrate species, fungi, micro-organisms, and 

of course people. If you would like to learn more 

about how other ecosystems and species may 

be affected by climate change in the area, helpful 

resources are available: 

• Changing Landscapes in the Chicago 

Wilderness Region: a Climate Change 

Update to the Biodiversity Recovery 

Plan (Chicago Wilderness 2012). This 

online resource summarizes the key 

effects of climate change on species 

and ecosystems across the Chicago 

Wilderness region. 

• Climate Change and Chicago: 

Projections and Potential Impacts. 

Chapter 5—Ecosystems (Hellmann 

et al. 2008). This chapter describes the 

likely impacts of warming temperatures 

and changing precipitation on plant 

species, wildlife, invasive species, pests, 

and agricultural ecosystems across the 

Chicago region.

• Climate Change Impacts on Terrestrial 

Ecosystems in Metropolitan Chicago 

and its Surrounding, Multi-state 

Region (Hellmann et al. 2010). This article 

summarizes the impacts of climate change 

on plants, wildlife, invasive species, pests, 

and agricultural ecosystems across the 

multi-state region centered on Chicago.

Riparian area, Riverside, IL. Photo by M.G. Collins, 

Village of Riverside, used with permission. 
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 46 |

to milder winters, however, the heat island effect can 

make summer temperatures higher, especially near dark 

pavements and buildings. Thus, some plants that are 

marginal for the area based on heat zones could exhibit 

negative effects. Excessive heat can cause scorching of 

leaves and twigs and sunburn on branches and stems. In 

addition, photosynthetic rates are often reduced at high 

temperatures, leading to reduced growth rates and early 

leaf senescence. 

Drought Stress 

Severe and long-term droughts can have dramatic 

impacts on the Chicago Wilderness region’s urban 

forest. During drought years, growth can be reduced, 

with variation among species and land use (Fahey et 

al. 2013). Despite uncertainty about future changes in 

drought over the next century, some evidence suggests 

that trees may become more water-stressed from 

increased evapotranspiration under higher temperatures 

or less frequent rain events (Chapter 2). Species known 

for their drought tolerance, such as Kentucky coffeetree, 

may show less of a negative effect than species that are 

adapted to more mesic environments, such as yellowpoplar and sugar maple. Trees in developed areas, 

such as near residences and along streets, may be less 

susceptible to drought due to reduced competition 

and increased maintenance. However, some street 

trees planted in confined spaces could also undergo 

drought stress if there is insufficient soil. Droughts in 

the Chicago Wilderness region tend to not be as severe 

as those in the desert Southwest, and growth tends to 

bounce back in the years after a drought (Fahey et al. 

2013). However, if droughts do become more severe in 

the future, large-scale mortality could occur (Breshears 

et al. 2005). 

Wind Damage 

On average, urban areas tend to have lower wind 

speeds than surrounding rural areas (Mishra et al. 

2015), but buildings and other urban infrastructure 

can create “street canyons” with localized areas of 

altered wind speed and turbulence (Rotach 1995). 

Wind damage to trees can be particularly problematic 

in urban areas because of the increased risk to human 

life and property. As noted in Chapter 2, conditions 

could become favorable for more thunderstorms and 

straight-line wind events in the future, and tornadoes 

could occur earlier in the season and in more clustered 

outbreak sequences. When the urban forest is subjected 

to severe wind disturbance, falling limbs and trees 

can result in loss of electric service, displace families 

and businesses, and impede emergency vehicles from 

reaching damaged areas. 

Several factors influence the resilience of trees to wind 

damage. Trees that have defects (e.g., codominant stems, 

decay, severed roots) are more likely to be damaged or 

uprooted by strong winds. Large trees sustain more 

damage because they have larger crowns exposed 

Daffodils coming up in early spring, Hazel Crest, IL. Photo by K. Persons, Village of Hazel Crest Open Lands Commission, used 

with permission.
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 47

to wind. Furthermore, wind speeds increase with 

increased distance from the ground, thereby exerting 

greater force on taller trees. Wind-related damage and 

mortality can also be species-specific and depend on 

factors such as tree architecture and wood material 

properties. For example, silver maple, Siberian elm, 

and yellow-poplar are among the species reported to be 

frequently damaged in wind storms, whereas white oak, 

baldcypress, and hickory species are among the most 

resilient to wind damage (Duryea et al. 2007). Certain 

cultivars (e.g., Bradford pear) have growth forms that 

make them highly susceptible to wind damage. Pruning 

can reduce wind loading and increase wind resistance 

if done properly (Ciftci et al. 2014, Gilman et al. 2015, 

Smiley and Kane 2006). 

Winter Storm Damage

Winter storm damage to trees can be of particular 

concern in urban areas because of the risk to 

infrastructure. Winter storms occur frequently across 

the region, and could become less frequent, but when 

they do occur under future climate conditions they 

could be more severe with wetter snow (Chapter 2). 

Less salt may have to be applied to roads and sidewalks 

with wetter snow, which might reduce stress on 

parkway landscaping and street trees, reducing the 

need to maintain and replace trees in the region (Jaffe 

and Woloszyn 2013). However, dense, wet snow may 

behave much like heavy ice deposited by ice storms and 

freezing rain, with the weight of the snow damaging 

trees and surrounding structures (Jaffe and Woloszyn 

2013). In general, tree species and cultivars that are 

more likely to have included bark, decurrent branching 

patterns, and a large branch surface area will be more 

susceptible to winter snow and ice damage (Hauer 

et al. 1994). Susceptible species include American 

basswood, hackberry, silver maple, and eastern 

cottonwood. Resistant trees generally lack included 

bark and have coarse branching patterns and excurrent 

branching. Resistant trees include baldcypress, eastern 

hophornbeam, Kentucky coffeetree, and swamp white 

oak (Hauer et al. 1994). 

Flooding and Stormwater Runoff 

Urban environments are more susceptible to 

stormwater runoff due to the high concentration of 

impervious surfaces. Increases in impervious cover can 

dramatically increase the size and frequency of socalled 100-year flood events (Hollis 1975). This effect 

is likely to be exacerbated by the increase in heavy 

rain events that is already occurring and projected 

to accelerate in the area under a changing climate 

(Chapter 2). Typically, urban floods are short-lived, 

but extended flooding can stress trees, leading to 

leaf yellowing, defoliation, and crown dieback. If 

damage is severe, mortality can occur. In addition, 

flooding can lead to secondary attacks by insect pests 

and diseases (Bratkovich et al. 1993). Some species 

are more tolerant of flooding than others. Floodintolerant species include upland species such as 

bitternut and shagbark hickory, Kentucky coffeetree, 

and white oak (Bratkovich et al. 1993). Species that 

tend to be tolerant of flooding include species that are 

generally native to wetlands and riparian areas such 

as baldcypress, American sycamore, and red maple 

(Bratkovich et al. 1993). In addition to differences 

among species, age class and vigor can affect floodrelated damage and mortality. 

Air and Soil Pollution

Air and soil pollution from ozone, nitrogen deposition, 

and sulfur dioxide can all affect tree health. In 2015, the 

Chicago region was ranked 19th in the nation for highest 

ozone pollution (American Lung Association® 2015). 

Elevated temperatures can increase the rate of groundlevel ozone formation (Jacob and Winner 2009), leading 

to leaf damage and secondary damage from insects and 

disease. Regional increases in summer temperatures 

could worsen effects already experienced due to the 

heat island effect (Wilby 2008). However, new air 

quality standards could help mitigate this effect (U.S. 

Environmental Protection Agency 2015b). Although 

nitrogen deposition and sulfur dioxide levels have 

decreased in the area in recent years, they are still far 

above ambient levels (Illinois Environmental Protection 

Agency 2013). Stress from sulfur dioxide and nitrogen 

deposition can interact with climate change-induced 

stressors to lead to tree decline and mortality (McNulty 

and Boggs 2010). 

Carbon Dioxide Increases

The urban forest in the Chicago region is estimated 

to absorb about 2.5 million tons of carbon dioxide 

(CO2) per year (Nowak et al. 2013). Elevated CO2

may enhance growth and water use efficiency of some 

species (Ainsworth and Rogers 2007, Norby et al. 

2005), potentially offsetting the negative effects of 

drier conditions later in the growing season. There 

is already some evidence for increased forest growth 

under elevated CO2 in the eastern United States (Cole 
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 48 |

et al. 2010, McMahon et al. 2010), but it remains 

unclear if long-term enhanced growth can be sustained 

(Bonan 2008, Foster et al. 2010). Nutrient and water 

availability, ozone pollution, and tree age and size 

all play major roles in the ability of trees to capitalize 

on carbon dioxide fertilization (Ainsworth and Long 

2005). 

Shifts in Phenology

Climate change may lead to shifts in the timing of leaf 

out, flowering, fruit production, and senescence in urban 

trees. Trees and shrubs may break dormancy earlier in 

the year in response to earlier, warmer springs (Chicago 

Wilderness 2012, Fahey 2016). This could have negative 

effects if the spring warming is followed by a frost 

period. For example, in 2012, the apple crop in the 

northern Midwest was severely reduced by an unusually 

warm March followed by a killing frost. Other negative 

effects are possible if leaf out and insect emergence occur 

before the arrival of insectivorous migratory birds; insect 

herbivory could increase as a result (Chicago Wilderness 

2012). Early bud burst may also lead to early flowering, 

which could be problematic in insect-pollinated trees 

if pollinators have not yet arrived. At the end of the 

growing season, senescence may occur later due to 

warmer temperatures in autumn. Although a longer 

growing season could be beneficial in absorbing carbon 

dioxide, it could also place additional stress on trees if 

there are also declines in water availability later in the 

growing season. 

Invasive Plant Species

Invasive species can be problematic in natural areas in 

the Chicago Wilderness region as mentioned in Chapter 

1. Many invasive species that currently threaten natural 

areas in the Chicago Wilderness region are likely to 

benefit from projected climate change. As noted earlier in 

this chapter, invasive species tend to have high adaptive 

capacity and low vulnerability, meaning they will be 

among those most successful in a changing climate. 

Changes in climate may allow some invasive plant 

species to survive farther north than they had previously 

(Dukes et al. 2009). Kudzu is an invasive vine that has 

blanketed forests in the southeastern United States, 

blocking access to sunlight for native trees and plants. 

The current northern distribution of kudzu is limited 

by winter temperature. A modeling study suggests that 

warmer temperatures could aid the spread of kudzu across 

Illinois by the end of the century (Bradley et al. 2010). 

Although the risk to the Chicago area was projected to be 

low, the urban heat island effect was not accounted for in 

this study. Another study examined the potential future 

distribution of kudzu for the year 2035 using trends in 

observed climate data; results also suggest that kudzu 

will move northward but will still be south of Chicago 

(Jarnevich and Stohlgren 2009). Chinese and European 

privet are invasive flowering shrubs that crowd out native 

species and form dense thickets. Model projections 

suggest that the risk of privet invasion into the Chicago 

Wilderness region may be similar to that of kudzu by the 

end of the century (Bradley et al. 2010). 

Riparian area invaded with European buckthorn, Riverside, IL. Photo by M.G. Collins, Village of Riverside, used with permission.
Climate Change Impacts and the Adaptive Capacity of the Urban Forest—CHAPTER 3 | 49

Focus on Oaks

Oak species once dominated the landscape of the Chicago 

Wilderness region (Fahey et al. 2012). Efforts are currently 

underway to restore oak ecosystems in natural areas in the 

region and to increase the component of native oak trees in the 

urban landscape (Chicago Wilderness 2015). Given projected 

changes in climate, one might wonder if oaks will continue to 

thrive in the region. Model projections mentioned earlier in this 

chapter are mixed for many oak species, including northern 

red, bur, and white oak. Despite a projected loss in suitable 

habitat under some scenarios, many of these species have 

adaptive capacity factors that could allow them to persist, such 

as drought tolerance and low susceptibility to breakage during 

storms. These factors may allow them to perform better than 

the models suggest. However, there are several pests and 

diseases of oaks that could become more problematic under 

a changing climate, such as oak wilt, two-lined chestnut borer, 

and bur oak blight. 

Taken together, these factors paint a complex picture for oaks in the area under a changing climate. 

Ultimately, management decisions regarding if and where to plant particular oak species will depend on a 

variety of factors. The benefits of restoring native biodiversity and the ecosystem service benefits that oaks 

provide, such as habitat for a wide diversity of insect species (Tallamy 2009), may be weighed with the direct 

and indirect risks and benefits of climate change. 

Insect Pests and Pathogens

Warmer temperatures and stressed trees may increase 

the abundance of pests and pathogens that are currently 

present in the Chicago Wilderness region. Milder 

winters could be beneficial for the emerald ash borer, 

which is already causing extensive damage to ash 

trees across the area (Venette and Abrahamson 2010). 

Drought stress, which could occur later in the growing 

season, may make trees susceptible to attacks by boring 

insects such as bronze birch borer and two-lined 

chestnut borer and to diseases such as Botryosphaeria 

canker. Oak wilt, a high-mortality disease of oaks 

in the region, benefits from cool, moist conditions 

for transmission and hot, dry conditions for disease 

progression. Thus, wetter springs followed by hot dry 

summers could make oak wilt a larger problem in the 

Chicago Wilderness region in the coming decades 

(see the box below). Gypsy moth, which attacks oaks 

and other shade trees, could benefit from warming 

temperatures (Logan et al. 2007). However, wetter 

springs could curtail its spread to some extent: a fungal 

pathogen of the larvae has been shown to reduce 

populations in years with wet springs (Andreadis and 

Weseloh 1990).

A changing climate is also likely to increase the 

susceptibility of tree species to pests and diseases that 

are not currently a problem in the Chicago Wilderness 

region. High spring precipitation has been associated 

with severe outbreaks of bur oak blight in Iowa, which 

could put Chicago-area bur oaks at risk if springs 

become wetter (Harrington et al. 2012). Milder winter 

temperatures could allow southern pine beetle to 

migrate northward (Ungerer et al. 1999). Beech bark 

disease and thousand cankers disease may benefit from 

warmer winter temperatures (Kasson and Livingston 

2012, Luna et al. 2013). However, the risk for some 

diseases may be reduced under a changing climate. 

Climate conditions are currently only marginal for 

sudden oak death in the Chicago Wilderness region 

(Kluza et al. 2007, Venette and Cohen 2006), and 

models suggest that conditions may become less suitable 

in the future (Venette 2009). Additional pests and 

diseases may invade for reasons other than climate 

change. The northern distribution of Asian longhorned 

beetle does not appear to be driven by winter 

temperatures, and this pest has already been found in 

Chicago (Antipin and Dilley 2004, Roden et al. 2009). 

Project to preserve historic oak genetic stock through 

propagation, Oak Park, IL. Photo by E. Johnson, Erika 

Hildegard Photography, used with permission. 
CHAPTER 3—Climate Change Impacts and the Adaptive Capacity of the Urban Forest 50 |

Tree- and Forest-dependent Wildlife

Wildlife that depends on the urban forest in the 

Chicago Wilderness region may also be subject to effects 

of climate change (Hellmann et al. 2010). Suitable 

habitat for wildlife may shift due to both direct effects 

of temperature and precipitation and indirect effects 

through changes in the vegetation and food sources 

upon which they depend. Based on species distribution 

modeling that accounts for both vegetation shifts and 

direct climate effects, habitat for 52 to 54 bird species 

may increase at least 10 percent in the greater Chicago 

region over the next century (Hellmann et al. 2010). 

Another 64 to 76 species could lose habitat within this 

area (Hellmann et al. 2010). There are currently 115 

butterfly species in the greater Chicago region, of which 

20 could lose suitable habitat and 19 could gain habitat 

based on range boundaries (Hellmann et al. 2010). 

Many species common in urban environments, such 

as raccoons, skunks, and gray squirrels, may remain 

unaffected by climate change, as they are tolerant of a 

wide range of climatic conditions (Hellmann et al. 2010). 

Some wildlife species, such as white-tailed deer, can 

have negative effects on the urban forest by feeding 

on seedlings or saplings (Urbanek and Nielsen 2012). 

Currently, there is little evidence to indicate how 

deer will respond to climate change in the Chicago 

Wilderness region. An analysis of climate change 

impacts on white-tailed deer in Wisconsin suggests 

that deer in that area are likely to experience a mixture 

of positive impacts from milder winters coupled with 

negative impacts from increased disease outbreaks 

(Wisconsin Initiative on Climate Change Impacts 

2011). How these two factors may influence deer 

populations in Chicago remains unknown.

Summary

Results from species distribution modeling suggest that 

habitat suitability for many tree species may shift across 

the Chicago Wilderness region, leading to declines 

in some species and increases in others. Species at the 

southern extent of their range are generally projected 

to decline in suitable habitat. Species at the northern 

extent of their range or south of the area could have an 

increase in suitable habitat, especially in areas where 

there is an urban heat island effect. Model projections 

are mixed for many oak species that were once dominant 

on the landscape before European settlement. Other 

factors that are not included in models, such as changes 

in extreme events, insects, and diseases, may also affect 

the survival of particular tree species and make them 

more or less adaptable to climate change-induced 

pressures than the models would suggest. Overall, the 

vulnerability of trees and the surrounding urban forest 

will need to be gauged based on the complex interaction 

of multiple stressors and benefits. 

Key Points

• Species distribution modeling for native species 

suggests that suitable habitat under a changing 

climate may decrease for 11 primarily northern 

species, and increase or become newly suitable for 40 

species. 

• For species for which no model information is 

available (rare, nonnative, or cultivars), shifts in 

hardiness and heat zones could have a positive effect 

on about 23 percent of species that are either present 

in the area or considered for planting, and a negative 

effect on about 19 percent. 

• Adaptive capacity of 179 species was evaluated 

by using scoring systems for planted and natural 

environments, with invasive species among those with 

the highest capacity to adapt to a range of stressors. 

• An analysis of vulnerability that combines model 

projections, shifts in hardiness and heat zones, and 

adaptive capacity showed that 17 percent of the tree 

species currently present in the region have either 

moderate-high or high vulnerability to climate 

change, and 77 percent of individual trees with low 

vulnerability are invasive species. 

• Key impacts to trees in the Chicago Wilderness 

region projected over the next century include 

increased drought and heat stress, increased 

stormwater runoff and flooding, increases in wind 

damage, and increases in tree pests and pathogens.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 51

CHAPTER 4

URBAN FOREST VULNERABILITY CASE STUDIES

This chapter focuses on the vulnerability of urban 

forests in the Chicago Wilderness region to climate 

change. Vulnerability is the susceptibility of a system to 

the adverse effects of climate change (Intergovernmental 

Panel on Climate Change [IPCC] 2007). It is a 

function of potential climate change impacts and the 

adaptive capacity of the system. We consider urban 

forests to be vulnerable if they are anticipated to 

suffer substantial declines in health or productivity, 

similar to assessments of natural ecosystems (Brandt 

et al. 2014; Butler et al. 2015; Handler et al. 2014a, 

2014b; Janowiak et al. 2014). However, unlike in 

previous assessments of forested areas largely away 

from urban areas, considerations of social, economic, 

and organizational factors are critical in determining 

the overall vulnerability of an urban forest (Ordóñez 

and Duinker 2014). Urban forests can be considered 

social-ecological systems, where resilience of human 

communities and the ecological systems they depend 

upon are intertwined (Hale et al. 2015, Mincey et al. 

2013, Tidball 2014, Vogt et al. 2015). In urban forests 

with a more natural composition of native species, 

such as those found in forest preserves, systems are also 

vulnerable if climate change would fundamentally alter 

their composition or character.

Urban Vulnerability 

Assessment Process

We developed a vulnerability assessment process to 

help urban forestry professionals assess the vulnerability 

of municipalities, park districts, and forest preserves 

to climate change (for more details on methods, see 

Appendix 8). This process built upon concepts for 

assessing the vulnerability of urban forests (Ordóñez 

and Duinker 2014) as well as vulnerability assessments 

focused on natural forests and completed for the Climate 

Change Response Framework (Brandt et al. 2014; Butler 

et al. 2015; Handler et al. 2014a, 2014b; Janowiak et al. 

2014). Our process was designed to draw upon urban 

forestry professionals’ knowledge about the urban forests 

that they manage and to consider how these systems may 

change given projected climate changes. 

Our process evaluated vulnerability for two key 

components: impacts and adaptive capacity (Glick et al. 

2011, Swanston and Janowiak 2016). Climate change 

impacts are the direct and indirect effects of climate 

change on the system in question. Impacts were 

subdivided into physical, biological, or human factors 

that could influence the exposure or sensitivity of the 

urban forest (Fig. 15). Considerations under the physical 

factor category were related to soil type, topography, and 

proximity to large bodies of water. Considerations under 

the biological factor category included projected shifts in 

habitat suitability for urban trees and anticipated effects 

of climate change on organisms that influence tree 

health, such as insect pests, pathogens, and herbivores. 

Human factor considerations included the relative 

magnitude of the urban heat island effect, amount of 

impervious surface, and capacity of the storm sewer 

system to absorb heavier storms. 

Adaptive capacity is the ability of a species or ecosystem 

to accommodate or cope with potential climate change 

impacts with minimal disruption. Adaptive capacity 

was subdivided into biological, organizational/technical, 

economic, or social factors that could enhance or inhibit 

the capacity of the urban forest to adapt to changes in 

climate. Biological factor considerations included the 

Figure 15.—Impact and adaptive capacity factors that were 

evaluated during the vulnerability assessment process. In 

general, impacts have a negative influence on vulnerability 

and adaptive capacity factors have a positive influence. For 

examples of each factor, see Appendix 6.
CHAPTER 4—Urban Forest Vulnerability Case Studies 52 |

overall condition of the current forest and the diversity 

of species, genotypes, and age classes. Organizational/

technical factor considerations included whether the area 

had trained forestry professionals, tree care ordinances, 

and plans, as well as the availability of nursery stock. 

Economic factor considerations focused on whether 

there was sufficient funding to plant or maintain trees. 

Social considerations included the value that the public 

places on trees and the extent to which volunteers or 

local organizations provide support. 

Potential impacts and adaptive capacity factors specific to 

the Chicago Wilderness region were used to prepopulate a worksheet for urban forestry professionals (see 

Appendix 8). Regional impacts were summarized from 

the information presented in Chapters 2 and 3, and 

adaptive capacity factors were identified from the 

literature (Ordóñez and Duinker 2014). Each 

consideration within a factor included a question to 

prompt the professionals to evaluate specific ways in 

which it could affect their work. Professionals were asked 

to answer the question, give an assessment of its overall 

effect on the urban forest (positive, negative, or neutral), 

and rate their level of confidence in that effect (high, 

medium, low). To allow for flexibility, the professionals 

using the worksheet could remove considerations that did 

not apply and add other considerations that may apply. 

Professionals were asked to rate each factor, and then use 

these factor ratings to determine overall impacts and 

adaptive capacity. They were then asked to place a mark 

on a vulnerability figure based on their determinations of 

impact and adaptive capacity (Fig. 16). 

Figure 16.—Vulnerability diagram used to determine overall 

vulnerability from impacts and adaptive capacity (based on 

Swanston and Janowiak 2012).

Measuring tree diameter for the 2010 tree census. Photo by E. Johnson, Erika Hildegard Photography, used with permission. 
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 53

Vulnerability Case Studies

Urban forestry professionals from four municipalities, 

three park districts, and three forest preserve districts in 

the Chicago Wilderness region (Fig. 17) completed the 

vulnerability assessment process to test the approach. 

Representatives from each organization participated in a 

focused workshop where they completed the worksheets 

and assessed the vulnerability of their management area 

to climate change. Professionals were asked to consider 

the impacts of climate change over the next century, and 

the adaptive capacity of the urban forest that they 

manage. After the workshops, information from the 

worksheets was summarized in narrative form for each 

location. In the following case study summaries, we 

highlight the key considerations for the impact and 

adaptive capacity factors that urban forestry professionals 

identified as having the greatest influence on climate 

change vulnerability for the areas they manage. When 

inventories were available, we also evaluated the number 

of species and total trees in the inventory considered 

vulnerable based on changes in habitat suitability and 

adaptive capacity (see Chapter 3). Overall ratings of 

vulnerability ranged from low-medium to medium-high, 

and were primarily driven by differences in adaptive 

capacity (Table 13). 

Figure 17.—Case study areas in the Chicago Wilderness region. Note that the municipal 

boundaries are shown for the park districts.
CHAPTER 4—Urban Forest Vulnerability Case Studies 54 |

Table 13.—Summary of vulnerability assessment results for four municipalities, three park districts, and three forest preserves in 

the Chicago Wilderness region 

Impacts Adaptive capacity Vulnerability

Location Type Physical Biological Human Overall Biological

Organizational/

Technical Economic Social Overall

Riverside M moderatenegativemoderate moderate moderate high high high high high low-moderate

Lake Forest M moderate negative moderate moderatenegativehigh moderate high high moderatehighmoderate

Hazel Crest M moderate moderatenegativemoderatenegativemoderatenegativelow moderate-high low moderate lowmoderatemoderatehigh

Glenview M moderatenegativemoderatenegativemoderatenegativemoderatenegativemoderate high high high high moderate

Glencoe Parks P moderate moderatenegativenegative moderatenegativemoderate low-moderate lowmoderatelow lowmoderatemoderatehigh

Wheaton 

Parks

P moderate negative negative moderatenegative

high high high moderatehighhigh moderate

Chicago Parks P moderatenegativemoderatenegativemoderatepositivemoderatenegativemoderate moderate-high moderatehighmoderatehigh moderatehighlow-moderate

Southern Des 

Plaines River 

Preserves

F negative moderatenegative

negative moderatenegativelowmoderatemoderate moderatehighhigh moderate moderate

Freeman 

Kame 

Preserve

F moderatenegative

moderatenegativenegative moderatenegativemoderate moderate moderate moderatehighmoderatehighmoderate

Swallow Cliff 

Preserve

F moderate moderatenegative

moderate moderatenegativelow moderate low lowmoderatelowmoderatemoderatehigh

M indicates municipality P indicates park district F indicates forest preserve
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 55

MUNICIPALITIES 

Village of Riverside

Low-Moderate Vulnerability 

This residential community has a diverse mix of tree 

species that are well-maintained by professionals and 

the community, thus a very high adaptive capacity. 

The primary concern in the area is an increase in 

pests and diseases due to milder winters. 

The Village of Riverside is a suburban village in Cook 

County, IL. A significant portion of the village is in the 

Riverside Landscape Architecture District, designated 

a National Historic Landmark in 1970, and designed by 

Frederick Law Olmstead. The population of the village 

was 8,875 at the 2010 census with a median income 

of $85,703 (note that population and income data for 

each municipality, park district, and forest preserve 

district are from the 2010 U.S. Census [U.S. Census 

Bureau 2011]). Riverside is located about 9 miles west of 

downtown Chicago and 2 miles outside city limits.

Impacts—moderate 

Physical factors—moderate-negative

The areas south and north of the railroad have different 

physical properties. On the south side, the area near the 

Des Plaines River can become flooded and may be more 

vulnerable if heavy storms increase. The soils in the area 

are relatively well-drained, however. On the north side 

of the railroad, the area is more developed and soils tend 

to be more compacted with heavy clay content. This can 

lead to temporary flooding during heavy rain events. 

Storm damage is a bigger concern on the south side of 

the village, where trees are more exposed because there 

are fewer structures to block the wind. 

Biological factors—moderate

The area tends to have high species diversity and 

does not have many trees that are expected to be 

particularly vulnerable to shifts in temperature or 

precipitation. Fewer than 6 percent of the trees in 

the village are considered to have moderate-high or 

high vulnerability, and more than half have low or 

Residential neighborhood, Chicago. Photo by L. Darling, The Morton Arboretum, used with permission.
CHAPTER 4—Urban Forest Vulnerability Case Studies 56 |

low-moderate vulnerability ratings (Table 14). However, 

there are many species of oak that could be susceptible 

to increased pest and disease pressure such as oak wilt, 

gypsy moth, and bur oak blight. Herbivory from deer is 

an issue throughout the area, and could become more 

problematic if winters become milder. Invasive species 

competition from European buckthorn is primarily a 

problem along the river and could become a larger issue 

as native species become more stressed under a changing 

climate. (For lists of the common and scientific names of 

species mentioned in this report, see Appendix 1.) 

Table 14.—Number of tree species and the proportion of 

total trees inventoried in each vulnerability class for the 

Village of Riverside, based on a 2014 inventory of all park 

and street trees

Vulnerability class Number of species Percentage of trees

Low 18 23.1

Low-Moderate 27 38.7

Moderate 26 25.2

Moderate-High 5 3.9

High 6 1.9

Not rated1 22 6.5

1 This category includes trees that were only identified to genus in the 

inventory and extremely uncommon species or cultivars.

Human factors—moderate

The area is primarily residential, with a mix of 

greenspace and impervious cover. The more developed 

north side has more impervious cover, and thus could 

experience increases in runoff and decreases in water 

infiltration into the soil if heavy rain events increase. 

The area is relatively densely populated and thus 

susceptible to air pollution. 

Adaptive Capacity—high

Biological factors—high

Riverside is a National Historic Landscape District that 

was designed by Olmsted to provide connected greenspace. 

In keeping with original plans, a wide mix of native species 

with a high genetic diversity has been planted throughout 

the village. Trees are a good mix of age classes with few 

trees approaching the end of their lifespan. 

Organizational/technical factors—high

The Village of Riverside has a forester on staff, 

comprehensive inventory, planting list, and long-term 

plan. The village is part of a mutual aid network and has 

conducted a risk assessment of its urban forest to natural 

disasters. Trees are regularly pruned and maintained to 

reduce vulnerability to storm damage. There is a lack of 

diverse nursery stock to keep up with demand, however. 

Economic factors—high

There is currently sufficient funding for the village to 

plant and maintain trees in the area, but it can fluctuate 

from year to year. Private homeowners typically have 

sufficient economic means to care for their trees, but this 

capability varies. 

Social factors—high

The area has extensive community support and 

involvement in planting and maintaining trees and a 

wide network of organizations for sharing ideas and 

resources. 

City of Lake Forest 

Moderate Vulnerability

The area has many trees that are vulnerable to the 

direct and indirect effects of a changing climate. 

Municipal trees are not maintained as often as would 

be optimal. However, the city has a well-funded 

forestry program and a community with the means 

and desire to care for its trees. 

Lake Forest is a suburb in the North Shore area along 

Lake Michigan in Lake County with a population of 

19,375. The median household income is $147,162. 

When Lake Forest was first developed in the 1850s, the 

original design limited road access in order to maintain 

a tranquil setting. More development has occurred on 

the west side of the city in recent decades, but the east 

Bitternut hickory planted in a Riverside neighborhood. Photo by 

M.G. Collins, Village of Riverside, used with permission. 
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 57

side remains relatively secluded. It is an affluent area and 

home to Lake Forest College. 

Impacts—moderate-negative 

Physical factors—moderate

The City of Lake Forest has soils with a high clay 

content and poor drainage, making the area susceptible 

to flooding and runoff during heavy rain events. 

However, the clay soils could help retain some moisture 

later in the growing season. 

Biological factors—negative

The city has many common trees that are vulnerable to 

projected changes in climate, especially white ash, black 

cherry, and several northern conifer species. Overall, 

about 10 percent of the trees in the area are considered 

to have moderate-high or high vulnerability based on 

the most recent inventory (Table 15). The city is also 

experiencing outbreaks of pests and diseases, especially 

emerald ash borer, and has a relatively large concentration 

of herbivores such as deer. All of these stressors could 

become worse with milder winters. Canopy cover, and 

thus cooling from trees, is being lost due to ash mortality. 

Many of the trees are susceptible to wind and ice damage.

Table 15.—Number of tree species and the proportion of 

total trees inventoried in each vulnerability class for the 

City of Lake Forest, based on a 2013 inventory of all park 

and street trees

Vulnerability class Number of species Percentage of trees

Low 23 14.7

Low-Moderate 26 27.4

Moderate 33 36.1

Moderate-High 8 7.3

High 12 2.4

Not rated1 73 12.0

1 This category includes trees that were only identified to genus in the 

inventory and extremely uncommon species or cultivars.

Human factors—moderate

This primarily residential area does not experience an 

urban heat island effect. Although it does not have a 

high amount of impervious cover, irrigation in the area 

combined with insufficient ravines to channel water can 

lead to runoff issues during heavy storm events. 

Adaptive Capacity—moderate-high

Biological factors—high

The mix of tree species and genetic diversity are 

high. Most of the trees are at mid-age so they are not 

particularly at risk for mortality from potential dry periods 

or storms. In the past, a single tree species was planted 

on a block, which increased vulnerability to canopy loss 

from climate change or pest outbreaks. However, newer 

planting programs are working to increase the diversity. 

Organizational/technical factors—moderate

The City of Lake Forest has professional forestry staff 

overseeing the selection and care of its street trees. It also 

has a planting list and tree care ordinance, a long-term 

plan, and a tree inventory. Private residents typically 

care for and maintain their trees. Maintenance of city 

trees is not as often as would be optimal, with pruning 

occurring every 13 to 17 years. The city does not have a 

risk assessment or disaster response plan. 

Economic factors—high

There is currently sufficient funding for the city to plant 

and maintain trees in the area. The area has a high 

median income, and private homeowners have sufficient 

economic means to care for their trees. 

Social factors—high

Residents tend to value their trees. Likewise, the city 

offers several programs as an incentive to residents. 

The Forestry Section offers a cost-sharing program to 

residents, which allows those residents who do not already 

have a parkway tree to split the cost 50/50 with the city. 

It also has a wholesale tax-exempt native tree sale for 

residents to encourage the planting of native trees. 

Field and woodlot, Lake Forest. Photo by P. Gordon, City of 

Lake Forest, used with permission.
CHAPTER 4—Urban Forest Vulnerability Case Studies 58 |

Village of Hazel Crest 

Moderate-High Vulnerability 

The vulnerability of the Village of Hazel Crest is 

primarily driven by its lack of biodiversity from an 

overdominance of silver maple. Its adaptive capacity 

is also hindered by an insufficient budget and a lack 

of public involvement and support. 

Hazel Crest is a village in Cook County, IL. The 

population of approximately 14,100 is racially diverse 

with a median income of $49,489. The village is located 

in an area known as the Chicago Southland and is about 

25 miles south of the Chicago Loop. 

Impacts—moderate-negative

Physical factors—moderate

Soils in the area tend to be very well-drained, which 

makes them susceptible to drying. Flooding is not a 

major issue in the area, with only temporary flooding 

occurring after large storm events. One exception is 

the open lands, which are left undeveloped due to their 

susceptibility to flooding. 

Biological factors—moderate-negative

Silver maple is the dominant tree planted on the 

village’s streets. It is extremely vulnerable to wind and 

ice damage, which could be more problematic as severe 

storms increase. Emerald ash borer is also killing ash 

trees in the area, leading to a reduction in canopy cover. 

Human factors—moderate-negative

Hazel Crest is situated at the intersection of Interstate 

80 and 294, which contributes vehicle pollution and an 

urban heat island effect. Like most suburban areas, it has 

impervious cover, but not any more than others in the 

Chicago Wilderness region. 

Adaptive Capacity—low-moderate

Biological factors—low

Biodiversity of street trees in the village is very low; 

most street trees are silver maple. Many of these trees 

are reaching the end of their lifespan. As they are 

replaced, a more diverse mix of species is being planted, 

but it will be a long time before the area has a diverse, 

mature canopy. 

Organizational/technical factors—moderate-high

The village has a certified arborist, tree board, and 

open lands commission. Its planting ordinance is being 

revised to increase flexibility. It has a risk assessment and 

participates in a mutual aid network. It does not have a 

comprehensive inventory or a long-term plan. In general, 

street trees are maintained, but routine maintenance has 

fallen behind due to the need to remove ash damaged by 

emerald ash borer. 

Economic factors—low

Funding for the planting and maintenance of trees is 

generally less than what is needed. Residents tend to 

be in the low-to-middle income range and do not have 

extra resources to care for trees in private residences. 

Social factors—moderate

Support for the planting and care of trees in the area is 

mixed. On average, older residents tend to place more 

value on trees than newer residents. There is a large 

volunteer base that helps with the planting and care of 

trees in open lands, but there is not a similar volunteer 

base to aid in the care of street trees. The village does 

offer incentives for the planting of street trees in front of 

private residences. 

Village of Glenview 

Moderate Vulnerability

This developed suburban area has many trees that 

could be susceptible to projected changes in climate, 

but has a large adaptive capacity due to trained staff, 

a sufficient budget, and a supportive community that 

values urban trees. 

Glenview is a suburban village located about 14 miles 

north of downtown Chicago in Cook County, IL. It is a 

mid-sized municipality of 50,692 people that is relatively 

affluent, with a median household income of $92,350. 

Flooded area in open land, Hazel Crest. Photo by K. Persons, 

Village of Hazel Crest Open Lands Commission, used with 

permission.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 59

Impacts—moderate-negative

Physical factors—moderate-negative

Some areas directly north of the Chicago River can 

become flooded occasionally, but it is not a severe issue. 

Soils in the area tend to be poorly drained, which could 

be helpful in retaining moisture as long as moisture is 

not excessive enough to cause uprooting. Wind storms 

are a major issue in the area, but not particularly severe 

compared to other areas in the region. 

Biological factors—moderate-negative

It is estimated that roughly two-thirds of the trees in 

Glenview may have reduced habitat suitability, with 

the remaining having improved habitat suitability 

(Table 16). Pests and pathogens can be an issue in the 

area, and are expected to become more problematic 

in the future. Herbivory by deer can be an issue on 

the east and west sides of town, and could increase if 

winters are milder. 

Table 16.—Number of tree species and the proportion 

of total trees inventoried in each vulnerability class for 

the City of Glenview, based on a 2012–2014 street tree 

inventory

Vulnerability class Number of species Percentage of trees

Low 27 18.1

Low-Moderate 31 37.9

Moderate 32 25.6

Moderate-High 10 8.2

High 14 2.2

Not rated1 54 8.1

1 This category includes trees that were only identified to genus in the 

inventory and extremely uncommon species or cultivars.

Human factors—moderate-negative

The area is a developed, primarily residential area with 

a large amount of impervious cover and soil pollution 

from road salt and fertilizers. If runoff increases from 

increased heavy rain events, it could lead to more 

frequent street flooding and reduced rainfall infiltration. 

Runoff could concentrate pollutants in low-lying areas 

and streams. 

Adaptive Capacity—high

Biological factors—moderate

The area has a relatively diverse mix of tree species, but 

there is an overemphasis on honeylocust and maples, 

which could be problematic if those species are affected 

by climate change. Many of the same species can be 

concentrated on a single block, which could also make 

some neighborhoods vulnerable to complete loss of 

canopy cover if those species are lost. This is an older 

suburb, and thus many of the trees are aging and 

susceptible to storm damage. 

Organizational/technical factors—high

The Village of Glenview has professional forestry 

staff overseeing the selection and care of its street 

trees. It also has a planting list, a long-term plan, a 

tree inventory, and a disaster response plan. Trees 

are routinely pruned, but do not receive additional 

maintenance between pruning cycles. The village does 

not have a risk assessment, however, and the long-term 

plan could be expanded to account for the impacts of 

climate change. 

Economic factors—high

There is currently sufficient funding for the village 

to plant and maintain trees in the area. Private 

homeowners typically have sufficient economic means to 

care for their trees. 

Social factors—high

The area has public support for planting and maintaining 

trees and a wide network of organizations for sharing 

ideas and resources. The village has not made use of 

volunteers for any planting or maintenance to date. 

Residential area, Glenview. Photo by R. Flackne, Village of 

Glenview, used with permission.
CHAPTER 4—Urban Forest Vulnerability Case Studies 60 |

PARK DISTRICTS 

Glencoe Park District 

Moderate-High Vulnerability

The area’s large number of mature oak trees and 

proximity to Lake Michigan make it vulnerable to 

wind-related damage. Despite abundant funding and 

flexibility to plant a wide range of species, a lack of 

funding for maintenance of older trees reduces the 

adaptive capacity in the area. 

Glencoe is an affluent residential village in Cook 

County, IL, located on the North Shore of Lake 

Michigan. It has a population of 8,723 and a median 

income of $161,976. It is bordered by natural areas to 

the north and west. The Glencoe Park District manages 

24 parks and other facilities in the village, including a 

beach and sanctuaries for birds and wildflowers. 

Impacts—moderate-negative 

Physical factors—moderate

Soils in the Glencoe Park District tend to be welldrained, reducing flood susceptibility but also increasing 

susceptibility to late-season drying. Low-lying areas 

still are susceptible to flooding. The area’s proximity 

to Lake Michigan increases exposure to heavy winds. 

Areas directly adjacent to the lake, though cooler than 

the surrounding area, also have experienced greater 

warming over the past century. 

Biological factors—moderate-negative

Some conifers and sugar maple may be vulnerable 

to increases in temperature, but many other species 

common in the area are not expected to suffer direct 

negative effects from climate change (Table 17). Other 

organisms, such as insect pests, herbivores, and invasive 

plants, may benefit from milder winters and cause 

negative impacts to trees in the area’s parks. Many of the 

trees are older and susceptible to storm damage. 

Table 17.—Number of tree species and the proportion of 

total trees inventoried in each vulnerability class for the 

Glencoe Park District, based on a 2012 inventory of all 

park trees

Vulnerability class Number of species Percentage of trees

Low 15 10.5

Low-Moderate 19 38.3

Moderate 22 37.5

Moderate-High 8 4.8

High 7 2.2

Not rated1 20 6.6

1 This category includes trees that were only identified to genus in the 

inventory and extremely uncommon species or cultivars.

Fall foliage, Glencoe. Photo by T. McDonald, Glencoe Park District, used with permission.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 61

Human factors—negative

The area is primarily residential and thus has a 

combination of pervious and impervious cover. There 

is not much of an urban heat island effect due in part 

to its proximity to Lake Michigan. A new threat is 

redevelopment, where older, smaller homes are replaced 

with larger new homes. This leads to direct disturbance to 

existing trees and loss of greenspace, decreasing infiltration 

into the soil and increasing runoff, which could become 

more problematic if heavy precipitation events increase. 

Adaptive Capacity—low-moderate

Biological factors—moderate

The Glencoe Park District has made a concerted effort 

to enhance species diversity and genetic diversity by 

growing trees from seeds instead of buying clonal 

cultivars. However, about one-third of the trees in 

the park district are older oaks, which could increase 

vulnerability to some extent. In addition, many of the 

mature trees have not been pruned recently, increasing 

susceptibility to storm damage.

Organizational/technical factors—low-moderate

The park district has two certified arborists on staff who 

ensure a diversity of trees are planted and maintained. 

At this time, however, only the youngest trees are 

pruned. Glencoe has an inventory, but it has not been 

updated recently and has not been used to assess risks of 

natural disasters. 

Economic factors—low-moderate

Glencoe has a sufficient budget for planting new trees, 

but not for maintenance. 

Social factors—low

Residents in the area value trees, but there is not a strong 

network of organizations engaged in supporting the 

area’s trees. No incentive programs are currently in place 

to encourage the planting and care of trees in the area. 

Wheaton Park District 

Moderate Vulnerability

Despite negative impacts of the interaction of climate 

change and stressors such as invasive species and 

development, the area has sufficient biological, 

organizational, economic, and social capacity to adapt. 

The City of Wheaton is the county seat of DuPage 

County, IL. It is about 25 miles west of Chicago. It has 

a population of 52,894 and a median income of $86,124. 

The Wheaton Park District received the National 

Gold Medal Award for Excellence from the National 

Recreation and Park Association four times, most 

recently in 2005. It has 52 parks covering more than 800 

acres, including the 135-acre Lincoln Marsh Natural 

Area, with over 300 species of prairie and wetland plants 

and animals.

Impacts—moderate-negative 

Physical factors—moderate

Soils in the Wheaton Park District tend to be heavily 

compacted with high clay content. This creates poorly 

drained soils that can be susceptible to flooding. The 

likely increase in local flooding as a result of more 

heavy rain events is expected to have negative effects 

on the area. However, the high clay content could aid 

in moisture retention later in the growing season when 

conditions may become drier.

Biological factors—negative

The park district tends to have many tree species that 

are planted from southern latitudes and few trees from 

northern climates, which should benefit adaptation to 

warmer temperatures (Table 18). However, stressors such 

as insect pests, diseases, and invasive species are likely to 

continue to be a problem in the area. The area’s ash trees 

are currently being affected by the emerald ash borer. 

Lincoln Marsh, Wheaton Park District. Photo by R. Sperl, 

Wheaton Park District, used with permission.
CHAPTER 4—Urban Forest Vulnerability Case Studies 62 |

Table 18.—Number of tree species and the proportion of 

total trees inventoried in each vulnerability class for the 

Wheaton Park District, based on a 2014 inventory of all 

park trees

Vulnerability class Number of species Percentage of trees

Low 15 18.3

Low-Moderate 19 24.2

Moderate 11 29.6

Moderate-High 7 8.1

High 5 7.4

Not rated1 17 12.3

1 This category includes trees that were only identified to genus in the 

inventory and extremely uncommon species or cultivars.

Human factors—negative

The Wheaton area has a large amount of green space 

and is far enough away from the urban core that the 

urban heat island effect and air pollution are not 

major issues. However, the area is heavily developed; 

redevelopment and existing roads have led to a large 

amount of impervious cover and soil and water pollution. 

Impervious cover can make the area more susceptible 

to flooding, which is projected to increase in the region 

from heavy precipitation events. Extreme precipitation 

could also transport pollutants to low-lying areas. 

Adaptive Capacity—high

Biological factors—high

Wheaton parks have a wide diversity of species, with the 

exception of a high abundance of Norway maple. The 

parks have many native species from local seed sources, 

and the forests contain a diverse array of age classes. All 

these factors are anticipated to allow forests in the park 

district to withstand a wide range of climate-induced 

stressors. 

Organizational/technical factors—high

The Wheaton Park District has trained foresters and 

horticulturists on staff and works to maintain and prune 

the trees in the area to reduce the risk of damage from 

storms. It has a list of preferred species and a long-term 

plan for planting that builds in flexibility to allow for 

adjustments. Wheaton parks do not currently have a 

comprehensive inventory, which limits the ability to 

assess risks, but one is underway. 

Economic factors—moderate-high

The area is relatively affluent, so both the city and 

homeowners have adequate funding to plant and 

maintain trees in the area. 

Social factors—high

Wheaton has an actively engaged citizenry that cares 

a great deal about its parks and trees. The park district 

has an annual plant sale that is an extremely popular 

fundraiser, and local organizations such as scout troops 

have helped plant trees.

Chicago Park District 

Low-Moderate Vulnerability

The Chicago Park District benefits from a large, 

trained staff and engaged public to facilitate the 

planting of a variety of trees. However, trees are not 

always maintained regularly enough, are exposed 

to the pollution associated with a heavily populated 

urban environment, and are sometimes vandalized. 

Chicago is a densely populated city with 2,695,598 

residents and a median income of $47,270. The Chicago 

Park District is the oldest park district in the United 

States. It has the largest budget of any U.S. park district, 

and among large U.S. municipalities, it spends the 

most per capita on its parks. The Chicago Park District 

oversees more than 580 parks with over 8,100 acres of 

municipal parkland as well as 24 beaches, 77 pools, 

11 museums, 2 world-class conservatories, 16 historic 

lagoons, and 10 bird and wildlife gardens.

Lincoln Marsh, Wheaton Park District. Photo by R. Sperl, 

Wheaton Park District, used with permission.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 63

Impacts—moderate-negative

Physical factors—moderate-negative

Many parks in the area are susceptible to flooding due 

to flat terrain and lack of sewer access. Soils in the area 

have been heavily disturbed from development, so not 

much is known about their characteristics. Although 

adjacent Lake Michigan may provide some cooling, the 

extent of urban development in the city results in an 

overall heat island effect.

Biological factors—moderate-negative

Because of the harsh environment in the city, most 

of the trees in the area have been selected for their 

adaptation to a range of conditions, reducing their 

vulnerability to climate change. However, some trees at 

the north end of their range may be susceptible to severe 

winters, if extremely cold winters like those experienced 

in 2013–2014 become more common. Many of the trees 

in the area are more mature and susceptible to wind and 

winter storm damage.

Human factors—moderate-positive

Chicago parks offer a refuge from the urban heat 

island effect and tend to be slightly cooler than the 

surrounding areas. However, their location in the middle 

of the city increases their susceptibility to air pollutants, 

some of which can become more problematic during 

extremely hot periods. 

Adaptive Capacity—moderate-high

Biological factors—moderate

Species and genetic diversity is somewhat lower 

because species selection is limited by the harsh urban 

environment. However, Chicago parks have a large 

number of trees and a mix of both young and mature 

individuals.

Organizational/technical factors—moderate-high

The Chicago Park District has an arborist on staff and 

a large crew of trained personnel. The district strives 

to plant a mix of species, following a city planting 

list. However, pruning and maintenance may not 

always be sufficient once trees are planted. The district 

could benefit from more in-depth long-term planning 

and risk assessments. It also lacks a comprehensive 

inventory. 

Economic factors—moderate-high

Chicago has sufficient funding for planting and 

replacing trees, but does not always have resources for 

the maintenance of existing trees.

Social factors—moderate-high

The large and diverse population holds a wide range 

of attitudes toward trees. Many organizations are 

engaged in helping to plant and care for trees in the area. 

However, some trees are vandalized by the public as well.

Two municipal parks in Chicago: Indian Boundary Park (l) and Harrison Park. Photos by J. Scott, Chicago Park District, used with 

permission. 
CHAPTER 4—Urban Forest Vulnerability Case Studies 64 |

FOREST PRESERVE DISTRICTS 

Southern Des Plaines 

River Preserves

within the Lake County Forest Preserve District 

Moderate Vulnerability

The location of the preserves along the Des Plaines 

River floodplain coupled with altered hydrology from 

drainage tile may make them vulnerable to heavy 

rain events. Nonetheless, the preserves have sufficient 

technical, economic, and social capacity to restore the 

area and conserve native vegetation. 

The Lake County Forest Preserve District manages 

and protects more than 30,100 acres and is the second 

largest forest preserve district in Illinois. Oak woodlands 

define the unique natural and historic landscape of Lake 

County, and efforts are underway to actively restore oak 

ecosystems in preserves. Several preserves located along 

the southern Des Plaines River, including MacArthur 

Woods, Grainger Woods, Captain Daniel Wright 

Woods, and Ryerson Conservation Area, are currently 

being targeted for restoration efforts. 

Impacts—moderate-negative

Physical factors—negative

Portions of the preserves are located in the Des Plaines 

River floodplain, and additional low-lying areas could 

be susceptible to flooding if heavy rains increase. This 

is further exacerbated by the fact that many soils in the 

area are poorly drained. Wetter winters and springs 

could also reduce the window for prescribed burning, 

which is used in oak restoration projects in the area. 

Biological factors—moderate-negative

The preserves have a large number of oaks that are in 

the center of their range, so they are not expected to be 

very positively or negatively affected by direct changes 

in temperature or precipitation. However, climate 

conditions could be more favorable for diseases that 

attack oaks, such as oak wilt and bur oak blight, and 

could also be more favorable for species such as European 

buckthorn that shade out oak seedlings in the understory. 

In more mesic areas, maples are more common, some of 

which could be vulnerable to potential dry periods later 

in the growing season or higher temperatures. 

Spring ephemerals growing in the first season after canopy clearing in a Lake County forest preserve. Photo by M. Ueltzen, Lake 

County Forest Preserves, used with permission.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 65

Human factors—negative

The area is largely forested with little impervious cover 

and low-density housing surrounding the preserve. But 

parts of the area have drain tiles remaining from former 

agricultural use, which exacerbates precipitation-induced 

changes in hydrology. Stormwater runoff from local 

residential areas could also concentrate in the preserves.

Adaptive Capacity—moderate

Biological factors—low-moderate

The preserves tend to have low species and age-class 

diversity; they are dominated by older oak trees. 

However, these trees are native, naturally regenerating 

populations that are adapted to local conditions and are 

likely to have high genetic diversity. 

Organizational and technical factors—moderate

The preserves have a management plan and a trained 

natural resources operations staff to carry it out. If new 

trees are planted, a diverse mix of native seed is used. 

Economic factors—moderate-high

Adequate funding is currently available to carry out 

management of natural areas and plant trees as needed. 

Social factors—high

Several conservation groups are heavily engaged and 

willing to assist the preserves in achieving management 

goals. There is a strong volunteer base and residents in 

the area value the preserves. 

Freeman Kame 

Forest Preserve 

within the Kane County Forest Preserve District 

Moderate Vulnerability

The preserves have a diverse mix of native species 

at the center of their range. However, they may be 

vulnerable to increased insect attack and invasive 

species competition in a changing climate due to lack 

of resources to manage these stressors. 

The Freeman Kame Forest Preserve is located on 

the northern edge of a kettle-moraine complex north of 

Gilberts, IL in north-central Kane County. It is named 

after the wooded glacial hills that can be found there. It 

is considered a high-risk natural area. 

Impacts—moderate-negative 

Physical factors—moderate-negative

The forest preserve has extremely well-drained soil with 

a high gravel content, which decreases its susceptibility to 

flooding. This soil condition makes it more susceptible to 

drying due to projected reductions in precipitation later 

in the growing season. The area’s topography also makes 

it susceptible to windthrow. More frequent fires could be 

beneficial to oaks in the area. 

Biological factors—moderate-negative

Most of the dominant trees in the area are at the 

center of their distribution range, and are not expected 

to be directly affected by changes in temperature or 

precipitation. However, milder winters could increase 

damage from insect pests and herbivores and competition 

from invasive species such as European buckthorn. 

Another concern is that earlier springs could lead to 

asynchrony between pollinator arrival and flowering, 

reducing seed production and thus reducing natural 

regeneration. 

Human factors—negative

The area is primarily rural and thus not susceptible 

to the urban heat island effect and urban pollution. 

However, the preserve already receives a large quantity 

of agricultural runoff, and more heavy precipitation 

events could exacerbate this further. If the late growing 

season becomes drier or droughts increase as some 

models project, groundwater demands could increase, 

decreasing baseflow in area streams.

Freeman Kame-Meagher Forest Preserve. Photo by K.A. Miller, 

Kane County Development Department, used with permission.
CHAPTER 4—Urban Forest Vulnerability Case Studies 66 |

Adaptive Capacity—moderate-high

Biological factors—moderate

Although the preserve is dominated by oak and hickory 

species, other species are also common. A large diversity 

of age classes is represented, and reforestation centers on 

local seed sources. 

Organizational and technical factors—moderate

The preserve has a long-term vision and a trained staff 

of trained forestry professionals to carry it out. If new 

trees are planted, a diverse mix of native seed is used. 

The preserve does not have a comprehensive inventory, 

however. 

Economic factors—moderate

There is currently sufficient funding for reforestation efforts 

from the district, but insufficient funding to carry out 

management of existing forests, such as buckthorn removal.

Social factors—moderate-high

The Kane County forest preserves are engaged with 

other collar counties in the Chicago area to share 

resources and expertise. Not enough volunteers are 

currently available in the area to help with management, 

such as invasive species removal. 

Swallow Cliff Preserve 

within the Forest Preserves of Cook County 

Moderate-High Vulnerability

The preserve is dominated by older oak trees that 

are susceptible to wind damage because of elevated 

topography. Lack of funding for management 

and replanting has reduced regeneration in the 

understory, increasing vulnerability. 

The Forest Preserves of Cook County encompass 

about 68,000 acres of open space around Chicago and its 

surrounding suburbs. Swallow Cliff Preserve is located 

in Palos Park, IL and features a 100-foot-high bluff that 

formed 12,000 years ago when glacial meltwater carved 

out the Sag Valley Steep walls and a varied landscape of 

morainal hills and pothole lakes were left behind. The 

area includes woodlands, prairies, wet marshes, and 

sedge meadows.

Impacts—moderate-negative

Physical factors—moderate

The area is not in a floodplain and has well-drained soils; 

thus, it is not susceptible to flooding. The hilly topography 

of the preserve increases its susceptibility to windthrow 

and soil erosion during heavy storm events, however. 

Biological factors—moderate-negative

Most of the dominant trees in the area are at the center of 

their distribution range, and are not expected to be directly 

affected by changes in temperature or precipitation. But 

milder winters could increase damage from insect pests 

and deer herbivory and competition from invasive species 

such as European buckthorn. Another concern is that 

earlier springs could lead to asynchrony between pollinator 

arrival and flowering, reducing seed production and thus 

reducing natural regeneration. 

Human factors—moderate

Although the Preserve is located in Cook County, a 

highly urban area, it has a large acreage around it that 

buffers it from the urban heat island effect and pollution. 

The preserve is largely upslope from the surrounding 

area, so it does not receive runoff. 

Adaptive Capacity—low-moderate

Biological factors—low

The preserve is dominated by oak and hickory species. 

The trees are primarily older, and little regeneration has 

happened in the understory. These mature trees are more 

susceptible to breakage and tip-ups during heavy storms, 

and have already been damaged by recent storms. The 

location in a fragmented urban area means that there is 

little opportunity for migration or genetic exchange. 

Near the falls at Swallow Cliff Preserve. Photo by J. Occhiuzzo, 

Joe Occhiuzzo Photography, used with permission.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 67

Organizational and technical factors—moderate

The preserve has ecologists and arborists on staff. They 

have a set of best management practices and a species 

list to help guide them, as well as a long-term plan. 

However, the preserve does not have a comprehensive 

inventory or a disaster response plan in place. 

Economic factors—low

There is currently insufficient funding for planting new 

trees or for management of the existing forest. 

Social factors—low-moderate

The people in the area do not appear to place a high 

value on public trees and there is an insufficient 

volunteer base to assist with management. But some 

restoration volunteers do provide assistance. 

Case Study Outcomes 

Participants indicated in written evaluations that the 

workshop presentations and worksheet exercise improved 

knowledge on climate change impacts and vulnerability 

in the places they worked. They reported the greatest 

knowledge gains in their understanding of the adaptive 

capacity of their systems. They also reported that they had 

increased their ability to identify those climate change 

impacts that are most relevant to the places they manage. 

They self-reported that they already had considerable 

knowledge about climate change going into the workshop; 

thus, gains in knowledge for these participants may not be 

as great as for groups with less prior knowledge. 

We asked these professionals to identify the factors 

that they perceived would contribute most strongly to 

their ratings of impacts and adaptive capacity for their 

respective municipality, park district, or forest preserve. 

Key physical considerations were generally related to 

potential impacts from intense precipitation and extreme 

storm events; areas susceptible to these impacts were 

perceived as being at greater risk. These included areas 

with very poorly drained soils, floodplains, and areas 

susceptible to flooding due to infrastructure. Areas with 

high susceptibility to wind events were also considered 

to have negative impacts due to dominance of a windsusceptible species, older trees, or topography. Key 

biological considerations pertained to shifts in habitat 

suitability for trees and the organisms that influence them, 

such as insects, pathogens, and invasive species. Key 

human-related considerations centered on how impervious 

cover could exacerbate issues associated with increased 

heavy precipitation events and how higher temperatures 

could potentially make ozone pollution more severe. 

The factors identified as most important for the adaptive 

capacity of ecosystems differed notably between 

natural areas and more developed areas. Urban forestry 

professionals from all areas emphasized funding and 

trained staff, and many identified the lack of funding 

for maintenance or management of their forests as a 

negative contributor to adaptive capacity. In contrast, 

representatives from forest preserve districts identified 

biological considerations, such as genetic and biological 

diversity, as more important contributors to overall 

adaptive capacity. Participants from park districts and 

municipalities placed a greater emphasis on social 

and organizational factors, such as planting lists and 

ordinances, the value residents place on trees, and the 

participation of volunteers. 

We also asked the professionals to add factors to 

the worksheet if they thought they were important. 

Additional physical impact factors identified were more 

frequent and severe fires in natural areas, susceptibility 

to erosion, and susceptibility to extreme cold events. 

Participants from forest preserve districts identified 

an asynchrony between plants and pollinators as 

another key biological impact factor. Additional human 

impact factors identified were population growth and 

development, more groundwater withdrawal during hot/

Riparian area in Riverside after removal of European buckthorn. 

Photo by M.G. Collins, Village of Riverside, used with 

permission.
CHAPTER 4—Urban Forest Vulnerability Case Studies 68 |

dry periods, and the potential for stress from road salt to 

exacerbate climate stress. 

A few additional adaptive capacity factors were 

identified. Several participants added education as a 

key adaptive capacity factor, stating that educational 

programs on the planting and maintenance of trees 

would enhance residents’ capacity to adapt to a changing 

climate. Capacity to allow for wider planting spaces on 

parkways was another factor identified. 

Participants differed between natural areas and 

developed areas in which factors they thought were less 

important in contributing to vulnerability. In developed 

areas, participants did not think that the following 

factors listed in the worksheet would contribute strongly 

to overall impacts: changes in herbivore damage, shifts 

in winter freeze-thaw cycles, and changes in invasive 

plant species. Representatives of forest preserve districts 

listed the urban heat island effect, impervious cover, and 

pollution as being less important contributors. 

Overall, levels of confidence tended toward a “medium” 

ranking for most factors. Impact factors that tended to 

have a high confidence level were susceptibility to severe 

storms and flooding, and increased temperatures. There 

was also high confidence that high species diversity 

would enhance adaptive capacity. Municipalities had 

high confidence in their organizational and technical 

capacity as a whole. There was high confidence that 

funding contributed significantly to adaptive capacity, 

but uncertainty was also expressed about future funding. 

There were several areas of uncertainty where 

participants would have liked more information. 

Although participants acknowledged the importance of 

shifts in habitat suitability of trees in the area, the overall 

confidence was low in this factor because of two issues. 

First, many places lacked a comprehensive inventory 

or the inventory was outdated. Without knowing what 

trees are present in the first place, it is hard to discern 

the proportion that may be vulnerable to climate change. 

Second, participants lacked confidence in some of the 

model results, especially when the results did not line up 

with expectations. For example, several oak species were 

in the center of their range in the Chicago area, yet were 

projected to decline in habitat suitability. 

Another area of low confidence was projected changes 

in pests and diseases. There is currently very little 

information on how insect pests and diseases may 

Baldcypress planted as part of reforestation efforts in Riverside. Photo by M.G. Collins, Village of Riverside, used with permission.
Urban Forest Vulnerability Case Studies—CHAPTER 4 | 69

change in a changing climate. Many participants made 

the assumption that the overall effect of climate change 

would be to increase these stressors, but currently only a 

limited amount of research is available to support these 

assumptions. In particular, participants were interested 

in how climate change may affect the many diseases and 

pests that afflict oaks, such as bur oak blight, oak wilt, 

and gypsy moth.

Although we selected a range of communities from 

across the Chicago Wilderness region, implications for 

the region as a whole should be considered with caution. 

The fact that these groups chose to participate in the 

workshop indicates an interest and engagement on 

climate change that may not be representative of other 

communities. Some communities that did not participate 

may be more vulnerable to climate change due to lower 

organizational adaptive capacity. Other communities 

may have chosen not to participate because they felt 

they already had a considerable understanding of local 

climate change impacts, and because of this greater 

organizational adaptive capacity could be less vulnerable. 

Summary

Urban forestry professionals in the Chicago Wilderness 

region representing several municipalities, park districts, 

and forest preserve districts used a common tool to 

assess the vulnerability of their forests to climate 

change. The professionals generally rated the impacts 

of climate change on the places they managed as 

moderately negative, mostly driven by the effects that 

extreme storms and heavy precipitation events could 

have on trees in the area. The capacity of forests to 

adapt to climate change ranged widely based on several 

economic, social, and organizational factors, as well as 

the species and genetic diversity of trees in the area. The 

assessment process and resultant case studies may be 

used by other communities in the Chicago Wilderness 

region or elsewhere to understand how specific urban 

forests may be vulnerable to climate change.

Key Points 

• We developed a process for municipalities, park 

districts, and forest preserve districts to assess their 

vulnerability to climate change based on impacts and 

adaptive capacity.

• Ten case studies were developed in the Chicago 

Wilderness region using this approach. 

• Most of the variation in vulnerability among case 

studies was in adaptive capacity, driven by differences 

in biological, organizational, economic, and social 

factors among communities.

Chicago Region Trees Initiative urban forestry basic training. 

Photo by E. Johnson, Erika Hildegard Photography, used with 

permission.
CHAPTER 5—Management Considerations 70 |

CHAPTER 5

MANAGEMENT CONSIDERATIONS

Projected changes in climate can pose both challenges 

and opportunities for the management of urban forests. 

Shifts in temperature and changes in extreme events 

may have effects on selection of species for planting, 

management of existing trees, and public engagement. 

This chapter provides an overview of climate change 

impacts on decisionmaking, management practices, and 

other issues related to urban and community forestry in 

the Chicago Wilderness region. This chapter does not 

make recommendations as to how management should 

be adjusted to account for these changes. A separate 

document, Forest Adaptation Resources: Climate 

Change Tools and Approaches for Land Managers, 2nd

edition (Swanston et al. 2016), has been developed to 

assist forest managers in a decisionmaking process to 

adapt their land management to projected impacts. 

Management implications are summarized by theme, 

which were selected to encompass a range of issues 

that urban foresters face. These themes, and their 

descriptions, are by no means comprehensive, but 

provide a springboard for thinking about management 

implications of climate change. The More Information 

sections provide links to key resources for urban forestry 

professionals about the impacts of climate change on 

that theme.

Wildlife 

Wildlife habitat is among the many benefits urban trees 

and natural communities provide that could be altered 

by a changing climate. Urban forests in the Chicago 

Wilderness region support a wide variety of wildlife 

species, including invertebrates, birds, and mammals 

(Chicago Wilderness 1999). The Chicago Wilderness 

region is a critical stopover area for more than 140 

migratory species of songbirds that have just crossed the 

extensive agricultural areas of Indiana and Illinois (D. 

Stotz, the Field Museum, pers. comm., 2015). Most 

of these species rely on tree-dwelling insects for food 

to refuel before heading farther north to their breeding 

grounds. Climate change is expected to affect the 

phenological patterns of trees, including the timing of 

spring leaf out, flowering, fruiting, and leaf drop in the 

fall (Groffman et al. 2014). Shifts in the availability of 

resources will certainly affect wildlife, but the degree to 

which different species are affected will vary depending 

on their life history traits and level of specialization. 

Phenological changes throughout the Midwest are 

already underway (Groffman et al. 2014). For example, 

extreme early and sustained warm-ups have occurred 

several times in the last two decades in the upper 

Midwest. A record warm March across most of North 

America in spring 2012 resulted in oak trees leafing out 

and oak-dependent insects emerging almost a month 

early near Chicago (Ellwood et al. 2013). In contrast, 

Neotropical migratory warblers that rely heavily on 

the insect flush that accompanies leaf out arrived only 

a week early because they time migration primarily in 

response to changes in day length instead of changing 

temperature. This mismatch of phenology between the 

trees, insects, and birds resulted in a decoupling of food 

availability and arrival time for the warblers (D. Stotz, 

pers. comm., 2015). Similar results were found in the 

warm springs of 2003 near Champaign, IL (Strode 

2009, 2015) and 2010 in southwestern Wisconsin 

(Wood and Pidgeon 2015a, 2015b). These studies 

documented increased leaf damage to canopy trees and 

decreased foraging rate in migratory birds in these years 

with a strong asynchrony between leaf out and the peak 

of migration. 

Climate change is already rearranging the geography of 

biological communities (Groffman et al. 2014). Some 

wildlife species will be able to track these changes and 

keep up with the movement of their “climate envelope.” 

Others will be limited in their dispersal ability and 

unable to do so. Even for species able to keep pace with 

a changing environment, it may be challenging for 

them to find appropriate habitats due to development. 

It is possible the ranges of long-lived trees will shift 

more slowly in response to changes in temperature, 

precipitation patterns, and other habitat features, 

compared to the ranges of wildlife dependent upon 

them. This mismatch in movement capability could 

result in a lag time between the range shifts of trees 

and wildlife, limiting the ability of wildlife, especially 
Management Considerations—CHAPTER 5 | 71

host-specific species, to adapt to a changing climate 

(Pelini et al. 2009). 

More Information

• The Climate Change Atlas documents the current 

and possible future distribution of 147 bird species 

in the eastern United States and gives detailed 

information on environmental characteristics defining 

these distributions: www.fs.fed.us/nrs/atlas/

• Changing Landscapes in the Chicago Wilderness 

Region: a Climate Change Update to the Biodiversity 

Recovery Plan summarizes the implications of 

climate change to natural areas in the Chicago 

Wilderness region along with some considerations 

for management: http://climatechicagowilderness.org/

index.php?title=Main_Page

• Audubon’s Birds and Climate Change Report 

presents the climate conditions birds need to survive, 

then maps where those conditions will be found in 

the future as the Earth’s climate responds to increased 

greenhouse gases: climate.audubon.org/

• The Impacts of Climate Change on Biodiversity, 

Ecosystems, and Ecosystem Services: Technical Input 

to the 2013 National Climate Assessment details 

current and expected climate impacts to species and 

systems: nca2014.globalchange.gov/report/sectors/

ecosystems

Natural Areas

Climate change will have important implications for 

management of natural areas in the Chicago Wilderness 

region. Warmer temperatures and longer growing 

seasons are likely to benefit many nonnative invasive 

species, making the management of invasive species and 

the need to be on the lookout for new nonnative invasive 

species an increasing challenge. Prescribed fire is often 

used as a management tool to control invasive species 

and create growing conditions more suitable to oaks 

and other shade-intolerant native species in grasslands, 

savannas, and woodlands. The number of days in which 

prescribed burning is possible could potentially be 

reduced or shifted to other seasons in the future. Drier 

conditions in the summer and fall could increase the 

difficulty of controlling a prescribed burn. Warmer, 

wetter springs could make it more difficult to carry a fire 

due to saturated vegetation. Thus, the timing of burn 

windows may need to be adjusted to later in the fall, 

earlier in the spring, or during winter months (Chicago 

Wilderness 2012). 

Restoring oak ecosystems is a key area of focus for 

many forested natural areas in the Chicago Wilderness 

region (Fahey et al. 2015). These systems tend to have 

greater species diversity than the maple- and Europeanbuckthorn-dominated systems that have replaced them. 

Changes in habitat suitability for many oak species 

along with climate change-induced changes in pests 

and diseases could make it more challenging to restore 

oak-dominated ecosystems (see Chapter 3). Managers 

of natural areas may choose to focus on specific species 

already present, consider introducing new species, or 

undertake additional strategies to reduce the risk of pests 

and diseases. 

More Information

• The Chicago Wilderness Biodiversity Recovery Plan: 

Climate Change Update summarizes the implications 

of climate change to natural areas in the Chicago 

Wilderness region along with some considerations for 

management: climate.chicagowilderness.org/

• Climate change considerations for management 

of natural areas and green spaces in the City 

of Chicago focus on adaptation, with the aim 

of helping resource managers in the Chicago 

Wilderness region jump-start the process of updating 

approaches to management to better incorporate 

these considerations—and reduce the rate of climate 

change: glslcities.org/library/climate-considerationsfor-management-of-natural-areas-and-green-spacesin-the-city-of-chicago/

Street Trees

Selection, planting, and maintenance of street trees 

may be affected by changes in climate. Municipal 

foresters currently rate tolerance of site conditions and 

maintaining species diversity as the two most important 

considerations for species selection (see Chapter 1). 

These considerations are often taken into account in 

developing planting lists, which guide species selection. 

However, planting lists are typically based on current 

climate and available nursery stock (see Nursery 

Industry section) and do not take future conditions 

into account. If these lists are not adjusted to account 

for future conditions, municipal foresters could observe 

more tree failure and a reduction in street tree diversity. 

In addition, municipalities that lack diversity of species 

or age classes may be more vulnerable to climate-related 

stressors such as pest and disease outbreaks. 
CHAPTER 5—Management Considerations 72 |

Climate change may also pose new challenges for 

planting and maintenance. Extremely wet springs 

followed by dry falls could make it difficult for newly 

planted street trees to get established. The timing of 

planting may need to be adjusted to ensure trees have 

the best chance of survival. An increase in extreme 

weather events could increase the amount of time and 

resources spent removing damaged trees and limbs and 

replacing those that are lost. Additional pruning may be 

required to help reduce the risk of loss and damage from 

these events. Increases in insect and disease outbreaks 

could also increase the amount of resources required to 

treat and remove diseased trees. Obtaining new species 

that are adapted to future conditions from nurseries may 

be difficult if nurseries are not engaged in the effort.

More Information

• The Trees for 2050 Urban Forest Adaptive Planting 

List includes considerations for future climate 

change based on habitat suitability modeling: www.

chicagobotanic.org/plantinfo/tree_alternatives

• Growing Greener: Eco-Structure for Climate 

Resilience offers a guide to help local governments, 

organizations, and others to develop tools for their 

own communities, while also learning about the ways 

in which green infrastructure can provide natural 

protection from the impacts of climate change: www.

nwf.org/What-We-Do/Energy-and-Climate/ClimateSmart-Conservation/Climate-Smart-Communities/

NWF-Programs/King-County/Forestry-CPRGuidebook.aspx

Municipal Parks

Municipal parks can range from highly developed to 

relatively natural areas. These parks provide active 

and passive recreational opportunities for residents 

while also serving an important role in mitigating the 

urban heat island effect. Many of the climate change 

considerations for management of natural areas and 

street trees also apply to municipal parks. However, the 

role of parks as areas for community recreation means 

that some additional considerations may need to be 

made. Warmer springs and falls are likely to improve 

conditions for outdoor recreation activities during those 

seasons (Nicholls 2012). But shifts in precipitation could 

also have negative impacts on spring and fall recreation 

activities. Increased spring precipitation could increase 

risks for flash flooding or simply lead to unpleasant 

conditions for recreation. During hot summer months, 

residents may take advantage of municipal parks if they 

offer respite from the sweltering heat via shade and water 

features. As park use shifts in response to a changing 

climate, so too may decisions about the planting of trees 

and other vegetation in parks. 

Arbor Day activities, Joliet, IL. Photo by E. Johnson, Erika Hildegard Photography, used with permission.
Management Considerations—CHAPTER 5 | 73

Some municipal parks may play an important role 

in stormwater management through retention and 

detention basins. If storm frequency and severity 

increases, these basins may no longer be sufficient to 

absorb all the increased runoff. Park managers may need 

to reevaluate their capacity and additional investments 

may need to be made to handle the increase in storms. 

More Information

• U.S. National Climate Assessment Midwest 

Technical Input Report: Outdoor Recreation and 

Tourism (Nicholls 2012) discusses implications of 

climate change for recreation and tourism: glisa.

umich.edu/media/files/NCA/MTIT_RecTourism.pdf

• How Cities Use Parks for Climate Change 

Management is a briefing paper that discusses the 

benefits city parks provide in reducing the heat 

island effect and storing carbon: www.planning.org/

cityparks/briefingpapers/climatechange.htm

Private Properties

Medium-to-large private properties such as commercial 

and industrial tracts and corporate or college campuses 

as well as golf courses and cemeteries will also 

experience stress from a changing climate. In addition 

to changes in habitat suitability for trees and other 

plants, some of these places may have incorporated 

green roofs or rain gardens that may also be affected. A 

few of the larger properties may have trained forestry 

professionals or horticulturists on staff with the skills to 

adapt to anticipated changes, but many may need outside 

assistance. 

Private homeowners face many of these same challenges, 

but often lack the specialized knowledge and skills to 

manage trees on their property in light of a changing 

climate. If temperatures increase, private homeowners 

may place greater value on their trees to provide cooling 

and shade and reduce energy costs. They may need 

additional assistance in species selection to ensure 

they plant a species that will tolerate future climate 

conditions given their specific site requirements. 

Additional training may be needed to ensure 

homeowners provide sufficient water to their trees 

during dry periods and that trees are planted properly. 

More Information

• The Climate-Friendly Gardener: a Guide to 

Combating Global Warming from the Ground Up 

explains the science linking soil, plants, and climate 

change; it provides practical tips for a more climatefriendly garden, and links to resources that will help 

gardeners adapt these tips to their own needs: www.

ucsusa.org/sites/default/files/legacy/assets/documents/

food_and_agriculture/climate-friendly-gardener.pdf

• The Morton Arboretum Plant Advice Web site 

offers gardeners, landscapers, and green industry 

professionals a wide array of help, information, 

and inspiration: www.mortonarb.org/trees-plants/

tree-and-plant-advice

Green Infrastructure

Green infrastructure is an interconnected network of 

greenspace that conserves natural ecosystem values and 

functions and provides associated benefits to human 

populations. Green infrastructure can range in scale 

from site design approaches such as rain gardens and 

green roofs to regional planning approaches such 

as conservation of large tracts of open land (U.S. 

Environmental Protection Agency 2015a). If the 

trend of rising temperatures and more-frequent storms 

continues, the importance of green infrastructure at the 

site scale may be heightened as a tool to reduce the urban 

heat island effect and control stormwater (Gaffin et al. 

2012, Gill et al. 2007). A larger regionally connected 

network of green infrastructure could also be important 

for facilitating natural migrations of wildlife to newly 

suitable habitat. 

More Information

• Green infrastructure at the regional scale is described 

by the Chicago Wilderness Green Infrastructure 

Vision (GIV). Developed through a collaborative 

and consensus-based process, the GIV consists 

of spatial data and policies describing the most 

important areas to protect in the region. The GIV 

was originally adopted by Chicago Wilderness in 

2004 and has been refined over many years with 

the help of The Conservation Fund: www.cmap.

illinois.gov/livability/sustainability/open-space/

green-infrastructure-vision

• The Value of Green Infrastructure for Urban Climate 

Adaptation provides information on the costs and 

benefits of green infrastructure solutions for bolstering 

local adaptation to climate change: ccap.org/resource/

the-value-of-green-infrastructure-for-urban-climateadaptation/
CHAPTER 5—Management Considerations 74 |

• Retrofitting Large Landscapes for Sustainability 

was developed to assist homeowner associations 

and corporate, commercial, and other large 

private property owners in establishing and 

maintaining green infrastructure: www.mortonarb.

org/trees-plants/community-trees-program/

community-tree-resources/sustainable-large-landscapes

Nursery Industry

As habitat suitability changes for tree species, 

managers of natural areas, street trees, and parks as 

well as private property owners may wish to select 

species that will be more likely to do well under future 

conditions. However, species selection will ultimately 

be limited by the nursery stock available. Many small 

local nurseries rely on large wholesalers for their supply. 

These wholesalers can be located in different parts of 

the United States and may not be familiar with local 

needs. If the proper economic incentives are not in 

place to encourage the production of new species or 

cultivars, nursery growers may not be able to provide 

them. In addition, the lack of certainty among models 

and the financial risks associated with expanding 

species diversity for a changing climate make it 

difficult for the nursery industry to know the climate 

conditions for which to develop new cultivars and what 

markets to target for existing stock based on future 

climate conditions. In light of those considerations, 

many nursery growers are working to develop cultivars 

that are adapted to a wide range of climate conditions.

More Information

• Changing Climates ... Changing Tree Species 

contains a list of trees developed by J. Frank 

Schmidt & Son Co. that are tailored to meet today’s 

sustainability goals: www.jfschmidt.com/pdfs/JFSMN-Short-Course-2015.pdf

Planning and Partnerships 

Climate change will remain an important consideration 

in planning activities for urban forestry. Many 

municipal foresters and other urban forest managers 

are recognizing the benefits of developing management 

plans to guide the future direction of the urban forests 

they manage. Currently, many municipalities lack 

these plans, and the ones that do often lack any specific 

considerations for climate change. In addition, many 

municipalities and metropolitan regions are beginning to 

develop climate action plans, of which the urban forest 

is a component. Many recognize the importance of the 

Glencoe Park District nursery. Photo by T. McDonald, Glencoe Park District, used with permission.
Management Considerations—CHAPTER 5 | 75

urban forest for its myriad benefits and services and 

believe it to be critical as a strategy, but fewer consider 

how the management and species selection of the urban 

forest may also need to be adjusted. 

Municipalities often have limited staff and budget 

resources. Thus, many cities rely on partnerships with 

other organizations to manage their urban forests. In 

some areas, citizens participate on tree boards or other 

related advisory committees to help guide decisions 

about the urban forest. In others, partnerships promote 

innovative greening strategies that complement or 

augment existing programs (Connolly et al. 2013, 

Mincey and Fischer 2013). As climate change places 

more strain on already limited resources, these 

partnerships may become even more important. 

Some communities rely on volunteers to assist with 

the planting and care of trees in cities and parks or in 

restoration efforts in natural areas. As extreme weather 

events and other stressors increase the need for planting 

new trees and maintenance of existing trees, the 

importance of using volunteers may increase. Volunteerbased urban forest initiatives may complement efforts 

by local governments to adapt to a changing climate 

(Westphal 1994, 2003). 

More Information

• Forest Adaptation Resources: Climate Change Tools 

and Approaches for Land Managers, 2nd edition, is 

a tool that can assist in incorporating climate change 

considerations into planning: www.nrs.fs.fed.us/

pubs/52760

• The Chicago Climate Action Plan details steps for 

organizations of all kinds and suggests actions for 

every individual for greenhouse gas mitigation and 

adaptation: www.chicagoclimateaction.org/

• Climate Adaptation Guidebook for Municipalities 

in the Chicago Region is a resource for communities 

interested in adapting their planning and investment 

decisions to a changing climate: www.cmap.illinois.

gov/livability/sustainability/climate-adaptation-toolkit

• Cities, Climate Change and Multilevel Governance 

presents a framework for multilevel governance. 

This framework entails integration across all levels 

of government and relevant stakeholders from local 

action plans to national policy frameworks (vertical 

integration), and cross-scale learning between relevant 

departments or institutions in local and regional 

governments (horizontal dimension): www.oecd.org/

governance/regional-policy/44232263.pdf

• The Chicago Community Climate Action toolkit 

helps residents to learn about how climate change 

is affecting the Chicago region and to explore a 

place-based approach to climate change and models 

for community-led climate action. It suggests 

what residents can do to help design and carry 

out community-led climate action projects that at 

the same time improve local quality of life: www.

climatechicago.fieldmuseum.org/

Summary

Changes in climate and impacts on the urban forest 

in the Chicago Wilderness area can have important 

implications for management. The management of 

natural areas, street trees, and landscaped parks may 

become more challenging due to more-severe storms and 

changes in habitat suitability for dominant trees. Greater 

financial investments may be required in the short term 

to maintain the urban forest so it can continue to provide 

benefits to the community, such as clean air, reduced heat 

island effects, and stormwater management, in the long 

term. At the same time, confronting the challenge of 

climate change also presents opportunities for managers 

and other decisionmakers to protect their investments 

by planning ahead, building resilient landscapes, 

expanding their volunteer base, and engaging with their 

communities to adapt to future change.

Algonquin (IL) Public Works nursery. Photo by E. Johnson, Erika 

Hildegard Photography, used with permission.
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GLOSSARY 86 |

GLOSSARY

adaptive capacity

the general ability of institutions, systems, and 

individuals to moderate the risks of climate change, or to 

realize benefits, through changes in their characteristics 

or behavior. Adaptive capacity can be an inherent 

property or it could have been developed as a result of 

previous policy, planning, or design decisions.

asynchronous quantile regression

a type of regression used in statistical downscaling. 

Quantile regression models the relation between a set of 

predictor variables and specific percentiles (or quantiles) 

of the response variable.

barrens

plant communities characterized by widely spaced 

trees (no greater than 50 percent woody cover) and a 

codominant understory of grasses and other prairie 

plants.

basal area

the cross-sectional area of all stems of a species or all 

stems in a stand measured at 4.5 feet above the ground 

and expressed per unit of land area.

carbon dioxide (CO2) fertilization 

increased plant uptake of CO2 through photosynthesis 

in response to higher concentrations of atmospheric CO2

(Norby et al. 2005).

Chicago Wilderness 

a regional alliance of more than 200 different 

organizations that work together to improve the quality 

of life of the humans and the many other species living 

in the Chicago area.

climate change 

a change in the state of the climate that can be 

identified (e.g., by using statistical tests) by changes 

in the mean and/or the variability of its properties, 

and that persists for an extended period, typically 

decades or longer. Climate change may be due to 

natural internal processes or external factors, or to 

persistent anthropogenic changes in the composition of 

the atmosphere or in land use. 

climate model

see general circulation model.

climate normal

the arithmetic mean of a climatological element 

computed over three consecutive decades.

collar county

one of the five Illinois counties that border Cook 

County, where Chicago is located: DuPage, Kane, Lake, 

McHenry, and Will.

derecho

widespread and long-lived convective wind storm that 

is associated with a band of rapidly moving showers 

or thunderstorms characterized by wind gusts that are 

greater than 57 miles per hour and that may exceed 100 

miles per hour. 

detention basin

a basin whose outlet has been designed to detain 

stormwater runoff for some minimum time (e.g., 24 

hours) to allow particles and associated pollutants to 

settle.

disturbance

stresses and destructive agents such as invasive 

species, diseases, and fire; changes in climate and 

serious weather events such as hurricanes and ice 

storms; pollution of the air, water, and soil; real estate 

development of forest lands; and timber harvest. Some 

of these are caused by humans, in part or entirely; others 

are not.

downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general climate models; involves examining 

the statistical relationship between past climate data and 

on-the-ground measurements. 

drought 

a long period of abnormally low rainfall, especially one 

that adversely affects growing or living conditions.

dune

a mound or ridge of sand or other loose sediment formed 

by the wind.
GLOSSARY | 87

ecological province

climatic subzones, controlled primarily by continental 

weather patterns such as length of dry season and 

duration of cold temperatures. Provinces are also 

characterized by similar soil orders and are evident as 

extensive areas of similar potential natural vegetation. 

ecosystem 

a system of living organisms interacting with each other 

and their physical environment. The boundaries of what 

could be called an ecosystem are somewhat arbitrary, 

depending on the focus of interest or study. Thus, the 

extent of an ecosystem may range from very small spatial 

scales to, ultimately, the entire Earth. 

emissions scenario

a plausible representation of the future development 

of emissions of greenhouse gases and aerosols that 

are potentially radiatively active, based on certain 

demographic, technological, or environmental 

developments.

ensemble average

the average value of a large number of output values 

from a climate model; a way to address some of the 

uncertainties in the system. 

esker

a long, winding ridge of stratified sand and gravel 

created by glaciers.

evapotranspiration

the sum of evaporation from the soil and transpiration 

from plants.

fragmentation

a disruption of ecosystem or habitat connectivity, caused 

by human or natural disturbance, creating a mosaic 

of successional and developmental stages within or 

between forested tracts of varying patch size, isolation 

(distance between patches), and edge length.

fundamental niche

the total habitat available to a species based on climate, 

soils, and land cover type in the absence of competitors, 

diseases, or predators.

general circulation model (GCM)

numerical representation of the climate system based 

on the physical, chemical, and biological properties of 

its components, their interactions, and their feedback 

processes, and accounting for all or some of its known 

properties (also called climate model).

green infrastructure 

an interconnected network of green space that conserves 

natural ecosystem values and functions and provides 

associated benefits to human populations.

growing season

the period in each year when the temperature is 

favorable for plant growth.

hardiness zone

a geographically defined area in which a specific 

category of plant life is capable of growing, defined by 

the average annual winter minimum temperature. 

heat zone

a geographically defined area in which a specific 

category of plant life is capable of growing, defined by 

the number of days above 86 degrees Fahrenheit. 

impact

the direct and indirect consequences of climate change 

on systems, particularly those that would occur without 

adaptation.

impact model

simulations of impacts on trees, animals, and 

ecosystems; these models use GCM projections as 

inputs, and include additional inputs such as tree species, 

soil types, and life history traits of individual species.

importance value

an index of the relative abundance of a species in a given 

community (0 = least abundant, 50 = most abundant).

intensity

amount of precipitation falling per unit of time.

invasive species

any species that is nonnative (or alien) to the ecosystem 

under consideration and whose introduction causes or is 

likely to cause damage, injury, or disruption to ecosystem 

processes or other species within that ecosystem.

kame

an irregularly shaped hill or mound composed of sand, 

gravel, and till that accumulate in a depression on a 

retreating glacier, and are then deposited on the land 

surface with further melting of the glacier.

lake effect 

the effect of any lake, especially the Great Lakes, in 

modifying the weather and climate in nearby areas.
GLOSSARY 88 |

mesic

referring to sites or habitats characterized by 

intermediate (moist, but neither wet nor dry) soil 

moisture conditions.

model reliability score

for the Tree Atlas: a “tri-model” approach to assess 

reliability of model predictions for each species, 

classified as high, medium, or low (Iverson et al. 2008).

modifying factor

environmental variables (i.e., site conditions, interspecies 

competition, disturbance, dispersal ability) that influence 

the way a tree may respond to climate change.

moraine

any glacially formed accumulation of unconsolidated 

glacial debris (soil and rock) that occurs in currently 

glaciated and formerly glaciated regions on Earth 

(i.e., a past glacial maximum), through 

geomorphological processes. 

natural community 

an assemblage of native plants and animals that tend to 

recur over space and time and that interact with each 

other and their physical environment in ways minimally 

modified by exotic species and negative human 

disturbances.

parcelization

the subdivision of a single forest ownership into 

two or more ownerships. Parcelization may result in 

fragmentation if habitat is altered under new ownership. 

peak flow

the maximum instantaneous discharge of a stream or 

river at a given location. 

phenology

the study of the timing of natural events such as the 

date that migrating birds return, the first flower dates 

for plants, and the date on which a lake freezes in the 

autumn or opens in the spring.

prairies 

natural communities dominated by perennial grasses 

and forbs with scattered shrubs and very few trees (less 

than 10 percent canopy cover). 

process model

a model that relies on computer simulations based on 

mathematical representations of physical and biological 

processes that interact over space and time.

productivity 

the rate at which biomass is produced per unit area by 

any class of organisms, or the rate of energy utilization 

by organisms.

projection 

a potential future evolution of a quantity or set of 

quantities, often computed with the aid of a model. 

Projections are distinguished from predictions in order 

to emphasize that projections involve assumptions 

concerning, for example, future socioeconomic and 

technological developments that may or may not 

be realized, and are therefore subject to substantial 

uncertainty. 

radiative forcing

the change in net irradiance between different layers 

of the atmosphere. A positive forcing (more incoming 

energy) tends to warm the system, whereas a negative 

forcing (more outgoing energy) tends to cool it. Causes 

include changes in solar radiation or concentrations of 

radiatively active gases and aerosols.

realized niche

the portion of potential habitat a species occupies; 

usually it is less than what is available because of 

predation, disease, and competition with other species.

refugia

locations and habitats that support populations of 

organisms that are limited to small fragments of their 

previous geographic range.

retention basin

an impoundment created by a dam or an excavation for 

the purpose of storing water and settling sediment and 

other pollutants from surface runoff. A retention basin 

is designed to hold a specific amount of water until the 

water can evaporate or infiltrate.

runoff

that part of the precipitation that appears in surface 

streams. It is the same as streamflow unaffected by 

artificial diversions or storage.

savanna

fire-maintained grasslands with open-grown, scattered, 

orchard-like trees or groupings of trees and shrubs. 
GLOSSARY | 89

scenario 

a plausible and often simplified description of how 

the future may develop, based on a coherent and 

internally consistent set of assumptions about driving 

forces and key relationships. Scenarios may be derived 

from projections, but are often based on additional 

information from other sources, sometimes combined 

with a narrative storyline (see also emissions scenario). 

significant trends

least-squares regression p-values of observed climate 

trends. In this report, significant trends (p < 0.10) are 

shown by stippling on maps of observed climate trends. 

Where no stippling appears (p > 0.10), observed trends 

have a higher probability of being due to chance alone. 

snow water equivalent 

the amount of water contained in snowpack. It is a way 

of measuring the amount of snow while accounting for 

differences in density.

snowpack

layers of accumulated snow that usually melts during 

warmer months.

species distribution model

a model that uses statistical relationships to project 

future change.

spring

a continual or intermittent natural flow of water from 

the ground following a rather well-defined channel. 

statistical downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCMs) by 

deriving statistical relationships between observed smallscale (often station-level) variables and larger (GCM) 

scale variables. Future values of the large-scale variables 

obtained from GCM projections of future climate are 

then used to drive the statistical relationships and so 

estimate the smaller-scale details of future climate.

streamflow 

discharge that occurs in a natural surface stream course

whether or not it is diverted or regulated.

stressor 

an agent, condition, change in condition, or other 

stimulus that causes stress to an organism.

suitable habitat

in the context of the Climate Change Tree Atlas 

(a species distribution model), the area-weighted 

importance value, or the product of tree species 

abundance and the number of cells with projected 

occupancy.

uncertainty 

an expression of the degree to which a value (such as 

the future state of the climate system) is unknown. 

Uncertainty can result from lack of information or 

from disagreement about what is known or even 

knowable. It may have many types of sources, from 

quantifiable errors in the data to ambiguously defined 

concepts or terminology, or uncertain projections of 

human behavior. Uncertainty can be described by using 

quantitative measures or by qualitative statements.

urban forest

all publicly and privately owned trees within an urban 

area—including individual trees along streets and in 

backyards, as well as stands of remnant forest. 

urban heat island effect

phenomenon where a city or metropolitan area is 

significantly warmer than its surrounding rural areas due 

to human activities.

vulnerability 

The degree to which a system is susceptible to, and 

unable to cope with, adverse effects of climate change, 

including climate variability and extremes. Vulnerability 

is a function of the impacts and adaptive capacity of a 

system.

weather 

the state of the atmosphere at a given time and place, 

with respect to variables such as temperature, moisture, 

wind velocity, and barometric pressure. 

wind shear 

the rate at which wind velocity changes from point to 

point in a given direction. 

woodland

highly variable natural communities with a canopy of 

trees ranging from 30- to 100-percent openness, a sparse 

understory, and a dense ground flora rich in grasses, 

sedges, and forbs.

xeric

pertaining to sites or habitats characterized by decidedly 

dry conditions.
APPENDIX 1—Species List 90 |

FLORA

Common name Scientific name

Accolade® elm Ulmus japonica x wilsonia 

‘Morton’

‘Accolade’ flowering cherry Prunus ‘Accolade’

Allegheny serviceberry Amelanchier laevis

American basswood (American linden) Tilia americana

American beech Fagus grandifolia

American elm Ulmus americana

American hornbeam Carpinus caroliniana

American plum Prunus americana

American smoketree Cotinus obovatus

American sycamore Platanus occidentalis

American witchhazel Hamamelis virginiana

Amur cherry Prunus maackii

Amur corktree Phellodendron amurense

Amur honeysuckle Lonicera maackii

Amur maackia Maackia amurensis

Amur maple Acer ginnala

apple serviceberry Amelanchier x grandifolia

apple/crabapple species Malus spp.

aspen species Populus spp.

Austrian pine Pinus nigra

‘Autumn Gold’ gingko Ginkgo biloba ‘Autumn Gold’

autumn-olive Elaeagnus umbellata

baldcypress Taxodium distichum

balsam fir Abies balsamea

bigtooth aspen Populus grandidentata

bitternut hickory Carya cordiformis

black ash Fraxinus nigra

black cherry Prunus serotina

blackhaw Viburnum prunifolium

Black Hills spruce Picea glauca var. densata

black hickory Carya texana

black locust Robinia pseudoacacia

black maple Acer nigrum

black oak Quercus velutina

black walnut Juglans nigra

black willow Salix nigra

blackgum Nyssa sylvatica

blackjack oak Quercus marilandica

blue spruce Picea pungens

boxelder Acer negundo

Bradford pear Pyrus calleryana ‘Bradford’

bur oak Quercus macrocarpa

Callery pear Pyrus calleryana

cedar elm Ulmus crassifolia

FLORA

Common name Scientific name

cherry plum Prunus cerasifera

chestnut oak Quercus prinus

Chinese catalpa Catalpa ovata 

Chinese chestnut Castanea mollissima

Chinese fringetree Chionanthus retusus

Chinese juniper Juniperus chinensis 

Chinese privet Ligustrum sinense

chinkapin oak Quercus muehlenbergii

cockspur hawthorn Crataegus acutifolia (crusgalli)

common chokecherry Prunus virginiana

common elderberry Sambucus canadensis

common hackberry Celtis occidentalis

common lilac Syringa vulgaris

common pear Pyrus communis

common reed Phragmites australis

common persimmon Diospyros virginiana

Cornelian cherry dogwood Cornus mas

Crimean linden Tilia x euchlora

cucumbertree Magnolia acuminata 

dawn redwood Metasequoia 

glyptostroboides

‘Discovery’ elm Ulmus davidiana ‘Discovery’

Douglas-fir Pseudotsuga mucronata 

(menziesii)

downy serviceberry Amelanchier arborea

eastern cottonwood Populus deltoides

eastern hemlock Tsuga canadensis

eastern hophornbeam (ironwood) Ostrya virginiana

eastern redbud Cercis canadensis

eastern redcedar Juniperus virginiana

eastern wahoo Euonymus atropurpurea 

(atropurpureus)

eastern white pine Pinus strobus

elm species Ulmus spp.

European alder Alnus alnus (glutinosa)

European beech Fagus sylvatica

European buckthorn Rhamnus cathardica

European filbert Corylus avellana

European hornbeam Carpinus betulus

European larch Larix decidua

European mountain-ash Sorbus aucuparia 

European planetree Platanus x acerifolia

European privet Ligustrum vulgare

European smoketree Cotinus coggygria

flowering dogwood Cornus florida

APPENDIX 1 

SPECIES LISTS
Species List—APPENDIX 1 | 91

FLORA

Common name Scientific name

Freeman maple Acer x freemanii

‘Frontier’ elm Ulmus carpinafolia x parvifolia 

‘Frontier’

garlic mustard Alliaria petiolata

glossy buckthorn Rhamnus frangula

gray alder Alnus incana 

gray birch Betula populifolia

gray dogwood Cornus racemosa

green ash Fraxinus pennsylvanica

‘Harvest Gold’ linden Tilia cordata x mongolica 

‘Harvest Gold’

hedge maple Acer campestre

Heritage® oak Quercus x macdanielii

hickory species Carya spp.

Higan cherry Prunus subhirtella 

‘Autumnalis’

honeylocust Gleditsia triacanthos

horse chestnut Aesculus hippocastanatum

jack pine Pinus banksiana

Japanese maple Acer palmatum

Japanese red pine Pinus densiflora

Japanese stiltgrass Microstegium vimineum

Japanese tree lilac Syringa reticulata

Japanese zelkova Zelkova serrata

katsura tree Cercidiphyllum japonicum

Kentucky coffeetree Gymnocladus dioicus

Korean mountain-ash Sorbus omnifolia

Korean SunTM pear Pyrus fauriei ‘Westwood’

Kousa dogwood Cornus kousa

kudzu Pueraria lobata

lakeside daisy Hymenoxys acaulis var. 

glabra

leatherleaf viburnum Viburnum rhytidophyllum

Legacy® sugar maple Acer saccharum ‘Legacy’

littleleaf linden Tilia cordata

London planetree Platanus x acerifolia

longleaf pine Pinus palustris

maidenhair tree Gingko biloba

maple species Acer spp.

Miyabe maple Acer miyabei

mockernut hickory Carya alba

Morden hawthorn Crataegus x mordenensis

multiflora rose Rosa multiflora

nannyberry Viburnum lentago

northern catalpa Catalpa speciosa

northern pin oak (Hill’s oak) Quercus ellipsoidalis

northern red oak Quercus rubra

northern white-cedar (arborvitae) Thuja occidentalis

Norway maple Acer platanoides

Norway spruce Picea abies

oak species Quercus spp.

Ohio buckeye Aesculus glabra

Oriental bittersweet Celastrus orbiculatus

Oriental spruce Picea orientalis 

Osage-orange Maclura pomifera

FLORA

Common name Scientific name

Pacific Sunset® maple Acer truncatum x platanoides 

‘Warrenred’

pagoda dogwood Cornus alternifolia

paper birch Betula papyrifera

pawpaw Asimina triloba

peach Prunus persica

peachleaf willow Salix amygdaloides

pecan Carya illinoinensis

Peking lilac Syringa pekinensis

pignut hickory Carya glabra

pin cherry Prunus pensylvanica

pin oak Quercus palustris

post oak Quercus stellata

‘Prairie Gem’ Ussurian pear Pyrus ussuriensis ‘MorDak’

prickly ash Zanthoxylum americanum

privet Ligustrum spp.

‘Prospector’ Wilson Elm Ulmus wilsoniana ‘Prospector’

pussy willow Salix discolor

quaking aspen Populus tremuloides

red maple Acer rubrum

red mulberry Morus rubra

red pine Pinus resinosa

river birch Betula nigra

Robusta poplar Populus x euramerica 

rock elm Ulmus thomasii

rose-of-Sharon Hibiscus syriacus

Russian-olive Elaeagnus angustifolia

Sargent cherry Prunus sargentii

sassafras Sassafras albidum

saucer magnolia Magnolia x soulangeana

scarlet oak Quercus coccinea

scholar tree Sophora japonica

Scotch pine Pinus sylvestris

Serbian spruce Picea omorika

shagbark hickory Carya ovata

Shantung maple Acer truncatum

shellbark hickory Carya laciniosa

shingle oak Quercus imbricaria

shortleaf pine Pinus echinata

Shumard oak Quercus shumardii

Siberian elm Ulmus pumila

silver linden Tilia tomentosa

silver maple Acer saccharinum

slippery elm Ulmus rubra

smoothleaf elm Ulmus carpinifolia

‘Snow Goose’ cherry Prunus ‘Snow Goose’

southern red oak Quercus falcata

staghorn sumac Rhus typhina

star magnolia Magnolia stellata

sugar maple Acer saccharum

sugarberry Celtis laevigata

swamp white oak Magnolia virginiana

sweetbay magnolia Quercus bicolor

sweetgum Liquidambar styraciflua
APPENDIX 1—Species List 92 |

FLORA

Common name Scientific name

sycamore maple Acer pseudoplatanus

tree of heaven Ailanthus altissima

TriumphTM elm Ulmus ‘Morton Glossy’

turkey oak Quercus laevis

Turkish hazelnut Corylus colurna

‘Village Green’ Japanese zelkova Zelkova serrata ‘Village 

Green’

walnut species Juglans spp.

Washington hawthorn Crataegus cordata 

(phaenopyrum)

weeping willow Salix babylonica

white ash Fraxinus americana

white fir Abies concolor

white fringetree Chionanthus virginicus

white mulberry Morus alba

white oak Quercus alba

white poplar Populus alba

white spruce Picea glauca

willow oak Quercus phellos

winged burningbush Euonymus alatus

winged elm Ulmus alata

‘Winter King’ green hawthorn Crataegus viridis ‘Winter King’

yellow birch Betula alleghaniensis

yellow buckeye Aesculus flava (octandra)

yellow-poplar (tuliptree) Liriodendron tulipifera

yellowwood Cladrastis kentukea

FAUNA

Common name Scientific name

Asian longhorned beetle Anoplophora glabripennis

beech bark disease (beech scale) Cryptococcus fagisuga

bronze birch borer Agrilus anxius

butterfly Lepidoptera family

emerald ash borer Agrilus planipennis

gypsy moth Lymantria dispar

gray squirrel Sciurus carolinensis

raccoon Procyon lotor

skunk Mephitis mephitis

southern pine beetle Dendroctonus frontalis

two-lined chestnut borer Agrilus bilineatus

walnut twig beetle 

(thousand cankers disease) Pityophthorus juglandis

white-tailed deer Odocoileus virginianus

BACTERIAL AND FUNGAL PATHOGEN SPECIES

Common name Scientific name

beech bark disease Neonectria faginata and N. ditissima

Botryosphaeria canker Botryosphaeria obtusa

bur oak blight Tubakia iowensis

Dutch elm disease Ophiostoma novo-ulmi

oak wilt Ceratocystis fagacearum

sudden oak death Phytophthora ramorum

thousand cankers disease Geosmithia morbida
Trend Analysis and Historical Climate Data—APPENDIX 2 | 93

APPENDIX 2

TREND ANALYSIS AND 

HISTORICAL CLIMATE DATA 

To examine historical trends in precipitation and 

temperature for the analysis area, we used the 

ClimateWizard Custom Analysis tool (ClimateWizard 

2011, Girvetz et al. 2009). Data for ClimateWizard are 

derived from PRISM (Parameter-elevation Regressions 

on Independent Slopes Model; Gibson et al. 2002). 

The PRISM model interpolates historical data from 

the National Weather Service cooperative stations, 

the Midwest Climate Data Center, and the Historical 

Climate Network, among others. Data undergo 

strict quality control procedures to check for errors in 

station measurements. The PRISM model finds linear 

relationships between these station measurements and 

local elevation using a digital elevation model (digital 

gridded version of a topographic map). Temperature 

and precipitation are then derived for each pixel on a 

2.5-mile grid across the conterminous United States. 

The closer a station is in distance and elevation to a grid 

cell of interest, and the more similar it is in its proximity 

to coasts or topographic features, the higher the weight 

the station will have on the final, predicted value for 

that cell. More information on PRISM can be found 

at: http://www.prism.oregonstate.edu/. Please note that 

Web addresses are current as of the publication date of 

this assessment but are subject to change. 

Linear trend analysis for 1901 through 2011 was 

performed by using restricted maximum likelihood 

(REML) estimation (Girvetz et al. 2009). Restricted 

maximum likelihood methods were used for trend 

analysis of past climate for the Intergovernmental Panel 

on Climate Change Working Group I Report and 

are considered an effective way to determine trends 

in climate data over time (Trenberth et al. 2007). A 

first-order autoregression was assumed for the residuals, 

meaning that values one time step away from each 

other are assumed to be correlated. This method was 

used to examine trends for every 2.5-mile grid cell. The 

slope and p-values for the linear trend over time were 

calculated annually, seasonally, for each month, and 

for each climate variable, and then mapped. An overall 

trend for an area is based on the trend analysis of the 

average value for all grid cells within the area over time 

(see Appendix 7 and Table 23). 

The developers of the ClimateWizard tool advise users 

to interpret the linear trend maps in relation to the 

respective map of statistical confidence (Figs. 18 and 19). 

In this case, statistical confidence is described by using 

p-values from a t-test applied to the linear regression. 

A p-value can be interpreted as the probability of the 

slope being different from zero by chance alone. For this 

assessment, p-values of less than 0.1 were considered 

to have sufficient statistical confidence. Areas with low 

statistical confidence in the rate of change (gray areas on 

the map) should be interpreted with caution. 

In addition, because maps are developed from 

weather station observations that have been spatially 

interpolated, developers of the ClimateWizard tool and 

PRISM dataset recommend that inferences about trends 

should not be made for single grid cells. The number of 

weather stations has also changed over time, and station 

data are particularly limited before 1948, meaning 

grid cells from earlier in the century are based on an 

interpolation of fewer points than later in the century 

(Gibson et al. 2002). Therefore, interpretations should 

be based on many grid cells showing regional patterns 

of climate change with high statistical confidence. For 

those interested in understanding trends in climate at a 

particular location, it is best to refer to weather station 

data for the closest station in the Global Historical 

Climatology Network (GHCN) from the National 

Centers for Environmental Information (NCEI) 

(National Oceanic and Atmospheric Administration 

[NOAA], National Climatic Data Center 2013, NOAA 

NCEI 2016). 

We selected the time period 1901 through 2011 as 

it was sufficiently long to capture interdecadal and 

intra-decadal variation in climate for the region. We 

acknowledge that different trends can be inferred by 

selecting different beginning and end points in the 

analysis. To test the sensitivity of our trends to the 

selection of beginning and end dates, we also analyzed 
APPENDIX 2—Trend Analysis and Historical Climate Data 94 |

Figure 18.—Maps of statistical confidence (p-values for the linear regression) for trends in the 111-

year time series for temperature in the Chicago Wilderness region. Gray values represent areas of low 

statistical confidence. Source: ClimateWizard (2011). 
Trend Analysis and Historical Climate Data—APPENDIX 2 | 95

the data for the years since 1951 and since 1971 (data 

not shown). In general, selecting this period resulted in 

trends that were similar in direction and spatial pattern 

to the 1901 through 2011 trends, but different in slope 

and sometimes different in their statistical significance. 

Therefore, trends should be interpreted based on their 

relative magnitude and direction, and the slope of the 

particular trend should be interpreted with caution. 

LITERATURE CITED

ClimateWizard. 2011. ClimateWizard Custom Analysis tool. The 

Nature Conservancy, University of Washington, University of 

Southern Mississippi, Climate Central, and Santa Clara University. 

Available at http://climatewizardcustom.org/ (accessed May 21, 

2012).

Gibson, W.P.; Daly, C.; Kittel, T.; Nychka, D.; Johns, C.; Rosenbloom, 

N.; McNab, A.; Taylor, G. 2002. Development of a 103-year 

high-resolution climate data set for the conterminous United 

States. Proceedings of the 13th American Meteorological Society 

Conference on Applied Climatology: 181-183.

Girvetz, E.H.; Zganjar, C.; Raber, G.T.; Maurer, E.P.; Kareiva, P.; 

Lawler, J.J. 2009. Applied climate-change analysis: the Climate 

Wizard tool. PLoS ONE. 4(12): e8320. https://doi.org/10.1371/

journal.pone.0008320.

Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun, L.; Janssen, E.; 

Wuebbles, D.; Hilber, S.D.; Timlin, M.S.; Stoecker, L.; Westcott, 

N.E.; Dobson, J.G. 2013. Regional climate trends and scenarios 

for the U.S. National Climate Asssessment. Part 3. Climate of 

the Midwest. NOAA Technical Report NESDIS 142-3. Washington, 

DC: U.S. Department of Commerce, National Oceanic and 

Atmospheric Administration, National Environmental Satellite, 

Data, and Information Service. 103 p.

National Oceanic and Atmospheric Administration [NOAA], National 

Centers for Environmental Information [NCEI]. 2016. GHCN–

monthly version 3. Available at http://www.ncdc.noaa.gov/ghcnm/

v3.php (accessed December 23, 2016).

National Oceanic and Atmospheric Administration [NOAA], National 

Climatic Data Center. 2013. State of the climate national 

overview: annual 2012. Available at http://www.ncdc.noaa.gov/

sotc/national/2012/13 (accessed January 9, 2013).

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 climate 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, 

UK and New York, NY: Cambridge University Press: 234-336. 

http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch3.html

(accessed October 31, 2016). Figure 19.—Map of statistical confidence (p-values for 

the linear regression) for trends in the 111-year time series 

for precipitation in the Chicago Wilderness region. Gray 

values represent areas of low statistical confidence. Source: 

ClimateWizard (2011).
APPENDIX 3—Downscaled Climate Models Used in this Report 96 |

APPENDIX 3

DOWNSCALED CLIMATE MODELS 

USED IN THIS REPORT

In this assessment, we report statistically downscaled 

climate projections for two model-emissions scenario 

combinations: GFDL A1FI and PCM B1 (unless 

otherwise noted). Both models and both scenarios 

were included in the Intergovernmental Panel on 

Climate Change (IPCC) Fourth Assessment Report 

(IPCC 2007). The IPCC assessment includes several 

other models, but for simplicity, we selected two that 

had relatively good skill at simulating climate in the 

eastern United States and that bracketed a range of 

temperature and precipitation futures. The Geophysical 

Fluid Dynamics Laboratory’s Climate Model (GFDL 

CM2; Delworth et al. 2006) is considered moderately 

sensitive to changes in radiative forcing. In other words, 

any change in greenhouse gas concentration included in 

the model would lead to a change in temperature that 

is higher than some models and lower than others. The 

National Center for Atmospheric Research’s Parallel 

Climate Model (PCM; Washington et al. 2000), in 

contrast, is considered to have low sensitivity to radiative 

forcing. As mentioned in Chapter 2, the A1FI scenario 

is at the higher end of greenhouse gas emissions. The B1 

scenario is the lowest greenhouse gas emission scenario 

used in the 2007 IPCC assessment, and is much lower 

than the trajectory for greenhouse gas emissions over 

the past decade. Therefore, the two model-scenario 

combinations span a large range of possible futures, with 

the GFDL A1FI model-scenario combination leading 

to a high-end projection of possible future temperature 

increases, and the PCM B1 projecting at the low end 

of the range. Although each projection is possible, the 

GFDL A1FI scenario represents a more likely projection 

of future greenhouse gas emissions and temperature 

increases (Raupach et al. 2007). It is important to note 

that it is possible that actual emissions and temperature 

increases could be lower or higher than these projections. 

This assessment uses a statistically downscaled dataset 

for the continental United States (Stoner et al. 2012). 

Daily mean, minimum, and maximum temperature 

and total daily precipitation were downscaled to an 

approximately 7.5-mile resolution grid across the United 

States. This dataset uses a sophisticated statistical 

approach (asynchronous quantile regression) to downscale 

daily general circulation model (GCM) output and 

historical climate data (Stoner et al. 2013). This approach 

is advantageous because GCM and historical data do not 

need to be temporally correlated, and it is much better at 

capturing extreme temperatures and precipitation events 

than a linear regression approach.

This dataset was chosen for several reasons. First, 

the dataset covered the entire United States, and thus 

allowed a consistent dataset to be used in this and other 

regional vulnerability assessments being conducted 

simultaneously. Second, it included downscaled 

projections for the A1FI emissions scenario, which is 

the scenario that most closely matches current trends 

in global greenhouse gas emissions (Raupach et al. 

2007). Third, the availability of data at daily time steps 

was advantageous because it was needed to calculate 

hardiness and heat zones. Fourth, the statistical 

technique used is more accurate at reproducing extreme 

values at daily time steps than simpler statistical 

downscaling methods (Hayhoe 2010). Last, the 

resolution was fine enough to be useful for informing 

land management decisions. 

To show projected changes in temperature and 

precipitation in Chapter 2, we calculated the average 

daily mean, minimum, and maximum temperature 

for each season and the entire year for three 30-year 

time periods (2010 through 2039, 2040 through 2069, 

2070 through 2099) (Figs. 20-26). Mean cumulative 

precipitation was also calculated for each season and 

annually for the same time periods (Fig. 27). We then 

subtracted these values from the corresponding 1971 

through 2000 averages to determine the departure 

from current climate conditions. Historical climate 

data used for the departure analysis were taken from 

ClimateWizard based on the PRISM dataset (see 

Appendix 2). This dataset was also used to develop 

hardiness and heat zone projections (see Chapter 2) and 

in the Tree Atlas model described in Chapter 3.
Downscaled Climate Models Used in this Report—APPENDIX 3 | 97

Figure 20.—Projected difference in mean daily mean temperature (°F) in the Chicago Wilderness 

region at the beginning of the century (2010 through 2039) compared to baseline (1971 through 2000), 

for two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
APPENDIX 3—Downscaled Climate Models Used in this Report 98 |

Figure 21.—Projected difference in mean daily minimum temperature (°F) in the Chicago Wilderness 

region at the beginning of the century (2010 through 2039) compared to baseline (1971 through 2000), 

for two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
Downscaled Climate Models Used in this Report—APPENDIX 3 | 99

Figure 22.—Projected difference in mean daily maximum temperature (°F) in the Chicago Wilderness 

region at the beginning of the century (2010 through 2039) compared to baseline (1971 through 2000), 

for two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
APPENDIX 3—Downscaled Climate Models Used in this Report 100 |

Figure 23.—Projected difference in mean daily mean temperature (°F) in the Chicago Wilderness 

region at mid-century (2040 through 2069) compared to baseline (1971 through 2000), for two climate 

model-emissions scenario combinations. Source: Stoner et al. (2013).
Downscaled Climate Models Used in this Report—APPENDIX 3 | 101

Figure 24.—Projected difference in mean daily minimum temperature (°F) in the Chicago Wilderness 

region at mid-century (2040 through 2069) compared to baseline (1971 through 2000), for two climate 

model-emissions scenario combinations. Source: Stoner et al. (2013).
APPENDIX 3—Downscaled Climate Models Used in this Report 102 |

Figure 25.—Projected difference in mean daily maximum temperature (°F) in the Chicago Wilderness 

region at mid-century (2040 through 2069) compared to baseline (1971 through 2000), for two climate 

model-emissions scenario combinations. Source: Stoner et al. (2013).
Downscaled Climate Models Used in this Report—APPENDIX 3 | 103

Figure 26.—Projected difference in mean annual and seasonal precipitation (inches) in the Chicago 

Wilderness region at the beginning of the century (2010 through 2039) compared to baseline (1971 

through 2000), for two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
APPENDIX 3—Downscaled Climate Models Used in this Report 104 |

Figure 27.—Projected difference in mean annual and seasonal precipitation (inches) in the Chicago 

Wilderness region at mid-century (2040 through 2069) compared to baseline (1971 through 2000), for 

two climate model-emissions scenario combinations. Source: Stoner et al. (2013).
Downscaled Climate Models Used in this Report—APPENDIX 3 | 105

LITERATURE CITED

Delworth, T.L.; Broccoli, A.J.; Rosati, A.; Stouffer, R.J.; Balaji, V.; 

Beesley, J.A.; Cooke, W.F.; Dixon, K.W.; Dunne, J.; Dunne, K.A.; 

Durachta, J.W.; Findell, K.L.; Ginoux, P.; Gnanadesikan, A.; 

Gordon, C.T.; Griffies, S.M.; Gudgel, R.; Harrison, M.J.; Held, I.M.; 

Hemler, R.S.; Horowitz, L.W.; Klein, S.A.; Knutson, T.R.; Kushner, 

P.J.; Langenhorst, A.R.; Lee, H.C.; Lin, S.J.; Lu, J.; Malyshev, S.L.; 

Milly, P.C.D.; Ramaswamy, V.; Russell, J.; Schwarzkopf, M.D.; 

Shevliakova, E.; Sirutis, J.J.; Spelman, M.J.; Stern, W.F.; Winton, 

M.; Wittenberg, A.T.; Wyman, B.; Zeng, F.; Zhang, R. 2006. GFDL’s 

CM2 global coupled climate models. Part I: Formulation and 

simulation characteristics. Journal of Climate. 19(5): 643-674. 

https://doi.org/10.1175/jcli3629.1.

Hayhoe, K.A. 2010. A standardized framework for evaluating the 

skill of regional climate downscaling techniques. UrbanaChampaign, IL: University of Illinois at Urbana-Champaign. 153 p. 

Ph.D. dissertation.

Intergovernmental Panel on Climate Change [IPCC]. 2007. 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. [Core Writing Team; Pachauri, R.K. and 

Reisinger, A., eds.]. Geneva, Switzerland: Intergovernmental Panel 

on Climate Change. 104 p. http://www.ipcc.ch/publications_and_

data/publications_ipcc_fourth_assessment_report_synthesis_

report.htm (accessed January 11, 2013).

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. 104(24): 10288-10293. https://doi.

org/10.1073/pnas.0700609104.

Roden, D.; Haack, R.; Keena, M.; McKenney, D.; Beall, F.; Roden, 

P. 2009. Potential northern distribution of Asian longhorned 

beetle in North America. In: McManus, K.A.; Gottschalk, K.W., 

eds. Proceedings, 19th U.S. Department of Agriculture interagency 

research forum on invasive species; 2008 Jan. 8-11; Annapolis, MD. 

Gen. Tech. Rep. NRS-P-36. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research Station: 65-67.

Stoner, A.; Hayhoe, K.; Yang, X. 2012. Downscaled climate 

projections by Katharine Hayhoe. Available at http://cida.usgs.

gov/climate/hayhoe_projections.jsp (accessed May 1, 2012).

Stoner, A.M.K.; Hayhoe, K.; Yang, X.; Wuebbles, D.J. 2013. An 

asynchronous regional regression model for statistical 

downscaling of daily climate variables. International Journal of 

Climatology. 33(11): 2473-2494. https://doi.org/10.1002/joc.3603.

Washington, W.M.; Weatherly, J.W.; Meehl, G.A.; Semtner, A.J. Jr.; 

Bettge, T.W.; Craig, A.P.; Strand, W.G. Jr.; Arblaster, J.; Wayland, 

V.B.; James, R.; Zhang, Y. 2000. Parallel climate model (PCM) 

control and transient simulations. Climate Dynamics. 16(10): 

755-774. https://doi.org/10.1007/s003820000079.F
APPENDIX 4—Plant Hardiness Zone and Heat Zone Mapping 106 |

APPENDIX 4

PLANT HARDINESS ZONE AND 

HEAT ZONE MAPPING

The plant hardiness zone map is based on minimum 

annual temperature and is used to guide gardeners and 

others on which plants may or may not be adapted to 

withstand winter cold temperatures. It was published in 

1990 (Cathey 1990) and updated in 2003 (Ellis 2003) 

and 2010 (Daly et al. 2010). This approach differs from 

other approaches that use plant distributions to map 

hardiness zones (McKenney et al. 2007). The heat zone 

map, produced in 1997 by the American Horticultural 

Society (Cathey 1997), is based on number of days 

exceeding 86 °F (30 °C) and can be used as an indicator 

of heat stress on organisms.

Data used for the production of plant hardiness 

zones and heat zones were downscaled daily values of 

minimum and maximum temperature, as projected for 

1980 through 2099 by Stoner et al. (2013) (see Appendix 

3 for more information on downscaling methods and 

model-scenarios). Each modeled daily record for the 

120-year period for each 1/8 degree (~8.6-mile × 8.6-

mile) cell was evaluated by using R statistical software 

(R Development Core Team 2010). For plant hardiness 

zones, the code searched minimum temperatures and 

cataloged the absolute minimum temperature achieved 

for each year (June 1 through May 31 to ensure coldest 

day within annual cycle). The absolute minimum 

temperature for each year was averaged across each of 

four 30-year periods (1980 through 2009, 2010 through 

2039, 2040 through 2069, and 2070 through 2099). 

Yearly and 30-year average data were imported into 

ArcGIS to prepare maps of plant hardiness zones and 

subzones (Fig. 28) according to the USDA definitions, 

which break subzones by increments of 5 °F (2.8 °C) 

from zone 1a (-60 to -55 °F; -51 to -48 °C) to zone 13b 

(65 to 70 °F; 18 to 20 °C) of annual extreme minimum 

temperature. For heat zones (Fig. 29), the maximum 

temperature of daily records was evaluated for the 

86 °F threshold. Each day in a calendar year passing that 

threshold was tallied and summed for each year. The 

yearly data were again averaged over 30-year periods to 

obtain the average number of days per year greater than 

86 °F for the same four 30-year periods. 

LITERATURE CITED

Cathey, H. 1990. USDA plant hardiness zone map. USDA Misc. 

Publ. 1475. Washington, DC: U.S. Department of Agriculture.

Cathey, H. 1997. Announcing the AHS plant heat-zone map. 

American Gardener. 76(5): 30-37.

Daly, C.; Widrlechner, M.P.; Halbleib, M.D.; Smith, J.I.; Gibson, 

W.P. 2010. Development of a new USDA plant hardiness 

zone map for the United States. Journal of Applied 

Meteorology and Climatology. 51(2): 242-264. https:// doi.

org/10.1175/2010jamc2536.1. 

Ellis, D.J. 2003. The USDA plant hardiness zone map, 2003 

edition. American Gardener. 82(3): 30-35.

McKenney, D.W.; Pedlar, J.H.; Lawrence, K.; Campbell, K.; 

Hutchinson, M.F. 2007. Beyond traditional hardiness zones: 

using climate envelopes to map plant range limits. Bioscience. 

57(11): 929-937. https://doi.org/10.1641/b571105.

R Development Core Team. 2010. R: a language and environment 

for statistical computing. Vienna, Austria: R Foundation for 

Statistical Computing. Available at www.r-project.org (accessed 

July 2014).

Stoner, A.M.K.; Hayhoe, K.; Yang, X.; Wuebbles, D.J. 2013. An 

asynchronous regional regression model for statistical 

downscaling of daily climate variables. International Journal of 

Climatology. 33(11): 2473-2494. http://dx.doi.org/10.1002/joc.3603.
Plant Hardiness Zone and Heat Zone Mapping—APPENDIX 4 | 107

Figure 28.—Current and projected future plant hardiness zones for the Chicago Wilderness region 

under the PCM B1 and GFDL model-scenario combinations. Cartographers: Stephen Matthews, Louis 

Iverson, and Matthew Peters (U.S. Forest Service, Northern Research Station).
APPENDIX 4—Plant Hardiness Zone and Heat Zone Mapping 108 |

Figure 29.—Current and projected future plant heat zones for the Chicago Wilderness region under 

the PCM B1 and GFDL model-scenario combinations. Cartographers: Stephen Matthews, Louis 

Iverson, and Matthew Peters (U.S. Forest Service, Northern Research Station).
Additional Tree Atlas Information—APPENDIX 5 | 109

APPENDIX 5

ADDITIONAL TREE ATLAS INFORMATION

This appendix provides supplementary information 

to Chapter 3. The following pages contain additional 

model results and information from the Climate Change 

Tree Atlas (U.S. Forest Service, n.d.) (Table 19). 

Scientific names for all species are provided in 

Appendix 1.

The table shows results of the DISTRIB model used 

in the Tree Atlas for the Chicago Wilderness region. 

Measured area-weighted importance values (IVs) from 

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

(FIA) as well as modeled current (1961 through 1990) 

and future IVs (2010 through 2039, 2040 through 2069, 

2070 through 2099) from the DISTRIB models were 

calculated for each time period. One hundred thirtyfour tree species were initially modeled. If a species 

never had an area-weighted IV (sum of IV for all 12.5-

mile by 12.5-mile pixels in the assessment area) greater 

than 3 (FIA, current modeled, or future) across the 

region, it was deleted from the list because there were 

not enough data. Therefore, only a subset of 70 of the 

134 possible species is shown. 

A set of rules was established to determine change 

classes for the years 2070 through 2099, which was used 

to create the tables in Chapter 3. For most species, the 

following rules applied:

Future:Current modeled IV Class

<0.5 large decrease

0.5 to 0.8 small decrease

>0.8 to <1.2 no change 

1.2 to 2.0 small increase

>2 large increase

A few exceptions applied to these general rules. When 

there was a zero in the numerator or denominator, a 

ratio could not be calculated. Instead, a species was 

classified as gaining new habitat if its FIA value was 0 

and the future IV was greater than 0. A species’ habitat 

was considered to have a large decrease if the future IV 

was zero and FIA values were greater than 0. 

Special rules were created for rare species. A species 

was considered rare if it had a current modeled areaweighted IV that equaled less than 10 percent of the 

number of pixels in the assessment area. The change 

classes are calculated differently for these species because 

their current infrequency tends to inflate the amount 

of change that is projected. For this assessment, a 

species was considered rare if its current area-weighted 

importance value was less than 8. For rare species, the 

following rules applied:

Future:Current modeled IV Class

<0.2 large decrease

0.2 to <0.6 small decrease

0.6 to <4 no change 

4 to 8 small increase

>8 large increase (not used when 

current modeled IV ≤3)

Special rules also applied to species that were present 

(current FIA IV >0) but not modeled as present (current 

modeled IV = 0). In these cases, the FIA IV was used in 

place of the current modeled IV to calculate ratios. Then, 

change class rules were applied based on the FIA IV.

LITERATURE CITED

U.S. Forest Service. [N.d.]. Climate change atlas. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. http://www.nrs.fs.fed.us/atlas.
APPENDIX 5

—Additional Tree Atlas Information 110 | Table 19.—Current and future modeled importance values, ratios, and change classes for two model-emissions scenario combinations, using the Climate Change Tree Atlas Common name Modeled IV Future:Current IV Change class Current 2010–2039 2040–2069 2070–2099 2010–2039 2040–2069 2070–2099 2070–2099 Model reliability FIAIV modeled IV PCM B1 GFDL A1FI† PCM B1 GFDL A1FI† PCM B1 GFDL A1FI PCM B1 GFDL A1FI† PCM B1 GFDL A1FI† PCM B1 GFDL A1FI PCM B1 GFDL A1FI American basswood (American linden) medium 130 164 149 269 132 264 143 138 0.91 1.32 0.8 1.29 0.87 0.84 no change no change American beech high 27 19 13 33 16 26 44 13 0.68 0.87 0.84 0.68 2.32 0.68 large increase small decrease American elm medium 372 496 615 784 685 916 936 597 1.24 1.2 1.38 1.41 1.89 1.2 small increase small increase American hornbeam medium 14 10 6 19 11 54 28 23 0.6 0.91 1.1 2.57 2.8 2.3 large increase large increase American plum low 18 6 4 38 5 86 4 63 0.67 2.53 0.83 5.73 0.67 10.5 decrease large increase American sycamore medium 5 25 40 76 72 94 110 100 1.6 2.17 2.88 2.69 4.4 4 large increase large increase Bigtooth aspen high 12 10 0 4 1 0 2 0 0 0.1 0.1 0 0.2 0 large decrease large decrease Bitternut hickory low 68 60 115 172 141 165 240 124 1.92 1.93 2.35 1.85 4 2.07 large increase large increase Black ash high 69 90 40 56 26 83 17 47 0.44 0.51 0.29 0.75 0.19 0.52 large decrease small decrease Black cherry high 752 596 370 372 328 116 467 77 0.62 0.5 0.55 0.16 0.78 0.13 small decrease large decrease Black hickory high 0 0 0 5 0 12 2 26 NA s NA s s s s s

Black locust low 145 63 96 153 101 478 118 335 1.52 1.87 1.6 5.83 1.87 5.32 small increase large increaseBlack oak high 206 220 226 316 225 273 255 188 1.03 1.02 1.02 0.88 1.16 0.85 no change no changeBlack walnut medium 42 132 208 358 226 352 381 249 1.58 1.88 1.71 1.85 2.89 1.89 large increase small increaseBlack willow low 90 103 135 165 117 225 142 184 1.31 1.27 1.14 1.73 1.38 1.79 small increase small increaseBlackgum high 22 10 12 19 12 18 20 14 1.2 0.83 1.2 0.78 2 1.4 small increase small increaseBlackjack oak medium 0 0 5 26 15 63 2 90 s s s s s s s sBoxelder medium 452 295 377 465 327 772 288 537 1.28 1.31 1.11 2.18 0.98 1.82 no change small increaseBur oak medium 403 352 384 479 325 508 238 375 1.09 1.11 0.92 1.18 0.68 1.07 small decrease no changeCedar elm low 0 0 0 0 0 6 0 23 NA NA NA s NA s NA sChinkapin oak medium 0 0 46 93 55 117 82 82 s 18.6 s 23.4 s s s sCommon chokecherry low 9 2 0 2 0 0 5 0 0 0.17 0 0 2.5 0 no change large decreaseCommon hackberry medium 57 212 327 562 376 620 507 424 1.54 2.05 1.77 2.26 2.39 2 large increase small increaseCommon persimmon medium 0 0 0 2 15 8 32 50 NA NA s s s s s sEastern cottonwood low 101 137 218 335 215 466 222 287 1.59 1.79 1.57 2.49 1.62 2.09 small increase large increaseEastern hophornbeam (ironwood)

medium 66 109 122 166 132 77 182 56 1.12 1.2 1.21 0.56 1.67 0.51 small increase small decrease

Eastern redcedar medium 3 25 109 169 112 252 133 170 4.36 3.93 4.48 5.86 5.32 6.8 large increase large increaseEastern redbud medium 0 5 54 162 66 254 80 199 10.8 16.2 13.2 25.4 16 39.8 s sEastern white pine high 18 43 6 1 3 2 20 0 0.14 0.02 0.07 0.03 0.47 0 large decrease large decreaseFlowering dogwood high 15 16 16 29 17 29 64 38 1 1 1.06 1 4 2.38 large increase large increaseGreen ash medium 166 233 257 402 276 541 332 368 1.1 1.38 1.18 1.86 1.42 1.58 small increase small increaseHoneylocust low 107 139 317 348 357 383 401 291 2.28 1.87 2.57 2.06 2.88 2.09 large increase large increaseJack pine high 5 11 9 13 0 40 0 50 0.82 0.57 0 1.74 0 4.55 large decrease large increaseKentucky coffeetree low 0 0 1 19 1 28 2 1 s 4.75 s 7 s s s sLongleaf pine high 0 0 0 0 0 0 7 0 NA NA NA NA s NA s NAMockernut hickory high 1 9 12 76 22 91 50 92 1.33 3.17 2.44 3.79 5.56 10.22 large increase large increase

(Continued)
Additional Tree Atlas Information—APPENDIX 5

| 111

Table 19.—(Continued) Current and future modeled importance values, ratios, and change classes for two model-emissions scenario combinations, using the Climate Change Tree Atlas

Common name

Modeled IV Future:Current IV Change class

Current 2010–2039 2040–2069 2070–2099 2010–2039 2040–2069 2070–2099 2070–2099

Model 

reliability

FIA

IV

modeled 

IV

PCM 

B1

GFDL 

A1FI†

PCM 

B1

GFDL 

A1FI†

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI†

PCM 

B1

GFDL 

A1FI†

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Northern pin oak (Hill’s oak) medium 66 33 16 23 12 31 8 13 0.48 0.56 0.36 0.76 0.24 0.39 large decrease large decrease

Northern red oak high 274 229 220 304 221 211 315 138 0.96 0.95 0.97 0.66 1.38 0.6 small increase small decreaseOhio buckeye low 0 6 0 29 0 26 11 13 0 2.9 0 2.6 1.83 2.17 s sOsage-orange medium 67 119 158 159 181 206 273 176 1.33 0.98 1.52 1.27 2.29 1.48 large increase small increasePaper birch high 25 21 0 6 0 6 2 0 0 0.17 0 0.17 0.1 0 large decrease large decreasePawpaw low 0 0 0 3 0 3 18 1 NA 1.5 NA 1.5 s s s sPecan low 0 0 1 0 9 0 21 33 s 0 s 0 s s s sPignut hickory high 42 38 35 136 50 87 80 107 0.92 2.03 1.32 1.3 2.11 2.82 large increase large increasePin oak medium 63 49 77 168 90 310 139 197 1.57 2.07 1.84 3.83 2.84 4.02 large increase large increasePost oak high 0 0 48 105 65 147 35 388 s s s s s s s sQuaking aspen high 80 47 11 16 4 36 8 15 0.23 0.22 0.09 0.49 0.17 0.32 large decrease large decreaseRed maple high 148 159 113 209 122 311 156 197 0.71 0.87 0.77 1.29 0.98 1.24 no change small increaseRed mulberry low 129 101 175 233 193 297 231 228 1.73 1.6 1.91 2.03 2.29 2.26 large increase large increaseRed pine medium 70 61 25 55 18 57 22 39 0.41 0.61 0.3 0.63 0.36 0.64 large decrease small decreaseRiver birch low 20 2 11 15 12 27 23 20 5.5 3 6 5.4 11.5 10 small increase small increaseRock elm low 4 0§ 0 7 0 29 0 4 0 7 0 29 0 1 large decrease no changeSassafras high 28 38 37 57 50 40 84 52 0.97 0.86 1.32 0.61 2.21 1.37 large increase small increaseScarlet oak high 13 0§ 7 7 6 4 7 3 0.54 1.17 0.46 0.67 0.54 0.23 small decrease large decreaseShagbark hickory medium 214 197 251 325 287 259 367 215 1.27 1.3 1.46 1.04 1.86 1.09 small increase no changeShellbark hickory low 1 0§ 4 30 0 27 0 17 4 30 0 27 0 17 no change small increaseShingle oak medium 1 0§ 51 81 64 59 62 53 51 20.25 64 14.75 62 53 small increase small increaseShortleaf pine high 4 0§ 0 1 0 1 0 0 0 0 0 0 0 0 large decrease large decreaseSilver maple medium 70 210 340 545 341 687 406 464 1.62 1.94 1.62 2.45 1.93 2.21 small increase large increaseSlippery elm medium 107 126 153 257 168 265 232 156 1.21 1.42 1.33 1.46 1.84 1.24 small increase small increaseSugar maple high 213 235 219 317 246 219 396 139 0.93 0.98 1.05 0.68 1.69 0.59 small increase small decreaseSugarberry medium 0 0 0 3 0 75 0 84 NA s NA s NA s NA sSwamp white oak low 26 12 39 83 43 99 56 62 3.25 2.59 3.58 3.09 4.67 5.17 large increase large increaseSweetgum high 1 0§ 0 0 0 2 8 0 NA NA NA NA 8 s small increase no changeTurkey oak high 0 0 0 0 0 0 5 0 NA NA NA NA s NA s NAWhite ash high 138 193 190 367 212 278 340 194 0.98 1.34 1.1 1.01 1.76 1.01 small increase no changeWhite oak high 431 367 416 584 396 279 421 178 1.13 1.15 1.08 0.55 1.15 0.49 no change large decreaseWhite spruce medium 5 0§ 0 1 0 1 0 0 0 0.5 0 0.5 0 0 large decrease large decreaseWinged elm high 0 0 0 0 0 38 0 126 NA NA NA s NA s NA sYellow-poplar (tuliptree) high 18 14 6 27 12 13 70 12 0.43 1 0.86 0.48 5 0.86 large increase no change

s indicates new habitat† GFDL values for 2010–2039 and 2040–2069 were calculated in a slightly different way, and ratios may have used slightly different values for the FIA and current modeled importance values. However, this should not affect the overall change class.§ FIA importance values were used in place of current modeled values when the current modeled value was zero and the FIA value was greater than zero.NA: no suitable habitat under current or projected climate conditions.
APPENDIX 6 —Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 112 |

APPENDIX 6

MODIFYING FACTORS FOR ASSESSING 

THE ADAPTIVE CAPACITY OF 

TREE SPECIES IN URBAN AREAS 

Modifying Factor scores, based on Matthews et al. 

(2011), were developed for 179 species that are either 

already present or being considered for planting in the 

Chicago area. The purpose of these scores is to provide 

regional information about individual species which will 

allow managers and policy-makers to consider potential 

suitable habitat distribution models in a local context 

based on specific variables within their jurisdiction. This 

approach will assist interpretation of modeled outputs 

as published for the Climate Change Atlas (U.S. Forest 

Service, n.d.) and other species distribution models. 

Several assumptions associated with climate change over 

the next 50 years in the Chicago Wilderness region were 

made to develop the scores. We assume, based on the 

literature reviews in this assessment:

• More drought conditions throughout the region 

because growing season average temperatures are 

projected to be higher in the future with only minimal 

increases in precipitation during this time for most 

scenarios

• Higher exposure to fire events in natural areas due to 

higher average temperatures

• Higher incidence of flooding due to more extreme 

precipitation patterns

• Higher wind damage due to more-intense pressure 

differences

• An increase in ice storms at mid-century, followed by 

a reduction later in the century as temperatures warm

• Increases in several air and soil pollutants, which will 

be especially harsh in urban areas, over the next 50 

years as the population, industry, and transportation 

increase in the area 

• Increases in disease, insects, herbivory from deer 

(especially in natural and suburban areas), and 

invasive plants

• The use of harvesting primarily for restoration efforts 

and for new land development.

There are several limitations to these scores. 

Landscapes—natural, urban, and rural—contain many 

diverse interactions between processes and patterns that 

influence the species inhabiting them. Although this 

analysis uses common factors that influence habitat at 

the local level to modify large-scale projections, local 

managers are encouraged to consider, where applicable, 

some factors that are not included. 

It is also important to understand that severe events 

can influence many factors used to modify habitat 

projections. A long drought can influence seed dispersal, 

fire, insect development, and seedling establishment. 

Therefore, these modifications are somewhat dynamic, 

and should be updated as needed by managers.

Scoring System

Each species was given a subscore for each modifying 

factor, which was then weighted and converted into 

overall disturbance, biological, and adaptability scores. 

The definitions for the scoring system are as follows: 

FactorType—One of two influential factor types 

(biological and disturbance) which describe the variables 

used to modify the outputs of individual species 

distribution models.

ModFactor—A modifying factor that is considered 

to affect the establishment, growth, mortality rate, 

and regeneration of a species which could reduce or 

increase the habitat suitability or future abundance 

for that species. See the following two sections for 

specific details relating to each Modfactor for naturally 

occurring and planted trees. 

Score—A score, given as an integer ranging from -3 

(negative effect on reproduction, growth, or survival) to 

+3 (positive effect on reproduction, growth, or survival), 
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6 | 113

which relates to the potential influence of a ModFactor 

on the species throughout its range at the present. 

Uncert—A default score (a multiplier to Score) 

reflecting the level of uncertainty about the ModFactor’s 

influence on the distribution of the species. Scores are 

0.5 = highly uncertain, 0.75 = somewhat uncertain, 

or 1.0 = low uncertainty that the ModFactor will 

provide the influence. These values are also assigned 

preliminarily by the modeling team based on literature 

research. For example, if there is contradictory 

information in the literature, the score would be 0.5.

FutureRelevance—A value (also a multiplier to Score) 

referring to the likely potential future relevance that a 

particular ModFactor could have on the distribution of 

a species over the next 50 years in a changing climate. 

Values range from 1 = not highly relevant over the next 

50 years to 5 = likely to be an extremely important 

ModFactor. 

Weighted—A weighted score based on multiplication 

of the three default values (Score × Uncert × 

FutureRelevance) for the species throughout its range. 

Average Disturbance Score—The average of all 

the weighted disturbance factor scores; it indicates the 

relative overall impact of these factors.

Average Biological Score—The average of all the 

weighted biological factor scores; it indicates the relative 

overall impact of these factors.

Converted Dist Score—The average of all disturbance 

factor scores (unweighted) + 3 to remove negative values. 

Values can range from 0 to 6. 

Converted Bio Score—The average of all biological 

factor scores (unweighted) + 3 to remove negative values. 

Values can range from 0 to 6.

Adapt Score—The hypotenuse of a right triangle 

created from the Converted Dist Score and Bio Score. 

Values can range from 0 to 8.5.

Adapt Class—Categories assigned based on Adapt 

Score. Low: less than 3.5. Moderate: 3.5 to 4.5. High: 

More than 4.5. 

Factors for Naturally 

Regenerating Trees in Natural 

and Other Undeveloped Areas 

Scores were developed for 75 species that are either 

native or naturalized in the Chicago Wilderness 

region. Scores for native species were primarily based 

on those developed by Matthews et al. (2011), with 

most information derived from Burns and Honkala 

(1990a, 1990b). For invasive species, information was 

gleaned from various sources, including the Natural 

Resources Conservation Service’s PLANTS Database 

(Natural Resources Conservation Service 2015) and 

invasive species fact sheets developed by federal and state 

agencies. Additional information for wind and ice storm 

susceptibility was taken from Hauer et al. (2006) and 

Duryea et al. (2007). 

Defaults were kept consistent with Matthews et al. 

(2011), with a few exceptions. Insect and disease scores 

were modified to account for local conditions. 

Factors that received a weighted score of less than -4.5 

or greater than 4.5 were listed in tables as contributing 

negatively or positively, respectively, to the overall 

adaptability score of the species. Weighted scores 

between these two values were not listed. Disturbance 

and biological factors are defined next; and default values 

for the associated Score, Uncert, and FutureRelevance 

are provided for each.

Disturbance Factors 

Disease—Accounts for the number and severity of 

known pathogens that attack a species. If a species is 

resistant to many pathogens, it is assumed that it will 

continue to be so in the future. If the mortality rate is 

low, it is assumed that the species is not greatly affected 

by diseases. Thus, those species would receive positive 

scores. Defaults for all species are: Score = -1, Uncert = 

0.75, and FutureRelevance = 2.

Insect pests—Accounts for the number and severity 

of insects that may attack a species. If a species is 

resistant to attacks from known insect pests now or is 

adapted to cope with them, then it is assumed to be 

at least partially resistant in the future. This factor, 

although highly uncertain in overall effects, is likely 

to be very important over the next 50 years. Defaults 

for all species are: Score = -2, Uncert = 0.5, and 

FutureRelevance = 4.

Browse—The extent to which browsing (by deer or 

other herbivores) has an effect on the species, either 

a positive effect by promoting growth or by effective 

strategies for herbivory avoidance, or a negative effect 

by overbrowsing. Defaults for all species are: Score = 

-2 (+1 if promoted by browsing), Uncert = 0.75, and 

FutureRelevance = 1.
APPENDIX 6 —Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 114 |

Invasive plants—The effects of invasive plants on a 

species, either through competition for nutrients or as 

a pathogen. This factor is not yet well researched as 

to effects on individual tree species, but could be very 

important in the future as invasive species are usually 

more readily adapted to changing environments, and 

can form monotypic stands that restrict regeneration. 

Defaults for all species are: Score = -3, Uncert = 0.5, and 

FutureRelevance = 4.

Drought—Extended periods without sufficient access 

to water. Certain species are better adapted to drier 

conditions, allowing them to survive more-frequent or 

prolonged droughts. Defaults for all species are: Score = 

-1, Uncert = 0.75, and FutureRelevance = 4. 

Flood—Frequent or prolonged periods of standing 

water. Species adapted to sustained flooding will be 

positively affected and species vulnerable to flooding will 

be negatively affected by the assumed greater exposure 

to flooding under climate change. Defaults for all species 

are: Score = -1, Uncert = 0.75, and FutureRelevance = 3.

Ice—The damaging effects of ice storms and potential 

for ice heaving on a species. Defaults for all species are: 

Score = -1, Uncert = 0.5, and FutureRelevance = 2.

Wind—The damaging effects of wind storms and 

uprooting potential (and top breakage) of a species. 

Defaults for all species are: Score = -1, Uncert = 0.75, 

and FutureRelevance = 2. If a species is susceptible to 

windthrow, Score = -2; if it is resistant to windthrow, 

Score = +1.

Fire topkill—The effects of fire or fire suppression on 

the larger stems of a species (poles and sawtimber). 

Species adapted to fire will be positively affected by the 

assumed greater exposure to fire under climate change, 

whereas species vulnerable to fire will be negatively 

affected. As a first approximation, bark thickness relates 

directly to this ModFactor. Defaults for all species are: 

Score = -1, Uncert = 0.75, and FutureRelevance = 2. 

Harvest—If the species is harvested by using best 

management practices, is the species generally enhanced 

or diminished through time? If the best management 

practice includes replanting, that is included in the 

ranking. If the species is not a target species currently 

being managed within a harvest context, managers 

may consider how the species responds when it is an 

incidental species in harvested stands. Harvesting is 

generally low in urban areas, so this disturbance factor 

defaults to 0 and is not factored in unless there is an 

active attempt at managing this species (e.g., removal 

of woody invasive species). Defaults for all species are: 

Score = 0, Uncert = 0.5, and FutureRelevance = 2.

Temperature gradients—The effects of variations 

in the temperature gradient associated with a species. 

Species that currently occupy regions with a wide 

range of temperatures are assumed to be better adapted 

to warmer and highly variable climates than species 

occupying regions with a narrow range of temperatures. 

Defaults for all species are: Score = 1, Uncert = 0.75, and 

FutureRelevance = 2.

Air pollution—Airborne pollutants, including acid 

rain and ozone, that affect, mostly negatively, the 

growth, health, and distribution of a species. Defaults 

for all species are: Score = -2, Uncert = 0.75, and 

FutureRelevance = 3.

Soil and water pollution—Pollutants in the soil and 

water that affect, mostly negatively, the growth, health, 

and distribution of a species. Defaults for all species are: 

Score = -1, Uncert = 0.5, and FutureRelevance = 1.

Salt—The sensitivity of a species to road salt and 

salt spray. This also would include tolerance of any 

alternative ice control substances (assuming that 

information is available). Defaults for all species are: 

Score = -1, Uncert = 0.5, and FutureRelevance = 1. If a 

species is particularly susceptible to salt, Score =-2; if it 

is resistant to salt, Score = +1.

Biological Factors 

Competition-light—The shade tolerance of a species. 

Does the species grow better in full sun, partial shade, 

or shade? Default values depend on the tolerance level of 

the species; default for all species is: FutureRelevance = 

3. Species intolerant to shade receive: Score = -3, Uncert 

= 0.75. Species with intermediate shade tolerance receive 

Score = -1, 0, or 1, where the default is 0, with flexibility 

to go +1 or -1; Uncert = 0.5. Shade-tolerant species 

receive Score = +3 and Uncert = 0.75. 

Edaphic specificity—The specific soil requirements 

(e.g., pH, texture, organic content, horizon thickness, 

permeability) for a species to survive in a suitable 

habitat; includes long-term soil moisture capacities of 

the soil. Species that tolerate a range of soil properties 

have positive scores, and species with specific 

requirements have negative defaults. Unsuitable soils 

north of the current range of a species can be a barrier to 

migration. Defaults for all species are: Score = 0, Uncert 

= 0.75, and FutureRelevance = 2.
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6 | 115

Environmental habitat specificity—Considers the 

range of non-edaphic environmental characteristics (e.g., 

slope, aspect, topographic position, climatic modulation, 

specific associates) that the species requires; also 

considers whether the species may be able to survive a 

changed climate in relatively small refugia (e.g., coves, 

north-facing slopes). Defaults for all species are: Score = 

0, Uncert = 0.75, and FutureRelevance = 3.

Dispersal—The ability of the species to effectively 

produce and distribute seeds; considers viability, 

production, production intervals, seed banking, 

dispersing agents (even humans), and other factors 

related to moving seeds across the landscape. Defaults 

for all species are: Score = 1, Uncert = 0.5, and 

FutureRelevance = 3.

Seedling establishment—The ability of the species to 

regenerate with seeds to maintain future populations; 

considers the conditions required for establishment 

of seedlings and survival rates for seedlings, but not 

necessarily to the sapling stage. Defaults for all species 

are: Score = 1, Uncert = 0.75, and FutureRelevance = 4.

Vegetative reproduction—The ability of the species 

to regenerate by means of stump sprouts or cloning (not 

necessarily growing into a sapling size). Species that can 

reproduce vegetatively have positive defaults and those 

that cannot have negative defaults. Defaults assume 

some vegetative reproduction, so for all species they are: 

Score = 1, Uncert = 0.75, and FutureRelevance = 2.

Fire regeneration—The capability for the species to be 

enhanced in regeneration through fire, usually surface 

fires. This score will never be less than 0 as it is used 

only if there is an extra benefit in fire to regenerate the 

species, above seedling establishment and vegetative 

reproduction. Defaults are: Score = 0, Uncert = 0.75, and 

FutureRelevance = 2.

An example score for naturally regenerating boxelder in 

a natural setting is given in Table 20.

Table 20.—Example of Natural Modifying Factor scores generated for the 

species boxelder

FactorType ModFactor Score Uncert

Future

Relevance Weighted

Disturbance Disease -1 0.75 4 -3

Disturbance Insect pests -1 0.5 4 -2

Disturbance Browse -1 0.75 1 -0.75

Disturbance Invasive plants -1 0.5 4 -2

Disturbance Drought 3 0.75 4 9

Disturbance Flood 2 0.75 3 4.5

Disturbance Ice -2 0.5 1

Disturbance Wind -2 0.75 2 -3

Disturbance Fire topkill -2 0.75 2 -3

Disturbance Harvest 0 0.5 2 0

Disturbance Temperature gradients 3 0.75 2 4.5

Disturbance Air pollution -2 0.75 3 -4.5

Disturbance Soil and water pollution -1 0.5 1 -0.5

Disturbance Salt 1 0.5 1 0.5

Biological Competition-light 2 0.75 3 4.5

Biological Edaphic specificity 2 0.75 2 3

Biological Environmental habitat 

specificity 1 0.75 3 2.25

Biological Dispersal 3 1 3 9

Biological Seedling establishment 3 0.75 4 9

Biological Vegetative reproduction 2 0.75 2 3

Biological Fire regeneration 1 0.75 2 1.5

Average Disturbance Score -0.09

Average Biological Score 4.61

Converted Dist Score 2.71

Converted Bio Score 5

Adapt Score 5.69

Adapt Class high
APPENDIX 6 —Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 116 |

Factors for Planted Trees in 

Developed Areas 

Scores for 179 species that are currently planted, being 

considered for planting lists in the area, or are native 

to the area and could be planted were evaluated for 

adaptability in planted environments. Factors, scores, 

and weighting were modified from naturally occurring 

trees to account for the different environments present 

in more-developed areas. Many biological factors were 

also altered to account for the fact that dispersal and 

reproduction are not typically factors for planted trees. 

Most information for native species was derived from 

Burns and Honkala (1990a, 1990b), with supplementary 

material relevant to cultivated environments from 

Gilman and Watson (1993). Most information for 

cultivars and nonnatives was taken from Gilman and 

Watson (1993). Additional information for wind and ice 

storm susceptibility were taken from Hauer et al. (2006) 

and Duryea et al. (2007).

Factors that received a weighted score of less than -4.5 

or greater than 4.5 were listed in tables as contributing 

negatively or positively to the overall adaptability 

score of the species. Weighted scores between these 

two values were not listed. Disturbance and biological 

factors are defined next; and default values for the 

associated Score, Uncert, and FutureRelevance are 

provided where they differ from those for naturally 

occurring trees (“natural scores”).

Disturbance Factors

Disease—Same as for natural scores. 

Insect pests—Same as for natural scores.

Browse—Same as for natural scores, but default Score 

is increased from -2 to -1 because it is assumed herbivory 

would be lower in planted environments. 

Invasive plants—Same as for natural scores, but 

defaults go to 0 because it is assumed that for the most 

part planted trees will be shielded from competition 

from invasive species. 

Drought—Same as for natural scores, but default 

FutureRelevance is reduced from 4 to 3 because it is 

assumed that many planted trees will be watered during 

drought periods. 

Flood—Same as for natural scores.

Ice—Same as for natural scores. 

Wind—Same as for natural scores.

Temperature gradients—Same as for natural scores, 

but default FutureRelevance is increased from 2 to 3 

because of the urban heat island effect. 

Air pollution—Same as for natural scores, but default 

Score is reduced from -2 to -3 to account for the 

increased air pollution in developed areas. 

Soil and water pollution—Same as for natural scores, 

but default Score is reduced from -1 to -2 to account for 

greater pollution in developed areas. 

Salt—Same as for natural scores, but default Scores are 

reduced from -1 to -2 and for susceptible species from -2 

to -3 to account for the greater road salt and salt spray on 

street and residential trees. 

Biological Factors 

Competition-light—Same as for natural scores. 

Edaphic specificity—Same as for natural scores. 

Land-use/Planting site specificity—The capability 

for the species to be planted in a variety of site types 

(street, residential, park, campus); also considers the 

range of non-edaphic environmental characteristics 

(e.g., slope, aspect, topographic position, climatic 

modulation, specific associates) that the species requires. 

Defaults for all species are: Score = 0, Uncert = 0.75, and 

FutureRelevance = 3.

Restricted rooting conditions and soil 

compaction—The ability of a species to grow and 

survive in narrow boulevards and other constrained 

spaces. Defaults for all species are: Score = -1, Uncert = 

0.75, and FutureRelevance = 3.

Nursery production potential—The ease and cost 

of producing the species in a nursery; also relates to 

how widely available it is. FutureRelevance is high 

for this factor because it will largely determine the 

extent to which the species is widely propagated and 

planted. Defaults for all species: Uncert = 0.75, and 

FutureRelevance = 4. If stock is widely available, Score = 

+2. If stock is not currently available, Score = -2. 

Planting establishment—The ease with which the 

species establishes itself after planting; also relates to 

the amount of care required for the species to become 

established. Defaults for all species are: Score = 1, 

Uncert = 0.75, and FutureRelevance = 2. If the species is 

not easily established, Score = -1. 
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6 | 117

Maintenance required—The degree to which pruning 

or other maintenance is needed after establishment. 

Negative score indicates that maintenance is required. 

Defaults for all species are: Score = -1, Uncert = 0.75, 

and FutureRelevance = 2. If minimal maintenance is 

required, Score = 1. 

Invasive potential—Likelihood that the species could 

become invasive if planted; applies to both native and 

nonnative species. Negative score indicates that a species 

is known to be or has the potential to be invasive. 

Defaults for all species are: Score = 0, Uncert = 0.75, and 

FutureRelevance = 3. If species is known to be invasive, 

Score = -3. 

An example score for boxelder planted in a developed 

area is given in Table 21.

On the following pages are the resulting scores using the 

two scoring systems (Table 22).

Table 21.—Example of Planted Modifying Factor scores generated for the 

species boxelder

FactorType ModFactor Score Uncert

Future

Relevance Weighted

Disturbance Disease -1 0.75 2 -1.5

Disturbance Insect pests -3 0.5 5 -7.5

Disturbance Browse -1 0.75 1 -0.75

Disturbance Invasive plants 0 0.5 2 0

Disturbance Drought 3 0.75 3 6.75

Disturbance Flood 2 0.75 3 4.5

Disturbance Ice -1 0.5 2 -1

Disturbance Wind -1 0.75 2 -1.5

Disturbance Temperature gradients 3 0.75 3 6.75

Disturbance Air pollution -2 0.75 3 -4.5

Disturbance Soil and water pollution -2 0.5 1 -1

Disturbance Salt 1 0.5 1 0.5

Biological Competition-light 2 0.5 1 1

Biological Edaphic specificity 2 0.75 2 3

Biological Land-use/Planting site 

specificity 1 0.75 3 2.25

Biological Restricted rooting conditions 1 0.75 3 2.25

Biological Nursery propagation -1 0.75 4 -3

Biological Planting establishment 2 0.75 2 3

Biological Maintenance required -1 0.75 2 -1.5

Biological Invasive potential -3 0.75 3 -6.75

Average Disturbance Score 0.06

Average Biological Score 0.03

Converted Dist Score 2.83

Converted Bio Score 3.38

Adapt Score 4.41

Adapt Class medium
APPENDIX 6

—Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 118 | Table 22.—Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments Common name Number of trees2 Planted Disturb Score Planted Bio Score Planted Adapt Score Planted Adapt Class Positive traits– planted3 Negative traits– planted3 Natural Disturb Score Natural Bio Score Natural Adapt Score Natural Adapt Class Positive traits– natural3 Negative traits– natural3 Accolade® elm 0 -0.48 2.59 4.76 high TEM LPS NUP -- n/a n/a n/a n/a -- -- ‘Accolade’ flowering cherry 0 -0.92 1.5 4.21 medium TEM NUP FLO n/a n/a n/a n/a -- -- Allegheny serviceberry 0 -1.67 1.88 4.66 high LPS DRO AIP -1.88 2.36 4.57 high COL INPL DRO AIP American basswood (American linden) 47 -0.79 0.28 4.01 medium FLO TEM NUP AIR RRC -1.36 1.5 4.18 medium FLO COL INPL AIP SES American beech 0 -1.58 -0.66 3.55 medium -- FLO AIP LPS RRC -1.89 1.5 4.25 medium TEM FLO FTK AIP COL American elm 261 -1.94 0.94 3.92 medium NUP DISE DRO -2.07 2.36 4.64 high EHS DISE DRO AIP American hornbeam 1 -0.64 2.46 4.75 high FLO TEM NUP DRO AIP -0.64 2.46 4.88 high FLO COL SES INPL DRO FTK American plum 8 -1.02 1.63 4.33 medium NUP AIP -1.11 0.64 3.96 medium -- INPL AIP American sycamore 0 -0.58 0.13 3.95 medium TEM NUP -- -0.88 0.75 4 medium -- INPL American witchhazel 11 -0.98 0.22 4.06 medium TEM INS AIP -1.2 2.79 4.95 high COL EHS INS INPL AIP Amur cherry 0 -2.25 2.31 4.57 high LPS RRC TEM AIP n/a n/a n/a n/a -- -- Amur corktree 3 0.1 0.66 4.46 medium TEM NUP RRC INPO -0.15 2.46 5.11 high SES COL Amur honeysuckle 184 1.42 1.91 6.15 high TEM AIP ESP LPS PLE MAR INPO 0.86 5.14 4.5 high TEM AIP COL ESP EHS DISP SES VRE -- Amur maackia 0 0.56 2.34 4.85 high DRO TEM LPS RRC NUP FLO n/a n/a n/a n/a -- -- Amur maple 36 -0.27 0.09 4.4 medium TEM LPS INPO n/a n/a n/a n/a -- INS Apple serviceberry 0 -2.06 3.13 5.06 high LPS RRC NUP DRO FLO AIP n/a n/a n/a n/a -- -- Apple/crabapple species 105 -0.96 1.84 4.01 medium TEM LPS RRC NUP DISE INS AIP n/a n/a n/a n/a -- -- Austrian pine 50 -0.65 0.72 3.91 medium TEM RRC DISE INS n/a n/a n/a n/a -- --

Autumn-olive 10 -0.83 -0.59 4.06 medium LPS AIP NUP INPO -0.48 2.89 5.09 high EHS DISP AIP Baldcypress 2 0.02 2.28 4.9 high FLO RRC NUP AIP n/a n/a n/a n/a -- --Balsam fir 10 -2.19 1.13 4.22 medium NUP DRO TEM AIP n/a n/a n/a n/a -- --Bigtooth aspen 0 -0.92 -0.25 3.45 low -- -- -1.07 1.14 4.57 high VRE FRG

INPL DRO FTK AIP COL 

Bitternut hickory 14 -0.5 -0.31 3.83 medium DRO AIP -0.59 0.21 4.06 medium DRO INPL COL 

(Continued)
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6

| 119

Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments

Common name

Number of trees2

Planted Disturb Score

Planted Bio Score

Planted Adapt Score

Planted Adapt Class

Positive traits–planted3

Negative traits–planted3

Natural Disturb Score

Natural Bio Score

Natural Adapt Score

Natural Adapt Class

Positive traits–natural3

Negative traits– natural3

Black cherry 437 -1.58 -2.34 2.39 low DRO TEM AIP LPS RRC -1.8 1.61 4.22 medium DRO EHS SES INPL FLO FTK AIP COL Blackgum 0 -0.5 1.41 4.72 high RRC AIP -0.05 2.79 5.31 high COL INPL Blackhaw 2 -0.6 2.22 4.85 high NUP TEM INS -0.91 2.25 4.73 high EHS INS INPLBlack Hills spruce 0 -1.65 1.03 3.92 medium -- TEM AIP n/a n/a n/a n/a -- --Black locust 135 -1.23 -0.56 3.55 medium DRO TEM 

FLO AIP LPS RRC INPO 

-1.29 2.04 4.66 high DRO INPL DRO AIP 

Black maple 3 -1.71 0.38 4.06 medium TEM AIP -2.02 2.89 4.78 high COL EHS INPL AIP INSBlack oak 4 -0.56 0.59 4 medium DRO TEM DISE INS -0.88 1.61 4.61 high DRO DISE INS INPLBlack walnut 130 -1.02 0.32 3.97 medium -- DRO AIP LPS RRC -1.02 0.32 3.97 medium SES INPL DRO COL Black willow 3 -1.58 -0.38 3.25 low FLO INS DRO AIP RRC -1.7 -1.29 3.26 low FLO INS INPL DRO COL FTK AIPBlue spruce 54 -1.4 0.75 3.95 medium NUP INS FLO AIP n/a n/a n/a n/a -- --Boxelder 481 0.06 0.03 4.41 medium DRO FLO TEM AIP INPO -0.09 4.61 5.69 high DRO FLO TEM COL DISP SES AIP Bur oak 87 0.58 0.84 4.54 high

DRO TEM AIP LPS NUP 

DISE INSE FLO 0.25 1.39 4.77 high DRO TEM AIP DISE INS FLO 

Callery pear 18 -0.73 1 4 medium DRO TEM RRC NUP INS INPO n/a n/a n/a n/a -- --Cherry plum 12 -1.48 1 3.82 medium NUP INS AIP n/a n/a n/a n/a -- --Chestnut oak 0 -1.04 1.94 4.7 high LPS INS AIP -0.59 3.54 5.5 high EHS SES VRE INS INPL Chinese catalpa 0 -0.94 1.06 4.12 medium FLO TEM AIP n/a n/a n/a n/a -- --Chinese chestnut 1 -0.52 -0.28 3.59 medium TEM -- n/a n/a n/a n/a -- --Chinese fringetree 0 -1.04 2.06 4.77 high LPS RRC -- n/a n/a n/a n/a -- --Chinese juniper 0 -0.31 1.69 4.5 high DRO LPS NUP FLO n/a n/a n/a n/a -- --Chinkapin oak 5 -0.42 1.22 4.4 medium DRO TEM LPS DISE INS AIP -0.57 1.07 4.48 medium DRO TEM DISE INS INPL Cockspur hawthorn 25 -1.02 2.5 4.59 high

DRO TEM LPS RRC NUP

INS AIP n/a n/a n/a n/a -- --

Common chokecherry 7 -1.46 0.66 3.56 medium NUP DISE FLO AIP -1.54 0.64 3.93 medium -- DISE INPL FLO AIP COL 

(Continued)
APPENDIX 6

—Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 120 | Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments Common name Number of trees2 Planted Disturb Score Planted Bio Score Planted Adapt Score Planted Adapt Class Positive traits– planted3 Negative traits– planted3 Natural Disturb Score Natural Bio Score Natural Adapt Score Natural Adapt Class Positive traits– natural3 Negative traits– natural3 Common elderberry 10 -0.23 0.34 3.91 medium FLO TEM AIP INPO -0.14 2.57 5.16 high FLO EHS DISP SES VRE AIP COL Common hackberry 53 0.31 1.66 4.7 high DRO TEM LPS NUP -- -0.38 2.36 4.96 high DRO INPL FTK Common lilac 8 -1.69 1.69 3.88 medium RRC NUP FLO AIP n/a n/a n/a n/a -- -- Common pear 17 -1.85 0.72 3.47 low TEM NUP DRO AIP n/a n/a n/a n/a -- -- Common persimmon 0 0.69 1.97 4.76 high DRO FLO TEM ESP RRC NUP -- n/a n/a n/a n/a -- -- Cornelian cherry dogwood 1 -0.81 0.13 4.06 medium TEM AIP n/a n/a n/a n/a -- -- Crimean linden 0 -0.88 3.13 5.33 high DRO TEM LPS RRC NUP -- n/a n/a n/a n/a -- -- Cucumbertree 0 -1.48 0.34 3.8 medium NUP DRO AIP n/a n/a n/a n/a -- -- Dawn redwood 0 0.1 1.03 4.35 medium TEM AIP n/a n/a n/a n/a -- -- ‘Discovery’ elm 0 -0.29 2.53 4.81 high TEM LPS NUP INS n/a n/a n/a n/a -- -- Douglas-fir 5 -2.17 0.09 3.26 low NUP DRO FLO TEM LPS n/a n/a n/a n/a -- -- Downy serviceberry 5 -0.5 2 4.97 high TEM NUP AIP -1.25 4.39 5.76 high COL EHS SES FRG INPL FTK Eastern cottonwood 98 -1.48 -0.38 3.1 low TEM NUP DISE INS AIP LPS RRC -1.45 0.64 3.93 medium TEM DIS INS INPL AIP COL Eastern hemlock 15 -2.54 -0.47 3.21 low NUP DRO AIP LPS n/a n/a n/a n/a -- -- Eastern hophornbeam (ironwood) 44 -0.4 3 5.41 high DRO TEM LPS RRC NUP FLO AIP -0.68 3.54 5.46 high DRO TEM COL EHS INPL FLO AIP Eastern redbud 6 -0.5 1.38 4.31 medium FLO TEM NUP AIP -0.5 2.36 4.96 high FLO INPL AIP Eastern redcedar 35 0.08 1.75 4.71 high DRO TEM LPS RRC AIP -0.32 -0.32 4.09 medium DRO INPL FTK COL 

Eastern wahoo 4 -1.73 0.22 3.84 medium -- AIP -1.88 1.93 4.41 medium COL INPL AIPEastern white pine 99 -3.25 -0.59 2.9 low NUP 

DISE INS DRO TEM AIP LPS RRC 

-3.07 1.29 3.75 medium --

DISE INS INPL DRO FTK AIP 

European alder 28 -0.35 0.13 4.25 medium FLO DISE AIP INPO n/a n/a n/a n/a -- --European beech 1 -2.04 0.44 3.76 medium NUP DRO LPS RRC n/a n/a n/a n/a -- --

(Continued)
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6

| 121

Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments

Common name

Number of trees2

Planted Disturb Score

Planted Bio Score

Planted Adapt Score

Planted Adapt Class

Positive traits–planted3

Negative traits–planted3

Natural Disturb Score

Natural Bio Score

Natural Adapt Score

Natural Adapt Class

Positive traits–natural3

Negative traits– natural3

European buckthorn 2,595 2.02 0.72 5.86 high DRO TEM ESP NUP INPO 1.68 5.14 6.74 high

DRO TEM AIP COL ESP DISP SES VRE 

--

European smoketree 1 -0.33 2.91 4.91 high DRO LPS RRC NUP FLO n/a n/a n/a n/a -- --European filbert 1 -1.48 0.66 3.71 medium NUP AIP RRC n/a n/a n/a n/a -- --European hornbeam 9 0.85 0.03 4.6 high TEM -- n/a n/a n/a n/a -- --European larch 0 -1.77 0.44 3.67 medium -- DRO TEM AIP n/a n/a n/a n/a -- --European mountain-ash 0 -1.65 2.81 4.51 high LPS RRC NUP -- n/a n/a n/a n/a -- --Flowering dogwood 4 -2.13 1.5 4.3 medium TEM LPS DRO FLO AIP RRC -2.32 3 4.86 high COL INPL DRO FLO AIPFreeman maple 12 -0.23 2.25 4.91 high TEM ESP LPS NUP -- n/a n/a n/a n/a -- INS‘Frontier’ elm 0 -0.15 2.5 4.91 high DRO TEM LPS RRC -- n/a n/a n/a n/a -- --Glossy buckthorn 29 1.83 0.72 5.8 high FLO TEM ESP NUP INPO 1.63 5.14 6.74 high FLO TEM AIP COL DIP SES --Gray alder 0 -1.73 1.09 4.17 medium FLO DRO AIP -2.04 1.29 4.22 medium FLO 

INPL DRO FTK AIP

Gray birch 8 -2.27 -0.28 3.22 low -- DISE INS AIP LPS -2.54 1.39 4.16 medium EHS DISE INS INPL FTK AIP COL Gray dogwood 5 -0.81 0.88 4.48 medium TEM -- -1.68 3 4.96 high SES INPL FTKGreen ash 413 -1.1 0.81 3.9 medium FLO NUP INS MAR -1.27 1.18 4.48 medium FLO INS INPL COL ‘Harvest Gold’ linden 0 -0.92 1.31 4.27 medium RRC AIP n/a n/a n/a n/a -- --Hedge maple 0 -0.94 2.66 5.07 high TEM LPS NUP INS n/a n/a n/a n/a -- INSHeritage® oak 0 -0.21 2.78 5.19 high DRO TEM LPS NUP DISE INS n/a n/a n/a n/a -- --Higan cherry 0 -1.92 0.66 3.58 medium -- FLO AIP RRC n/a n/a n/a n/a -- --Honeylocust 52 0.19 1.06 4.26 medium DRO TEM RRC NUP -- -0.09 1.61 4.72 high DRO INPL Horse chestnut 3 -0.96 0.88 4.1 medium TEM AIP n/a n/a n/a n/a -- --Jack pine 2 -0.92 -0.38 3.4 low DRO AIP LPS RRC n/a n/a n/a n/a -- --Japanese maple 1 -1.6 1.84 4.25 medium NUP DRO AIP n/a n/a n/a n/a -- INSJapanese red pine 1 -1.6 -0.22 3.31 low -- AIP n/a n/a n/a n/a -- --

(Continued)
APPENDIX 6

—Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 122 | Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments Common name Number of trees2 Planted Disturb Score Planted Bio Score Planted Adapt Score Planted Adapt Class Positive traits– planted3 Negative traits– planted3 Natural Disturb Score Natural Bio Score Natural Adapt Score Natural Adapt Class Positive traits– natural3 Negative traits– natural3 Japanese tree lilac 1 -0.71 2.5 4.72 high RRC LPS NUP AIP n/a n/a n/a n/a -- -- Japanese zelkova 1 0.17 1.94 4.6 high TEM LPS RRC NUP -- n/a n/a n/a n/a -- -- Katsura tree 1 -1.85 -0.41 3.31 low DISE INS NUP DRO WIN AIP RRC n/a n/a n/a n/a -- -- Kentucky coffeetree 1 0.04 1.84 4.6 high DRO LPS NUP AIP -0.23 0.32 4.26 medium -- DRO INPL AIP COL Korean mountain-ash 0 -1.88 1.22 3.65 medium NUP AIP n/a n/a n/a n/a -- -- Korean SunTM pear 0 -0.38 2.81 4.91 high TEM LPS RRC NUP -- n/a n/a n/a n/a -- -- Kousa dogwood 0 -1.94 1.88 4.63 high NUP DRO AIP n/a n/a n/a n/a -- -- Leatherleaf viburnum 1 -1.75 1.72 4.51 high NUP AIP n/a n/a n/a n/a -- -- Littleleaf linden 10 -0.94 1.81 4.59 high LPS NUP INS SAL n/a n/a n/a n/a -- -- London planetree 0 -0.67 1.81 4.42 medium DRO FLO TEM NUP DISE AIP n/a n/a n/a n/a -- -- Maidenhair tree 7 1.52 3 5.97 high DRO TEM LPS RRC NUP FLO n/a n/a n/a n/a -- -- Miyabe maple 0 0.58 0.84 4.91 high -- AIP n/a n/a n/a n/a -- INS Mockernut hickory 6 0.25 2.31 4.8 high TEM AIP -0.79 1.18 4.44 medium -- INPL FTK COL Morden hawthorn 69 -1.17 0.91 4.29 medium NUP AIP n/a n/a n/a n/a -- -- Nannyberry 3 -0.48 2.66 5.15 high TEM RRC NUP -- -1.05 2.25 4.73 high COL INPL Northern catalpa 2 -0.65 -0.56 3.45 low -- AIP -1.05 -0.96 3.44 low -- INPL AIP COL EHS Northern pin oak (Hill’s oak) 1 -1.5 0.84 3.77 medium DRO LPS TEM AIP -1.27 1.07 4.22 medium DRO INPL Northern red oak 170 -0.23 2.53 5.02 high TEM LPS NUP DISE INS INPL -0.59 1.82 4.88 high -- DISE INS INPL Northern white-cedar (arborvitae) 133 -1.96 1.5 4.44 medium NUP DRO AIP -2.23 2.04 4.38 medium COL INPL DRO SIP 

Norway maple 106 -0.19 2.34 5.21 high

DRO FLO ESP RRC NUP 

INS INPO -0.14 4.71 5.97 high

DRO FLO COL ESP EHS SES 

INS 

Norway spruce 21 -1.67 0.72 3.61 medium NUP INS FLO AIP n/a n/a n/a n/a -- --Ohio buckeye 3 -1.81 0.38 3.88 medium -- AIP -1.68 -1.29 3.08 low COL NPL SES Oriental spruce 0 -2.1 0.91 3.99 medium -- AIP n/a n/a n/a n/a -- --Osage-orange 3 0.23 2.66 5.15 high

DRO TEM ESP RRC NUP

AIP -0.2 2.04 4.95 high DRO ESP EHS INPL 

(Continued)
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6

| 123

Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments

Common name

Number of trees2

Planted Disturb Score

Planted Bio Score

Planted Adapt Score

Planted Adapt Class

Positive traits–planted3

Negative traits–planted3

Natural Disturb Score

Natural Bio Score

Natural Adapt Score

Natural Adapt Class

Positive traits–natural3

Negative traits– natural3

Pacific Sunset® maple 0 -0.54 2.75 5.22 high DRO TEM LPS RRC INS n/a n/a n/a n/a -- INSPagoda dogwood 2 -1.73 1.66 4.29 medium NUP AIP -2.2 2.14 4.46 medium COL INPL FTKPaper birch 23 -2.42 1.03 3.65 medium NUP DRO TEM AIP -2.91 1.61 4.36 medium EHS FRG INPL DRO FTK AIP COL Peach 8 -1.42 0.38 3.61 medium NUP DRO FLO n/a n/a n/a n/a -- --Peachleaf willow 6 -1.75 0.09 3.32 low FLO AIP -1.75 -1.07 3.49 low FLO INPL AIP COL SESPecan 0 -1.85 -0.31 3.15 low NUP AIP LPS RRC n/a n/a n/a n/a -- --Peking lilac 0 -1.04 2.03 4.67 high LPS NUP FLO TEM n/a n/a n/a n/a -- --Pignut hickory 0 -0.5 0.16 4.1 medium TEM -1.14 2.25 4.73 high EHS Pin cherry 2 -1.71 -0.47 3.1 low -- DRO AIP LPS -1.8 0.86 4.06 medium SES FRG INLP DRO AIP COL Pin oak 22 -0.94 1.31 3.91 medium FLO TEM RRC NUP DISE INS AIP -0.82 -0.43 3.66 medium FLO DISE INS INPL FTK COL ‘Prairie Gem’ Ussurian pear 0 -0.63 2.34 4.57 high TEM LPS NUP AIP n/a n/a n/a n/a -- --Prickly ash 9 -0.5 -1.44 3.48 low -- AIP LPS INPO -0.68 2.68 5.05 high EHS VRE INPL AIP Privet 1 -1.54 0.69 4.1 medium TEM NUP AIP INPO -1.45 4.71 5.81 high

COL EHS DISP SES VRE

AIP

‘Prospector’ Wilson elm 0 -0.6 0.38 4.1 medium DRO LPS n/a n/a n/a n/a -- --Pussy willow 5 -1.27 -0.19 3.3 low FLO TEM NUP DRO AIP LPS RRC -1.13 0.75 4.24 medium FLO DISP SES FRG INPL DRO AIP COL EHS Quaking aspen 17 -1.73 0.19 3.37 low TEM NUP 

INS DRO AIP RRC INPO 

-2.52 1.82 4.41 medium TEM EHS VRE INS INPL DRO FTK AIP COL 

Red maple 17 -1.23 1.25 4.25 medium

FLO TEM ESP LPS NUP 

INS DRO AIP -1.46 4.39 5.52 high

FLO COL ESP EHS SES VRE 

INS INPL DRO

Red mulberry 4 -1.13 0.38 3.97 medium TEM NUP AIP -1.63 1.93 4.44 medium COL INPL AIP FTK Red pine 1 -2.04 -1.03 2.7 low -- INS DRO AIP RRC n/a n/a n/a n/a -- --River birch 29 -0.9 1.22 3.85 medium TEM LPS NUP DISE INS DRO -1.75 0.86 4.06 medium --

DISE INS INPL DRO FTK COL 

(Continued)
APPENDIX 6

—Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 124 | Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments Common name Number of trees2 Planted Disturb Score Planted Bio Score Planted Adapt Score Planted Adapt Class Positive traits– planted3 Negative traits– planted3 Natural Disturb Score Natural Bio Score Natural Adapt Score Natural Adapt Class Positive traits– natural3 Negative traits– natural3 Robusta poplar 0 -0.25 1.5 4.4 medium DRO NUP -- n/a n/a n/a n/a -- -- Rose-of-Sharon 3 -1.33 1.97 4.55 high NUP AIP n/a n/a n/a n/a -- -- Russian -olive 3 0.77 1.41 4.89 high DRO TEM NUP INPO 0.61 2.89 5.67 high DRO TEM DISP EHS SES COL Sargent cherry 5 -1.23 1.19 3.82 medium TEM LPS RRC WIN AIP n/a n/a n/a n/a -- -- Sassafras 3 -1.08 2 4.12 medium DRO NUP n/a n/a n/a n/a -- -- Saucer magnolia 2 -0.83 2.06 4.63 high TEM NUP DRO FLO n/a n/a n/a n/a -- -- Scarlet oak 0 -0.96 2 4.59 high ESP LPS AIP -1.38 2.68 5.02 high ESP EHS VRE INPL AIP Scholar tree 0 -0.08 1.94 4.5 high DRO TEM LPS RRC NUP FLO n/a n/a n/a n/a -- -- Scotch pine 1 -0.79 1.97 4.42 medium NUP INS n/a n/a n/a n/a -- -- Serbian spruce 4 0.1 1.88 4.06 medium NUP INS n/a n/a n/a n/a -- -- Shagbark hickory 102 -1.08 -1.06 3.51 medium TEM AIP -1.59 2.14 4.66 high -- INPL AIP Shantung maple 0 -0.31 3 5.41 high DRO TEM LPS RRC NUP INS n/a n/a n/a n/a -- INS Shellbark hickory 1 -2.04 -1.03 3.26 low -- -- -2.14 1.18 3.99 medium COL INPL DRO AIP EHS Shingle oak 1 -0.08 1.88 4.8 high DRO NUP DISE INS AIP -0.45 0.75 4.33 medium DRO EHS DISE INS INPL AIP COL Shumard oak 0 0.79 1.69 4.57 high DRO FLO TEM LPS RRC NUP -- 0.29 0.11 4.3 medium DRO FLO TEM INPL COL Siberian elm 122 -0.17 -1.25 3.51 medium DRO WIN INPO -0.04 2.25 4.83 high DRO EHS SES WIN COL Silver linden 0 -1.17 0.91 4.01 medium TEM NUP INS AIP n/a n/a n/a n/a -- -- Silver maple 155 -0.33 0.13 4.05 medium FLO TEM NUP RRC -1.05 3.32 5.18 high FLO COL DISP SES INS INPL 

Slippery elm 37 -1.42 0.94 3.99 medium TEM LPS DISE -1.93 1.61 4.22 medium --

DISE INPL FTK AIP COL 

Smoothleaf elm 0 -2.35 -0.47 3.02 low -- DISE AIP n/a n/a n/a n/a -- --‘Snow Goose’ cherry 0 -1.19 2.59 4.85 high NUP -- n/a n/a n/a n/a -- --Staghorn sumac 0 -0.08 -0.19 3.95 medium DRO TEM AIP RRC -0.73 1.93 4.77 high

DRO TEM SES VRE FRG

INPL FLO FTK COL 

Star magnolia 4 -1.85 1.84 4.37 medium NUP DRO AIP n/a n/a n/a n/a

(Continued)
Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas—APPENDIX 6

| 125

Table 22.—(Continued) Overall Disturb, Bio, and Adapt score, and adaptability class, for species in natural1 and planted environments

Common name

Number of trees2

Planted Disturb Score

Planted Bio Score

Planted Adapt Score

Planted Adapt Class

Positive traits–planted3

Negative traits–planted3

Natural Disturb Score

Natural Bio Score

Natural Adapt Score

Natural Adapt Class

Positive traits–natural3

Negative traits– natural3

Sugar maple 213 -1.83 1.03 4.17 medium NUP INS FLO AIP -2.07 3.21 4.78 high COL INS INPL FLO AIP Sugarberry 0 -0.42 -0.59 3.81 medium DRO FLO TEM WIN AIP NUP n/a n/a n/a n/a -- --Swamp white oak 6 -0.58 1.84 4.57 high TEM RRC NUP AIP -0.63 1.18 4.36 medium -- INPL AIP Sweetgum 1 -0.44 -0.28 3.74 medium -- INS DRO RRC n/a n/a n/a n/a -- --Sycamore maple 0 -1.75 1.16 4.25 medium NUP INS AIP n/a n/a n/a n/a -- INS Tree of heaven 71 1.4 -1.88 4.54 high DRO TEM AIP ESP LPS NUP INPO 1.18 4.82 6.58 high

DRO AIP ESP EHS DISP SES VRE FRG

--

TriumphTM elm 0 -0.52 3 5.15 high

DRO TEM LPS RRC NUP 

-- n/a n/a n/a n/a -- --

Turkish hazelnut 0 0.38 1.75 4.71 high DRO TEM LPS RRC -- n/a n/a n/a n/a -- --Washington hawthorn 1 -1.25 1.97 4.32 medium DRO TEM RRC NUP DISE INS n/a n/a n/a n/a -- --Weeping willow 1 -0.5 -0.88 3.57 medium FLO TEM NUP INS AIP LPS RRC n/a n/a n/a n/a -- --White ash 234 -2.52 -0.28 3.22 low TEM NUP INS AIP RRC -2.39 1.39 4.12 medium -- INS INPL AIP COL White fir 0 -0.63 -0.59 3.87 medium -- DLO AIP n/a n/a n/a n/a -- --White fringetree 0 -1.08 2.47 4.92 high LPS RRC -- n/a n/a n/a n/a -- --White mulberry 90 0.33 -0.22 4.06 medium NUP LPS INPO 0.13 2.46 4.99 high EHS SES White oak 101 -0.81 0.53 4.25 medium TEM ESP LPS FLO AIP -0.86 3.11 5.37 high TEM ESP EHS INPL AIP White poplar 6 -0.29 -0.56 3.59 medium DRO TEM ESP NUP INS WIN LPS RRC n/a n/a n/a n/a -- --White spruce 91 -0.79 0.88 4.15 medium INS n/a n/a n/a n/a -- --Willow oak 0 0.25 2.31 4.8 high FLO LPS RRC NUP -- n/a n/a n/a n/a -- --Winged burningbush 9 -0.42 1.44 4.81 high TEM NUP INPO -0.66 3.21 5.4 high COL EHS FTK‘Winter King’ green hawthorn 0 -1.15 3.09 4.77 high LPS RRC NUP -- n/a n/a n/a n/a -- --Yellow buckeye 0 -1.21 0.47 4.1 medium -- DRO AIP n/a n/a n/a n/a -- --Yellow-poplar (tuliptree) 1 -1.92 0.09 3.47 low NUP DRO AIP RRC -1.66 3.75 5.39 high EHS SES FRG INPL DRO AIP Yellowwood 0 -0.33 1.47 4.51 high TEM RRC AIP n/a n/a n/a n/a -- --

1 The designation “n/a” means that this tree does not occur outside of cultivated settings in this area.2 Number of individuals in the most recent tree census (Nowak et al. 2013).3 See Table 5 for trait codes.
APPENDIX 6 —Modifying Factors for Assessing the Adaptive Capacity of Tree Species in Urban Areas 126 |

LITERATURE CITED

Burns, R.M.; Honkala, B.H., tech. coords. 1990a. Silvics of North 

America: 1. Conifers. Agric. Handb. 654. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 675 p. Available at 

http://www.na.fs.fed.us/spfo/pubs/silvics_manual/table_of_

contents.htm (accessed September 11, 2015).

Burns, R.M.; Honkala, B.H., tech. cords. 1990b. Silvics of North 

America: 2. Hardwoods. Agric. Handb. 654. Washington, DC: 

U.S. Department of Agriculture, Forest Service. 877 p. Available 

at http://www.na.fs.fed.us/spfo/pubs/silvics_manual/table_of_

contents.htm (accessed September 11, 2015).

Duryea, M.L.; Kampf, E.; Littell, R.C. 2007. Hurricanes and the urban 

forest: I. Effects on southeastern United States coastal plain tree 

species. Arboriculture & Urban Forestry. 33(2): 83-97.

Gilman, E.F.; Watson, D.G. 1993. 680 Tree fact sheets. Gainesville, 

FL: University of Florida, Environmental Horticulture. Available 

at http://hort.ifas.ufl.edu/database/trees/trees_scientific.shtml

(accessed September 11, 2015).

Hauer, R.J.; Dawson, J.O.; Werner, L.P. 2006. Trees and ice storms: 

the development of ice storm-resistant urban tree populations.

2nd ed. Joint Publ. 06-1. Stevens Point, WI: University of Wisconsin 

– Stevens Point, College of Natural Resources; Urbana-Champaign,

IL: University of Illinois at Urbana-Champaign, Department of

Natural Resources and Environmental Sciences and the Office of

Continuing Education. 20 p.

Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, M.P.; Rodewald, 

P.G. 2011. Modifying climate change habitat models using tree 

species-specific assessments of model uncertainty and life 

history-factors. Forest Ecology and Management. 262(8): 1460-

1472. https://doi.org/10.1016/j.foreco.2011.06.047.

Natural Resources Conservation Service. 2015. The PLANTS 

database. Greensboro, NC: U.S. Department of Agriculture, 

Natural Resources Conservation Service, National Plant Data 

Team. http://plants.usda.gov (accessed March 3, 2015).

U.S. Forest Service. [N.d.]. Climate change atlas. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. http://www.nrs.fs.fed.us/atlas.
Tree Species Vulnerability—APPENDIX 7 | 127

APPENDIX 7

TREE SPECIES VULNERABILITY

Overall vulnerability of trees present in the Chicago 

Wilderness region and those being considered for 

planting was assessed by combining information about 

projected changes in habitat suitability and adaptive 

capacity (see Appendix 6 and Table 23). 

For species where information was available from the 

Tree Atlas, the following matrix was used to determine 

vulnerability: 

Projected 

Change in 

Habitat Suitability 

(Tree Atlas)

Adapt Class

Low Medium High

Decrease 

(both scenarios) high moderate-high moderate

Mixed results moderate-high moderate lowmoderate

No effect moderate-high low-moderate low

Increase 

(both scenarios) moderate low-moderate low

For species where no model information was 

available, the following matrix was used to determine 

vulnerability: 

Hardiness/Heat 

Zone Effect

Adapt Class

Low Medium High

Negative high moderate-high moderate

No effect moderate-high low-moderate low

Positive moderate low-moderate low

Confidence ratings were generally based on the 

level of evidence to support the vulnerability rating 

and the level of agreement among that evidence 

(Intergovernmental Panel on Climate Change 2014). 

Reliable model projections that tended to agree across 

climate scenarios resulted in higher confidence ratings. 

If adaptive capacity was high and model projections 

were favorable for that species, this also resulted in 

a higher confidence rating. If model results were not 

available, confidence was higher if there was sufficient 

information on the adaptive capacity of the species and 

the hardiness and heat zones to make a determination. 

Species for which there was very little information on 

adaptive capacity and no model projections received a 

low vulnerability rating. 

LITERATURE CITED

Intergovernmental Panel on Climate Change. 2014. Climate change 

2014: impacts, adaptation, and vulnerability. Part A: Global 

and sectoral aspects. Contribution of Working Group II to the 

Fifth Assessment Report of the Intergovernmental Panel on 

Climate Change [Field, C.B.; Barros, V.R.; Dokken, D.J.; Mach, 

K.J.; Mastrandrea, M.D.; Bilir, T.E.; Chatterjee, M.; Ebi, K.L.; 

Estrada, Y.O.; Genova, R.C.; Girma, B.; Kissel, E.S.; Levy, A.N.; 

MacCracken, S.; Mastrandrea, P.R.; White, L.L., eds.]. Cambridge, 

UK and New York, NY: Cambridge University Press. 1132 p.
APPENDIX 7—Tree Species Vulnerability 128 |

Table 23.—Overall vulnerability, and associated information, for tree species in the Chicago Wilderness region

Common name Origin

Estimated 

number of 

trees

Planted 

Adapt 

Class

Natural 

Adapt 

Class1

Projected 

change: 

Tree Atlas

Heat/

Hardiness 

zone effect

Overall 

vulnerability Confidence

Accolade® elm cultivar 0 high n/a not modeled positive low medium

‘Accolade’ flowering 

cherry cultivar 0 medium n/a not modeled positive low-moderate low-medium

Allegheny serviceberry native 0 high high not modeled no effect low low-medium

American basswood 

(American linden) native 822,780 medium medium no change not evaluated low-moderate medium

American beech native 0 medium medium

mixed 

results not evaluated moderate medium

American elm native 5,363,030 medium high increase not evaluated low-moderate medium-high

American hornbeam native 26,130 high high increase not evaluated low high

American plum native 150,100 medium medium

mixed 

results not evaluated moderate low-medium

American sycamore native 7,970 medium medium increase not evaluated low-moderate medium

American witchhazel native 206,360 medium high not modeled no effect low-moderate low

Amur cherry nonnative 0 high n/a not modeled • low low

Amur corktree invasive 7,970 medium high not modeled no effect low-moderate low

Amur honeysuckle Invasive 3,370,400 high high not modeled no effect low low

Amur maackia nonnative 744,480 high n/a not modeled negative moderate low

Amur maple invasive 36 medium n/a not modeled negative moderate-high low

Apple serviceberry nonnative 0 high n/a not modeled negative moderate low

Apple/crabapple species cultivar 1,724,980 medium n/a not modeled no effect moderate low

Austrian pine nonnative 983,160 medium n/a not modeled positive low-moderate low

Autumn-olive invasive 228,040 medium high not modeled no effect low-moderate low

Baldcypress native 26,030 high n/a not modeled positive low medium

Balsam fir native 205,390 medium n/a not modeled negative high medium

Bigtooth aspen native 0 low high decrease not evaluated high medium-high

Bitternut hickory native 186,540 medium medium increase not evaluated low-moderate medium-high

Black cherry native 7,737,030 low medium decrease not evaluated high medium-high

Blackgum native 0 high high increase not evaluated low high

Blackhaw native 68,650 high high not modeled no effect low low

Black Hills spruce native 0 medium n/a not modeled negative moderate-high low

Black locust native 2,972,090 medium high increase not evaluated low high

Black maple native 69,910 medium high not modeled no effect moderate low

Black oak native 53,670 medium high no change not evaluated low medium

Black walnut native 2,469,240 medium medium increase not evaluated moderate medium

Black willow native 44,830 low low increase not evaluated moderate medium

Blue spruce native 1,107,240 medium n/a not modeled no effect moderate low

Boxelder native 8,597,890 medium high increase not evaluated low high

Bur oak native 1,603,410 high high no change not evaluated low medium-high

Callery pear invasive 257,690 medium n/a not modeled positive low-moderate low

Cherry plum nonnative 157,440 medium n/a not modeled positive low-moderate low

Chestnut oak native 0 high high not modeled no effect low low

Chinese catalpa nonnative 0 medium n/a not modeled • moderate low

Chinese chestnut nonnative 11,090 medium n/a not modeled no effect moderate low

Chinese fringetree nonnative 0 high n/a not modeled positive low low

Chinese juniper nonnative 0 high n/a not modeled no effect low low

Chinkapin oak native 79,770 medium medium new habitat not evaluated moderate medium

Cockspur hawthorn native 320,200 high n/a not modeled negative moderate low

Common chokecherry native 114,910 medium medium decrease not evaluated moderate-high low-medium

Common elderberry native 197,340 medium high not modeled no effect low-moderate low

Common hackberry native 1,020,060 high high increase not evaluated low high

(Continued)
Tree Species Vulnerability—APPENDIX 7 | 129

Table 23.—(Continued) Overall vulnerability, and associated information, for tree species in the Chicago 

Wilderness region

Common name Origin

Estimated 

number of 

trees

Planted 

Adapt 

Class

Natural 

Adapt 

Class1

Projected 

change: 

Tree Atlas

Heat/

Hardiness 

zone effect

Overall 

vulnerability Confidence

Common lilac nonnative 109,050 medium n/a not modeled no effect moderate low

Common pear nonnative 266,140 low n/a not modeled no effect moderate-high low

Common persimmon native 0 high n/a new habitat not evaluated low high

Cornelian cherry 

dogwood cultivar 11,090 medium n/a not modeled no effect moderate low

Crimean linden nonnative 0 high n/a not modeled negative moderate low

Cucumbertree native 0 medium n/a not modeled no effect moderate low

Dawn redwood nonnative 0 medium n/a not modeled positive moderate low

‘Discovery’ elm cultivar 0 high n/a not modeled • low low

Douglas-fir native 108,410 low n/a not modeled negative high low

Downy serviceberry native 57,460 high high not modeled no effect low low

Eastern cottonwood native 2,198,060 low medium increase not evaluated moderate high

Eastern hemlock native 268,660 low n/a not modeled no effect high low-medium

Eastern hophornbeam 

(ironwood) native 602,120 high high

mixed 

results not evaluated low-moderate medium

Eastern redbud native 110,420 medium high new habitat not evaluated low-moderate medium-high

Eastern redcedar native 563,500 high medium increase not evaluated low-moderate medium-high

Eastern wahoo native 46,320 medium medium not modeled • moderate low

Eastern white pine native 1,525,970 low n/a not modeled negative high high

European alder invasive 382,610 medium n/a not modeled positive moderate-high low

European beech nonnative 20,240 medium high not modeled no effect low-moderate low

European buckthorn invasive 44,281,470 high n/a not modeled no effect low low

European filbert nonnative 17,440 medium n/a not modeled no effect moderate low

European hornbeam nonnative 99,760 high n/a not modeled negative low low

European larch nonnative 0 medium n/a not modeled negative moderate-high low

European mountain-ash nonnative 0 high n/a not modeled positive moderate low

European smoketree nonnative 13,070 high high increase not evaluated low low

Flowering dogwood native 81,590 medium n/a not modeled no effect low-moderate medium-high

Freeman maple cultivar 280,470 high n/a not modeled • low low-medium

‘Frontier’ elm cultivar 0 high high not modeled no effect low low

Glossy buckthorn invasive 500,900 high medium not modeled negative low low

Gray alder native 0 medium medium not modeled negative moderate-high low

Gray birch native 145,590 low high not modeled no effect high low

Gray dogwood native 68,010 medium medium increase not evaluated moderate low

Green ash native 8,657,000 medium n/a not modeled negative moderate medium-low

‘Harvest Gold’ linden cultivar 0 medium n/a not modeled positive moderate-high low

Hedge maple nonnative 0 high n/a not modeled • low low

Heritage® oak cultivar 0 high n/a not modeled positive low low

Higan cherry cultivar 0 medium high increase not evaluated low-moderate low

Honeylocust native 997,510 medium n/a not modeled no effect low-moderate medium-high

Horse chestnut nonnative 40,250 medium n/a

mixed 

results not evaluated moderate low

Jack pine native 25,720 low n/a not modeled no effect high medium

Japanese maple nonnative 36,060 medium n/a not modeled negative moderate low

Japanese red pine nonnative 11,090 low n/a not modeled negative high low

Japanese tree lilac nonnative 19,020 high n/a not modeled positive moderate low

Japanese zelkova nonnative 11,090 high n/a not modeled no effect low low

Katsura tree nonnative 11,090 low medium new habitat not evaluated high low

Kentucky coffeetree native 33,380 high n/a not modeled positive low medium-high

Korean mountain-ash nonnative 0 medium n/a not modeled • low-moderate low

(Continued)
APPENDIX 7—Tree Species Vulnerability 130 |

Table 23.—(Continued) Overall vulnerability, and associated information, for tree species in the Chicago 

Wilderness region

Common name Origin

Estimated 

number of 

trees

Planted 

Adapt 

Class

Natural 

Adapt 

Class1

Projected 

change: 

Tree Atlas

Heat/

Hardiness 

zone effect

Overall 

vulnerability Confidence

Korean SunTM pear nonnative 0 high n/a not modeled positive low low

Kousa dogwood nonnative 0 high n/a not modeled positive low low

Leatherleaf viburnum nonnative 17,440 high n/a not modeled no effect low low

Littleleaf linden nonnative 789,950 high n/a not modeled • low low

London planetree nonnative 0 medium n/a not modeled no effect moderate low

Maidenhair tree nonnative 199,650 high n/a not modeled no effect low low

Miyabe maple cultivar 0 high medium new habitat not evaluated low low

Mockernut hickory native 6 high n/a not modeled no effect low-moderate medium

Morden hawthorn cultivar 121,430 medium high not modeled no effect moderate low

Nannyberry native 69,310 high low not modeled no effect low low

Northern catalpa native 59,440 low medium decrease not evaluated moderate-high low

Northern pin oak (Hill’s 

oak) native 20,240 medium high

mixed 

results not evaluated high high

Northern red oak native 3,087,850 high medium not modeled negative low-moderate medium

Northern white-cedar 

(arborvitae) native 2,457,220 medium high not modeled negative moderate-high low

Norway maple invasive 1,858,800 high n/a not modeled no effect moderate low

Norway spruce nonnative 377,510 medium low new habitat not evaluated moderate low

Ohio buckeye native 64,160 medium n/a not modeled no effect moderate medium

Oriental spruce nonnative 0 medium high increase not evaluated moderate low

Osage-orange native 80,910 high medium not modeled no effect low high

Pacific Sunset® maple cultivar 0 high medium increase not evaluated low low

Pagoda dogwood native 34,590 medium medium decrease not evaluated moderate low

Paper birch native 352,400 medium n/a not modeled positive high high

Peach nonnative 107,320 medium low not modeled no effect low-moderate low

Peachleaf willow native 77,720 low n/a new habitat not evaluated moderate-high low

Pecan native 0 low n/a not modeled negative moderate medium

Peking lilac nonnative 0 high high increase not evaluated moderate medium

Pignut hickory native 0 medium medium not modeled • low-moderate medium

Pin cherry native 40,550 low medium increase not evaluated moderate-high low

Pin oak native 360,430 medium n/a not modeled • low-moderate medium

‘Prairie Gem’ Ussurian 

pear cultivar 0 high high not modeled no effect low low

Prickly ash native 207,940 low high not modeled positive low low

Privet invasive 7,940 medium n/a not modeled • low-moderate low

‘Prospector’ Wilson elm cultivar 0 medium medium not modeled no effect moderate low

Pussy willow native 55,420 low medium decrease not evaluated moderate low

Quaking aspen native 230,070 low high increase not evaluated high high

Red maple native 340,290 medium medium increase not evaluated low-moderate medium-high

Red mulberry native 66,440 medium n/a decrease not evaluated low-moderate medium-high

Red pine native 15,010 low medium increase not evaluated high high

River birch native 552,800 medium n/a not modeled • low-moderate medium-high

Robusta poplar cultivar 0 medium n/a not modeled positive moderate low

Rose-of-Sharon nonnative 77,240 high high not modeled no effect low low

Russian-olive invasive 54,970 high n/a not modeled no effect low low

Sargent cherry cultivar 80,070 medium n/a increase not evaluated moderate low

Sassafras native 47,370 medium n/a not modeled positive low-moderate medium-high

Saucer magnolia cultivar 26,030 high high decrease not evaluated low low

Scarlet oak native 0 high n/a not modeled positive moderate low-medium

Scholar tree nonnative 0 high n/a not modeled negative low low

(Continued)
Tree Species Vulnerability—APPENDIX 7 | 131

Table 23.—(Continued) Overall vulnerability, and associated information, for tree species in the Chicago 

Wilderness region

Common name Origin

Estimated 

number of 

trees

Planted 

Adapt 

Class

Natural 

Adapt 

Class1

Projected 

change: 

Tree Atlas

Heat/

Hardiness 

zone effect

Overall 

vulnerability Confidence

Scotch pine nonnative 23,500 medium n/a not modeled no effect moderate-high low

Serbian spruce nonnative 78,160 medium high increase not evaluated moderate low

Shagbark hickory native 1,711,410 medium n/a not modeled no effect low-moderate medium

Shantung maple nonnative 0 high medium increase not evaluated low low

Shellbark hickory native 9,750 low medium increase not evaluated moderate low-medium

Shingle oak native 23,500 high medium not modeled positive low-moderate medium

Shumard oak native 0 high high not modeled no effect low-moderate low

Siberian elm invasive 2,240,590 medium n/a not modeled • low-moderate low

Silver linden nonnative 0 medium high increase not evaluated moderate low

Silver maple native 3,209,940 medium medium increase not evaluated low-moderate medium-high

Slippery elm native 453,470 medium n/a not modeled positive low-moderate medium-high

Smoothleaf elm nonnative 0 low n/a not modeled positive moderate low

‘Snow Goose’ cherry cultivar 0 high high not modeled no effect low low

Staghorn sumac native 0 medium n/a not modeled no effect low-moderate low

Star magnolia nonnative 69,320 medium high

mixed 

results not evaluated moderate low

Sugar maple native 4,457,170 medium n/a new habitat not evaluated moderate medium

Sugarberry native 0 medium n/a not modeled no effect low-moderate medium

Swamp white oak native 104,750 high n/a increase not evaluated low-moderate medium-high

Sweetgum native 17,090 medium n/a not modeled positive low-moderate low-medium

Sycamore maple nonnative 0 medium high not modeled positive low-moderate low

Tree of heaven invasive 1,830,940 high n/a not modeled no effect low low

TriumphTM elm cultivar 0 high n/a not modeled negative low low

Turkish hazelnut nonnative 0 high n/a not modeled no effect moderate low

Washington hawthorn native 23,100 medium n/a not modeled no effect moderate low

Weeping willow nonnative 11,090 medium medium increase not evaluated moderate low

White ash native 4,025,410 low n/a not modeled negative moderate-high low-medium

White fir native 0 medium n/a not modeled no effect moderate-high low

White fringetree nonnative 0 high high not modeled no effect low low

White mulberry invasive 1,584,250 medium high decrease not evaluated low-moderate low

White oak native 1,857,380 medium medium decrease not evaluated moderate-high low-medium

White poplar nonnative 95,600 medium n/a not modeled no effect moderate low

White spruce native 1,786,850 medium n/a decrease not evaluated high high

Willow oak native 0 high n/a not modeled positive low low

Winged burningbush invasive 148,650 high high not modeled no effect low low

‘Winter King’ green 

hawthorn cultivar 0 high n/a not modeled negative moderate low

Yellow buckeye native 0 medium n/a not modeled no effect moderate low

Yellow-poplar (tuliptree) native 17,440 low high increase not evaluated moderate low-medium

Yellowwood native 0 high n/a not modeled no effect low low

1 The designation “n/a” means that this tree does not occur outside of cultivated settings in this area.

A “0” indicates that no trees of this species or cultivar were detected in the most recent tree census (Nowak et al. 2013).

• indicates insufficent information.
APPENDIX 8—Urban Forest Vulnerability Worksheet and Instructions 132 |

APPENDIX 8

URBAN FOREST VULNERABILITY 

WORKSHEET AND INSTRUCTIONS

Case Study Process 

Representatives from four municipalities, three park districts, and three forest preserve districts in 

the Chicago Wilderness region were selected to participate in a 1-day workshop at The Morton 

Arboretum in November 2014. Participant selection was based on a combination of participants’ 

interest/availability and the need for a range of levels of development (urban-rural), economic 

means, and geographic locations. We recruited participants through phone calls and in-person 

conversations at local urban forestry-related events in the Chicago region. 

Participants were given presentations on past and projected climate change, model projections for 

tree species, and the adaptive capacity of trees. After presentations were completed, each participant 

was given instructions and a worksheet (see following sections). When available, additional 

information such as soil drainage class and inventory data was also provided. Each participant 

completed his or her own worksheet, but was grouped with like organizations to share ideas (e.g., 

all municipalities were together). Each group had a facilitator to walk group members through 

worksheet questions and a note-taker to record group discussion. Participants were given about 

3 hours to complete the worksheet and were asked to rely primarily on their personal knowledge 

about the place they manage. 

After all participants in the group had completed their worksheets, the facilitator asked the 

following questions within each group:

• As you look over the worksheet factors, are there some that you don’t feel will have 

a large influence on tree growth or survival? 

• Which of the factors stand out as having the largest influence and should be given 

the greatest weight?

• Are there other factors that we’re missing? 

• Which factors most strongly contributed to your ratings? 

• Are there factors which may interact in such a way that the effects cancel each 

other out or multiply the effect? 

• In what areas did you have the most confidence in your determination, and in what 

areas did you have the least? 

• For those who completed worksheets for more than one area, did some areas stand 

out as more or less vulnerable, and what factors contributed to that outcome? 

After the smaller group discussion, all participants joined in a large-group discussion to compare 

results, and anonymous written evaluations were also collected from each participant.

We collected all completed worksheets from participants and summarized their responses in 

narrative form. We sent the completed narrative summaries back to each participant for review of 

accuracy and tone and made revisions accordingly.
Urban Forest Vulnerability Worksheet and Instructions—APPENDIX 8 | 133

Worksheet Instructions

A. Read through the list of regional impacts and adaptive capacity factors and their 

accompanying questions. These questions will prompt you to consider local factors that 

may modify regional impacts or affect local capacity to adapt to climate change. 

B. For each impact or adaptive capacity factor: 

1. Answer yes or no to the question based on any data you have or your personal knowledge/

experience (if you aren’t sure, you can give a lower confidence rating). If the question simply does 

not apply to your area (e.g., a question about residents’ tree care and there are no residents), please 

write N/A.

2. Write down your explanation about whether and how you would expect this to affect your 

impacts or adaptive capacity to climate change in your area (see glossary). 

3. Record the overall effect of the factor on your area’s trees and/or forests. 

↓ Negative effect: You expect this factor to increase mortality, physical damage, or disease/

pest susceptibility of your trees or reduce growth and vigor.

↔ Neutral effect: You do not expect this factor to have a negative or positive effect on your 

trees, or the positive and negative factors will balance out.

↓ Positive effect: You expect this factor to reduce mortality, physical damage, or disease/pest 

susceptibility of your trees and/or enhance growth and vigor.

4. Record your confidence in the direction of the effect you determined in step 3.

Low: You do not have much information to support your conclusion and/or the information is 

conflicting. 

Medium: You have some information which seems to support your conclusion. 

High: Your conclusion is supported by area-specific data that all support your conclusion.

Example:

Regional Impact Local considerations Yes or no Explanation Overall effect Confidence

Heavy rain events 

could increase 

flooding, especially in 

the spring. 

Is all or part of the area 

currently susceptible to 

flooding (in a flood plain 

and/or has high number 

of flood-related insurance 

claims)?

Yes According to FEMA, only 

the area within 100 feet 

of the Chicago River is 

at risk for flooding. Flood 

maps are not up-to-date, 

however. 

↔ Medium
APPENDIX 8—Urban Forest Vulnerability Worksheet and Instructions 134 |

C. Blank spots are left at the end of each section for additional impacts and factors not 

listed. Feel free to add to the list. 

Example:

Regional Impact Local considerations Yes or no Explanation Overall effect Confidence

Increased 

temperatures 

and late-season 

droughts 

could increase 

susceptibility to 

wildfire. 

Does the area have a 

high density of fuels 

or other factors that 

put it at risk for 

wildfire? 

Yes Area is heavily forested 

and overstocked with many 

declining trees. 

↓ High

D. Go through your responses and circle the factors you think would have the greatest 

influence on your area. 

Example:

Regional Impact Local considerations Yes or no Explanation Overall effect Confidence

Heavy rain events 

could increase 

flooding, especially in 

the spring. 

Is all or part of the area 

currently susceptible to 

flooding (in a flood plain 

and/or has high number 

of flood-related insurance 

claims)?

Yes According to FEMA, only 

the area within 100 feet 

of the Chicago River is 

at risk for flooding. Flood 

maps are not up-to-date, 

however. 

↔ Medium

Increased 

temperatures 

and late-season 

droughts 

could increase 

susceptibility to 

wildfire. 

Does the area have a 

high density of fuels 

or other factors that 

put it at risk for 

wildfire? 

Yes Area is heavily forested 

and overstocked with many 

declining trees. 

↓ High

E. For each factor (e.g., physical, biological, human): 

Review the circled positive/negative effects. Determine an overall impact or adaptive capacity 

rating by placing a mark along a continuous line. 

Use your best judgment for the final determination based on the relative weight given to each factor, 

but as a general guideline: 

For impacts:

• Negative Impact: Negative effects outweigh positive effects.

• Moderate Impact: Negative effects are approximately equal to positive effects or most factors 

are neutral. 

• Positive Impact: Positive effects outweigh negative effects. 

For adaptive capacity:

• Low Adaptive Capacity: Negative effects outweigh positive effects.

• Moderate Adaptive Capacity: Negative effects are approximately equal to positive

 effects or most factors are neutral. 

• High Adaptive Capacity: Positive effects outweigh negative effects. 

Example:

Overall influence of human factors (place mark along line): 

Negative X Moderate Positive
Urban Forest Vulnerability Worksheet and Instructions—APPENDIX 8 | 135

F. For each section:

Review the overall effects for all factor types to determine an overall rating of impacts and adaptive 

capacity by placing a mark along a continuous line. In general, this will be an average of all factor 

types within the section, but use your best judgment for the final determination based on the 

relative weight given to each factor type.

Example: 

Overall impacts (place mark along line):

Negative X Moderate Positive

G. Evaluate overall vulnerability:

Based on your determination of impacts and adaptive capacity, plot your assessment of vulnerability 

for your area on the figure (as shown in the example below). 
APPENDIX 8—Urban Forest Vulnerability Worksheet and Instructions 136 |

Urban Forest Vulnerability Worksheet

Name Organization/Municipality 

Area assessed 

(e.g., neighborhood, community, county, quadrant, or zone)

Section 1: Impacts

Impacts are the direct and indirect consequences of climate change on systems. Impacts are a 

function of a system’s exposure to climate change and its sensitivity to any changes. Impacts could 

be beneficial or harmful to a particular forest or ecosystem type.

Physical Factors

Regional Impact Local considerations Yes or no Explanation Overall effect Confidence

Heavy rain events could 

increase flooding, especially 

in the spring. 

Is all or part of your area currently 

susceptible to flooding (in a flood 

plain and/or has high number of 

flood-related insurance claims)?

Increases in precipitation 

could lead to increased soil 

moisture in the winter and 

spring. 

Are the soils in your area well or 

poorly drained compared to the rest 

of the region? 

Decreases in summer or fall 

precipitation could lead to 

decreases in soil moisture.

Are the soils in your area well or 

poorly drained compared to the rest 

of the region?

Heavy storm events may 

increase and tornado seasons 

may shift. 

Is your area particularly susceptible 

to wind or tornadoes compared to 

the rest of the region? 

Temperatures have increased 

more dramatically in the 

counties directly adjacent to 

Lake Michigan over the past 

100 years.

Is your area along the shores of Lake 

Michigan? 

Snow may increase on the 

east side of Lake Michigan.

Is your area on the east side of Lake 

Michigan?

Overall influence of physical factors (place mark along line):

Negative Moderate Positive
Urban Forest Vulnerability Worksheet and Instructions—APPENDIX 8 | 137

Biological Factors

Regional Impact Local considerations Yes or no Explanation Overall effect Confidence

Many trees, especially at the 

southern end of their natural or 

planted range, are expected 

to experience a reduction in 

habitat suitability. 

Based on the model projections, are many 

of the dominant trees in your area expected 

to have a reduction in habitat suitability 

and/or be otherwise vulnerable to climate 

change and associated stressors? 

Many trees, especially at the 

northern end of their natural or 

planted range, are expected 

to experience an increase in 

habitat suitability.

Based on the model projections, are many 

of the dominant trees in your area expected 

to have an increase in habitat suitability 

and/or have low vulnerability to climate 

change? 

Many pests and pathogens are 

expected to benefit from longer 

growing seasons and warmer 

temperatures. 

Is your area currently experiencing a 

pest or disease outbreak of one or more 

pests/pathogens that could benefit from 

projected changes in climate? 

Some pests or pathogens that 

prefer cool, wet conditions may 

decrease.

Is your area currently experiencing a pest 

or disease outbreak of one or more pests/

pathogens that could see a reduction 

in favorable conditions from projected 

changes in climate? 

Warmer winters could be 

beneficial to some herbivores. 

Is your area particularly vulnerable to 

herbivory from deer or other wildlife?

As temperatures increase, 

suitable habitat for many 

invasive species could 

increase. 

Are invasive plant species outcompeting 

native and/or desired planted species in 

your area?

Temperatures are projected to 

increase.

Tree canopy cover can reduce local 

temperatures by creating shade and 

contributing to evaporative cooling. Are 

tree cover and/or density at or near desired 

levels for your area?

Winter storms may become 

more intense.

Are a large percentage of trees in the area 

susceptible to winter storm damage?

Severe thunderstorms with 

straight-line winds may become 

more frequent in the future.

Are a large percentage of trees in the area 

susceptible to wind damage?

Winter freeze-thaw cycles are 

expected to see seasonal shifts 

to later in the fall and earlier in 

the spring. 

Overall influence of biological factors (place mark along line):

Negative Moderate Positive
APPENDIX 8—Urban Forest Vulnerability Worksheet and Instructions 138 |

Human-Influenced Factors

Regional Impact Local considerations Yes or no Explanation Overall effect Confidence

Temperatures are 

projected to increase. 

Does your area experience a strong urban 

heat island effect that could make it warmer 

than the surrounding area? 

Increased temperatures 

and longer growing 

seasons could lead 

to increased ozone 

pollution. 

Does your area have higher levels of air 

pollution from automobiles or industry 

compared to the region as a whole?

Increased heavy 

precipitation events 

could increase runoff, 

leading to shifts in local 

soil and water pollution.

Does your area have relatively high levels of 

soil or water pollution (e.g., from agricultural 

or industrial sources)?

Increased heavy 

precipitation events 

could increase runoff 

and flooding, especially 

in the spring. 

Does the area have a large amount of 

impervious cover that could reduce soil 

infiltration and increase runoff, reducing 

soil moisture in some areas and flooding in 

others? 

Overall influence of human factors (place mark along line):

Negative Moderate Positive
Urban Forest Vulnerability Worksheet and Instructions—APPENDIX 8 | 139

Section 2: Adaptive Capacity

Adaptive capacity is the ability of a system to accommodate or cope with potential climate change 

impacts with minimal disruption. This could be intrinsic to the organism (ability to acclimate).

Biological Factors

Adaptive Capacity Factor Local considerations Yes or no Explanation Overall effect Confidence

Species-rich communities have 

exhibited greater resilience to 

extreme environmental conditions 

and greater potential to recover 

from disturbance. 

Is there a high level of diversity of 

tree species in your area (e.g., no 

more than 20% from one family, 

10% of one genus, 5% of one 

species)?

Greater genetic diversity may help 

species adjust to new conditions 

or sites by increasing the 

likelihood that some individuals 

within a species will be able 

to withstand climate-induced 

stressors.

Is there a high level of genetic/

seed source diversity within the 

tree species planted in your area?

Reduced concentrations of 

one species or genotype in 

a particular area can reduce 

the spread of pests and/or 

pathogens.

Are species or genotypes 

arranged spatially (either naturally 

or artificially) across your area 

to reduce high concentrations of 

one type in a particular location 

(e.g., alternating street tree 

species within a block or between 

blocks)?

Older trees can be more 

vulnerable to breakage from wind 

storms or susceptible to pests 

and/or pathogens as they reach 

the end of their lifespan. 

Are many trees in your area 

reaching the end of their lifespan?

Younger trees may be more 

vulnerable before their root 

systems are well-established.

Are many trees in your area newly 

planted (e.g., under 5 years old)?

Damaged trees can be 

susceptible to infection and other 

stressors.

Have trees in your area recently 

(within the past year) experienced 

damage from a storm or other 

disturbance event?

Overall biological adaptive capacity (place mark along line):

Low Moderate High
APPENDIX 8—Urban Forest Vulnerability Worksheet and Instructions 140 |

Organizational/Technical Factors 

Adaptive Capacity Factors Local considerations Yes or no Explanation Overall effect Confidence

Trained forestry professionals 

may be more likely to recognize 

potential problems and identify 

appropriate solutions. 

Is your area currently overseen 

by a tree board and/or 

department staffed with forestry 

professionals? 

Tree care ordinances can be 

helpful in ensuring best practices 

when followed unless they are 

not updated in light of new 

information. 

Is a tree care ordinance or 

planting list in place that is 

sufficiently flexible to allow for 

adjustments in species in light of 

climate change?

A diverse mix of species and 

genotypes relies on the availability 

of young trees for planting. 

Are current nursery suppliers able 

to provide a wide mix of species 

and cultivars? 

Knowing the mix of species, age 

classes, and conditions of trees 

can help determine how many 

trees could be vulnerable. 

Does your area have a current 

and comprehensive tree 

inventory?

Long-term plans can be helpful in 

identifying goals and objectives, 

as long as they can be adjusted 

given new information. 

Does your area have a long-term 

plan (such as a tree management 

plan, or similar) that is sufficiently 

flexible to allow for adjustments in 

species in light of climate change?

A disaster response/recovery plan 

can help ensure that damaged 

trees are properly managed and 

replaced with a resilient mix of 

trees that are properly planted 

and maintained. 

Does your area currently have a 

disaster recovery/response plan?

Trees that are properly watered 

and fertilized are less likely to 

experience disease, mortality, and 

drought stress.

Are the majority of trees in the 

area receiving routine care and 

maintenance?

Proper pruning can reduce the 

susceptibility of trees to damage 

or mortality from storms.

Are the majority of trees pruned 

routinely (every 5-7 years) to 

reduce storm-related damage?

Sharing resources can help with 

recovery and response following 

extreme storms.

Is your area part of a mutual aid 

network?

Conducting risk and hazard 

assessments can help reduce 

vulnerability to extreme storm 

events.

Does your organization 

conduct tree risk/hazard tree 

assessments?

Overall organizational/technical adaptive capacity (place mark along line):

Low Moderate High
Urban Forest Vulnerability Worksheet and Instructions—APPENDIX 8 | 141

Economic Factors

Adaptive Capacity Factor Local considerations Yes or no Notes Overall effect Confidence

As trees die, planting new 

trees presents an opportunity 

to increase diversity and plant 

more resilient species.

Is there sufficient funding to 

purchase and plant new trees 

to replace each one that is lost 

(excluding recent EAB-related 

funding issues)? 

Proper care and maintenance 

can reduce the vulnerability of 

existing trees.

Is there sufficient funding to 

maintain existing street trees and 

those on public lands? 

Proper care and maintenance 

can reduce the vulnerability of 

existing trees.

Do private landowners 

(homeowners, businesses) 

in your area have sufficient 

financial resources to plant and 

care for trees?

Overall economic adaptive capacity (place mark along line):

Low Moderate High

Social Factors

Adaptive Capacity Factor Local considerations Yes or no Climate change considerations Overall effect Confidence

Residents who value trees 

are more likely to care for 

them. 

Do residents in your area 

value trees as an important 

resource? 

Even if funding is limited, a 

large volunteer base can 

help reduce costs and 

increase awareness about 

trees and tree care. 

Is there a sufficient volunteer 

base to aid in the planting 

and care of trees in the area? 

Organizations can help find 

and pool resources to help 

care for and plant trees. 

Is there an active network 

of organizations engaged in 

caring for forest resources in 

your area?

Incentives can increase 

public participation and 

interest. 

Are any public incentive 

programs in place to 

encourage the planting and/

or care of trees? 

Overall social adaptive capacity (place mark along line): 

Low Moderate High
APPENDIX 8—Urban Forest Vulnerability Worksheet and Instructions 142 |

Section 3: Vulnerability

Vulnerability is the susceptibility of a system to the adverse effects of climate change. It is a

function of its potential impacts and its adaptive capacity.

Based on your determination of impacts and adaptive capacity, plot your assessment of vulnerability 

for your area on the figure.
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complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department 

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USDA is an equal opportunity provider, employer, and lender.

Brandt, Leslie A.; Derby Lewis, Abigail; Scott, Lydia; Darling, Lindsay; Fahey, Robert T.; Iverson, Louis; 

Nowak, David J.; Bodine, Allison R.; Bell, Andrew; Still, Shannon; Butler, Patricia R.; Dierich, Andrea; 

Handler, Stephen D.; Janowiak, Maria K.; Matthews, Stephen N.; Miesbauer, Jason W.; Peters, Matthew; 

Prasad, Anantha; Shannon, P. Danielle; Stotz, Douglas; Swanston, Christopher W. 2017. Chicago 

Wilderness region urban forest vulnerability assessment and synthesis: a report from the Urban 

Forestry Climate Change Response Framework Chicago Wilderness pilot project. Gen. Tech. Rep. 

NRS-168. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 

142 p. https://doi.org/10.2737/NRS-GTR-168.

The urban forest of the Chicago Wilderness region, a 7-million-acre area covering portions of Illinois, Indiana, 

Michigan, and Wisconsin, will face direct and indirect impacts from a changing climate over the 21st century. 

This assessment evaluates the vulnerability of urban trees and natural and developed landscapes within the 

Chicago Wilderness region to a range of future climates. We synthesized and summarized information on the 

contemporary landscape, provided information on past climate trends, and illustrated a range of projected 

future climates. We used this information to inform models of habitat suitability for trees native to the area. 

Projected shifts in plant hardiness and heat zones were used to understand how nonnative species and 

cultivars may tolerate future conditions. We also assessed the adaptability of planted and naturally occurring 

trees to stressors that may not be accounted for in habitat suitability models such as drought, flooding, wind 

damage, and air pollution. 

The summary of the contemporary landscape identifies major stressors currently threatening the urban forest 

of the Chicago Wilderness region. Major current threats to the region’s urban forest include invasive species, 

pests and disease, land-use change, development, and fragmentation. Observed trends in climate over the 

historical record from 1901 through 2011 show a temperature increase of 1 °F in the Chicago Wilderness 

region. Precipitation increased as well, especially during the summer. Mean annual temperature is projected to 

increase by 2.3 to 8.2 °F by the end of the century, with temperature increases across all seasons. Projections 

for precipitation show an increase in winter and spring precipitation, and summer and fall precipitation 

projections vary by model. Species distribution modeling for native species suggests that suitable habitat may 

decrease for 11 primarily northern species and increase or become newly suitable for 40 species. An analysis 

of tree species vulnerability that combines model projections, shifts in hardiness and heat zones, and adaptive 

capacity showed that 15 percent of the trees currently present in the region have either moderate-high or high 

vulnerability to climate change, and many of those trees with low vulnerability are invasive species. 

We developed a process for self-assessment of urban forest vulnerability that was tested by urban forestry 

professionals from four municipalities, three park districts, and three forest preserve districts in the region. 

The professionals generally rated the impacts of climate change on the places they managed as moderately 

negative, mostly driven by the potential effects of extreme storms and heavy precipitation on trees in the 

area. The capacity of forests to adapt to climate change ranged widely based on economic, social, and 

organizational factors, as well as on the diversity of species and genotypes of trees in the area. These 

projected changes in climate and their associated impacts and vulnerabilities will have important implications 

for urban forest management, including the planting and maintenance of street and park trees, management of 

natural areas, and long-term planning. will have important implications for urban forest management, including 

the planting and maintenance of street and park trees, management of natural areas, and long-term planning. 

KEYWORDS: climate change, vulnerability, adaptive capacity, urban forests, Climate Change Tree Atlas, 

expert elicitation, climate projections, impacts
Northern Research Station

www.nrs.fs.fed.us