United States Department of Agriculture

Forest Ecosystem Vulnerability Assessment 

and Synthesis for Northern Wisconsin 

and Western Upper Michigan: 

A Report from the Northwoods Climate Change 

Response Framework Project

Forest

Service

Northern

Research Station August 2014

General Technical

Report NRS-136
Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies, contact:

USDA FOREST SERVICE USDA Forest Service

11 CAMPUS BLVD., SUITE 200 Publications Distribution

NEWTOWN SQUARE, PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

August 2014 Fax: 740-368-0152

Manuscript received for publication February 2014

Forest ecosystems across the Northwoods will face direct and indirect impacts from a changing 

climate over the 21st century. This assessment evaluates the vulnerability of forest ecosystems in 

the Laurentian Mixed Forest Province of northern Wisconsin and western Upper Michigan under 

a range of future climates. We synthesized and summarized information on the contemporary 

landscape, provided information on past climate trends, and described a range of projected 

future climates. This information was used to parameterize and run multiple vegetation impact 

models, which provided a range of potential vegetative responses to climate. Finally, we brought 

these results before a multidisciplinary panel of scientists and land managers familiar with the 

forests of this region to assess ecosystem vulnerability through a formal consensus-based expert 

elicitation process. 

The summary of the contemporary landscape identifies major forest trends and stressors 

currently threatening forests in the region. Observed trends in climate over the past century 

reveal that precipitation increased in the area, particularly in summer and fall, and that daily 

maximum temperatures increased, particularly in winter. Projected climate trends for the next 

100 years using downscaled global climate model data indicate a potential increase in mean 

annual temperature of 2 to 9 °F for the assessment area. Projections for precipitation indicate 

an increase in winter and spring precipitation, and summer and fall precipitation projections 

vary by scenario. We identified potential impacts on forests by incorporating these future 

climate projections into three forest impact models (Tree Atlas, LANDIS-II, and PnET-CN). Model 

projections suggest that northern boreal species such as black spruce, quaking aspen, and paper 

birch may fare worse under future conditions, but other species may benefit from projected 

changes in climate. Published literature on climate impacts related to wildfire, invasive species, 

and forest pests and diseases also contributed to the overall determination of climate change 

vulnerability. We assessed vulnerability for nine forest communities in the assessment area. The 

assessment was conducted through a formal elicitation process of 19 science and management 

experts from across the area, who considered vulnerability in terms of the potential impacts 

and the adaptive capacity for an individual community. Upland spruce-fir, lowland conifers, 

aspen-birch, lowland-riparian hardwoods, and red pine forests were determined to be the 

most vulnerable ecosystems. White pine and oak forests were perceived as less vulnerable to 

projected changes in climate. These projected changes in climate and the associated impacts and 

vulnerabilities will have important implications for economically valuable timber species, forestdependent wildlife and plants, recreation, and long-term natural resource planning.

ABSTRACT

Cover Photo

Lake of the Clouds in western Upper Michigan. Photo by Scott Pearson, used with permission.
Forest Ecosystem Vulnerability Assessment 

and Synthesis for Northern Wisconsin 

and Western Upper Michigan: 

A Report from the Northwoods Climate Change 

Response Framework Project

Maria K. Janowiak, Louis R. Iverson, David J. Mladenoff, Emily Peters, 

Kirk R. Wythers, Weimin Xi, Leslie A. Brandt, Patricia R. Butler, 

Stephen D. Handler, P. Danielle Shannon, Chris Swanston, Linda R. Parker, 

Amy J. Amman, Brian Bogaczyk, Christine Handler, Ellen Lesch, Peter B. Reich, 

Stephen Matthews, Matthew Peters, Anantha Prasad, Sami Khanal, Feng Liu, 

Tara Bal, Dustin Bronson, Andrew Burton, Jim Ferris, Jon Fosgitt, Shawn Hagan, 

Erin Johnston, Evan Kane, Colleen Matula, Ryan O’Connor, Dale Higgins, 

Matt St. Pierre, Jad Daley, Mae Davenport, Marla R. Emery, David Fehringer, 

Christopher L. Hoving, Gary Johnson, David Neitzel, Michael Notaro, 

Adena Rissman, Chadwick Rittenhouse, and Robert Ziel
Authors

MARIA K. JANOWIAK is a climate change adaptation and carbon 

management scientist with the Northern Institute of Applied 

Climate Science, U.S. Forest Service, 410 MacInnes Drive, 

Houghton, MI 49931, mjanowiak02@fs.fed.us.

LOUIS R. 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. MLADENOFF is a professor in the Forest Landscape 

Ecology lab at the University of Wisconsin, Department of 

Forest and Wildlife Ecology, 1630 Linden Drive, Madison, WI 

53706, djmladen@wisc.edu.

EMILY PETERS is a data analyst with Minnesota Pollution 

Control Agency, 520 Lafayette Road N., St. Paul, MN 55155, 

Emily.Peters@state.mn.us.

KIRK R. WYTHERS is a research fellow at the University of 

Minnesota, 1530 Cleveland Avenue N., St. Paul, MN 55108, 

kwythers@umn.edu.

WEIMIN XI, formerly a research associate in the Forest 

Landscape Ecology lab at the University of Wisconsin – 

Madison, is an assistant professor at Texas A&M University – 

Kingsville, Department of Biological and Health Sciences, 

700 University Boulevard, Kingsville, TX 78363, 

weimin.xi@tamuk.edu. 

LESLIE A. BRANDT is a climate change specialist with the 

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

Service, Northern Research Station, 1992 Folwell Avenue, 

St. Paul, MN 55108, lbrandt@fs.fed.us.

PATRICIA R. BUTLER is a climate change outreach specialist 

with the Northern Institute of Applied Climate Science, 

Michigan Technological University, U.J. Noblet Forestry Building, 

1400 Townsend Drive, Houghton, MI 49931, prbutler@mtu.edu.

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.

P. DANIELLE SHANNON is a climate change outreach specialist 

with the Northern Institute of Applied Climate Science, 

Michigan Technological University, U.J. Noblet Forestry Building, 

1400 Townsend Drive, Houghton, MI 49931, 

dshannon@mtu.edu.

CHRIS SWANSTON is a research ecologist with the U.S. 

Forest Service, Northern Research Station, and director 

of the Northern Institute of Applied Climate Science, 

410 MacInnes Drive, Houghton, MI 49931, 

cswanston@fs.fed.us.

LINDA R. PARKER is a forest ecologist with the U.S. 

Forest Service, Chequamegon-Nicolet National Forest, 

1170 4th Avenue S., Park Falls, WI 54552, lrparker@fs.fed.us.

AMY J. AMMAN is a soil scientist with the U.S. Forest Service, 

Ottawa National Forest, E6248 US 2, Ironwood, MI 49938, 

amyjor@fs.fed.us.

BRIAN BOGACZYK is a wildlife biologist with the U.S. Forest 

Service, Ottawa National Forest, E6248 US 2, Ironwood, MI 

49938, bbogaczyk@fs.fed.us.

CHRISTINE HANDLER is a forest planner with the U.S. Forest 

Service, Ottawa National Forest, E6248 US 2, Ironwood, MI 

49938, cahandler@fs.fed.us.

ELLEN LESCH is an aquatic ecologist/hydrologist with the U.S. 

Forest Service, Ottawa National Forest, E6248 US 2, Ironwood, 

MI 49938, elesch@fs.fed.us.

PETER B. REICH is a professor at the University of Minnesota, 

1530 Cleveland Avenue N., St. Paul, MN 55108, 

preich@umn.edu.

STEPHEN MATTHEWS is a research assistant professor at 

Ohio State University, School of Environment and Natural 

Resources, and an ecologist with the U.S. Forest Service, 

Northern Research Station, 359 Main Road, Delaware, OH 

43015, snmatthews@fs.fed.us.

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.

SAMI KHANAL is a postdoctoral research associate in the 

Forest Landscape Ecology lab at the University of Wisconsin – 

Madison, 1630 Linden Drive, Madison, WI 53706, 

skhanal2@wisc.edu.

FENG LIU, formerly a research associate in the Forest Landscape 

Ecology lab at the University of Wisconsin – Madison, is a 

research scientist in Wuhan Botanical Garden, Chinese Academy 

of Sciences, Wuhan China, liufeng@wbgcas.cn. 

TARA BAL is a research assistant professor at Michigan 

Technological University, U.J. Noblet Forestry Building, 

1400 Townsend Drive, Houghton, MI 49930, tlbal@mtu.edu.

DUSTIN BRONSON is a forest research ecologist with the 

Wisconsin Department of Natural Resources, 

107 Sutliff Avenue, Rhinelander WI 54501, 

dustin.bronson@wisconsin.gov.

ANDREW BURTON is a professor at Michigan Technological 

University, U.J. Noblet Forestry Building, 1400 Townsend Drive, 

Houghton, MI 49931, ajburton@mtu.edu.

JIM FERRIS is a silviculturist with the Michigan Department of 

Natural Resources, 1990 U.S. Highway 41 South, Marquette, MI 

49855, ferrisj@michigan.gov.
JON FOSGITT is a forest management specialist with Compass 

Land Consultants, 7750 U.S. Highway 51 South, P.O. Box 846, 

Minocqua, WI 54548, jon@compasslandconsultants.com.

SHAWN HAGAN is the Director-Forest Operations, The 

Forestland Group, LLC, 33687 Lake Gerald Road, Toivola, MI 

49965, shawn@forestlandgroup.com.

ERIN JOHNSTON is the Lake Superior Program Coordinator 

for Keweenaw Bay Indian Community Natural Resources 

Department, 14359 Pequaming Road, L’Anse, MI 49946, 

ejohnston@kbic-nsn.gov.

EVAN KANE is an assistant professor at Michigan Technological 

University, U.J. Noblet Forestry Building, 1400 Townsend Drive, 

Houghton, MI 49931, eskane@mtu.edu. 

COLLEEN MATULA is a forest ecologist/silviculturist with the 

Wisconsin Department of Natural Resources, P.O. Box 709, 

Mellen, WI 54546, colleen.matula@wisconsin.gov.

RYAN O’CONNOR is an ecologist & inventory coordinator at the 

Natural Heritage Conservation at the Wisconsin Department of 

Natural Resources, 101 S. Webster Street, Madison WI 53703, 

ryan.oconnor@wisconsin.gov.

DALE HIGGINS is a forest hydrologist with the U.S. 

Forest Service, Chequamegon-Nicolet National Forest, 

500 Hanson Lake Road, Rhinelander, WI 54501, 

dhiggins@fs.fed.us.

MATT ST. PIERRE is a biologist with the U.S. Forest Service, 

Chequamegon-Nicolet National Forest, 500 Hanson Lake Road, 

Rhinelander, WI 54501, mstpierre@fs.fed.us.

JAD DALEY is a program director at the Trust for Public Land, 

660 Pennsylvania Avenue SE., Suite 401, Washington, DC 20003, 

jad.daley@tpl.org.

MAE DAVENPORT is an associate professor at the University 

of Minnesota, 1530 Cleveland Avenue N., St. Paul, MN 55108, 

mdaven@umn.edu.

MARLA R. EMERY is a research geographer with the U.S. 

Forest Service, Northern Research Station, 81 Carrigan Drive, 

Burlington, VT 05405, memery@fs.fed.us.

DAVID FEHRINGER is the Great Lakes Assistant Regional Director 

with The Forestland Group, LLC, 33687 Lake Gerald Road, 

Toivola, MI 49965, dave@forestlandgroup.com.

CHRISTOPHER L. HOVING is an adaptation specialist with the 

Michigan Department of Natural Resources, P.O. Box 30028, 

Lansing, MI 48909, hovingc@michigan.gov.

GARY JOHNSON is an extension professor at the University of 

Minnesota, 1530 Cleveland Avenue N., St. Paul, MN 55108, 

johns054@umn.edu.

DAVID NEITZEL is an epidemiologist with the Minnesota 

Department of Health, 625 N. Robert Street, St. Paul, MN 

55164, david.neitzel@state.mn.us.

MICHAEL NOTARO is an associate scientist and associate 

director of the Nelson Institute Center for Climatic Research, 

University of Wisconsin – Madison, 1225 West Dayton Street, 

Madison, WI 53706, mnotaro@wisc.edu.

ADENA RISSMAN is an assistant professor at the University of 

Wisconsin – Madison, 1630 Linden Drive, Madison, WI 53706, 

arrissman@wisc.edu.

CHADWICK RITTENHOUSE is an assistant research professor 

at the University of Connecticut, Wildlife and Fisheries 

Conservation Center, 1376 Storrs Road, Storrs, CT 06269, 

chadwick.rittenhouse@uconn.edu.

ROBERT ZIEL (retired) was the program coordinator for the 

Lake States Fire Science Consortium, 1680 Madison Avenue, 

Wooster, OH 44691. He can be reached at rhziel1@gmail.com.
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Preface

CONTEXT AND SCOPE

This assessment is a fundamental component of the 

Northwoods Climate Change Response Framework 

project. The Framework is a collaborative, crossboundary approach among scientists, managers, 

and landowners to incorporate climate change 

considerations into natural resource management. 

Six Framework projects are currently underway, 

covering approximately 250 million acres in 

the northeastern and midwestern United States: 

Northwoods, Central Appalachians, Central 

Hardwoods, Mid-Atlantic, New England, and 

Urban Forests. Each project interweaves four 

components: science and management partnerships, 

vulnerability assessments, adaptation resources, and 

demonstration projects. 

We designed this assessment to be a synthesis of the 

best available scientific information on the topic of 

climate change and forest ecosystems. Its primary 

goal is to inform forest managers in northern 

Wisconsin and western Upper Michigan, in addition 

to people who study, recreate, and live in these 

forests. As new scientific information arises, we will 

develop future versions to reflect that accumulated 

knowledge and understanding. Most importantly, 

this assessment does not make recommendations 

about how this information should be used. 

The scope of the assessment is terrestrial forested 

ecosystems, with a particular focus on tree species. 

We acknowledge that climate change will also have 

impacts on aquatic systems, wildlife, and human 

systems, but addressing these issues in depth is 

beyond the scope of this assessment. 

The large list of authors reflects the highly 

collaborative nature of this assessment. The overall 

document structure and much of the language 

was a coordinated effort among Leslie Brandt, 

Patricia Butler, Maria Janowiak, Stephen Handler, 

and Chris Swanston. Danielle Shannon conducted 

much of the data analysis and developed maps 

for Chapters 1, 3, and 4. Louis Iverson, Steve 

Matthews, Matthew Peters, and Anantha Prasad 

provided and interpreted Tree Atlas information for 

Chapter 5, and assisted with the data processing 

for the climate data presented in Chapter 4. David 

Mladenoff, Weimin Xi, and Sami Khanal provided 

results and interpretation of the LANDIS-II model. 

Emily Peters, Kirk Wythers, and Peter Reich 

provided results and interpretation of the PnETCN model. All modeling teams coordinated their 

efforts impressively. Amy Amman, Brian Bogaczyk, 

Christine Handler, Ellen Lesch, and Linda Parker 

provided substantial input throughout the document. 

Among the many others who made valuable 

contributions to the assessment, Scott Pearson 

furnished numerous photographs of the forests and 

wildlife present in the assessment area, and Keith 

Cherkauer (Purdue University) provided hydrologic 

data for Chapter 4. We also thank Jack Williams 

(University of Wisconsin – Madison), Eric Kruger 

(University of Wisconsin – Madison), and Christel 

Kern (U.S. Forest Service, Northern Research 

Station), who provided formal technical reviews 

of the assessment. Their thorough reviews greatly 

improved the quality of this assessment. 
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Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Chapter 1: The Contemporary Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Chapter 2: Climate Change Science and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter 3: Observed Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Chapter 4: Projected Changes in Climate and Physical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Chapter 5: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Chapter 6: Forest Ecosystem Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Chapter 7: Management Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

Appendix 1: Species Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Appendix 2: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

Appendix 3: Additional Future Climate Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

Appendix 4: Supplementary Model Results – Tree Atlas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Appendix 5: Supplementary Model Results – LANDIS-II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

Appendix 6: Vulnerability and Confidence Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

Appendix 7: Contributors to Implications Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Contents
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Executive Summary

This assessment evaluates key ecosystem 

vulnerabilities for forest ecosystems in the 

Laurentian Mixed Forest Province in northern 

Wisconsin and western Upper Michigan across a 

range of future climate scenarios. This assessment 

was completed as part of the Northwoods 

Climate Change Response Framework project, 

a collaborative approach among researchers, 

managers, and landowners to incorporate climate 

change considerations into 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 published research, 

local knowledge, and expertise, are used to identify 

the factors that contribute to the vulnerability of 

major forest systems within the assessment area 

through this century. A final chapter summarizes the 

implications of these impacts and vulnerabilities for 

forest management across the region. 

CHAPTER 1: THE CONTEMPORARY

LANDSCAPE

This chapter describes the forests and related 

ecosystems across the Laurentian Mixed Forest 

Province in northern Wisconsin and western 

Upper Michigan and summarizes current threats 

and management trends. The information lays the 

foundation for understanding how shifts in climate 

may contribute to changes in forest ecosystems, and 

how climate may interact with other stressors on the 

landscape.

Main Points 

● The assessment area of northern Wisconsin and 

western Upper Michigan contains approximately 

16 million acres of forest land. Private individuals 

and organizations own more than 60 percent of 

forest land.

● Current major stressors and threats to forest 

ecosystems in the region include:

• Fragmentation and land-use change

• Fire regime shifts 

• Nonnative species invasion

• Forest diseases and insect pests

• Overbrowsing by deer

• Extreme weather events 

● Historical land use and past management 

practices tended to favor younger forests across 

the landscape and often reduced species diversity 

and structural complexity. 

● The forest products industry is a major 

contributor to the region’s economy, and much 

of the forest land in the assessment area is 

managed according to at least one sustainability 

certification standard. 

CHAPTER 2: CLIMATE CHANGE 

SCIENCE AND MODELING

This chapter provides a brief background on climate 

change science, models that simulate future climate 

change, and models that project the effects of 

climate change on tree species and ecosystems. This 

chapter also describes the climate data used in this 

assessment.
Executive Summary

Main Points 

● Temperatures have been increasing at a global 

scale and across the United States over the past 

century.

● More than 95 percent of climate scientists 

attribute this increase in temperature to human 

activities.

● Major contributors to warming are greenhouse 

gases from fossil fuel burning, agriculture, and 

changes in land use.

CHAPTER 3: OBSERVED 

CLIMATE CHANGE

Many of the climatic changes that have been 

observed across the world over the past century are 

also evident in the assessment area. This chapter 

summarizes our current understanding of observed 

changes and current climate trends in the assessment 

area and across the Midwest region, with a focus on 

the last 50 to 100 years.

Main Points 

● Mean, minimum, and maximum temperatures 

increased across all seasons over the past century, 

with winter temperatures warming the most 

rapidly. 

● Precipitation patterns have changed across the 

region. The number of intense precipitation 

events has increased.

● Snowfall has generally decreased across the 

assessment area, although the severity of large 

winter storms may have increased. 

● Climate change has also been indicated by trends 

in lake ice, growing season length, and wildlife 

range shifts.

CHAPTER 4: PROJECTED CHANGES 

IN CLIMATE AND 

PHYSICAL PROCESSES

This chapter describes climate projections for the 

assessment area over the 21st century, including 

projections related to patterns of extreme weather 

events and other climate-related processes. 

Temperature and precipitation projections are 

derived from downscaled simulations of climate 

models. Published scientific literature provides the 

basis for describing possible trends in a range of 

climate-driven processes, such as extreme weather 

events and snowfall. 

Main Points

● Temperatures are expected to continue to 

increase over the next century. A range of climate 

scenarios project warming in all seasons.

● Precipitation is projected to increase in winter 

and spring across a range of climate scenarios. 

Projections of summer and fall precipitation are 

more variable, and summer precipitation may 

decrease.

● Intense precipitation events are expected to 

continue to become more frequent. 

● Snowfall is projected to continue to decline 

across the assessment area, with more winter 

precipitation falling as rain. 

● Soils are projected to be frozen for shorter 

periods during winter. 

CHAPTER 5: FUTURE CLIMATE 

CHANGE IMPACTS ON FORESTS

This chapter summarizes the potential impacts of 

climate change on forests in the Laurentian Mixed 

Forest Province in northern Wisconsin and western 

Upper Michigan, drawing on information from 

a coordinated series of model simulations and 

published research. 
Executive Summary

Main Points

● Boreal species such as black spruce, balsam fir, 

quaking aspen, paper birch, and white spruce are 

projected to have reductions in suitable habitat 

and biomass over the next century.

● Species with ranges that extend to the south such 

as American basswood, black cherry, northern red 

oak, and red maple may have increases in suitable 

habitat and biomass.

● Many species currently common in northern 

Wisconsin and western Upper Michigan are 

projected to decline under a hotter, drier future 

climate scenario.

● Forest productivity will be influenced by 

a combination of factors such as carbon 

dioxide (CO2 ) fertilization, water and nutrient 

availability, and species migration. 

● The model projections used in this assessment 

do not account for many other factors that may 

change under a changing climate. Scientific 

literature was used to provide additional 

information on these factors, including:

• Altered precipitation and hydrology

• Drought stress

• Wildfire frequency and severity

• Altered nutrient cycling

• Changes in invasive species, insect pests, and 

forest diseases 

• Interactions among these factors

CHAPTER 6: FOREST ECOSYSTEM 

VULNERABILITIES

Forest ecosystems across the Northwoods will 

experience direct and indirect impacts from a 

changing climate over the 21st century. We assessed 

the vulnerability of major forest systems in the 

assessment area to climate change over the next 100 

years, focusing on shifts in dominant species, system 

drivers, and stressors. The adaptive capacity of forest 

systems was also examined as a key component 

to overall vulnerability. Synthesis statements 

are provided to capture general trends. Detailed 

vulnerability determinations are also provided for 

nine major forest systems (Table 1).

Main Points

Potential Impacts on Drivers and Stressors

● Temperatures will increase (robust evidence, 

high agreement). All global climate models 

project that temperatures will increase with 

continued increases in atmospheric greenhouse 

gas concentrations.

● Growing seasons will get longer (robust 

evidence, high agreement). There is high 

agreement among information sources that 

projected temperature increases will lead to 

longer growing seasons in the assessment area.

Table 1.―Vulnerability determination summaries for the forest systems considered in this assessment

Forest system Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Aspen-birch Moderate-Negative Moderate Moderate-High Medium-High Medium-High

Jack pine Moderate-Negative Moderate-High Moderate Medium Medium-High

Lowland conifers Negative Moderate-Low High Medium Medium-High

Lowland-riparian hardwoods Moderate-Negative Moderate Moderate-High Limited-Medium Medium

Northern hardwoods Moderate-Negative Moderate-High Moderate Medium-High Medium

Oak Moderate Moderate-High Moderate-Low Medium Medium-High

Red pine Moderate-Negative Moderate-Low Moderate-High Medium-High Medium-High

Upland spruce-fir Negative Moderate-Low High Medium-High Medium-High

White pine Moderate-Positive High Moderate-Low Medium-High Medium
Executive Summary

● Winter processes will change (robust evidence, 

high agreement). All evidence agrees that 

temperatures will increase more in winter than in 

other seasons across the assessment area, leading 

to changes in snowfall, soil frost, and other winter 

processes. 

● The amount and timing of precipitation will 

change (medium evidence, high agreement).

All global climate models agree that there will 

be changes in precipitation patterns across the 

assessment area. 

● Intense precipitation events will continue to 

become more frequent (medium evidence, 

medium agreement). There is some agreement 

that the number of heavy precipitation events will 

continue to increase in the assessment area. If 

they do increase, impacts from flooding and soil 

erosion may also become more damaging. 

● Soil moisture patterns will change (medium 

evidence, high agreement), with drier soil 

conditions later in the growing season 

(medium evidence, medium agreement).

Studies show that climate change will affect 

soil moisture, but there is disagreement among 

climate and impact models on how soil moisture 

will change during the growing season. 

● Droughts will increase in duration and area 

(limited evidence, low agreement). A study 

using multiple climate models indicates that 

drought may increase in extent and area, and an 

episodic precipitation regime could mean longer 

dry periods between events. 

● Climate conditions will increase fire risks 

by the end of the century (medium evidence, 

medium agreement). Some national and global 

studies suggest that wildfire risk will increase 

in the region, but few studies have specifically 

looked at wildfire potential in the assessment 

area. 

● Many nonnative species, insect pests, and 

pathogens will increase or become more 

damaging (limited evidence, high agreement).

Evidence indicates that an increase in temperature 

and greater ecosystem stress will lead to increases 

in these threats, but research to date has examined 

few species. 

Potential Impacts on Forests 

● Boreal species will face increasing stress 

from climate change (medium evidence, 

high agreement). Ecosystem models agree 

that boreal or northern species will experience 

reduced suitable habitat and biomass across the 

assessment area, and that they may be less able 

to take advantage of longer growing seasons 

and warmer temperatures than temperate forest 

species. 

● Southern species will be favored by 

climate change (medium evidence, medium 

agreement). Ecosystem models agree that many 

temperate species will gain suitable habitat and 

biomass across the assessment area, and that 

longer growing seasons and warmer temperatures 

will lead to productivity increases for temperate 

forest types. 

Amnicon Falls in northern Wisconsin. Photo by 

Scott Pearson, used with permission.
Executive Summary

● Forest ecosystems will change across the 

landscape (medium evidence, high agreement).

Although few models have specifically examined 

how communities may change, model results 

from individual species and ecological principles 

suggest that species composition and recognized 

forest communities will change.

● Forest productivity will increase across the 

assessment area (medium evidence, low 

agreement). Some model projections and 

other evidence suggest forest productivity may 

increase in the assessment area, although there is 

uncertainty about the effects of and limitations 

to CO2

 fertilization. It is also anticipated that 

productivity will be reduced in localized areas. 

Adaptive Capacity Factors

● Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies 

have consistently shown that more-diverse 

systems are more resilient to disturbance, and 

low-diversity systems are more vulnerable to 

change.

● Species in fragmented landscapes will have less 

opportunity to migrate in response to climate 

change (limited evidence, high agreement). The 

dispersal ability of individual species is reduced 

in fragmented landscapes, but the future degree 

of landscape fragmentation and the potential 

for human-assisted migration are two areas of 

uncertainty. 

● Systems that are limited to particular 

environments will have less opportunity to 

migrate in response to climate change (limited 

evidence, high agreement). Despite a lack of 

published research demonstrating this concept 

in the assessment area, our current ecological 

understanding indicates that migration to new 

areas will be particularly difficult for species and 

systems with narrow habitat requirements. 

● Systems that are more tolerant of disturbance 

have less risk of declining on the landscape 

(medium evidence, high agreement). Basic 

ecological theory and other evidence support 

the idea that systems that are adapted to more 

frequent disturbance will be at lower risk. 

CHAPTER 7: MANAGEMENT

IMPLICATIONS

This chapter summarizes the implications of 

potential climate change to forest management 

and planning in northern Wisconsin and western 

Upper Michigan. This chapter does not make 

recommendations as to how management should be 

adjusted to cope with these impacts, because impacts 

and responses will vary by ecosystem, ownership, 

and management objective. 

Main Points

● Plants, animals, and people that depend on forests 

may face additional challenges as the climate 

shifts. 

● Greater financial investments may be required to 

manage forests and infrastructure and to prepare 

for severe weather events. 

● Management activities such as wildfire 

suppression or recreation activities such as 

snowmobiling may need to be altered as 

temperatures and precipitation patterns change. 

● Climate change may present opportunities for 

the forest products industry, recreation, and other 

sectors if changing conditions are anticipated.
Introduction

CONTEXT

This assessment is part of a regional effort across 

the Northwoods region of Minnesota, Wisconsin, 

and Michigan called the Northwoods Climate 

Change Response Framework (Framework; www.

forestadaptation.org). The Framework project 

was initiated in 2009 in northern Wisconsin 

with the overarching goal of helping managers 

incorporate climate change considerations into 

forest management. To meet the challenges brought 

about by climate change, a team of federal and state 

land management agencies, private forest owners, 

conservation organizations, and others have come 

together to accomplish three objectives: 

1. Provide a forum for people working across the 

Northwoods to effectively and efficiently share 

experiences and lessons learned. 

2. Develop new user-friendly information and tools 

to help land managers factor climate change 

considerations into decisionmaking. 

3. Support efforts to implement actions for 

addressing climate change impacts in the 

Northwoods. 

The Framework process is designed to work at 

multiple scales. The Northwoods Framework is 

coordinated across the region, but activities are 

generally conducted at the state level to allow for 

greater specificity. Therefore, this assessment will 

focus on northern Wisconsin and western Upper 

Michigan and will serve as a companion for similar 

assessments completed in northern Minnesota 

and Michigan. Additionally, regional Framework 

projects are underway in several other regions: 

Central Appalachians, Central Hardwoods, MidAtlantic, New England, and Urban Forests.

The Northwoods Framework is an expansion of the 

original northern Wisconsin effort, and has been 

supported in large part by the U.S. Department of 

Agriculture, Forest Service. Across the Northwoods, 

the project is being guided by an array of partners 

with an interest in forest management, including:

● Northern Institute of Applied Climate Science

● U.S. Forest Service, Eastern Region

● U.S. Forest Service, Northern Research Station

● U.S. Forest Service, Northeastern Area 

(State & Private Forestry)

● Trust for Public Land

● The Nature Conservancy

● American Forest Foundation

● Wisconsin Department of Natural Resources

● Minnesota Department of Natural Resources

● Michigan Department of Natural Resources

This assessment is designed to provide detailed 

information for forest ecosystems across northern 

Wisconsin and western Upper Michigan. Several 

independent efforts related to climate change, 

natural ecosystems, and human well-being are also 

occurring in this area. This assessment complements 

other assessments that have been created for the 

assessment area and for the broader Northwoods 

region. The Framework project will also work 

to integrate the results and outcomes from other 

projects related to climate change and natural 

resource management. 

This assessment bears some similarity to other 

synthesis documents about climate change science, 

such as the National Climate Assessment (Melillo 

et al. 2014) and the Intergovernmental Panel on 


Climate Change (IPCC) reports (e.g., IPCC 2007a, 

2013). Where appropriate, we refer to these largerscale documents when discussing national and 

global changes. However, this assessment differs 

from these reports in a number of ways. This 

assessment was not commissioned by any federal 

government agency nor does it give advice or 

recommendations to any federal government agency. 

It also does not evaluate policy options or provide 

input into federal priorities. Instead, this report was 

developed by the authors to fulfill a joint need of 

understanding local impacts of climate change on 

forests and assessing which tree species and forest 

systems may be the most vulnerable in northern 

Wisconsin and western Upper Michigan. Although 

it was written to be a resource for forest managers, 

it is first and foremost a scientific document that 

represents the views of the authors.

SCOPE AND GOALS

The primary goal of this assessment is to summarize 

potential changes to the forest ecosystems of 

northern Wisconsin and western Upper Michigan 

under a range of future climates, and determine the 

vulnerability of forest communities to these changes 

during the next century. Included is 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 about 16.0 million acres of 

forest land in northern Wisconsin and Michigan 

(Fig. 1). The assessment area boundaries are defined 

by the Laurentian Mixed Forest Province (Ecological 

Province 212) within northern Wisconsin and the 

western Upper Peninsula of Michigan (McNab and 

Avers 1994, McNab et al. 2007). The assessment 

area includes Ecological Sections VIII, IX, and X in 

Wisconsin and Ecological Section IX in Michigan 

of Albert’s Regional Landscape Ecosystems (Albert 

1995). In addition to these ecological boundaries, 

we used county-level information that most closely 

represented the assessment area when eco-regional 

data were not available, limiting our selections 

to the 41 counties that are most analogous to the 

assessment area. 

The northern Wisconsin portion of the assessment 

area is substantially larger than the Michigan portion 

and covers 11.5 million acres. The western Upper 

Peninsula of Michigan contains 4.5 million acres 

of forest land. Land ownership is fairly similar 

across the two states. Overall, more than 60 percent 

of forest land in the assessment area is owned by 

private individuals and organizations. Approximately 

12 percent of land is federally owned, with the 

Chequamegon-Nicolet and Ottawa National Forests 

making up the bulk of federal lands. State agencies 

own nearly 6 percent of forest land; and county, 

municipal, and local governments own 17 percent. 

This assessment synthesizes information covering all 

forest lands in the assessment area in recognition of 

the area’s varied patterns of forest composition and 

land ownership.

Figure 1.—The assessment area and the 41 counties used 

to approximate the Laurentian Mixed Forest Province when 

county-level data were required.
Introduction



ASSESSMENT CHAPTERS

This assessment contains the following chapters: 

Chapter 1: The Contemporary Landscape

describes existing conditions, providing background 

on the physical environment, ecological character, 

and broad socioeconomic dimensions of the 

assessment area.

Chapter 2: Climate Change Science and 

Modeling contains background on climate change 

science, projection models, and impact models. It 

also describes the techniques used in developing 

climate projections to provide context for the model 

results presented in later chapters.

Chapter 3: Observed Climate Change provides 

information on the past and current climate of the 

assessment area in northern Wisconsin and western 

Upper Michigan, summarized from the interactive 

ClimateWizard database and published literature. 

This chapter also summarizes some relevant 

ecological indicators of observed climate change. 

Chapter 4: Projected Changes in Climate and 

Physical Processes presents downscaled climate 

change projections for the assessment area, including 

future temperature and precipitation data. It also 

includes summaries of other climate-related trends 

that have been projected for the assessment area and 

the Midwest region. 

Chapter 5: Future Climate Change Impacts on 

Forests summarizes ecosystem model results that 

were prepared for this assessment. Three modeling 

approaches were used to model climate change 

impacts on forests: a species distribution model 

(Climate Change Tree Atlas), a forest simulation 

model (LANDIS-II), and a biogeochemical model 

(PnET-CN). This chapter also includes a literature 

review of other climate-related impacts on forests. 

Chapter 6: Forest Ecosystem Vulnerabilities 

synthesizes the potential effects of climate change on 

the forested ecosystems of the assessment area and 

provides detailed vulnerability determinations for 

each major forested ecosystem.

Chapter 7: Management Implications draws 

connections from the forest ecosystem vulnerability 

determinations to a wider network of related 

concerns shared by forest managers, including forest 

management, recreation, cultural resources, and 

forest-dependent wildlife. 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE 

The contemporary landscape of northern Wisconsin 

and the western Upper Peninsula of Michigan results 

from numerous physical, ecological, economic, 

and social factors. This chapter includes a brief 

introduction to the complex variables that shape the 

northern forests in the assessment area and provides 

context for the modeling results and interpretations 

provided in later chapters.

LANDSCAPE SETTING

Northern Wisconsin and the western portion of 

the Upper Peninsula of Michigan are part of the 

Laurentian Mixed Forest system (Ecological 

Province 212), which covers much of the northern 

Great Lakes region (McNab and Avers 1994, McNab 

et al. 2007) (Fig. 2). Ecological provinces are broad 

geographic areas that share similar characteristics, 

such as climate, glacial history, and vegetation types. 

Albert (1995) characterized the region and outlined 

Ecological Sections based upon these factors, 

including a bedrock consisting of Precambrian 

granites, gneiss, and the Negaunee iron formation 

that underlies most of the assessment area. Below, 

we summarize the major physical and biological 

features of the assessment area.

Figure 2.—The area used for this assessment includes the Laurentian Mixed Forest Ecological Province within northern 

Wisconsin and the western Upper Peninsula of Michigan (McNab and Avers 1994, McNab et al. 2007). The area covers 

Ecological Sections VIII, IX, and X in Wisconsin and Ecological Section IX in Michigan (Albert 1995).
Chapter 1: The Contemporary Landscape

10

Physical Environment 

Climate

Located largely north of the 45th parallel (halfway 

between the equator and the North Pole), the 

assessment area is shaped by a convergence of 

several climatic systems (Eichenlaub 1979). 

Frequent polar air masses and a lower annual 

amount of solar energy typically keep this area 

colder than areas farther south (Sommers et al. 

1984). Proximity to two Great Lakes—Superior and 

Michigan—creates a lake-effect zone across much 

of the assessment area that moderates temperatures 

throughout the year (Stearns 1997) (Box 1). The 

mean annual temperature for the assessment area 

from 1971 through 2000 was 41.5 °F (5.3 °C). July 

was the warmest month with an average temperature 

of 67.2 °F (19.7 °C) during this period, and January 

was the coldest month, with an average temperature 

of 11.6 °F (-11.3 °C) (ClimateWizard 2012). 

There are few days with temperatures above 90 °F 

(32.2 °C), and only four or fewer extremely hot days 

occur per year on average (Great Lakes Integrated 

Sciences and Assessments [GLISA] 2013, National 

Oceanic and Atmospheric Administration [NOAA] 

National Climatic Data Center 2006a). The growing 

season lasts for 100 to 140 frost-free days per 

year (Host et al. 1995, Michigan State University 

2010), which has a strong influence on the type 

of vegetation that can be sustained. Winters in the 

assessment area are long and somewhat severe. 

Temperatures of -30 °F (-34.7 °C ) are reported from 

northern Wisconsin weather stations almost every 

winter (NOAA 2006a). Many lakes and streams are 

covered in ice from late November to early April, 

and snow covers the ground all winter long 

in northern portions of the assessment area. 

During 1971 through 2000, average annual 

precipitation across the assessment area totaled 

32 inches, about two-thirds of which occurred during 

the growing season (ClimateWizard 2012, NOAA 

National Climatic Data Center 2006b). Seasonally, 

the most precipitation falls in summer (11.8 inches), 

followed by fall (8.8 inches), spring (7.5 inches), and 

winter (3.9 inches). August is the wettest month 

(4 inches) and February is the driest month (1 inch). 

An average of between 160 and 220 inches of snow 

falls in the assessment area every year (NOAA 

National Climatic Data Center 2006a), and snow 

cover and snowmelt play an important role in the 

hydrologic regime of the assessment area (Cherkauer 

and Sinha 2010). The climate of much of the 

assessment area is influenced by lake-effect weather 

patterns (Box 1), and the western Upper Peninsula 

Box 1: What is Lake Effect?

A significant lake-effect zone along both Lake 

Superior and Lake Michigan produces temperatures 

that are warmer in winter and cooler in summer 

than inland areas at the same latitude (Eichenlaub 

1970). The massive Great Lakes take much longer 

to adjust to temperature changes. They absorb 

heat in the summer and autumn and cool the 

land near the shore; in autumn and winter they 

release heat, thereby warming the land near shore 

(Stearns 1997). In the late fall and early winter, the 

air over the Great Lakes is generally warmer and 

moister than over land. As this air moves inland, 

the water condenses and enhanced cloud cover 

and precipitation (often in the form of snow) occur 

downwind of the lakes (NOAA National Climatic Data 

Center 2006a, Notaro et al. 2012). Higher, rougher 

terrain amplifies the lake-effect resulting in even 

more snowfall than many other locations in the 

United States (Albert et al. 1986, Eichenlaub 1979). 

In 2011, the Weather Channel ranked Hancock, 

Michigan, the third snowiest city in America based 

on 30-year average annual snowfall data; some 

unincorporated towns nearby were not included in 

the ranking, but annually receive even more snow 

than Hancock (Erdman 2011).
Chapter 1: The Contemporary Landscape

11

generally receives more precipitation than northern 

Wisconsin partly due to this phenomenon (NOAA 

2010). Melting snow, combined with spring rains, 

contributes to a distinct hydrologic regime that can 

result in frequent and sometimes severe flooding in 

April.

Chapter 3 provides more details about current and 

historical climate trends, and Chapter 4 describes the 

projected future climate of the assessment area.

Geology, Landform, and Soils 

The assessment area lies on the ancient rocks of 

the Precambrian Shield. Its geological foundation 

is composed of highly resistant igneous and 

metamorphic rock, lava flows, iron formations, 

granite and gneiss intrusions, conglomerates, 

limestone, and sandstone (Albert 1995). Exposed 

bedrock is found throughout the northern part of 

the assessment area, and the Porcupine and Huron 

Mountains in the western Upper Peninsula are 

remnants of some of the oldest mountains in the 

world (Sommers et al. 1984). Past glaciations are 

responsible for the area’s surface geology. At the 

end of the Pleistocene epoch (2.6 million to 11,000 

years ago), glaciers covered the assessment area 

with ice thousands of feet thick. As the glaciers 

advanced southward, and again as the ice age 

ended and the glaciers retreated, great volumes 

of soil and rock were moved substantial distances 

(Sommers et al. 1984). Glacial runoff cut channels 

throughout the assessment area. A massive deposit 

of glacial drift (more than 200 feet thick in some 

areas) and the subsequent melting of the glacial ice 

combined to create a diverse landscape of glacially 

scoured bedrock ridges and irregular glacial features 

including moraines, glacial lake beds, and outwash 

channels and clay plains (Albert 1995). Exposed 

bedrock knobs can be found in the northern portion 

of the assessment area; swamps, bogs, and other 

wetland areas are very common throughout (Jerome 

2006). 

Soils in the assessment area are relatively young, 

having developed since the retreat of the Wisconsin 

ice sheets at the end of the ice age approximately 

10,000 years ago (Stearns 1997). Glacial deposits 

of coarse outwash sands, fine-textured clays, and 

tills serve as the parent material for the relatively 

infertile soils in this region (Hole 1968, Jerome 

2006). Early logging practices and subsequent 

fires on newly exposed soils have destroyed the 

organic layer of many sandy loam forest soils, and 

tree-tipping during centuries of storms has resulted 

in occasional cradle-knoll formation (Hole 1976). 

Sandy barrens scattered across the north often have 

sands tens of feet deep with very little variation, and 

support vegetation adapted to low-productivity soils, 

in such ecosystems as pine barrens, aspen forests, 

and grasslands (Hole 1976). The depressions, or 

kettles, left by melting glacial ice masses are often 

filled by lakes or by peat bogs containing sphagnum 

moss. Fine-textured lacustrine deposits rich in silt 

and clay can be 2 to 3 feet deep in glacial lake 

plains. The poor drainage of these soils is limiting 

to agricultural production due to excess water in 

the spring and droughty conditions in late summer 

(Hole 1976). Red clay soils arising from pulverized 

iron redistributed by glacial lakes are found along 

the shorelines of Lake Superior and Lake Michigan 

(Hole 1976). 

Hydrology 

Water resources are abundant and diverse across the 

assessment area. The national forests alone (covering 

15 percent of the assessment area) contain more 

than 4,000 miles of perennial streams and rivers, 

more than 1,000 lakes larger than 10 acres, and more 

than 550,000 acres of wetlands. Groundwater is 

generally abundant in unconfined sand and gravel 

aquifers that interact with surface water bodies to 

influence flow rates, water chemistry, and aquatic 

ecology. Inextricably linked with water supplies and 

cycling, forests play a critical role in protecting soil, 
Chapter 1: The Contemporary Landscape

12

moderating streamflow, sustaining water quality, 

and protecting sources of drinking water for human 

populations (Barnes et al. 2009).

Hydrology in the assessment area often follows a 

seasonal pattern. Climate is the primary driver of this 

seasonal pattern; the amount of runoff in a watershed 

results from a combination of snowfall, snowmelt, 

rainfall, and evapotranspiration. Stream flows 

are generally low in winter because most water is 

stored as snow or ice, which is followed by high 

stream flows in spring that result from snowmelt 

and increased precipitation. Flows often decline in 

summer as a result of greater evapotranspiration 

losses, with occasional runoff from rainstorms. 

Autumn is similar to summer but with higher 

flows due to reduced evapotranspiration and wetter 

conditions.

At a more local level, differences in watershed 

characteristics, such as landform, soil, geology, 

vegetation, and land use, have a greater influence on 

the hydrologic regime. In the assessment area, this 

response can be divided into three broad hydrologic 

regimes: surface, groundwater, and mixed. Surface 

runoff regimes are most responsive to rainfall and 

snowmelt events, particularly in watersheds where 

there is a limited capacity to absorb and store 

water from precipitation events, leading to more 

water in streams. Groundwater runoff regimes are 

A small lake in Michigan’s Upper Peninsula. Photo by Maria Janowiak, U.S. Forest Service.
Chapter 1: The Contemporary Landscape

13

least responsive to surface runoff from storms or 

snowmelt and have high, stable baseflows fed by a 

continuous supply of groundwater. Watersheds with 

groundwater runoff regimes generally have coarsetextured glacial outwash or till with high porosity 

that favors groundwater recharge. They also tend 

to have sufficient topographic change to produce a 

steady flow of groundwater from uplands to streams 

and fen-type wetlands. Mixed runoff watersheds 

typically have landform and soil characteristics 

that are intermediate between the surface and 

groundwater regimes. The predominant watershed 

type in the assessment area is mixed runoff. 

Groundwater runoff watersheds are least common. 

Flood flows in the assessment area tend to be 

relatively low because of the area’s high storage 

capacity in the form of wetlands and lakes, gentle 

relief, and soils with high infiltration capacity. There 

are some exceptions, however. For example, the clay 

plains along Lake Superior and Lake Michigan have 

a low infiltration capacity, less storage, and steep 

slopes in some locations. The Penokee-Gogebic 

Range contains shallow soils over bedrock, and 

its steeper slopes have a lower ability to absorb 

precipitation and runoff. Annual flood peaks in 

northern Wisconsin are caused by both snowmelt 

and rainfall runoff in about equal proportions; the 

western Upper Peninsula has a greater influence 

from snowmelt. 

Ecosystem Composition

Land Cover 

Although dominated by forest (50 percent of land 

cover), the assessment area also contains substantial 

components of wetlands (19.3 percent) and 

agricultural lands (19.2 percent). Developed or urban 

land (5.1 percent), water (3.7 percent), and other 

shrubland, herbaceous, or barren lands (2.8 percent) 

complete the landscape (U.S. Geological Survey 

2011). The western Upper Peninsula of Michigan 

is generally less favorable for agriculture and urban 

development than northern Wisconsin, resulting in 

a greater percentage of forested land in the western 

Upper Peninsula compared to northern Wisconsin. 

A major vegetation change occurs along the tension 

zone (Fig. 3), where the more open landscape of 

southern Wisconsin (once prairie and oak savanna 

but now predominantly agricultural lands) transitions 

into the mixed deciduous-coniferous forests of the 

assessment area (Wisconsin Department of Natural 

Resources [WDNR] 2009a). The proximity of 

these ecosystems to the boreal forests that extend 

northward into Canada creates a complex and 

unique set of ecological conditions. Species that are 

at or near the southern extent of their range in the 

assessment area include jack pine, red pine, white 

pine, northern white-cedar, tamarack, balsam fir, 

yellow birch, paper birch, black spruce, and white 

spruce (Burns and Honkala 1990). American beech, 

absent in most of the western Upper Peninsula and 

limited to the Lake Michigan shoreline in Wisconsin, 

is at the western extent of its range. Eastern hemlock 

is close to the western edge of its range in the 

assessment area, and is rare in western Wisconsin. 

Tables 14 and 15 in Appendix 1 list common and 

scientific names of plant, fauna, and other species 

mentioned in this assessment.

Figure 3.—Approximate location of the tension zone (blue 

line) in Minnesota, Wisconsin, and Michigan. Adapted from 

Andersen (2005), Curtis (1959), and Wisconsin Department 

of Natural Resources (1995).
Chapter 1: The Contemporary Landscape

14

Ecosystems Within the Assessment Area 

In this assessment, we focus on forest ecosystems 

in the region. At the same time, it is important 

to recognize that although the assessment area 

is covered by extensive forest, many different 

types of ecosystems are present. Differences in 

landform, soils, and natural and human disturbances 

produce a great deal of ecological variation 

across the landscape. Within the forested areas, 

other ecosystems, including riparian and wetland 

systems, shrublands, and grasslands (described 

below), depend on, influence, or otherwise coexist 

with a variety of forested ecosystems. Great Lakes 

coastal systems and aquatic systems are also key 

components of the landscape, but are outside the 

scope of this assessment (Michigan Department of 

Natural Resources [MDNR] 2011b, WDNR 2009b). 

Here, we describe several of these regionally 

important ecosystems.

• Forests within the assessment area are influenced 

largely by differences in soil moisture and 

include northern hardwoods, lowland hardwoods, 

lowland conifers, aspen, and pine forest types. 

These forests vary in their association with 

understory plant communities, wildlife, and 

dominant species. Openings in the forest canopy 

may be composed of other ecosystem types, such 

as wetlands or grasslands (MDNR 2011b). These 

forested systems are described in greater detail 

later in this chapter.

• Wetlands are found in diverse forms throughout 

the assessment area and can include swamp 

(areas with peat or muck soils dominated by trees 

or shrubs), open bog (a carpet of living sphagnum 

moss over a layer of acid peat), shallow and 

deep marshes (areas of shallow seasonal or 

permanent water, supporting emergent aquatic 

plants such as cattails, bulrushes, wild rice, and 

arrowheads), and sedge meadows (peat or muck 

soils dominated by the sedge genus Carex, but 

also spike-rushes, bulrushes, and nut-grasses) 

(Eggers and Reed 1997, Shaw and Fredine 1971). 

Additionally, the assessment area contains many 

small (less than 1 acre) and isolated wetlands 

referred to as woodland ponds, vernal pools, or 

seasonal ponds that provide important habitat 

for salamanders, frogs, and many birds (MDNR 

2011a). Forested swamps and bogs are the most 

common types of wetland (Box 2).

• Grassland systems include generally open lands 

(trees are absent or widely scattered) that are 

categorized as prairie, savanna, abandoned field, 

hayland, pasture, row crop, orchard, right-ofway, or fence row (MDNR 2011b). Many birds, 

including the sharp-tailed grouse, northern 

bobwhite, American woodcock, and red-headed 

woodpecker, require grassland habitat close to 

forests (MDNR 2011b). 

• Shrublands are dominated by woody vegetation 

in seasonally or permanently saturated soils 

(lowland shrub) or moist to dry soils (upland 

shrub) (MDNR 2011b). Birds such as the 

American black duck, American bittern, and 

golden-winged warbler, and mammals such as the 

northern bat, bobcat, and snowshoe hare, depend 

on shrublands for habitat (MDNR 2011b).

SOCIOECONOMIC CONDITIONS 

Approximately 1.6 million people live within the 

assessment area. Northern Wisconsin makes up the 

greater proportion of the assessment area by acreage 

and also has a higher population density. Almost 

1.4 million people live in northern Wisconsin and 

the population has increased steadily since 1970 

(Headwaters Economics 2011). In contrast, far 

fewer (172,774) people live in the western Upper 

Peninsula, and the population has decreased slightly 

during the same period.

Other socioeconomic conditions are very similar 

across the assessment area. Unemployment across 

the entire area was 9 percent in 2010 and more 

than doubled between 2000 and 2010 (Headwaters 

Economics 2011). Per capita income was $34,494 
Chapter 1: The Contemporary Landscape

15

Box 2: Forested Wetlands

Forested bogs and swamps are common throughout 

the region (Eggers and Reed 1997). Differences 

in topography, soil characteristics, hydrology, 

vegetation, water chemistry, and other factors 

create a diversity of wetland types. 

Inland wetlands occur primarily in low-lying areas 

where the groundwater is near the soil surface, 

or where precipitation saturates the soil above an 

impervious layer. Wetlands can be permanent or 

seasonal, becoming saturated for weeks or months 

each year. Wetlands are recognized for their waterfiltering and recharging services, and their role in 

carbon storage. Forested wetlands can be broadly 

grouped into three main types: 

• Coniferous swamps are dominated by northern 

white-cedar and tamarack, growing on peat or 

muck soils that are saturated during much of the 

growing season, and that may be temporarily 

inundated by as much as a foot of standing water. 

Balsam fir may be a component in some stands. 

Soils can vary from nutrient-poor and acidic to 

fertile and alkaline. Tamarack typically dominates 

on acidic soils, and northern white-cedar on 

alkaline soils (Eggers and Reed 1997). Northern 

white-cedar swamps are among the most fragile 

plant communities in the assessment area, 

containing many rare orchid species. Species 

relying on coniferous swamps include the wood 

turtle, spruce grouse, frogs, salamanders, great 

blue heron, and snowshoe hare (MDNR 2011b). 

• Coniferous bogs are dominated by black spruce 

and tamarack, growing on a carpet of living 

sphagnum moss over a layer of acid peat. 

Sphagnum moss is the dominant ground layer 

species, but sedges, orchids, and pitcher plants 

are often present, along with shrubs in the 

heath family (Ericaceae) (Eggers and Reed 1997). 

Species that rely on coniferous bogs include 

dragonflies, Blanding’s turtle, smooth green 

snake, American marten, and the southern bog 

lemming (MDNR 2011b).

• Lowland hardwood swamps are dominated 

by silver maple, black ash, red maple, and 

yellow birch growing on soils that are saturated 

during much of the growing season, and may 

be inundated by as much as a foot of standing 

water (Shaw and Fredine 1971). American elm 

and northern white-cedar can be a component 

in some stands. Bird species relying on lowland 

swamps include the green heron, great blue 

heron, and osprey; mammals include the arctic 

shrew, pygmy shrew, and snowshoe hare (MDNR 

2011b). 

and 5 percent of families were living below 

the poverty line during this time (Headwaters 

Economics 2011). As the assessment area has 

become a more popular retirement destination 

(especially in northern Wisconsin), the median 

population age has increased (Headwaters 

Economics 2011), along with the percentage of 

housing units used for seasonal, recreational, or 

occasional purposes. Across the assessment area, 

about 17 percent of all housing units are seasonal. In 

some Wisconsin counties (Vilas, Florence, Forest, 

Burnett, and Sawyer) and one Michigan county 

(Keweenaw), there are more seasonal than primary 

units (U.S. Census Bureau 2011). 

Economic Sectors

Forest Products Industry

The forest products industry, much of which is 

supported by the heavily forested local land base 

rather than imports, is important throughout the 

assessment area, accounting for 5 percent of all 

jobs in 2009 (Headwaters Economics 2011). At 

the same time, recent economic conditions have 

resulted in reduced capacity or closings at many 

mills, leading to job losses. Within the assessment 

area, employment in the timber/forest products 

sector decreased by 36 percent between 1998 and 

2009 (Headwaters Economics 2011). As fewer new 
Chapter 1: The Contemporary Landscape

16

residential homes are built or remodeling projects 

undertaken, the demand for solid wood panels and 

other building materials has plummeted (Smith 

and Guldin 2012). Across the northeastern United 

States alone, 505 sawmills have closed since 2005 

(Smith and Guldin 2012). The number of active 

mills in Wisconsin has stayed fairly steady within 

the past decade, but the number of mills in Michigan 

decreased from 288 in 2004 to 201 in 2008 (Perry 

et al. 2012, Pugh et al. 2012). 

Recreation

Many northern and rural counties depend on 

resource-based tourism (Stynes 1997), with 

14.5 percent of the jobs in the assessment area being 

related to travel and tourism (Headwaters Economics 

2011). Statewide in both Michigan and Wisconsin, 

popular recreational activities include walking and 

hiking, fishing, birdwatching, camping, boating, 

hunting, and swimming (MDNR 2012c). Many 

recreational opportunities are centered around water 

features (MDNR 2013, WDNR 2011). Participation 

in some recreational activities such as biking, 

cross-country skiing, and off-highway vehicle 

driving has increased over the past decade (MDNR 

2013; WDNR 2006, 2011). Off-highway vehicle 

use, in particular, continues to grow rapidly in the 

assessment area. Snowmobiling remains popular, but 

demand for this recreational activity is declining due 

to fewer people taking up the activity and low snow 

levels in the past decade (WDNR 2006). Hunting is 

another important recreational activity in Michigan 

and Wisconsin. Although hunting participation has 

generally been decreasing regionally and nationally, 

due in part to land access and generational changes, 

there is some evidence that this decline may have 

recently slowed in Michigan (Cordell 2012, MDNR 

2012c, WDNR 2011).

Agriculture

Agriculture is an important industry in Michigan 

and Wisconsin. The agricultural sector provided 

33,740 on-farm jobs and more than $3 billion of 

farm business income to the assessment area in 

2009 (Headwaters Economics 2011). Within the 

assessment area, 19.3 percent of the total land area 

is used for agriculture, with most of that acreage 

located at the southern portion of the assessment 

area (U.S. Geological Survey 2011, WDNR 1998a). 

Most of this acreage is dedicated to the production 

of hay and corn. Soybeans, wheat, oats, and barley 

are also common crops in the assessment area. 

Wisconsin is also the top producer of cranberries in 

the country, with 14,000 of the state’s 17,700 acres 

of cranberry bogs located in the northern counties 

(National Agricultural Statistics Service 2009, 

WDNR 1998a). 

FOREST ECOSYSTEMS

The assessment area is noted as a landscape of 

woods and water. Many forest communities, each 

with different characteristics, are represented in the 

area’s 16 million acres of forest land (U.S. Forest 

Service 2013). Local factors, including landform, 

soils, climate, hydrology, and management history, 

result in a diversity of forest communities across the 

assessment area.

Forest Types Used in this Assessment

Different organizations describe forests using 

different classification systems. This assessment 

uses two classification systems (Table 2), which are 

useful for different reasons and convey different 

types of information. Although there are some 

general relationships between the two systems, they 

are organized differently enough that one cannot be 

substituted for the other. Both types of information 

are relevant to this assessment, so we use both 

classification systems. 
Chapter 1: The Contemporary Landscape

17

FIA forest-type group

Aspen/birch

Elm/ash/cottonwood

Exotic softwoods 

Maple/beech/birch

Oak/hickory

Oak/pine

Other eastern softwoods 

Other hardwoods 

Spruce/fir

White/red/jack pine

Forest type

Aspen-birch

Jack pine

Lowland conifer

Lowland hardwood

Northern hardwood

Oak

Red pine

Upland spruce-fir

White pine

Table 2.—Forest classification systems used in this 

assessment

One system was created by the U.S. Forest 

Service, Forest Inventory and Analysis (FIA) 

program to characterize forests across the Nation. 

In this assessment, we describe acres, ownership 

category, and volume of timber using “foresttype groups” based upon the FIA classification 

system (Woudenberg et al. 2010). There are several 

advantages to the FIA classification system. The 

FIA system measures tree species composition on 

a set of systematic plots across the country and 

uses that information to provide area estimates 

for each forest type, making it a good way of 

estimating what is currently on the landscape and 

the relative abundance of different forest types. In 

this assessment, FIA forest-type groups were used to 

provide quantitative data about forest conditions in 

the assessment area. Additionally, forest-type groups 

have been mapped for the assessment area and the 

Nation (Fig. 4).

Importantly, FIA forest-type groups are intentionally 

broad in order to characterize diverse forests across 

the country. For this reason, they are less useful 

to local natural resource managers and forest 

owners in describing the forests common in the 

assessment area. For example, the red/white/jack 

pine FIA forest-type group combines several unique 

forest communities that correspond to different 

site conditions, have different assemblages of tree 

species, and are subject to different types of natural 

disturbances and forest management activities; this 

grouping is too broad for use at a regional or local 

level. Throughout this assessment, we also used a 

classification of “forest systems” as the primary 

classification system whenever possible because 

these better describe the forest ecosystems present 

in the assessment area (Table 2). We developed this 

classification based upon the classification systems 

used by national forests, state agencies, and other 

forest management organizations in the assessment 

area. We also used these forest systems to assess 

forest vulnerability to climate change (Chapter 6). 

Although these are different systems, there are many 

similarities. For example, the FIA maple/beech/birch 

forest-type group is largely synonymous with the 

northern hardwood forest system (though beech 

is present only on the far eastern extent of the 

assessment area), and the FIA elm/ash/cottonwood 

forest-type group includes forests comparable to the 

lowland hardwood forest system. 

Forest Composition and Abundance

Several different kinds of forest are found across the 

16 million acres of forest land in the assessment area 

(Fig. 4, Table 3). Maple/beech/birch (5.8 million 

acres) and aspen/birch (3.5 million acres) are the 

most abundant forest-type groups across the area 

(Table 3). More than half of the forest in the Upper 

Peninsula of Michigan is of the maple/beech/birch 

group, with aspen/birch and spruce/fir being the next 

most abundant. There is a more even distribution 

of forest-type groups across northern Wisconsin. 

Maple/beech/birch and aspen/birch forests each 

cover about a quarter of the forested land base 

in the Wisconsin portion of the assessment area. 

Elm/ash/cottonwood, oak/hickory, spruce/fir, and 

white/red/jack pine are also relatively common, with 

each making up about one-tenth of the forest land in 

northern Wisconsin. Differences between forest-type 

groups can influence the amount of carbon stored 

aboveground and belowground (Box 3). 
Chapter 1: The Contemporary Landscape

18

Figure 4.—Distribution of forest-type groups across the assessment area and surrounding region. Data source: U.S. Forest 

Service (2013).

a Forest-type groups are used to present broad-scale information on forest trends based upon U.S. Forest Service, Forest Inventory 

and Analysis (FIA) data (Woudenberg et al. 2010). In this assessment, forest systems are often used to describe forest ecosystems as 

commonly grouped by local forest management organizations.

Table 3.—Forest land (in acres and as a percentage of total forest land) in the assessment area by FIA forest-type 

groups (U.S. Forest Service 2013)

 Total assessment area Assessment area (MI) Assessment area (WI)

FIA forest-type groupa Acres Percent Acres Percent Acres Percent

Aspen/birch 3,473,558 21.7 761,823 16.9 2,711,735 23.5

Elm/ash/cottonwood 1,346,436 8.4 170,611 3.8 1,175,825 10.2

Exotic softwoods 25,644 0.2 11,797 0.3 13,847 0.1

Maple/beech/birch 5,826,887 36.3 2,525,778 56.0 3,301,109 28.6

Oak/hickory 1,571,067 9.8 68,701 1.5 1,502,366 13.0

Oak/pine 365,369 2.3 51,959 1.2 313,410 2.7

Other eastern softwoods 3,236 0.0 0 0.0 3,236 0.0

Other hardwoods 82,132 0.5 30,483 0.7 51,649 0.4

Spruce/fir 1,958,103 12.2 613,056 13.6 1,345,047 11.7

White/red/jack pine 1,267,350 7.9 272,116 6.0 995,234 8.6

Nonstocked 123,690 0.8 7,160 0.2 116,530 1.0

 Total 16,043,474 100% 4,513,486 100% 11,529,988 100%
Chapter 1: The Contemporary Landscape

19

Box 3: Forest Carbon

Forest ecosystems around the world play a valuable 

role as carbon sinks. The accumulated terrestrial 

carbon pool within forest soils, belowground 

biomass, dead wood, aboveground live biomass, 

and litter represents an enormous store of carbon 

(Birdsey et al. 2006). Terrestrial carbon stocks in 

the region have generally been increasing for the 

past few decades (Rhemtulla et al. 2009), and there 

is increased attention on the potential to manage 

forests to maximize and maintain this carbon pool 

(Malmsheimer et al. 2011, Price 2010). Carbon 

sequestration and storage in forest ecosystems 

depends on the health and function of those 

ecosystems in addition to human management, 

episodic disturbances, climate variability, and forest 

stressors. 

Forest lands within the assessment area are 

estimated to hold approximately 1.5 billion metric 

tons of carbon, or roughly 95 metric tons per 

acre. There is relatively little variation in carbon 

density across the different major forest ownership 

categories, ranging from 94 to 109 metric tons 

per acre across different land ownerships. Among 

different forest-type groups, however, there is 

greater variation in the amount of carbon stored per 

acre (Fig. 5). The spruce/fir forest-type group holds 

more carbon per acre than any other forest-type 

group, most of which is in the soil organic carbon 

pool. Maple/beech/birch forests tend to store the 

most aboveground carbon compared to other FIA 

forest-type groups. 

Figure 5.—Forest carbon density (metric tons per acre) by FIA forest-type group. Data source: U.S. Forest Service 

(2013).

0

25

50

75

100

125

150

175

T otal ca rb o n (m etric to ns/ac re)

S o il o rg anic Liv e b elo wg ro und Litter D ead wo o d Liv e ab o v eg ro und
Chapter 1: The Contemporary Landscape

20

Drivers of Change in Forest Ecosystems

Past Forest Ecosystem Change 

Climatic changes occurring since the retreat of the 

glaciers about 10,000 years ago have influenced the 

migration and composition of the region’s forests 

over time. During the early Holocene (about 10,000 

years ago), regional forests underwent dynamic 

transformations, including species migration and 

forest succession, in response to climatic change and 

biological processes including species migration and 

forest succession. Most of the plant species present 

in the Lake States today had migrated into the region 

by 6,000 years ago, with hemlock and beech being 

among the most recent arrivals, about 2,000 to 3,000 

years ago (Booth et al. 2012, Davis 1986). Beech 

is still slowly expanding westward (Bennett 1985, 

Burns and Honkala 1990) and other tree species 

distributions have continued to shift when measured 

at the scale of tens of thousands of acres. In addition 

to changes in distribution, the rate of succession 

and direction of compositional change have been 

influenced by glacial activity and the resulting 

landform and soil factors that influence moisture and 

nutrient availability (Host et al. 1987).

Before the 1850s, wind, fire, herbivory, insect 

infestation, and beaver activity were the primary 

natural drivers of change in the region’s forests 

(Schulte and Mladenoff 2005). Intense wind events 

occurred more frequently than fires and affected 

a greater area than fires in all forest types across 

most of northern Wisconsin (Schulte and Mladenoff 

2005). Fire events, both natural and human-caused, 

were most prevalent in fire-dependent forest types, 

such as jack, red, and white pine forests. Fire 

rotation periods estimated from Public Land Survey 

records suggest that a combination of frequent 

surface fire and less frequent stand-replacing fires 

constituted the natural fire regime (Schulte and 

Mladenoff 2005). 

Profound changes occurred within the assessment 

area between the 1850s and the early 1930s, when 

Euro-American settlement and industrial logging 

began to greatly affect forests. Logging of eastern 

white pine began as early as the 1830s and peaked 

at the end of the 19th century. About the time pine 

logging was ending, removal of northern hardwoods 

began, followed by removal of hemlock for tannic 

acid, and cedar for fence posts and mine timbers. 

By the 1930s nearly all of the primary forest had 

been harvested or burned (Cleland et al. 2004). The 

amount and extent of slash left after logging fueled 

intense and catastrophic fires across most of the 

assessment area. Clearcutting, slash burning, and 

stream and river modifications during the logging 

era, combined with repeated cutting and intense 

wildfires, have resulted in long-term changes in the 

ecosystems within the assessment area. Long-lived 

conifers (e.g., hemlock, white pine, and northern 

white-cedar) have declined precipitously, often 

having been replaced by deciduous trees (Cleland 

et al. 2004). Pioneer tree species (e.g., aspen and 

cottonwood) represented no more than 5 percent 

of the northern hardwood forest before European 

settlement (Frelich and Lorimer 1991); in contrast, 

a single pioneer community, the aspen-birch foresttype group, currently occupies about 23 percent 

of the area (U.S. Forest Service 2013). Pine 

plantations have largely replaced pine barrens, and 

fire suppression has also allowed some successional 

advance of pine barrens to pine forests (WDNR 

1995). Jack pine, aspen, oak, and maple forests have 

largely replaced white and red pine forests. 

Natural Disturbance Regimes 

Wind and fire events are still the primary natural 

disturbances influencing vegetation patterns in 

the assessment area, although anthropogenic 

disturbances are also major drivers of forest 

composition (Schulte and Mladenoff 2005, White 

and Mladenoff 1994). Wind and fire events can 

affect forest structure and composition on a broad 

spatial scale over time, although singular standreplacing events are relatively rare (Canham and 

Loucks 1984). Logging that occurred from 1850 
Chapter 1: The Contemporary Landscape

21

until the early 1900s was followed by frequent slash 

fires. Mature forests that were historically resistant 

to catastrophic fire or wind had many large trees 

and various size-class distributions. The secondgrowth forests that are managed today tend to 

remain young and even-aged. The return intervals of 

stand-replacing fires are approximately 6,500 years 

for sugar maple-basswood forests and 14,300 years 

for yellow birch-hardwood forests (Schulte and 

Mladenoff 2005). Fire-return intervals are about 

10 times longer today than they were in presettlement times (Cleland et al. 2004). 

Across most of the assessment area, small-scale 

blowdown events have been the primary natural 

disturbance agent. Windthrow events vary in extent 

and in the degree of tree mortality that they cause, 

in part because some tree species and age classes 

are more susceptible to windthrow (Rich et al. 

2007). Return intervals for windthrow events vary 

geographically and have not been consistent over the 

past 40 years (Coniglio and Stensrud 2004). 

Pests and Diseases 

Insect and disease outbreaks have also influenced 

the vegetation of the assessment area (Pugh et al. 

2012, WDNR 2007). Before European settlement, 

outbreaks were caused by native species. For 

example, outbreaks of jack pine budworm and 

spruce budworm have historically been an important 

agent of mortality for their host species (Bergeron 

and Leduc 1998, Fleming et al. 2002). More 

recently, insect and disease outbreaks have occurred 

at an increasing frequency as a consequence of 

introduction and establishment of nonnative insects 

and disease agents. For example, the emerald ash 

borer, a beetle that can cause nearly complete 

mortality of ash tree populations throughout the 

eastern United States, has been confirmed in the 

western Upper Peninsula and in northern Wisconsin 

near Green Bay. Outbreaks of the nonnative gypsy 

moth have caused widespread oak mortality, and 

fungal diseases such as oak wilt and butternut 

canker have resulted in tree mortality on smaller 

scales. Earthworm introduction has dramatically 

altered soil composition and structure and organic 

matter decay rates and processes, making seedbed 

and germination conditions less favorable for some 

native plants (Hale et al. 2006). 

Nonnative Plant Species 

Nonnative plant species have become an increasing 

concern across the assessment area because of their 

potential to outcompete native species and influence 

species interactions that are important to ecosystem 

function. Some nonnatives can establish more 

rapidly than native species, in part because native 

diseases or pests are not adapted to compete against 

them (Tu et al. 2001). Across the assessment area, 

numerous cooperative weed management areas have 

been established to control invasive plant species 

across political boundaries. Land management 

organizations in the assessment area have been 

actively combating the spread of nonnative, invasive 

plants (Table 4) with integrated pest management 

tools that include prescribed fire, mechanical 

treatments, and herbicide application. 

Current Stressors and Threats

Each of the forest systems addressed in this 

assessment faces a particular suite of stressors and 

threats (Table 5). We define these as agents that 

tend to disrupt the natural functioning of forest 

ecosystems or impair forest health and productivity. 

This information is collected from published 

literature as well as from local forest managers. The 

impacts of particular stressors and threats are very 

dependent on local conditions and are not consistent 

across a landscape as large and diverse as the 

assessment area. 

These particular threats should be considered in 

addition to landscape-level threats such as forest 

fragmentation, the legacy of past management 

practices, and altered disturbance regimes. It is 

often difficult to examine the effects of just one of 
Chapter 1: The Contemporary Landscape

22

these landscape-level threats in isolation, because 

they have all interacted across the assessment 

area over the past century. Fragmentation caused 

by agricultural and urban development, forest 

management, and other factors has tended to reduce 

the ratio of interior to edge conditions in forests 

(Pugh et al. 2012, Radeloff et al. 2005). The legacy 

of forest management and land use in the region has 

been well documented, with the general outcomes 

being a transition to more early-succession forests 

with reduced structural, spatial, and species diversity 

(Dickmann and Leefers 2003). The disruption 

of natural disturbance regimes has included fire 

suppression in upland systems as well as hydrologic 

disruption in riparian and lowland forests. Natural 

regeneration and succession of forest ecosystems 

is strongly tied to disturbance regimes, so in many 

cases alteration of disturbance regimes has resulted 

in less regeneration of disturbance-adapted species 

and reduced landscape diversity (Nowacki and 

Abrams 2008, Romano 2010).

Forest Wildlife

The assessment area is home to hundreds of native 

animal species, including more than 50 mammal 

species and approximately 250 bird species. A 

handful of mammal species have been extirpated 

from the assessment area, including woodland 

caribou, bison, and wolverine. Others have been 

reintroduced, or are returning by migration, 

including the moose, gray wolf, elk, fisher, and 

American marten. 

The gray wolf population was eliminated from 

Wisconsin and decimated in Michigan through 

state bounty programs in the late 1950s and early 

1960s. State and Federal protection as an endangered 

species has promoted a population rebound. From 

approximately 25 wolves in 1980, the population 

across the two states has grown to nearly 1,500 in 

the winter of 2011-12 (MDNR 2014, Wydeven et 

al. 2011), with the majority of packs located in the 

northernmost forests. 

Woody shrubs

Asiatic honeysuckles

Autumn olive

Buckthorn

Japanese barberry

Oriental bittersweet vine**

Siberian pea shrub**

Grasses

Common reed grass

Reed canarygrass

Composites

Bull thistle

Canada thistle

European marsh thistle (swamp thistle)

Spotted knapweed

Aquatic plants

Curly pondweed

Eurasian water milfoil

Other herbaceous plants

Bishop’s goutweed

Brittle-stem hemp-nettle**

Common mullein**

Forget-me-not

Garden (common) valerian*

Garlic mustard

Giant knotweed*

Japanese knotweed

Leafy spurge

Purple loosestrife

Tansy ragwort (stinking willie)*

Wild chervil*

Wild parsnip

*Species is a priority only on the Ottawa National Forest

**Species is a priority only on the Chequamegon-Nicolet 

National Forest

Table 4.—Nonnative plant species that are present in 

the assessment area and being prioritized for control 

measures on National Forest lands
Chapter 1: The Contemporary Landscape

23

Community Major current stressors and impacts Reference

Aspen-birch 

Stands are dominated by 

quaking aspen, bigtooth 

aspen, paper birch, or 

balsam poplar. Some 

stands may have codominant tree species 

such as balsam fir or 

white spruce.

Suppression of natural fire regimes has allowed 

succession to other forest types and limited 

suitable conditions for natural regeneration.

(Cleland et al. 2004, Nowacki and Abrams 2008)

Insect pests such as forest tent caterpillar, birch 

leaf miner, bronze birch borer, and gypsy moth 

cause reduced growth or mortality of target 

species.

(Michigan Department of Natural Resources 

2011a; Pugh et al. 2009, 2012; Romano 2010; 

Wisconsin Department of Natural Resources 

2012)

Forest diseases such as hypoxylon canker, 

Armillaria, and white trunk rot lead to damage and 

mortality.

(Burns and Honkala 1990, Romano 2010, 

Weber et al. 2007, Wisconsin Department of 

Natural Resources 2013)

Excessive drought causes reduced growth or 

mortality. 

(Auclair et al. 2010, Cornett et al. 2000b, 

Hanson and Weltzin 2000, Nowacki and Abrams 

2008, Wisconsin Department of Natural 

Resources 2005, Worrall et al. 2013)

Deer herbivory results in reduced growth and 

mortality of seedlings and saplings of target browse 

species.

(Cornett et al. 2000a, Côté et al. 2004, Waller 

and Alverson 1997, Weber et al. 2006, 

Wisconsin Department of Natural Resources 

2013)

Jack pine

Stands are generally 

dominated by jack pine, 

with some composed 

primarily of mixed pine 

species or occasionally 

Scotch pine. Oak species 

may be co-dominant in 

some stands.

Suppression of natural fire regimes has reduced 

structural and species diversity, allowed hardwood 

encroachment on many sites, and limited suitable 

conditions for natural regeneration.

(Cleland et al. 2004, Cohen 2002a, Nowacki and 

Abrams 2008, Wisconsin Department of Natural 

Resources 2012)

Insect pests such as jack pine budworm, white pine 

tip weevil, pine tussock moth, and bark beetles 

cause reduced growth or mortality of target 

species.

(Michigan Department of Natural Resources 

2011a, Pugh et al. 2009, 2012; Wisconsin 

Department of Natural Resources 2013)

Diseases such as Armillaria and scleroderris lead to 

damage and mortality.

(Burns and Honkala 1990)

Limited ability to apply prescribed fire makes it 

difficult to simulate natural fire regimes.

(Cohen 2002a, Nowacki and Abrams 2008)

Past management practices have led to reduced 

age class diversity across the landscape and a 

concentration of trees in young age classes.

(Wisconsin Department of Natural Resources 

2012, 2013) 

(Table 5 continued on next page)

Table 5.—Current major stressors and threats for forest systems in the assessment area
Chapter 1: The Contemporary Landscape

24

Community Major current stressors and impacts Reference

Lowland conifers

Stands are in low-lying 

sites and are dominated 

primarily by black spruce, 

northern white-cedar, 

tamarack, or a mixture of 

these species. Quaking 

aspen, paper birch, and 

other species may be codominant in some stands.

Road or ditch building leads to altered drainage 

patterns.

(Cohen 2006, Kost 2002, Swanson and Grigal 

1991, Wisconsin Department of Natural 

Resources 2013)

Raised water tables can result in tree mortality, 

and lowered water tables can lead to improved tree 

growth but also susceptibility to drought.

(Cohen 2006, Kost 2002, Swanson and Grigal 

1991)

Insect pests such as tamarack sawfly, larch case 

bearer, eastern larch beetle, and spruce budworm 

cause reduced growth or mortality of target 

species.

(Cohen 2002b; Michigan Department of Natural 

Resources 2011a; Pugh et al. 2009, 2012; 

Kost 2002; Wisconsin Department of Natural 

Resources 2012)

Diseases such as mistletoe lead to damage and 

mortality.

(Baker et al. 2012)

Invasive plants such as reed canarygrass, multiflora 

rose, and European buckthorn reduce suitable 

conditions for natural regeneration, facilitate 

other exotic species, and alter understory plant 

communities.

(Pugh et al. 2009, 2012; Wisconsin Department 

of Natural Resources 2012)

Deer herbivory results in reduced growth and 

mortality of seedlings and saplings of target browse 

species.

(Cornett et al. 2000a, Côté et al. 2004, 

Rooney et al. 2002, Waller and Alverson 1997, 

Wisconsin Department of Natural Resources 

2012)

Past management practices removed coarse 

woody debris, reduced species diversity, or led to 

alder encroachment.

(Cohen 2006, Kost 2002)

Excessive drought causes reduced growth or 

mortality.

(Cornett et al. 2000b, Hanson and Weltzin 2000, 

Kost 2002, Swanson and Grigal 1991, Wisconsin 

Department of Natural Resources 2005)

Lowland-riparian 

hardwoods

Stands are in low-lying 

sites and are dominated 

primarily by black ash, red 

maple, American elm, or a 

mixture of these species.

Altered hydrologic regimes lead to excessively 

wet or dry soils and result in reduced growth, 

lack of suitable conditions for regeneration, and 

susceptibility to dieback and decline.

(Opperman et al. 2010, Romano 2010, 

Slaughter et al. 2007, Tepley et al. 2004, Weber 

et al. 2007, Wisconsin Department of Natural 

Resources 2013)

Ash decline causes reduced growth, crown dieback, 

or mortality of ash species.

(Benedict and Frelich 2008, Palik et al. 2011, 

Weber et al. 2007, Tepley et al. 2004)

Invasive plants such as reed canarygrass, Japanese 

barberry, and European buckthorn reduce suitable 

conditions for natural regeneration, facilitate 

other exotic species, and alter understory plant 

communities.

(Pugh et al. 2009, 2012; Tepley et al. 2004; 

Weber et al. 2007)

Insect pests such as emerald ash borer and gypsy 

moth cause reduced growth or mortality of target 

species.

(Michigan Department of Natural Resources 

2011a; Pugh et al. 2009, 2012; Slaughter et al. 

2007, Tepley et al. 2004; Weber et al. 2007)

Diseases such as mistletoe lead to damage and 

mortality.

(Baker et al. 2012)

Excessive drought causes reduced growth or 

mortality.

(Hanson and Weltzin 2000, Slaughter et al. 

2007, Wisconsin Department of Natural 

Resources 2005)

Deer herbivory results in reduced growth and 

mortality of seedlings and saplings of target browse 

species.

(Côté et al. 2004, Slaughter et al. 2007, Waller 

and Alverson 1997, Wisconsin Department of 

Natural Resources 2013)

(Table 5 continued on next page)

Table 5 (continued).
Chapter 1: The Contemporary Landscape

25

Community Major current stressors and impacts Reference

Northern hardwoods

Forests are composed 

largely of sugar and red 

maple. Eastern hemlock, 

yellow birch, basswood, 

red oak, and black cherry 

are common associates, 

found in varying 

amounts based upon 

site conditions.

Exotic earthworms reduce forest litter, alter 

nutrient and water cycling, alter soil conditions, 

facilitate exotic plant species, decrease 

regeneration suitability for many forest species, 

and increase drought susceptibility for sugar maple.

(Frelich et al. 2006, Hale et al. 2005)

Invasive plants such as garlic mustard, Pennsylvania 

sedge, Japanese barberry, and European buckthorn 

reduce suitable conditions for natural regeneration, 

facilitate other exotic species, and alter understory 

plant communities.

(Powers and Nagel 2009; Pugh et al. 2009, 

2012; Wisconsin Department of Natural 

Resources 2004, 2010a)

Insect pests such as emerald ash borer, forest 

tent caterpillar, gypsy moth, and white pine tip 

weevil cause reduced growth or mortality of target 

species.

(Michigan Department of Natural Resources 

2011a; Pugh et al. 2009, 2012; Wisconsin 

Department of Natural Resources 2013)

Diseases such as beech bark disease, white pine 

blister rust, and Armillaria lead to damage and 

mortality.

(Michigan Department of Natural Resources 

2011a; Pugh et al. 2009, 2012)

Soil frost and freeze-thaw cycles damage roots 

and new growth, and may cause crown dieback or 

widespread decline of maple and birch species.

(Auclair et al. 2010; Bourque et al. 2005; 

Tierney et al. 2001; Wisconsin Department of 

Natural Resources 2012, 2013)

Excessive drought dries ephemeral ponds and 

causes reduced growth or mortality.

(Auclair et al. 2010; Hanson and Weltzin 2000; 

Mladenoff and Stearns 1993; Rooney et al. 

2000; Swanson and Grigal 1991; Wisconsin 

Department of Natural Resources 1995, 2005, 

2013)

Deer herbivory results in reduced growth and 

mortality of seedlings and saplings of browsed 

species.

(Cornett et al. 2000a, Côté et al. 2004, Powers 

and Nagel 2009, Rooney and Waller 2003, 

Waller and Alverson 1997) 

Past management practices removed coarse 

woody debris, reduced species diversity, simplified 

forest structure, or altered species composition. 

(Cohen 2000, Crow et al. 2002, Powers and 

Nagel 2009, Wisconsin Department of Natural 

Resources 2013)

(Table 5 continued on next page)

Table 5 (continued).
Chapter 1: The Contemporary Landscape

26

Community Major current stressors and impacts Reference

Oak

Stands are dominated by 

one or more oak species. 

Aspen, eastern white pine, 

and other species may 

be co-dominant in some 

stands.

Suppression of natural fire regimes has reduced 

structural and species diversity, allowed mesic 

hardwood encroachment on many sites, and 

limited suitable conditions for natural regeneration.

(Cleland et al. 2004, Cohen 2001, Courteau et 

al. 2006, Nowacki and Abrams 2008, Nowacki 

et al. 1990)

Limited ability to apply prescribed fire makes it 

difficult to simulate natural fire regimes.

(Cohen 2001, Courteau et al. 2006, Nowacki 

and Abrams 2008)

Diseases such as oak wilt, oak decline, gypsy 

moth, and two-lined chestnut borer cause reduced 

growth, crown dieback, or mortality of oak species.

(Courteau et al. 2006; Michigan Department 

of Natural Resources 2011a; Pugh et al. 2009, 

2012; Wisconsin Department of Natural 

Resources 2013)

Deer and rabbit herbivory results in reduced 

growth and mortality of seedling and saplings of 

target browse species.

(Alverson et al. 1988, Côté et al. 2004, Courteau 

et al. 2006, Waller and Alverson 1997)

Invasive plants such as garlic mustard and 

European buckthorn reduce suitable conditions for 

natural regeneration, facilitate other exotic species, 

and alter understory plant communities.

(Pugh et al. 2009, 2012b)

Excessive drought causes regeneration failure or 

mortality.

(Radeloff et al. 1999; Wisconsin Department of 

Natural Resources 2005, 2012)

Soil frost damages roots and new growth, and may 

cause crown dieback or widespread decline of oak 

species.

(Burns and Honkala 1990)

Red pine

Stands are dominated by 

red pine. Some stands 

have an oak component 

in the understory 

and sometimes as a 

co-dominant.

Suppression of natural fire regimes has reduced 

structural and species diversity, allowed hardwood 

encroachment on many sites, and limited suitable 

conditions for natural regeneration.

(Cleland et al. 2004; Cohen 2002a, 2002b; 

Nowacki and Abrams 2008; Radeloff et al. 1999)

Limited ability to apply prescribed fire makes it 

difficult to simulate natural fire regimes.

(Cohen 2002a, 2002b; Nowacki and Abrams 

2008)

Insect pests such as jack pine budworm, white 

pine tip weevil, and redheaded pine sawfly cause 

reduced growth or mortality of target species 

insect pests.

(Michigan Department of Natural Resources 

2011a; Munck et al. 2009; Pugh et al. 2009, 

2012; Stanosz et al. 2001)

Invasive plants such as spotted knapweed and 

Japanese barberry reduce suitable conditions for 

natural regeneration, facilitate other exotic species, 

and alter understory plant communities.

(Powers and Nagel 2009; Pugh et al. 2009, 

2012; Wisconsin Department of Natural 

Resources 2004, 2013)

Diseases such as white pine blister rust, red pine 

shoot blight, and Armillaria lead to damage and 

mortality.

(Burns and Honkala 1990)

Excessive drought causes reduced growth or 

mortality.

(Hanson and Weltzin 2000; Rogers et al. 2008; 

Wisconsin Department of Natural Resources 

2005, 2013)

Deer herbivory results in reduced growth and 

mortality of seedlings and saplings of target browse 

species.

(Cohen 2002a, Côté et al. 2004, Waller and 

Alverson 1997)

(Table 5 continued on next page)

Table 5 (continued).
Chapter 1: The Contemporary Landscape

27

Community Major current stressors and impacts Reference

White pine

Stands are dominated 

by eastern white pine. 

Some stands may include 

a component of eastern 

hemlock or northern red 

oak and white ash.

Suppression of natural fire regimes has reduced 

structural and species diversity, allowed hardwood 

encroachment on many sites, and limited suitable 

conditions for natural regeneration.

(Cleland et al. 2004; Cohen 2002a, 2002b; 

Nowacki and Abrams 2008)

Limited ability to apply prescribed fire makes it 

difficult to simulate natural fire regimes.

(Cohen 2002a, 2002b; Nowacki and Abrams 

2008)

Insect pests such as jack pine budworm, white 

pine tip weevil, and redheaded pine sawfly cause 

reduced growth or mortality of target species 

insect pests.

(Michigan Department of Natural Resources 

2011a; Munck et al. 2009; Pugh et al. 

2009, 2012; Stanosz et al. 2001; Wisconsin 

Department of Natural Resources 2013)

Diseases such as white pine blister rust, red pine 

shoot blight, and Armillaria lead to damage and 

mortality.

(Burns and Honkala 1990, Wisconsin 

Department of Natural Resources 2013)

Deer herbivory results in reduced growth and 

mortality of seedlings and saplings of target browse 

species.

(Cohen 2002b, Côté et al. 2004, Waller and 

Alverson 1997)

Upland spruce-fir

Stands are generally 

dominated by white 

spruce (occasionally black 

spruce or Norway spruce). 

Some stands may have 

co-dominant tree species 

such as balsam fir or 

quaking aspen.

Insect pests such as spruce budworm and balsam 

fir bark beetle cause reduced growth or mortality 

of target species.

(Cohen 2007; Michigan Department of Natural 

Resources 2011a; Pugh et al. 2009, 2012; 

Wisconsin Department of Natural Resources 

2013)

Excessive drought causes reduced growth or 

mortality.

(Cornett et al. 2000b; Hanson and Weltzin 

2000; Wisconsin Department of Natural 

Resources 2005, 2013)

Deer and moose herbivory results in reduced 

growth and mortality of seedlings and saplings of 

target browse species.

(Cohen 2007, Côté et al. 2004, Waller and 

Alverson 1997)

Table 5 (continued).

American marten in western Upper Michigan. Photo by Scott Pearson, used with permission.
Chapter 1: The Contemporary Landscape

28

Moose populations disappeared from the area by the 

1890s, largely due to hunting pressure and habitat 

change following increased settlement of the area 

(Beyer et al. 2011). Moose were reintroduced to 

the western Upper Peninsula from Ontario in 1985 

and 1987 and have successfully grown from 59 

individuals to a population of about 430 in January 

2011 (Beyer et al. 2011). A reintroduction of 25 

elk in 1995 into the Clam Lake area of Wisconsin 

(Ashland County) has resulted in somewhat slower 

population growth; there were approximately 130 

in the summer of 2009 (Stowell and McKay 2009). 

Vehicle collisions, accidental shooting by hunters, 

and predation by wolves are leading mortality 

factors for elk.

Fishers, one of the largest members of the weasel 

family, were extirpated from the assessment area 

in the early 1900s following widespread logging 

and over-trapping. After reintroduction in the 1950s 

and 1960s, the population expanded rapidly in 

the 1980s (Kohn et al. 1993), and the trapping of 

fisher across the assessment area resumed in the 

1980s and continues today. American marten also 

were extirpated from the assessment area after the 

logging era, but reintroduction efforts have not 

been as successful. The species remains protected 

from trapping in Wisconsin despite population 

sizes sufficient to allow harvesting in neighboring 

Minnesota and Michigan (Skalski et al. 2011, 

Williams et al. 2007). The Michigan population has 

been slowly expanding its range and has supported 

a modest recreational trapping season since 2000 

(Skalski et al. 2011).

White-tailed deer are perhaps the wildlife species of 

highest recreational interest across the assessment 

area. Deer hunting is a strong tradition throughout 

the region (Willging 2008). The 2010 post-hunt 

white-tailed deer population in the northern forest 

region of Wisconsin was an estimated 341,300 

animals, 32 percent higher in 2010 than in 2009 and 

12 percent above the goal in 2010 (Rolley 2010). 

The long-term goal is to maintain a population of 

270,000 animals, which is 70 percent of the carrying 

capacity (WDNR 1998b). Across the western Upper 

Peninsula, deer density is generally lower than 

in northern Wisconsin, due in part to the deeper 

snowpack and less agriculture (MDNR 2010). 

Currently, white-tailed deer in the western Upper 

Peninsula are managed based on deer population 

trends, with current deer populations generally 

trending downward (C. Albright, MDNR, pers. 

commun.). Deer have been called a keystone species 

due to their profound effect on forest structure and 

composition through their browsing patterns (Côté 

et al. 2004, Waller and Alverson 1997). Chronically 

high deer populations can suppress the regeneration 

of some tree species and can result in lower diversity 

of the whole forest community (Rooney and Waller 

2003, Waller 2007). 

Populations of the snowshoe hare naturally fluctuate, 

peaking every 7 to 10 years (Shefferly 2007). It is an 

important herbivore and key prey species for many 

predators across the assessment area. Primarily a 

boreal species, it occurs at the southern edge of its 

range in the assessment area (Shefferly 2007). The 

reddish brown or gray fur of the snowshoe hare 

species molts to white during the winter (Kuvlesky 

and Keith 1983) and reduced snow cover may 

increase vulnerability to predation. 

Ruffed grouse is a popular game bird across the 

assessment area. At the time of European settlement, 

it was thought to be common in most areas 

(Schorger 1945). After the logging era, regenerating 

forests in central and northern Wisconsin provided 

high-quality grouse habitat (Schorger 1945). The 

relationship between aspen acreage, particularly 

7- to 25-year-old aspen, and grouse numbers 

(McCaffery et al. 1997) suggests that decreasing 

grouse populations may be the result of declines in 

aspen acreage (Perry et al. 2008). 
Chapter 1: The Contemporary Landscape

29

Beaver were hunted and trapped heavily for 

their pelts during European settlement and their 

population was dramatically reduced by 1900. 

Restricted trapping and favorable habitat changes 

resulted in a rapidly growing beaver population, 

and from 1940 to 1960 the population (120,000 to 

170,000 individuals in northern Wisconsin) may 

have exceeded the historical level (Knudsen 1963). 

Beaver populations peaked again in 1995 at 126,000, 

but are now on the decline throughout northern 

Wisconsin, with only 45,000 reported in 2008

(Rolley et al. 2008). Similar declining trends are 

evident in the western Upper Peninsula. 

Brook trout is the only trout native to streams in 

the assessment area. The introduction of nonnative 

trout (e.g., brown and rainbow), habitat alteration 

by beaver and by humans, and heavy fishing 

pressure in some areas have caused declines in the 

distribution, numbers, and sizes of brook trout since 

European settlement (Becker 1983). Brook trout is 

often considered an indicator species for coldwater 

communities because of its sensitivity to water 

temperature.

Breeding bird surveys conducted over roughly 20 

years across the national forests have recorded 

the presence of approximately 175 species on the 

Chequamegon-Nicolet National Forest and 140 

species on the Ottawa National Forest (Etterson et al. 

2007, Howe and Roberts 2005, Johnson 2004). Both 

positive and negative trends in abundance have been 

observed for some of those species. The declines 

in abundance observed for some species are often 

attributed to loss or fragmentation of mature forest 

(no such trend revealed with Ottawa National Forest 

data), loss of habitat in wintering areas, mortality 

during migration, and pesticide use on wintering 

grounds. For example, several species associated 

with early successional, wetland, and shrub habitats 

have declining populations, such as red-winged 

blackbird, common yellowthroat, mourning warbler, 

and brown thrasher. 

Hermit thrush nest and eggs. Photo by Scott Pearson, used with permission.
Chapter 1: The Contemporary Landscape

30

FOREST OWNERSHIP AND USE

Ownership 

There are numerous types of forest landowners 

within the assessment area (Fig. 6). About onethird of the forest land in the assessment area is 

publically owned, which includes Federal, State, 

and local ownership. The U.S. Forest Service 

owns approximately 2 million acres when the 

Chequamegon-Nicolet and Ottawa National Forests 

are combined. State ownership accounts for more 

than a million acres, with the part of the assessment 

area in Michigan having a somewhat greater 

proportion of state-owned forest land compared to 

Wisconsin (12 percent in Michigan vs. 7 percent in 

Wisconsin). County and municipal governments own 

more than 2 million acres, nearly all of which is held 

by Wisconsin county governments.

Most of the forest land, covering 10 million acres or 

about two-thirds of the assessment area, is privately 

held (Box 4). This category reflects a diversity of 

landowner types, including industrial and corporate 

entities, conservation organizations, families, 

individuals, and tribes. As a result, private ownership 

patterns are complex and change over time. For 

example, in recent decades a substantial amount of 

forest owned by the forest products industry has 

been sold to other corporate owners, largely through 

sales of company lands to real estate investment 

trusts (REITs) and timber investment management 

organizations (TIMOs) (Pugh et al. 2012, WDNR 

2012). Across Wisconsin, the amount of land owned 

by forest product companies fell from 62 percent of 

private lands to 24 percent between 2002 and 2008

(WDNR 2012). A similar transition is taking place 

in the western Upper Peninsula; more than 1 million 

acres of industry-owned lands were sold to REITs or 

TIMOs in 2005 and 2006 alone (Froese et al. 2007). 

Forest Harvest and Products 

As mentioned above, forest industry is a major 

economic contributor in the region. In 2009, 

Michigan and Wisconsin produced 319 million and 

261 million cubic feet of industrial roundwood, 

respectively, across the entire state (Perry et al. 2012, 

Pugh et al. 2012). This material includes saw logs, 

veneer logs, pulpwood, and other wood products 

used by wood processing mills within the region, 

and most of this wood is from hardwood species. In 

both states, more than half of this roundwood is in 

the form of pulpwood; this proportion is somewhat 

higher in Wisconsin (Perry et al. 2012, Pugh et al. 

2012), reflecting the greater importance of the pulp 

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

Assessment area

in Wisconsin

11,529,988 acres

Assessment area

in Michigan

4,513,485 acres

Total assessment area

16,043,474 acres

Private National Forest National Park Service Other Federal State County, municipal, and local

Figure 6.—Forest land ownership in the assessment area, as a proportion of total forest land. Data source: U.S. Forest Service 

(2013).
Chapter 1: The Contemporary Landscape

31

Box 4: Forest Management Takes Many Forms

Family forest owners were asked about their 

reasons for owning forest land as part of the 

National Woodland Owner Survey (Butler 2008). 

The top reasons that most families acquired 

or retained forest lands include: scenic quality, 

nature protection, privacy, family legacy, hunting 

or fishing, land investment, or other recreation 

activities (Butler 2008). Less than 8 percent of 

families owned forest for the primary purposes of 

firewood production, timber production, or the 

collection of nontimber forest products. Family 

owners can enroll their lands in conservation 

easements or forest certification programs such 

as the American Tree Farm System, which require 

forests to have written management plans. Engaged 

family forest owners often look to extension agents, 

Conservation Districts, and private consultants to 

provide technical assistance and other resources for 

managing forests. 

Industrial forest land owners manage for timber 

products. Many industrial forest land owners 

voluntarily participate in third-party certification, 

but millions of acres of corporate land has been 

transferred to real estate investment trusts 

(REITs) and timberland investment management 

organizations (TIMOs) in the last decade (Froese et 

al. 2007). Considered private (nonindustrial) forest 

landowners, REITs and TIMOs have been acquiring 

much land and receiving substantial attention in 

the last 20 years. REITs own and operate incomeproducing real estate and timberland holdings, 

sometimes made public through trading of shares on 

a stock exchange. REITs are required to distribute at 

least 90 percent of taxable income to shareholders 

annually, which is an allowable deduction from 

corporate taxable income. TIMOs act as investment 

managers for institutional clients, who own the 

timberlands as investments or partnership shares 

(Fernholz et al. 2007) with the goal of maximizing 

growth of the timberland asset. In contrast with 

corporate holdings, the risk of large investment 

losses is spread out among investors, as are the 

frequency and rate at which capital gains are taxed. 

The purchase of timberland by REITs and TIMOs 

raises concerns about parcelization, development, 

and high-yield management practices (Fernholz et 

al. 2007). 

Public (Federal, State, and county) agencies and 

tribal organizations own extensive tracts of forest in 

the assessment area. These lands are often managed 

to provide many benefits, often including wildlife 

habitat, water protection, nature preservation, 

timber production, recreation, cultural resources, 

and a variety of other uses (Pugh et al. 2012). 

and paper industry in Wisconsin (Prentiss & Carlisle 

2008, WDNR 2012). In both states, about one-third 

of the roundwood produced is saw log products.

The amount of wood harvested annually in Michigan 

and Wisconsin is less than the amount that is grown 

each year, suggesting that the harvest of timber 

products is biologically sustainable (Perry et al. 

2012, Pugh et al. 2012). The net annual growth-toremoval ratio is based upon FIA data and provides 

a primary measure of sustainability. This ratio 

compares net growth (i.e., gross growth minus 

mortality) to removals from forest management for 

forested lands; values greater than 1.0 indicate that 

net annual growth is greater than annual removals 

and that the removal rate is sustainable (Perry 

et al. 2012). Across all ownership classes in the 

assessment area, the growth-to-removal ratio was 1.6 

for the most recent inventory period (2008 through 

2012), meaning that growth was more than 50 

percent greater than removals (Table 6).

The FIA data also provide more information 

about the amount of wood removed from forests 

in the assessment area through timber harvest or 

conversion of forest to nonforest, with the vast 

majority of removals in this region being due to 

timber harvest. Among the major forest-type groups, 
Chapter 1: The Contemporary Landscape

32

Forest-type group Annual net growth (cubic feet) Annual removals (cubic feet) Annual net growth : removals

Aspen/birch 86,496,227 62,698,908 1.4

Elm/ash/cottonwood 35,855,663 8,659,921 4.1

Exotic softwoods 2,309,062 0 n/a

Maple/beech/birch 173,827,074 122,088,446 1.4

Oak/hickory 44,475,288 39,294,233 1.1

Oak/pine 18,947,689 3,056,096 6.2

Other hardwoods 463,509 3,299,590 0.1

Spruce/fir 40,465,110 10,241,705 4.0

White/red/jack pine 79,064,680 34,794,979 2.3

Other* 433,437 9,923,185 0.0

 Total 482,337,739 294,057,063 1.6

* “Other” represents estimated net growth and removals for lands converted from forest to nonforest.

Table 6.—Net growth and removals and the growth-to-removal ratio for forest-type groups in the assessment area 

(U.S. Forest Service 2013)

oak/pine had the highest growth-to-removal ratio 

(6.2), and elm/ash/cottonwood, spruce/fir, and 

white/red/jack pine forests all had higher ratios 

than the assessment area average. The only foresttype group with a ratio less than 1.0 was the other 

hardwoods group, which represents only one-half 

of 1 percent of forest land in the assessment area. 

Removals from forest management and timber 

harvest in the assessment area were greatest in the 

most commercially important forest-type groups: 

maple/beech/birch (42 percent of total removals), 

aspen/birch (21 percent), oak/hickory (13 percent), 

and white/red/jack pine (12 percent).

Programs for Private Landowners 

Both states offer incentives to private forest 

landowners, with the intent of maintaining larger 

parcels of privately owned forest and promoting 

sustainable production of forest products. The 

Commercial Forest (CF) program in Michigan 

provides a property tax reduction to private 

landowners as an incentive to keep and manage 

land as commercial forest land. There are currently 

almost 1.6 million acres enrolled in the CF program 

in the western Upper Peninsula, which accounts for 

more than half of the 2.2 million CF acres statewide 

(MDNR 2012a). Land enrolled in the CF program 

is required to be open to the public for hunting, 

trapping, and fishing. More recently, the Qualified 

Forest (QF) program was developed, which also 

provides tax benefits to private landowners without 

the requirement for public access. 

In Wisconsin, two programs provide tax relief 

to qualified forest landowners. The Forest Crop 

Law program was enacted in 1927 as a means to 

promote private forestry. Enrollment closed in 1986, 

when the Managed Forest Law (MFL) program 

was started, and the last participant contracts will 

expire in 2034 (Wisconsin Department of Revenue 

2009). Within the assessment area, 210,767 acres 

are enrolled in the Forest Crop Law program, 

which is approximately 80 percent of the total 

enrollment within the state (WDNR 2003, 2012). 

The Managed Forest Law is a property tax reduction 

program for landowners with 10 acres or more. 

Landowners are required to develop and implement 

comprehensive Forest Stewardship Plans which are 

a prerequisite for Federal cost-sharing assistance. 

There were 2,293,205 acres in the assessment 
Chapter 1: The Contemporary Landscape

33

area participating in the MFL program as of 2009, 

which represents nearly all of the acres enrolled 

statewide (P.E. Pingrey, Forest Stewardship Council, 

pers. commun.). In the last decade, the number of 

enrollments has more than doubled, which means 

that more management plans have been written 

for small parcels (WDNR 2012). Although public 

access is often allowed on enrolled lands, many 

newly enrolled owners do not permit public access 

(Wisconsin Council on Forestry 2006). 

Forest Certification 

Forest certification is a process designed to ensure 

that forest products originate from forests that 

are sustainably managed. In both Wisconsin and 

Michigan, forest lands are certified through several 

systems, including the Forest Stewardship Council 

(FSC), the Sustainable Forestry Initiative (SFI), and 

the American Tree Farm System (ATFS). Across 

the entire state, about 42 percent (7.0 million 

acres) of forest land is certified in Wisconsin and 

approximately 31 percent (6.1 million acres) of 

forest land is certified in Michigan (Table 7). In 

2005, Wisconsin’s Managed Forest Law program 

received third-party forest certification under ATFS, 

making it the largest group-certification program 

usable by private landowners in North America, with 

2.2 million acres enrolled in northern Wisconsin 

(P.E. Pingrey, Forest Stewardship Council, pers. 

commun.; Wisconsin Council on Forestry 2006).

Forest land enrolled in certification program (thousand acres)

Forest Stewardship Sustainable Forestry American Tree Farm Dual-certified Dual-certified

Council (FSC) Initiative (SFI) System (ATFS) (FSC & SFI) (ATFS & FSC)

Michigan

State of Michigan 0 4,200 0 0 0

Industry/REIT/TIMO 584 1,125 0 0 0

Private/nongovernment 0 0 235 0 0

Wisconsin

State of Wisconsin 0 57 0 1,541 0

County 166 724 0 1,465 0

Industry/REIT/TIMO 354 282 0 5 0

Private/nongovernment 0 0 194 0 2,239

Table 7.—Forest land (in thousand acres) enrolled in forest certification programs (P. Pingrey and S. Robbins, 

pers. commun.)
34

CHAPTER 2: CLIMATE CHANGE SCIENCE 

AND MODELING

This chapter provides a brief background on climate 

change science, models that simulate climate, and 

models that project the effects of changes in climate 

on species and ecosystems. Throughout the chapter, 

boxes point to recent reports based on the best 

available science. A more detailed scientific review 

of climate change science, trends, and modeling can 

be found in the Intergovernmental Panel on Climate 

Change (IPCC) reports (IPCC 2007a, 2013), the 

National Climate Assessment (Melillo et al. 2014), 

and the whitepaper contributions to the Midwest 

Chapter of the National Climate Assessment 

(Andresen et al. 2012, Winkler et al. 2012).

CLIMATE CHANGE

Climate is not the same thing as weather. Climate 

is defined as the average, long-term meteorological 

conditions and patterns for a given area. Weather, in 

contrast, is the set of the meteorological conditions 

for a given point in time in one particular place. 

The IPCC (2007a: 30, 2013) defines climate 

change as “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.” A key finding 

of the IPCC in its Fourth Assessment Report 

(2007a) was that “warming of the climate system 

is unequivocal.” This was the first Assessment 

Report in which the IPCC considered the evidence 

strong enough to make such a statement. In addition 

to evidence of increased global surface, air, and 

ocean temperatures, this conclusion was based on 

thousands of long-term (more than 20 years) data 

series from all continents and most oceans. These 

data showed significant changes in snow, ice, 

and frozen ground; hydrology; coastal processes; 

and terrestrial, marine, and biological systems. 

Additional assessments are underway internationally, 

nationally, and regionally to provide updated 

information regarding the potential effects of climate 

change. Selected global and national assessments are 

listed in Box 5.

The Warming Trend

The Earth is warming, and the rate of warming is 

increasing (IPCC 2007a, 2013). Measurements from 

weather stations across the globe indicate that the 

global mean temperature has risen by 1.4 °F 

(0.8 °C) over the past 50 years, nearly twice the rate 

of the last 100 years (Fig. 7) (IPCC 2007a), although 

annual and decadal fluctuations do occur (Box 6). 

The first 12 years in the 21st century rank among the 

warmest 14 years in the 133-year period of record of 

global temperature (NOAA National Climatic Data 

Center 2012). The average temperature across the 

entire United States has risen by 1 to 2 °F (0.6 to 

1.1 °C) in the last 50 years (Karl et al. 2009). The 

year 2012 ranked as the warmest on record in the 

United States, 1.0 °F (0.6 °C) warmer than the 

previous record year of 1998 and 3.3 °F above the 

20th-century average (NOAA National Climatic 

Data Center 2013). 

Average temperature increases are simplifications 

of a more complex pattern of regional and seasonal 

climatic changes. For example, the frequency of 

cold days, cold nights, and frosts has decreased over 

many regions of the world while the frequency of 

hot days and nights has increased (IPCC 2007a). 

Within the United States, 356 all-time high 
Chapter 2: Climate Change Science and Modeling

35

Box 5: Global and National Assessments 

Intergovernmental Panel on Climate Change

The Intergovernmental Panel on Climate Change 

(IPCC; www.ipcc.ch/) is the leading international 

body for the assessment of climate change. It was 

established by the United Nations Environment 

Programme (UNEP) and the World Meteorological 

Organization (WMO) in 1988 to provide the world 

with a clear scientific view on the current state 

of knowledge in climate change and its potential 

environmental and socioeconomic impacts. The most 

recent report is available for download at the Web 

address below.

Climate Change 2007: Synthesis Report 

www.ipcc.ch/publications_and_data/ar4/syr/en/

contents.html

U.S. Global Change Research Program

The The U.S. Global Change Research Program 

(USGCRP; www.globalchange.gov) is a Federal 

program that coordinates and integrates global 

change research across 13 government agencies to 

ensure that it most effectively and efficiently serves 

the Nation and the world. Mandated by Congress 

in the Global Change Research Act of 1990, the 

USGCRP has since made the world’s largest scientific 

investment in the areas of climate science and 

global change research. It has released a number of 

national synthesis reports on climate change in the 

United States, which are available for download at 

the Web addresses below. 

National Climate Assessment

http://nca2014.globalchange.gov/downloads

Global Change Impacts on the United States

www.globalchange.gov/what-we-do/assessment/

nca-overview.html

Synthesis and Assessment Products

http://library.globalchange.gov/products/

assessments/2004-2009-synthesis-and-assessmentproducts

Effects of Climatic Variability and Change on Forest 

Ecosystems: a Comprehensive Science Synthesis for 

the U.S.

www.treesearch.fs.fed.us/pubs/42610

Midwest Technical Input Report for the National 

Climate Assessment (coordinated by the Great 

Lakes Integrated Science and Assessment [GLISA] 

Center)

http://glisa.msu.edu/resources/nca

Figure 7.—Trends in global temperature compared to the 1951 through 1980 mean. Data source: NASA Goddard Institute for 

Space Studies. Image courtesy of NASA Earth Observatory, Robert Simmon; www.giss.nasa.gov/research/news/20120119/.
Chapter 2: Climate Change Science and Modeling

36

Box 6: Is the Climate Cooling?

Global temperature trends during the 2000s 

generated some discussion about climatic change 

in the popular media. Lines plotted through specific 

subsets of years during this period appear to have 

negative or flat slopes, leading some to conclude 

that warming has slowed, stopped, or reversed 

since 1998 (Easterling and Wehner 2009, Knight 

et al. 2009). Trends over such a short period should 

be interpreted with great caution, however, because 

meaningful global trends need to be calculated 

over a multi-decadal period in order to account 

for natural annual variation in the Earth’s climate 

(Easterling and Wehner 2009, IPCC 2007b). Global 

mean temperature can increase or decrease from 

year to year because of volcanic eruptions, solar 

activity, and large-scale ocean circulation patterns 

like El Niño. Since 1880 there have been several 

5- to 10-year periods during which trends appeared 

to be flat or even decreasing, including a long, level 

period from the 1940s to the 1970s. Nonetheless, 

the overall trend has been increasing over the last 

century, and recent decades are clearly warmer 

than preceding decades (Fig. 7). In fact, the NASA 

Goddard Institute for Space Studies (2010) ranked 

the decade from January 2000 to December 2009 

as the warmest decade observed since recording 

began in 1880. Information from multiple years, data 

sets, and organizations provides no valid statistical 

evidence for long-term global cooling, and the 

weight of evidence still supports a long-term trend 

of global warming. 

temperature records were broken in 2012, compared 

to only 4 all-time low temperature records (NOAA 

National Climatic Data Center 2013). There is also 

strong evidence that the frequency of heat waves 

and heavy precipitation events has increased over 

this period (IPCC 2012, WMO 2008). Global rises 

in sea level, decreasing extent of snow and ice, 

and shrinking of mountain glaciers have all been 

observed over the past 50 years, and are consistent 

with a warming climate (IPCC 2007a). 

Average global temperature increases of a few 

degrees may seem small, but even small increases 

can result in large changes to the average severity 

of storms, the nature and timing of seasonal 

precipitation, droughts and heat waves, ocean 

temperature and volume, and snow and ice—all 

of which affect humans and ecosystems. The end 

of the last ice age, roughly 10,000 years ago, was 

triggered by a global warming on the order of 

5.4 to 9 °F (3 to 5 °C) (Annan and Hargreaves 2013, 

Shakun and Carlson 2010, Shakun et al. 2012). 

Further, an average change of a few degrees means 

that some areas of the globe may experience much 

more change, while other areas experience very little 

change. The synthesis report of the International 

Scientific Congress on Climate Change concluded 

that “recent observations show that societies and 

ecosystems are highly vulnerable to even modest 

levels of climate change, with poor nations and 

communities, ecosystem services and biodiversity 

particularly at risk” (Richardson et al. 2009: 12). 

Temperature increases above 3.6 °F (2 °C) will 

be difficult for contemporary societies to cope 

with, and are expected to cause major societal and 

environmental disruptions through the rest of the 

century and beyond (Richardson et al. 2009). 

Scientists have been able to attribute these changes 

to human causes by using climate model simulations 

of the past, both with and without human-induced 

changes in the atmosphere, and then comparing 

those simulations to observational data. Overall, 

these studies have shown a clear human effect on 

recent changes in temperature, precipitation, and 

other climate variables due to changes in greenhouse 

gases and particulate matter in the air (Stott et al. 

2010). 
Chapter 2: Climate Change Science and Modeling

37

Chapter 3 provides specific information about 

observed climate trends for the assessment area and 

the surrounding region, and Chapter 4 describes a 

range of anticipated future climate simulations.

The Greenhouse Effect

The greenhouse effect is the process by which 

certain gases in the atmosphere absorb and re-emit 

energy that would otherwise be lost into space 

(Fig. 8). The greenhouse effect is necessary for 

human survival: without it, Earth would have an 

average temperature of about 0 °F (-18 °C) and 

would be covered in ice. Several naturally occurring 

gases in the atmosphere, including carbon dioxide 

(CO2 ), methane (CH4 ), nitrous oxide (N2

O), and 

water vapor, contribute to the greenhouse effect. 

Water vapor is the most abundant greenhouse gas, 

but its residence time in the atmosphere is on the 

order of days as it quickly responds to changes in 

temperature and other factors. Carbon dioxide, 

CH4 , N2

O, and other greenhouse gases reside in 

the atmosphere for decades to centuries. Therefore, 

these long-lived gases are the primary concern with 

respect to long-term warming. 

Human Influences on Greenhouse Gases

Human activities have increased emissions of CO2 , 

CH4 , and N2

O since the beginning of the industrial 

era (Fig. 9) and perhaps even earlier (Ruddiman 

2003, 2013), leading to an enhanced greenhouse 

Figure 8.—Idealized model of the natural greenhouse effect. Figure courtesy of IPCC (2007).
Chapter 2: Climate Change Science and Modeling

38

effect. More CO2 has been released by humans 

into the atmosphere than any other greenhouse gas. 

Carbon dioxide levels have been increasing at a 

rate of 1.4 parts per million (ppm) per year for the 

past 50 years (IPCC 2007a), reaching 395 ppm in 

January 2013 (Tans and Keeling 2013). In recent 

decades, fossil fuel burning has been responsible 

for approximately 83 to 94 percent of the humaninduced increase in CO2 . The remaining 6 to 

17 percent of human-caused emissions has come 

primarily from deforestation of land for conversion 

to agriculture. However, increases in fossil fuel 

emissions over the past decade mean that the 

contribution from land-use changes has become 

a smaller proportion of the total (Le Quéré et al. 

2009).

Methane is responsible for roughly 14 percent of the 

greenhouse gas effect (IPCC 2007a). Concentrations 

of this gas have also been increasing as a result of 

human activities, including production of livestock 

and of rice. Livestock production contributes to CH4

emissions primarily from fermentation in the guts of 

cattle and other ruminants. Rice production requires 

Figure 9.—Concentrations of greenhouse gases over the 

past 2005 years, showing increases in concentrations since 

1750 attributable to human activities in the industrial era. 

Concentration units are parts per million (ppm) or parts 

per billion (ppb), indicating the number of molecules of the 

greenhouse gas per million or billion molecules of air. Figure 

courtesy of IPCC (2007).

wet conditions that are also ideal for microbial CH4

production. Other sources of CH4

 include biomass 

burning, microbial emissions from landfills, fossil 

fuel combustion, and leakage of natural gas during 

mining and distribution. 

Nitrous oxide accounts for about 8 percent of the 

global greenhouse gas effect (IPCC 2007a). The 

primary human source of N2

O is agriculture. Using 

more fertilizer increases emissions from soil as soil 

microbes break down nitrogen-containing products. 

In addition, converting tropical forests to agricultural 

lands increases microbial N2

O production. Other 

sources of N2

O from human activities include nylon 

production and combustion of fossil fuels.

Humans have also reduced ozone in the stratosphere 

through the use of chlorofluorocarbons (CFCs) 

in refrigeration, air conditioning, and other 

applications. Restrictions against the use of CFCs 

under the Montreal Protocol led to a decline in 

CFC emissions and reductions in ozone have 

subsequently slowed. After CFCs were banned, 

another class of halocarbons, hydrofluorocarbons 

(HFCs, also known as F-gases), largely replaced 

CFCs in refrigeration and air conditioning. Although 

HFCs do not deplete stratospheric ozone, many are 

powerful greenhouse gases. Currently HFCs account 

for about 1 percent of greenhouse gas emissions 

(IPCC 2007a).

CLIMATE MODELS

Scientists use models, which are simplified 

representations of reality, to simulate future climates. 

Models can be theoretical, mathematical, conceptual, 

or physical. General circulation models (GCMs), 

which combine complex mathematical formulas 

representing physical processes in the ocean, 

atmosphere, and land surface within large computer 

simulations, are important in climate science. They 

are used in short-term weather forecasting as well as 

long-term climate projections.
Chapter 2: Climate Change Science and Modeling

39

General Circulation Models

General circulation models simulate physical 

processes in the Earth’s surface, oceans, and 

atmosphere through time using mathematical 

equations in three-dimensional space. They can 

work in time steps as small as minutes or hours in 

simulations covering decades to centuries. Because 

of their complexity, GCMs require intensive 

computing power, and must be run on immense 

supercomputers. 

Although climate models use highly sophisticated 

computers, limits on computing power mean that 

projections are limited to relatively coarse spatial 

scales (although this is improving as computer 

power increases). Instead of simulating climate for 

every single point on Earth, modelers divide the 

land surface, ocean, and atmosphere into a threedimensional grid (Fig. 10). Each cell within the 

grid is treated as an individual unit, and able to 

interact with adjacent cells. Although each model 

Figure 10.—Schematic describing climate models, which are systems of differential equations based on the basic laws of 

physics, fluid motion, and chemistry. The planet is divided into a three-dimensional grid that is used to apply basic equations 

and evaluate results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology 

within each grid and evaluate interactions with neighboring points. Figure courtesy of NOAA (2008).
Chapter 2: Climate Change Science and Modeling

40

is slightly different, each square in the grid used 

in the IPCC (2007a) modeling is usually between 

2 and 3° latitude and longitude, or for the middle 

latitudes, about the size of the assessment area. 

These horizontal grids are stacked in interconnected 

vertical layers that simulate ocean depth or 

atmospheric thickness at increments usually ranging 

from 650 to 3,280 feet.

Several research groups from around the world have 

developed GCMs that have been used in climate 

projections for the IPCC reports and elsewhere 

(Box 7). These models have been developed by 

internationally renowned climate research centers 

such as NOAA’s Geophysical Fluid Dynamics 

Laboratory (GFDL CM2; Delworth et al. 2006), the 

United Kingdom’s Hadley Centre (HadCM3; Pope et 

al. 2000), and the National Center for Atmospheric 

Research (PCM and CCSM; Washington et al. 

2000). These models use slightly different grid sizes 

and ways of quantitatively representing physical 

processes. They also differ in sensitivity to changes 

in greenhouse gas concentrations, which means that 

some models will tend to project higher increases in 

temperature than others under increasing greenhouse 

gas concentrations (Winkler et al. 2012). In some 

instances, the choice of GCM can have a larger 

influence on the projected climate trends than the 

choice of greenhouse gas emissions scenario.

Like all models, GCMs have strengths and 

weaknesses (Box 8). In general, they are useful 

and reliable tools because they are based on wellunderstood physical processes and have been 

successful at projecting climate and weather 

conditions. Simulations of past climates by GCMs 

generally correspond well with proxy-based 

estimates of ancient climates and actual historical 

measurements of recent climates (Maslin and 

Austin 2012). These models are judged in part by 

their ability to accurately simulate past climate 

against proxy estimates, but GCM projections are 

not perfect (Maslin and Austin 2012). Sources of 

error in model output include incomplete scientific 

understanding of some climate processes and the 

fact that some influential climate processes occur at 

spatial scales too small to be modeled with current 

computing power. Additionally, future climate 

projections regarding changes in precipitation and 

extreme events are particularly uncertain. Lastly, 

Box 7: Resources on Climate Models and Emissions Scenarios

U.S. Forest Service

Climate Projections FAQ

www.treesearch.fs.fed.us/pubs/40614

U.S. Global Change Research Program

Climate Models: an Assessment of Strengths and 

Limitations

http://library.globalchange.gov/products/

assessments/2004-2009-synthesis-and-assessmentproducts 

Intergovernmental Panel on Climate Change

Chapter 8: Climate Models and Their Evaluation

www.ipcc.ch/publications_and_data/ar4/wg1/en/

ch8.html

Special Report on Emissions Scenarios: 

Summary for Policymakers

http://www.ipcc.ch/ipccreports/sres/emission/index.

php?idp=0

Great Lakes Integrated Science and Assessment 

(GLISA) Center

Midwest Technical Input Report for the National 

Climate Assessment

http://glisa.msu.edu/resources/nca 
Chapter 2: Climate Change Science and Modeling

41

future GCM projections are driven by future 

conditions that have never previously occurred, 

making it impossible to perfectly validate the 

models. Instead, partial analogs can be found in the 

geological record and used to test the ability of the 

models to recreate past climate conditions (IPCC 

2013, Williams et al. 2013). Additional resources on 

climate models and emissions scenarios are listed 

in Box 7.

Box 8: Model Limitations and Uncertainty

“All models are wrong, some are useful.” 

–George Box (Box and Draper 1987) 

Models are conceptual or mathematical 

representations of reality, so they will always depart 

from reality in some ways. Hence, any model output 

must be evaluated for its accuracy to simulate any 

biological or physical response or process. The 

overall intention is to provide the best information 

possible for land managers and to characterize the 

amount of uncertainty and limitations inherent in 

models.

Model results from both climate models and forest 

impact models are not considered standalone 

components of this vulnerability assessment 

because there are many assumptions made about 

the processes simulated by GCMs and impact 

models, uncertainty in future greenhouse gas 

concentrations, and limitations on the numbers of 

inputs that a model can reliably handle. Precipitation 

projections usually have much more variability across 

future climate projections than do temperature 

projections. Regions with complex topography 

contain much more diversity in microclimates than 

many models can capture, even after downscaling. 

Many nonclimate stressors, such as insect pests or 

pathogens, can overshadow the impact of climate on 

a species or community, especially in the short term. 

Therefore, model results are best interpreted by local 

experts to identify regional caveats and limitations 

of each model, and subsequently integrated with 

additional knowledge and experience in the forest 

ecosystems being assessed. 

We integrated fundamentally different types 

of impact models into our assessment of forest 

vulnerability to climate change. These models 

operate at different spatial scales and provide 

different kinds of information. The DISTRIB model 

projects the distribution and amount of available 

suitable habitat for a species. The LANDIS-II 

model projects changes in biomass and species 

distribution. The PnET-CN model projects ecosystem 

productivity for existing forests, but does not 

model shifts in species or ecosystem distribution. 

There are similarities between some inputs into 

these models—downscaled climate models and 

scenarios, simulation periods, and many of the 

same species—but because of the fundamental 

differences in their architecture, their results are not 

directly comparable. Their value lies in their ability to 

provide insights into how various interrelated forest 

components may respond to climate change under a 

range of possible future climates. 

In this assessment, we used an integrated approach 

drawing from multiple models and expert judgment. 

The basic inputs, outputs, and architecture of each 

model are summarized in this chapter with clear 

descriptions of the limitations and caveats of each 

model. Limitations of these models with specific 

applicability to forest ecosystems are discussed 

in more detail in Chapter 5. The integration of 

model results and expert judgment to assess forest 

vulnerability to climate change is described in 

Chapter 6 and Appendix 6.

Technological advances in the computing industry 

along with scientific advances in our understanding 

of Earth’s physical processes will lead to continued 

improvements in GCM projections. Future 

projections may still have a considerable range 

of future values, however, because adding greater 

modeling complexity introduces new sources of 

uncertainty (Maslin and Austin 2012).
Chapter 2: Climate Change Science and Modeling

42

Emissions Scenarios

General circulation models require significant 

amounts of information to project future climates. 

Some of this information, like future greenhouse gas 

concentrations, is not known and must be estimated. 

Although human population growth, economic 

circumstances, and technological developments will 

certainly have dramatic effects on future greenhouse 

gas concentrations, these developments cannot be 

completely foreseen. One common approach for 

dealing with uncertainty about future greenhouse gas 

concentrations is to develop storylines about how the 

future may unfold and then calculate the potential 

greenhouse gas concentrations for each alternative 

storyline. The use of different emissions scenarios 

to run GCM simulations results in different climate 

projections. The IPCC’s set of standard emissions 

scenarios is a widely accepted set of storylines that 

are used throughout this assessment (IPCC 2007a). 

Emissions scenarios are a quantitative representation 

of alternative storylines given certain demographic, 

technological, or environmental developments. 

None of the current scenarios include any changes 

in national or international policies directed 

specifically at greenhouse gas mitigation such as the 

Kyoto Protocol. However, some of the scenarios that 

include a reduction in greenhouse gases via other 

means suggest what we could expect if these policies 

were implemented. Six different emissions scenarios 

are commonly used in model projections (Fig. 11).

The A1FI scenario is the most fossil-fuel intensive, 

and thus projects the highest future greenhouse gas 

concentrations; GCM simulations using the A1FI 

scenario project the highest future warming. On the 

other end of the spectrum, the B1 scenario represents 

a future where alternative energies and new 

technologies decrease our reliance on fossil fuels 

and greenhouse gas concentrations increase the least. 

GCM simulations using the B1 scenario project the 

lowest increase in global temperature. Although 

these scenarios were designed to describe a range 

of future emissions over the coming decades, it is 

important to note that the future could conceivably 

be different from any of the developed scenarios. 

It is highly improbable that future greenhouse gas 

emissions will be less than described by the B1 

scenario even if national or international policies 

were implemented immediately. In fact, current 

emissions more closely track the greenhouse gas 

emissions of the A1FI scenario, and global emissions 

since the year 2000 have even exceeded the A1FI 

scenario values in some years (Raupach et al. 2007).

Downscaling

As mentioned previously, GCMs simulate climate 

conditions for relatively large areas. To examine 

the future climate of areas within the assessment 

area, a smaller grid scale is needed. One method 

of projecting climate on smaller spatial scales 

is statistical downscaling, a technique by which 

Figure 11.—Projected global greenhouse gas (GHG) 

emissions (in gigatons [Gt] of carbon dioxide equivalent 

per year) assuming no change in climate policies under 

six scenarios (B1, A1T, B2, A1B, A2, and A1FI) originally 

published in the Special Report on Emissions Scenarios 

(SRES) (IPCC 2000), and the 80th-percentile range (gray 

shaded area) of recent scenarios published since SRES. 

Dashed lines show the full range of post-SRES scenarios. 

Figure courtesy of IPCC (2007).
Chapter 2: Climate Change Science and Modeling

43

statistical relationships between GCM model outputs 

and on-the-ground measurements are derived for 

the past. These statistical relationships are then 

used to adjust large-scale GCM simulations of the 

future for much smaller spatial scales. The level of 

downscaling is limited by the resolution of the initial 

climate data set, with many data sets available at a 

resolution on the order of 6.2 miles.

Statistical downscaling has advantages and 

disadvantages (Daniels et al. 2012). It is a relatively 

simple and inexpensive way to produce smallerscale projections from GCMs. However, statistical 

downscaling assumes that past relationships between 

modeled and observed temperature and precipitation 

will hold true under future change, which may 

or may not be true. Statistical downscaling also 

depends on local climate data. If there are no 

weather stations in the area of interest, it may be 

difficult to obtain a good downscaled estimate of 

future climate for that area. Finally, local influences 

on climate that occur at finer scales (such as 

land cover type, lake-effect snow, topography, or 

particulate matter) also add to uncertainty when 

climate projections are downscaled.

Another approach, dynamical downscaling, uses a 

regional climate model (RCM) embedded within 

a GCM. Like GCMs, RCMs simulate physical 

processes through mathematical representations on 

a grid. However, RCMs operate on a finer resolution 

than GCMs, typically ranging from 15.5 to 31.0 

miles, but can be as fine as 6.2 miles or less. Thus, 

they can simulate the effects of topography, land 

cover, lakes, and regional circulation patterns that 

operate on smaller scales. 

As with statistical downscaling, dynamical 

downscaling has pros and cons (Daniels et al. 2012). 

It is advantageous for simulating the effects of 

climate change on regional phenomena such as lakeeffect snow and extreme weather events. However, 

like GCMs, RCMs require a lot of computer power. 

Therefore, dynamically downscaled data are usually 

available only for one or two GCMs or emissions 

scenarios and for limited geographic areas. Because 

dynamically downscaled data were unavailable for 

the assessment area at the time it was developed, we 

use statistically downscaled data in this assessment. 

Downscaled Climate Projections 

Used in this Assessment 

In this assessment, we report statistically downscaled 

climate projections for two GCM-emissions 

scenario combinations: GFDL A1FI and PCM B1. 

Both models and both scenarios were included 

in the IPCC Fourth Assessment Report (IPCC 

2007a). The latest version of the National Climate 

Assessment (Melillo et al. 2014) also draws on 

statistically downscaled data based on IPCC models 

and scenarios but uses the A2 scenario as an upper 

bound, which projects lower emissions compared 

to A1FI. The IPCC Assessment includes roughly 

20 other models, which are represented as a multimodel average in its reports. The National Climate 

Assessment takes a similar approach in using a 

multi-model average (Melillo et al. 2014). For this 

assessment, we instead selected two models that 

had relatively good skill at simulating climate in the 

eastern United States and that bracketed a range of 

temperature and precipitation futures. This approach 

gives readers a better understanding of the range of 

projected changes in climate and provides a set of 

alternative scenarios that can be used by managers in 

planning and decisionmaking. 

The Geophysical Fluid Dynamics Laboratory’s 

Climate Model (GFDL CM2) is considered 

moderately sensitive to changes in greenhouse gas 

concentrations (Delworth et al. 2006). In other 

words, any change in greenhouse gas concentration 

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), by contrast, is 

considered to have low sensitivity to greenhouse 
Chapter 2: Climate Change Science and Modeling

44

gas concentrations (Washington et al. 2000). As 

mentioned above, the A1FI scenario is the highest 

greenhouse gas emissions scenario used in the 2007

IPCC assessment, and is the most similar to current 

trends in greenhouse gas emissions globally. The 

B1 scenario is the lowest greenhouse gas emissions 

scenario used in the 2007 IPCC assessment, and 

is much lower than the trajectory for greenhouse 

gas emissions over the past decade. Therefore, the 

GFDL A1FI and PCM B1 scenarios span a large 

range of possible futures. Although both projections 

are possible, the GFDL A1FI scenario is closer to 

current trends in greenhouse gas emissions (Raupach 

et al. 2007). It is important to note that actual 

emissions and temperature increases could be lower 

or higher than these projections. 

This assessment relies on a statistically downscaled 

climate data set (Hayhoe 2010a). Daily mean, 

maximum, and minimum temperature and total daily 

precipitation were downscaled to an approximately 

7.5-mile grid across the United States. This data set 

uses a modified statistical asynchronous quantile 

regression method to downscale daily 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 (Hayhoe 2010b). This 

statistically downscaled data set is different from 

that used in the National Climate Assessment, which 

uses a simpler “delta” approach (Kunkel et al. 2013). 

This data set was chosen for several reasons. First, 

the data set covers the entire United States, and thus 

allows a consistent data set to be used in this and 

other regional vulnerability assessments. Second, 

it includes 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 

data set includes daily values, which are needed for 

some impact models used in this report and provide 

the opportunity to examine questions related to 

growing season length, heavy precipitation events, 

and droughts. Fourth, the statistical technique used 

is more accurate at reproducing extreme values at 

daily time steps than simpler statistical downscaling 

methods (Hayhoe 2010b). Finally, the 7.5-mile 

resolution of the downscaled data is useful for 

informing land management decisions. 

To show projected changes in temperature and 

precipitation, we calculated the average daily mean, 

maximum, and minimum temperature and mean 

precipitation for each month for three 30-year 

periods (2010 through 2039, 2040 through 2069, and 

2070 through 2099). Using 30-year periods reduces 

the influence of natural year-to-year variation that 

may bias calculations of change. The use of 30-year 

periods also allows for more direct comparison with 

the 1971 through 2000 historical data, highlighting 

longer-term trends over annual fluctuations. Monthly 

averages were used to calculate seasonal and annual 

values. We then subtracted the corresponding 1971 

through 2000 average from these values to determine 

the departure from current climate conditions. 

Historical climate data used for the departure 

analysis were taken from ClimateWizard (Girvetz et 

al. 2009). Chapter 3 and Appendix 2 contain more 

information about the observed climate data from 

ClimateWizard. 

Importantly, this set of downscaled future climate 

projections was also used for all the forest impact 

models described below (Fig. 12). The complete data 

set containing daily mean, minimum, and maximum 

temperature and total daily precipitation data was 

provided to each modeling team and incorporated 

into the models. This consistency in future climate 

data allows for more effective comparison of results 

across different forest impact models. 
Chapter 2: Climate Change Science and Modeling

45

1.

Em is sion s

s c en a rio s a re

d e v e lo p e d a n d

u s e d a s in p uts

2.

M o d e l

p ro je ctio n s a re

ru n b y u s in g

G CM s

3.

G CM

p ro je ctio n s a re

dow n s c aled

to a sm a lle r

g rid s c a le

4.

D ow n s c a le d

G CM s a n d

oth e r d ata a re

u s e d a s

im p a c t m od el

in p uts

B 1 o r A 1FI PCM o r G FD L

~3 °  1 /8°

lo n g itu d e/

latitu d e

Tre e Atla s,

LAN DIS-II,

P nET-C N

G e n e ra l

P ro c e s s

T h is

A s s e s sm e nt

Figure 12.—Steps in the development of climate impact models using climate projections from general circulation models 

(GCMs) and the specific steps taken in this assessment.

MODELS FOR ASSESSING 

FOREST CHANGE

Downscaled climate projections from GCMs 

provide us with important information about future 

climate, but they tell us nothing about how climate 

change might affect forests and other ecosystems. 

Other models, commonly called impact models, 

are needed to project impacts on trees, animals, 

and ecosystems. In this assessment, we used forest 

impact models with GCM projections as inputs, as 

well as information about tree species, life-history 

traits of individual species, and soil types. Several 

different models are used to simulate impacts 

on species and forest ecosystems. These models 

generally fall into one of two main categories: 

species distribution models (SDMs) and process 

models. We used one species distribution model, the 

DISTRIB component of the Climate Change Tree 

Atlas (U.S. Forest Service 2014), and two process 

models, LANDIS-II (Scheller et al. 2007) and PnETCN (Aber et al. 1997). These models operate at 

different spatial scales and provide different kinds of 

information. We chose them because they have been 

used to assess climate change impacts on forests in 

our geographic area of interest, and have stood up to 

rigorous peer review in scientific literature.

Species distribution models establish a statistical 

relationship between the current distribution of a 

species or a community and climate, habitat, or other 

environmental variables. This relationship is used 

to model how the range of the species will shift as 

climate change affects those attributes. These models 

are less computationally expensive than process 

models, so they can typically provide projections 

for the suitable habitat of many species over a large 

area. There are some caveats that users should be 

aware of when using them, however (Wiens et al. 

2009). These models use a species’ realized niche 

instead of its fundamental niche. The realized 

niche is the actual habitat a species occupies given 

interactions with other species (e.g., predation, 

disease, and competition), limitations on dispersal, 

and the existence of suitable climates (Soberón 

2007). A species’ fundamental niche, in contrast, is 

the full set of habitats a species potentially could 

occupy. Given that a species’ fundamental niche 
Chapter 2: Climate Change Science and Modeling

46

may be greater than its realized niche, 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. Furthermore, fragmentation 

or other physical barriers to migration may create 

obstacles for species otherwise poised to occupy new 

habitat. Therefore, a given species might not actually 

be able to enter the assessment area in the future, 

even if Tree Atlas projects it will gain suitable 

habitat. Additionally, SDMs like Tree Atlas do not 

project that existing trees will die if suitable habitat 

moves out of an area; rather, this is an indication that 

they will be living farther outside their ideal range 

and will be exposed to more climate-related stress. 

Lastly, SDMs may have difficulty in accurately 

extrapolating into projected climates with no current 

analog (Fitzpatrick and Hargrove 2009, Williams et 

al. 2012).

In contrast to SDMs, process models such as 

LANDIS-II and PnET-CN simulate community 

and tree species dynamics based on interactive 

mathematical representations of physical and 

biological processes. Process models can simulate 

future change in tree species dispersal, succession, 

biomass, and nutrient dynamics over space and 

time. Because these models simulate the spatial 

and temporal dynamics of a variety of complex 

processes, they typically require more computer 

power than a species distribution model. Therefore, 

fewer species or forest types can be modeled 

compared to a SDM. Process models have several 

assumptions and uncertainties that should be 

taken into consideration when results are applied 

to management decisions. Process models rely 

on empirical and theoretical relationships that are 

specified by the modeler. Any uncertainties in these 

relationships can be compounded over time and 

space, leading to an erroneous result.

Although useful for projecting future changes, both 

process models and SDMs share some important 

limitations. They assume that species will not 

adapt evolutionarily to changes in climate. This 

assumption may be true for species with long 

generation times (such as trees), but some shortlived species may be able to adapt even while 

climate is rapidly changing. The inputs to the forest 

impact models for current distribution of trees, site 

characteristics, and downscaled GCM projections are 

all based on estimates, each with its own uncertainty. 

No single model can include all possible variables, 

so there are important inputs that may be excluded 

from individual models, such as competition from 

understory vegetation, herbivory, and pest outbreaks. 

Given these limitations, it is important for all model 

results to pass through a filter of local expertise to 

ensure that results match with reality on the ground. 

Chapter 6 and Appendix 5 explain the approach used 

in this assessment for determining the vulnerability 

of forest ecosystems based on local expertise and 

model synthesis. 

Climate Change Tree Atlas

The Climate Change Tree Atlas (Tree Atlas) 

incorporates a diverse set of information about 

potential shifts in the distribution of tree species’ 

habitat in the eastern United States over the next 

century (Iverson et al. 2008, U.S. Forest Service 

2013). Tree Atlas is actually a set of different models 

and information that work together. The species 

distribution model 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 model current species abundance 

with respect to current habitat distributions by using 

statistical techniques. DISTRIB then projects future 
Chapter 2: Climate Change Science and Modeling

47

importance values and suitable habitat for individual 

tree species by using the downscaled GCM data 

described above. 

Each tree species is further evaluated for additional 

factors not accounted for in the statistical models 

(Matthews et al. 2011b). These modifying 

factors (Appendix 4) are based on supplementary 

information about life-history characteristics 

such as dispersal ability or fire tolerance as well 

as information on current pests and diseases that 

have been negatively affecting the species. This 

supplementary information indicates when an 

individual species may do better or worse than 

model projections would suggest.

For this assessment, the DISTRIB model uses 

the GFDL A1FI and PCM B1 model-scenario 

combinations. Data are presented for three periods 

(2010 through 2039, 2040 through 2069, and 2070 

through 2099) in Appendix 4, with analysis of the 

results for 2070 through 2099 discussed in 

Chapter 5. The GFDL A1FI and PCM B1 modelscenario combinations used for this assessment 

are those described earlier in this chapter, which 

differ slightly from the scenarios used in the online 

Tree Atlas. Modifying factors are based on general 

species traits that are consistent across the entire 

range of a species, so the modifying factor values 

presented in the assessment are not unique for the 

assessment area.

Union Bay in western Upper Michigan. Photo by Scott Pearson, used with permission.
Chapter 2: Climate Change Science and Modeling

48

LANDIS-II

The LANDIS-II model is a spatially explicit and 

dynamic model for simulating forest landscape 

changes that is process-driven and flexible for a 

variety of applications (Scheller et al. 2007). It 

simulates forest succession, natural disturbance 

(e.g., fire, wind, and insect outbreak), management 

(i.e., harvesting and planting), and other processes 

(e.g., climate change and drought, seed dispersal) 

in a grid-based framework that emphasizes spatial 

interactions across the landscape and among 

processes. Thus processes occur within a given 

grid cell, and also between cells. This model can 

run simulations over many decades and over large 

spatial extents (e.g., greater than 20 million acres in 

this study). Some processes are simulated to occur 

randomly based on probabilities and cell conditions, 

such as fire disturbance or seed dispersal (see 

www.landis-ii.org for further details).

Inputs to LANDIS-II include an initial forest 

conditions map with tree species assigned to age 

cohorts in all forested cells, an ecoregion map 

with similar ecological (i.e., climate, soils and 

topographical conditions), and other spatial data 

related to forest management. Climate change 

is incorporated by integrating specific species 

parameters to calculate maximum aboveground 

net primary productivity (Aber et al. 1997) and the 

probability of establishment (Xu et al. 2009) using 

the PnET-II model at every time step. LANDISII calculates these parameters by using monthly 

maximum and minimum temperature, precipitation, 

and solar radiation under different climate change 

scenarios. Other inputs include species foliar 

nitrogen content, maximum foliar mass area, and 

ecoregion parameters such as soil water-holding 

capacity and actual evapotranspiration. LANDISII also requires modelers to specify timber harvest 

methods and rates of harvesting (Ravenscroft et 

al. 2010) and rotation periods and size information 

for fire and wind disturbances (Cleland et al. 2004, 

White and Host 2008). Projections of aboveground 

biomass are provided for individual species and for 

aggregated forest types as aboveground biomass 

provides a useful metric for comparing tree and 

forest growth over time. 

For this assessment, two future climate scenarios, 

PCM B1 and GFDL A1FI, were used to simulate a 

range in potential future climate. The simulations 

used a landscape with 15-acre grid cells. A 300-year 

time horizon from 2000 to 2300 was simulated, and 

the results from 2000 through 2100 are discussed 

in this assessment (Chapter 5 and Appendix 5). 

Biomass values in three individual years (2040, 

2070, and 2100) were compared to year 2000 

biomass to make comparisons of change over time. 

The landscape modeled covered most counties in 

northern Wisconsin, the western Upper Peninsula 

of Michigan (Baraga, Gogebic, Houghton, Iron, 

and Ontonagon Counties), and Chisago County in 

eastern Minnesota, an area of more than 20 million 

acres of forest across much of the assessment area. 

LANDIS-II simulations included 27 tree species 

currently present within this landscape (Chapter 5). 

Forest management practices were described with 

a business-as-usual scenario using the current 

harvesting rates in the study area under different 

ownership types.

PnET-CN

The PnET-CN model is an ecosystem-level 

process model that simulates carbon, water, and 

nitrogen dynamics in forests over time (Aber 

et al. 1997, 2001; Ollinger et al. 2008; Peters 

et al. 2013). This model accounts for physiological 

and biogeochemical feedbacks, which allows 

carbon, water, and nitrogen cycles to interact with 

each other. This enables PnET-CN to simulate the 

effects of water and nitrogen limitation on forest 

productivity. A strength of the PnET-CN model is 

its ability to simulate forest responses over time 

to many simultaneously changing environmental 

factors, including climate, nitrogen deposition, 

tropospheric ozone, and atmospheric CO2 . Although 
Chapter 2: Climate Change Science and Modeling

49

PnET-CN can be applied to large geographical 

regions, it is not a spatially dynamic model and does 

not simulate ecological processes such as succession 

or migration. PnET-CN assumes forest composition 

does not change over time. Rather, the utility of 

PnET-CN is to assess the physiological response of 

existing forests to projected environmental change. 

PnET-CN requires input information on climate, 

soil, and vegetation. Climate and atmospheric 

inputs include monthly air temperature, 

precipitation, photosynthetically active radiation, 

tropospheric ozone concentration, atmospheric CO2

concentration, and atmospheric N deposition rate. 

Soils are defined by their water holding capacity. 

Vegetation inputs include a suite of parameters, such 

as specific leaf area or leaf lifespan, that define a 

particular forest type. Forest types used by PnETCN in this assessment are similar to FIA forest-type 

groups, such as maple/beech/birch (Pugh et al. 

2012). Output from PnET-CN includes many 

variables related to carbon, water, and nitrogen 

cycling, including key ecosystem processes such as 

net primary production, net ecosystem production, 

evapotranspiration, and nitrogen mineralization. Full 

information on the PnET-CN simulations used in this 

assessment, including inputs, methods, and results, 

can be found in Peters et al. (2013).

For this assessment, the PnET-CN model simulations 

were run from 1960 through 2100 with a grid 

resolution of 0.6 miles (Chapter 5). Relative 

change in aboveground net primary productivity 

was calculated by subtracting the modeled values 

for the years 1971 through 2000 from the 2070 

through 2099 period. Two future climate scenarios, 

PCM B1 and GFDL A1FI, were used to simulate a 

range in potential future climate and atmospheric 

CO2

 concentration. Current tropospheric ozone 

concentrations and nitrogen deposition rates (data 

provided by the U.S. Environmental Protection 

Agency) were held constant into the future. Soil 

water-holding capacity was characterized by the 

Natural Resources Conservation Service’s Soil 

Survey Geographic Database (Matthew Peters, U.S. 

Forest Service, pers. commun.). Current vegetation 

cover was defined by using a vegetation map based 

on FIA data and satellite imagery (Wilson et al. 

2012), which included six forest types (maple/

beech/birch, elm/ash/cottonwood, oak/hickory, 

aspen/birch, spruce/fir, and pine). Although PnETCN can account for discrete disturbance events, for 

this assessment we did not include any disturbances 

related to harvest, fire, or wind.

SUMMARY

Temperatures have been increasing in recent 

decades at global and national scales, and the 

overwhelming majority of scientists attribute this 

change to increases in greenhouse gases from human 

activities. Even if dramatic changes are made to help 

curtail greenhouse gas emissions, these greenhouse 

gases will persist in our atmosphere for decades to 

come. Scientists can model how these increases in 

greenhouse gases may affect global temperature 

and precipitation patterns by using GCMs. These 

large-scale climate models can be downscaled to 

finer resolution and incorporated into multiple 

different forest impact models that project changes 

in forest composition and ecosystem processes for 

use in informing management decisions. There 

are inherent uncertainties in what the future holds, 

but all of these types of models can help us frame 

a range of possible futures. This information can 

then be used in combination with the local expertise 

of researchers and managers to provide important 

insights about the potential effects of climate change 

on forests.
50

CHAPTER 3: OBSERVED CLIMATE CHANGE 

Climate is one of the principal factors that have 

determined the composition and extent of forest 

ecosystems of this region over the past several 

thousand years. Many of the climatic changes that 

have been noted across the world over the past 

century are also evident in the assessment area. This 

chapter describes changes in climate that have been 

observed since 1900 in the assessment area. We also 

present a few case studies that illustrate the effects 

of climate change on ecological indicators such as 

growing season, lake ice, and bird migration. 

CURRENT CLIMATE

Weather stations in Wisconsin and Michigan 

have recorded measurements of temperature and 

precipitation for more than 100 years, providing a 

rich set of information to evaluate changes in climate 

over time. The ClimateWizard Custom Analysis 

application was used to estimate the changes in 

temperature and precipitation across the assessment 

area (ClimateWizard 2012, Girvetz et al. 2009). This 

tool uses data from PRISM (Gibson et al. 2002), 

which converts measured point data from weather 

stations onto a continuous 2.5-mile grid over the 

entire United States. Temperature and precipitation 

data for the assessment area were used to derive 

annual, seasonal, and monthly values for the 30-year 

“climate normal” during 1971 through 2000 

(Table 8), as well as long-term trends during 1901 

through 2011. Additional information regarding 

confidence in trends, the PRISM data, and the 

ClimateWizard Custom Analysis application is 

available in Appendix 2. 

Mean Minimum Maximum Mean

temperature (°F) temperature (°F) temperature (°F) precipitation (inches)

Winter 15.4 5.6 25.1 3.9

 December 17.4 8.9 26.0 1.5

 January 11.6 1.6 21.7 1.4

 February 17.0 6.2 27.7 1.0

Spring 40.9 29.2 52.6 7.5

 March 27.7 17.2 38.3 1.9

 April 41.1 29.5 52.7 2.4

 May 53.8 41.0 66.7 3.2

Summer 65.0 53.1 76.8 11.8

 June 62.4 50.1 74.8 3.8

 July 67.2 55.4 79.0 3.9

 August 65.3 53.8 76.7 4.0

Fall 44.2 34.3 54.1 8.8

 September 56.5 45.3 67.6 3.8

 October 45.2 34.8 55.7 2.7

 November 30.9 22.8 38.9 2.3

Annual 41.3 30.5 52.2 32.0

Table 8.—Average temperature and precipitation for the assessment area, 1971 through 2000 (ClimateWizard 2012)
Chapter 3: Observed Climate Change

51

The regional climate is generally characterized by 

cold winters and relatively hot, humid summers 

(Albert 1995). Annual temperature and precipitation 

patterns are generally similar across the assessment 

area, with some notable exceptions in areas heavily 

influenced by Lake Superior and, to a lesser extent, 

Lake Michigan (see Chapter 1 and Box 1). Mean 

annual air temperature fluctuates by about 50 °F 

(27.8 °C) between winter and summer. July is the 

warmest month with a mean temperature of 

67.2 °F (19.6 °C), and January is the coldest month 

with a mean temperature of 11.6 °F (-11.3 °C; 

Table 8, Fig. 13). Areas to the south and east of 

Lake Superior tend to be cooler and wetter during 

the summer months than areas farther inland 

(Fig. 13). Precipitation is greatest in summer and 

least in winter (Table 8, Fig. 14). 

Figure 13.—Thirty-year annual and seasonal averages of mean, minimum, and maximum temperature across the assessment 

area from 1971 through 2000. Data source: ClimateWizard (2012).
Chapter 3: Observed Climate Change

52

Figure 14.—Thirty-year averages of mean annual and 

seasonal precipitation across the assessment area from 1971 

through 2000. Data source: ClimateWizard (2012).

TRENDS IN TEMPERATURE AND 

PRECIPITATION (1901-2011)

Temperature 

The Midwest has experienced substantial changes 

in temperature and precipitation over the past 

100 years, and the rate of change appears to be 

increasing (Andresen et al. 2012). In addition to 

the 30-year climate normal (1971 through 2000) 

data described above, the long-term climate record 

(1901 through 2011) was analyzed by using the 

ClimateWizard Custom Analysis tool to gain a better 

understanding of how the climate has changed over 

the last century (Appendix 2). Although there is 

variation in annual mean (average) temperature from 

year to year, there is a long-term warming trend 

(Fig. 15) that is consistent with changes observed at 

state, continental, and global scales (Andresen et al. 

2012, IPCC 2007b, Kunkel et al. 2008, Wisconsin 

Initiative on Climate Change Impacts [WICCI] 

2011b). Across the assessment area, the mean annual 

temperature increased by 1.4 °F (0.8 °C) between 

1901 and 2011 (Fig. 15). 

The greatest change in temperature during the 

20th century was observed during winter, with an 

increase in mean temperature of 2.2 °F (1.2 °C) and 

an increase in minimum (low) temperature of 3.0 °F 

(1.6 °C). The greatest increases in mean, minimum, 

and maximum temperatures have all been observed 

during February (Fig. 16), with an increase in mean 

temperature of 4.6 °F (2.6 °C). Mean and minimum 

temperatures have increased over nearly all 

seasons and months, whereas changes in maximum 

temperature have been more variable (Fig. 16, 

Appendix 2). 
Chapter 3: Observed Climate Change

53

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

190 1-201 1 T em p e rat u re ch an g e (°F)

M ean M inim um M ax im um

Figure 16.—Change in mean monthly temperatures across the assessment area from 1901 through 2011. Data source: 

ClimateWizard (2012). Numeric change values are provided in Appendix 2.

Figure 15.—Mean annual temperature across the assessment area from 1901 through 2011. The blue line represents the 

rolling 5-year mean. The red regression line shows the trend across the entire period (0.012 °F/year). Source: ClimateWizard 

(2012).
Chapter 3: Observed Climate Change

54

Within the assessment area, there are local 

differences in the magnitude and direction of 

temperature change observed over the last century. 

Mean annual temperature has increased by 2 to 3 °F 

(1.1 to 1.7 °C) in northwestern Wisconsin and much 

of the western Upper Peninsula. Parts of northcentral and northeast Wisconsin have not changed 

as much or have even cooled slightly during some 

seasons (Fig. 17). Increases in minimum temperature 

have generally been the most widespread across 

the assessment area, although the magnitude varies. 

Minimum temperature increases during the 1900 

to 2011 time period ranged from 2 °F (1.1 °C) in 

central Wisconsin to 6 °F (3.3 °C) in northwestern 

Wisconsin.

Figure 17.—Annual and seasonal change in mean, minimum, and maximum temperatures across the assessment area from 

1901 through 2011. Stippling indicates there is less than 10-percent probability that the trend could have occurred by chance 

alone. Data source: ClimateWizard (2012). Additional information is available in Appendix 2.
Chapter 3: Observed Climate Change

55

The general trend toward increasing temperatures 

in the assessment area is similar to observations 

reported elsewhere. Much of the United States has 

warmed by 1 to 2 °F (0.6 to 1.1 °C) since the 1960s 

and 1970s (Karl et al. 2009), and annual average 

temperatures across the lower 48 states increased 

by 1.3 °F (0.7 °C) from 1901 to 2009 (U.S. 

Environmental Protection Agency 2013). Across 

the Midwest, temperatures rose an average of 

0.1 °F (0.06 °C) per decade during 1900 through 

2010, for a total increase of approximately 1.2 °F 

(0.6 °C) during that period (Andresen et al. 2012); 

further, this rate of warming has increased to more 

than 0.4 °F (0.2 °C) per decade during the past 40 

years (Andresen et al. 2012). 

These data are also similar to results from other 

regional studies. Measurements from Wisconsin 

weather stations between 1950 and 2006 show that 

minimum (low) temperatures have increased 1.1 to 

4.0 °F (0.6 to 2.2 °C) across the state (Kucharik et al. 

2010a). This is greater than the increase in maximum 

temperatures, which have risen 0.5 to 1.1 °F (0.3 to 

0.6 °C) during the same time period. Similarly, data 

from 16 weather stations across the Upper Peninsula 

showed a 3.7 °F (2.1 °C) increase in minimum daily 

temperatures from 1970 to 2007, and the fastest rise 

was observed during winter (Myers et al. 2009). 

These changes in temperature lead to other important 

changes in climate. For example, the length of 

the growing season increased by 12 days across 

Wisconsin between 1950 and 2006, with the greatest 

change in central and northwestern Wisconsin 

(Kucharik et al. 2010a). The date of the last spring 

freeze is occurring 5.6 days earlier on average, and 

the first autumn freeze is occurring 6.5 days later. 

PRECIPITATION

Precipitation patterns have also changed over the 

past century (Figs. 18 and 19). Although annual 

mean precipitation varies widely from year to 

year, there is a slight trend toward greater annual 

precipitation in the assessment area. This trend 

is consistent with changes observed in statewide 

analyses of Michigan and Wisconsin (Andresen 

2007, WICCI 2011b). Mean annual precipitation 

increased by 2.0 inches, or about 6.5 percent, 

across the assessment area from 1901 through 

2011. Observed precipitation changes were variable 

across months and seasons (Figs. 18 through 20). 

Precipitation generally increased during the late fall 

and winter and decreased during May through June 

(Fig. 19).

There were also geographic differences in 

precipitation change. The greatest increase in 

precipitation was observed in northwestern 

Wisconsin, where annual precipitation increased 

by more than 6 inches from 1901 through 2011 

(Fig. 20). The area near Iron County, Michigan, 

experienced the greatest decrease in precipitation 

with an annual decrease of as much as 8 inches. 

Seasonal changes also varied geographically, and 

were generally less pronounced (Fig. 20). Summer 

precipitation increased as much as 3 inches in some 

areas yet decreased by the same amount in parts of 

the Upper Peninsula and north-central Wisconsin. 

Winter precipitation increased slightly over much of 

the assessment area, with notable increases of 3 to 

4 inches along parts of the Lake Superior shoreline. 

This change may be related to increases in lakeeffect snowfall; long-term, upward trends in lakeeffect snowfall were recorded in the Great Lakes 

region from 1931 to 2000 (Burnett et al. 2003). 
Chapter 3: Observed Climate Change

56

-0.25

0.00

0.25

0.50

0.75

1901-2011 P recip iatatio n

ch an g e (in ch es)

Figure 19.—Change in mean monthly precipitation across the assessment area from 1901 through 2011. Data source: 

ClimateWizard (2012). Numeric change values are provided in Appendix 2.

Figure 18.—Mean annual precipitation across the assessment area from 1901 through 2011. The blue line represents the 

rolling 5-year mean. The red regression line shows the trend across the entire time period (0.018 inches/year). Source: 

ClimateWizard (2012).
Chapter 3: Observed Climate Change

57

Figure 20.—Change in mean annual and seasonal 

precipitation across the assessment area from 1901 through 

2011. Stippling indicates there is less than 10-percent 

probability that the trend could have occurred by chance 

alone. Data source: ClimateWizard (2012). Additional 

information is available in Appendix 2.

TRENDS IN EXTREME 

WEATHER EVENTS

Although it can be very instructive to examine 

long-term means of climate and weather data, in 

many circumstances extreme events can have a 

greater impact on forest ecosystems and the human 

communities that depend on them. Weather or 

climate extremes are defined as individual weather 

events or long-term patterns that are unusual in their 

occurrence or have destructive potential (Climate 

Change Science Program [CCSP] 2008b). These 

events can trigger catastrophic disturbances in forest 

ecosystems, along with significant socioeconomic 

impacts. Evidence suggests that extreme events 

are becoming more frequent and severe across the 

United States and globally, and that global climate 

change is part of the explanation (Coumou and 

Rahmstorf 2012, IPCC 2012, Kunkel et al. 2008). 

At the same time, it is difficult to directly attribute 

the occurrence of a single event to climate change 

(Coumou and Rahmstorf 2012, Stott et al. 2010).

Many physical processes important for forest 

ecosystems are driven by climate and weather 

patterns. These factors, such as snowpack and 

soil frost, can regulate annual phenology, nutrient 

cycling, and other ecosystem dynamics. Changes 

to these physical processes can result in impacts 

and stress that might not be anticipated from mean 

climate values alone. This section presents a few key 

trends that have been observed in the assessment 

area and throughout the Midwest. 

Temperature Extremes

Warmer mean temperatures are often correlated 

with higher extreme temperatures (Kling et al 2003, 

Kunkel et al. 2008). For example, the number of 

heat waves has increased across the United States 

since 1960, driven largely by an increase in the 

frequency of extremely high nighttime temperatures 

(Kunkel et al. 2008). Multi-day heat waves have also 

become more common over the past 60 years in the 
Chapter 3: Observed Climate Change

58

Midwest, and summer cool days have become less 

frequent over this same time period (Perera et al. 

2012). These trends correspond to global patterns 

of increasing occurrence of extreme hot weather 

and decreasing occurrence of extreme cool weather 

(Hansen et al. 2012). There is less evidence for 

this trend in the assessment area. In Wisconsin, the 

number of hot days with a maximum temperature 

greater than 90 °F (32.2 °C) stayed relatively stable 

from 1950 through 2006 (Kucharik et al. 2011).

Additionally, the number of extremely cold weather 

events may be decreasing. Minimum temperatures 

have increased more than maximum temperatures 

in parts of the United States, including the Midwest 

(Peterson et al. 2008). Similarly, cold days with a 

minimum temperature below 0 °F (-17.8 °C) are 

becoming less frequent in Wisconsin. There were 9.5 

fewer such days statewide and 20 fewer such days 

in the northwest portion of the state between 1950 

and 2006 (Kucharik et al. 2010a). A study across the 

entire Midwest region found that intense cold waves 

(4-day durations of temperatures below a 1-in-5-year 

recurrence threshold) have been less frequent over 

the past 17 years, but there has not been a clear trend 

across the 20th century (Perera et al. 2012).

Intense Precipitation

Intense heavy precipitation events have become 

more frequent throughout North America, the 

Midwest, and the assessment area (Groisman et 

al. 2004, Karl et al. 2009, Saunders et al. 2012). 

Evidence of change across the United States 

includes a doubling of heavy downpours compared 

to a century ago and a 50-percent increase in the 

frequency of days with more than 4 inches of 

precipitation (Karl et al. 2009). Greater changes have 

been observed in the Midwest than other parts of the 

country (Kunkel et al. 1999, Saunders et al. 2012). 

The frequency of rainstorms of 3 inches or more 

between 1961 and 2011 increased by 203 percent in 

Wisconsin and 180 percent in Michigan (Saunders et 

al. 2012). At the same time, the number of light and 

average precipitation days (less than 1 inch) changed 

very little or even decreased (Groisman et al. 2004, 

2005; Saunders et al. 2012). Events on the all-time 

record rainfall list are quickly being replaced by 

record rainfalls that have occurred in recent decades. 

In Wisconsin, 6 of the 10 heaviest downpours 

from 1961 through 2011 have occurred since 2000, 

resulting in 5 flooding disasters (Saunders et al. 

2012). The Wisconsin record for the most rainfall 

ever recorded in a single day occurred in 2008. In 

Michigan, four of the heaviest downpours on record 

have occurred since 2000 (Saunders et al. 2012). 

Extreme Storms and Wind

Strong thunderstorms occur most frequently in the 

summer in the assessment area, and these weather 

events can be particularly damaging if they also 

occur with tornadoes or severe wind or rain. Based 

on long-term data from 1896 through 1995, the 

assessment area averaged 25 to 35 thunderstorm 

days per year with fewer storms occurring in the 

Upper Peninsula (Changnon 2003). Damaging or 

dangerous storms are relatively infrequent in the 

assessment area and are more likely to occur in 

the southern parts of Wisconsin and Michigan and 

in states to the south and west (Changnon 2003, 

National Oceanic and Atmospheric Administration 

[NOAA] 2006a). From 1981 through 2010, there 

were 23 tornadoes per year in Wisconsin and 17 in 

Michigan, and tornado frequency appears to have 

remained stable in recent decades (National Weather 

Service 2012). The U.S. Annual Tornado Maps from 

1950 to 2009 show that few of these tornadoes occur 

within the assessment area, with the most tornadoes 

occurring in the southern portion (National Weather 

Service 2012). 

In warm months of the year the assessment 

area occasionally experiences very powerful, 

convectively generated windstorms, or derechos. 

These events can result in substantial windthrow 

disturbances in forest ecosystems. Smaller-scale 

wind disturbances also introduce complexity in 
Chapter 3: Observed Climate Change

59

forest stands throughout the region (Schulte and 

Mladenoff 2005, White and Host 2008). The 

frequency of derechos decreases with increasing 

latitude in the assessment area, and these events 

are relatively rare in the assessment area (Coniglio 

and Stensrud 2004). The understanding of historical 

trends in derecho frequency and geographic location 

is limited by a lack of long-term data in the first half 

of the 20th century (Peterson 2000). 

CHANGES IN WATER AND SOILS

Numerous studies have investigated hydrologic 

processes and have found that snow density and 

extent, snow and frost depth and duration, soil 

moisture and structure, and soil temperature are 

critical components of seasonal hydrology in the 

Great Lakes region. Trends in temperature and 

precipitation can influence hydrologic cycling, 

leading to changes in soil moisture, groundwater 

availability, and streamflow. 

Lake Temperature and Ice Cover

The Great Lakes are drivers of the region’s local 

climate, with water temperature and ice cover 

playing important roles. With increases in air 

temperatures, water temperatures also increase. 

From 1979 through 2006, Lake Superior water 

temperatures increased 2.5 °F (1.3 °C), an increase 

much greater than that observed in regional air 

temperatures. Lake Michigan showed a similar trend 

(Austin and Colman 2007).

The timing and extent of lake ice formation have 

been recorded for more than 150 years across 

the Great Lakes region. These records show that 

the duration of ice cover has decreased by 1 to 2 

days per decade (U.S. Environmental Protection 

Agency 2010). This decrease is a result of later ice 

formation in the fall and earlier ice breakup in the 

spring. During a period of rapid warming from 1975 

to 2004, average ice duration in the Great Lakes 

region decreased by more than 5 days per decade 

(Jensen et al. 2007). Lakes and bays in northern 

Wisconsin showed a similar rate of decrease in lake 

ice duration from 1850 through 2000 (Magnuson 

2003). A long-term simulation of historical lake ice 

trends across Michigan, Minnesota, and Wisconsin 

estimated that breakup dates are occurring earlier 

and freeze dates are occurring later, both by about 

1.4 days per decade (Mishra et al. 2011). Within 

the region of Michigan, Minnesota, and Wisconsin, 

observed changes in lake ice duration indicate that 

breakup and freeze dates have been shifting three 

to four times more rapidly since 1980 than over 

the entire 20th century (Kling et al. 2003). Another 

study found an average decline of 71 percent in ice 

coverage on the Great Lakes between 1973 and 2010 

(Wang et al. 2012). These state and regional patterns 

of lake ice duration correspond with observed trends 

across the entire Northern Hemisphere (Johnson 

and Stefan 2006, Kling et al. 2003, Magnuson et al. 

2000).

Warmer water temperatures and reduced ice cover 

often interact in a positive feedback cycle where 

warmer winter air and water temperatures reduce 

ice cover and increase the duration of open water 

conditions. The ensuing open water conditions allow 

the water to absorb more heat, further increasing 

water temperatures (Assel 2005, Austin and Colman 

2007). Similarly, thermal stratification is another 

important process in many lakes where a layer of 

warm water is present on the surface of the lake and 

separated from colder, deeper water during warmer 

times of the year. Earlier onset of lake stratification 

(currently occurring about 2 weeks earlier) increases 

the period during which the lake is able to warm 

during the summer months and further reinforces the 

warming trend (Assel 2009, Lofgren et al. 2002). 

These changes can strongly influence near-shore 

climates and weather events, such as lake-effect snow.
Chapter 3: Observed Climate Change

60

Snowfall

Cold and snowy winters are characteristic of the 

assessment area. There are clear patterns of winter 

precipitation across the assessment area, with 

areas near the Lake Superior shoreline having the 

highest amounts of winter precipitation (Fig. 14). 

These trends are dictated by the prevailing wind 

direction, topography, and lake-effect snow from 

Lake Superior and, to a much lesser extent, Lake 

Michigan. 

During the 20th century, winter (December through 

February) precipitation increased slightly across 

the assessment area (0.3 inches), with the most 

substantial increases observed in the lake-effect zone 

along Lake Superior (Figs. 19 and 20). Long-term 

records from across the Great Lakes also indicate 

that lake-effect snow increased gradually across 

the region during the 20th century (Burnett et al. 

2003, Kunkel et al. 2013). This increase is largely 

attributed to declining ice cover on the Great Lakes 

and greater evaporation from lakes; this moisture 

is then deposited over land as snowfall (see 

Box 1). Other regional studies of snow 

measurements also observed increased snowfall 

and snowfall frequency during the winter months 

(December through February) and decreases during 

the month of April (Burnett et al. 2003, Ellis and 

Johnson 2004, Groisman and Easterling 1994, 

Norton and Bolsenga 1993). 

Regional trends indicate that snowfall amounts are 

quite variable from year to year, but fewer heavy 

snowfall years have occurred over the last 30 years 

(Kunkel et al. 2013). Individual snowfall events may 

have become more severe, however. During the 20th 

century, there was an increase in snowstorms of 6 

inches or more across the upper Midwest (CCSP 

The “ice road” from Bayfield to Madeline Island in Wisconsin. Photo by Maria Janowiak, U.S. Forest Service.
Chapter 3: Observed Climate Change

61

2008b). Similarly, a four-state region including 

Michigan and Wisconsin had fewer extreme lowsnow years over the 20th century (Kunkel et al. 

2009).

As described above, there is substantial evidence 

that winter air temperature and precipitation have 

increased over the assessment area during the last 

century. Whether precipitation falls as rain or snow 

is strongly influenced by air temperature, and 

evidence suggests that the proportion of precipitation 

falling as snow has decreased in some parts of the 

assessment area (Feng and Hu 2007, Mishra and 

Cherkauer 2011). 

Soil Temperature and Soil Frost

Soil frost dynamics are important for forest 

ecosystems because soil temperatures can impact 

water infiltration rates, nutrient cycling, and tree 

growth. Snowcover insulates the soil surface from 

changes in air temperature, thereby helping reduce 

both the number of freeze-thaw cycles and the 

depth to which frost penetrates the soil (Hardy 

et al. 2001, Isard et al. 2007, Sinha et al. 2009). 

Research has shown that deeper snow depth can 

result in shallower soil frost depth in northern 

forests, whereas thinner snowpack results in colder 

soil temperatures and deeper soil frost where 

temperatures are sufficiently cold (Hardy et al. 

2001). 

Observations of how soil temperature and soil 

frost have changed across the assessment area are 

complex and influenced by local factors including 

soil characteristics and lake effect. However, some 

general trends are notable. In the northeastern extent 

of the assessment area, winter soil temperatures 

have decreased (Isard et al. 2007) and the number 

of soil frost days increased (Sinha et al. 2010). 

This area often has greater snowfall and snowcover 

(and therefore, insulated soils) than other parts of 

the assessment area, so it is possible that warmer 

temperatures have resulted in more variable 

snowpack conditions and increased occurrence of 

soil frost. 

Conversely, in northwestern Wisconsin where 

winter warming has generally been greatest 

(Fig. 17), soil temperatures increased since 1950 

(Isard et al. 2007) and fewer frost days occurred in 

recent decades (Sinha et al. 2010). Weather stations 

from six locations across Wisconsin also point 

to a shorter duration of frozen ground, especially 

in the southern and western portions of the state 

(C. Rittenhouse, University of Connecticut, and 

A. Rissman, University of Wisconsin – Madison, 

unpublished data). For example, the greatest 

reduction in the number of days of frozen soil 

(4 inches deep) was observed in Eau Claire County, 

where days with frozen soil decreased 39 days since 

1949 and winter soil temperature increased slightly.

Runoff, Streamflow, and Flooding

Long-term data on flooding are difficult to 

interpret because of the variety of measures used to 

describe floods. From 1961 to 1979, the National 

Weather Service reported no severe flood years 

in the four-state region including Michigan and 

Wisconsin, and there were 4 such years between 

1983 and 2001 (Cartwright 2005). There are several 

Amnicon Falls in northern Wisconsin. Photo by 

Scott Pearson, used with permission.
Chapter 3: Observed Climate Change

62

complicating factors in attributing this trend. In 

particular, human-caused land-use change over the 

past century has had a considerable influence on 

flooding frequency in the upper Midwest. Increased 

flood peaks in the upper Midwest may be driven by 

land use practices, agricultural practices, and dam 

construction (Villarini et al. 2011). Even after these 

factors are accounted for, Midwestern watersheds 

still exhibited increased discharge over the past 

several decades and this trend has been attributed 

to climate change (Tomer and Schilling 2009). Data 

from streamflow stations in the Great Lakes show an 

earlier occurrence of spring peak flows, which are 

attributed to increased air temperature and earlier 

melting of snowpack (Hodgkins and Dudley 2006, 

Small et al. 2006).

Soil Moisture and Drought

Droughts are among the greatest stressors on forest 

ecosystems, and can often lead to secondary effects 

of insect and disease outbreaks on stressed trees 

and increased fire risk. In North America and the 

United States, there has been a trend toward wetter 

conditions since 1950, and there is no detectable 

trend for increased drought based on the Palmer 

Drought Severity Index (Dai et al. 2004, Karl et 

al. 1996). Other studies of hydrologic trends in the 

United States over the last century also observed 

reduced duration and severity of droughts across the 

upper Midwest as a result of increased precipitation 

(Andreadis and Lettenmaier 2006, Peterson et al. 

2013), although this observation was not as strong in 

the assessment area (Peterson et al. 2013).

Statewide data from Michigan and Wisconsin 

support this general pattern. Between 1895 and 

2013, the Midwest trend has been toward slightly 

less common and less severe droughts during the 

growing season (June through September), with 

the years between 1920 and 1940 representing the 

most extreme droughts during the period of record 

(NOAA National Climatic Data Center 2013). This 

trend, however, has not been observed across the 

assessment area. The western Upper Peninsula in 

particular has been drier than average in recent 

decades, and moderate drought during the growing 

season has been recorded in nearly every year since 

2000. North central Wisconsin has also experienced 

more frequent periods of drought than many other 

parts of the Midwest, although not to the same 

degree as the Upper Peninsula.

ECOLOGICAL INDICATORS OF 

CLIMATE CHANGE

Phenology is the timing of biological events, such 

as bird migration, wildlife breeding, and plant 

flowering and fruiting, or the study of these events. 

The timing of these events is determined by many 

variables, including seasonal temperature, food 

availability, and pollination mechanisms (Bradley 

et al. 1999). Increases in the growing season length 

and other climatic changes are causing noticeable 

changes in the timing of biological activities 

(Ellwood et al. 2013, Schwartz et al. 2006a, Walther 

et al. 2002), and changes in events such as plant 

flowering and bird migration have been observed 

across the region (Bradley et al. 1999, Notaro 

et al. 2010). The following case studies present 

some examples of early indications of climate 

change within the assessment area. These examples 

are by no means comprehensive, but they are 

intended to highlight a few of the ways that shifts 

in temperature, precipitation, and other factors 

may be influencing the environment and natural 

communities. 

Tree Phenology and Growth

Certain aspects in the annual life cycle of trees 

are governed by seasonal cues that are relatively 

constant from year to year, like day length. Other 

aspects are controlled by cues that can vary 

substantially from year to year, like temperature. 

For sugar maple, a common northern hardwood 
Chapter 3: Observed Climate Change

63

species across the assessment area, leaf expansion 

in the spring is triggered by temperature. Rather 

than sending out new leaves on the first warm day, 

sugar maples adjust the date of leaf-out based on 

aggregated temperature. Growing-degree days, an 

indicator of heat accumulation above a speciesspecific base temperature, can be used to predict 

the progress of sugar maple leaf expansion and 

development. Leaf expansion marks the beginning of 

a tree’s growing season, and growing season length 

can help dictate how much trees grow over time. 

Researchers have been measuring leaf phenology 

and tree growth in sugar maple stands across a 

latitudinal gradient in northern Michigan for more 

than 20 years (Fig. 21) (Burton et al. 1996). Annual 

growing season across the gradient of study sites 

differs by about 3 weeks, being longer in southern 

Michigan where annual temperatures are warmer by 

more than 5 °F (2.8 °C). Mean annual temperatures 

increased by 2.3 °F (1.3 °C) across the study sites 

between 1989 and 2009 (A.J. Burton, Michigan 

Technological University, unpublished data). This 

change had a corresponding influence on growing 

season length, which increased by an average of 

11.5 days over the study period. Growing season 

length seems to be shifting earlier into the spring for 

sugar maple, as leaf-out dates advanced more rapidly 

than leaf-fall dates were delayed (Fig. 22). Tree 

growth increased at each of the study sites in the 

Lower Peninsula, with an average 26-percent gain 

in aboveground biomass. The study site near Twin 

Lakes, in the Upper Peninsula, appears to have been 

limited by late-season droughts for most of the years 

from 2001 through 2009, and therefore sugar maple 

in this location was less able to take advantage of 

the extended growing season. These phenology and 

growth trends highlight the influence that shifting 

temperatures can have on northern Michigan forests. 

This work also illustrates that forest productivity 

may be limited by moisture availability, even as 

temperatures rise and growing seasons lengthen. 

Bird Populations and Range Shifts

Spring is coming earlier across the assessment area, 

and migratory birds are returning earlier, too. In 

the Upper Peninsula, a study shows that among the 

47 bird species that migrate through the region, 20 

species arrived an average 19 days earlier in 1994 

than in 1965 (Price and Root 2000). Another study 

indicates that birds that winter in the southern United 

States have been arriving disproportionately earlier 

(13 days) than birds that winter in Central or South 

America (4 days), suggesting that short-distance 

migrants are better able to track changes in local 

conditions than are long-distance migrants (Butler 

2003). 

Nonmigratory species are also taking up new 

residency in northern locations. In the Upper 

Peninsula, a few bird species that historically 

migrated south for the winter have now become 

year-round residents (Price and Root 2000). The redbellied woodpecker has expanded its winter range 

into northern Wisconsin and the western Upper 

Peninsula, possibly because the milder winters 

A

B

C

D

N

Figure 21.—Location of four long-term study sites (A, B, 

C, D) tracking phenology in hardwood forests in northern 

Michigan (Burton et al. 1996). © Canadian Science 

Publishing or its licensors.
Chapter 3: Observed Climate Change

64

Figure 22.—Leaf phenology trends across four northern hardwood study sites in Michigan. Leaf duration is the number of days 

between leaf-out and leaf-drop (A.J. Burton, Michigan Technological University, unpublished data). All trends are consistent 

with and likely caused by rising temperatures in the region.

allow birds to survive farther north than previously 

(Adams and Wenger 2011). 

Changes in the climate can affect the timing of 

events both for particular species and for the species 

they depend on. Migration dates are closely tied 

to availability of food and spring temperatures. 

If arrival dates are not aligned with similar 

phenological changes in food sources and other 

resources, reproductive success may decline (Both 

et al. 2004). Many species rely on day length rather 

than temperature for their phenological cues. Even 

though temperature is warming, the day length 

stays the same, which suggests the potential for 

a future mismatch with required resources, such 

as insect larvae for food. The complexities of 

phenological shifts may pose a greater challenge for 

conservationists and land managers in the future.
Chapter 3: Observed Climate Change

65

SUMMARY

The assessment area has observed several notable 

shifts in climate, climate-driven processes, and 

extreme weather events. In general, the assessment 

area is experiencing warmer weather across the 

year, particularly with respect to mean minimum 

temperatures and winter temperatures. Precipitation 

has increased over the 20th century and the 

precipitation regime has intensified, resulting in 

more large precipitation events. Characteristic winter 

conditions are diminishing, and growing seasons 

appear to be lengthening. These trends are consistent 

with regional, national, and global observations 

related to anthropogenic climate change. Ecological 

indicators are beginning to reflect these changes as 

well, as evidenced by changing ranges of wildlife 

species and changing phenology. 

A swan nesting in a wetland on the Ottawa National Forest. Photo by Maria Janowiak, U.S. Forest Service.
66

CHAPTER 4: PROJECTED CHANGES IN CLIMATE 

AND PHYSICAL PROCESSES

Climate across the Great Lakes region has changed 

over the past century, and it will continue to 

change in the future. This chapter describes climate 

projections for the assessment area over the 

21st century, including projections related to patterns 

of extreme weather events and other climate-related 

processes. Temperature and precipitation projections 

are derived from downscaled simulations of climate 

models. The models, data sources, and methods 

used to generate these downscaled projections, as 

well as the inherent uncertainty in making longterm projections, are described in greater detail in 

Chapter 2. In some cases, these downscaled data 

are then incorporated into hydrologic models to 

better understand impacts on such variables as 

soil moisture, evapotranspiration, and streamflow. 

Information related to future weather extremes and 

other impacts is drawn from published research. 

PROJECTED CHANGES 

IN TEMPERATURE 

AND PRECIPITATION

Projections of future climate show the potential for 

dramatic changes over this century. Temperature and 

precipitation are projected to change, with important 

seasonal variations and associated changes in snow 

and ice cover, growing season length, soil moisture, 

lake levels, and streamflow. 

In this chapter, we report downscaled climate 

projections for two combinations of global climate 

models and emissions scenarios: GFDL A1FI and 

PCM B1 (unless otherwise noted). The GFDL 

A1FI model-scenario combination projects greater 

increases in regional temperature than does the 

PCM B1 scenario (see Chapter 2). It is possible that 

actual emissions and temperature increases could 

be lower or higher than either of these projections. 

The GFDL A1FI scenario represents a projection of 

future greenhouse gas emissions and temperature 

increases that is closest to current trends (Raupach 

et al. 2007). The future will probably 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.

Daily mean, minimum, and maximum temperature 

and total daily precipitation were downscaled to 

an approximately 7.5-mile grid across the United 

States (see Chapter 2). Projected values were 

determined for the average daily mean, minimum, 

and maximum temperature for each season and the 

entire year for three 30-year periods (2010 through 

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

Daily precipitation values were summed by year 

and season, and 30-year means were calculated. We 

compared these means with the baseline historical 

data from 1971 through 2000, presented in 

Chapter 3, in order to determine the future change 

from current climate conditions. 

Temperature 

Scientists agree with greater than 90-percent 

certainty that the global climate will get warmer 

in the next 100 years (Intergovernmental Panel 

on Climate Change [IPCC] 2007a). This warming 

will translate into a wide-ranging set of changes 

to the climate system, globally as well as locally. 
Chapter 4: Projected Changes in Climate and Physical Processes

67

The assessment area is projected to experience 

substantial warming over the 21st century 

(Figs. 23 through 26; see also Appendix 3). Although 

projected temperature increases are fairly small from 

2010 through 2039, the projections under the two 

future scenarios diverge mid-century, with the GFDL 

A1FI scenario projecting much larger temperature 

increases. Compared to the 1971 through 2000 

baseline period, the mean annual temperature is 

projected to increase 2.6 °F (1.5 °C) under PCM B1 

and 8.7 °F (4.8 °C) under GFDL A1FI by 2100 

(Fig. 23).

Figure 23.—Projected mean, maximum, and minimum temperatures in the assessment area averaged over 30-year periods 

for the entire year and by season. The 1971 to 2000 value is based on observed data from weather stations. Note that the 

panels have different Y-axis values. See Appendix 3 for values of projected change in the early century (2010 through 2039) 

and mid-century (2040 through 2069).

30

40

50

60

70

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

T emperature (0 F)

Annual

GFDL A1FI mean GFDL A1FI maximum GFDL A1FI minimum

P C M B 1 mean P C M B 1 maximum P C M B 1 minimum

0

10

20

30

40

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

T emperature (0 F)

Winter (Dec - F eb)

50

60

70

80

90

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

T emperature (0 F)

S ummer (J une - Aug)

20

30

40

50

60

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

S pring (Mar - May)

30

40

50

60

70

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

F all (S ep - Nov)
Chapter 4: Projected Changes in Climate and Physical Processes

68

Figure 24.—Projected difference in mean daily temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios. Sufficient data were not available for Isle Royale, Michigan.
Chapter 4: Projected Changes in Climate and Physical Processes

69

Figure 25.—Projected difference in minimum daily temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios. Sufficient data were not available for Isle Royale, Michigan.
Chapter 4: Projected Changes in Climate and Physical Processes

70

Figure 26.—Projected difference in maximum daily temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios. Sufficient data were not available for Isle Royale, Michigan.
Chapter 4: Projected Changes in Climate and Physical Processes

71

The projected increase in mean temperature is not 

equal across all seasons. The PCM B1 scenario 

projects relatively similar warming throughout the 

year, with increases of 2.0 to 3.2 °F (1.1 to 1.8 °C) 

during each season. In contrast, the GFDL scenario 

projects mean temperature increases of 6.0 °F 

(3.3 °C) and greater in each season. Under this 

scenario, summer is projected to have the most 

substantial warming with an increase of 11.6 °F 

(6.5 °C). Appendix 3 contains numerical values and 

maps of projected temperature change in the early 

century (2010 through 2039) and mid-century 

(2040 through 2069). 

Minimum annual temperature is projected to 

increase across the assessment area by 2.7 and 

9.7 °F (1.5 and 5.4 °C) by the end of the century 

under the PCM B1 and GFDL A1FI scenarios, 

respectively (Fig. 25). Minimum temperature under 

the PCM B1 scenario is projected to increase the 

most in winter, warming 4.1 °F (2.3 °C) by the end 

of the century. Under GFDL A1FI, the projected 

increase in winter mean temperature is 10.9 °F 

(6.0 °C). 

Minimum temperatures are generally projected to 

increase more than maximum temperatures under 

both scenarios during nearly all seasons. Maximum 

annual temperature is projected to increase by 2.4 

to 7.5 °F (1.3 to 4.2 °C) under the two scenarios 

(Fig. 26). Summer is the only season where 

increases in maximum temperatures are expected to 

exceed increases in minimum temperatures. Summer 

maximum temperatures are projected to increase 

3.0 °F (1.7 °C) by the end of the century under 

PCM B1 and 11.6 °F (6.5 °C) by the end of the 

century under GFDL A1FI. 

Although the two climate scenarios project different 

amounts of warming, they are in agreement that 

mean, maximum, and minimum temperature will 

increase in the assessment area during all seasons. 

The PCM B1 scenario projects much less warming 

than the GFDL A1FI scenario for the end of the 

century. Under PCM B1, the mapped results 

show few areas that are projected to experience 

temperature changes more than 2.0 °F (1.1 °C; 

Figs. 24 through 26). Conversely, the GFDL A1F1 

scenario generally indicates warming greater than 

6.0 °F (3.3 °C) across much of the assessment area. 

This divergence is due in part to the two climate 

scenarios we chose for this assessment, which are 

intended to bracket the potential range of future 

temperature for the assessment area.

Precipitation

The two climate scenarios we chose for this 

assessment also describe two markedly different 

scenarios of future precipitation for the assessment 

area (Figs. 27 and 28). Substantial variation exists 

among future projections of precipitation across 

the Midwest (Center for Climatic Research 2013, 

Kunkel et al. 2013, Winkler et al. 2012). For this 

reason, it is important to keep in mind that other 

climate model and emissions scenario combinations 

vary widely in projected future precipitation. Other 

scenarios may project precipitation values outside 

of the range presented in this assessment. 

For the assessment area, mean annual precipitation 

is projected to increase by 0.5 inches under the 

GFDL A1FI scenario by the end of the 21st 

century (Fig. 28; see also Appendix 3) compared 

to the 1971 through 2000 baseline. By contrast, 

annual precipitation is projected to increase more 

substantially under the PCM B1 scenario, by an 

average of 2.7 inches. Appendix 3 contains tables 

and maps of projected precipitation change in the 

early century (2010 through 2039) and mid-century 

(2040 through 2069).

The seasonal precipitation trends show even more 

departure between the two scenarios. In particular, 

most of the difference between these two climate 

scenarios exists in spring and summer. Under the 

PCM B1 scenario, spring months are projected 

to receive 1.5 inches more precipitation over the 
Chapter 4: Projected Changes in Climate and Physical Processes

72

Figure 27.—Projected trends in average precipitation in the assessment area averaged over 30-year periods for the 

entire year and by season. The 1971 through 2000 value is based on observed data from weather stations. Note that 

the panels have different Y-axis values.

21st century. Summer precipitation under this 

scenario is projected to increase by 1.1 inches by 

mid-century. The GFDL A1FI scenario projects a 

much sharper distinction between these seasons, 

with spring precipitation increasing 3.2 inches and 

summer precipitation declining by 4.8 inches. Those 

projections represent a 47-percent increase in spring 

precipitation, followed by a 41-percent decrease 

in summer precipitation. Winter precipitation is 

expected to increase slightly over time under both 

scenarios. Fall precipitation is expected to decline 

slightly by the end of the century under the PCM 

B1 scenario (-0.3 inches), and to increase 1.6 inches 

under GFDL A1FI.

28

29

30

31

32

33

34

35

36

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches )

Annual

GFDL A1FI P C M B 1

0

1

2

3

4

5

6

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches )

Winter (Dec - F eb)

0

2

4

6

8

10

12

14

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches )

S ummer (J une - Aug)

0

2

4

6

8

10

12

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

S pring (Mar - May )

0

2

4

6

8

10

12

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

F all (S ep - Nov)
Chapter 4: Projected Changes in Climate and Physical Processes

73

Figure 28.—Projected difference in mean precipitation at the end of the century (2070 through 2099) compared to baseline 

(1971 through 2000) for two climate scenarios. Sufficient data were not available for Isle Royale, Michigan.
Chapter 4: Projected Changes in Climate and Physical Processes

74

Evapotranspiration and 

Precipitation Ratios

Temperature and precipitation are important 

climatic factors governing forest ecosystems, and 

it is projected that both will continue to change 

within the assessment area over the coming century. 

Trees and other plants derive water from soils, and 

the amount of soil moisture is governed by the 

hydrologic balance between evapotranspiration—

that is, the combined amount of water lost through 

evaporation from soils and transpiration from 

plants—and precipitation. Warmer temperatures 

generally increase evapotranspiration, which means 

that a corresponding increase in precipitation is 

needed to maintain the same level of soil moisture 

under warmed conditions.

A given amount of change in temperature or 

precipitation may be ecologically significant, but it 

is difficult to know how changes in one value might 

buffer or amplify changes in the other. For example, 

a given increase in temperature may not result in 

significant ecological change if precipitation also 

increases, but the same increase in temperature 

could result in a severe change if accompanied by a 

reduction in precipitation. As temperatures increase 

the atmosphere is able to hold more water, which 

causes evaporation and transpiration to increase. 

Increasing evaporation and transpiration both lead 

to drier soils and vegetation (Drever et al. 2009). 

Therefore, precipitation generally needs to increase 

significantly to compensate for even moderate 

temperature increases. One way to examine the 

potential interaction between temperature and 

precipitation shifts is to examine changes in the 

ratio of evapotranspiration to precipitation. This 

ratio of evapotranspiration to precipitation 

(ET:P) can be used as a metric to describe the 

balance between demand and supply of available 

water in forest ecosystems. Projected changes 

in this ratio can indicate whether a forest may 

experience drier or wetter conditions in the future 

compared to recent conditions. A positive change 

in ET:P values indicates that evapotranspiration has 

increased relative to available moisture and that 

forests are expected to be under increased moisture 

stress. Conversely, a negative change in ET:P values 

indicates that more moisture is available to forests.

We used the ecosystem model PnET-CN to calculate 

projected changes in ET:P for the assessment area, 

comparing the baseline period (1971 through 

2000) to the years 2070 through 2099 under both 

PCM B1 and GFDL A1FI (Fig. 29). Because 

evapotranspiration is an output of the PnET-CN 

model, the values incorporate projected changes 

in forest productivity due to temperature and 

precipitation changes, growing season length, 

carbon dioxide (CO2 ) fertilization, and other factors. 

Chapter 2 describes the PnET-CN model in greater 

detail, and additional results from this model are 

presented in Chapter 5. 

The PnET-CN model projects little change in 

annual ET:P across the assessment area under both 

climate scenarios (Fig. 29). Projected changes are 

also relatively small for the fall, winter, and spring 

seasons, with some geographic variability. Summer 

months are projected to have the greatest change in 

ET:P of all seasons, with the two climate scenarios 

projecting substantially different outcomes. Under 

PCM B1, slightly wetter conditions are projected 

based on a decrease in the ET:P ratio. Conversely, 

the GFDL A1FI scenario projects substantially 

drier conditions during the summer (large increase 

in ET:P). This overall trend is consistent with the 

precipitation trends discussed above. 

The changes in ET:P values indicate that the GFDL 

A1FI scenario may result in a much higher degree 

of moisture stress in summer months than indicated 

by precipitation values alone. The projected summer 

temperature increase of 10.8 °F (6.0 °C) results 

in higher evapotranspiration for forests across the 

assessment area, which essentially intensifies the 

effects of the projected decreases in precipitation. 
Chapter 4: Projected Changes in Climate and Physical Processes

75

Figure 29.—Projected changes in the ratio of evapotranspiration to precipitation (ET:P) under two future climate scenarios 

for the assessment area during 2070 through 2099. Data source: Stoner et al. (2013) and Peters et al. (2013). Positive values 

indicate that ET is projected to increase relative to available moisture, increasing the potential for forests to be under moisture 

stress. Negative values indicate that more moisture is projected to be available.
Chapter 4: Projected Changes in Climate and Physical Processes

76

Importantly, the ET:P values projected by 

PnET-CN include the effects of CO2

 fertilization, 

which results in significantly higher water-use 

efficiency and lower evapotranspiration for forest 

communities (Ollinger et al. 2002). Projections that 

do not include the CO2

 fertilization benefit resulted 

in substantially drier conditions (higher ET:P ratios) 

for the assessment area during the growing season 

(not shown). These results suggest that forests could 

undergo more frequent and extreme moisture stress 

in the future if water-use efficiency benefits from 

CO2

 fertilization are less significant than modeled by 

PnET-CN. Chapter 5 includes more information on 

the potential for CO2

 fertilization to influence forest 

productivity and water-use efficiency.

GROWING SEASON LENGTH

The assessment area has experienced an expansion 

of the growing (i.e., freeze-free) season over the past 

century, as noted in Chapter 3, and models project 

that these changes will continue into the future. 

Growing season length in the assessment area is 

projected to increase by 14 to more than 49 days 

under a range of emissions scenarios (Center for 

Climatic Research 2013) (Fig. 30). The projected 

expansion of the growing season is a result of both 

earlier spring freeze-free dates and later fall freezes 

(Center for Climatic Research 2013). These results 

are similar to those from another study across the 

Midwest region, which projected that the growing 

season will be extended by 30 days under the B1 

emissions scenario and 70 days under the A1FI 

scenario by the end of the century (Wuebbles and 

Hayhoe 2004). The last spring frost dates were 

projected to shift earlier into the year at about the 

same rate that first fall frost dates were projected 

to retreat later into the year (Wuebbles and Hayhoe 

2004).

EXTREME WEATHER EVENTS

Many extreme weather events are expected to 

change as a result of a changing climate. In general, 

there is less confidence in model projections of 

the magnitude and direction of change in extreme 

events over the next century compared with 

general temperature and precipitation changes. The 

infrequent nature of extreme events increases the 

difficulty of detecting changes in the frequency or 

intensity of events over time, as well as attributing 

these alterations to climate change (Coumou and 

Rahmstorf 2012, Stott et al. 2010). However, there is 

mounting evidence for projected increases in many 

extreme weather events across the Midwest (Kunkel 

et al. 2013, WICCI 2013).

Temperature Extremes

In addition to projecting mean temperatures, 

downscaled daily climate data can be used to 

estimate the frequency of extreme high and low 

temperatures in the future. Studies from across the 

Midwest region point to an increasing frequency of 

hot days across the assessment area, with roughly 

20 to 30 more days per year above 95 °F (35 °C) 

and a greater frequency of multi-day heat waves 

(extended periods of extremely hot weather) by 

the end of the century (Diffenbaugh et al. 2005, 

Perera et al. 2012, Winkler et al. 2012). These trends 

are consistent with another assessment covering 

the entire Midwest region, which projected that 

the assessment area could experience up to 10 

more days above 95 °F (35 °C) by the end of the 

21st century (Kunkel et al. 2013). Similarly, an 

assessment for Wisconsin projected 6 to 22 more 

days above 90 °F (32.2 °C) for northern Wisconsin 

by the end of the century (WICCI 2011b). Heat 

waves are also projected to occur more frequently 

across the Midwest (Wuebbles and Hayhoe 2004, 

Karl et al. 2009), driven largely by the increase 

in the frequency of extremely high nighttime 

temperatures (Karl et al. 2009, Meehl and Tebaldi 

2004). 
Chapter 4: Projected Changes in Climate and Physical Processes

77

Figure 30.—Projected change in the growing season length in days (number of days between the last spring freeze and the 

first autumn freeze, where a freeze is when the daily low temperature drops below 32 °F [0 °C]), based on nine global climate 

models from the Coupled Model Intercomparison Project Phase Three (CMIP3), computed as the difference between 2081 to 

2100 and 1981 to 2000. Results are shown for the B1 (upper map) and A2 (lower map) emissions scenarios. The A2 emissions 

scenario projects greenhouse gas emissions similar to the A1FI scenario. The source of the data is a daily, statisticallydownscaled climate product from the Center for Climatic Research, University of Wisconsin – Madison.
Chapter 4: Projected Changes in Climate and Physical Processes

78

Extremely cold temperatures are projected 

to become less frequent, without completely 

disappearing, as the climate warms. Downscaled 

climate scenarios project that the Midwest will 

experience between 25 and 38 fewer days below 

freezing by the end of the 21st century (Sinha and 

Cherkauer 2010), and 12 to 15 fewer days that 

are colder than the current 95th-percentile cold 

event (Diffenbaugh et al. 2005). Another study 

shows similar results, with as many as 24 fewer 

nights below 0 °F (-17.8 °C) projected in northern 

Wisconsin by 2100 (WICCI 2011b). It is important 

to note, however, that the enhanced warming 

occurring in polar regions greatly influences weather 

patterns in the mid-latitudes and can lead to periods 

of extreme cold, even as the overall climate becomes 

warmer (Francis and Vavrus 2012, Vavrus et al. 

2006).

Intense Precipitation

As described in Chapter 3, there is a clear trend 

toward more extreme precipitation events in the 

assessment area and the entire Midwest region 

(Kunkel et al. 2008, Saunders et al. 2012). Rainfall 

from these high-intensity events is representing a 

larger proportion of the total annual and seasonal 

rainfall, meaning that the precipitation regime is 

becoming more episodic. An assessment covering 

the entire Great Lakes region projected that the 

frequency of single-day and multi-day heavy rainfall 

events could double by 2100 (Kling et al. 2003). 

More recent assessments across a combination of 

climate projections indicate that the entire Midwest 

region is projected to receive 23 percent more 

rainfall events greater than 1 inch by 2100, with 

larger events increasing by progressively larger 

amounts (Kunkel et al. 2013). Other climate data 

project up to 1.5 more days per year having events 

greater than 1 inch by the end of the century across 

the assessment area under a low emissions scenario 

and greater than 1.5 additional days per year under a 

high emissions scenario (Fig. 31).

It is important to consider this trend in combination 

with the projected increases or decreases in 

mean precipitation over the 21st century. A given 

increase or decrease in precipitation may not be 

distributed evenly across a season or even a month. 

Additionally, large-scale modeling efforts have 

also suggested that climate change will increase 

the year-to-year variation of precipitation across 

the northern United States (Boer 2009). Therefore, 

the assessment area may experience more extreme 

wet and dry years in the future. Further, ecological 

systems are not all equally capable of holding 

moisture that comes in the form of extreme events 

due to landscape position, soils, and other factors. 

For example, areas with shallow soils may not 

have the water-holding capacity to retain moisture 

received in intense rainstorms, and areas with finetextured soils might not have fast enough infiltration 

rates to absorb water from these kinds of storms. 

For these reasons, if rainfall becomes more episodic, 

these areas may suffer from additional drought stress 

even if there is an overall increase in moisture or 

precipitation. 

Thunderstorms

General circulation models do not operate at a scale 

small enough to model thunderstorms explicitly 

and many of the climatic processes that produce 

thunderstorms are not well modeled. Nevertheless, 

evidence suggests that temperature increases may 

lead to conditions more favorable to convective 

storms such as thunderstorms due to increased 

atmospheric water vapor in the lower portions of 

the atmosphere (Kunkel et al. 2008; Trapp et al. 

2007, 2009). Researchers examined changes in 

thunderstorm potential over the 21st century using 

a mid-range emissions scenario (A1B; Trapp et al. 

2009). They found a slight increase in the frequency 

of conditions favorable for severe thunderstorms in 

the Midwest. A similar study found an increase in 

severe thunderstorm potential in the region at the 

end of the century under a higher emissions scenario 

(A2; Trapp et al. 2007). 
Chapter 4: Projected Changes in Climate and Physical Processes

79

Figure 31.—Projected change in the frequency of heavy precipitation events per year (days with at least 1 inch of 

precipitation), based on nine global climate models from the Coupled Model Intercomparison Project Phase Three (CMIP3), 

computed as the difference between 2081 to 2100 and 1981 to 2000. Results are shown for the B1 (upper map) and A2 

(lower map) emissions scenarios. The A2 emissions scenario projects greenhouse gas emissions similar to the A1FI scenario. 

The source of the data is a daily, statistically-downscaled climate product from the Center for Climatic Research, University of 

Wisconsin – Madison.
Chapter 4: Projected Changes in Climate and Physical Processes

80

Tornadoes and Hail

Very little is known about how the frequency, 

severity, and seasonal patterns of tornadoes and hail 

may change over the next century. A recent synthesis 

report on extreme weather events stated that “there 

is low confidence in projections of small spatialscale phenomena such as tornadoes and hail because 

competing physical processes may affect future 

trends and because current climate models do not 

simulate such phenomena” (IPCC 2012: 8). As the 

sophistication of global and regional climate models 

increases, so will our understanding of how patterns 

in tornadoes and hail may change in the future. 

PROJECTED CHANGES AFFECTING 

WATER AND SOILS

As discussed above, several climatic changes are 

projected to occur over the next century in the 

assessment area, including warmer temperatures, 

altered patterns of seasonal precipitation, and 

increased potential for intense rain events. All of 

these changes, when combined, have the potential to 

cause substantial changes to hydrologic regimes and 

soil moisture.

Lake Temperature and Ice Cover

Many changes in lake temperature and ice cover 

have been observed on the Great Lakes and inland 

lakes. Surface temperatures of Lake Superior 

during spring, summer, and fall are projected to 

continue increasing through the end of the century 

(Trumpickas et al. 2009). Increased temperatures are 

expected to cause further decreases in the duration, 

thickness, and extent of ice cover during winter 

(Wuebbles and Hayhoe 2004). In fact, ice cover on 

both Lake Michigan and Lake Superior is projected 

to decline and at the current rate of ice cover loss, 

Lake Superior is expected to remain largely free of 

ice in a typical winter by mid-century (Austin and 

Colman 2007). Changes in temperature may also 

affect lake levels. Lake levels are projected to peak 

earlier in the year in response to earlier snowmelt 

and higher precipitation in the winter and spring. 

Peak lake levels, however, may be lower than levels 

observed during the past century as evaporation 

increases with temperature and as longer ice-free 

periods permit more evaporation (Karl et al. 2009, 

Wuebbles and Hayhoe 2004). 

Cold-season Precipitation

Increases in winter temperatures are expected to 

continue to alter cold-season precipitation patterns 

across the assessment area. Total snowfall is 

projected to decrease more than 10 percent across 

the assessment area under a low emissions scenario 

and more than 30 percent under a high emissions 

scenario by the end of the 21st century (Center for 

Climatic Research 2013), even as most climate 

projections indicate increases in winter precipitation 

(Fig. 27) (Center for Climatic Research 2013, 

Notaro et al., in press). The largest reductions in 

snowfall are generally projected to occur in the early 

and late portions of the snow season—November, 

December, March, and April (Notaro et al., in press). 

As one specific example, projections for Ashland, 

Wisconsin, show a shortening of the season during 

which frozen precipitation is more likely as well 

as an overall increase in the probability of rain 

during December through February (Fig. 32) 

(WICCI 2013).

Studies have shown that across much of the 

Midwest region, an increasing percentage of winter 

precipitation is being delivered as rain rather than 

snow (Feng and Hu 2007, Notaro et al. 2011). As 

winter temperatures rise across the assessment 

area, it is projected that an increasing proportion 

of winter precipitation will be delivered as rain 

in the assessment area (Notaro et al., in press, 

Sinha and Cherkauer 2010, WICCI 2011b). One 

modeling study also projected that climate change 

will result in less frequent freezing rain events 

across the assessment area (Lambert and Hansen 

2011). The projected decreases were slight (2.5 to 

10 fewer events per decade over the 21st century), 
Chapter 4: Projected Changes in Climate and Physical Processes

81

Figure 32.—Projected change in the probability that precipitation is frozen in Ashland, Wisconsin, in 1980 (modern) and 2090 

(future) under a low (B1; left) and high (A2; right) emissions scenario. The A2 emissions scenario projects greenhouse gas 

emissions similar to the A1FI scenario. The red line indicates a 50-percent chance of either rain or snow under current climate 

(solid line) or future climate (dashed line) conditions. Source: WICCI (2013).

but correspond with the projected increase in winter 

temperature and the shift from snowfall to rain. 

It is more difficult to project snowfall in areas 

subject to lake-effect precipitation, largely because 

of interactions among multiple factors, including 

lake temperature, lake ice cover, and over-water 

and over-land air temperatures (Notaro et al. 2012, 

Notaro et al., in press). It is possible that areas 

that typically receive lake-effect snow may see 

increased snowfall amounts during the early- and 

mid-21st century as reduced ice cover allows for 

greater evaporation from the surface of the Great 

Lakes (Burnett et al. 2003, Wright et al. 2011). 

As temperatures continue to warm through the 

21st century, however, the potential exists for 

increasingly warm winter temperatures to negate the 

effect of decreased lake ice, leading to less lakeeffect snowfall and more rain events (Kunkel et al. 

2002). 

Snow depth during the winter season is expected 

to decline even more than snowfall amounts, 

because snow depth will also be reduced by warm 

temperatures between snowfall events (Notaro 

et al. 2011, Notaro et al., in press). Even areas 

that maintain constant or possibly increased 

snowfall could still experience a decrease in snow 

depth if winter temperatures prevent snow from 

accumulating (Kling et al. 2003). November through 

April snow depth is projected to decrease more 

than 20 percent by the end of the century across the 

assessment area under both low and high emissions 

scenarios, with greater decreases projected for areas 

farther south (Center for Climatic Research 2013). 

Similarly, the number of days with snow depth 

of 3 inches or more is also projected to decrease 

substantially across the assessment area by the end 

of the century (Fig. 33).
Chapter 4: Projected Changes in Climate and Physical Processes

82

Figure 33.—Projected change in the duration (in days per year) of snowpack of at least 3 inches, based on nine global climate 

models from the Coupled Model Intercomparison Project Phase Three (CMIP3), computed as the difference between 2081 to 

2100 and 1981 to 2000. Results are shown for the B1 (upper map) and A2 (lower map) emissions scenarios. The A2 emissions 

scenario projects greenhouse gas emissions similar to the A1FI scenario. The source of the data is a daily, statisticallydownscaled climate product from the Center for Climatic Research, University of Wisconsin – Madison.
Chapter 4: Projected Changes in Climate and Physical Processes

83

Soil Temperature and Frost

The increase in winter temperatures projected 

across the assessment area under both PCM B1 

and GFDL A1FI is generally expected to increase 

soil temperatures and reduce soil frost. One study 

projected that cold-season soil temperatures may 

increase between 1.8 and 5.4 °F (1 and 3 °C) and 

that the soil frost season may be shortened by 1 to 

2 months across the assessment area by 2100 

(Fig. 34) (Sinha and Cherkauer 2010). Total frost 

depth is projected to decline by 40 to 80 percent 

across the assessment area. The exception may be 

areas that currently have deep winter snowpack, 

which insulates soils and prevents soil frost; in 

these areas, reductions in snow may expose soils to 

Figure 34.—Baseline and projected number of annual soil frost days for the Midwest under a range of climate scenarios, from 

Cherkauer and Sinha (2010). Base refers to the average annual number of soil frost days, 1977 through 2006. Early-base, 

mid-base, and late-base refer to the difference in mean soil frost days from the baseline period for 2010 through 2039, 2040 

through 2069, and 2070 through 2099, respectively. The A2 emissions scenario is roughly equivalent to the A1FI scenario in 

terms of greenhouse gas emissions, and the A1B scenario is approximately a middle range between A1FI and B1.

sufficiently cold temperatures and allow for deeper 

soil frost (Hardy et al. 2001, Isard et al. 2007a). 

The number of freeze-thaw cycles is also expected 

to increase, due in part to daytime and nighttime 

temperature variability (Cherkauer and Sinha 2010). 

These projections are generally consistent with 

studies of snowpack and soil frost in New England 

forests (Campbell et al. 2010). 

Runoff, Streamflow, and Flooding

The shifts in winter precipitation and temperature 

described above are expected to shift the timing 

of snowmelt, runoff, and peak streamflow earlier 

into the year (Karl et al. 2009, Kling et al. 2003, 

Wuebbles et al. 2009). Researchers project that total 
Chapter 4: Projected Changes in Climate and Physical Processes

84

winter runoff values may more than double across 

much of the assessment area by the end of the 21st 

century, with the most-dramatic increases in the 

Keweenaw Peninsula and along the Lake Superior 

shoreline in Michigan (Fig. 35) (Cherkauer and 

Sinha 2010). This localized increase is associated 

with the high winter precipitation amounts in this 

region and a greater likelihood of winter melt 

and rain events; because winter runoff levels 

are generally low, this projected increase was 

substantial—more than 400 percent in some areas 

(Cherkauer and Sinha 2010). 

Figure 35.—Spatial distribution of average seasonal cumulative runoff for the Midwest projected for 2069 through 2099 as a 

percentage change from the base (1977 through 2006) for three emissions scenarios. The A2 emissions scenario is roughly 

equivalent to the A1FI scenario in terms of greenhouse gas emissions, and the A1B scenario is approximately a middle range 

between A1FI and B1. Source: Cherkauer and Sinha (2010).
Chapter 4: Projected Changes in Climate and Physical Processes

85

Earlier peak flows coupled with increases in spring 

precipitation are expected to contribute to increased 

flood frequency across the Midwest (Karl et al. 

2009, Kling et al. 2003). Similarly, high-intensity 

rainfall events are linked to both flash flooding and 

widespread floods, although the severity depends on 

soil saturation and stream levels at the time of the 

event. For example, a modeling study examining 

climate change impacts on streamflow across the 

Midwest projected that runoff and streamflow 

may shift substantially across the assessment area, 

with increased runoff during the winter and spring 

(Cherkauer and Sinha 2010).

As with precipitation, the potential changes in 

runoff and streamflow during summer and autumn 

are more complex and less certain. Summer 

runoff is generally projected to increase across the 

assessment area under a low emissions scenario, 

but to vary geographically under a high emissions 

scenario (Fig. 35) (Cherkauer and Sinha 2010). In 

fall, total runoff is projected to decline by 8 to 

32 percent across the assessment area. Another study 

indicates similar results for the upper Mississippi 

River Basin, including the Chippewa and Wisconsin 

River watersheds (Wuebbles et al. 2009). Stream 

flashiness has the potential to increase during 

summer and autumn if future conditions increase 

the occurrence of both high-flow days and low-flow 

days (Cherkauer and Sinha 2010).

Soil Moisture and Drought

Changes in soil moisture are largely driven by the 

balance of precipitation and evapotranspiration, and 

thus there is some uncertainty about future changes. 

At the same time, projections of higher temperatures 

combined with decreases or small increases in 

precipitation during the growing season suggest a 

potential for reduced soil moisture and increased 

drought. As discussed earlier in this chapter, 

warmer temperatures combined with reduced or 

unchanged precipitation amounts would increase 

evapotranspiration levels, and such changes are 

projected under higher emissions scenarios (Fig. 29). 

Earlier spring snowmelt and runoff may contribute 

to late-summer moisture deficits (Cherkauer and 

Sinha 2010), and shifts in precipitation patterns 

may result in longer periods between rainfall, with 

greater amounts of precipitation occurring over 

fewer precipitation events (Karl et al. 2009). For 

these reasons, increases in drought are more likely 

to occur in areas where precipitation is not sufficient 

to recharge late summer soil moisture deficits 

(Cherkauer and Sinha 2010). 

SUMMARY

The assessment area is projected to undergo 

profound changes in climate by the end of the 

century. Direct changes include shifts in mean 

temperature and precipitation as well as altered 

timing and extremes. Projected changes also extend 

to more indirect climate-controlled factors such as 

an increasing frequency of extreme rainstorms and 

decreased soil frost during winter. By the end of 

the 21st century, the assessment area is generally 

projected to experience a climate that is hotter and 

more variable, with more moisture stress towards 

the end of the growing season and less characteristic 

winter weather. In the next chapter, we examine the 

ecological implications of these anticipated changes 

for forest ecosystems. 
86

CHAPTER 5: FUTURE CLIMATE CHANGE 

IMPACTS ON FORESTS

In this chapter, we describe the potential effects 

of climate change on forest ecosystems in the 

assessment area. These effects include the direct 

impacts of climate change, as well as indirect 

impacts from forest pests, invasive species, altered 

disturbance regimes, and other interacting factors. 

To gain a better understanding of how forests in the 

assessment area may respond to climate change, we 

rely on forest impact models and scientific literature. 

This information provides us with the foundation 

to assess the potential vulnerability of forest 

ecosystems in the assessment area (Chapter 6). 

MODELED PROJECTIONS 

OF FOREST CHANGE

Forest ecosystems in the assessment area are 

expected to respond to climate change in various 

ways. Potential effects include changes in tree 

species composition and diversity, as well as 

shifts in the spatial distribution, abundance, and 

productivity of tree species. For this assessment, 

we rely on a combination of three forest impact 

models to describe these potential changes: Climate 

Change Tree Atlas, LANDIS-II, and PnET-CN 

(Table 9). Tree Atlas uses statistical techniques to 

model changes in suitable habitat for individual 

species over broad geographic areas. LANDIS-II 

is a spatially explicit, dynamic model that includes 

succession, migration, natural disturbances, timber 

harvest, and competition to simulate the abundance 

and distribution of individual tree species. PnETCN simulates carbon, water, and nitrogen in forest 

ecosystems and calculates the productivity of several 

forest types. 

No single model offers a perfect projection of future 

change in forest ecosystems, but each tool provides 

useful information. Similar results across models 

suggest more plausible outcomes, and differences 

between model projections provide opportunities 

to better understand the nuances of ecological 

responses given the strengths and limitations of the 

models (Iverson et al. 2011). Chapter 2 contains 

more thorough descriptions of the different forest 

impact models and how they were applied for this 

assessment.

Importantly, all of these modeling investigations 

relied on a consistent set of future climate data. 

All three research teams used the same two 

climate model-emissions scenario combinations: 

GFDL A1FI and PCM B1 (described in detail in 

Chapters 2 and 4). The GFDL A1FI model-scenario 

combination is on the higher end of the spectrum 

for future temperature increases, and PCM B1 

represents a less substantial temperature increase. 

This consistency in the input climate data means that 

the forest impact models are describing potential 

forest changes over the same range of plausible 

future climates. A more complete description of 

general circulation models and emissions scenarios 

is also provided in Chapter 2. 

These model results are best used to describe trends 

across large areas and over long time scales. These 

models are not designed to deliver precise results 

for individual forest stands or a particular year in the 

future, despite the temptation to examine particular 

data points or locations on a map. In this chapter, we 

present model results for the end of the 21st century. 
Chapter 5: Future Climate Change Impacts on Forests

87

a See Chapter 2 for model descriptions.

* The SHIFT component of the Climate Change Tree Atlas includes dispersal and colonization, but this is not discussed in this 

document with the exception of the results presented in Box 10.

** This parameter can be an output for this model, but was not investigated in this assessment.

Feature Climate Change Tree Atlas LANDIS-II PnET-CN

Summary Suitable habitat distribution 

model (DISTRIB) and 

supplementary information 

(modifying factors)

Spatially explicit, dynamic process 

model

Ecosystem-level carbon, 

water, and nitrogen process 

model

Primary outputs for 

this assessment

Area-weighted importance 

values and modifying factors 

by species

Aboveground biomass by species 

and distribution maps by forest 

type

Aboveground net primary 

productivity by forest type

Climate scenarios PCM B1, GFDL A1FI PCM B1, GFDL A1FI PCM B1, GFDL A1FI

Area modeled Full assessment area: 

Ecological Section IX in 

Michigan and Sections VIII, 

IX, and X in Wisconsin (Albert 

1995) (Fig. 2)

Most of the assessment area: 

Ecological Sections IX and X and 

Ecological Subsection VIII.3.1 in 

Wisconsin (Albert 1995); Baraga, 

Gogebic, Houghton, Iron, and 

Ontonagon Counties in the Upper 

Peninsula of Michigan; Chisago 

County in eastern Minnesota 

(Fig. 41)

Full assessment area: 

Ecological Section IX in 

Michigan and Sections VIII, 

IX, and X in Wisconsin (Albert 

1995) (Fig. 2)

Dispersal and 

colonization

No* Yes, for 27 species currently 

present in the assessment area

No

Competition, survival, 

and reproduction

Not modeled, but addressed 

through the modifying 

factors.

Yes No

Forest management No Yes No**

Disturbances Not modeled, but modifying 

factors include sensitivity to 

disturbance. 

Yes: wind (Fire, insects, browse, 

and other disturbances were not 

modeled.)

No**

CO2

 fertilization No No Yes

Succession and 

community shifts

No Yes No

Biogeochemical 

feedbacks

No No** Yes

Table 9.—Overview of impact models used for this assessment and the different features included in future 

simulations a
Chapter 5: Future Climate Change Impacts on Forests

88

Data for intermediate time periods are provided in 

Appendix 4 (Tree Atlas) and Appendix 5 

(LANDIS-II).

Tree Atlas

Importance values of 134 eastern tree species were 

modeled for potential habitat suitability in the 

assessment area by using the DISTRIB component 

of the Tree Atlas (Iverson and Prasad 1998, 

Iverson et al. 2008). Importance value is an index 

of the relative abundance of a species in a given 

community, and can range from 0 (not present) to 

100 (only species present in the area) in a single 

12.4-mile grid cell (Iverson and Prasad 1998). 

Single-cell importance values were calculated and 

then summed across the assessment area to reach the 

area-weighted importance value for a species. In the 

assessment area, 78 of the 134 species are of interest 

because suitable habitat is currently present or is 

projected to be present in the assessment area by the 

end of the century. Chapter 2 contains more detail on 

the Tree Atlas methods. 

The projected change in potential suitable habitat 

for the 78 species was calculated for the years 

2070 through 2099 by using the GFDL A1FI and 

PCM B1 scenarios and compared to present values 

(Table 10). Species are categorized based upon 

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 are mixed. Further, 

some tree species that are currently not present in 

the assessment area are identified as having potential 

suitable habitat in the future under one or both 

scenarios. Appendix 4 contains complete results 

from the DISTRIB model, including projections for 

three different periods (2010 through 2039, 2040 

through 2069, and 2070 through 2099). 

Additionally, when examining these results, it is 

important to keep in mind that model reliability is 

generally higher for common species than for rare 

species. When model reliability is low, less 

certainty exists for the model results. See 

Appendix 4 for specific rankings of model 

reliability for each species.

Modifying factors have also been incorporated into 

the Tree Atlas to provide additional information on 

potential forest change. Modifying factors include a 

species’ life-history traits, known stressors, and other 

environmental factors that make a species more or 

less likely to persist on the landscape (Matthews et 

al. 2011b). These factors are not explicitly included 

in the DISTRIB outputs, and are based on a literature 

review of each species. Examples of modifying 

factors are drought tolerance, dispersal ability, shade 

tolerance, site specificity, and susceptibility to insect 

pests and diseases, all of which are highly related to 

a species’ adaptive capacity (see Chapter 6). 

Positive and negative modifying factors have 

been identified for each tree species (Appendix 

4). Modifying factors that are primarily negative 

suggest that a species may be limited by biological 

traits or susceptibility to disturbance, and that it 

may fare worse than the DISTRIB results suggest. 

Conversely, modifying factors that are primarily 

positive indicate that the species may have a 

greater capacity to adapt to future conditions, 

potentially allowing it to occupy more habitat than 

modeled by DISTRIB. For example, although the 

DISTRIB results project no change in red maple 

suitable habitat under the PCM B1 scenario and a 

decrease in habitat under GFDL A1FI, the species 

has the highest adaptive capacity potential of all 

the species assessed (Table 11). Several positive 

modifying factors, such as shade tolerance and a 

high dispersal ability, suggest that red maple may 

occupy more habitat in the future than the DISTRIB 

results indicate. Additionally, the combination of the 

DISTRIB results and modifying factors can be used 

to better understand the potential risk to a species 

from climate change (Box 9).
Chapter 5: Future Climate Change Impacts on Forests

89

Declines under Both Scenarios

Balsam fir (-) Decrease Large decrease

Black ash (-) Decrease Decrease

Black spruce Large decrease Large decrease

Mountain maple (+) Large decrease Extirpated

Northern white-cedar Decrease Large decrease

Paper birch Decrease Large decrease

Quaking aspen Decrease Large decrease

White spruce Decrease Large decrease

Yellow birch Decrease Large decrease

No Change under Both Scenarios

Northern pin oak (+) No change No change

Red pine No change No change

Striped maple No change No change

Increases under Both Scenarios

American beech Large increase Increase

American elm Increase Large increase

American hornbeam Increase Large increase

Bitternut hickory (+) Large increase Large increase

Black cherry (-) Large increase Increase

Black locust Large increase Large increase

Black oak Large increase Large increase

Black walnut Large increase Large increase

Black willow (-) Large increase Large increase

Boxelder (+) Increase Large increase

Bur oak (+) Increase Large increase

Eastern cottonwood Large increase Large increase

Hackberry (+) Large increase Large increase

Red mulberry Large increase Large increase

River birch Large increase Large increase

Shagbark hickory Large increase Large increase

Silver maple (+) Large increase Large increase

Slippery elm Large increase Large increase

White ash (-) Increase Increase

White oak (+) Increase Large increase

Mixed Results Across Scenarios

American basswood No change Increase

Balsam poplar Large decrease No change

Bigtooth aspen No change Large decrease

Butternut (-) Increase Extirpated

Chokecherry No change Large decrease

Eastern hemlock (-) Increase Large decrease

Eastern hophornbeam (+) No change Increase

Eastern redbud Large decrease Large increase

Eastern white pine No change Decrease

Green ash Decrease Increase

Jack pine No change Decrease

Northern red oak (+) Increase No change

Peachleaf willow -- Increase

Pin cherry No change Large decrease

Red maple (+) No change Decrease

Rock elm (-) Decrease No change

Sugar maple (+) No change Large decrease

Swamp white oak No change Increase

Tamarack (native) (-) No change Decrease

Wild plum Decrease Large increase

New Suitable Habitat

Black hickory -- New habitat

Blackgum (+) New habitat New habitat

Blackjack oak (+) New habitat New habitat

Chestnut oak (+) New habitat New habitat

Chinquapin oak New habitat New habitat

Common persimmon (+) -- New habitat

Eastern redcedar New habitat New habitat

Flowering dogwood New habitat New habitat

Gray birch New habitat New habitat

Honeylocust (+) New habitat New habitat

Mockernut hickory (+) New habitat New habitat

Northern catalpa New habitat New habitat

Ohio buckeye New habitat New habitat

Osage-orange (+) New habitat New habitat

Pignut hickory New habitat New habitat

Pin oak (-) New habitat New habitat

Post oak (+) New habitat New habitat

Sassafras New habitat New habitat

Scarlet oak New habitat New habitat

Shellbark hickory -- New habitat

Shingle oak New habitat New habitat

Sugarberry -- New habitat

Sweet birch (-) New habitat New habitat

Sweetgum -- New habitat

Sycamore New habitat New habitat

Yellow-poplar (+) New habitat New habitat

Species are grouped according to change classes based on 

the percentage change in the area-weighted importance 

value projected by the DISTRIB model for the end of century 

(2070 through 2099) under two climate-emissions scenarios. 

Species with positive and negative modifying factor scores 

are marked with plus (+) and minus (–) signs, respectively. 

See Appendix 4 for further explanation of the data and 

complete results for all 78 species.

Table 10.—Potential change in suitable habitat for 78 tree species in the assessment area 

Common name PCM B1 GFDL A1FI Common name PCM B1 GFDL A1FI
Chapter 5: Future Climate Change Impacts on Forests

90

Species Modifying factors that affect rating

Highest adaptive capacity

1. Red maple high seedling establishment rate, wide range of habitats, shade-tolerant, high dispersal ability

2. Boxelder high seedling establishment rate, shade-tolerant, high dispersal ability, wide range of 

temperature tolerances, drought-tolerant

3. Bur oak drought-tolerant, fire-tolerant

4. Eastern hophornbeam shade-tolerant, wide range of temperature tolerances, wide range of habitats 

5. Osage-orange wide range of habitats

Lowest adaptive capacity

1. Black ash emerald ash borer susceptibility, poor light competitor, limited dispersal ability, 

poor seedling establishment, fire-intolerant, dependent on specific hydrological regime

2. Butternut butternut canker, drought-intolerant, fire-intolerant, poor light competitor

3. Balsam fir spruce budworm and other insect pests, fire-intolerant, drought-intolerant

4. White ash emerald ash borer, drought-intolerant, fire-intolerant

5. Eastern hemlock hemlock wooly adelgid, drought-intolerant

a See Appendix 4 for a more complete listing of modifying factors for each species.

Table 11.—Species with the five highest and five lowest ratings for adaptive capacity potential, based on Tree Atlas 

modifying factorsa

Box 9: Assessing Risk for Tree Species and Forest Habitats

The Climate Change Tree Atlas results presented 

in this chapter and in greater detail in Appendix 

4 provide information about how individual 

tree species may respond to a changing climate. 

Projections of suitable habitat from the DISTRIB 

model describe the environmental and climatic 

factors that could affect species distribution and 

abundance across the landscape. The modifying 

factors detail life-history traits that may influence the 

ability of a tree species to cope with disturbances 

and biological stressors at both broad and fine scales. 

The combined use of these Tree Atlas components, 

as well as the associated SHIFT model, allows for a 

more comprehensive understanding of the response 

of tree species to climate change and can inform 

policy and management (Iverson et al. 2011).

As climate change research grows, there is an 

increasing emphasis on providing information in 

terms of “risk” to help inform decisionmaking. 

The use of a risk matrix model was encouraged 

as part of the development of the most recent 

National Climate Assessment as a tool for organizing 

information about key vulnerabilities and risks 

(Dalton and Mote 2012, Melillo et al. 2014). In this 

model, qualitative or quantitative estimates are used 

to describe the likelihood of impact (X-axis) and the 

magnitude of consequence (Y-axis). 

As one example of the potential application of this 

approach, Tree Atlas results for northern Wisconsin 

(Swanston et al. 2011) were translated into a risk 

matrix for three future periods: 2010 to 2040, 2040 

to 2070, and 2070 to 2100 (Iverson et al. 2012a, 

2012b) (Fig. 36). This effort was intended as a “proof 

of concept” on how complex information could 

be represented in a way that helped to organize 

thinking regarding climate change vulnerability and 

risk. In translating the Tree Atlas information into this 

framework, projected changes in suitable habitat 

from DISTRIB were used to indicate the likelihood 

of impact. Thus, a large decrease in suitable habitat 

suggests a greater likelihood that that species 

will have reduced habitat under future climatic 

conditions. The magnitude of consequence was 

inversely related to the adaptability of the species 
Chapter 5: Future Climate Change Impacts on Forests

91

Box 9 (continued)

Figure 36.—Risk matrix for sugar maple in the northern Wisconsin portion of the assessment area. The numbers 

on the X-axis reflect projected suitable habitat, where 1.0 indicates no change from current values and 0 indicates 

complete loss of habitat. The numbers on the Y-axis are based on modifying factors, with increasing influence of 

disturbance factors over time. Values are plotted for three 30-year periods: 2040 (2010 to 2040), 2070 (2040 to 

2070), and 2100 (2070 to 2100). The HAD A1FI scenario is roughly equivalent to the GFDL A1FI scenario presented 

elsewhere in this assessment. See Iverson et al. (2012b) for complete methods and additional examples.

to climate change based upon the modifying 

factors; thus, the lower the capacity to cope, the 

greater the risk for habitat loss and the greater the 

consequences from climate change (Iverson et al. 

2012a, 2012b). To assess changes in consequence 

over time, adaptability scores were adjusted to 

account for projected increases in disturbance over 

time (Iverson et al. 2012b).

S pe cie s ability to adapt to clim ate ch an ge

R ela tive lik elih o o d o f im p a c t

1.0 0.75 0.5 0.25 0

P C M B 1

H A D A 1 F I

S ugar m ap le

N o c hange Large decrease

L o w H ig h

2 0 4 0

2 1 0 0

2 0 7 0

2 1 0 0

2 0 7 0

R is k:

9

0

6

3

C h an ge in su itab le h ab itat

M a g nitu de o f c o n s e q uen ce

H igh Low
Chapter 5: Future Climate Change Impacts on Forests

92

Decreases in Suitable Habitat

For the assessment area, 9 of the 78 modeled species 

are projected to undergo large or small declines in 

suitable habitat under the full range of examined 

climate futures. The projected declines in habitat (as 

measured by a ratio of potential future importance 

value to current importance value; see Appendix 4) 

are more severe for these species under the 

GFDL A1FI scenario than under PCM B1. These 

reductions in suitable habitat do not imply that all 

or most mature trees will die or the species will be 

extirpated; rather, these results indicate that these 

species will be living outside of their ideal climatic 

envelope. As a result, trees living on less suitable 

habitats may have greater susceptibility to new or 

existing stressors such as drought, pests, diseases, or 

competition from other species including invasives. 

Climate-related stress may also increase the risk of 

regeneration failure. 

Many of the species projected to decline are 

boreal or northern species that are currently near 

the southern limit of their range in the assessment 

area, including black spruce, balsam fir, quaking 

aspen, paper birch, and white spruce. These species 

are currently very common across the landscape 

and play a dominant role in many forests, and the 

reduction of suitable habitat for these species may 

affect a large portion of northern Wisconsin and 

the western Upper Peninsula. Suitable habitat for 

northern white-cedar, yellow birch, and black ash 

is also projected to decline under both climate 

scenarios, although not by as much as the boreal 

species. Mountain maple is currently rare across 

the landscape and is projected to have the greatest 

percentage decrease in suitable habitat, although 

positive modifying factors suggest that it may persist 

in localized areas with the most suitable conditions. 

Highly negative modifying factors are associated 

with two of the species projected to decline, balsam 

fir and black ash, suggesting that there are lifehistory traits or disturbance stressors that may cause 

these species to lose even more suitable habitat than 

the DISTRIB model results indicate. For example, 

the expanding presence of emerald ash borer in the 

assessment area is expected to greatly reduce the 

importance of black ash in the area; its impact on 

black ash and other ash species is expected to be 

greater than the impacts from changing climatic 

conditions over the next few decades. 

No Change in Suitable Habitat

Three species—northern pin oak, red pine, and 

striped maple—are projected to undergo less than 

a 20-percent change in suitable habitat under either 

of the two scenarios. Red pine is relatively common 

across the landscape. Several negative modifying 

factors, such as susceptibility to insect pests and 

low dispersal characteristics, are associated with red 

pine, which may cause it to fare worse in the future 

than the model results suggest. Positive modifying 

factors were identified for northern pin oak, but oak 

wilt may still be a concern for this species. Striped 

maple is relatively infrequent across the current 

landscape, and suitable habitat is not projected to 

change substantially in the future. 

Mixed Results across Scenarios

There are several species (20 of 78) for which the 

model projects different outcomes under GFDL 

A1FI and PCM B1. For 10 of these species, the 

DISTRIB model projects that suitable habitat will 

decrease under GFDL A1FI, but there is generally 

less than a 20-percent change projected under PCM 

B1. Many of these species, including bigtooth 

aspen, eastern hemlock, eastern white pine, northern 

red oak, red maple, and sugar maple, are currently 

common in the assessment area. Northern red oak, 

red maple, and sugar maple have positive modifying 

factors that indicate the species may fare better than 

the model suggests. Red maple in particular has 
Chapter 5: Future Climate Change Impacts on Forests

93

the most positive modifying factors among all 134 

species that were assessed across the eastern United 

States. Eastern hemlock, butternut, and tamarack 

are associated with negative modifying factors and 

are projected to have greater decreases in suitable 

habitat under the GFDL A1FI scenario.

Ten of the species in this category are projected to 

have larger increases or smaller decreases in suitable 

habitat under GFDL A1FI compared to PCM B1. 

Currently, many of these species, including eastern 

redbud, rock elm, swamp white oak, and wild plum, 

are relatively infrequent in the assessment area. 

These species are more frequently found south of the 

assessment area, suggesting that suitable habitat will 

move northward under future conditions. Eastern 

hophornbeam is currently a common species in the 

assessment area, frequently associated with northern 

hardwood forests, and it is projected to have no 

change in suitable habitat under PCM B1 and have 

increased suitable habitat under GFDL A1FI. The 

positive modifying factors associated with this 

species, such as shade tolerance and an ability to 

occupy a wide range of sites, suggest that it may fare 

better than DISTRIB projects. 

Increases in Suitable Habitat

Suitable habitat for 20 species is projected to 

increase under both models by the end of the 

century. Of these species, few are currently 

widespread in the assessment area. The more 

common species that are projected to have increases 

in suitable habitat under future climatic conditions 

include American elm, white oak, bur oak, white 

ash, black oak, and boxelder. Other species that are 

projected to increase in suitable habitat are more 

common in the mixed hardwood forests found along 

the southern boundary of the assessment area and 

include black walnut, shagbark hickory, and bitternut 

hickory. 

Importantly, the DISTRIB model results project 

only changes in suitable habitat, which does not 

necessarily mean that a given species will be able 

to migrate to newly available habitat and colonize 

successfully. Migration models suggest that most 

species will not be able to colonize new habitats at 

the rate that climate changes (Iverson et al. 2004a, 

2004b). A few species within the large increaser 

category, such as black cherry, black willow, and 

white ash, are associated with negative modifying 

factors, which suggest that they may be less able to 

take advantage of increases in suitable habitat. At the 

same time, several species associated with positive 

modifying factors, such as boxelder, white oak, 

silver maple, and bitternut hickory, may be able to 

increase beyond what the models suggest. 

New Suitable Habitat

The DISTRIB model results also project that suitable 

habitat will be available in the future under at least 

one of the climate scenarios for 26 species that are 

not currently present in the assessment area. This 

projection does not necessarily mean that a given 

species will be able to migrate to newly available 

habitat and colonize successfully, but rather that 

conditions may be suitable for a species to occupy 

the site if it is established. Many species that are 

not currently present in the assessment area would 

require long-distance migration, whether intentional 

or unintentional, in order to establish and occupy 

suitable habitat in the assessment area. Habitat 

fragmentation and the limited dispersal ability of 

seeds could also hinder the northward movement 

of the more southerly species, despite the increase 

in habitat suitability (Ibáñez et al. 2008). Further, 

species are generally expected to migrate more 

slowly than their habitats will shift (Iverson et al. 

2004a, 2004b). Of course, human-assisted migration 

is a possibility for some species and may be tested 

and used over the coming decades (Pedlar et al. 

2012). 
Chapter 5: Future Climate Change Impacts on Forests

94

Of the 26 new habitat species, 21 are projected 

to gain new suitable habitat under both climate 

scenarios. Five species are projected to have new 

habitat only under the more extreme GFDL A1FI 

scenario, and these species are located in areas that 

are far south of the assessment area. Of the species 

that are projected to have new habitat under one or 

both scenarios, nine have positive modifying factors 

and two have negative modifying factors.

Geographic Trends

Outputs from DISTRIB can be visualized spatially, 

and these results can provide greater context for 

interpreting the projected changes in suitable habitat. 

Three sets of maps (Figs. 37 through 39) provide 

examples of the changes in suitable habitat for three 

species: quaking aspen, sugar maple, and white oak. 

These maps highlight that projected changes are not 

uniform across the assessment area, and that areas of 

suitable habitat are related to local conditions as well 

as to projected climate change. Quaking aspen is 

projected to retain a large amount of suitable habitat 

in the assessment area under PCM B1. Suitable 

habitat decreases more under GFDL A1FI, with 

most remaining suitable habitat found in the central 

part of the assessment area along the MichiganWisconsin border. Sugar maple is projected to lose 

areas of suitable habitat along the southern boundary 

of the assessment area under both climate scenarios, 

with a much greater loss of habitat projected under 

GFDL A1FI. White oak is virtually absent from the 

assessment area today, occurring primarily along the 

tension zone, a transitional zone between the more 

open landscape of southern Wisconsin and the mixed 

deciduous-coniferous forests of the assessment 

area (Wisconsin Department of Natural Resources 

[WDNR] 2009a). Under PCM B1, white oak is 

projected to gain suitable habitat in the southern 

portion of the assessment area. Under GFDL A1FI, 

however, suitable habitat for white oak is projected 

to occur at moderate levels across the assessment 

area. 

Figure 37.—Modeled importance values for quaking aspen 

across the Laurentian Mixed Forest Province under current 

climate conditions (top) and projected for the years 2070 

through 2099 under the PCM B1 (middle) and GFDL A1FI 

(bottom) climate scenarios, from the Tree Atlas model. 

Importance values can range from 0 to 100. An importance 

value of zero (light yellow) indicates that the species is not 

present currently (top), or will not have suitable habitat at 

the end of the century (middle, bottom).
Chapter 5: Future Climate Change Impacts on Forests

95

Figure 39.—Modeled importance values for white oak across 

the Laurentian Mixed Forest Province under current climate 

conditions (top) and projected for the years 2070 through 

2099 under the PCM B1 (middle) and GFDL A1FI (bottom) 

climate scenarios, from the Tree Atlas model. Importance 

values can range from 0 to 100. An importance value of 

zero (light yellow) indicates that the species is not present 

currently (top), or will not have suitable habitat at the end of 

the century (middle, bottom).

Figure 38.—Modeled importance values for sugar maple 

across the Laurentian Mixed Forest Province under current 

climate conditions (top) and projected for the years 2070 

through 2099 under the PCM B1 (middle) and GFDL A1FI 

(bottom) climate scenarios, from the Tree Atlas model. 

Importance values can range from 0 to 100. An importance 

value of zero (light yellow) indicates that the species is not 

present currently (top), or will not have suitable habitat at 

the end of the century (middle, bottom).
Chapter 5: Future Climate Change Impacts on Forests

96

As mentioned above, DISTRIB results indicate 

only a change in suitable habitat, not necessarily 

the ability of a given species to migrate to newly 

available habitat (Box 10). Additionally, these 

results do not incorporate the positive influence of 

modifying factors into the maps for sugar maple or 

white oak. Suitable habitat maps for all the species 

addressed in this assessment are available online at 

the Climate Change Tree Atlas Web site (www.nrs.

fs.fed.us/atlas/tree; also see Appendix 4). As is the 

case for interpreting any spatial model outputs, local 

knowledge of soils, landforms, and other factors is 

necessary to determine if particular sites may indeed 

be suitable habitat for a given species in the future. 

These maps serve only as an illustration of broad 

trends. 

Hardwood forest canopy. Photo by Linda Parker, Chequamegon-Nicolet National Forest.
Chapter 5: Future Climate Change Impacts on Forests

97

Figure 40.—Projected colonization of black oak 

to future suitable habitat under the PCM B1 

scenario. Results under the A1FI scenario (not 

shown) were similar. See Prasad et al. (2013) for 

a full description.

Box 10: Colonization of Tree Species to New Locations

The DISTRIB results from the Climate Change Tree 

Atlas provide useful information about the potential 

of suitable habitat to change over the next century 

for a large number of tree species. However, these 

results provide only an indication of whether suitable 

habitat is likely to be available and do not account 

for the ability of tree species to colonize sites where 

they are not currently present. The SHIFT model 

is another component of the Climate Change Tree 

Atlas that simulates the likelihood of tree species 

colonization into newly available habitats (Iverson 

et al. 2010, Prasad et al. 2013). The SHIFT model is 

a process-based model that estimates colonization 

likelihoods of tree species across fragmented 

landscapes. Current land cover data are used to 

identify which areas may be suitable for colonization, 

and species movement is based upon Holocene 

migration rates among trees migrating into forested 

environments (Prasad et al. 2013).

As one example, black oak is currently found only at 

the southern extent of the assessment area along 

the tension zone that runs through the center of 

Wisconsin (Fig. 40). Suitable habitat for black oak is 

expected to more than double within the assessment 

area during the next century under PCM B1, and 

even greater increases are projected under 

GFDL A1FI.

When migration and colonization are modeled for 

black oak under the PCM B1 scenario, the species 

is expected to colonize new sites, particularly those 

close to the current range. Habitat fragmentation 

and limits on dispersal limited the ability of black oak 

to naturally colonize newly available habitat that was 

not near the current habitat (Prasad et al. 2013).
Chapter 5: Future Climate Change Impacts on Forests

98

Figure 41.—Analysis area modeled by LANDIS-II for this assessment and major forest ownership.

LANDIS-II

Results from the LANDIS-II model include 

projections of aboveground biomass for 27

tree species, as well as biomass data and maps 

aggregated into 10 forest types. The LANDIS-II 

model was used across most of the assessment area; 

however, portions of northern Wisconsin near Green 

Bay as well as the portions of the assessment area 

that fall within Michigan’s Keweenaw, Marquette, 

and Dickinson Counties were not modeled (Fig. 41). 

The LANDIS-II model projects changes in 

aboveground biomass for individual tree species 

through the year 2100 for three climate scenarios: 

a current climate scenario and the GFDL A1FI and 

PCM B1 climate scenarios (Fig. 42). The current 

climate scenario is based upon observed temperature 

and precipitation values from 1960 through 2010 

and also incorporates climate variability at levels 

observed during that period. Climate conditions 

under GFDL A1FI and PCM B1 are described in 

Chapter 4.
Chapter 5: Future Climate Change Impacts on Forests

99

Figure 42.—LANDIS-II biomass projections (kilograms per square meter) through the year 2100 for 27 modeled tree species 

under three climate scenarios. Note that the Y-axis differs by species. The model results maintain current levels of forest 

harvest throughout the simulation. One kilogram per square meter is roughly equivalent to 4 tons per acre. (Figure 42 

continued on next page.)
Chapter 5: Future Climate Change Impacts on Forests

100

Figure 42 (continued).—LANDIS-II biomass projections (kilograms per square meter) through the year 2100 for 27 modeled 

tree species under three climate scenarios. Note that the Y-axis differs by species. The model results maintain current levels 

of forest harvest throughout the simulation. One kilogram per square meter is roughly equivalent to 4 tons per acre.

Biomass provides an indication of the productivity 

of individual tree species based in different locations 

across the landscape, which are provided as averages 

across the LANDIS-II analysis area. Species are 

limited to the most common tree species that are 

currently found within the LANDIS-II study area, 

as well as a few oak species that are found primarily 

at the southern extent of the assessment area. 

The LANDIS-II model incorporates natural wind 

disturbance, but other disturbances, such as fire, 

insect pest outbreaks, forest diseases, and herbivory, 

were not modeled. Additionally, model simulations 

maintain current harvest levels throughout the 

21st century. The current harvest levels are based 

on recent harvest rates for individual ownership 

types from U.S. Forest Service, Forest Inventory 

and Analysis (FIA) data for all of Wisconsin 

between 2005 and 2011 (Appendix 5). Multiple 

types of forest harvest, including clearcut, seed 

tree, single-tree (gap), group or patch selection, 

and shelterwood, are modeled, and are selected to 

realistically represent methods commonly used for 

the individual tree species in this region.

The LANDIS-II model results presented in 

this chapter do not incorporate the effects of 

elevated atmospheric carbon dioxide (CO2 ) on 

plant physiology, commonly referred to as “CO2

fertilization.” In this assessment, the LANDIS-II 

model used a steady CO2

 concentration of 

369.5 ppm (i.e., year 2000 levels; 2012 levels were 

approximately 390 ppm) through the year 2100 
Chapter 5: Future Climate Change Impacts on Forests

101

for all model simulations. Although LANDIS-II is 

able to incorporate rising CO2

 concentrations into 

future scenarios, several challenges are associated 

with modeling CO2

 fertilization. Few observational 

studies have evaluated the effects of CO2

 fertilization 

beyond 600 ppm, and the results from observational 

studies are quite mixed, particularly about the role of 

water and nitrogen in biomass production (Finzi et 

al. 2007, Franks et al. 2013, Leakey et al. 2009). For 

comparison, the A1FI emissions scenario projects 

approximately 900 ppm by 2100. 

The LANDIS-II model simulated only change in 

upland forests in the assessment area, which is an 

important consideration when evaluating the results 

of species that are commonly found in lowland 

systems. Forested wetlands were not modeled 

because of limitations with the model in simulating 

ecosystem dynamics in areas with saturated soils. 

This is particularly important to keep in mind for 

species like black ash, black spruce, and northern 

white-cedar. Further, LANDIS-II results were not 

incorporated into the vulnerability determinations 

for the lowland conifer or the lowland and riparian 

hardwood forest types that are presented in 

Chapter 6.

Aboveground Biomass

The LANDIS-II model simulated changes in 

aboveground biomass for 27 common tree species 

under the current climate, PCM B1, and GFDL 

A1FI scenarios (Fig. 42). These species were also 

organized according to the proportional changes 

under the PCM B1 and GFDL A1FI scenarios 

relative to the current climate scenario (Table 12; 

see also Appendix 5). 

Although the current climate is expected to change 

(Chapters 2 and 4), the current climate scenario 

is useful for highlighting trends that might be 

expected if the climate were to remain stable over 

the next 100 years. This projection can provide a 

baseline for comparing the relative increases or 

a Species are grouped into change classes based on the 

proportional change between the year 2100 biomass under 

the PCM B1 and GFDL A1FI scenarios and the biomass under 

the current climate scenario in the year 2100. Large increases 

or decreases indicate that the proportional change under one 

or both climate change scenarios is greater than 50 percent. 

“No change” indicates that biomass is within 20 percent of what 

is projected under the current climate scenario. See Appendix 5 

for complete results for all 27 species.

Declines under Both Scenarios

Balsam fir Decrease Large decrease

Black spruce Large decrease Large decrease

Jack pine Decrease Large decrease

Paper birch Large decrease Large decrease

Quaking aspen Decrease Large decrease

Red pine Decrease Decrease

White spruce Large decrease Large decrease

Increases under Both Scenarios

American basswood Large increase Large increase

American beech Large increase Large increase

Bitternut hickory Large increase Large increase

Black oak Large increase Large increase

Bur oak Increase Increase

Eastern hemlock Increase Increase

Northern pin oak Large increase Large increase

Red maple Increase Increase

White ash Large increase Large increase

White oak Large increase Large increase

No Change under Both Scenarios

Northern red oak No change No change

Mixed Results Across Scenarios

Balsam poplar No change Large decrease

Bigtooth aspen Increase No change

Black ash No change Decrease

Black cherry No change Increase

Eastern white pine No change Decrease

Green ash Increase No change

Northern white-cedar No change Decrease

Sugar maple No change Decrease

Yellow birch No change Large decrease

Table 12.—Potential change in biomass for 27 tree 

species in the LANDIS-II analysis areaa

Common name PCM B1 GFDL A1FI
Chapter 5: Future Climate Change Impacts on Forests

102

decreases in biomass under the two climate change 

scenarios. The current climate scenario makes it 

easier to observe changes in tree species biomass 

and forest composition that occur as a result of 

natural succession and management, as opposed 

to changes driven by climate. Natural succession 

is important in the forests for this region as areas 

continue to recover from historic widespread 

and intensive logging. Forests in Michigan and 

Wisconsin are actively growing, with forest growth 

outpacing natural mortality, land-use change, and 

timber harvest (Perry et al. 2012, Pugh et al. 2012). 

Thus, the increases in biomass that are observed for 

nearly all species under the current climate scenario 

are due to the continued maturation of forests across 

the region. Succession is also occurring within the 

two climate change scenarios, but the effects of 

climate change on forest dynamics are evident when 

compared to the current climate scenario. 

According to recent FIA data for the assessment 

area, the annual net growth is greater than annual 

harvest removals (growth-to-removal ratio for the 

assessment area = 1.6; see Chapter 1). This positive 

ratio helps explain the increasing trends for many 

of the species under the current climate scenario. 

The biomass projections of nearly all the species 

modeled for this assessment indicate at least a 

short-term biomass increase, regardless of climate 

scenario. All forested landscapes have a degree 

of “landscape inertia,” in that current trends are 

projected to continue into the near future (the next 

several decades). This momentum was built into the 

LANDIS-II simulations based on recent observed 

patterns of forest growth and regeneration, so that 

even species that are projected to eventually decline 

in biomass often show initial increases.

Decreases in Biomass

Biomass is projected to decrease for seven species 

by the year 2100 under both PCM B1 and GFDL 

A1FI, relative to the current climate scenario. These 

species are generally characterized as northern and 

boreal species, including paper birch, balsam fir, 

white spruce, and black spruce (Table 12). Under 

both scenarios, black spruce, white spruce, and paper 

birch are projected to have more than a 50-percent 

decrease in biomass by the end of the century when 

compared to the current climate scenario. Three 

species—balsam fir, jack pine, and quaking aspen—

are projected to have greater decreases in biomass 

under GFDL A1FI than under PCM B1 (Fig. 42). 

These trends indicate that climate-related shifts are 

driving the biomass projections for these species, 

and that the hotter and drier conditions projected by 

GFDL A1FI amplify the decline. The LANDIS-II 

model does not incorporate artificial regeneration as 

part of management, which may underestimate the 

potential future biomass of red pine and jack pine 

because these species are often planted.

Mixed Results across Scenarios

Different outcomes are projected for nine species 

under the two climate scenarios. Six species—

balsam poplar, black ash, eastern white pine, 

northern white-cedar, sugar maple, and yellow 

birch—are projected to have no substantial change 

in biomass under the PCM B1 scenario (i.e., biomass 

projected to be within 20 percent of the current 

climate scenario) but to have substantial biomass 

decreases under GFDL A1FI (Table 12). This result 

suggests that the changes in climate projected under 

the milder PCM B1scenario may not be severe 

enough to negatively affect these species, but that 

the hotter and drier conditions projected under 

GFDL A1FI may lower productivity. Similarly, 

bigtooth aspen and green ash are projected to have 

slight biomass increases under PCM B1 and no 

change in biomass under GFDL A1FI. This result 

suggests that the slight increases in temperature 

under PCM B1 may make conditions more favorable 

for the growth of these species. In contrast, the 

greater temperature increases and summer moisture 

limitations projected under GFDL A1FI might 

not benefit these species. Only one species in this 

group, black cherry, is projected to have greater 

productivity gains under the GDFL A1FI scenario 

than under PCM B1.
Chapter 5: Future Climate Change Impacts on Forests

103

No Change in Biomass

Northern red oak is the only species to have no 

substantial change in biomass projected under either 

PCM B1 or GFDL A1FI (Table 12). For this species, 

aboveground biomass is projected to increase 

17 percent under PCM B1 and decline 8 percent 

under GFDL A1FI relative to the current climate 

scenario. All three scenarios project steady increases 

in biomass through the end of the century, with little 

difference between scenarios until mid-century 

(Fig. 42).

Increases in Biomass

Ten species are projected to have increased biomass 

under both PCM B1 and GFDL A1FI relative to 

the control scenario (Table 12). Several of these 

species, including American beech, bur oak, eastern 

hemlock, red maple, and white oak, are projected to 

have comparable biomass increases under both PCM 

B1 and GFDL A1FI scenarios. American basswood, 

northern pin oak, and white ash are projected to have 

higher levels of biomass under the PCM B1 scenario 

(Table 12; see also Appendix 5). The slightly warmer 

temperatures and slightly wetter growing season 

conditions projected under PCM B1 might benefit 

these species, whereas the more severe change 

to hotter temperatures, wetter springs, and drier 

summers projected under GFDL A1FI may not have 

as great a benefit for these species. Bitternut hickory 

and black oak are projected to have large biomass 

increases under both scenarios, and are among 

the few species that are projected to have greater 

biomass increases under GFDL A1FI than PCM B1.

Trends over Time

Individual species show different patterns in 

biomass change over time (Fig. 42). For some 

species that are projected to have reduced biomass 

by 2100, reductions in biomass as a result of 

climate change become evident by mid-century. 

This result is particularly notable with characteristic 

boreal species, such as balsam fir, black spruce, 

paper birch, and white spruce. Other species, such 

as balsam poplar and quaking aspen, show more 

gradual changes throughout the century. For species 

that increase as a result of climate change, the 

patterns of biomass increases are variable. Several 

species such as bitternut hickory, black oak, red 

maple, and white oak show distinct increases in 

biomass under the GFDL A1FI scenario near the 

end of the 21st century. These hardwood species 

are currently near the northern edge of their 

historic ranges in the assessment area, and the latecentury increase suggests enhanced migration and 

establishment of these species in the assessment 

area or a greater competitive advantage under the 

projected hotter and drier conditions. These trends 

are similar to those projected by other simulations 

in northern Wisconsin and the Great Lakes (e.g., 

Duveneck et al. 2014; Handler et al. 2014a, 2014b; 

Scheller and Mladenoff 2005).

The regional total of aboveground biomass for all 

species across the LANDIS-II analysis area also 

varies over time (Fig. 43). For the PCM B1 scenario, 

biomass is expected to rise steadily across the 

landscape, reaching a plateau around the year 2080. 

This trajectory closely tracks the combined results 

for the current climate scenario. The GFDL A1FI 

scenario projects a slight increase to the year 2030, 

followed by a steady decline until around 2090. 

A slight increase is then projected during the last 

decade of the century. When viewed in concert with 

the projected declines in northern or boreal species, 

this trend appears to support the possibility of a 

landscape-level transition to a hardwood-dominated 

landscape around the year 2100 for the GFDL A1FI 

scenario. Noticeable biomass gains for southern 

hardwood species may be delayed until late-century 

because of lag times associated with projected 

rates of migration, colonization, and growth to the 

overstory forest layers. It is important to reiterate 

that LANDIS-II assesses only the 27 species listed 

above and for upland forests, and does not account 

for the possibility of other species that may enter the 

analysis area.
Chapter 5: Future Climate Change Impacts on Forests

104

Figure 43.—LANDIS-II biomass projections through the year 

2100 for all tree species combined under three climate 

scenarios. The model results maintain current levels of 

forest harvest throughout the simulation. One teragram 

equals one million metric tons, or about 1.1 million tons.

Geographic Trends

Maps of projected forest cover indicate the potential 

for landscape-level change under the two climate 

scenarios (Fig. 44). The LANDIS-II model allows 

for species cohorts to migrate, compete, reproduce, 

and undergo disturbances across the landscape, 

and these transitions are governed by a range of 

factors including soils and landform. To create 

these forest-type maps, individual locations (cells) 

in the LANDIS-II simulations were classified into 

10 forest types based on characteristic species 

composition (Appendix 5). These forest types are 

not perfectly correlated to the forest types used in 

other portions in this assessment, and cannot be 

directly compared. Because the LANDIS-II model 

does not effectively simulate lowland forest types 

or nonforested systems, the lowland and riparian 

forest types are not included in this classification. 

Additionally, LANDIS-II simulations account for 

only the 27 species modeled in this assessment. 

Therefore, these maps do not represent the potential 

for new species to migrate into the landscape or the 

potential for currently low-abundance species to 

increase within the assessment area.

When the individual species are grouped into forest 

types, changes in forest communities over time 

become more apparent (Fig. 44). The boreal forest 

types show substantial reductions over time, with 

very little of the landscape containing upland sprucefir, aspen/birch, or jack pine forest by the year 

2100 under PCM B1 or GFDL A1FI. Under both 

climate scenarios, the northern hardwoods forest 

type is projected to increase substantially across the 

landscape, but individual species respond differently 

as described above. Although the forest type remains 

common on the landscape, species composition is 

expected to change in conjunction with climatic 

changes.
Chapter 5: Future Climate Change Impacts on Forests

105

Figure 44.—Proportion of area in each forest type projected by the LANDIS-II model through 2100 under the current climate 

(top) and two climate scenarios: PCM B1 (center) and GFDL A1FI (bottom). The model results maintain current levels of forest 

harvest throughout the simulation.
Chapter 5: Future Climate Change Impacts on Forests

106

Figure 45.—Spatial distribution of the aggregated foresttype groups used in PnET-CN simulations. These foresttype groups remain fixed for the duration of the PnET-CN 

simulations.

were included in the modeling. Natural disturbances, 

forest management, and tree species competition and 

migration were not included in the model.

Productivity 

Productivity is projected to increase under both 

PCM B1 and GFDL A1FI through the end of the 

century in comparison to the 1971 through 2000 

baseline period (Fig. 46). Across all forest types 

in the assessment area, PCM B1 resulted in an 

average productivity increase of 66 percent 

(399 grams biomass per meter per year) compared 

to the baseline period, although this value ranged 

from 9 to 97 percent. The productivity increases 

projected under GFDL A1FI are roughly two times 

greater than the increases projected under PCM B1. 

Under this scenario, the average relative increase in 

productivity from baseline to end-of-century is 

145 percent (825 grams biomass per meter per year), 

and ranged from 41 to 253 percent. Productivity 

increases generally leveled off around the year 2080 

under the PCM B1 scenario, but increases continue 

under GFDL A1FI until the end of the model period.

PnET-CN

The PnET-CN model projects changes in 

aboveground net primary productivity (productivity). 

Productivity is commonly used as a measure of 

how fast forest ecosystems are photosynthesizing 

and accumulating biomass and can provide some 

information pertaining to ecosystem function. In 

this assessment, we report absolute productivity as 

well as relative percentage changes in productivity. 

The PnET-CN model uses 1971 through 2000 as a 

baseline period, and simulates productivity changes 

from 2000 through 2099. 

For this assessment, PnET-CN results describe 

six aggregated forest types rather than individual 

species. These forest types are based on FIA 

forest-type groups (Pugh et al. 2012). The six 

forest types used by PnET-CN in this assessment 

are: aspen/birch, maple/beech/birch, oak/hickory, 

elm/ash/cottonwood, pine, and spruce/fir. These 

roughly correspond to the forest types used in this 

assessment (Fig. 45).

PnET-CN was used to evaluate change in 

productivity across the full assessment area. 

Biological changes associated with nitrogen cycling, 

tropospheric ozone pollution, and CO2

 fertilization 

Visitors viewing Lake of the Clouds in western Upper 

Michigan. Photo by Scott Pearson, used with permission.
Chapter 5: Future Climate Change Impacts on Forests

107

Figure 46.—Projected trends in aboveground net primary productivity (ANPP) from PnET-CN for six aggregated forest-type 

groups under the PCM B1 (top) and GFDL A1FI (bottom) future climate scenarios. Outputs have been smoothed based on a 

5-year running mean. One kilogram (one thousand grams) per square meter is roughly equivalent to 4 tons per acre.
Chapter 5: Future Climate Change Impacts on Forests

108

All forest types show increases in productivity in 

both scenarios. The greatest projected increases 

are in the broadleaf forest types: aspen/birch, 

oak/hickory, maple/beech/birch, and elm/ash/

cottonwood. In comparison, the conifer-dominated 

spruce/fir and pine forest types have much smaller 

increases, with spruce/fir consistently having the 

lowest productivity throughout the simulations. 

The main drivers of the increased forest productivity 

projected by PnET-CN are growing season length 

and CO2

 fertilization. Warmer temperatures enhance 

carbon uptake earlier in the spring and later in the 

fall, but carbon uptake is reduced in mid-summer 

due to water limitations on photosynthesis. Growing 

season length increased more under GFDL A1FI 

(1 to 2 months across the assessment area) than 

PCM B1 (roughly 1 month). In general, this longer 

growing season allowed forests to accumulate more 

biomass per year in the simulation. 

Elevated atmospheric CO2

 concentrations enable 

trees to absorb more carbon through pores on 

their leaves. As a result water loss is reduced 

and photosynthesis is increased for a given amount 

of water use. Carbon dioxide fertilization effects 

are larger under GFDL A1FI compared to 

PCM B1 because of the higher levels of greenhouse 

gases that are projected under the A1FI emissions 

scenario. In a separate set of simulations where 

the level of atmospheric CO2

 was fixed at 

350 parts per million and all other variables were 

held constant (not shown in this assessment), there 

was little change in productivity under PCM B1 

and declines in productivity under GFDL A1FI by 

the end of the 21st century. The lower levels of 

CO2

 in these simulations reduced the fertilization 

effect, and increased water stress led to reduced 

forest productivity. This outcome suggests that 

the predicted increases in productivity presented 

here are largely due to CO2

 fertilization. PnET-CN 

tends to predict a larger CO2

 fertilization effect 

on productivity than other ecosystem models, so 

this effect may be a generous estimate (Medlyn 

et al. 2011, Norby and Zak 2011). It is difficult 

to know the fertilization effect on forests at high 

CO2

 concentrations because no field studies have 

directly tested ecosystem responses at levels 

similar to those projected under the A1FI emissions 

scenario at the end of the century. For comparison, 

updates to the CO2

 routine in the PnET-CN model 

(Franks et al. 2013) conducted subsequent to this 

assessment decreased this CO2

 fertilization effect 

by approximately 20 percent relative to the data 

presented in this assessment at CO2

 concentrations 

equal to 1100 ppm (E. Peters, University of 

Minnesota, unpublished work).

Although PnET-CN accounts for biogeochemical 

feedbacks like water and nutrient limitation, 

this model does not account for multiple other 

factors that could cause mortality or otherwise 

reduce the productivity of forest ecosystems in the 

future. For example, the model does not account 

for competition, forest management, natural 

disturbances, or climate effects on regeneration. 

Additionally, the model does not account for 

forest-type change over time; forest composition is 

essentially static through the 100-year simulations. 

Therefore, it may be most helpful to think of these 

results as a best-case representation of the potential 

ecosystem productivity response of existing forests 

to climate change. 

Geographic Trends

Productivity is projected to increase under both 

future climate scenarios across the assessment 

area (Fig. 47). Under the PCM B1 scenario, small 

increases are projected throughout much of the 

assessment area, with localized areas of no change. 

Productivity increases are projected to be much 

greater under the GFDL A1FI scenario. Increases 

of about 100 percent are projected in the western 

portion of the assessment area, which is also where 

productivity was highest during the 1971 through 

2000 baseline period. Increases of 150 percent and 
Chapter 5: Future Climate Change Impacts on Forests

109

Figure 47.—Projected aboveground net primary productivity (ANPP) changes across the assessment area under the PCM B1 

and GFDL A1FI future climate scenarios, from the PnET-CN model. For the baseline period of 1971 through 2000, productivity 

is shown as an absolute value (top panels). For the future scenarios, changes in productivity are shown for 2070 to 2099 

relative to the 1971 to 2000 baseline (lower panels). Baseline values are slightly different between the two climate scenarios 

because of slight variations in the downscaled climate data.
Chapter 5: Future Climate Change Impacts on Forests

110

more are projected in the central portion of the 

assessment area under GFDL A1FI, compared to 

smaller increases on the eastern and western edges 

of the assessment area. Greater biomass increases 

appear to occur in parts of the landscape that are 

dominated by maple/beech/birch forests.

The PnET-CN simulations indicate the productivity 

of forests in the assessment area could switch from 

being temperature limited to water limited by the end 

of the 21st century. See Chapter 4 for a discussion of 

area-wide changes in the ratio of evapotranspiration 

to precipitation, which is a related output of the 

PnET-CN model. In the PnET-CN simulations, areas 

with lower water-holding capacity are less buffered 

from water limitation and more prone to reductions 

or smaller increases in productivity (Peters et al. 

2013a). Therefore, soil water-holding capacity could 

play a critical role in determining how forests in the 

assessment area respond to future climatic changes. 

Discussion of Model Results

The three different models used in this assessment 

were selected because of the ability to model and 

represent different facets of potential forest change 

as a result of a changing climate. Therefore, the 

ability to make comparisons between the different 

models facilitates a deeper understanding of 

which parts of a forest ecosystem may be most 

responsive or vulnerable to change. However, 

the differences between the models, in terms of 

design, outputs, strengths, and weaknesses, also 

make direct comparisons among model results 

difficult. This section describes areas of agreement 

and disagreement between the results and provides 

context for how the results from multiple models can 

be integrated to better understand forest change.

Agreements 

Despite the differences between the modeling 

approaches, Tree Atlas, LANDIS-II, and PnET-CN 

show some strong similarities in forest change over 

the next century under a range of future climates. 

All three models agree that characteristic boreal 

species or northern species that are currently at their 

southern range limits will face increasing climate 

stress. For example, the Tree Atlas and LANDIS-II 

models both project that several species, including 

black spruce, white spruce, paper birch, and 

northern-white cedar, will decrease. Additionally, 

the PnET-CN results project a weaker potential 

productivity response for spruce/fir forests compared 

to other forest types. These species and forest types 

are characteristic of boreal environments and are 

currently major components of regional ecosystems. 

As the climate warms through the 21st century, 

they are projected to face increasing climate-related 

stress. These findings are similar to a number of 

other studies that evaluated tree species responses 

to climate change in northern Wisconsin and saw 

transitions away from boreal species toward 

species more representative of warmer climates 

(e.g., Chiang et al. 2008, Duveneck et al. 2014, 

Scheller and Mladenoff 2005, Williams et al. 2012).

Moreover, both Tree Atlas and LANDIS-II tend to 

agree that many species within the assessment area 

may fare better under the PCM B1 conditions and 

have greater decreases under GFDL A1FI. These 

results support the idea that GFDL A1FI represents 

a future climate that is beyond the tolerance of 

many species. These results also suggest that 

many temperate species currently present in the 

assessment area could tolerate a mild degree of 

warming with corresponding increases in growing 

season precipitation, as represented by the PCM B1 

scenario. 

Tree Atlas and LANDIS-II also agree that some 

species have the potential to increase under a range 

of future climates. These temperate hardwood 

species, including American beech, bitternut hickory, 

black oak, bur oak, white ash, and white oak, may 

be more tolerant of warmer year-round conditions 

and a slightly drier growing season. PnET-CN 

outputs also indicate that elm/ash/cottonwood, 
Chapter 5: Future Climate Change Impacts on Forests

111

maple/beech/birch, and oak/hickory forests have the 

potential for large productivity increases across the 

assessment area. 

Both Tree Atlas and LANDIS-II project that several 

species currently present south of the assessment 

area may become more widespread throughout the 

landscape, assuming higher regeneration success 

under future forest conditions. At the same time, 

many of the species that are projected to decrease 

are still expected to be major components of forest 

ecosystems at the end of the century. For these 

reasons, it is possible that forests in the assessment 

area may be able to contain a higher diversity of 

species in the future, with a blend of temperate and 

boreal species.

Disagreements

As mentioned above, there are differences in 

productivity projections between the LANDIS-II 

and PnET-CN simulations used for this assessment. 

The LANDIS-II results are driven by projections of 

an overall decline in productivity under GFDL A1FI 

and a very small increase under PCM B1. The 

PnET-CN simulations in this assessment, however, 

project large productivity increases under both 

scenarios, with productivity gains nearly twice as 

large under the GFDL A1FI scenario. One major 

reason for this difference is that the LANDIS-II 

model includes several processes that lead to tree 

mortality, such as competition, natural disturbances, 

and forest management. These processes are not 

included in the PnET-CN model.

Another important reason for this discrepancy is the 

way that these models account for the potential CO2

fertilization effect. Again, the PnET-CN simulations 

appear to be driven mainly by the potential CO2

fertilization effect, and the productivity estimates 

used by the LANDIS-II model do not account for 

CO2

 fertilization. It is unclear how substantial this 

factor will be over the long term. Experiments with 

CO2

 enrichment in forests suggest net primary 

productivity will increase under elevated CO2 , 

although this response can diminish over time due 

to water or nutrient limitation and tree age (Norby 

and Zak 2011, Norby et al. 2005). Additionally, 

productivity increases under elevated CO2

 could be 

partially offset by reductions in productivity from 

warming-induced drought stress or the effects of 

future disturbances (Dieleman et al. 2012). Unique 

factors incorporated into the LANDIS-II model, 

such as wind disturbance, forest harvest, and tree 

species competition and movement, may also cause 

some smaller differences between the model results.

There do not appear to be any major discrepancies 

between individual species when the LANDIS-II 

and Tree Atlas model results are compared, although 

there are some differences. Particularly under the 

PCM B1 scenario, LANDIS-II projects better 

outcomes for some species, such as northern pin 

oak, northern white-cedar, red maple, white ash, and 

white oak. This difference may be due to several 

factors. Most importantly, the LANDIS-II model 

simulates changes in tree growth and biomass, 

whereas the Tree Atlas describes potential suitable 

habitat that is available to a species. Therefore, the 

biomass values at the year 2100 that are provided 

by LANDIS-II include biomass for trees that are 

established well before the year 2100, even trees that 

are currently present. Trees that are able to establish 

by mid-century may be able to persist and even 

grow on a site as establishment conditions become 

less and less suitable over time. By the end of the 

century, however, the habitat conditions may no 

longer be suitable for many species, especially at 

the regeneration stage.

Limitations

All models are simplified representations of reality, 

and no model can fully consider the entire range 

of ecosystem processes, stressors, interactions, and 

future changes to forest ecosystems. Each model 

omits processes or drivers that may critically 

influence ecosystem change in the future. Future 
Chapter 5: Future Climate Change Impacts on Forests

112

uncertainty is not limited to climate scenarios; there 

is also uncertainty associated with future human 

interactions with forests. Some examples of factors 

that are not considered in these models are:

• Land management and policy responses to 

climate change or impacts to forests

• Land-use change or forest fragmentation

• Future changes in forest industry, both in 

products and in markets

• Changes in phenology and potential timing 

mismatches for key ecosystem processes

• Responses of understory vegetation, soil 

microorganisms, and soil mycorrhizal 

associations

• Extreme weather events, which are not captured 

well in climate data or forest impact models

• Future wildfire behavior, fire suppression, and 

ability to apply prescribed fire 

• Novel successional pathways for current forest 

ecosystems

• New major insect pests or disease agents 

• Future herbivory pressure, particularly from 

white-tailed deer

• Interactions among all these factors

Most of these factors could drive large changes in 

forest ecosystems throughout the assessment area, 

depending on how much change occurs in the future. 

The potential for interactions among these factors 

adds additional layers of complexity and uncertainty. 

Despite these limitations, impact models are still 

the best tools available and can simulate a range of 

possible future outcomes. It is important to keep 

the above limitations in mind when weighing the 

results from different models and use them to inform 

an overall assessment. The comparison across 

several different kinds of models allows for a better 

understanding of the range of possibilities. In the 

following section, we draw upon published literature 

to address other factors that may dictate how forest 

ecosystems in the assessment area respond to climate 

change. 

SUMMARY OF CURRENT

SCIENTIFIC KNOWLEDGE 

The results presented above provide us with 

important projections of tree species distributions 

and forest response across a range of future climates, 

but these models do not account for all factors that 

may influence tree species and forest communities 

under a changing climate. Climate change has the 

potential to alter the distribution, abundance, and 

productivity of forests and their associated species in 

several ways (CCSP 2008a, Vose et al. 2012). These 

impacts can broadly be divided into the direct effects 

of changing temperature, precipitation, and CO2

levels on forests and the indirect effects of altered 

stressors or the development of additional stressors. 

It is also important to note that some of the impacts 

may in fact be positive or beneficial to native forests 

in the assessment area. The remainder of this chapter 

summarizes the current state of scientific knowledge 

on additional direct and indirect effects of climate 

change on forests in the assessment area and the 

wider Midwest region.

Wind Events

Wind disturbance is a primary driver of many 

regional forests (Johnson 1995). Both small-scale 

and stand-replacing wind events influence the 

structure and species composition of many forests 

in the assessment area and increase landscape 

complexity (Frelich and Reich 1995a, 1995b; White 

and Host 2008). Storm severity, forest composition, 

stand age, soils, topography, and a host of other 

factors influence the physical effects from a given 

wind event (Peterson 2000; Rich et al. 2007, 2010). 

Some models project an overall increase in the 

frequency of extreme wind events across the central 

United States, but it is not known whether any 

increase in severe forest disturbances from wind in 

the assessment area will be outside the already high 

range of variability (Chapter 4). 
Chapter 5: Future Climate Change Impacts on Forests

113

Under climate change, stand-replacing wind 

events could potentially act as a catalyst for 

more rapid ecosystem change than would occur 

through migration and competition alone. Climatic 

conditions following a major wind event in the 

future may not favor typical successional pathways, 

particularly if regeneration consists of novel species 

mixes. Moreover, tree mortality as a result of future 

wind events may further increase the risk of wildfire.

Altered Precipitation

Climate change is expected to alter precipitation 

regimes and hydrologic conditions throughout the 

assessment area. As discussed in Chapters 3 and 

4, heavy precipitation events have been increasing 

across the assessment area over the past century 

and this trend is expected to continue. In a review 

of the consequences of more-extreme precipitation 

regimes, Knapp et al. (2008) also proposed that 

mesic systems may be most negatively affected 

because of increasing duration and severity of soil 

water stress. Xeric systems may be less affected by 

a more extreme precipitation regime, because they 

already are limited by moisture stress and larger 

pulses of precipitation might afford them slightly 

longer periods of available soil moisture. Hydric 

systems, on the other end of the spectrum, are 

already limited by anoxic conditions so longer dry 

periods between precipitation pulses might increase 

some ecosystem functions like biomass productivity. 

This conceptual framework does not incorporate 

modifiers like soil texture and root depth, but the 

general principles are useful.

In addition to more episodic precipitation events, 

future climate scenarios also project a wide possible 

range of seasonal precipitation and soil moisture 

(Chapter 4). Such variability may expose forest 

ecosystems to greater risk of hydrologic extremes, 

including flooding and drought. For example, 

species such as northern white-cedar and eastern 

cottonwood have particular seedbed requirements 

that are tightly linked to hydrologic conditions 

(Burns and Honkala 1990, Cornett et al. 2000b). 

Tree species and assemblages that are accustomed 

to seasonal or annual variations in water availability 

may be better able to tolerate this variability. In 

particular, riparian forests are more tolerant of 

varying degrees of hydrologic fluctuation, and pinedominated forests are generally adapted to periodic 

moisture stress and drought. Forests that depend 

on a more stable regime of soil moisture or water 

levels throughout the year or between years, such 

as northern hardwoods or lowland conifers, may be 

more stressed by hydrologic variation. 

Drought Stress 

There is evidence for an increased risk of drought 

stress in the future in the assessment area. 

Temperatures are expected to rise over the next 

century, which will increase evapotranspiration 

in ecosystems. Moisture stress may occur when 

increases in evapotranspiration are not offset by 

a corresponding increase in precipitation and soil 

moisture. Within the climate scenarios used in 

this assessment, the potential for more frequent 

droughts and moisture stress during the growing 

season appears to be much greater under the GFDL 

A1FI scenario. Even under the milder PCM B1 

scenario, warmer temperatures may also lead to 

increased evapotranspiration and physiological 

stress if increases in precipitation do not offset 

water losses (Chapter 4). Although precipitation 

projections have greater uncertainty, these results are 

generally consistent with other projections of future 

precipitation in the region (Center for Climatic 

Research 2013, Kunkel et al. 2013). Additionally, 

there is evidence that precipitation is more likely 

to occur during more intense precipitation events 

(Kucharik et al. 2010b), which may increase the 

interval between rainfall events (Knapp et al. 2008). 
Chapter 5: Future Climate Change Impacts on Forests

114

Drought can affect forests in many ways, including 

altering ecosystem processes, reducing forest 

productivity, increasing susceptibility to other 

stressors, and increasing tree mortality (Dale et al. 

2001). Nearly all forests are susceptible to drought. 

For example, a recent study found that forests in 

both wet and dry environments around the world 

typically operate within a relatively narrow range of 

tolerance for drought conditions (Choat et al. 2012). 

Drought stress causes air bubbles to form in the 

xylem of growing trees (cavitation), which reduces 

the ability of trees to move water and causes reduced 

productivity or mortality. Forest species from rain 

forests, temperate forests, and dry woodlands all 

showed a similarly low threshold for resisting 

hydraulic failure (Choat et al. 2012).

The potential effects of drought on forests will 

depend upon many factors, including drought 

duration and severity, as well as site-level 

characteristics of the forest. In the Upper Midwest, 

the duration of drought events may have a greater 

effect on tree mortality than drought severity or 

average dryness over a period of years (Gustafson 

and Sturtevant 2013). High stand density may 

compound susceptibility to moisture stress as 

high-density stands face increased competition for 

available moisture (D’Amato et al. 2011, Magruder 

et al. 2012). Furthermore, drought-stressed trees 

are typically more vulnerable to insect pests and 

diseases (Dukes et al. 2009b, Minnesota Department 

of Natural Resources 2011). 

Tree species also respond differently to drought. 

For example, drought has been linked to dieback 

in sugar maple, birch species, and ash species in 

Maine over the past century (Auclair et al. 2010). 

Recently, a widespread aspen decline was observed 

in northern Minnesota and linked to the combined 

effects of a multi-year drought and insect defoliation 

(Worrall et al. 2013), and future projections suggest 

greater losses of suitable habitat for aspen in the 

upper Great Lakes region by mid-century (Worrall 

et al. 2013). Conversely, ecosystem modeling in 

northern Wisconsin suggests that drought events 

might benefit pioneer forest types like aspen and 

birch, even though individuals of these species 

are generally drought-intolerant (Gustafson and 

Sturtevant 2013). Additionally, elevated atmospheric 

CO2

 reduces the rate of water loss from trees through 

evapotranspiration, but it is unclear to what degree 

enhanced water use efficiency may be able to offset 

the combined effects of warmer temperatures and 

drier conditions (Ryan and Vose 2012, Wyckoff and 

Bowers 2010). 

Snow and Soil Frost

Winter processes, such as snowfall and soil frost, are 

projected to continue to change substantially under 

a changing climate (Chapter 4). Shifts in the timing 

of the soil frost season may have cascading impacts 

on various ecosystem processes. Unfrozen soils are 

better able to absorb snowmelt and rainfall, leading 

to increased infiltration (Sinha and Cherkauer 

2010). Increased infiltration can lead to increased 

nutrient leaching from forest soils if the phenology 

of plant communities does not closely track the 

change in soil frost (Campbell et al. 2010). Studies 

from northern hardwood forests in New England 

have shown that snowmelt and soil thawing are 

advancing rapidly in the spring and that overstory 

leaf-out dates are lagging behind (Groffman et al. 

2012), so these systems may be losing additional soil 

nutrients. Northern hardwood species are generally 

shallow-rooted and more vulnerable to freezing, 

and frost-related mortality in this forest type has 

been observed elsewhere in the northern United 

States (Auclair et al. 2010). Interactions of these 

effects may be increasing hardwood decline in the 

assessment area (Box 11). Altered winter processes 

on forest ecosystems in the assessment area may 

also affect regeneration conditions for some species. 

For example, yellow birch is best able to disperse 

seeds over snow, and therefore may be impaired by 

less consistent snowpack (Burns and Honkala 1990, 

Groffman et al. 2012). 
Chapter 5: Future Climate Change Impacts on Forests

115

Box 11: Hardwood Decline in the Upper Great Lakes Region

Northern hardwood stands with sugar maple crown 

dieback have recently been reported in the upper 

Great Lakes region (Michigan Department of Natural 

Resources and Environment 2009, 2010; Michigan 

Department of Natural Resources [MDNR] 2012b). 

Permanent plots have been established on industrial, 

Federal, and State land in the Upper Peninsula 

of Michigan, northern Wisconsin, and eastern 

Minnesota in order to investigate the cause of this 

dieback (Bal 2013). Mean sugar maple crown dieback 

percentage of live trees at all plots varied from 15 

percent in 2009 to approximately 7 percent in 2012. 

Healthy sugar maple stands typically have less than 

10-percent dieback.

Analysis has indicated that sugar maple dieback is 

related to many factors, including exotic earthworms, 

climate, and soil nutrient variability (Bal 2013). 

Out of all plot variables measured, the forest floor 

condition was the significant factor related to mean 

sugar maple crown dieback (2009-12). The removal 

of the duff layer by high densities of European 

earthworms exposes roots, disturbs biogeochemical 

cycling, reduces soil moisture, increases soil 

temperature, affects mycorrhizal communities, 

and generally exacerbates further stresses on trees 

(Bohlen et al. 2004, Larson et al. 2010). Evaluation 

of basal area growth indicates a significant positive 

relationship with total winter snowfall, number of 

days with snowcover on the ground, and number of 

days below freezing temperatures across the region, 

all of which have been decreasing in recent decades. 

Tree roots of sugar maple and other northern 

hardwoods are generally frost intolerant, and lack 

of adequate snowcover exposes these shallow roots 

to freezing conditions. Moderate drought conditions 

in recent years, especially in the Upper Peninsula of 

Michigan, have likely further contributed to maple 

dieback (Bal 2013). The presence of earthworms and 

poor soil fertility are also likely contributing to poor 

crown conditions and decline in many areas.

Winter visitors to the sea caves at Apostle Islands National Lakeshore. Photo by Maria Janowiak, U.S. Forest Service.
Chapter 5: Future Climate Change Impacts on Forests

116

Soil Erosion

As climate change continues to intensify the 

hydrologic cycle, the increase in heavy rainfall 

events is projected to continue across the assessment 

area. One of the potential impacts of this trend is that 

soil erosion rates will increase (Nearing et al. 2004, 

2005). Most studies examining the effects of climate 

change on soil erosion have focused on agricultural 

settings, rather than forest ecosystems. Although 

additional vegetative cover and root stabilization in 

forest systems may make forests less prone to soil 

erosion, not all forest soils will be equally protected. 

Reductions in vegetative cover from climate-related 

impacts or disturbance events such as prolonged 

drought, wildfire, or increased tree mortality, could 

lead to greater susceptibility to erosion. Additionally, 

the projected decline in snowcover and the transition 

from snowfall to rain in winter months might make 

forest soils particularly vulnerable to erosion during 

the late fall and early spring (Sinha and Cherkauer 

2010).

Wildfire

Wildfire is an important driver for some forest 

ecosystems in the assessment area. Jack pine, red 

pine, and aspen-birch forests are often linked to 

wildfire dynamics, but fire could also become an 

increasing source of disturbance in other forest 

types if climatic shifts over the 21st century result 

in different fire behavior. The climate of an area can 

directly affect the frequency, size, and severity of 

fires, and climate also indirectly affects fire regimes 

through its influence on vegetation vigor, structure, 

and composition (Sommers et al. 2011). 

Many aspects of the fire regime within the 

assessment area are expected to be affected by 

changes in climate, with response to climate 

change varying over time and space. Authors of 

a review paper on climate and wildfire conclude 

that fire-related impacts may be more important to 

some ecosystems than the direct effects of climate 

change on species fitness and migration (Sommers 

et al. 2011). Fire can be a catalyst for change in 

vegetation, perhaps prompting more rapid change 

than would be expected based only on the changes 

in temperature and moisture availability. As with 

wind disturbances, the potential exists for novel 

successional pathways following wildfire if climatic 

conditions, seed sources, or management decisions 

favor different forest types. 

Even if uncertainty exists for the near term, model 

simulations from around the world tend to agree 

that there will be increases in fire activity by the end 

of the 21st century under climate change (Moritz 

et al. 2012). This agreement is particularly high 

for boreal forests, temperate coniferous forests, 

and temperate broadleaf and mixed forests. These 

global assessments correspond with more local 

research on climate and wildfire. Projections for 

boreal forests in Canada estimate that there may be 

a 100-percent increase in the annual area burned 

by the end of the century, along with a 50-percent 

increase in fire frequency (Flannigan et al. 2009). 

Research on boreal forest systems in Quebec 

projects that the wildfire season may shift later into 

the growing season and that wildfire risk may double 

in August (Le Goff et al. 2009). Future fire activity 

may depend most on the relationship between 

temperature, precipitation, and evapotranspiration. 

If temperature and evapotranspiration increases 

amplify the effects of declining precipitation or 

overwhelm modest precipitation increases, fires may 

become more frequent (Drever et al. 2009). 

Research suggests that human activities may have a 

larger influence on wildfire activity than biophysical 

drivers in some landscapes (Miranda et al. 2012). 

Land use and management decisions often determine 

whether a change in fire risk might translate to an 

actual increase in wildfire activity. Because future 

policies on wildfire suppression and prescribed 

fire are unknown, there is greater uncertainty about 

the potential effects of wildfire on forests in the 

assessment area.
Chapter 5: Future Climate Change Impacts on Forests

117

Invasive Species

Nonnative invasive species are currently a major 

threat to forests in the assessment area (Chapter 1). 

It is generally expected that many invasive plants 

will “disproportionally benefit” under climate 

change due to more effective exploitation of changed 

environments and more aggressive colonization 

of new areas (Dukes et al. 2009a). The invasion 

of nonnative plant species into new environments 

depends on a complex combination of factors related 

to both the specific species and environmental 

conditions in question. Climatic factors that could 

influence the ability of a species to invade include 

warmer temperatures, earlier springs, and reduced 

snowpack (Ryan and Vose 2012). As one example, 

Japanese barberry is limited by cold temperatures, 

which may increase its ability to expand to new 

areas as temperatures increase. Further, some 

invasive plant species have been identified as having 

increased productivity in response to elevated CO2

(Ryan and Vose 2012). 

Further, as discussed throughout this chapter, many 

potential effects of climate change are expected 

to increase stress and disturbance within forest 

ecosystems, which certainly raises the potential for 

invasive species to exploit altered environments. 

Disturbances such as flooding and wildfire can open 

forest canopies, expose mineral soil, and reduce tree 

cover, providing greater opportunities for invasion 

(Ryan and Vose 2012). Once established, invasive 

species can also limit regeneration of native tree 

species through increased competition.

Nonnative species may facilitate the invasion and 

establishment of other nonnative species. This 

interaction appears to be the case with European 

earthworms and European buckthorn, which appear 

to have a co-facilitating relationship (Heimpel et 

al. 2010). Similarly, studies in northern Minnesota 

found that a combination of invasive earthworms 

and warming conditions could benefit nonnative 

understory plant species (Eisenhauer et al. 2012). 

Forest Pests and Diseases

Forest pests and diseases, some of which are 

nonnative, are also generally expected to increase 

in a changing climate; this expected outcome 

reflects the ability of these species to increase in 

abundance and distribution in response to altered 

climatic conditions combined with anticipated 

increases in forest stress and disturbance that may 

make forests more susceptible to pests and diseases 

(Weed et al. 2013). At the same time, the effect of 

climate on specific forest insects remains uncertain 

in many cases (Ryan and Vose 2012). For example, 

gypsy moth is currently limited by cold winter 

temperatures across the Midwest and is anticipated 

to expand its range northward under future climate 

change scenarios (Frelich and Reich 2010, Vanhanen 

et al. 2007). Similarly, hemlock wooly adelgid is 

limited by winter low temperatures of -10 to -15 °F 

(MDNR 2011a). Locations currently identified as 

being at greater risk from this pest are based on 

average winter minimum temperatures (MDNR 

2012a) but do not account for the rapid rise in winter 

minimum temperatures projected across a range of 

climate scenarios (Chapter 4). 

It is more difficult to anticipate the response of forest 

pathogens under a warmer future due to complex 

modes of infection, transmission, survival, and tree 

response (Dukes et al. 2009a). Reviews examining 

forest pests and diseases in light of potential 

climate change impacts highlight the potential for 

interactions involving other stress agents that make 

trees more susceptible to these agents (Sturrock et 

al. 2011,Weed et al. 2013). Pests and pathogens are 

generally expected to become more damaging in 

forest ecosystems as the climate changes, because 

they will be able to adapt more quickly to new 

climatic conditions, migrate more quickly to suitable 

habitat, and reproduce at faster rates than host tree 

species. One example of a potential disease migrant 

to the assessment area is sudden oak death, a fungal 

pathogen currently limited by cold temperatures 

to the West Coast and southeastern United States. 
Chapter 5: Future Climate Change Impacts on Forests

118

However, the current risk maps for sudden oak 

death are based on the climate during 1971 through 

2000, and do not account for projected climate shifts 

(Venette and Cohen 2006). The suitability maps for 

sudden oak death based on historical climate data 

already include most of Michigan and Wisconsin 

as marginally suitable habitat. Particularly under 

scenarios with greater temperature increases, it is 

expected that this disease could survive in the region 

(Venette and Cohen 2006). 

Herbivory

As mentioned above, changes in snowfall amount 

and duration throughout the assessment area 

may change the wintertime foraging behavior 

for herbivores such as moose, white-tailed deer, 

and snowshoe hare. Climate change is expected 

to influence both white-tailed deer and moose 

populations in the assessment area (Frelich et al. 

2012, Rempel 2011). Warmer winter temperatures 

and reduced snow depth are expected to reduce the 

energy requirements for deer, and increase access 

to forage during winter months (WICCI 2011a). 

Larger deer herds could have impacts on forest 

ecosystems across the assessment area. Research 

has found that deer browsing pressure may limit the 

ability of forest ecosystems to respond to climate 

change (Fisichelli et al. 2012). Tree species that are 

anticipated to expand their ranges northward in the 

assessment area, such as many hardwood species, 

are browsed much more heavily than boreal conifers 

such as balsam fir and white spruce. Deer herbivory 

may also favor species which are not preferred 

browse species, such as eastern hophornbeam and 

black cherry, or invasive species like buckthorn 

or Japanese barberry. Tree Atlas, LANDIS-II, and 

PnET-CN models project that most mesic hardwood 

species and eastern white pine will gain in suitable 

A white-tailed deer in western Upper Michigan. Photo by Maria Janowiak, U.S. Forest Service.
Chapter 5: Future Climate Change Impacts on Forests

119

habitat, biomass, and productivity in the assessment 

area over the 21st century, but none of these models 

accounts for selective herbivory. 

Further, climate change may influence the 

interaction between white-tailed deer and moose 

populations. Climate change is expected to have 

multiple negative impacts on moose populations. 

Although the winter browse opportunities may 

increase as snow depths decrease, moose are 

intolerant of heat and may be susceptible to summer 

heat stress (Rodenhouse 2009). Moose distribution is 

also limited by the presence of the deer brainworm, 

a disease that is carried by deer and lethal to moose 

(Frelich et al. 2012). Warmer temperatures and less 

severe winters could result in increased mortality of 

moose from brainworm as deer populations expand 

into areas currently dominated by moose. Similarly, 

warmer temperatures may also increase populations 

of winter ticks, another major cause of moose 

mortality (Rodenhouse 2009). Dynamics between 

moose and deer populations are also tightly linked 

to forest composition. The ability of either species 

to thrive will depend in part upon the tree species 

that are available for browse, and changes in the 

distribution or abundance of deer and moose will 

have a strong influence on forest composition in the 

future (Frelich et al. 2012, Rodenhouse 2009).

Carbon Dioxide Fertilization

As discussed earlier in this chapter, CO2 itself can 

affect plant productivity and species composition 

in addition to its direct, positive effect on 

photosynthesis. Elevated CO2

 reduces the rate of 

water loss from trees, which may enhance growth 

in some species and potentially offset some of the 

effects of drier growing seasons (Ainsworth and 

Rogers 2007, Franks et al. 2013, Norby and Zak 

2011, Wang et al. 2006). There is already some 

evidence for increased forest growth in the eastern 

United States (Cole et al. 2010, McMahon et al. 

2010), but it remains unclear if enhanced growth can 

be sustained (Bonan 2008, Foster et al. 2010). 

The potential for water-use efficiency gains to 

buffer against moisture deficits could be particularly 

important for forests in the assessment area, given 

the potential for moisture stress late in the growing 

season. Research on bur oak in Minnesota suggests 

that this effect may have improved the ability of 

adult trees to withstand seasonal moisture stress 

(Wyckoff and Bowers 2010). Precipitation and 

humidity influence the effects of elevated CO2

 on 

ecosystem water balance (Ryan and Vose 2012).

As mentioned in the discussion of PnET-CN 

results, several factors might actually limit the CO2

fertilization effect. Nutrient and water availability, 

ozone pollution, and tree age and size all play 

major roles in the ability of trees to capitalize on 

CO2

 fertilization (Ainsworth and Long 2005). Fire, 

insects, disease, and management could reduce 

forest productivity in discrete locations, and longterm ecosystem transitions might also influence the 

ability of forests to take advantage of additional 

atmospheric CO2 . Furthermore, productivity 

increases under elevated CO2

 could be partially 

offset by reductions in productivity from warminginduced drought stress or the effects of future 

disturbances (Dieleman et al. 2012, Franks et al. 

2013).

It is unclear how substantial this factor will be 

over the long term. Carbon dioxide enrichment 

experiments in forests suggest net primary 

productivity will increase under elevated CO2 , 

although this response can diminish over time due 

to water or nutrient limitation and tree age (Norby 

and Zak 2011, Norby et al. 2005). Moreover, few 

observational studies have evaluated the effects of 

CO2

 fertilization beyond 600 ppm, making it hard to 

predict how forests may respond under even higher 

levels of atmospheric CO2 , such as the 900-ppm 

levels projected under the A1FI emissions scenario 

for 2100.
Chapter 5: Future Climate Change Impacts on Forests

120

Nutrient Cycling

As air temperatures warm and precipitation 

patterns change, changes may also occur in the way 

nutrients are cycled between plants, soils, and the 

atmosphere. Alterations in nutrient cycling have 

important implications for the productivity of forest 

ecosystems, which can be limited by nutrients such 

as phosphorus, calcium, and nitrogen. Studies across 

the northeastern United States can give some insight 

into potential effects of climate change on nutrient 

cycling. 

Decomposition of detritus is carried out primarily 

by enzymes released from bacteria and fungi. These 

enzymes are sensitive to changes in temperature, 

and thus there is generally a positive effect of 

temperature on the rate of enzymatic activity as long 

as moisture is also sufficient (Brzostek and Finzi 

2012, Rustad et al. 2001). In addition to increases in 

temperature, changes in growing season, soil frost, 

soil moisture, soil pH, nitrogen deposition, and the 

interaction among these factors can affect nutrient 

cycling (Campbell et al. 2009). For example, 

more nutrients may leach from forest soils as a 

result of earlier spring thaws because the onset of 

photosynthesis in plant communities may not be 

advancing as rapidly and plants are not ready to 

take up the products of overwinter decomposition 

(Campbell et al. 2010). 

A review of nutrient cycling and climatic factors 

for sugar maple concluded that extremes in light 

environment, temperature, precipitation, pathogen 

attack, and herbivory can induce or amplify nutrient 

imbalances (St. Claire et al. 2008). For example, 

excessive or inadequate soil moisture can limit 

nutrient acquisition by tree roots. A number of 

studies have examined the effects of extended dry 

periods followed by moisture pulses on nutrient 

cycling (Borken and Matzner 2009). Although 

these moisture pulses do lead to a flush of mineral 

nitrogen, it is not sufficient to compensate for the 

lack of microbial activity during dry periods. Thus, 

an increase in wet-dry cycles appears to lead to a 

reduction in nutrient availability for trees. These 

results suggest that the increasingly episodic 

precipitation regime in the assessment area may add 

further stress to forest ecosystems in the future. 

Interactions

Clearly, none of the changes described above are 

expected to occur in isolation. Climate change has 

the potential to alter the entire suite of ecosystem 

processes and stressors, in addition to others not 

considered here. The potential for interactions 

among these impacts will be critically important 

in determining the resulting changes to forest 

ecosystems across the assessment area. Just as 

there are typically several interacting drivers for 

individual tree mortality (Dietze and Moorcroft 

2011), overall ecosystem shifts will be influenced by 

multiple factors (Frelich and Reich 2010). 

Recognizing the potential for these interactions 

will be necessary to accurately assess the risks that 

climate change poses to forest ecosystems. Scientific 

research is beginning to clarify how biotic and 

abiotic stressors can operate in concert, but these 

types of studies are still relatively rare (Gellesch et 

al. 2013). For example, it has long been suggested 

that stressed trees are more susceptible to insect 

pests and diseases. Recent research has found that 

drought stress leads to more-damaging forest tent 

caterpillar outbreaks (Babin-Fenske and Anand 

2011). Earthworm invasion tends to create warmer, 

drier soil surface conditions with more bare soil in 

forest systems, which may favor species that can 

germinate in these conditions (Eisenhauer et al. 

2012). Earthworm invasion may also make northern 

hardwood forests more vulnerable to the effects 

of drought (Larson et al. 2010), leading to greater 

risk of disease and pest outbreak. This example 

is simply one chain of interactions, and many 

more connections could be drawn to phenological 

changes, fire seasons, and other climate-mediated 

impacts. 
Chapter 5: Future Climate Change Impacts on Forests

121

SUMMARY

Climate change has the potential to affect forest 

ecosystems in numerous ways. Some of these 

potential impacts have been investigated through 

a coordinated set of model projections. The model 

results from Tree Atlas, LANDIS-II, and PnET-CN 

each contribute particular kinds of information 

about how tree species and forest ecosystems 

could potentially respond to a range of possible 

climate futures. Generally, these model projections 

agree that characteristic boreal or northern species 

and forest types may undergo declines in suitable 

habitat, landscape-level biomass, and productivity. 

These model projections indicate that temperate 

species may perform better, raising the possibility 

for potentially large ecosystem shifts across the 

assessment area. 

Additionally, research on the direct and indirect 

impacts of climate change on forest ecosystems 

highlights several potential drivers of change in 

the assessment area. These impacts may arise from 

chronic stress (e.g., extended drought), gradual 

changes (e.g., warming winter temperatures and 

declining snow levels), or discrete disturbance 

events (e.g., stand-replacing wildfires or insect pest 

outbreaks). Many of these factors may operate in 

concert, and synergistic or multiplying interactions 

may be the most difficult to understand and forecast. 

Chapter 6 uses the information presented in this 

chapter to assess the vulnerability of the forests of 

the assessment area to climate change.

A creek in northern Wisconsin in the spring. Photo by Maria Janowiak, U.S. Forest Service.
122

CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

Changes in species distribution and abundance due 

to climate change can have important implications 

for the habitats in which those species live, leading 

to shifts in community composition and changes 

in ecosystem processes (Climate Change Science 

Program [CCSP] 2008a, Ryan and Vose 2012). 

In addition, climate change itself can alter system 

drivers and exacerbate or ameliorate current 

stressors (CCSP 2008a, Ryan and Vose 2012). This 

chapter describes the climate change vulnerability 

of nine forest systems in the assessment area over 

the next century. Vulnerability is the susceptibility 

of a system to the adverse effects of climate change 

(Intergovernmental Panel on Climate Change [IPCC] 

2007a). It is a function of potential climate change 

impacts and the adaptive capacity of the system 

(Fig. 48). We consider a system to be vulnerable 

if it is at risk of a shift in composition that leads 

to a substantially different identity for the system, 

or if the system is anticipated to suffer substantial 

declines in health or productivity. The vulnerability 

of a system to climate change is independent of 

the economic or social values associated with the 

system, and the ultimate decision of whether to 

conserve vulnerable systems or allow them to shift 

to an alternate state will depend on the individual 

objectives of land managers. 

This chapter is organized into two sections. We 

first present an overall synthesis of climate change 

vulnerability of the assessment area, organized 

according to drivers and stressors, ecosystem 

impacts, and factors that influence adaptive capacity. 

This synthesis is based on the current scientific 

consensus of published literature (Chapters 4 and 

5). In the following section, we present individual 

Figure 48.—Key components of vulnerability, illustrating 

the relationship among exposure, sensitivity, and adaptive 

capacity. Adapted from Glick et al. (2011b).

vulnerability determinations for the nine forest types 

considered in this assessment.

Throughout this chapter, statements about potential 

impacts and adaptive capacity factors are qualified 

with a confidence statement, phrased according 

to the IPCC’s guidance for authors (Mastrandrea 

et al. 2010) (Fig. 49). Confidence was determined 

by gauging both the level of evidence and level of 

agreement among information. Evidence refers to 

the body of information available based upon theory, 

data, models, expert judgment, and other sources. It 

was considered robust when multiple observations 

or models were available as well as an established 

theoretical understanding to support a statement. 

Agreement refers to the agreement among the 

multiple lines of evidence. A high level of agreement 

was suggested if theories, observations, and models 
Chapter 6: Forest Ecosystem Vulnerabilities

123

tended to point toward similar outcomes. Agreement 

does not refer to the level of agreement among the 

authors of this assessment. 

SYNTHESIS OF CLIMATE CHANGE 

IMPACTS ON FOREST ECOSYSTEMS 

Climate change creates the potential for a wideranging set of direct and indirect impacts on 

ecosystems, which are a function of the degree 

to which a system is exposed to climatic change 

and its sensitivity to these changes. Impacts may 

be beneficial or harmful to a particular forest or 

ecosystem type. Impacts could be beneficial to a 

system if the changes result in improved heath or 

productivity, a greater area occupied by the system, 

or a tendency to maintain the current identity of 

the system. Negative potential impacts would tend 

toward declining health and productivity, reduced 

territory occupied by the system, or a composition 

shift that leads to a substantially different identity 

for the system. The summary below presents the 

potential impacts of climate change on major drivers 

and stressors in the assessment area over the next 

century based on the current scientific consensus 

of published literature, which is described in more 

detail in the preceding chapters.

Figure 49.—Confidence determination diagram used in the 

assessment. Adapted from Mastrandrea et al. (2010).

Potential Impacts on Drivers 

and Stressors 

Many physical and biological factors contribute to 

the current state of forest ecosystems in northern 

Wisconsin and the western Upper Peninsula of 

Michigan. These factors include drivers―the most 

fundamental variables that shape a particular system, 

and stressors―agents that can reduce forest health 

or productivity or impair ecosystem functions. Many 

factors, such as flooding or fire, may be drivers in 

one situation and stressors in another. Similarly, 

some disturbances, such as flooding or fire, act as 

drivers in certain systems, but can be stressors if the 

timing or intensity of the disturbance changes.

Temperatures will increase (robust evidence, high 

agreement). All global climate models project that 

temperatures will increase with continued increases 

in atmospheric greenhouse gas concentrations.

A large amount of evidence from across the globe 

shows that temperatures have been increasing and 

will continue to increase due to human activities 

(Chapter 2). Temperatures across the assessment 

area have already exhibited substantial increases 

(Chapter 3), and continued temperature increases 

are projected for the assessment area even under 

the most conservative future climate scenario 

(Chapter 4). 

Growing seasons will get longer (robust evidence, 

high agreement). There is high agreement among 

information sources that projected temperature 

increases will lead to longer growing seasons in the 

assessment area.

Evidence at both global and local scales indicates 

that growing seasons have been getting longer, 

and this trend is expected to become even more 

pronounced over the next century (Chapters 3 and 

4). Longer growing seasons have the potential to 

affect the timing and duration of ecosystem and 

physiological processes across the region (Bradley 

et al. 1999, Dragoni and Rahman 2012). Earlier 
Chapter 6: Forest Ecosystem Vulnerabilities

124

springs and longer growing seasons are expected to 

cause shifts in phenology for plant species that rely 

on temperature as a cue for the timing of leaf-out, 

reproductive maturation, and other developmental 

processes (Schwartz et al. 2006b, Walther et al. 

2002), and some of these effects have already 

been observed (Bradley et al. 1999, Ellwood et al. 

2013). Longer growing seasons may also result in 

greater growth and productivity of trees and other 

vegetation, but only if balanced by available water 

and nutrients (Chapter 5).

Winter processes will change (robust evidence, 

high agreement). All evidence agrees that 

temperatures will increase more in winter than in 

other seasons across the assessment area, leading 

to changes in snowfall, soil frost, and other winter 

processes. 

Both climate scenarios for the assessment area 

project that winter temperatures will increase more 

than temperatures in other seasons (Chapter 4). 

Projected temperature increases indicate that a 

greater proportion of moisture will be delivered as 

rain rather than snow during the winter. Combined 

with increased snowmelt from higher temperatures, 

the amount of snow on the ground is expected to 

decrease across the assessment area by the end 

of the 21st century (Notaro et al. 2011, Sinha and 

Cherkauer 2010, Wisconsin Initiative on Climate 

Change Impacts [WICCI] 2011). In addition, the 

assessment area is projected to experience fewer 

days of soil frost by the end of the century (Sinha 

and Cherkauer 2010). Although these conditions 

could increase water infiltration into the soil and 

reduce runoff, they may also lead to greater soil 

water losses through increased evapotranspiration. 

This decrease in snow cover and frozen soil may 

affect a variety of ecosystem processes, including 

decomposition, nutrient cycling, and the onset of 

the growing season.

The amount and timing of precipitation will 

change (medium evidence, high agreement).

All global climate models agree that there will 

be changes in precipitation patterns across the 

assessment area.

For the climate projections used in this assessment 

(Chapter 4) and other publications, large variation 

exists for projected changes in precipitation for the 

assessment area (Kling et al. 2003, Kucharik et al. 

2010b, Winkler et al. 2012). Although individual 

model projections for the assessment area may 

differ, there is general agreement that total annual 

precipitation is projected to remain consistent or 

increase slightly during the 21st century. Models 

also tend to agree that precipitation patterns between 

seasons may shift substantially (Kunkel et al. 2013). 

Precipitation increases are generally expected to be 

larger in winter and spring, which is in agreement 

with both climate scenarios presented in this 

assessment (Chapter 4). There is disagreement in the 

projections of summer precipitation in the models 

used in this assessment (a slight increase under 

PCM B1 or a sharp decrease under GFDL A1FI; 

Chapter 4) and other data sets (Center for Climatic 

Research 2013, WICCI 2013). 

Intense precipitation events will continue to 

become more frequent (medium evidence, 

medium agreement). There is some agreement 

that the number of heavy precipitation events will 

continue to increase in the assessment area. If they 

do increase, impacts from flooding and soil erosion 

may also become more damaging.

Heavy precipitation events have been increasing in 

number and severity in the upper Midwest in general 

and the assessment area in particular (Groisman 

et al. 2012, Kunkel et al. 2008, Saunders et al. 

2012), and many models agree that this trend will 

continue over the next century (IPCC 2007a, Kling 

et al. 2003, Kunkel et al. 2013, WICCI 2011). Most 
Chapter 6: Forest Ecosystem Vulnerabilities

125

heavy precipitation events currently occur during 

summer in the assessment area, although changes in 

precipitation patterns are projected to increase the 

frequency of extreme precipitation events in spring, 

summer, and fall (Kucharik et al. 2010b). Projected 

increases in total runoff and peak stream flow 

during the winter and spring (Cherkauer and Sinha 

2010) could potentially increase the magnitude or 

frequency of flooding. Increases in runoff following 

heavy precipitation events could also lead to an 

increase in soil erosion (Nearing et al. 2004). 

The risk from floods, erosion, and other impacts 

will ultimately depend on local geological and 

topographic conditions as well as future decisions 

regarding infrastructure and land use, which remain 

unknown. 

Soil moisture patterns will change (medium 

evidence, high agreement), with drier soil 

conditions later in the growing season (medium 

evidence, medium agreement). Studies show that 

climate change will affect soil moisture, but there is 

disagreement among climate and impact models on 

how soil moisture will change during the growing 

season. 

Given that warmer temperatures and seasonal 

changes in precipitation are expected across the 

assessment area, it is reasonable to expect that 

soil moisture regimes will also shift. Longer 

growing seasons and warmer temperatures may 

result in greater evapotranspiration losses and 

lower soil-water availability later in the growing 

season (Chapter 4). Outputs from the PnET-CN 

Canyon Falls in western Upper Michigan. Photo by Scott Pearson, used with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

126

model indicate that forests in the assessment area 

may become increasingly moisture-limited under 

climate change (Chapter 5). This condition may 

be particularly true in locations where soils and 

landforms cannot retain the water from intense 

precipitation events. There is substantial variation 

among model projections, however, and it is also 

possible that the assessment area will experience an 

increase in precipitation sufficient to offset increases 

in evapotranspiration (Winkler et al. 2012). 

Droughts will increase in duration and area 

(limited evidence, low agreement). A study using 

multiple climate models indicates that drought 

may increase in extent and area, and an episodic 

precipitation regime could mean longer dry periods 

between events.

With an increasingly episodic precipitation regime, 

it has been suggested that there may be longer 

intervals between heavy rainfall events in the 

future (Knapp et al. 2008a). Studies examining 

a range of climate model projections disagree 

with this conclusion, projecting that the region 

may experience fewer consecutive days without 

precipitation in the future (Kunkel et al. 2013). 

Overall, there is relatively low confidence in the 

projected future frequency of droughts across the 

central United States. Climate projections described 

in this assessment also highlight the possibility of 

reduced precipitation and increased moisture stress 

during summer months, particularly under the GFDL 

A1F1 scenario (Chapter 4). 

Climate conditions will increase fire risks by the 

end of the century (medium evidence, medium 

agreement). Some national and global studies 

suggest that wildfire risk will increase in the region, 

but few studies have specifically looked at wildfire 

potential in the assessment area. 

At a global scale, the scientific consensus is that fire 

risk will increase by 10 to 30 percent due to higher 

summer temperatures (IPCC 2007a). For the early 

part of the 21st century, there is low agreement 

in this trend across climate models (Moritz et al. 

2012). By the end of the century, however, most 

models project an increase in wildfire probability, 

particularly for boreal forests, temperate coniferous 

forests, and temperate broadleaf forests. Studies 

from southern Canada also project more-active 

wildfire regimes in the future (Drever et al. 2009, 

Flannigan et al. 2009, Le Goff et al. 2009). In 

addition to the direct effects of temperature and 

precipitation, increases in fuel loads from pestinduced mortality or blowdown events could 

increase fire risk, but the relationship between 

these factors can be complex (Hicke et al. 2012). 

Forest fragmentation and unknown future decisions 

regarding wildfire management also make fire 

projections more uncertain for the assessment area.

Many nonnative species, insect pests, and 

pathogens will increase or become more 

damaging (limited evidence, high agreement).

Evidence indicates that an increase in temperature 

and greater ecosystem stress will lead to increases in 

these threats, but research to date has examined few 

species. 

Changes in climate may allow some nonnative plant 

species, insect pests, and pathogens to expand their 

ranges farther north (Dukes et al. 2009a) as the 

climate warms and the assessment area loses some 

of the protection offered by a traditionally cold 

climate and short growing season. The abundance 

and distribution of some nonnative plant species 

may be able to increase directly in response to a 

warmer climate and also indirectly through increased 

invasion of stressed or disturbed forests (Ryan and 

Vose 2012). Similarly, forest pests and pathogens 

are generally able to respond rapidly to changes in 

climate and also disproportionately damage stressed 

ecosystems (Weed et al. 2013). Thus, there is high 

potential for pests and pathogens to interact with 

other climate-mediated stressors. Unfortunately, we 

lack basic information on the climatic thresholds 

that apply to many invasive plants, insect pests, 

and pathogens. Further, our ability to predict the 
Chapter 6: Forest Ecosystem Vulnerabilities

127

mechanisms of infection (in the case of pests 

and diseases), dispersal, and spread for specific 

agents remains low. Furthermore, it is not possible 

to predict all future nonnative species, pests, or 

pathogens that may enter the assessment area during 

the 21st century. 

Potential Impacts on Forests 

Shifts in drivers and stressors mentioned above are 

expected to change forest ecosystems throughout the 

assessment area. Indirect impacts of climate change 

may become manifest through shifts in suitable 

habitat, species composition, or function of forest 

ecosystems. 

Boreal species will face increasing stress 

from climate change (medium evidence, high 

agreement). Ecosystem models agree that boreal or 

northern species will experience reduced suitable 

habitat and biomass across the assessment area, 

and that they may be less able to take advantage of 

longer growing seasons and warmer temperatures 

than temperate forest species. 

Across northern latitudes, warmer temperatures are 

expected to be more favorable to individuals near 

the northern extent of their species’ range and less 

favorable to those near the southern extent (Iverson 

and Prasad 1998). Results from climate impact 

models project a decline in suitable habitat and 

landscape-level biomass for northern species such 

as black spruce, white spruce, tamarack, jack pine, 

yellow birch, and paper birch (Chapter 5). PnET-CN 

results also suggest that spruce/fir forests may have 

smaller productivity gains than other forest types 

across the range of anticipated climate futures. These 

northern species may persist in the assessment area 

throughout the 21st century, although with declining 

vigor. Boreal species may remain in areas with 

favorable soils, management, or landscape features. 

Additionally, boreal species may be able to persist in 

the assessment area if competitor species are unable 

to colonize these areas (Iverson et al. 2008).

Southern species will be favored by climate 

change (medium evidence, medium agreement).

Ecosystem models agree that many temperate 

species will gain suitable habitat and biomass 

across the assessment area, and that longer growing 

seasons and warmer temperatures will lead to 

productivity increases for temperate forest types. 

Model results project that species currently near 

their northern range limits in the assessment area 

may become more abundant and more widespread 

under a range of climate futures (Chapter 5). Species 

projected to increase in both suitable habitat and 

biomass in the assessment area include bitternut 

hickory, black oak, bur oak, and white oak 

(Chapter 5). PnET-CN outputs also indicate that 

deciduous forest types have the potential for large 

productivity increases across the assessment area. 

In addition, Tree Atlas results project that suitable 

habitat may become available for species not 

currently found in the assessment area by the end 

of the century (Chapter 5). Habitat fragmentation 

and dispersal limitations could hinder the northward 

movement of southerly species, despite the increase 

in habitat suitability. Most species can be expected 

to migrate more slowly than their suitable habitats 

will shift (Iverson et al. 2004a, 2004b; McLachlan 

et al. 2005; Scheller and Mladenoff 2008). Pests 

and diseases such as emerald ash borer, beech bark 

disease, and Dutch elm disease are also expected 

to limit some species projected to be increasers by 

impact models.

Forest ecosystems will change across the 

landscape (medium evidence, high agreement). 

Although few models have specifically examined 

how communities may change, model results from 

individual species and ecological principles suggest 

that species composition and recognized forest 

communities will change. 

Species will respond individually to climate change, 

which may lead to the dissolution of traditional 

community relationships (Davis et al. 2005, 
Chapter 6: Forest Ecosystem Vulnerabilities

128

Root et al. 2003). The model results presented in 

Chapter 5 raise the possibility of potentially large 

changes in tree species distribution across the 

assessment area. The models indicate that climate 

trends may generally favor hardwood species 

across the landscape by the end of the century. Past 

climatic changes resulted in large shifts in species 

composition (Davis 1983, Williams et al. 2004). 

Conceptual models based on ecological principles 

lend support to this possibility, particularly along 

ecological transition zones (Frelich and Reich 

2010). Modeling studies also project that forest 

communities may move across the assessment 

area (Iverson et al. 2008, Lenihan et al. 2008) and 

that tree species may also rearrange into novel 

communities. Observed trends have suggested 

that forest species may be more prone to range 

contraction at southern limits and less able to 

expand ranges northward to track climate change 

(Murphy et al. 2010, Woodall et al. 2013, Zhu et 

al. 2011). Similarly, the possibility also exists for 

nonnative species to take advantage of shifting 

forest communities and unoccupied niches if 

native forest species are limited (Hellmann et al. 

2008). Major shifts in species composition may 

not be observable until well into the 21st century 

because of the long timeframes associated with 

many ecosystem processes and responses to climate 

change. Major stand-replacing disturbance events or 

forest management could accelerate shifts in forest 

composition, however (He et al. 2002, Climate 

Change Science Program [CCSP] 2008b).

Forest productivity will increase across the 

assessment area (medium evidence, low 

agreement). Some model projections and other 

evidence suggest forest productivity may increase in 

the assessment area, although there is uncertainty 

about the effects of and limitations to carbon 

dioxide (CO2 ) fertilization. It is also anticipated that 

productivity will be reduced in localized areas. 

Numerous studies have tried to project the effects 

of climate change on forest productivity and 

carbon balance through modeling simulations and 

manipulative experiments (Handler et al. 2012, 

Ryan and Vose 2012). Studies of CO2

 fertilization, 

including the PnET-CN results presented in this 

assessment, indicate that productivity may generally 

increase across the assessment area (Chapter 5). 

Warmer temperatures may speed nutrient cycling 

and increase photosynthetic rates for most tree 

species in the assessment area. Longer growing 

seasons could also result in greater growth and 

productivity of trees and other vegetation, but 

only if sufficient water and nutrients are available 

(Chapter 5). Conversely, LANDIS-II modeling 

results, which do not include the possible effects 

of CO2

 fertilization, project gradual productivity 

declines under the GFDL A1FI scenario across the 

assessment area (Chapter 5). Episodic disturbances 

such as fires, wind events, droughts, and pest 

outbreaks may reduce productivity in certain areas 

over different time scales. In addition, lags in the 

migration of species to newly suitable habitat may 

reduce productivity until a new equilibrium is 

reached. For these reasons, future forest productivity 

is dependent upon complex interactions among the 

degree of warming, ecosystem water balance, and 

disturbance events (Chiang et al. 2008, Duveneck 

et al. 2014, He et al. 2002, Scheller and Mladenoff 

2005). 

Adaptive Capacity Factors

Adaptive capacity is the ability of a species or 

ecosystem to accommodate or cope with potential 

climate change impacts with minimal disruption 

(Glick et al. 2011). It is strongly related to the 

concept of resilience (CCSP 2008a). Below, we 

summarize factors that could reduce or increase 

the adaptive capacity of forest systems within the 

assessment area. Greater adaptive capacity tends 

to reduce climate change vulnerability, and lower 

adaptive capacity tends to increase vulnerability. 
Chapter 6: Forest Ecosystem Vulnerabilities

129

Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies have 

consistently shown that more-diverse systems are 

more resilient to disturbance, and low-diversity 

systems are more vulnerable to change.

In general, species-rich communities have exhibited 

greater resilience to extreme environmental 

conditions and greater potential to recover 

from disturbance than less diverse ecosystems 

(Tilman 1996, 1999). Consequently, less diverse 

ecosystems may be inherently more susceptible 

to future changes and stressors (Duveneck et al. 

2014, Swanston et al. 2011). Elmqvist et al. (2003) 

emphasize that “response diversity,” or the diversity 

of potential responses of a system to environmental 

change, is a critical component of ecosystem 

resilience. Response diversity is generally reduced 

in less diverse ecological systems. For example, 

northern hardwood forests generally support a large 

number of tree species and therefore have many 

possible future trajectories, but aspen-birch and red 

pine forests have fewer potential paths. Genetic 

diversity within species is also critical for the ability 

of populations to adapt to climate change, because 

species with high genetic variation are more apt to 

have individuals that can withstand extreme events 

and adapt to changes over time (Reusch et al. 2005). 

Species in fragmented landscapes will have less 

opportunity to migrate in response to climate 

change (limited evidence, high agreement). The 

dispersal ability of individual species is reduced 

in fragmented landscapes, but the future degree 

of landscape fragmentation and the potential 

for human-assisted migration are two areas of 

uncertainty. 

Habitat fragmentation can hinder the ability of tree 

species to migrate to more suitable habitat on the 

landscape. The degree to which fragmentation limits 

dispersal depends upon the level of fragmentation, 

the composition of the surrounding area (e.g., forest 

versus nonforest), and the dispersal characteristics 

of individual species (Ibáñez et al. 2006, Iverson 

et al. 2004a). Modeling results indicate that mean 

centers of suitable habitat for tree species will 

migrate between 60 and 350 miles by the year 2100 

under a high emissions scenario and between 30 and 

250 miles under milder climate change scenarios 

(Iverson et al. 2004a). Based on gathered data of 

seedling distributions, it has been estimated that 

many northern tree species could possibly migrate 

northward at a rate of 60 miles per century (Woodall 

et al. 2009), but other evidence indicates that natural 

migration rates could be far slower for some species 

(McLachlan et al. 2005, Murphy et al. 2010). 

Fragmentation makes this disparity even more 

challenging, because the landscape is essentially less 

permeable to migration (Jump and Peñuelas 2005, 

Scheller and Mladenoff 2008). Humans may be able 

to assist in the migration of species to newly suitable 

areas to counteract the effects of fragmentation. 

Assisted migration is a contentious issue for some 

species, especially those of conservation concern 

(Pedlar et al. 2012, Schwartz et al. 2012).

Systems that are limited to particular 

environments will have less opportunity to 

migrate in response to climate change (limited 

evidence, high agreement). Despite a lack of 

published research demonstrating this concept 

in the assessment area, our current ecological 

understanding indicates that migration to new areas 

will be particularly difficult for species and systems 

with narrow habitat requirements.

Several species and forest types in the assessment 

area are confined to particular habitats on the 

landscape, whether through particular requirements 

for hydrologic regimes or soil types, or other 

reasons. Similar to species occurring in fragmented 

landscapes, isolated species and systems face 

additional barriers to migration (Jump and Peñuelas 

2005). More-widespread species may also have 

particular habitat requirements. For example, 

sugar maple is often limited to soils that are rich in 
Chapter 6: Forest Ecosystem Vulnerabilities

130

nutrients like calcium, so this species may actually 

have less newly suitable habitat in the assessment 

area than might be projected solely from temperature 

and precipitation patterns. Species restricted to 

riparian forests are not expected to migrate to upland 

areas because many species depend on seasonal 

flood dynamics for regeneration and a competitive 

advantage. Similarly, lowland conifer systems often 

contain a unique mix of species that are adapted to 

low pH values, peat soils, and particular water table 

regimes. These systems face additional challenges 

in migration compared to more-widespread species 

with broad ecological tolerances. 

Systems that are more tolerant of disturbance 

have less risk of declining on the landscape 

(medium evidence, high agreement). Basic 

ecological theory and other evidence support the 

idea that systems that are adapted to more frequent 

disturbance will be at lower risk.

Disturbances such as wildfire, flooding, and pest 

outbreaks are projected to increase in the assessment 

area (Chapters 4 and 5). Northern hardwoods in 

particular are adapted to gap-phase disturbances, 

with stand-replacing events occurring over hundreds 

or thousands of years. Therefore, these systems 

may be less tolerant of more frequent widespread 

disturbances. Mesic systems can create conditions 

that could buffer against fire and drought to some 

extent, but these systems are not expected to do well 

if soil moisture declines significantly (Nowacki and 

Abrams 2008). Forest systems in the assessment 

area that are more tolerant of drought, flooding, or 

fire may be better able to withstand climate-driven 

disturbances. This principle is limited, however, 

because it is also possible for disturbance-adapted 

systems to undergo too much disruption. For 

example, jack pine systems might cover a greater 

extent under drier conditions with more frequent 

fire, but these systems might also convert to barrens 

or open grasslands if fire becomes too frequent or 

drought becomes too severe. 

VULNERABILITY DETERMINATIONS 

FOR INDIVIDUAL FOREST SYSTEMS

Climate-induced shifts in drivers, stressors, and 

dominant tree species will result in different impacts 

to forested systems within the assessment area. 

Some communities may have a greater capacity to 

adapt to these changes than others, whereas some 

may be susceptible to relatively minor impacts. 

Therefore, it is helpful to consider these factors for 

individual forest systems in addition to describing 

general principles related to vulnerability and 

adaptive capacity. 

We assessed the vulnerability of nine forest 

systems to climate change impacts, drawing on 

the information presented in previous chapters, as 

well as an expert panel assembled from a variety of 

organizations and disciplines across the assessment 

area. The 20 panelists evaluated anticipated climate 

trends for the assessment area and ecosystem 

model projections (Chapters 3 through 5), and 

used their expertise to interpret the information. 

For each forest system, panelists considered the 

potential impacts and adaptive capacity to assign a 

vulnerability determination and a level of confidence 

in that determination using the same confidence 

scale described above. For a complete description 

of the methods used to determine vulnerability, see 

Appendix 6.

Overall vulnerability determinations ranged from 

moderate-low (oak and white pine) to high (upland 

spruce-fir and lowland conifers) (Table 13). Impacts 

were rated as being most negative for upland sprucefir and lowland conifers, and most moderate for 

oak forests. Adaptive capacity was rated lowest 

for upland spruce-fir, lowland conifers, and red 

pine forests, and highest for white pine. Panelists 

tended to rate the amount of evidence as medium 

to medium-high (between limited and robust) for 

most forest systems. Incomplete knowledge of future 
Chapter 6: Forest Ecosystem Vulnerabilities

131

wildfire regimes, interactions among stressors, 

and precipitation regimes were common factors 

limiting this component of overall confidence. 

The ratings of agreement among information also 

tended to be in the medium to medium-high range. 

Contrasting information about precipitation regimes 

under the PCM B1 and GFDL A1FI climate change 

scenarios was a major factor that limited the level 

of agreement among information. The classification 

system for forest ecosystems for this assessment 

also limited the agreement in some instances where 

species-level impacts and vulnerability varied widely 

within a single forest type. In general, ratings were 

slightly higher for agreement than for evidence. 

In the sections that follow, we summarize the 

climate-related impacts on drivers, stressors, and 

dominant tree species that were major contributors 

to the vulnerability determination for each forest 

system across the assessment area. In addition, 

we summarize the main factors contributing to the 

adaptive capacity of each system. It is critical to 

note that climate change impacts and the adaptive 

capacity of a forest system were assessed across the 

entire assessment area. Because forest systems vary 

widely at a local level due to differences in climate, 

landform, soils, disturbance, past management, 

and numerous other factors, the vulnerability in a 

particular location may be different—even markedly 

so—from the broad-scale information highlighted 

in this chapter. For this reason, the following 

summaries provide a starting point for considering 

vulnerability at finer spatial scales.

Forest system Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Aspen-birch Moderate-Negative Moderate Moderate-High Medium-High Medium-High

Jack pine Moderate-Negative Moderate-High Moderate Medium Medium-High

Lowland conifers Negative Moderate-Low High Medium Medium-High

Lowland-riparian hardwoods Moderate-Negative Moderate Moderate-High Limited-Medium Medium

Northern hardwoods Moderate-Negative Moderate-High Moderate Medium-High Medium

Oak Moderate Moderate-High Moderate-Low Medium Medium-High

Red pine Moderate-Negative Moderate-Low Moderate-High Medium-High Medium-High

Upland spruce-fir Negative Moderate-Low High Medium-High Medium-High

White pine Moderate-Positive High Moderate-Low Medium-High Medium

Table 13.—Climate change vulnerability determinations for the forest systems considered in this assessment
Chapter 6: Forest Ecosystem Vulnerabilities

132

Aspen-Birch

Moderate-High Vulnerability (medium-high evidence, medium-high agreement)

Ecosystem models project substantial declines for aspen and birch across the assessment area, and 

the potential exists for multiple stressors to interact under climate change, particularly drought and 

forest pests. This forest system, however, is also distributed across a wide range of sites and adapted to 

disturbance.

Moderate-Negative Potential Impacts 

Drivers: There is a greater likelihood of reduced 

soil moisture and enhanced evapotranspiration as the 

climate warms, especially late in the growing season. 

Drought stress and mortality may consequently 

increase, with the greatest risk on dry and poorquality sites. Additionally, projected temperatures in 

the assessment area may be beyond the physiological 

limits of aspen and birch species by the end of the 

21st century, particularly under hotter conditions 

like those projected under the GFDL A1FI scenario. 

Disturbance from stand-replacing wildfire or wind 

events could benefit this forest type, although 

small-scale disturbances could increase the rate of 

succession to other forest types. 

Dominant Species: The dominant species within 

this forest system, such as balsam fir, paper birch, 

and quaking aspen, are near their southern range 

limits in the assessment area and are projected to 

decline in suitable habitat and biomass under a range 

of possible climate futures. Model results are mixed 

for balsam poplar and bigtooth aspen, but models 

agree that these species are not projected to increase 

substantially under future climate scenarios. Red 

maple is a common associate and is projected to fare 

better under future climate conditions relative to the 

other species in this forest system, suggesting that it 

may have a competitive advantage in the future.

Stressors: Climate change is expected to intensify 

several key stressors for aspen-birch forests. 

Insect pests such as forest tent caterpillar and 

gypsy moth may become more damaging under a 

warmer climate, and stressed forests may be more 

susceptible to diseases like hypoxylon canker. 

White-tailed deer herbivory may also increase 

with warmer winters and reduced snow cover. The 

possibility exists for interactions among multiple 

stressors to lead to more severe climate change 

impacts. For example, warmer temperatures may 

simultaneously increase drought stress on trees and 

create conditions more suitable to some forest pests 

and diseases, which could combine to increase the 

risk of insect and disease outbreaks, tree mortality, 

and ultimately, wildfire risk.

Moderate Adaptive Capacity

Aspen-birch forests are prevalent across the 

landscape on a wide variety of soils and landforms. 

The early-successional characteristics of these 

species, including abundant wind-dispersed seed 

production and vegetative reproduction, make 

them highly resilient to many forms of disturbance. 

Forests dominated by paper birch may be at greater 

risk than aspen-dominated forests, because paper 

birch forests are currently much less common 

across the landscape and face greater challenges 

to regeneration. Additionally, natural succession 

in recent decades is reducing the amount of earlysuccessional forests present across the landscape. 

Younger aspen-birch forests are therefore less 

common on the landscape, which may limit future 

opportunities for maintaining this forest type.
Chapter 6: Forest Ecosystem Vulnerabilities

133

Natural resource professionals in an aspen forest at the 

Chequamegon-Nicolet National Forest. Photo by Maria 

Janowiak, U.S. Forest Service.

Paper birch trees. Photo by Maria Janowiak, U.S. Forest 

Service.

Aspen-birch forest at Isle Royale National Park. Photo by 

Maria Janowiak, U.S. Forest Service.
Chapter 6: Forest Ecosystem Vulnerabilities

134

Jack Pine

Moderate Vulnerability (medium evidence, medium-high agreement)

Ecosystem models project declines in suitable habitat and biomass for jack pine forests under moreextreme climatic change. The ability of this system to be competitive on poor-quality sites and withstand 

disturbance increases the adaptive capacity, although jack pine may be less able to persist and regenerate 

under substantial warming. 

Moderate-Negative Potential Impacts

Drivers: Jack pine forests are generally found on 

sites with coarse-textured soils, and they may be 

able to tolerate the projected soil moisture decreases 

during summer. Although some current jack pine 

sites may become too hot or dry in the future, other 

sites currently dominated by red pine, white pine, 

or other species may become more suitable for jack 

pine. The potential for increased fire frequency or 

intensity under warmer and drier conditions would 

favor jack pine forests relative to many other forest 

types. Greater wildfire activity could be positive for 

these forests, but too much change to the fire regime 

might hamper regeneration and cause these forests to 

shift to barrens. 

Dominant Species: Considering the range of 

possible future climates, jack pine is generally 

expected to decline in suitable habitat and biomass 

across the assessment area over the next 100 years. 

This species is currently at the southern extent of 

its range in Wisconsin. Model results are unclear 

as to whether eastern white pine and red pine, 

two commonly associated species, will decrease 

in habitat suitability or biomass; these species are 

not projected to increase substantially, although 

species characteristics suggest they may persist 

on more favorable sites. Conditions may become 

more suitable for some species, such as red maple, 

northern red oak, or northern pin oak.

Stressors: Insect pests like jack pine budworm 

and diseases like Scleroderis may become more 

damaging under a warmer climate. Additionally, the 

possibility exists for new pests, such as western bark 

beetles, to arrive in the assessment area. The window 

of opportunity to apply prescribed fire to jack pine 

forests may shift under future climate change, but it 

is unclear how this change would affect the ability to 

use fire as a management tool. 

Moderate-High Adaptive Capacity 

Jack pine forests are highly tolerant of drought 

and disturbances, and thus have greater adaptive 

capacity to climate change. These forests can persist 

on dry and poor soils and in the future may be 

able to colonize relatively mesic sites that become 

drier as a result of climate change. The ability of 

jack pine to be competitive in extreme conditions 

also suggests that it will be able to persist in many 

places as conditions become less suitable for other 

species. Jack pine seedlings are more susceptible to 

drought stress than are established trees, however, 

and regeneration failure may occur more frequently. 

Low tree species diversity in this forest type also 

provides few options if conditions shift beyond the 

physiological limits of jack pine.
Chapter 6: Forest Ecosystem Vulnerabilities

135

A young jack pine forest in western Upper Michigan. Photo by Maria 

Janowiak, U.S. Forest Service.

Logging in a jack pine stand. Photo by Maria Janowiak, U.S. 

Forest Service.

Cone of a jack pine tree. Photo by Colleen Matula, Wisconsin 

Department of Natural Resources.
Chapter 6: Forest Ecosystem Vulnerabilities

136

Lowland Conifers

High Vulnerability (medium evidence, medium-high agreement)

Lowland conifer forests have limited tolerance to hydrologic change, including altered precipitation 

patterns and water table depth. Additionally, the dominant species in these forests are expected to 

decline under a range of climate futures. Future precipitation and groundwater levels are the primary 

uncertainties for this forest system. 

Negative Potential Impacts

Drivers: Lowland conifer forests function in a 

relatively narrow window of hydrologic and soil 

conditions. These conditions are expected to be 

perturbed in a variety of ways, including through 

increased likelihood of severe precipitation events 

and flooding, increased risk of drought, and 

changes in the water table or relative influence of 

precipitation versus groundwater. Sphagnum moss, 

characteristic of peatland forests, may be susceptible 

to warmer conditions. Stand-replacing fire, typically 

a rare event in these systems, may become more 

frequent if sites become particularly dry. 

Dominant Species: Most lowland conifer species 

are near their southern range limits in the assessment 

area, which suggests they may not tolerate warmer 

conditions. Most of the dominant conifer species 

in this system, including balsam fir, black spruce, 

northern white-cedar, and tamarack, are expected 

to undergo significant declines in suitable habitat 

and biomass across the landscape, although there 

are several limitations to modeling forest change in 

lowland forests. Associated hardwood species, such 

as paper birch and quaking aspen, are also expected 

to decline. These forests may not maintain their 

current identity if dominant species decline due to 

warmer conditions or hydrologic change.

Stressors: Roads and other watershed modifications 

are already harming lowland conifer forests in some 

parts of the assessment area. Additional hydrologic 

changes spurred by a changing climate could 

increase stress on these forests. In peatlands, warmer 

growing seasons may increase evapotranspiration 

rates and reduce the rate of peat accumulation, and 

peat layers may begin to erode as decomposition 

rates increase. The potential for extensive droughts 

also increases the possibility for more frequent 

outbreaks of pests like tamarack sawfly and spruce 

budworm, which may subsequently increase fire 

risk. Forests dominated by northern white-cedar may 

be particularly susceptible to changes in seasonal 

stream flow. Moreover, it is unclear how warmer 

winters and reduced snow cover across much of 

the assessment area may influence landscape-level 

patterns of deer herbivory. Some lowland conifers, 

particular northern white-cedar, are preferred browse 

species during winter. 

Moderate-Low Adaptive Capacity

Lowland conifer forests that are connected to 

groundwater may be less vulnerable to seasonal or 

short-term moisture deficits. Low-lying areas on the 

landscape may remain cooler than the surrounding 

uplands (i.e., frost pockets) and also be protected 

from summer droughts if increased winter and 

spring precipitation is retained through the summer. 

Prolonged droughts, however, are generally expected 

to be harmful to this forest type. Lowland conifer 

forests are not expected to expand to new territory 

within the assessment area or outcompete other 

forest types, but acid or alkaline soil conditions may 

make them less susceptible to encroachment by 

invasive species or competing upland forest types.
Chapter 6: Forest Ecosystem Vulnerabilities

137

Tamarack trees and pond. Photo by Scott Pearson, used with 

permission.

Black spruce in a lowland setting. Photo by Linda Parker, 

Chequamegon-Nicolet National Forest.

Northern white-cedar forest. Photo by Linda Parker, 

Chequamegon-Nicolet National Forest.

Lowland conifer forest and bog. Photo by Linda Parker, 

Chequamegon-Nicolet National Forest.
Chapter 6: Forest Ecosystem Vulnerabilities

138

Lowland and Riparian Hardwoods

Moderate-High Vulnerability (limited-medium evidence, medium agreement)

Climate change is expected to alter the water regimes in riparian and lowland systems, and may amplify 

the effects of insect pests and invasive species. High diversity and the presence of southern species raise 

the adaptability of these forests. There is high uncertainty regarding future precipitation patterns and the 

associated effects.

Moderate-Negative Potential Impacts 

Drivers: Climate change has the potential to alter 

the hydrologic regimes in lowland and riparian 

systems across the assessment area. These hardwood 

forests are particularly adapted to annual and 

seasonal fluxes in water tables, and the regeneration 

requirements of several species within this forest 

type are linked to these cycles. Shifts in the timing 

or amount of precipitation could disrupt the function 

of these forests. 

Dominant Species: Many lowland and riparian 

hardwood species, such as American elm, black 

willow, eastern cottonwood, silver maple, swamp 

white oak, and white ash, are expected to gain 

suitable habitat across the assessment area under a 

range of climate futures. Sycamore and hackberry 

are two southern species expected to gain new 

suitable habitat in the assessment area. Elm/ash/

cottonwood forests could have large potential 

productivity gains under a range of climate 

scenarios. Species in these systems expected to 

undergo decreased habitat suitability include 

northern white-cedar, black ash, balsam fir, yellow 

birch, and paper birch. 

Stressors: Invasive species such as reed canarygrass, 

Japanese barberry, and buckthorn are existing threats 

to these forests, and invasive species have the 

potential to increase in abundance in the assessment 

area under climate change. Emerald ash borer may 

reduce or eliminate ash species in lowland hardwood 

forests in the future. Gypsy moth and other forest 

pests may also be more damaging in climate-stressed 

forests. White-tailed deer populations may increase 

with warmer winters, which may hinder regeneration 

of some species in these forests. The trend toward 

more intense and variable precipitation events 

may present risks to this system through excessive 

waterlogging or prolonged droughts. 

Moderate Adaptive Capacity 

Many species in riparian and lowland forests can 

tolerate intermittent wet and dry conditions, as well 

as periodic floods and moisture stress. Extended 

droughts could cause significant damage to shallowrooted species, but increased winter and spring 

precipitation may buffer summer droughts in lowlying areas on the landscape. Groundwater-fed 

systems may also have some additional resilience 

where cooler, wetter soil conditions are maintained 

over time. These forests are relatively diverse with 

tree species occupying a variety of microsites, which 

reduces the risk of some species declining under 

future conditions. Riparian forests tend to contain 

species with more southerly distributions, such as 

silver maple and eastern cottonwood, which may 

increase their ability to adapt to changing conditions.
Chapter 6: Forest Ecosystem Vulnerabilities

139

A lowland hardwood forest in northern Wisconsin. Photo by 

Maria Janowiak, U.S. Forest Service.

Yellow birch and other species along a stream. Photo by 

Maria Janowiak, U.S. Forest Service.

A lowland hardwood forest. Photo by Joshua Cohen, 

Michigan Natural Features Inventory.

A floodplain hardwood forest. Photo by Joshua Cohen, 

Michigan Natural Features Inventory.
Chapter 6: Forest Ecosystem Vulnerabilities

140

Northern Hardwoods

Moderate Vulnerability (medium-high evidence, medium agreement)

Climate change may intensify several major stressors for northern hardwoods, such as drought, invasive 

species, and forest pests. High species diversity may increase resilience to future change. Uncertainty 

regarding future moisture regimes and potential interactions between stressors limits the confidence in 

this determination.

Moderate-Negative Potential Impacts

Drivers: Climate change poses several threats to 

these forests. Altered precipitation patterns and 

other hydrologic changes have the potential to 

greatly change soil moisture regimes. Disturbance 

dynamics in these forests may also change. If 

conditions become substantially drier, wildfire risk 

may increase. Increases in extreme weather events 

may lead to more frequent or widespread windthrow, 

which could affect the gap-phase dynamics that 

foster regeneration of shade-tolerant species.

Dominant Species: Model projections are mixed 

for many species common to these forests. Sugar 

maple, yellow birch, and to a lesser degree, eastern 

hemlock have more substantial projected declines in 

habitat suitability and biomass under the warmer and 

drier GFDL A1FI scenario than under the PCM B1 

scenario, suggesting that greater changes in climate 

will lead to more-negative consequences. Pests and 

diseases, such as emerald ash borer and Dutch elm 

disease, may limit the ability of certain common 

associate species, such as American elm and white 

ash, to increase. Some more southerly-distributed 

hardwood species that are currently infrequent 

or absent in the assessment area, including white 

oak, sycamore, sweet birch, and yellow-poplar, are 

projected to gain new suitable habitat. Although 

individual species may increase or decrease as the 

climate changes, there is evidence that the northern 

hardwoods forest type as a whole may be better 

able to maintain productivity relative to other forest 

types.

Stressors: Climate change may amplify several 

major stressors to northern hardwoods. Reduced 

snow cover and more frequent freeze-thaw events 

could exacerbate ongoing hardwood dieback in 

the assessment area. Forest tent caterpillar, gypsy 

moth, and other pests may cause more frequent and 

severe damage in climate-stressed forests, and new 

pests such as hemlock wooly adelgid may be able 

to persist if introduced. White-tailed deer herbivory 

may also increase with warmer winters or reduced 

snow cover. Unanticipated interactions may also 

occur between multiple stressors, such as drought, 

invasive species, and forests pests and diseases. 

Overall, it is anticipated that these impacts may 

be greatest on sites where conditions are currently 

less suitable for this type or where soil conditions 

become substantially drier.

Moderate-High Adaptive Capacity 

Northern hardwood forests are prevalent across 

the assessment area on a wide variety of soils and 

landforms, and many tree species are often present. 

North-facing slopes and other localized areas 

may undergo less change and continue to support 

northern hardwoods in the future. Additionally, 

although some sites may become too dry to support 

northern hardwoods, other sites that are currently too 

wet may become suitable over time and be colonized 

by these species. Sites that currently have reduced 

species or structural diversity because of past 

management may have lower adaptive capacity.
Chapter 6: Forest Ecosystem Vulnerabilities

141

A northern hardwood forest in the Ottawa National Forest. Photo by Maria Janowiak, 

U.S. Forest Service.

Canopy of a northern hardwood forest. Photo by Maria 

Janowiak, U.S. Forest Service.

Sugar maple seedlings. Photo by Maria Janowiak, U.S. Forest 

Service.
Chapter 6: Forest Ecosystem Vulnerabilities

142

Oak

Moderate-Low Vulnerability (medium evidence, medium-high agreement)

Oaks are relatively tolerant of drought and warmer temperatures and many species are projected to have 

increased habitat suitability in the future, although some stressors are also expected to increase. This 

forest system may expand in the future, but the extent may be influenced by interactions between oak 

and more mesic species. 

Moderate Potential Impacts

Drivers: Oak-dominated forests are relatively 

drought-tolerant and may tolerate some degree 

of greater precipitation variability under climate 

change. Oaks are limited by cold temperatures in the 

assessment area, so warming may allow this forest 

type to expand into previously unsuitable areas. 

Past management and wildfire suppression have 

allowed oak systems to flourish in areas that were 

previously barrens or pine forests, but continued fire 

suppression is allowing mesic species like red maple 

to invade these stands. Therefore, climate change 

influences on the wildfire regime and ability to apply 

prescribed fire will have great consequence for oakdominated forests. Excessive fire may encourage a 

shift to pine forests and barrens, whereas a continued 

lack of fire may promote hardwood forests. 

Dominant Species: Models project that black, bur, 

and white oak may gain suitable habitat and biomass 

in the assessment area, although results are mixed 

for northern red and northern pin oak. Most oaks are 

near their northern range limits in the assessment 

area, so they may gain suitable habitat under 

projected warming. Several new oak (pin oak, post 

oak, scarlet oak, and others) and hickory (mockernut 

hickory, pignut hickory, and others) species may 

gain suitable habitat across the assessment area 

and become a component of these systems where 

introduced. At the same time, red maple and 

other mesic species may be able to outcompete 

oak and hickory species where fire is suppressed. 

Additionally, results from the PnET-CN model 

indicate that oak-hickory forests may have greater 

productivity under future conditions than other forest 

systems.

Stressors: Climate change could amplify several 

stressors to oak forests. Forest tent caterpillar, 

gypsy moth, and other insect pests may cause more 

frequent and severe damage under climate change, 

and stressed forests may also be more susceptible 

to oak wilt and oak decline. Earlier springs may 

increase the risk of late spring frost damage on 

seedlings. White-tailed deer populations may also 

increase with warmer winters, which could hinder 

regeneration and reduce the potential for this forest 

type to expand. 

Moderate-High Adaptive Capacity 

Oak species are generally expected to fare better 

under climate change, and the species and genetic 

diversity of these forests provides for many 

possible future trajectories. These forests could 

gain territory lost by other forest types under drier 

future conditions, although the oak-dominated cover 

type may suffer from increased competition with 

hardwoods if fire suppression continues. 
Chapter 6: Forest Ecosystem Vulnerabilities

143

Leaves of a northern red oak tree. Photo by Maria Janowiak, 

U.S. Forest Service.

An oak seedling in fall. Photo by Maria Janowiak, U.S. Forest 

Service.

Oak forest. Photo by Greg Edge, Wisconsin Department of Natural Resources.
Chapter 6: Forest Ecosystem Vulnerabilities

144

Red Pine

Moderate-High Vulnerability (medium-high evidence, medium-high agreement)

The potential for increased pest and disease activity is a major threat to red pine forests, along with the 

potential for interactions among stressors. Tolerance for drought and disturbance increases the adaptive 

capacity of these forests, and the future fire regime is a primary uncertainty.

Moderate-Negative Potential Impacts

Drivers: This forest type is relatively droughttolerant and may not be greatly affected by more 

frequent moisture stress or more extensive droughts, 

except on the driest of sites. Additionally, increased 

frequency of surface fires could be a positive 

impact for these forest types, but it is possible that 

very frequent fires could hamper regeneration. 

Management is responsible for maintaining red pine 

across much of the assessment area, often using 

planted seedlings. Seasonal shifts in precipitation 

patterns, particularly the trend toward wetter springs 

and drier summers, may impair seedling success 

even though mature trees are more tolerant of 

moisture stress.

Dominant Species: Model projections are mixed 

for red pine and many common associate species. 

For most species, the warmer and drier GFDL 

A1FI scenario results in more substantial projected 

declines in habitat suitability and biomass relative 

to the PCM B1 scenario, suggesting that greater 

changes in climate will lead to more-negative 

consequences. Mature red pine trees are generally 

drought-tolerant, but the species may be limited by 

warm temperatures, especially if future temperatures 

exceed the physiological limits of the species. 

Stressors: Climate change is expected to intensify 

several stressors. Insect pests and diseases may 

become more virulent and damaging under a warmer 

climate, particularly where trees are already stressed 

or overstocked. White-tailed deer populations are 

also anticipated to increase with warmer winters, 

so herbivory on preferred species may continue to 

hinder regeneration. Moisture stress could favor 

jack pine or northern pin oak on already marginal 

red pine sites. In some areas, the shift toward mesic 

species in these forests may continue as ongoing fire 

suppression facilitates increases in red maple, black 

cherry, and other hardwoods species projected to 

increase under climate change. 

Moderate-Low Adaptive Capacity 

Red pine forests are generally tolerant of drought 

and disturbances, which lends these forests greater 

adaptive capacity to climate change. This forest type 

could also expand to new favorable locations on the 

landscape if overall conditions result in increased 

drying; for example, some current aspen-birch, 

oak, or northern hardwood sites may become more 

suitable for red pine forest in the future. There is 

relatively little genetic diversity among red pine 

individuals and the forest system tends to have 

lower species diversity, which could make it more 

vulnerable to changing conditions. Red pine forests 

are often planted. Natural regeneration of red pine is 

sometimes limited following harvest, particularly in 

the southern portion of the assessment area. 
Chapter 6: Forest Ecosystem Vulnerabilities

145

Red and white pine trees along a lakeshore. Photo by Linda 

Parker, Chequamegon-Nicolet National Forest.

A forest dominated by red pine at the Apostle Islands 

National Lakeshore. Photo by Maria Janowiak, U.S. Forest 

Service.

Red pine forest in northern Wisconsin. Photo by Maria Janowiak, U.S. Forest 

Service.
Chapter 6: Forest Ecosystem Vulnerabilities

146

Upland Spruce-Fir

High Vulnerability (medium-high evidence, medium-high agreement)

The boreal species within upland spruce-fir forests are not expected to tolerate warmer temperatures, 

increased competition from other forest types, and increased forest pest activity. These forests are not 

well equipped to adapt to climate change. 

Negative Potential Impacts

Drivers: Several species in this system are limited 

by high growing-season temperatures, so projected 

warming in the assessment area may exceed 

the physiological limits of this forest system. 

Increases in stand-replacing wildfire could provide 

opportunities for regeneration where conditions 

remain suitable for the dominant species. 

Dominant Species: Considering the range of 

possible climate futures, the majority of dominant 

species that constitute upland spruce-fir forests 

(balsam fir, black spruce, paper birch, quaking 

aspen, and white spruce) are projected to decline in 

suitable habitat and biomass across the assessment 

area. These boreal species are near their southern 

range limits in Wisconsin and Michigan. The same 

modeling studies offer mixed results for several 

associate species, such as bigtooth aspen, northern 

white-cedar, and red maple, but these species are all 

generally projected to fare worse under the hotter, 

drier GFDL AFF1 scenario. Spruce-fir forests may 

be less able to take advantage of warmer conditions 

and longer growing seasons for productivity 

increases. 

Stressors: Extensive droughts and warmer 

temperatures expected under climate change will 

be particularly stressful for this forest type. Spruce 

budworm and other insect pests may become more 

active and damaging under a warmer climate, 

especially where forests are already stressed 

by drought or other changes. White-tailed deer 

populations are also anticipated to increase with 

warmer winters, so herbivory on preferred species 

may continue to hinder regeneration for certain 

species like northern white-cedar. Conversely, nonpalatable boreal conifers may benefit from reduced 

competition if deer herbivory prevents hardwood 

expansion into these sites.

Moderate-Low Adaptive Capacity 

Upland spruce-fir forests can persist on sandy, 

nutrient-poor soils, so they may be able to tolerate 

short-term moisture stress. Several of these species 

produce seed and regenerate well after fire or 

other disturbances that provide a suitable seed bed. 

However, these forests have relatively low diversity 

or contain primarily boreal species, which leads 

to fewer possible trajectories in the future. Many 

planted upland spruce-fir forests in the region have 

also been negatively affected by spruce decline and 

other forest health issues, which are expected to 

reduce their resilience to climate change impacts.
Chapter 6: Forest Ecosystem Vulnerabilities

147

Forester coring a spruce tree to determine its age. Photo by 

Maria Janowiak, U.S. Forest Service.

A spruce plantation with an aspen component. Photo by 

Maria Janowiak, U.S. Forest Service.

A previously burned upland spruce-fir forest. Photo by 

Joshua Cohen, Michigan Natural Features Inventory.

A timber harvest in an upland spruce-fir stand. Photo by 

Maria Janowiak, U.S. Forest Service.
Chapter 6: Forest Ecosystem Vulnerabilities

148

White Pine

Moderate-Low Vulnerability (medium-high evidence, medium agreement)

Climate change may intensify some stressors for white pine forests, such as drought and insect pests. 

Several characteristics of this forest type suggest that it will have higher resilience, including high species 

diversity, the ability to persist across a range of site conditions, and the ability to respond favorably after 

disturbance.

Moderate-Positive Potential Impacts

Drivers: White pine forests are relatively droughttolerant and may tolerate some degree of greater 

precipitation variability under climate change. This 

forest system is present across a fairly wide range of 

sites, and some sites may be more greatly affected by 

altered soil moisture regimes. Disturbance dynamics 

in these forests may also change. Increased storm 

events may damage or kill mature trees, while also 

creating opportunities for regeneration. Increases in 

periodic fire may be beneficial by reducing ladder 

fuels and competition, although substantially more 

frequent or severe fire may favor red or jack pine 

forests.

Dominant Species: Suitable habitat and 

aboveground biomass are not expected to change 

substantially for eastern white pine under the PCM 

B1 scenario, but decreases are projected under the 

warmer and drier GFDL A1FI scenario, suggesting 

that greater changes in climate will lead to more 

negative consequences. Under the most extreme 

temperature increases, warming in the assessment 

area may exceed the physiological limits of white 

pine. Minor components of this forest type, such as 

northern red oak, northern pin oak, and red maple, 

also have mixed projections, although jack pine is 

generally expected to decline.

Stressors: Some common insect pests and diseases 

may become more damaging under a warmer 

climate, especially where increased moisture 

stress increases susceptibility. Moisture stress 

could also favor red pine, jack pine, or northern 

pin oak on marginal white pine sites, especially 

if fire also becomes more prevalent. White-tailed 

deer populations are also anticipated to increase 

with warmer winters, so herbivory on white pine 

seedlings may hinder regeneration. 

High Adaptive Capacity 

White pine forests are present across the landscape 

on a wide variety of soils and landforms, and many 

tree species are often present. White pine forest 

is less prevalent than other types and occupies 

less than its historical distribution but has been 

increasing across the assessment area in recent 

decades. Although white pine is not projected to 

have increased habitat suitability or productivity, its 

wide ecological amplitude may enhance its ability 

to persist across a range of sites. The ability of white 

pine to disperse seed and be a pioneer species in 

gaps and open areas also suggests that it will be able 

to effectively colonize newly suitable habitat.
Chapter 6: Forest Ecosystem Vulnerabilities

149

Cones of a white pine tree. Photo by Maria Janowiak, U.S. Forest Service.

A mixed forest dominated by red and white pine. Photo by Linda Parker, 

Chequamegon-Nicolet National Forest.
Chapter 6: Forest Ecosystem Vulnerabilities

150

SUMMARY

Forest ecosystems across the assessment area will 

be affected by climate change, although systems and 

species will respond to these changes individually. 

The synthesis statements in the first half of this 

chapter can be applied as rules of thumb when 

specific information about expected climate change 

impacts is lacking. Overall, we expect forest systems 

that are adapted to a narrow range of conditions 

or that contain few species to be more vulnerable 

to changing conditions. Communities with higher 

diversity that are adapted to tolerate a wide range of 

conditions and disturbances are expected to be better 

able to persist under a range of plausible climates. 

The vulnerability determinations for individual 

forest systems are best interpreted as broad trends 

and expectations across the assessment area. 

This assessment makes use of the most up-todate information from the scientific literature, a 

coordinated set of ecosystem modeling results and 

climate projections, and the input of a large team 

of local experts. Even so, there are limitations and 

unknowns that make these determinations imperfect. 

As new information continues to be generated on the 

potential impacts of climate change on forests in this 

region, this assessment should be supplemented with 

additional resources. 

It is essential to consider local characteristics such 

as past management history, soils, topographic 

features, species composition, forest health issues, 

and recent disturbances when applying these general 

vulnerabilities to local scales. Some site-level 

factors may amplify these expected vulnerabilities, 

yet others may buffer the effects of climate change. 

Developing a clear understanding of potential 

vulnerabilities across relevant scales will then 

enable forest managers, landowners, planners, or 

other resource specialists to consider appropriate 

adaptation responses. This is true whether the task 

is to manage a single stand over a few years, or to 

design a long-term management plan for a large tract 

of land. 

In the following chapter, we extend the discussion 

to consider the implications of climate trends and 

forest ecosystem vulnerabilities for other ecosystem 

services and resource areas that are often important 

for forest managers. 
151

CHAPTER 7: MANAGEMENT IMPLICATIONS

The previous chapters of this assessment have 

described observed and anticipated climate trends, 

potential impacts to forest ecosystems, and the 

climate-related vulnerability of major forest systems 

in the assessment area. This chapter takes one 

additional step and summarizes some implications 

of these climate change impacts and vulnerabilities 

for a variety of topics important to forest managers. 

Changes in climate, impacts on forests, and 

ecosystem vulnerability will combine to create both 

challenges and opportunities in forest management.

Topics were selected to encompass major resource 

areas that are priorities for public and private land 

managers. These topics, and the descriptions of 

climate change implications, are not comprehensive. 

Some topics have received less scientific attention 

or contain greater uncertainty. For some topics we 

relied on input from subject-area experts to discuss 

climate change implications (Table 26 in Appendix 

7). Our goal is to provide a springboard for thinking 

about management implications of climate change 

and to connect managers to other relevant resources. 

When available, the “more information” sections 

provide links to key resources for managers to find 

more information about the impacts of climate 

change on that particular topic.

This chapter does not make recommendations as to 

how management should be adjusted to cope with 

climate impacts. We recognize that climate change 

will have varying implications for different forest 

systems, ownerships, and management objectives. 

Additionally, climate change is only one of many 

factors considered in making land management 

decisions. Therefore, we provide broad summaries 

rather than focusing on particular management 

issues. A separate document, Forest Adaptation 

Resources, has been developed to assist land 

managers in a decisionmaking process to adapt their 

natural resource management to projected impacts 

(Swanston and Janowiak 2012). 

WILDLIFE 

Climate change effects on fish and wildlife species 

and their management are areas of active research, 

and the subject is summarized only briefly here. 

More thorough assessments are available for both 

Michigan (Hoving et al. 2013) and Wisconsin 

(Wisconsin Initiative on Climate Change Impacts 

[WICCI] 2011a). The wildlife communities found 

within the assessment area are the result of many 

interacting factors, including weather and climate. 

Weather and climate affect wildlife species directly 

through heat stress, snowfall, or annual saturation 

of ephemeral wetlands, for example. Climate and 

weather also affect wildlife indirectly through 

climate-related habitat shifts, pests and diseases, 

disturbance events, and other factors. For example, 

spruce grouse occur in the assessment area because 

past climate has favored spruce regeneration and 

competition with deciduous trees. Many species in 

the assessment area, such as the gray jay and the 

American marten, are not common farther south. If 

boreal conifer species decrease in the assessment 

area, these wildlife species may decrease as their 

habitats change. Conversely, species like whitetailed deer and wild turkey are hindered by severe 

winters. A decline in the frequency of severe winters 

may favor those species. Because forests in the 

assessment area are habitat for many wildlife species 
Chapter 7: Management Implications

152

at the northern or southern edge of their range, even 

small climate-induced changes may have noticeable 

impacts. 

Wildlife species throughout the Midwest are 

responding to climate change, and several 

assessments and vulnerability analyses suggest that 

there will continue to be various effects on wildlife 

(Hall 2012). Several tools have been developed 

to help managers evaluate the climate change 

vulnerabilities of wildlife species. For example, the 

Climate Change Bird Atlas examines the potential 

for climate change to alter suitable habitat for 

147 bird species across the eastern United States 

(Matthews et al. 2011a). Results from this work 

suggest that resident bird species are projected 

to fare better under a changing climate than are 

species that migrate over greater distances, which 

is consistent with recent observations of change 

(Chapter 3). Additionally, changes in suitable habitat 

of various bird species in the future are closely tied 

to changes in forest conditions (Matthews et al. 

2011a). 

More Information

• The Wildlife Working Group report from the 

Wisconsin Initiative on Climate Change Impacts 

documents past and current climate change 

impacts on wildlife, as well as anticipated 

changes in wildlife diversity and abundance: 

www.wicci.wisc.edu/report/Wildlife.pdf

• Changing Climate, Changing Wildlife, a report 

from the Michigan Department of Natural 

Resources – Wildlife Division, assesses how 

climate change may affect 400 wildlife species 

in the state: 

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

• Many states are working to incorporate climate 

change information into their state wildlife action 

plans. These plans identify wildlife species 

and associated habitats that are in greatest 

conservation need. Many species of greatest 

conservation need may be particularly affected by 

climate change.

Common loon in western Upper Michigan. Photo by Scott Pearson, used with permission.
Chapter 7: Management Implications

153

- Voluntary guidance has been provided by the 

Association of Fish and Wildlife Agencies: 

www.fishwildlife.org/files/AFWA-VoluntaryGuidance-Incorporating-Climate-Change_

SWAP.pdf 

- Wisconsin Wildlife Action Plan: 

http://dnr.wi.gov/topic/WildlifeHabitat/

ActionPlan.html

- Michigan State Wildlife Action Plan: 

www.michigan.gov/dnr/0,1607,7-153-10370_

30909---,00.html 

• The Climate Change Bird Atlas is a companion

to the Climate Change Tree Atlas and uses

information about climate change and effects on

forest habitat to project changes in bird species

distributions:

www.nrs.fs.fed.us/atlas/bird/

• Season’s End, a collaborative effort comprising

many hunting and conservation organizations,

offers many resources on how climate change

may affect wildlife:

http://www.cakex.org/virtual-library/784

• The Forest Service Climate Change Resource

Center provides a summary of potential climate

change effects on wildlife species:

www.fs.fed.us/ccrc/topics/wildlife/

THREATENED AND 

ENDANGERED SPECIES 

As discussed in Chapter 6, it is expected that plant 

or animal species that are already rare, threatened, 

or endangered may be especially vulnerable to 

shifts in temperature and precipitation. Rare plants 

and rare plant communities often rely on very 

particular combinations of environmental and habitat 

conditions, in many cases as relict populations 

from previous climate conditions (Devall 2009). 

Threatened and endangered species often face 

population declines due to a variety of other factors, 

including habitat loss, competition from invasive 

species, and disease. As temperatures become 

warmer and the precipitation regime changes, 

already rare or declining species may therefore be 

among the first to be susceptible to climate-related 

stress. The limited range of rare species makes it 

difficult to model the effects of climate and climate 

change on distribution and abundance (Schwartz et 

al. 2006b). In the absence of human intervention, 

rare or threatened species may face greater 

extinction risks. Alternatively, rare species that live 

in habitats that are buffered from climate shifts (i.e., 

refugia) may be able to persist. 

More Information

• The Wildlife Working Group report from the

Wisconsin Initiative on Climate Change Impacts

documents past and current climate change

impacts on wildlife, as well as anticipated

changes in wildlife diversity and abundance:

www.wicci.wisc.edu/report/Wildlife.pdf

• The Plants and Natural Communities Working

Group report from the Wisconsin Initiative on

Climate Change Impacts describes the potential

effects of climate change on groups of natural

communities:

www.wicci.wisc.edu/report/Plants-and-NaturalCommunities.pdf

• Changing Climate, Changing Wildlife, a report

from the Michigan Department of Natural

Resources – Wildlife Division, assesses how

climate change may impact 400 wildlife species

in the state:

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

WATER RESOURCES 

There are many potential interactions and 

relationships between climate change, forest 

ecosystems, and water resources in the assessment 

area. Below, we outline a few examples of these 

potential implications. In addition to reflecting 
Chapter 7: Management Implications

154

land-use decisions, water resources in the 

assessment area are influenced by a diverse array 

of management decisions and policies, including 

infrastructure planning and maintenance, water 

quality discharge permitting, water extraction/

diversion permitting, and biological resource 

management. These layers of policy and 

management decisions complicate the picture, but 

reinforce the notion that management decisions will 

be intertwined with ecological changes in the future. 

Infrastructure on Forest Land

Changes in climate and extreme weather events are 

expected to have impacts on infrastructure on forest 

lands throughout the region, such as roads, bridges, 

and culverts. Rising temperatures alone could have 

important impacts. A recent report suggests that 

heat stress may have substantial effects on surface 

transportation infrastructure in the assessment area 

(Posey 2012).

Many landowners and agencies are responsible 

for managing water-related infrastructure such as 

dams, drainage ditches, and culverts. The current 

specifications for infrastructure are generally based 

on past climate patterns, and the current trend of 

intensifying precipitation has placed additional 

strains on old and fragile infrastructure. As a recent 

regional example, the flood event in June 2012 

in Duluth and across northern Minnesota caused 

more than $100 million in damage, primarily to 

roads, bridges, and private property (Passi 2012). 

In addition, associated landslides and stream 

bank erosion extensively damaged area streams; 

restoration costs are estimated at $1 million per 

stream. 

Heavy precipitation events, which are already 

increasing and projected to increase more in 

the future (Chapter 4), may overload existing 

infrastructure that has not been built to that capacity. 

For example, older road systems may be susceptible 

to increased rainfall events due to improper 

location or outdated building standards. Many of 

these aging structures are being replaced, with the 

expectation that new culverts will need to last up 

to 100 years into the future and be able to sustain 

heavier precipitation events. As one example, an 

assessment performed by the Chequamegon-Nicolet 

National Forest reviewed culvert sizing criteria to 

determine how the replacement of culverts and other 

infrastructure may need to be altered as precipitation 

patterns change and extreme precipitation events 

occur more frequently (Higgins 2013). Hydrologic 

modeling was used to identify watersheds that may 

be more vulnerable to flood flows from climate 

change based upon stream width, runoff potential, 

and the number of stream crossings within a 

watershed. The most vulnerable watersheds were 

generally found to have a high runoff potential 

and a higher density of stream crossings within the 

watersheds, and this information is being used to 

inform the sizing of new culverts (Higgins 2013).

Replacing infrastructure often results in greater 

costs in order to upgrade to higher standards 

and capacity. Extreme events may also require 

more frequent maintenance of roads and other 

infrastructure, even if designed to appropriate 

specifications. Additionally, forest managers may 

find it necessary to take additional precautions to 

prevent erosion when designing road networks or 

other infrastructure. 

Aquatic Organisms

Thermal habitat in cold-water lakes and streams 

may also continue to be impaired as temperatures 

continue to warm. For example, models of water 

temperature in cold-water streams and stratified 

lakes in Wisconsin project water temperature 

increases of 1.4 to 7.2 °F (0.8 to 4.0 °C) by the 

end of the 21st century (Higgins 2013, Lyons et 

al. 2010, Mitro et al. 2010). These changes may be 

compounded by changes in forests: if tree cover 

is reduced or if conifer species are replaced by 

deciduous species, stream shading could decrease, 
Chapter 7: Management Implications

155

further increasing water temperatures. Altered 

precipitation patterns may influence stream flows, 

and increased storm intensity and frequency, rainon-snow events, and other hydrologic changes may 

promote stream bank and shoreline erosion, leading 

to increased turbidity and reduced water quality. 

Lakes may also be affected by reduced ice cover, 

with altered water temperature and oxygen profiles 

in shallow and moderate-depth lakes (Fang et al. 

2004a, 2004b, 2004c; Stefan et al. 2001). 

Fish and other aquatic organisms are projected 

to be affected by water quality changes, moreintense precipitation events, and other changes 

to the hydrology of the assessment area. These 

impacts may not occur equally across species or 

even across life stages for a given organism. For 

example, evaluation of the effects of warmer water 

temperatures on 50 stream fish species in Wisconsin 

indicated declines in several cold-water and coolwater species and increases in most warm-water 

fish species (Higgins 2013, Lyons et al. 2010). An 

assessment of 400 wildlife species in Michigan 

(Hoving et al. 2013) found that, compared to other 

taxa, fish are among the most vulnerable fauna in 

Michigan. More than 80 percent of the fish species 

were assessed as vulnerable to climate change. 

More Information 

• The Coldwater Fish and Fisheries Working Group 

report from the Wisconsin Initiative on Climate 

Change Impacts describes potential climate 

change impacts on several fish species: 

www.wicci.wisc.edu/report/Coldwater-Fish-andFisheries.pdf

• The Coastal Communities Working Group report 

from the Wisconsin Initiative on Climate Change 

Impacts describes the potential impacts to coastal 

systems, including coastal infrastructure: 

www.wicci.wisc.edu/report/CoastalCommunities.pdf

The Bad River in northern Wisconsin. Photo by Maria Janowiak, U.S. Forest Service.
Chapter 7: Management Implications

156

• Changing Climate, Changing Wildlife, a report 

from the Michigan Department of Natural 

Resources – Wildlife Division, assesses how 

climate change may affect fish species in the 

state: 

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

• The Great Lakes Environmental Assessment and 

Mapping (GLEAM) project compiles spatial 

information regarding many threats to Great 

Lakes ecosystems, including climate change: 

www.greatlakesmapping.org/ 

• A technical report summarizing climate change 

impacts on the transportation sector (including 

infrastructure) was recently released as input 

for the Midwest region for the National Climate 

Assessment: 

glisa.msu.edu/docs/NCA/MTIT_Transportation.

pdf 

FIRE AND FUELS 

Climate change is expected to have implications 

for fire and fuels management in the assessment 

area. As discussed above, this summary does not 

address the ways that land managers should adapt 

to the potential changes. A wide range of potential 

choices in policy and funding, as well as concerns of 

the public, will ultimately define the response that 

makes the most sense, and these responses may be 

different for different organizations and land owners. 

As described in Chapter 5, weather and climate 

are major drivers of fire behavior. Across the 

Great Lakes region, the fire season is controlled 

by a combination of day length, weather, and 

fuel conditions. Typically, short day lengths, 

cool temperatures, and wet fuels delay the onset 

of fire season until April or May. Although the 

summer months have the longest days and warmest 

temperatures, living vegetation requires extended 

dry periods of 2 weeks or more to increase the 

potential for fire ignition and spread. Live trees drop 

leaves and go dormant in the fall, but most forests 

become receptive to fire around the same time that 

short days and cool temperatures return. The type 

and condition of available fuels may lead to surface 

fires, which consume ground fuels, or crown fires, 

which burn across the forest canopy. 

Drought periods can exacerbate wildfire risk during 

any of these periods, and drought is a critical 

precursor for large summer fire events. Droughts 

may increase fire potential quickly, and indicators 

of fire potential suggest that hot and dry periods of 

weeks rather than months may be sufficient to stress 

live fuels and make them more receptive to ignition 

and spread. The projected trend toward more-intense 

precipitation could raise the potential for longer 

intervening dry periods between rain events (Chapter 

4). Combined with warmer temperatures and a range 

of other climate-driven stressors, the potential exists 

for more forests to be prone to wildfire throughout 

the growing season. The two climate scenarios 

examined in this assessment reveal a wide range 

of possible precipitation values (Chapter 4), so it is 

uncertain to what degree drought stress may harm 

forests in the assessment area. 

As with other parts of the country, critical fire 

weather conditions have been responsible for many 

of the major fire events across the Great Lakes 

region. Large, intensely burning fires generally 

require a combination of strong gradient winds, 

significant atmospheric instability, and dry air. The 

fires that occur in fire-prone landscapes during 

these weather events tend to produce the most 

severe fire effects. Fire-weather events are poorly 

captured by current modeling tools. Because large 

wildfires are driven primarily by these fire-weather 

events, it is difficult to forecast exactly how the 

projected climate trends may translate into changes 

in fire activity. Additionally, complex interactions 

between climate change, vegetation communities, 

seasonal precipitation, and discrete fire-weather 
Chapter 7: Management Implications

157

events will dictate whether fires are manifested as 

surface fires or crown fires. This distinction has 

important consequences for forest communities 

and fire management, and the current limits of our 

understanding are a major uncertainty. 

Projected changes in climate could also affect the 

ability to apply prescribed fire in the assessment 

area. Wetter springs could make it difficult to 

conduct prescribed burns in spring, shifting 

opportunities for dormant-season burning to the fall. 

If summer or fall becomes drier, burning under those 

conditions could involve greater risk and managers 

may be less inclined to implement this practice. 

More Information 

• The Lake States Fire Science Consortium 

provides fire science information to resource 

managers, landowners, and the public about 

the use, application, and effects of fire: 

www.lakestatesfiresci.net/index.html 

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect wildland fire in forest 

ecosystems: 

 www.fs.fed.us/ccrc/topics/wildfire/ 

FOREST PRODUCTS

The forest products industry is important to the 

economy of the assessment area (Chapter 1). Tree 

species and forest composition are projected to 

change during the 21st century (Chapters 5 and 6). 

Changes in forest composition across the landscape 

will be influenced by forest management, and in 

turn will influence forest management and the forest 

products industry. Several commercially important 

species, such as quaking aspen, are projected to 

decline substantially under a range of possible 

climate futures over the next century. Conversely, 

hardwood species like American basswood and 

white oak are projected to increase in the assessment 

area. Large potential shifts in commercial species 

availability may pose risks for the forest products 

sector if the shifts are rapid and the industry is 

unprepared. The forest products industry may 

benefit from awareness of anticipated climate 

trends and shifts in forest species. In many cases, 

forest managers can take actions to reduce potential 

risks associated with climate change or proactively 

encourage species and forest types anticipated to 

fare better under future conditions (Swanston and 

Janowiak 2012). There may be regional differences 

in forest responses, as well as potential opportunities 

for new merchantable species to gain suitable habitat 

in the assessment area. If the industry can adapt 

effectively, it is possible that the net effect of climate 

change on the forest products industry across the 

Midwest will be positive (Handler et al. 2012).

Overall, the effects of climate change on the forest 

products industry depend not only on ecological 

responses to the changing climate, but also on 

socioeconomic factors that will undoubtedly 

continue to change over the coming century. Major 

socioeconomic factors include national and regional 

economic policies, demand for wood products, and 

competing values for forests (Irland et al. 2001). 

Great uncertainty is associated with each of these 

factors. The forest products industry has adjusted 

to substantial changes over the past 100 years, and 

continued responsiveness can help the sector remain 

viable. 

More Information

• The Forest Service 2010 Resources Planning Act 

Assessment includes future projections for forest 

products and other resources through the year 

2060 and examines social, economic, land use, 

and climate change influences: 

www.fs.fed.us/research/rpa/ 

• The Climate Change Tree Atlas provides 

information on the projected suitable habitat for 

tree species under climate change: 

www.nrs.fs.fed.us/atlas/bird/ 
Chapter 7: Management Implications

158

FOREST MANAGEMENT

OPERATIONS

Climate variability and change present many 

challenges for forest managers who seek to maintain 

the diverse goods and services that forests provide. 

In particular, changes in winter conditions in the 

assessment area and throughout the northern Great 

Lakes region may shorten the available time window 

for conventional forest management operations. 

Most management in lowland areas and on soils 

prone to compaction or erosion is accomplished 

during the winter. Climate change is projected 

to result in shorter seasons of frozen ground, 

more midwinter thaws, less snowpack, and more 

rain during winter months (Chapter 4). Frozen 

ground facilitates timber harvest and transport, 

and snowpack provides protection for soils during 

harvest operations. Although special equipment is 

available to increase flotation on shallow snowpack 

or in the absence of snowpack, this equipment is 

costly. Additionally, a lack of frozen ground might 

increase the need to build roads to facilitate winter 

harvest, which would drive up costs compared to 

conventional practices.

Projected changes in precipitation during the 

growing season could also have important 

implications for forest management operations. 

Intense precipitation events could delay harvest 

operations in areas of poor drainage, but these 

events may be less disruptive in areas with coarse, 

sandy soils. Alternatively, summer dry periods 

and droughts could extend operating windows in 

low-lying areas or clay soils. Extended or severe 

droughts could present problems in sandy areas, 

however, if it becomes necessary to install gravel 

over logging roads. 

Changes in severe weather patterns could increase 

the number of salvage harvests that are undertaken. 

Harvesting green timber allows resource managers 

to strategically achieve desired objectives and 

outcomes. Salvage harvesting after a wind event or 

pest or disease outbreak, by contrast, generally arises 

from a more immediate need to remove hazardous 

fuels or clear affected forest areas. A salvage sale 

also does not garner the same financial return as 

does a green timber sale opportunity.

Analysis of timber harvest records in northern 

Wisconsin has identified some consequences of the 

changes in frozen ground condition (C. Rittenhouse, 

University of Connecticut, and A. Rissman, 

University of Wisconsin – Madison, unpublished 

data). In years with warm winters, there has been 

a shift toward greater harvest of jack pine and less 

harvest of black spruce, hemlock, and red maple. 

Interviews with loggers indicate that growingseason restrictions on harvest designed to limit oak 

wilt and other diseases reduced the annual harvest 

window. Additionally, such ongoing stressors as 

overcapitalization, loan and insurance payments, 

and high fuel prices increased pressure on loggers to 

harvest year-round. Interviews with transportation 

officials indicate their concerns that operating trucks 

on marginally frozen roads (or “over-weighting”) 

contributed to conflicts over roads between industry 

and local governments. Thus, climate change 

Natural resource professionals at the ChequamegonNicolet National Forest. Photo by Maria Janowiak, U.S. 

Forest Service.
Chapter 7: Management Implications

159

impacts on forestry operations have complex 

implications for management and governance of 

timber production, logger livelihoods, water quality, 

and transportation systems.

FOREST CARBON 

The accumulated carbon pool within forest soils, 

belowground biomass, dead wood, and aboveground 

live biomass is enormous (Chapter 1) (Birdsey et al. 

2006). Climate change and associated impacts 

to forest ecosystems may change the ability of 

forests in the assessment area to store carbon. A 

longer growing season and carbon dioxide (CO2 ) 

fertilization may lead to increased productivity 

and carbon storage in forests in the assessment 

area (Chapter 5). This increase could be offset by 

climate-related physical and biological disturbances 

(Gough et al. 2008, Hicke et al. 2011), leading 

to increases in carbon storage in some areas and 

decreases in others. As long as forests recover after 

a disturbance, total carbon losses may be negligible 

over the long term. If forests convert to nonforested 

conditions or if carbon stored in peat soils is lost to 

the atmosphere, then carbon storage is reduced over 

much longer time scales. 

Different forest types in the assessment area store 

different amounts of carbon (Chapter 1). On average, 

spruce/fir forests are the most carbon-dense, but 

most of this carbon occurs in organic soils (Birdsey 

et al. 2014). Maple/beech/birch forests generally 

contain the most aboveground carbon, so an increase 

in these species and a decline in spruce/fir forests 

may affect carbon storage in some areas. Modeling 

studies in northern Wisconsin examining the effects 

of species composition changes on landscapescale carbon stocks, suggest that some forests may 

increase in biomass and overall productivity, despite 

declines in boreal or northern species (Birdsey et al. 

2014, Chiang et al. 2008, Scheller and Mladenoff 

2005). The LANDIS-II model results presented in 

Chapter 5 raise the possibility that existing species in 

the assessment area may decline in landscape-level 

biomass across the assessment area under the GFDL 

A1FI climate scenario, but these projections do not 

account for increases in other species or the potential 

effects of CO2

 fertilization. As long as forests are 

maintained as forests in the assessment area, a 

large-scale decline in carbon stocks is not expected. 

Moreover, management options have the potential 

to increase forest carbon stores and reduce carbon 

emissions (Birdsey et al. 2014).

More Information

• The report Past and Prospective Carbon Stocks 

in Forests of Northern Wisconsin provides a 

baseline assessment of carbon stocks in the 

area, as well as an analysis of the impacts of 

disturbance and management on carbon stocks: 

www.treesearch.fs.fed.us/pubs/45578

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect the ability of forests to store 

carbon: 

www.fs.fed.us/ccrc/topics/forests-carbon/

• A recent article, A Synthesis of the Science on 

Forests and Carbon for U.S. Forests, summarizes 

the key issues related to forest management and 

carbon: 

www.treesearch.fs.fed.us/pubs/38959

NONTIMBER FOREST PRODUCTS

Changes in climate will have implications for 

nontimber forest products in the assessment area 

and throughout the Great Lakes region. Hundreds 

of these products are used for food, medicine, craft 

materials, and other purposes. Many of these will 

be affected by changes in temperature, hydrology, 

and species assemblages. As illustrations, effects 

of climate change on four regionally important 

nontimber forest products with broad cultural and 

economic importance are discussed briefly here.
Chapter 7: Management Implications

160

Natural wild rice is a cultural keystone species in 

the assessment area. It is central to the migration 

story of the Anishinaabe (also known as Ojibwe, 

Chippewa, or Odawa), for whom wild rice is a 

sacred food and medicine. Wild rice growth and 

productivity are sensitive to hydrologic conditions 

including water depth and temperature. Although 

wild rice is adapted to some seasonal variation, it 

thrives in water depths of 0.5 to 3 feet. Germination 

requires a 3- to 4-month period of dormancy in 

water at temperatures at or below 35 °F (1.6 °C). 

Wild rice seed does not survive prolonged drying. 

With regional and global models predicting 

increased heavy precipitation events, higher average 

temperatures, later winter onset, and earlier spring 

onset, the future of natural wild rice in the region 

may be at risk. Specific threats include:

• prolonged droughts leading to lowered water 

depths or seed desiccation, or both 

• flooding, particularly in the early summer 

“floating leaf” life stage, 

• shortened periods of cold water temperatures, and

• predation or displacement by species favored by 

warmer water temperatures (e.g., carp and reed 

canarygrass).

Balsam fir boughs enter regional, national, and 

international markets as wreaths, holiday greens, 

and fragrant souvenirs. The balsam bough industry 

provides seasonal employment for thousands of 

residents regionally and is especially important in 

rural areas where job opportunities are limited. For 

example, the Minnesota bough industry was reported 

to be worth more than $23 million in the mid-2000s 

(Jacobson 2005). Models predict a northward shift 

in the area of suitable habitat for balsam fir, with 

substantial reductions in suitable habitat by the end 

of the century (Chapter 5; see also Appendix 4).

Hunting morel mushrooms is a passion for many 

people throughout the assessment area (Fine 2003). 

Annual morel festivals and sales to restaurants 

provide supplemental income for many people, 

communities, and small businesses in the region. 

Under climate change, increased fire frequency and 

severity may result in increased morel fruiting. In 

a process similar to the spike in morel fruiting with 

the massive die-off of American elms due to Dutch 

elm disease, climate-related deaths of associated tree 

species also may result in immediate increases in 

morel fruiting. However, evidence from the midAtlantic suggests such a spike would be followed by 

a decline in fruiting frequency (Emery and Barron 

2010). In addition, because morel fruiting is highly 

sensitive to temperature and humidity, changes 

in these regimes also can be expected to alter the 

timing and intensity of morel fruiting.

Sugar maple is another cultural keystone species 

of the Midwest and Northeast. Maple syrup and 

sugar, made from boiling sugar maple sap, provided 

treasured sources of sweetness and a critical source 

of late-winter nourishment well before the arrival 

of the first Europeans on the continent. Fur trader 

records show that maple sugar was an important 

exchange good from the early days of settlement 

(Emery 2002). Today, gathering and boiling sugar 

maple sap continues to have profound cultural and 

economic importance in the region. During the 2013 

season, U.S. market production of maple syrup was 

approximately 3,253,000 gallons of maple syrup 

(265,000 and 148,000 gallons in Wisconsin and 

Michigan, respectively). The total value of maple 

syrup on the market for 2012 (the most recent 

year for which statistics are available) was nearly 

$74 million for the Nation as a whole and more than 

$5.5 million for Wisconsin and Michigan combined 

(National Agricultural Statistics Service 2013). Not 

included in these figures are the volume and value of 

maple syrup that never enters the market. Gathering 

and boiling sap and sharing syrup knits together 

families and communities. Tribes in the region 

still hold sugar festivals as an important marker of 

the seasons, as do many non-Native communities. 

Sap flow necessary for maple syrup production 
Chapter 7: Management Implications

161

requires a combination of warm days and freezing 

nights that is highly seasonal. Climate records show 

such conditions now occur earlier than in the past 

and this trend likely will continue, although there 

is disagreement on whether the total length of the 

season will be shortened and sap yield reduced 

(Groffman et al. 2012, Rustad et al. 2012, Skinner et 

al. 2010).

CULTURAL RESOURCES

Certain species can hold unique cultural importance, 

often based on established uses. Changes in forest 

composition and extent may alter the presence 

or availability of culturally important species 

throughout the region. For example, Dickmann 

and Leefers (2003) compiled a list of more than 

50 tree species from Michigan that are used by 

several Native American tribes in the region, and the 

Great Lakes Indian Fish and Wildlife Commission 

compiled information for numerous species used 

by the Great Lakes Ojibwe (Meeker et al. 1993). 

Among these species, northern white-cedar and 

paper birch stand out as having particular importance 

for defining a culture and way of life. Under climate 

change, however, these two species are expected to 

decline in suitable habitat and biomass over the next 

century (Chapter 5).

More Information

• The “Gikinoo’wizhiwe Onji Waaban” (Guiding 

for Tomorrow) initiative increases awareness 

of how climate change is affecting the people, 

culture, and economies of the Lake Superior 

region by using examples from traditional 

Ojibwe lifeways and other cultures: 

www.g-wow.org/

ARCHAEOLOGICAL AND 

HISTORICAL RESOURCES

Climate change may also present a variety of 

challenges for managers of heritage resources on 

public lands. Extreme wind events such as tornadoes 

and derechos can directly damage buildings and 

other structures. Storm-damaged cultural resources 

may subsequently be further damaged as a result of 

salvage or disaster-response management activities. 

A change in the frequency, severity, or duration 

of heavy precipitation and flooding could affect 

archaeological and historical resources as well. 

Historical and prehistoric habitation sites are often 

located near lakes or waterways. Flood events or 

storm surges can result in increased erosion or 

obliteration of significant archaeological sites. 

Similarly, torrential rains can trigger or exacerbate 

erosion of cultural resources. Erosion from storm 

surges in the Great Lakes has already begun to 

wash away cultural sites within the Apostle Islands 

National Lakeshore (Saunders et al. 2011).

More Information

• Climate Change and World Heritage: Report on 

Predicting and Managing the Impacts of Climate 

Change on World Heritage includes a list of 

climate change threats to cultural heritage sites: 

whc.unesco.org/documents/publi_wh_papers_22_

en.pdf 

Shoreline at the Apostle Islands National Lakeshore. Photo 

by Maria Janowiak, U.S. Forest Service.
Chapter 7: Management Implications

162

WILDERNESS 

There are several federally designated wilderness 

areas in the assessment area, including McCormick, 

Sylvania, and Headwaters Wilderness areas (all 

located on National Forest System Lands), as well as 

the State-designated and State-managed Porcupine 

Mountains Wilderness State Park. These areas are 

often regarded as some of the last remaining primary 

forests in the region because they include some areas 

that were not harvested in the early 20th century. 

As such, they play a special role in natural resource 

and recreation management. The potential for 

extensive ecosystem change resulting from climate 

change raises difficult questions about the future 

management of these and other wilderness areas. 

Climate change is poised to influence the forest 

ecosystems in the assessment area, including 

wilderness areas, in a variety of ways. Fire seasons 

are expected to shift, and more area is projected 

to burn each year under climate change (Chapter 

5). Additionally, many of the characteristic boreal 

species in the assessment area are projected to 

decline, and invasive species may increase in 

abundance and vigor (Chapter 5). Depending on 

the amount and timing of future precipitation, lake 

levels and aquatic ecosystems in the assessment 

area could be affected as well. Weather and climate 

could also influence recreational use, if spring 

and fall seasons become more attractive for visits 

or the threat of wildfires reduces visits in certain 

months. Furthermore, managers accept the fact 

that natural hazards and obstacles are inherently a 

part of the wilderness experience, but they try to 

remove trees that are posing immediate threats to 

visitors. Weather-related tree mortality from storm 

events, drought, or insect and disease attack could 

increase the complexity of providing for public 

safety. Weather conditions also affect the need for 

maintenance of recreational infrastructure like trails 

and trailheads, particularly when heavy rain events 

cause excessive erosion, or when wind events uproot 

trees and leave craters in parts of the trail. 

It is difficult to anticipate how climate-related 

impacts will influence management in wilderness 

areas, because of the legal requirement to protect 

and manage federally designated wilderness areas 

to preserve natural conditions and ensure the area 

remains undeveloped and “untrammeled” (i.e., 

where human manipulation is not apparent). Due 

to this minimal-management approach, wilderness 

often plays a key role in helping scientists to 

understand ecological systems and natural processes 

and to monitor change in those systems. At the 

same time, some arguments favor more-proactive 

management for wilderness areas to help create a 

“graceful transition” under climate change based on 

maintaining native tree species and natural processes 

like fire (Frelich and Reich 2009). In either 

approach, wilderness areas could play an important 

role in landscape-level adaptation planning. 

More Information

• The Wilderness.net Climate Change Toolbox 

offers information about climate change and 

wilderness, including management guidelines and 

strategies: 

www.wilderness.net/climate

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect wilderness area management: 

www.fs.fed.us/ccrc/topics/wilderness/ 

• The Wilderness.net Minimum Requirements 

Decision Guide is designed to assist managers 

in making Wilderness stewardship decisions 

in the context of the legal requirements of the 

Wilderness Act: 

www.wilderness.net/MRA 

RECREATION 

Forests are the centerpieces of outdoor recreation in 

the assessment area (Chapter 1). People throughout 

this region enjoy hunting; fishing; camping; wildlife 

watching; and exploring trails on foot, bicycles, 

skis, snowshoes, horseback, and off-highway 
Chapter 7: Management Implications

163

vehicles, among many other recreational pursuits. 

The vulnerabilities associated with climate change in 

forests may result in shifted timing or participation 

opportunities for forest-based recreation (Saunders 

et al. 2011). Forest-based recreation and tourism are 

strongly seasonal, and most visits to public lands 

are planned during times when the weather is most 

conducive to particular activities. 

Projections indicate that seasonal shifts will continue 

toward shorter, milder winters and longer, hotter 

summers, which could reduce opportunities for 

popular winter-based recreation activities in the 

long term. Climate change has already caused 

reductions in lake ice (Chapter 4), and activities 

such as ice fishing have the potential to be harmed 

as conditions continue to change. Observed changes 

in snowfall and snow depth have been more variable 

across the landscape, but it is expected that much 

of the assessment area will have substantially less 

snow by the end of the century. Reduced snowfall 

could create additional challenges to popular 

and economically important activities, such as 

snowmobiling and cross-country and downhill 

skiing. Recreationists may change the ways in 

which they participate in these activities, perhaps by 

changing the time or location of their participation, 

or switch to activities that do not require snow. 

Some warm-weather forms of nature-based 

recreation have the potential to increase due to 

changing conditions (Dawson and Scott 2010, Jones 

and Scott 2006, Mcboyle et al. 2007). Warm-weather 

recreation activities such as mountain biking, offhighway vehicle riding, and fishing may benefit 

from extended seasons in the Midwest (Nicholls 

2012). High spring precipitation could increase risks 

for flash flooding or lead to unpleasant conditions 

for recreation, however. Severe storms and flash 

flooding might also threaten infrastructure such 

as visitor centers, campsites, and trails. Fall will 

potentially be drier, which could lead to reduced 

water levels and thus diminished water recreation 

opportunities. Warmer, drier conditions in the 

summer and fall may raise the risk of wildfire, 

increasing visitor safety risk and restrictions on open 

flames. Lengthening of spring and fall recreation 

seasons will also have implications for staffing, 

especially for recreation-related businesses that rely 

on student labor—which will be unavailable during 

the school year (Nicholls 2012).

Climate can also have important influences on 

hunting and fishing. The timing of certain hunts or 

fishing seasons correspond to seasonal events, which 

are in part driven by climate. Waterfowl hunting 

seasons, for example, are designed to correspond to 

the times when birds are migrating south in the fall, 

an event that is expected to shift later in the year as 

temperatures warm. As mentioned above, climate 

change may also result in substantial changes in 

habitat availability and quality for wildlife and fish 

species. In a recent assessment of climate change 

Fishermen on a lake in northern Wisconsin. Photo by Maria 

Janowiak, U.S. Forest Service.
Chapter 7: Management Implications

164

vulnerability for wildlife species in Michigan, game 

species were generally rated as less vulnerable than 

Species of Greatest Conservation Need (Hoving 

et al. 2013), but nearly 20 percent of game species 

were rated as moderately, highly, or extremely 

vulnerable to climate change. Projected changes 

in Wisconsin water temperatures and fish species 

habitat (described above) may reduce opportunities 

for cold-water stream fishing but increase 

opportunities for warm-water lake fishing.

More Information

• The Wildlife Working Group report from the

Wisconsin Initiative on Climate Change Impacts

documents past and current climate change

impacts on game species and other wildlife:

www.wicci.wisc.edu/report/Wildlife.pdf

• Changing Climate, Changing Wildlife, a report

from the Michigan Department of Natural

Resources – Wildlife Division, assesses how

climate change may affect 400 wildlife species in

the state, including many fish and game species:

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

• A recent report submitted for the National

Climate Assessment summarizes the impacts of

climate change on outdoor recreational tourism

across the Midwest, including the assessment

area:

glisa.msu.edu/docs/NCA/MTIT_RecTourism.pdf

• Season’s End, a collaborative effort comprising

many hunting and conservation organizations,

includes many resources on how climate change

may affect wildlife:

http://www.cakex.org/virtual-library/784

HUMAN HEALTH

Climate change can influence human health in 

numerous ways (Patz et al. 2011), which can affect 

people who live, work, or recreate in the forests of 

the assessment area. Vector-borne diseases, such as 

Lyme disease and West Nile virus, pose an important 

risk to natural resource managers, local residents, 

and tourists alike, and this issue may become 

increasingly important over the 21st century. Vectorborne diseases are transmitted by arthropod vectors 

(e.g., ticks or mosquitoes) and cycle back and forth 

between arthropod vectors and animal hosts, usually 

mammals or birds. Humans are typically infected 

incidentally when they are bitten instead of animal 

hosts.

Climate is one of many important interacting 

variables that affect people’s risk for vector-borne 

diseases. Changes in climate can influence vectorborne disease risk by affecting the abundance and 

distribution of ticks or mosquitoes, the percentage 

of infected vectors, the abundance and distribution 

of animal hosts, the presence of suitable habitat 

for these vectors, and the behaviors that bring 

humans into contact with infected vectors. Most 

arthropod vectors of disease are sensitive to physical 

conditions, such as levels of humidity, daily high 

and low temperatures, rainfall patterns, and winter 

snowpack. For example, blacklegged ticks (i.e., 

“deer ticks”), which are the vector for Lyme disease 

and several other diseases, are most active on warm, 

humid days. They are most abundant in wooded 

or brushy habitats (especially mesic hardwoods 

and aspen) that contain abundant small mammals 

and deer. Projected expansion of mesic hardwoods 

with changing climate conditions may increase the 

incidence of Lyme disease and other tick-borne 

diseases if those habitats are frequently visited by 

humans. 

More Information

• The Human Health Working Group report from

the Wisconsin Initiative on Climate Change

Impacts documents the effect of climate change

on human health:

www.wicci.wisc.edu/report/Human-Health.pdf
Chapter 7: Management Implications

165

• The Michigan Department of Community Health 

has a Web site on the climate change implications 

for human health: 

www.michigan.gov/mdch/0,1607,7-132-54783_

54784_55975---,00.html

• The Natural Resources Defense Council hosts 

an online Web viewer that provides state-level 

information about various threats to human health 

associated with climate change: 

www.nrdc.org/health/climate/

• The Centers for Disease Control and Prevention 

Climate and Health Program includes information 

on a variety of subjects:

www.cdc.gov/climateandhealth/ 

URBAN FORESTS

Climate change is also expected to affect urban 

forests in the assessment area. In contrast with 

natural environments, urban environments can 

pose additional stresses to trees, such as pollution 

from vehicle exhaust, confined root environments, 

and road salts. Urban environments also cause a 

“heat island effect,” and thus warming in cities 

may be even greater than in natural communities. 

Impervious surfaces can make urban environments 

more susceptible to floods, placing flood-intolerant 

species at risk. Urban settings are often where exotic 

insect pests are first introduced. All of these abiotic 

stressors can make urban forests more susceptible 

to exotic species invasion, and insect and pathogen 

attack, especially because a limited range of species 

and genotypes is often planted in urban areas. 

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. Native species projected 

to decline under climate change may not tolerate 

the even more-extreme conditions presented by 

urban settings. Conversely, urban environments 

may favor heat-tolerant or drought-tolerant native 

species or even facilitate migration of trees across 

the landscape (Woodall et al. 2010). Determining 

appropriate species for planting may be a challenge, 

but community foresters are already familiar 

with the practice of planting species novel to an 

area. Because of urban effects on climate, many 

community forests already contain species that are 

from planting zones south of the area or cultivars 

that tolerate a wide range of climate conditions 

(Woodall et al. 2010). 

Large disturbance events may also become more 

frequent or intense in the future, necessitating 

informed decisions in response. For example, wind 

events or pest outbreaks may be more damaging 

to already-stressed trees. If leaf-out dates advance 

earlier in the spring due to climate change, 

community forests may be increasingly susceptible 

to early-season frosts or snow storms. More people 

and larger budgets may be required to handle an 

increase in the frequency or intensity of these events, 

which may become more difficult as many cities 

have reduced their budgets and staffing. 

More Information

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect urban forests: 

www.fs.fed.us/ccrc/topics/urban-forests/ 

• The Clean Air Partnership has developed a 

climate change impact assessment and adaptation 

plan for Toronto’s urban forest: 

www.cleanairpartnership.org/pdf/climate_

change_adaptation.pdf 

• British Columbia has developed an urban forestry 

climate adaptation guide that includes some 

general considerations for adapting urban forests 

to climate change:

www.toolkit.bc.ca/Resource/Urban-ForestsClimate-Adaptation-Guide
Chapter 7: Management Implications

166

FOREST-ASSOCIATED TOWNS 

AND CITIES

The ability of human communities to respond 

to environmental changes is directly related 

to their adaptive capacity—resources that can 

be leveraged by the community to monitor, 

anticipate, and proactively manage stressors and 

disturbances. Although ecosystem models can 

predict ecological community responses to climate 

change, considerably less is known about the social 

and cultural impacts of climate or forest change 

and how human communities might best respond. 

Many towns and cities in the assessment area are 

particularly tied to the health and functioning of 

surrounding forests, whether for economic, cultural, 

recreational, or other reasons. 

Every forest-associated community has particular 

conditions, capacities, and constraints that might 

make it more vulnerable or resilient to climate 

change. Moreover, the effects of climate change 

and forest impacts are not evenly distributed 

geographically or socially. Some communities (e.g., 

indigenous communities with forest-dependent 

cultural practices, tourism-dependent communities) 

and social groups within communities (e.g., 

individuals working in the forest products industry) 

may be more vulnerable to these impacts and less 

able to adapt.

If resource professionals, community leaders, 

and local organizations are to help communities 

adapt to changes, they must identify community 

vulnerabilities and sensitivities to climate-related 

impacts and also build community capacity to 

organize and engage community members and 

other resources. In the Great Lakes region, much 

of the work done to date to assess the vulnerability 

of human communities has focused on coastal 

communities (e.g., Minnesota Sea Grant 2012, Moy 

et al. 2010). Some community-based climate change 

assessment and adaptation efforts are underway in 

the assessment area (see below).

When planning for climate change, decisionmakers 

can consider how ecological events or changes (e.g., 

floods, droughts, wildfire, windstorms, introduced 

species, insect or pathogen outbreaks) will affect 

their communities and community members by 

asking several questions (Davenport et al. 2013). 

These include: 

• Is access to healthy ecosystems at risk? 

• Is there a potential for resource scarcity?

• Are cultural practices or recreational 

opportunities at risk?

• Is there potential for loss of social connectedness 

or increased social or cultural conflict?

• Is there potential for disproportionate impacts to 

certain populations?

• Is there potential for human health problems 

including stress, anxiety, despair, or sense of 

powerlessness?

More Information

• The Wisconsin Initiative on Climate Change 

Impacts sponsored working groups that created 

vulnerability assessments for Green Bay, 

Milwaukee, and other Great Lakes coastal 

communities: 

www.wicci.wisc.edu/publications.php

• The Superior Watershed Partnership and Lake 

Trust has prepared a Lake Superior Climate 

Adaptation, Mitigation, and Implementation 

Plan focused on communities within Michigan’s 

Upper Peninsula: 

www.superiorwatersheds.org/images/climate-jan.

pdf

• Michigan Sea Grant produced a community selfassessment to address climate change readiness, 

and its Web site includes several resources useful 

for communities: 

www.miseagrant.umich.edu/ 

• The Resilience Alliance has created a workbook 

for practitioners to assess resilience of socialecological systems: 

www.resalliance.org/index.php/resilience_

assessment 
Chapter 7: Management Implications

167

LAND ACQUISITION 

Climate change has many important implications for 

land conservation planning in the Great Lakes, and 

climate change science can be used to help prioritize 

land conservation investments and help guide 

project design. For example, it may be important to 

identify parcels that have large carbon mitigation 

potential and prioritize these for land acquisition and 

conservation. This is particularly important in the 

upper Great Lakes region, where private forest lands 

have some of the highest stored carbon levels in the 

entire country. Climate change trends and ecosystem 

models can also be used to identify lands that have 

long-term potential to provide refugia for at-risk 

species and habitats, enhance landscape connectivity, 

or protect water supplies. 

In the design of land conservation projects, there 

are important decisions to be made about long-term 

ownership and management prescriptions attached to 

the conservation agreement. In some cases, the best 

strategy may be to leave lands in private ownership, 

and to develop conservation easement terms that 

support adaptive management by the landowner to 

address climate shifts. In other cases, perhaps where 

complex restoration or species-specific management 

is needed, an appropriate conservation strategy 

might be to seek a public agency that can provide the 

necessary financial and technical resources. 

Private nonprofits, government agencies, 

landowners, and potential funders will increasingly 

need research-based results on anticipated climate 

trends and impacts, including spatially explicit 

information on how these shifts will play out over 

the land. This science can enable effective use 

of funding, staff time, and other resources that 

are essential to advancing “climate-informed” 

conservation of forests in the region and shaping 

conservation efforts to deliver a more resilient 

landscape. 

LAND MANAGEMENT PLANS

Until recently, climate change has not played a large 

role in natural resource planning. Many federal and 

state-level land management agencies are beginning 

to address the issue. For example, the recently 

updated Forest Service regulations for National 

Forest System Land Management Planning (also 

known as the 2012 Planning Rule) directly address 

the impacts and ramifications of climate change 

(36 CFR 219). In fact, climate change was among 

the stated purposes for revising the rule (FR Vol. 77, 

No. 68, 21163 & 21164). When the ChequamegonNicolet and Ottawa National Forests revise their 

management plans in the future, they will be 

required to address the issue of climate change under 

this new rule. Similarly, the state-level management 

Hiking trail at Isle Royale National Park. Photo by Maria 

Janowiak, U.S. Forest Service.
Chapter 7: Management Implications

168

plans for Michigan and Wisconsin have not 

historically addressed climate change. However, the 

statewide forest strategies for these states identify 

climate change as an issue that could influence the 

long-term sustainability of forests. 

Incorporating climate change considerations into 

natural resource planning will always be a difficult 

endeavor. The uncertainties associated with planning 

over long time horizons are only compounded with 

climate change. Management plans for federal, state, 

and local agencies, as well as private lands, are 

typically written to guide management for a 10- to 

25-year period, and it may be difficult to address 

the potential long-term effects of climate change 

within this shorter planning horizon. Additionally, 

major storms or disturbance events are inherently 

unpredictable, and often force managers to deviate 

from planned analysis or treatment cycles. If climate 

change results in more frequent disturbances or 

unanticipated interactions among major stressors, it 

may be more difficult to adhere to the stated goals, 

objectives, and priorities in current plans. Future 

land management plans may have to incorporate 

adaptive management principles, include greater 

flexibility, or coordinate across land ownerships to 

address shifting conditions and priorities. 

More Information

• Forest action plans have been prepared for 

both Wisconsin and Michigan. These statewide 

assessment and strategy documents include 

discussions of climate change: 

www.forestactionplans.org/regional-state

• The three Regional State Forest Management 

Plans developed for Michigan State Forest lands 

explicitly include climate change considerations 

as part of the management direction for each 

plan:

www.michigan.gov/dnr/0,4570,7-153-30301_

30505_62551-284917--,00.html

• More information on the Forest Service’s 2012 

Planning Rule can be found here: 

www.fs.usda.gov/planningrule 

• Forest Adaptation Resources: Climate Change 

Tools and Approaches for Land Managers

provides concepts and tools for integrating 

climate change considerations into natural 

resource planning and management: 

www.nrs.fs.fed.us/pubs/40543

SUMMARY

The breadth of these topics highlights the wide 

range of effects climate change may have on forest 

management in the assessment area. It is not the 

role of this assessment to identify adaptation actions 

that should be taken to address these climaterelated risks and vulnerabilities, nor would it be 

feasible to prescribe suitable responses for all future 

circumstances. Decisions to address climate-related 

risks for forest ecosystems in the region will be 

affected by economic, political, ecological, and 

societal factors. These factors will be specific to 

each land owner and agency, and are unpredictable. 

Confronting the challenge of climate change 

presents opportunities for managers and other 

decisionmakers to plan ahead, manage for resilient 

landscapes, and ensure that the benefits that forests 

provide are sustained into the future. Resources 

are available to help forest managers and planners 

incorporate climate change considerations into 

existing decisionmaking processes (Swanston and 

Janowiak 2012) (www.forestadaptation.org). This 

assessment will be a useful foundation for land 

managers in that process, to be further enriched by 

local knowledge and site-specific information. 
169

GLOSSARY

aerosol

a suspension of fine solid particles or liquid droplets 

in a gas, such as smoke, oceanic haze, air pollution, 

and smog. Aerosols may influence climate by either 

scattering and absorbing radiation or by acting 

as condensation nuclei for cloud formation or 

modifying the properties and lifetime of clouds.

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.

agreement

the extent to which evidence is consistent in support 

of a vulnerability statement or rating (see also 

confidence, evidence). 

alluvial

referring to a deposit of clay, silt, sand, and gravel 

left by flowing streams in a river valley or delta, 

typically producing fertile soil.

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.

biomass

the mass of living organic matter (plant and 

animal) in an ecosystem; also organic matter 

(living and dead) available on a renewable basis 

for use as a fuel; biomass includes trees and plants 

(both terrestrial and aquatic), agricultural crops 

and wastes, wood and wood wastes, forest and 

mill residues, animal wastes, livestock operation 

residues, and some municipal and industrial wastes.

carbon dioxide (CO2 ) fertilization 

increased plant uptake of CO2 through 

photosynthesis in response to higher concentrations 

of atmospheric CO2 .

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.

community

an assemblage of plants and animals living together 

and occupying a given area.
Glossary

170

confidence

a qualitative assessment of uncertainty as determined 

through evaluation of evidence and agreement (see 

also evidence, agreement). 

convective storm

convection is a process whereby heat is transported 

vertically within the atmosphere. Convective storms 

result from a combination of convection, moisture, 

and instability. Convective storms can produce 

thunderstorms, tornadoes, hail, heavy rains, and 

straight-line winds. 

derecho

widespread and long-lived convective windstorm 

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. 

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

methods for obtaining high-resolution climate or 

climate change information from coarse-resolution 

general circulation models. 

driver

any natural or human-induced factor that directly or 

indirectly causes a change in an ecosystem.

dynamical downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCMs) 

by using a limited-area, high-resolution model 

(a regional climate model, or RCM) driven by 

boundary conditions from a GCM to derive smallerscale information.

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. 

ecoregion

a region characterized by a repetitive pattern of 

ecosystems associated with commonalities in climate 

and landform.

ecosystem 

a volumetric unit of the Earth’s surface that includes 

air (climate), land (landform, soil, water), and biota. 

Ecosystems are defined by land area, and contain all 

the interactions between living organisms and their 

physical environment. 

emissions scenario

a plausible representation of the future development 

of emissions of greenhouse gases and aerosols 

that are potentially radiatively active, based on 

demographic, technological, or environmental 

developments.

evapotranspiration

the sum of evaporation from the soil and 

transpiration from plants.
Glossary

171

evidence

mechanistic understanding, theory, data, models, 

or expert judgment used to determine the level of 

confidence in a vulnerability statement or rating (see 

also agreement, confidence). 

exposure 

the nature and degree to which a system is exposed 

to significant climate variations.

fire-return interval

the number of years between two successive fire 

events at a specific location.

forest land

land that is at least 10 percent stocked by forest trees 

of any size, or land formerly having such tree cover, 

and not currently developed for a nonforest use.

forest type

a classification of forest vegetation based on the 

dominant species present, as well as associate 

species commonly occurring with the dominant 

species.

forest-type group 

based on FIA definitions, a combination of forest 

types that share closely associated species or site 

requirements and are generally combined for brevity 

of reporting.

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).

greenhouse effect

the rise in temperature that the Earth experiences 

because certain gases in the atmosphere (water 

vapor, carbon dioxide, nitrous oxide, and methane, 

for example) absorb and emit energy from the sun.

growing season

the period in each year when the temperature is 

favorable for plant growth.

hardwood

a dicotyledonous tree, usually broad-leaved and 

deciduous. Hardwoods can be split into soft 

hardwoods (red maple, paper birch, quaking aspen, 

and American elm) and hard hardwoods (sugar 

maple, yellow birch, black walnut, and oaks). 

hydric

referring to sites or habitats with abundant moisture 

throughout the year, frequently including saturation, 

ponding, or flooding.

impact

direct and indirect consequences of climate change 

on systems, particularly those that would occur 

without adaptation.

impact model

a model simulating impacts on trees, animals, 

and ecosystems. It uses general circulation model 

projections as inputs, and includes additional inputs 

such as tree species, soil types, and life-history traits 

of individual species.
Glossary

172

importance value

in the Climate Change Tree Atlas model, an index 

of the relative abundance of a species in a given 

location or pixel cell (0 = least abundant, 

100 = most abundant).

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.

Kyoto Protocol

adopted at the 1997 Third Session of the Conference 

of Parties to the U.N. Framework Convention on 

Climate Change in Kyoto, Japan, it contains legally 

binding commitments to reduce anthropogenic 

greenhouse gas emissions by at least 5 percent below 

1990 levels in the period 2008-2012.

mesic

referring to sites or habitats where soil moisture is 

available to plants throughout the growing season.

model reliability score

in the Climate Change Tree Atlas model, a “trimodel” approach to assess reliability of model 

predictions for each species, classified as high, 

medium, or low.

modifying factor

in the Climate Change Tree Atlas model, 

environmental variables (e.g., site conditions, 

interspecies competition, disturbance, dispersal 

ability) that influence the way a tree may respond to 

climate change.

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 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. Also refers to the 

study of this subject.

prairie 

a natural community 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. 
Glossary

173

proxy

a figure or data source that is used as a substitute 

for another value in a calculation. Ice and sediment 

cores, tree rings, and pollen fossils are all examples 

of things that can be analyzed to infer past climate. 

The size of rings and the isotopic ratios of elements 

(e.g., oxygen, hydrogen, and carbon) in rings and 

other substrates allow scientists to infer climate and 

timing.

pulpwood

roundwood, whole-tree chips, or wood residues used 

for the production of wood pulp for making paper 

and paperboard products. 

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.

resilience

capacity of a system to absorb a disturbance and 

continue to develop with similar fundamental 

function, structure, identity, and feedbacks.

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. 

saw log 

a log meeting minimum standards of diameter, 

length, and defect, including logs at least 8 feet long, 

sound and straight, and with a minimum diameter 

inside bark of 6 inches for softwoods and 8 inches 

for hardwoods, or meeting other combinations of 

size and defect specified by regional standards.

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). 

sensitivity

the degree to which a system is affected, either 

adversely or beneficially, by climate-related stimuli.

severity

the proportion of aboveground vegetation killed and 

the degree of forest floor and soil disruption.

significant trend

in this report, least-squares regression p-values of 

observed climate trends are significant when p<0.10. 

For trends where p>0.10, observed trends have a 

higher probability of being due to chance alone. 

softwood

a coniferous tree, usually evergreen, having needles 

or scale-like leaves.

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.
Glossary

174

snowpack

layers of accumulated snow that usually melts during 

warmer months.

species distribution model

a model that uses statistical relationships to project 

future change.

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 

small-scale (often station-level) variables and largerscale (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.

stratosphere

the layer of the Earth’s atmosphere which lies 

between 6 and 30 miles above the Earth.

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 Climate Change Tree Atlas model, the areaweighted importance value, or the product of tree 

species abundance and the number of cells with 

projected occupancy.

tension zone

a transitional band that corresponds to several 

climatic factors. Vegetation north and south of the 

tension zone reflects varied habitat conditions as a 

result of climatic differences.

timberland

forest land that is producing or capable of producing 

more than 20 cubic feet per acre per year of wood. 

topkill

death of aboveground tree stem and branches.

transpiration

liquid water phase change occurring inside plants 

with the vapor diffusing to the atmosphere.

troposphere

the lowest part of the atmosphere from the surface 

to about 6 miles in altitude in mid-latitudes (ranging 

on average from 5 miles in high latitudes to 9 miles 

in the tropics) where clouds and weather phenomena 

occur.

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.
Glossary

175

veneer

a roundwood product from which veneer is sliced 

or sawn and that usually meets certain standards of 

minimum diameter and length, and maximum defect. 

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. For this assessment, 

a system may be considered to be vulnerable if it 

is at risk of a composition change leading to a new 

identity, or if the system is anticipated to suffer 

substantial declines in health or productivity.

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. 

windthrow 

trees uprooted or broken by wind.

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.
176

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A moose in spring in western Upper Michigan. Photo by Maria Janowiak, U.S. Forest Service.
207

APPENDIX 1: SPECIES LISTS

Common Name Scientific Name

American basswood Tilia americana

American beech Fagus grandifolia

American elm Ulmus americana

American hornbeam Carpinus caroliniana

American mountain-ash Sorbus americana

balsam fir Abies balsamea

balsam poplar Populus balsamifera

bigtooth aspen Populus grandidentata

bitternut hickory Carya cordiformis

black ash Fraxinus nigra

black cherry Prunus serotina

black hickory Carya texana

black locust Robinia pseudoacacia

black oak Quercus velutina

black spruce Picea mariana

black walnut Juglans nigra

black willow Salix nigra

blackgum Nyssa sylvatica

blackjack oak Quercus marilandica

blue flag iris Iris versicolor

boxelder Acer negundo

bur oak Quercus macrocarpa

butternut Juglans cinerea

chestnut oak Quercus prinus

chinquapin oak Quercus muehlenbergii

chokecherry Prunus virginiana

eastern cottonwood Populus deltoides

eastern hemlock Tsuga canadensis

eastern hophornbeam 

(ironwood)

Ostrya virginiana

eastern redbud Cercis canadensis

eastern white pine Pinus strobus

European buckthorn Rhamnus cathartica

Common Name Scientific Name

flowering dogwood Cornus florida

garlic mustard Alliaria petiolata

goblin fern Botrychium mormo

green ash Fraxinus pennsylvanica

hackberry Celtis occidentalis

jack pine Pinus banksiana

Japanese barberry Berberis thunbergii

large-flowered trillium Trillium grandiflorum

mockernut hickory Carya tomentosa

mountain maple Acer spicatum

northern catalpa Catalpa speciosa

northern pin oak Quercus ellipsoidalis

northern red oak Quercus rubra

northern white-cedar Thuja occidentalis

Ohio buckeye Aesculus glabra

paper birch Betula papyrifera

peachleaf willow Salix amygdaloides

Pennsylvania sedge Carex pensylvanica

pignut hickory Carya glabra

pin cherry Prunus pensylvanica

pin oak Quercus palustris

pink lady slipper Cypripedium acaule 

post oak Quercus stellata

quaking aspen Populus tremuloides

red maple Acer rubrum

red mulberry Morus rubra

red pine Pinus resinosa

reed canarygrass Phalaris arundinacea

river birch Betula nigra

rock elm Ulmus thomasii

sassafras Sassafras albidum

scarlet oak Quercus coccinea

sensitive fern Onoclea sensibilis

Table 14.—Common and scientific names of plant species mentioned in this assessment

(Continued on next page)
Appendix 1

208

Common Name Scientific Name

shagbark hickory Carya ovata

shingle oak Quercus imbricaria

silver maple Acer saccharinum

slippery elm Ulmus rubra

sphagnum moss Sphagnum spp.

striped maple Acer pensylvanicum

sugar maple Acer saccharum

sugarberry Celtis laevigata

Common Name Scientific Name

swamp white oak Quercus bicolor

sweetgum Liquidambar styraciflua

tamarack Larix laricina

white ash Fraxinus americana

white oak Quercus alba

white spruce Picea glauca

wild rice Zizania palustris

yellow birch Betula alleghaniensis

Table 14 (continued).—Common and scientific names of plant species mentioned in this assessment

Common Name Scientific Name

American marten Martes americana

American woodcock Scolopax minor

Armillaria Armillaria mellea

bark beetles Ips spp. and 

Dendroctonus spp.

beaver Castor canadensis

birch leaf miner Fenusa pusilla

blacklegged tick Ixodes scapularis

Blanding’s turtle Emys blandingii

brook trout Salvelinus fontinalis

cisco Coregonus artedi

common loon Gavia immer

earthworms (nonnative) Dendrobaena octaedra, 

Lumbricus rubellus, and 

L. terrestris

eastern larch beetle Dendroctonus simplex

emerald ash borer Agrilus planipennis

forest tent caterpillar Malacosoma disstria

fox snake Elaphe spp.

Franklin’s ground squirrel Spermophilus franklinii

golden-winged warbler Vermivora chrysoptera

gray jay Perisoreus canadensis

gray wolf Canis lupus

green frog Lithobates clamitans

Common Name Scientific Name

gypsy moth Lymantria dispar dispar

hermit thrush Catharus guttatus

hypoxylon canker Hypoxylon mammatum

jack pine budworm Choristoneura pinus pinus

larch casebearer Coleophora laricella

Lyme disease Borrelia burgdorferi

moose Alces alces

morel mushroom Morchella spp.

porcupine Erethizon dorsatum

river jewelwing damselfly Calopteryx aequibilis

smooth green snake Liochlorophis vernalis

snowshoe hare Lepus americanus

spruce budworm Choristoneura fumiferana

sudden oak death Phytophthora ramorum

tamarack sawfly Pristiophora erichsonii

West Nile virus Flavivirus spp.

white pine blister rust Cronartium ribicola

white pine tip weevil Pissodes strobi

white-tailed deer Odocoileus virginianus

wild turkey Meleagris gallopavo

wood frog Lithobates sylvaticus

wood turtle Glyptemys insculpta

Table 15.—Common and scientific names of other species mentioned in this assessment
209

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 application (www.

climatewizardcustom.org) (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 by using a digital elevation 

model (digital gridded version of a topographic 

map). Temperature and precipitation are then derived 

for each pixel on a continuous 2.5-mile grid across 

the conterminous United States. The closer a station 

is to a grid cell of interest in distance and elevation, 

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: 

www.prism.oregonstate.edu/. 

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 1 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, and monthly 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 (Table 16). 

The developers of the ClimateWizard application 

advise users to interpret the linear trend maps 

in relation to the respective map of statistical 

confidence (Figs. 50 and 51). In this case, statistical 

confidence is described 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 data set recommend that inferences 

about trends should not be made for single grid cells 

or even small clusters of 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 
Appendix 2

210

Mean temperature Minimum temperature Maximum temperature Precipitation

1900-2011 1900-2011

Month/ 1900-2011 1900-2011 Change 1900-2011 1900-2011 Change 1900-2011 1900-2011 Change Mean Change Change

Season Mean (°F) Change (°F) p-value Mean (°F) Change (°F) p-value Mean (°F) Change (°F) p-value (inches) (inches) p-value

January 12.33 0.97 0.57 2.42 2.05 0.28 22.23 -0.11 0.94 1.25 0.19 0.44

February 15.31 4.57 0.01 4.33 5.21 0.01 26.28 3.93 0.01 1.07 -0.16 0.34

March 26.52 2.09 0.15 15.64 2.37 0.10 37.38 1.82 0.24 1.70 0.26 0.29

April 40.88 2.03 0.07 29.26 2.37 0.01 52.50 1.68 0.23 2.42 0.51 0.12

May 52.84 1.77 0.08 40.08 2.43 0.01 65.61 1.12 0.34 3.38 -0.18 0.63

June 62.25 0.87 0.40 49.86 2.04 0.03 74.65 -0.31 0.81 4.00 -0.20 0.72

July 67.27 0.52 0.49 55.01 1.78 0.00 79.54 -0.74 0.44 3.71 0.03 0.94

August 65.08 1.91 0.02 53.10 3.14 0.00 77.06 0.68 0.47 3.67 0.42 0.31

September 56.95 0.13 0.88 45.48 1.10 0.18 68.44 -0.84 0.41 3.64 0.01 0.99

October 45.89 -0.36 0.73 35.21 0.56 0.55 56.57 -1.28 0.32 2.57 0.68 0.11

November 31.38 0.99 0.41 23.14 1.67 0.15 39.62 0.30 0.82 2.04 0.11 0.72

December 18.01 0.92 0.52 9.46 1.74 0.29 26.55 0.10 0.94 1.41 0.34 0.08

Winter 15.22 2.17 0.06 5.41 3.02 0.02 25.02 1.32 0.19 1.24 0.12 0.35

Spring 40.07 1.95 0.02 28.33 2.38 0.00 51.83 1.51 0.12 2.50 0.20 0.32

Summer 64.86 1.11 0.06 52.65 2.33 0.00 77.08 -0.12 0.88 3.79 0.08 0.76

Fall 44.73 0.26 0.70 34.61 1.11 0.08 54.87 -0.60 0.43 2.75 0.27 0.27

Annual 41.22 1.37 0.01 30.25 2.21 0.00 52.20 0.53 0.33 30.87 2.00 0.12

Table 16.—Mean annual, seasonal, and monthly values and linear trend analysis for selected climate variables from 

1901 through 2011 for the assessment area*

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 from the National Climatic 

Data Center (www.ncdc.noaa.gov/). 

We selected the period 1901 through 2011 because 

it was sufficiently long to capture inter- and intradecadal variation in climate for the region. We 

acknowledge that different trends can be inferred 

by selecting different beginning and end points in 

the analysis. Therefore, trends should be interpreted 

based on their relative magnitude and direction, and 

the slope of any single trend should be interpreted 

with caution. 

*P-values represent the probability of observing that trend by chance alone. Boldface p-values indicate a 10-percent probability (or less) that the 

trend was due to chance alone. Data source: ClimateWizard (2012).
Appendix 2

211

Figure 50.—Statistical confidence (p-values for the linear regression) for trends in temperature from 1901 through 2011. 

Gray values represent areas of low statistical confidence. Data source: ClimateWizard (2012).
Appendix 2

212

Figure 51.—Statistical confidence (p-values for the linear 

regression) for trends in precipitation from 1901 through 

2011. Gray values represent areas of low statistical 

confidence. Data source: ClimateWizard (2012).

LITERATURE CITED

ClimateWizard. 2012. ClimateWizard Custom 

Analysis. The Nature Conservancy, University of 

Washington, University of Southern Mississippi, 

Climate Central, and Santa Clara University. 

Available at http://climatewizardcustom.org/. 

(Accessed April 2, 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 climatechange analysis: the ClimateWizard tool. PLoS 

ONE. 4(12): e8320.

Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun, L.; 

Janssen, E.; Wuebbles, D.; Hilberg, S.D.; Timlin, 

M.S.; Stoeck, L.; Westcott, N.E.; Dobson, J.G. 

2013. Regional climate trends and scenarios 

for the U.S. National Climate Assessment. Part 

3. Climate of the Midwest U.S. Washington, 

DC: U.S. Department of Commerce, National 

Oceanic and Atmospheric Administration. 103 p.

Trenberth, K.E.; Jones, P.D.; Ambenje, P.; Bojariu, 

R.; Easterling, D.; Klein Tank, A.; Parker, D.; 

Rahimzadeh, F.; Renwick, J.A.; Rusticucci, M.; 

Soden, B.; Zhai, P. 2007. Observations: surface 

and atmospheric 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. 

Available at https://www.ipcc.ch/publications_

and_data/ar4/wg1/en/ch3.html. (Accessed 

April 17, 2014).
213

APPENDIX 3: ADDITIONAL FUTURE 

CLIMATE INFORMATION

This appendix provides maps of projected change 

for the early (2010 through 2039) and middle 

(2040 through 2069) years of the 21st century as 

supplementary information to Chapter 4.

1971-2000 Temperature departure from 1971-2000 (°F)

 Temperature (°F) Scenario 2010-2039 2040-2069 2070-2099

Mean temperature

Annual 41.4 PCM B1 1.2 1.9 2.6

GFDL A1FI 2.3 6.6 8.7

Winter (Dec.-Feb.) 15.3 PCM B1 1.2 2.4 3.2

GFDL A1FI 2.7 7.5 8.7

Spring (Mar.-May) 40.9 PCM B1 0.0 1.1 2.0

GFDL A1FI 0.6 4.2 6.0

Summer (June-Aug.) 65.0 PCM B1 1.5 1.9 2.4

GFDL A1FI 3.5 8.9 11.6

Fall (Sept.-Nov.) 44.1 PCM B1 2.5 2.4 3.2

GFDL A1FI 2.8 6.0 8.5

Minimum temperature

Annual 30.5 PCM B1 1.2 1.9 2.7

GFDL A1FI 2.6 7.2 9.7

Winter (Dec.-Feb.) 5.4 PCM B1 1.4 3.0 4.1

GFDL A1FI 3.3 9.1 10.9

Spring (Mar.-May) 29.1 PCM B1 0.3 1.3 2.3

GFDL A1FI 1.6 5.6 7.9

Summer (June-Aug.) 53.1 PCM B1 0.7 1.3 1.6

GFDL A1FI 3.3 8.5 11.5

Fall (Sept.-Nov.) 34.2 PCM B1 2.7 2.3 3.2

GFDL A1FI 2.7 5.8 8.8

Maximum temperature

Annual 52.3 PCM B1 1.2 1.8 2.4

GFDL A1FI 1.9 6.0 7.5

Winter (Dec.-Feb.) 25.1 PCM B1 1.0 1.8 2.1

GFDL A1FI 2.1 5.9 6.6

Spring (Mar.-May) 52.6 PCM B1 -0.4 0.9 1.6

GFDL A1FI -0.4 2.7 4.0

Summer (June-Aug.) 76.9 PCM B1 2.2 2.5 3.0

GFDL A1FI 3.7 9.4 11.6

Fall (Sept.-Nov.) 54.1 PCM B1 2.4 2.4 3.1

GFDL A1FI 2.8 6.3 8.2

Table 17.—Projected change in mean average, minimum, and maximum temperatures under two future climate 

scenarios for the assessment area over the next century
Appendix 3

214

1971-2000 precipitation Precipitation departure from 1971-2000 (inches)

(inches) Scenario 2010-2039 2040-2069 2070-2099

Annual 32.0 PCM B1 0.2 2.7 2.7

GFDL A1FI 2.3 -1.6 0.5

Winter (Dec.-Feb.) 3.8 PCM B1 0.7 0.9 1.1

GFDL A1FI 0.4 0.3 0.6

Spring (Mar.-May) 7.5 PCM B1 -0.1 1.1 1.5

GFDL A1FI 1.5 2.1 3.2

Summer (June-Aug.) 11.8 PCM B1 -0.1 1.1 0.4

GFDL A1FI 0.2 -3.8 -4.8

Fall (Sept.-Nov.) 8.8 PCM B1 -0.2 -0.4 -0.3

GFDL A1FI 0.2 -0.2 1.6

Table 18.—Projected change in precipitation under two future climate scenarios for the assessment area over the 

next century

An informational sign in western Upper Michigan. Photo by Maria Janowiak, U.S. Forest Service.
Appendix 3

215

Figure 52.—Projected difference in mean daily temperature at the beginning of the century (2010 through 2039) compared to 

baseline (1971 through 2000) for two climate scenarios.
Appendix 3

216

Figure 53.—Projected difference in mean minimum daily temperature at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

217

Figure 54.—Projected difference in mean maximum daily temperature at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

218

Figure 55.—Projected difference in precipitation at the beginning of the century (2010 through 2039) compared to baseline 

(1971 through 2000) for two climate scenarios.
Appendix 3

219

Figure 56.—Projected difference in mean daily temperature for the middle of the century (2040 through 2069) compared to 

baseline (1971 through 2000) for two climate scenarios.
Appendix 3

220

Figure 57.—Projected difference in mean minimum daily temperature for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

221

Figure 58.—Projected difference in mean maximum daily temperature for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

222

Figure 59.—Projected difference in precipitation for the middle of the century (2040 through 2069) compared to baseline 

(1971 through 2000) for two climate scenarios.
223

APPENDIX 4: SUPPLEMENTARY MODEL RESULTS – 

TREE ATLAS

This appendix contains additional model details and 

results from the Climate Change Tree Atlas.

RESULTS OF THE DISTRIB MODEL

Table 19 provides additional outputs from the 

DISTRIB model for the 78 species considered 

for this assessment. More information about the 

modeling approach is available online through 

the Climate Change Tree Atlas Web site (www.

nrs.fs.fed.us/atlas/tree/tree_atlas.html), including 

detailed methods, maps of changes in importance 

value, and additional statistics. Publications 

describing the Tree Atlas tools also include key 

definitions and methods descriptions (Iverson et al. 

1999, 2008, 2011; Matthews et al. 2011). 

For this assessment, current area-weighted 

importance values (IVs) were derived from Forest 

Inventory and Analysis (FIA) data. Using the 

DISTRIB model, these were used to develop 

modeled current (1961-1990) IVs, as well as future 

IVs for three time periods (2010 through 2039, 

2040 through 2069, 2070 through 2099) under the 

PCM B1 and GFDL A1FI climate scenarios. Across 

the eastern United States, 134 tree species were 

initially modeled. If a species never had an areaweighted IV greater than 3 (FIA, current modeled, 

or future) across the assessment area, it was deleted 

from the list because the species has either no 

current or no future suitable habitat in the region, or 

there were not enough data. This step resulted in the 

list of 78 tree species for which data are shown.

A set of rules was established to determine change 

classes for 2070 through 2099, which was used to 

create tables in Chapter 5. For most species, the 

following rules applied, based on the ratio of future 

IVs to current modeled IVs:

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 3. A species’ habitat was considered to be 

extirpated if the future IV was 0 and FIA values 

were greater than 3. 

Special rules were created for rare species. A species 

was considered rare if it had a current modeled 

area-weighted IV that equaled less than 10 percent 

of the number of 12.5- by 12.5-mile pixels in the 

assessment area. The change classes are calculated 

differently for these species because their current 

infrequency tends to inflate the projected percentage 

change. The cutoff for the assessment area was 

27 pixels, or 10 percent of the total 274 pixels.
Appendix 4

224

When a species was below the cutoff above, 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)

“Extirpated” was not used in this case because of 

low confidence.

Special rules also applied to species that were known 

to be present (current FIA IV >0) but not modeled 

as present (current modeled = 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.

Modifying Factors 

and Adaptability Scores

Tables 20 and 21 describe the modifying factors 

and adaptability scores used in the Tree Atlas. 

These factors were developed by using a literaturebased scoring system to capture the potential 

adaptability of species to changes in climate that 

cannot be adequately captured by the DISTRIB 

model (Matthews et al. 2011). This approach was 

used to assess the capacity for each species to adapt 

and considered nine biological traits reflecting 

innate characteristics like competition ability for 

light and edaphic specificity. Twelve disturbance 

characteristics addressed the general response of 

a species to events such as drought, insect pests, 

and fire. This information distinguishes between 

species likely to be more tolerant (or sensitive) to 

environmental changes than the habitat models alone 

suggest. 

For each biological and disturbance factor, a species 

was scored on a scale from -3 to +3. A score of -3 

indicated a very negative response of that species to 

that factor. A score of +3 indicated a very positive 

response to that factor. To account for confidence 

in the literature about these factors, each of these 

scores was then multiplied by 0.5, 0.75, or 1, with 

0.5 indicating low confidence and 1 indicating high 

confidence. Finally the score was further weighted 

by its relevance to future projected climate change 

by multiplying it by a relevance factor. A 4 indicated 

highly relevant and a 1 indicated not highly relevant 

to climate change. Means for individual biological 

scores and disturbance scores were then calculated 

to arrive at an overall biological and disturbance 

score for the species. 

To arrive at an overall adaptability score for the 

species that could be compared across all modeled 

tree species, the mean, rescaled (0-6) values for 

biological and disturbance characteristics were 

plotted to form two sides of a right triangle; the 

hypotenuse was then a combination (disturbance 

and biological characteristics) metric, ranging from 

0 to 8.5 (Fig. 60). For this assessment, adaptability 

scores 3.2 and less are considered low, and scores of 

5.3 and greater are considered high.

Note that modifying factors and adaptability scores 

are calculated for a species across its entire range. 

Many species may have higher or lower adaptability 

in certain areas. For example, a species with a low 

flooding tolerance may have higher adaptability in 

areas not subject to flooding. Likewise, local impacts 

of insects and disease may reduce the adaptability of 

a species in that area.
Append

ix 4

225

American basswood 885 880 Medium 875 1189 950 1246 931 1219 0.99 1.35 1.08 1.42 1.06 1.39 No change Increase

American beech 108 167 High 190 328 358 362 358 321 1.14 1.96 2.14 2.17 2.14 1.92 Large increase Increase

American elm 922 950 Medium 1021 1522 1348 2179 1349 2313 1.08 1.60 1.42 2.29 1.42 2.44 Increase Large increase

American hornbeam 153 138 Medium 150 224 193 294 191 288 1.09 1.62 1.40 2.13 1.38 2.09 Increase Large increase

Balsam fir 1649 1682 High 1265 434 990 200 837 186 0.75 0.26 0.59 0.12 0.50 0.11 Decrease Large decrease

Balsam poplar 155 191 High 144 84 87 195 89 156 0.75 0.44 0.46 1.02 0.47 0.82 Large decrease No change

Bigtooth aspen 666 670 High 677 614 661 354 650 244 1.01 0.92 0.99 0.53 0.97 0.36 No change Large decrease

Bitternut hickory 103 115 Low 136 285 261 277 259 363 1.18 2.48 2.27 2.41 2.25 3.16 Large increase Large increase

Black ash 1207 1163 High 1019 892 914 786 868 695 0.88 0.77 0.79 0.68 0.75 0.60 Decrease Decrease

Black cherry 407 561 High 748 1246 1317 950 1354 807 1.33 2.22 2.35 1.69 2.41 1.44 Large increase Increase

Black hickory 0 0 High 0 0 0 90 0 213 NA NA NA New NA New NA New habitat

Black locust 2 6 Low 11 114 69 447 80 625 1.83 19.00 11.50 74.50 13.33 104.17 Large increase Large increase

Black oak 67 270 High 340 925 520 928 608 1365 1.26 3.43 1.93 3.44 2.25 5.06 Large increase Large increase

Black spruce 670 706 High 498 143 358 63 288 63 0.71 0.20 0.51 0.09 0.41 0.09 Large decrease Large decrease

Black walnut 2 11 Medium 46 282 138 656 178 757 4.18 25.64 12.55 59.64 16.18 68.82 Large increase Large increase

Black willow 57 82 Low 93 309 206 527 219 580 1.13 3.77 2.51 6.43 2.67 7.07 Large increase Large increase

Blackgum 0 2 High 0 0 1 62 1 90 0.00 0.00 0.50 31.00 0.50 45.00 NA New habitatBlackjack oak 0 0 Medium 0 5 7 126 4 267 NA New New New New New New habitat New habitat

Boxelder 61 205 Medium 194 513 316 766 320 1028 0.95 2.50 1.54 3.74 1.56 5.02 Increase Large increase

Bur oak 261 457 Medium 398 824 684 875 651 1056 0.87 1.80 1.50 1.92 1.43 2.31 Increase Large increase

Butternut 54 27 Low 31 65 42 2 46 0 1.15 2.41 1.56 0.07 1.70 0.00 Increase Extirpated

Chestnut oak 0 2 High 0 0 1 72 0 71 0.00 0.00 0.50 36.00 0.00 35.50 NA New habitatChinquapin oak 0 0 Medium 0 7 1 143 1 285 NA New New New New New New habitat New habitatChokecherry 134 139 Low 126 176 172 141 163 42 0.91 1.27 1.24 1.01 1.17 0.30 No change Large decrease

Common persimmon 0 0 Medium 0 0 0 0 0 10 NA NA NA NA NA New NA New habitatEastern cottonwood 20 57 Low 62 221 120 1027 126 955 1.09 3.88 2.11 18.02 2.21 16.75 Large increase Large increase

Eastern hemlock 467 409 High 460 372 530 195 495 154 1.13 0.91 1.30 0.48 1.21 0.38 Increase Large decrease

Eastern hophornbeam 397 443 Medium 420 576 538 677 524 681 0.95 1.30 1.21 1.53 1.18 1.54 No change Increase

Eastern redbud 28 0 Medium 1 5 3 160 3 331 New New New New New New Large decrease Large increase

Eastern redcedar 0 29 Medium 43 573 111 1902 147 2067 1.48 19.76 3.83 65.59 5.07 71.28 New habitat New habitatEastern white pine 628 636 High 690 682 742 548 720 319 1.09 1.07 1.17 0.86 1.13 0.50 No change DecreaseFlowering dogwood 0 0 High 3 18 27 216 26 298 New New New New New New New habitat New habitatGray birch 0 5 Medium 4 16 7 19 11 20 0.80 3.20 1.40 3.80 2.20 4.00 New habitat New habitatGreen ash 638 593 Medium 509 523 452 1117 449 890 0.86 0.88 0.76 1.88 0.76 1.50 Decrease Increase

Hackberry 2 12 Medium 24 358 164 770 178 935 2.00 29.83 13.67 64.17 14.83 77.92 Large increase Large increase

Honeylocust 0 2 Low 3 127 43 422 41 559 1.50 63.50 21.50 211.00 20.50 279.50 New habitat New habitatJack pine 695 812 High 693 749 672 501 654 492 0.85 0.92 0.83 0.62 0.81 0.61 No change DecreaseMockernut hickory 0 1 High 1 11 17 126 18 189 1.00 11.00 17.00 126.00 18.00 189.00 New habitat New habitatMountain maple 59 53 High 27 2 17 0 13 0 0.51 0.04 0.32 0.00 0.25 0.00 Large decrease Extirpated

Northern catalpa 0 2 Low 0 0 0 2 0 3 0.00 0.00 0.00 1.00 0.00 1.50 NA New habitat

Northern pin oak 329 319 Medium 292 483 361 343 341 341 0.92 1.51 1.13 1.08 1.07 1.07 No change No changeNorthern red oak 940 1078 High 1292 1829 1435 1491 1441 1216 1.20 1.70 1.33 1.38 1.34 1.13 Increase No changeNorthern white-cedar 1010 1003 High 869 460 744 439 704 361 0.87 0.46 0.74 0.44 0.70 0.36 Decrease Large decrease

DISTRIB results*

Modeled IV Future:Current Suitable Habitat Change Class

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2099

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI

Table 19.—Complete Tree Atlas results for the 78 tree species in the assessment area 

(Table 19 continued on next page)
Append

ix 4

226

DISTRIB results

Modeled IV Future:Current Suitable Habitat Change Class

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2099

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FITable 19 (continued).

Ohio bucke*Current importance values (Current IV) are based on modeled results. Early-century, mid-century, and late-century importance values are average values for the indicated years. Future:Current suitable habitat is a ratio of projected importance value to current importance value. Species are assigned to change classes based on the comparison between end-of-century (2070 through 2099) and current figures for area-weighted importance value. Descriptions of the change classes are given elsewhere in this appendix. NA = not applicable, no suitable habitat projected.Mig. = new migrant, new suitable habitat projected. 

ye 0 0 Low 1 11 2 109 2 136 New New New New New New New habitat New habitat

Osage-orange 0 8 Medium 9 50 55 144 57 231 1.13 6.25 6.88 18.00 7.13 28.88 New habitat New habitatPaper birch 1699 1628 High 1339 1118 1173 329 1068 258 0.82 0.69 0.72 0.20 0.66 0.16 Decrease Large decrease

Peachleaf willow 6 0 Low 0 0 0 3 0 47 NA NA NA New NA New NA Increase

Pignut hickory 0 1 High 2 43 49 175 64 211 2.00 43.00 49.00 175.00 64.00 211.00 New habitat New habitatPin cherry 56 48 Medium 49 62 56 36 55 10 1.02 1.29 1.17 0.75 1.15 0.21 No change Large decrease

Pin oak 0 7 Medium 5 40 34 206 32 294 0.71 5.71 4.86 29.43 4.57 42.00 New habitat New habitatPost oak 0 0 High 0 7 8 515 6 957 NA New New New New New New habitat New habitatQuaking aspen 3888 3921 High 3138 2278 2405 1076 2230 866 0.80 0.58 0.61 0.27 0.57 0.22 Decrease Large decrease

Red maple 3149 2910 High 3032 2603 3104 2054 3034 1803 1.04 0.90 1.07 0.71 1.04 0.62 No change Decrease

Red mulberry 1 0 Low 2 188 35 566 45 714 New New New New New New Large increase Large increase

Red pine 571 685 Medium 659 695 617 700 626 575 0.96 1.02 0.90 1.02 0.91 0.84 No change No change

River birch 4 1 Low 1 112 23 11 16 20 1.00112.00 23.00 11.00 16.00 20.00 Large increase Large increase

Rock elm 73 52 Low 43 45 42 61 39 62 0.83 0.87 0.81 1.17 0.75 1.19 Decrease No change

Sassafras 0 3 High 4 19 39 211 35 247 1.33 6.33 13.00 70.33 11.67 82.33 New habitat New habitatScarlet oak 0 0 High 0 0 7 56 9 122 NA NA New New New New New habitat New habitatShagbark hickory 86 117 Medium 203 608 429 525 455 621 1.74 5.20 3.67 4.49 3.89 5.31 Large increase Large increase

Shellbark hickory 0 0 Low 0 1 0 24 0 26 NA New NA New NA New NA New habitat

Shingle oak 0 1 Medium 0 15 10 138 11 172 0.00 15.00 10.00 138.00 11.00 172.00 New habitat New habitat

Silver maple 72 127 Medium 166 617 416 908 401 996 1.31 4.86 3.28 7.15 3.16 7.84 Large increase Large increase

Slippery elm 75 81 Medium 143 414 249 697 290 769 1.77 5.11 3.07 8.61 3.58 9.49 Large increase Large increase

Striped maple 14 24 High 15 27 27 25 24 18 0.63 1.13 1.13 1.04 1.00 0.75 No change No changeSugar maple 3574 3375 High 3210 2012 3095 1462 2851 1172 0.95 0.60 0.92 0.43 0.85 0.35 No change Large decreaseSugarberry 0 0 Medium 0 0 0 13 0 52 NA NA NA New NA New NA New habitat

Swamp white oak 32 25 Low 29 45 33 154 32 147 1.16 1.80 1.32 6.16 1.28 5.88 No change Increase

Sweet birch 0 2 High 2 4 3 53 4 41 1.00 2.00 1.50 26.50 2.00 20.50 New habitat New habitatSweetgum 0 0 High 0 0 0 10 0 22 NA NA NA New NA New NA New habitatSycamore 0 0 Medium 0 3 14 31 10 83 NA New New New New New New habitat New habitat

Tamarack (native) 508 566 High 475 510 514 381 500 367 0.84 0.90 0.91 0.67 0.88 0.65 No change DecreaseWhite ash 382 427 High 521 580 667 830 677 791 1.22 1.36 1.56 1.94 1.59 1.85 Increase Increase

White oak 349 459 High 621 1260 796 1314 912 1410 1.35 2.75 1.73 2.86 1.99 3.07 Increase Large increase

White spruce 363 369 Medium 259 178 224 158 209 154 0.70 0.48 0.61 0.43 0.57 0.42 Decrease Large decrease

Wild plum 2 6 Low 2 12 2 160 2 269 0.33 2.00 0.33 26.67 0.33 44.83 Decrease Large increaseYellow birch 628 589 High 508 300 546 136 470 125 0.86 0.51 0.93 0.23 0.80 0.21 Decrease Large decrease

Yellow-poplar 0 1 High 0 3 19 33 15 75 0.00 3.00 19.00 33.00 15.00 75.00 New habitat New habitat
Appendix 4

227

Model Modifying Factors* Adaptability Scores

Common Name Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

American basswood Medium COL FTK 0.3 0.2 4.6 ○

American beech High COL INS FTK -1.1 0.0 3.6 ○

American elm Medium ESP DISE INS -0.8 0.3 4.0 ○

American hornbeam Medium COL SES FTK DRO 0.6 0.6 5.1 ○

Balsam fir High COL INS FTK DRO -3.0 -0.4 2.7 ―

Balsam poplar High FRG VRE COL DRO 0.1 -0.6 4.0 ○

Bigtooth aspen High FRG DISP COL DRO FTK 1.0 0.2 5.1 ○

Bitternut hickory Low DRO COL 2.2 -0.8 5.6 +

Black ash High INS COL DISP DRO SES FTK ESP -1.3 -3.0 1.7 ―

Black cherry High DRO ESP INS FTK COL -1.6 -0.3 3.0 ―

Black hickory High ESP COL 1.0 -2.3 4.1 ○

Black locust Low COL INS 0.0 -0.6 3.8 ○

Black oak High DRO ESP INS DISE 0.5 0.4 4.9 ○

Black spruce High COL ESP DISP FTK INS DRO -2.1 1.2 4.3 ○

Black walnut Medium SES COL DRO 0.4 -0.8 4.0 ○

Black willow Low COL FTK DRO -0.3 -2.1 2.8 ―

Blackgum High COL FTK 1.5 0.8 5.9 +

Blackjack oak Medium DRO SES FRG VRE COL FTK 1.6 0.2 5.6 +

Boxelder Medium SES DISP DRO COL SES FTK 2.4 2.1 7.4 +

Bur oak Medium DRO FTK 2.8 -0.2 6.4 +

Butternut Low FTK COL DRO DISE -1.4 -1.3 2.3 ―

Chestnut oak High SES VRE ESP FTK INS DISE 1.4 1.3 6.1 +

Chinquapin oak Medium SES 1.2 -0.7 4.8 ○

Chokecherry Low COL 0.2 -0.9 3.8 ○

Common persimmon Medium COL ESP 1.2 1.0 5.8 +

Eastern cottonwood Low SES INS COL DISE FTK 0.2 -0.8 3.9 ○

Eastern hemlock High COL INS DRO -1.3 -0.9 2.7 ―

Eastern hophornbeam Medium COL ESP SES 1.7 1.3 6.4 +

Eastern redbud Medium 0.9 0.0 4.9 ○

Eastern redcedar Medium DRO FTK COL INS 0.6 -1.5 3.9 ○

Eastern white pine High DISP DRO FTK INS -2.0 0.1 3.3 ○

Flowering dogwood High COL 0.1 1.0 5.0 ○

Gray birch Medium DISP ESP FTK COL INS DISE -1.1 0.0 3.6 ○

Green ash Medium INS FTK COL -0.1 -0.3 4.0 ○

Hackberry Medium DRO FTK 1.7 0.3 5.7 +

Honeylocust Low COL 1.9 -0.5 5.5 +

Jack pine High DRO COL INS 1.9 -1.2 5.2 ○

Mockernut hickory High FTK 1.7 -0.3 5.4 +

Mountain maple High COL VRE ESP DRO FTK 0.8 1.5 5.9 +

Northern catalpa Low COL ESP 0.9 -1.6 4.2 ○

Northern pin oak Medium DRO FTK COL 2.5 -0.6 6.0 +

Table 20.—Modifying factors for the 74 tree species in the assessment area 

(Table 20 continued on next page)
Appendix 4

228

Northern red oak High INS 1.4 0.1 5.4 +

Northern white-cedar High COL FTK -0.7 0.5 4.2 ○

Ohio buckeye Low COL SES FTK 0.4 -1.9 3.5 ○

Osage-orange Medium ESP ESP 2.3 0.3 6.3 +

Paper birch High FRG DISP ESP FTK COL INS DRO -1.7 0.2 3.4 ○

Peachleaf willow Low COL 0.1 -1.7 3.4 ○

Pignut hickory High ESP INS DRO 0.2 0.4 4.7 ○

Pin cherry Medium SES FRG FTK COL 0.5 -0.7 4.2 ○

Pin oak Medium FTK COL INS DISE -0.7 -1.4 2.8 ―

Post oak High DRO SES FTK COL INS DISE 2.2 -0.6 5.7 +

Quaking aspen High SES FRG ESP COL DRO FTK 0.6 0.0 4.7 ○

Red maple High SES ESP ESP COL DISP 3.0 3.0 8.5 +

Red mulberry Low COL DISP FTK 0.1 0.6 4.7 ○

Red pine Medium INS COL DISP 0.9 -2.4 3.9 ○

River birch Low DISP FTK COL DRO -0.5 -0.3 3.7 ○

Rock elm Low ESP ESP SES -0.2 -2.6 2.8 ―

Sassafras High COL FTK 0.5 -0.6 4.2 ○

Scarlet oak High VRE ESP ESP INS DISE FTK -0.4 0.7 4.6 ○

Shagbark hickory Medium INS FTK -0.2 0.4 4.4 ○

Shellbark hickory Low COL FTK ESP -0.5 -0.3 3.7 ○

Shingle oak Medium ESP COL 1.3 -0.7 4.9 ○

Silver maple Medium DISP SES COL DRO FTK 0.1 1.6 5.6 +

Slippery elm Medium COL FTK DISE 0.0 0.7 4.8 ○

Striped maple High COL SES DRO 1.0 0.3 5.1 ○

Sugar maple High COL ESP 0.9 1.3 5.8 +

Sugarberry Medium COL SES FTK -0.2 0.6 4.6 ○

Swamp white oak Low 1.0 -0.3 4.9 ○

Sweet birch High DISP FTK COL INS DISE -1.3 -0.3 3.2 ―

Sweetgum High VRE ESP FTK COL DRO -0.4 0.2 4.1 ○

Sycamore Medium 1.3 -0.9 4.8 ○

Tamarack (native) High FTK COL INS -0.5 -1.2 3.1 ―

White ash High INS FTK COL -2.0 -0.5 2.7 ―

White oak High ESP ESP SES FTK INS DISE 1.7 1.0 6.1 +

White spruce Medium INS 0.1 -0.6 3.9 ○

Wild plum Low COL 0.5 -1.3 3.9 ○

Yellow birch High DISP FTK INS DISE -1.4 0.0 3.4 ○

Yellow-poplar High SES DISP ESP INP 0.1 1.3 5.3 +

*Modifying factor codes are described in Table 21. Adaptability scores are described in the appendix text. 

Model Modifying Factors* Adaptability Scores

Common Name Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

Table 20 (continued).
Appendix 4

229

Table 21.—Description of Tree Atlas modifying factor codes* 

Code Description (if positive) Description (if negative)

COL Tolerant of shade or limited light conditions Intolerant of shade or limited light conditions

DISE Has a high number or severity of known pathogens that 

attack the species

DISP High ability to effectively produce 

and distribute seeds

DRO Drought-tolerant Susceptible to drought

ESP Wide range of soil requirements Narrow range of soil requirements 

FRG Regenerates well after fire

FTK Resistant to fire topkill Susceptible to fire topkill

INS Has a high number and/or severity of insects that may 

attack the species

INP Strong negative effects of invasive plants on the species, 

either through competition for nutrients or as a pathogen

SES High ability to regenerate with seeds to maintain 

future populations

Low ability to regenerate with seeds to maintain future 

populations

VRE Capable of vegetative reproduction through stump 

sprouts or cloning

*These codes are used to describe positive or negative modifying factors used in Table 20. A species was given a code if information 

from the literature suggested that it had these characteristics. See Matthews et al. (2011) for a more thorough description of these 

factors and how they were assessed.

Figure 60.—Schematic showing how adaptability was determined for information for the 78 tree species in the assessment 

area modeled using the Climate Change Tree Atlas. Modifying factor codes are described in Table 21. Adaptability scores are 

described in the appendix text.
Appendix 4

230

LITERATURE CITED

Iverson, L.R.; Prasad, A.; Schwartz, M.W. 1999. 

Modeling potential future individual treespecies distributions in the eastern United 

States under a climate change scenario: a 

case study with Pinus virginiana. Ecological 

Modelling. 115(1): 77-93.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M. 2008. Estimating potential habitat for 

134 eastern US tree species under six climate 

scenarios. Forest Ecology and Management. 

254(3): 390-406.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M.P. 2011. Lessons learned while integrating 

habitat, dispersal, disturbance, and lifehistory traits into species habitat models under 

climate change. Ecosystems. 14(6): 1005-1020.

Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, 

M.P.; Rodewald, P.G. 2011. Modifying climate 

change habitat models using tree speciesspecific assessments of model uncertainty 

and life history-factors. Forest Ecology and 

Management. 262(8): 1460-1472.

A wetland on Madeline Island in Wisconsin. Photo by Maria Janowiak, U.S. Forest Service.
231

APPENDIX 5: SUPPLEMENTARY MODEL RESULTS – 

LANDIS-II

This appendix contains additional model details and 

results from the LANDIS-II model.

FOREST TYPE CLASSIFICATION 

The forest-type maps presented in Chapter 5 

(Fig. 41) rely on a simple classification scheme, 

which is different from the FIA forest-type groups 

and the forest ecosystem classifications used in 

other parts of this assessment. To create these foresttype maps, individual locations (“cells”) in the 

LANDIS-II simulations were classified into 

10 forest categories based on characteristic species 

composition. These classifications are based on the 

dominance of key indicator species (Table 22). 

The assignment of species to groups is based on 

unique species within groups and a balance of high 

abundance species within groups. Certain species 

that do not contribute to the unique forest type 

dominance are subtracted from the dominance 

calculation. Species assignment adjustments were 

made based on matching the proportion of individual 

forest types found in regional FIA plots to the 

proportion of individual forest types found 

in LANDIS-II cells for the year 2000. 

Table 22.—Classification rules for creating the forest-type maps based on LANDIS-II outputs (Fig. 41)

Forest type Species included in forest type

Aspen-birch paper birch, balsam poplar, bigtooth aspen, quaking aspen

Hemlock eastern hemlock

Jack pine jack pine

Northern hardwoods sugar maple, red maple, yellow birch, white ash, black ash, green ash, American beech, 

American basswood

Northern oaks northern pin oak, northern red oak

Red pine red pine

Southern mesic hardwoods bitternut hickory, black cherry

Southern oaks bur oak, white oak, black oak

Upland spruce-fir balsam fir, white spruce, black spruce, northern white cedar

White pine eastern white pine
Appendix 5

232

FOREST HARVEST

The LANDIS-II model was used to project changes 

in tree species under three harvest scenarios 

BIOMASS PROJECTIONS

(Tables 23 and 24). Results from the Current 

Harvest scenario are presented in Chapter 5. 

Table 23.—Harvest scenarios used for LANDIS-II modeling 

Forest ownership No harvest (%) Current harvest (%)* Double harvest (%)

National Forest 0 1.15 2.30

Other Federal 0 1.30 2.61

State 0 1.02 2.03

County and municipal 0 2.25 4.50

Other local government 0 2.87 5.75

Native American reservations 0 1.00 2.00

Private groups 0 3.07 6.13

Private individuals 0 2.21 4.41

*Current harvest rate reflects the annual percentage of total acreage harvested for the entire state of Wisconsin based on the 

2005-2011 FIA database. Harvest types include clearcut, seed tree, single-tree (gap) selection, group selection, patch selection, and 

shelterwood methods. 

American basswood 671 2040 690 103% 1048 52% 970 41%

2070 738 110% 1435 94% 1200 63%

2100 755 113% 1799 138% 1567 107%

American beech 9 2040 12 134% 16 28% 14 13%

2070 17 192% 27 55% 21 20%

2100 21 234% 39 83% 39 85%

Balsam fir 658 2040 785 119% 693 -12% 524 -33%

2070 1018 155% 829 -19% 262 -74%

2100 1135 173% 797 -30% 90 -92%

Balsam poplar 43 2040 39 90% 32 -18% 27 -30%

2070 23 55% 19 -19% 12 -50%

2100 6 13% 5 -6% 1 -79%

Bigtooth aspen 443 2040 370 83% 448 21% 390 6%

2070 283 64% 385 36% 281 -1%

2100 120 27% 168 40% 104 -13%

Current Climate PCM B1 GFDL A1FI

Biomass change Biomass change

Year 2000 Biomass Change Biomass relative to Biomass relative to

Species biomass (g/m2

) Year (g/m2) from 2000 (g/m2) current climate (g/m2) current climate 

Table 24.—Projected aboveground biomass for 27 species modeled with the LANDIS-II model 

(Table 24 continued on next page)
Appendix 5

233

Current Climate PCM B1 GFDL A1FI

Biomass change Biomass change

Year 2000 Biomass Change Biomass relative to Biomass relative to

Species biomass (g/m2) Year (g/m2) from 2000 (g/m2) current climate (g/m2) current climate 

Table 24 (continued).

Bitternut hickory 78 2040 79 101% 87 10% 78 -1%

2070 78 100% 99 27% 87 11%

2100 61 78% 99 63% 124 103%

Black ash 462 2040 642 139% 609 -5% 570 -11%

2070 644 139% 601 -7% 524 -19%

2100 542 117% 487 -10% 407 -25%

Black cherry 617 2040 608 98% 591 -3% 577 -5%

2070 533 86% 508 -5% 513 -4%

2100 339 55% 309 -9% 452 33%

Black oak 13 2040 13 102% 16 21% 15 16%

2070 13 97% 19 49% 17 33%

2100 10 74% 19 93% 22 131%

Black spruce 375 2040 482 129% 348 -28% 319 -34%

2070 496 132% 283 -43% 210 -58%

2100 472 126% 167 -65% 94 -80%

Bur oak 151 2040 171 113% 200 18% 189 11%

2070 173 115% 228 32% 199 15%

2100 156 103% 221 42% 213 37%

Eastern hemlock 462 2040 505 109% 560 11% 530 5%

2070 562 122% 675 20% 609 8%

2100 596 129% 778 30% 742 24%

Eastern white pine 1715 2040 2589 151% 2554 -1% 2298 -11%

2070 3621 211% 3682 2% 2635 -27%

2100 4785 279% 4713 -2% 2949 -38%

Green ash 133 2040 184 138% 215 17% 197 7%

2070 209 156% 256 23% 215 3%

2100 203 152% 251 24% 224 10%

Jack pine 186 2040 249 134% 197 -21% 179 -28%

2070 176 95% 136 -23% 105 -41%

2100 162 87% 111 -31% 46 -72%

Northern pin oak 137 2040 119 87% 165 38% 136 14%

2070 84 62% 143 70% 106 25%

2100 46 34% 110 139% 74 60%

Northern red oak 1073 2040 1354 126% 1500 11% 1363 1%

2070 1660 155% 1962 18% 1456 -12%

2100 1798 168% 2108 17% 1649 -8%

Northern white cedar 782 2040 1064 136% 991 -7% 919 -14%

2070 1230 157% 1120 -9% 904 -27%

2100 1273 163% 1095 -14% 801 -37%

Paper birch 884 2040 1118 126% 621 -44% 544 -51%

2070 1152 130% 482 -58% 256 -78%

2100 953 108% 328 -66% 91 -90%

Quaking aspen 1614 2040 1389 86% 1120 -19% 990 -29%

2070 908 56% 671 -26% 423 -53%

2100 346 21% 259 -25% 66 -81%

(Table 24 continued on next page)
Appendix 5

234

Red maple 4034 2040 3849 95% 4294 12% 3917 2%

2070 3034 75% 3917 29% 3183 5%

2100 2113 52% 3161 50% 2892 37%

Red pine 1097 2040 1446 132% 1222 -16% 1164 -20%

2070 1673 153% 1322 -21% 1101 -34%

2100 1726 157% 1210 -30% 932 -46%

Sugar maple 5542 2040 7016 127% 7134 2% 6436 -8%

2070 8152 147% 8818 8% 6413 -21%

2100 8572 155% 9477 11% 6433 -25%

White ash 219 2040 201 92% 273 36% 254 26%

2070 173 79% 308 77% 281 62%

2100 134 61% 339 153% 404 201%

White oak 143 2040 145 101% 177 23% 167 15%

2070 148 104% 208 40% 186 26%

2100 146 102% 225 54% 231 59%

White spruce 497 2040 693 140% 475 -31% 429 -38%

2070 760 153% 407 -46% 292 -62%

2100 751 151% 249 -67% 136 -82%

Yellow birch 614 2040 928 151% 1023 10% 874 -6%

2070 1273 207% 1410 11% 840 -34%

2100 1469 239% 1574 7% 727 -51%

Change from year 2000 is calculated as the change in biomass from year 2000 (100% equals no net change). Change relative to 

current climate biomass is calculated as the proportional change compared to the biomass under the current climate scenario for 

the same year. These data are based on three climate change scenarios under current harvest practices.

Current Climate PCM B1 GFDL A1FI

Biomass change Biomass change

Year 2000 Biomass Change Biomass relative to Biomass relative to

Species biomass (g/m2) Year (g/m2) from 2000 (g/m2) current climate (g/m2) current climate 

Table 24 (continued).
235

APPENDIX 6: VULNERABILITY AND 

CONFIDENCE DETERMINATION

EXPERT PANEL PROCESS

To assess vulnerabilities to climate change for 

each natural community type, we elicited input 

from a panel of 23 experts from a variety of land 

management and research organizations across the 

assessment area (Table 25). We sought a team of 

panelists who would be able to contribute a diversity 

of subject area expertise, management history, 

and organizational perspectives. Most panelists 

had extensive knowledge about the ecology, 

management, and climate change impacts on forests 

in the assessment area. This panel was assembled 

at an in-person workshop in Houghton, Michigan, 

in May 2013. Here we describe the structured 

discussion process that the panel used. 

Forest Systems Assessed

The authors of this assessment used the forest 

ecosystem classification described in Chapter 1. For 

each forest type, we collected information related 

Name Organization

Amy Amman Ottawa National Forest

Tara Bal Michigan Technological University

Brian Bogaczyk Ottawa National Forest

Dustin Bronson Wisconsin Department of Natural Resources

Andy Burton Michigan Technological University

Patricia Butler* Michigan Technological University & Northern Institute of Applied Climate Science

Jim Ferris Michigan Department of Natural Resources

Jon Fosgitt Compass Land Consultants

Shawn Hagan The Forestland Group

Christine Handler Ottawa National Forest

Louis Iverson U.S. Forest Service, Northern Research Station

Maria Janowiak* U.S. Forest Service, Northern Research Station & Northern Institute of Applied Climate Science

Erin Johnston Keweenaw Bay Indian Community

Evan Kane Michigan Technological University

Ellen Lesch Ottawa National Forest

Colleen Matula Wisconsin Department of Natural Resources

David Mladenoff University of Wisconsin-Madison

Ryan O’Connor Wisconsin Natural Heritage Program

Linda Parker Chequamegon-Nicolet National Forest

Kirk Wythers University of Minnesota

Weimin Xi University of Wisconsin-Madison

* Workshop facilitators

Table 25.—Participants in the May 2013 expert panel workshop
Appendix 6

236

to the major system drivers, dominant species, and 

stressors that characterize that community from the 

relevant ecological literature. The panel was asked 

to comment on and suggest modifications to the 

community descriptions, and those suggestions were 

incorporated into the descriptions. 

Potential Impacts 

To examine potential impacts, the panel was given 

several sources of background information on past 

and future climate change in the region (summarized 

in Chapters 3 and 4) and projected impacts on 

dominant tree species and forest productivity 

(summarized in Chapter 5). The panel was directed 

to focus on impacts to each forest type from the 

present through the end of the century, but more 

weight was given to the end-of-century period. The 

panel assessed impacts by considering a range of 

climate futures bracketed by two scenarios: GFDL 

A1FI and PCM B1. Panelists were then led through 

a structured discussion process to consider this 

information for each forest community considered in 

the assessment. 

Potential impacts on community drivers and 

stressors were summarized based on climate 

model projections, the published literature, and 

insights from the panelists. Impacts on drivers 

were considered positive or negative if they would 

alter system drivers in a way that would be more 

or less favorable for that community type. Impacts 

on stressors were considered negative if they 

increased the influence of that stressor or positive 

if they decreased the influence of that stressor on 

the community type. Panelists were also asked to 

consider the potential for climate change to facilitate 

new stressors in the assessment area over the next 

century. 

To assess potential impacts on dominant tree species, 

the panelists examined results from Tree Atlas, 

LANDIS-II, and PnET-CN, and were asked to 

consider those results in addition to their knowledge 

of life history traits and ecology of those species 

(Box 12). The panel evaluated how much the models 

agreed with each other, between climate scenarios, 

and across space and time. Finally, panelists were 

asked to consider the potential for interactions 

among anticipated climate trends, species impacts, 

and stressors. Input on these future ecosystem 

interactions relied primarily on the panelists’ 

expertise and judgment because there are not 

many examples of published literature on complex 

interactions, nor are future interactions accurately 

represented by ecosystem models. 

Adaptive Capacity

Panelists discussed the adaptive capacity of each 

forest system based on their ecological knowledge 

and management experience with the community 

types in the assessment area. Panelists were told 

to focus on community characteristics that would 

increase or decrease the adaptive capacity of that 

system. Factors that the panel considered included 

characteristics of dominant species within each 

community (e.g., dispersal ability, genetic diversity, 

range limits) and comprehensive community 

characteristics (e.g., functional and species diversity, 

tolerance to a variety of disturbances, distribution 

across the landscape). The panelists were directed 

to base their considerations on the current condition 

of the system given past and current management 

regimes, with no consideration of potential 

adaptation actions that could take place in the future. 

Vulnerability

Following extensive group discussion, each panelist 

evaluated the potential impacts and adaptive 

capacity of each community type to arrive at a 

vulnerability rating. Participants were provided 

with individual worksheets and asked to list which 

impacts they felt were most important to that system 

in addition to the major factors that would contribute 

to the adaptive capacity of that system. 
Appendix 6

237

Box 12: Note on Forest Impact Models Used in This Assessment

During the expert panel workshop, preliminary 

LANDIS-II results were used that included the effects 

of carbon dioxide (CO2 ) fertilization on the 27 tree 

species that were modeled. The inclusion of CO2

fertilization projected unrealistically large increases 

in biomass for many tree species. The CO2 routine in 

the PnET-CN model predicts a larger CO2 fertilization 

effect on productivity than do other ecosystem 

models (Franks et al. 2013, Medlyn et al. 2011). 

Further, few observational studies have evaluated 

the effects of CO2 levels over 600 ppm, although the 

GFDL scenario projects CO2 levels over 900 ppm by 

the year 2100. Chapter 5 contains a more detailed 

description of the CO2 fertilization effect and its 

influence in PnET-CN modeling.

The LANDIS-II model results presented in this 

assessment rely on the PnET-CN model to provide 

estimates of species establishment. The preliminary 

LANDIS-II model results used in the expert panel 

workshop included the potential benefits of 

CO2 fertilization on tree growth, which panelists 

identified as an issue because fertilization effect was 

so great that it overrode many of the potentially 

negative effects of warming and associated 

climatic changes. During the workshop, members 

of the expert panel also expressed their tendency 

to “discount” the biomass increases that were 

projected under both scenarios, and especially 

under the GFDL A1FI scenario. After the expert panel 

workshop, we chose to use new LANDIS-II results 

that did not include CO2

 fertilization. All LANDIS-II 

results presented in Chapters 5 and 6 and Appendix 

5 were subsequently vetted with the expert panelists 

to ensure their vulnerability rankings were still 

consistent with the final LANDIS-II model results. 

The new LANDIS-II results (that is, without CO2

fertilization) tended to be more consistent with the 

results from the Climate Change Tree Atlas presented 

in this assessment. Further, these new results are 

also comparable to those developed for Michigan 

and Minnesota by a different modeling team 

(Handler et al. 2014a, 2014b).

Panelists were directed to mark their rating in 

two-dimensional space on the individual worksheet 

and on a large group poster (Fig. 61a). This 

vulnerability figure required the participants to 

evaluate the degree of potential impacts related 

to climate change as well as the adaptive capacity 

of the system to tolerate those impacts (Swanston 

and Janowiak 2012). Individual ratings were 

compared and discussed and used to arrive at a 

group determination. In many cases, the group 

determination was at or near the centroid of all 

individual determinations. Sometimes the group 

determination deviated from the centroid because 

further discussion convinced some group members 

to alter their original response. 

Confidence 

Panelists were also directed to give a confidence 

rating to each of their individual vulnerability 

determinations (Fig. 61b). Panelists were asked 

to evaluate the amount of evidence they felt was 

available to support their vulnerability determination 

and the level of agreement among the available 

evidence (Mastrandrea et al. 2010). Panelists 

evaluated confidence individually and as a group, in 

a similar fashion to the vulnerability determination. 

Vulnerability and Confidence Figures

For reference, figures of individual and group 

determinations for all nine forest systems 

considered in this assessment are displayed 

(Figs. 62 through 70). In each figure, individual 

panelist votes are indicated with a small circle and 

the group determination is indicated with a large 

square. We do not intend for direct comparison 

between these figures because the axes represent 

subjective, qualitative scales. 
Appendix 6

238

Figure 61.—Figure used for (a) vulnerability determination by expert panelists, based on Swanston and Janowiak (2012), and 

(b) confidence rating among expert panelists, adapted from Mastrandrea et al. (2010).

Figure 62.—Aspen-birch vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.
Appendix 6

239

Figure 63.—Jack pine vulnerability and confidence determinations. Circles indicate individual determinations by each panelist 

and squares indicate the group determination after consensus was reached.

Figure 64.—Lowland conifers vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.
Appendix 6

240

Figure 65.—Lowland-riparian hardwoods vulnerability and confidence determinations. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 66.—Northern hardwoods vulnerability and confidence determinations. Circles indicate individual determinations by 

each panelist and squares indicate the group determination after consensus was reached.
Appendix 6

241

Figure 67.—Oak vulnerability and confidence determinations. Circles indicate individual determinations by each panelist and 

squares indicate the group determination after consensus was reached.

Figure 68.—Red pine vulnerability and confidence determinations. Circles indicate individual determinations by each panelist 

and squares indicate the group determination after consensus was reached.
Appendix 6

242

Figure 69.—Upland spruce-fir vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.

Figure 70.—White pine vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.
Appendix 6

243

VULNERABILITY STATEMENTS

Recurring themes and patterns that transcended 

individual community types were identified and 

developed into vulnerability statements (in boldface) 

and supporting text in Chapter 6. The lead author 

developed the statements and supporting text based 

on workshop notes and literature pertinent to each 

statement. An initial confidence determination 

(evidence and agreement) was assigned based on 

the lead author’s interpretation of the amount of 

information available to support each statement and 

the extent to which the information agreed. Each 

statement and its supporting literature discussion 

were sent to the expert panel for review. Panelists 

were asked to review each statement for accuracy, 

whether the confidence determination should be 

raised or lowered, if there was additional literature 

that was overlooked, and if there were any additional 

statements that needed to be made. Any changes that 

were suggested by a single panelist were brought 

forth for discussion and approved by the entire 

panel.

LITERATURE CITED

Franks, P.J.; Adams, M.A.; Amthor, J.S.; Barbour, 

M.M.; Berry, J.A.; Ellsworth, D.S.; Farquhar, 

G.D.; Ghannoum, O.; Lloyd, J.; McDowell, 

N.; Norby, R.J.; Tissue, D.T.; von Caemmerer, 

S. 2013. Sensitivity of plants to changing 

atmospheric CO2

 concentration: from the 

geological past to the next century. New 

Phytology. 197(4): 1077-1094.

Handler, S.; Duveneck, M.J.; Iverson, L.; Peters, 

E.; Scheller, R.M.; Wythers, K.R.; Brandt, 

L.; Butler, P.; Janowiak, M.; Shannon, P.D.; 

Swanston, C.; Barrett, K.; Kolka, R.; McQuiston, 

C.; Palik, B.; Reich, P.B.; Turner, C.; White, M.; 

Adams, C.; D’Amato, A.; Hagell, S.; Johnson, 

P.; Johnson, R.; Larson, M.; Matthews, S.; 

Montgomery, R.; Olson, S.; Peters, M.; Prasad, 

A.; Rajala, J.; Daley, J.; Davenport, M.; Emery, 

M.R.; Fehringer, D.; Hoving, C.L.; Johnson, 

G.; Johnson, L.; Neitzel, D.; Rissman, A.; 

Rittenhouse, C.; Ziel, R. 2014a. Minnesota 

forest ecosystem vulnerability assessment 

and synthesis: a report from the Northwoods 

Climate Change Response Framework project.

Gen. Tech. Rep. NRS-133. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 228 p.

Handler, S.; Duveneck, M.J.; Iverson, L.; Peters, E.; 

Scheller, R.M.; Wythers, K.R.; Brandt, L.; Butler, 

P.; Janowiak, M.; Shannon, P.D.; Swanston, C.; 

Eagle, A.C.; Cohen, J.G.; Corner, R.; Reich, 

P.B.; Baker, T.; Chhin, S.; Clark, E.; Fehringer, 

D.; Fosgitt, J.; Gries, J.; Hall, C.; Hall, K.R.; 

Heyd, R.; Hoving, C.L.; Ibanez, I.; Kuhr, D.; 

Matthews, S.; Muladore, J.; Nadelhoffer, K.; 

Neumann, D.; Peters, M.; Prasad, A.; Sands, M.; 

Swaty, R.; Wonch, L.; Daley, J.; Davenport, M.; 

Emery, M.R.; Johnson, G.; Johnson, L.; Neitzel, 

D.; Rissman, A.; Rittenhouse, C.; Ziel, R. 2014b. 

Michigan forest ecosystem vulnerability 

assessment and synthesis: a report from 

the Northwoods Climate Change Response 

Framework project. Gen. Tech. Rep. 

NRS-129. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, 

Northern Research Station. 229 p.
Appendix 6

244

Mastrandrea, M.D.; Field, C.B.; Stocker, T.F.; 

Edenhofer, O.; Ebi, K.L.; Frame, D.J.; Held, 

H.; Kriegler, E.; Mach, K.J.; Matschoss, 

P.R.; Plattner, G.-K.; Yohe, G.W.; Zwiers, 

F.W. 2010. Guidance note for lead authors 

of the IPCC Fifth Assessment Report on 

consistent treatment of uncertainties. Geneva, 

Switzerland: Intergovernmental Panel on 

Climate Change (IPCC). Available at www.ipcc.

ch/activities/activities.shtml. (Accessed May 6, 

2014).

Medlyn, B.E.; Duursma, R.A.; Zeppel, M.J.B. 2011. 

Forest productivity under climate change: a 

checklist for evaluating model studies. Wiley 

Interdisciplinary Reviews: Climate Change. 

2(3): 332-355.

Swanston, C.W.; Janowiak, M.K. 2012. Forest 

adaptation resources: climate change tools and 

approaches for land managers. Gen. Tech. Rep. 

NRS-87. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 121 p. Available at www.nrs.fs.fed.us/

pubs/40543. (Accessed May 6, 2014).
Appendix 6

245

Example Vulnerability Determination Worksheet

Name: Ecosystem/Forest Type: 

How familiar are you with this ecosystem? (circle one)

What do you think are the greatest potential impacts to the ecosystem?

What factors do you think contribute most to the adaptive capacity of the ecosystem?

Medium

I do some management 

or research in this 

system, or have read 

a lot about it.

Low

I have some basic 

knowledge about this 

system and how it 

operates

High

I regularly do 

management or 

research in this system

(Continued on next page)
Appendix 6

246

Vulnerability Determination

Use the handout for the vulnerability determination 

process and the notes that you have taken to plot 

your assessment of vulnerability on the figure below.

Confidence Rating

Use the handout for the confidence rating process 

and the notes that you have taken to rate confidence 

using the figure below.

The ratings above are for the entire analysis area. Please note where you think potential impacts 

or adaptive capacity may vary substantially within the analysis area (e.g., forests in the eastern 

portion may be more prone to impact X).
247

APPENDIX 7: CONTRIBUTORS TO 

IMPLICATIONS CHAPTER 

We relied on input from several subject-area experts 

from a variety of organizations to summarize the 

management implications of climate change in 

Chapter 7.

Name Organization Subject area

Jad Daley Trust for Public Land Land acquisition

Mae Davenport University of Minnesota Forest-associated towns and cities

Marla Emery U.S. Forest Service, Northern Research Station Nontimber forest products

Dave Fehringer The Forestland Group Forest management operations & infrastructure

Chris Hoving Michigan Department of Natural Resources Wildlife

Lucinda Johnson Natural Resources Research Institute Water resources

Gary Johnson University of Minnesota Urban forests

David Neitzel Minnesota Department of Health Human health concerns

Adena Rissman University of Wisconsin – Madison Forest management operations

Chadwick Rittenhouse University of Connecticut Forest management operations

Robert Ziel Lake States Fire Science Consortium Fire and fuels

Table 26.—Contributors to implications summaries in Chapter 7
Printed on recycled paper

Janowiak, Maria K.; Iverson, Louis R.; Mladenoff, David J.; Peters, Emily; Wythers, 

Kirk R.; Xi, Weimin; Brandt, Leslie A.; Butler, Patricia R.; Handler, Stephen 

D.; Shannon, P. Danielle; Swanston, Chris; Parker, Linda R.; Amman, Amy J.; 

Bogaczyk, Brian; Handler, Christine; Lesch, Ellen; Reich, Peter B.; Matthews, 

Stephen; Peters, Matthew; Prasad, Anantha; Khanal, Sami; Liu, Feng; Bal, 

Tara; Bronson, Dustin; Burton, Andrew; Ferris, Jim; Fosgitt, Jon; Hagan, Shawn; 

Johnston, Erin; Kane, Evan; Matula, Colleen; O’Connor, Ryan; Higgins, Dale; 

St. Pierre, Matt; Daley, Jad; Davenport, Mae; Emery, Marla R.; Fehringer, 

David; Hoving, Christopher L.; Johnson, Gary; Neitzel, David; Notaro, Michael; 

Rissman, Adena; Rittenhouse, Chadwick; Ziel, Robert. 2014. Forest ecosystem 

vulnerability assessment and synthesis for northern Wisconsin and 

western Upper Michigan: a report from the Northwoods Climate Change 

Response Framework project. Gen. Tech. Rep. NRS-136. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 

247 p.

Forest ecosystems across the Northwoods will face direct and indirect impacts 

from a changing climate over the 21st century. This assessment evaluates the 

vulnerability of forest ecosystems in the Laurentian Mixed Forest Province of 

northern Wisconsin and western Upper Michigan under a range of future climates. 

Information on current forest conditions, observed climate trends, projected climate 

changes, and impacts to forest ecosystems was considered in order to assess 

vulnerability to climate change. Upland spruce-fir, lowland conifers, aspen-birch, 

lowland-riparian hardwoods, and red pine forests were determined to be the 

most vulnerable ecosystems. White pine and oak forests were perceived as less 

vulnerable to projected changes in climate. These projected changes in climate 

and the associated impacts and vulnerabilities will have important implications 

for economically valuable timber species, forest-dependent wildlife and plants, 

recreation, and long-term natural resource planning.

Key words: climate change, vulnerability, adaptive capacity, 

 Climate Change Tree Atlas, LINKAGES, LANDIS-II, 

 PnET-CN, expert elicitation, Wisconsin, Michigan

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