Ecosystem Vulnerability Assessment 

and Synthesis: A Report from the 

Climate Change Response Framework 

Project in Northern Wisconsin

Northern

Research Station

United States 

Department of Agriculture

Forest

Service

General Technical

Report NRS-82

2011
The forests of northern Wisconsin will likely experience dramatic changes over the 

next 100 years as a result of climate change. This assessment evaluates key forest 

ecosystem vulnerabilities to climate change across northern Wisconsin under a range 

of future climate scenarios with a focus on the Chequamegon-Nicolet National Forest. 

We describe the contemporary landscape and major existing climate trends using 

state climatological data, as well as potential future climate trends for this region 

using downscaled global data from general circulation models. We identify potential 

vulnerabilities by incorporating these future climate projections into species distribution 

and ecosystem process models and assessing potential changes to northern Wisconsin 

forests. Warmer temperatures and shifting precipitation patterns are expected to 

infl uence ecosystem drivers and increase stressors, including more frequent disturbances 

and increased amount or severity of pests and diseases. Forest ecosystems will continue 

to adapt to changing conditions. Even under conservative climate change scenarios, 

suitable habitat for many tree species is expected to move northward. Many species, 

including balsam fi r, white spruce, paper birch, and quaking aspen, are projected 

to decline as their suitable habitat decreases in quality and extent. Certain species, 

communities, and ecosystems may not be particularly resilient to the increases in stress 

or changes in habitat, and they may be subject to severe declines in abundance or may 

be lost entirely from the landscape. These include fragmented and static ecosystems, 

as well as ecosystems containing rare species or species already in decline. Identifying 

vulnerable species and forests can help landowners, managers, regulators, and 

policymakers establish priorities for management and monitoring.

Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies:

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

June 2011 Fax: 740-368-0152

Manuscript received for publication November 2010

Cover Photo

Cover photo used with permission of Linda R. Parker, Chequamegon-Nicolet National 

Forest.

ABSTRACT
Ecosystem Vulnerability Assessment 

and Synthesis: A Report from the 

Climate Change Response Framework 

Project in Northern Wisconsin

Chris Swanston, Maria Janowiak, Louis Iverson, Linda Parker, 

David Mladenoff, Leslie Brandt, Patricia Butler, Matt St. Pierre, 

Anantha Prasad, Stephen Matthews, Matthew Peters, 

Dale Higgins, and Avery Dorland

Photo by Tom Matthiae, Chequamegon-Nicolet National Forest
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

MARIA JANOWIAK is a climate change adaptation and carbon management scientist 

with the Northern Institute of Applied Climate Science, 410 MacInnes Drive, Houghton, 

MI 49931, mjanowiak02@fs.fed.us 

LOUIS IVERSON is a landscape ecologist with the U.S. Forest Service, Northern 

Research Station, 359 Main Road, Delaware, OH 43015, liverson@fs.fed.us

LINDA PARKER is a forest ecologist with the U.S. Forest Service, Chequamegon-Nicolet 

National Forest, 500 Hanson Lake Road, Rhinelander, WI 54501, lrparker@fs.fed.us

DAVID MLADENOFF is a professor with the University of Wisconsin, Department of 

Forest and Wildlife Ecology, 1630 Linden Drive, Madison, WI 53706, djmladen@wisc.edu

LESLIE BRANDT is a climate change specialist with the Northern Institute of Applied 

Climate Science, 626 East Wisconsin Avenue, Milwaukee, WI 53202, lbrandt@fs.fed.us

PATRICIA BUTLER is a climate change outreach specialist with the Northern Institute 

of Applied Climate Science, Michigan Technological University School of Forest 

Resources and Environmental Science, 1400 Townsend Drive, Houghton, MI 49931, 

prbutler@mtu.edu

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

ANANTHA PRASAD is an ecologist with the U.S. Forest Service, Northern Research 

Station, 359 Main Road, Delaware, OH 43015, aprasad@fs.fed.us

STEVEN MATTHEWS is 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 GIS technician with the U.S. Forest Service, Northern Research 

Station, 11328 East Quintana Avenue, Mesa, AZ 85212, mpeters@fs.fed.us 

DALE HIGGINS is a forest hydrologist with the U.S. Forest Service, ChequamegonNicolet National Forest, 500 Hanson Lake Road, Rhinelander, WI 54501, 

dhiggins@fs.fed.us

AVERY DORLAND is forest geneticist and nursery coordinator with the Wisconsin 

Department of Natural Resources, 101 South Webster Street, Madison, WI 53707, 

avery.dorland@wisconsin.gov

AUTHORS
PREFACE

This is a fi rst assessment of ecological vulnerabilities 

in northern Wisconsin, with a focus on the 

Chequamegon-Nicolet National Forest. It includes 

new information created in conjunction with this effort 

and synthesizes previous information. Its primary goal 

is to inform; it does not make recommendations. This 

assessment is a fundamental component of the Climate 

Change Response Framework Project in northern 

Wisconsin. This project incorporates information 

and perspectives from numerous sources, compiles 

strategies and approaches for responding to climate 

change in forests, provides tools for climate adaptation 

planning, and initiates boundary-spanning partnerships 

and communication.

This is the fi rst version of this assessment. Scientifi c 

understanding of climate change and ecosystem 

response is rapidly growing. Additionally, ongoing 

research is being conducted specifi cally to provide 

expanded information to future versions of this 

assessment. We expect these future versions of the 

assessment to refl ect new understandings as well as 

include results from the ongoing studies.

The scope of this assessment is primarily ecological; 

it considers ecological vulnerabilities in northern 

Wisconsin with particular emphasis on tree species. It 

provides very limited direct consideration of water and 

wildlife, although these are vital issues. Additionally, 

because pest and diseases will continue to have 

profound roles in forest ecology, future modeling 

exercises will provide insights to their interactions 

with climate change. Although we provide limited 

consideration of ecosystem management implications, 

this assessment does not provide an analysis of wider 

human dimensions, such as social, infrastructural, 

and economic vulnerabilities to climate change. The 

Wisconsin Initiative on Climate Change Impacts 

(WICCI) has initiated complementary assessment 

efforts, providing deeper consideration of many these 

topics. We have closely collaborated with WICCI, 

and future versions of this assessment will refl ect 

the combined fi ndings. The Northern Forest Futures 

Project, led by the U.S. Forest Service Northern 

Research Station, is also engaged in modeling to assess 

climate impacts and forest ecosystem interactions, and 

results will prove useful in future versions.

The format we adopted for this assessment is that of a 

single document with highly interdependent chapters. 

Nonetheless, each chapter bears the imprint of a 

smaller subgroup of authors. Particular leadership and 

input was provided by Linda Parker, Leslie Brandt, 

Patricia Butler, and Matt St. Pierre. Louis Iverson 

and his team were instrumental in providing and 

interpreting information from the Climate Change Tree 

Atlas. Likewise, David Mladenoff greatly assisted 

synthesis of LANDIS-II modeling work, even while 

beginning a new project to inform the next version 

of this assessment. Dan Vimont and Michael Notaro 

(University of Wisconsin-Madison; WICCI Climate 

Group) generously provided data, fi gures, and 

expertise to this assessment. Eric Gustafson (USFS) 

and Don Waller (UW-Madison) kindly provided 

formal reviews of the assessment.
Given the uncertainty of future greenhouse gas 

emissions and related climate change, there is a 

consequent level of uncertainty in ecosystem change 

that simply cannot be fully resolved. We have 

endeavored to consider a range of climate projections, 

use different vegetation models, and combine the 

resulting mixture of information with professional 

experience to assess ecological implications. Future 

versions will include more details, but continue to 

balance the desire for defi nitive and precise predictions 

with the inherent uncertainties of the issues.

Chris Swanston and Maria Janowiak

Editors, Ecosystem Vulnerability 

and Assessment Synthesis 

A young spruce grouse in the nest.

Photo by Tom Matthiae, Chequamegon-Nicolet National Forest
CONTENTS

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

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

Chapter 2: Climate Change Science and Modeling . . . . . . . . . . . . . . . .31

Chapter 3: Climate Change in Northern Wisconsin . . . . . . . . . . . . . . . .44

Chapter 4: Climate Change Effects on Forests . . . . . . . . . . . . . . . . . . .54

Chapter 5: Implications for Forest Ecosystems . . . . . . . . . . . . . . . . . . .69

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .82

Literature Cited. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .88

Appendix 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105

Appendix 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .106

Appendix 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109

Appendix 4. Tree Species Vulnerability within 

 Northern Wisconsin Forests to Climate Changes . . . . . . 111

Abbreviations and Acronyms; Units of Measure. . . . . . . . . . . . . . . . . .142
1

EXECUTIVE SUMMARY

Wisconsin is already experiencing the effects of 

climate change, and impacts are expected to increase 

in the future. Data gathered from weather stations 

across Wisconsin indicate that the state has been 

warming since at least 1950 (Kucharik et al. 2010, 

WICCI 2009). The length of the growing season has 

increased by at least 5 days statewide, and up to 20 

days in the central and northwest regions of the state. 

Winters have fewer days below 0 °F (-17 °C) and 

the nighttime lows and daytime highs have become 

warmer throughout the year. Lakes freeze later in the 

year and thaw sooner. Phenological changes consistent 

with warming have also been observed, including 

earlier spring blooms and leafout dates. 

Although these changes may initially appear to 

be subtle, they have an increasingly strong ripple 

effect across northern Wisconsin ecosystems and 

their many interconnected components and drivers. 

Many of the most important factors that infl uence 

forest composition and distribution are expected to 

change: seasonal temperatures, the timing and type of 

precipitation, soil moisture, the severity and frequency 

of natural disturbances, and the range of pests and 

diseases. As these factors change, the ecosystems 

themselves are likely to change, often refl ecting an 

increase in the amount of stress on existing systems. 

This assessment was created to evaluate key ecosystem 

vulnerabilities to climate change across northern 

Wisconsin under a range of future climate scenarios. 

We describe the contemporary landscape and present 

major existing climate trends using state climatological 

data. We present potential future climate trends for 

this region using downscaled global data from general 

circulation models. We incorporated these future 

climate projections into species distribution and 

ecosystem process models to gain understanding of 

potential changes to northern Wisconsin forests and 

identify potential vulnerabilities. 

The following statements represent our assessment 

of potential ecosystem responses and vulnerabilities 

to a range of future climatic changes as presented 

in Chapters 3 and 4. Though the trends are already 

established for many of these responses and 

vulnerabilities, the climate scenarios evaluated are 

targeted to year 2100. Each assessment statement is 

followed by our qualitative view of its likelihood of 

occurring, using specifi c language established by the 

Intergovernmental Panel on Climate Change (IPCC 

2005).

Shifting Stressors

Climate change may relieve some stressors, while 

exacerbating others. Warmer temperatures and 

shifting precipitation patterns are expected to strongly 

infl uence ecosystem drivers.

• Temperatures will increase (virtually certain). 

Annual increases in temperature represent the 

broadest possible stressor, strongly infl uencing 

other stressors and ecosystem responses. 

• Growing seasons will get longer (virtually 

certain). Earlier spring thaws and later fi rst frosts 

in autumn could result in greater growth and 

productivity, but only if there is enough water. 

• The nature and timing of precipitation will 

change (virtually certain). Annual precipitation 

may increase, but a greater proportion of 

precipitation may occur during winter, leaving 

longer, drier summers.
2

EXECUTIVE SUMMARY

• Soil moisture patterns will change (virtually 

certain), with drier soil conditions later in the 

growing season (likely). Changing rainfall patterns 

and increased evapotranspiration are expected to 

decrease soil moisture.

• The frequency, size, and severity of natural 

disturbances will change across the landscape 

(very likely). Wind storms, ice storms, droughts, 

wildfi res, and fl oods are likely to cause greater 

damage. 

• Pests and diseases will increase or become more 

severe (very likely). Better able to survive warmer 

winters or complete a second lifecycle in one year, 

pests may expand their range and abundance.

Ecosystem Response 

to Shifting Stressors

Forest ecosystems will continue to adapt to changing 

conditions.

• Suitable habitat for many tree species will move 

northward (virtually certain). Species at the 

southern end of their range may experience greater 

stress as the suitable range moves northward, even 

as southern and invasive species gain a competitive 

advantage. 

• Many of the current dominant tree species will 

decline (likely). Many species, including balsam 

fi r, white spruce, paper birch, and quaking aspen, 

are projected to decline as their suitable habitat 

decreases in quality and extent. 

• Forest succession will change, making 

future trajectories unclear (very likely). As 

species distributions change, communities may 

fundamentally change or even disaggregate as 

increased stress, disturbance, and competition from 

nonnative species alter competitive dynamics. 

• Interactions of multiple stressors will reduce 

forest productivity (likely). Changes in hydrology, 

disturbances, and other stressors may combine to 

reduce growth rates, vigor, and health of many 

important species. 

Ecosystem Vulnerabilities

Certain species, communities, and ecosystems may be 

particularly vulnerable to severe declines in abundance 

or may be lost entirely from the landscape. 

• Risk will be greater in low diversity ecosystems

(very likely). Ecosystems dominated by a single 

species are more likely to experience severe 

degradation if that species declines. 

• Disturbance will destabilize static ecosystems

(very likely). Systems that are not resilient to 

disturbances may be particularly vulnerable as 

natural disturbances increase. 

• Climate change will exacerbate problems for 

species already in decline (very likely). Eastern 

hemlock, northern white-cedar, and yellow birch 

have been declining in northern Wisconsin. Models 

project these species’ suitable habitat to decrease 

further. 

• Resilience will be weakened in fragmented 

ecosystems (very likely). Smaller, separated 

patches often support lower species and genetic 

diversity, reducing species’ ability to adapt or 

migrate. 

• Altered hydrology will jeopardize lowland forests

(very likely). Altered precipitation regimes could 

dry peat systems or other sites that rely on saturated 

soils, leaving them vulnerable to extreme stress or 

severe wildfi re. 

• Changes in habitat will disproportionately 

affect boreal species (virtually certain). Projected 

decreases in potential suitable habitat are especially 

signifi cant for many boreal species. 

• Further reductions in habitat will impact 

threatened, endangered, and rare species 

(virtually certain). Species with very specifi c 

habitat requirements and low resilience will be 

vulnerable to changes. 

• Ecosystem changes will have signifi cant effects 

on wildlife (very likely). Species that rely on trees 

for food or habitat are likely to be impacted by 

changes in tree community composition.
3

EXECUTIVE SUMMARY

Management Implications 

Management practices have always had an important 

infl uence on forest composition, structure, and 

function, and will continue to infl uence the way that 

forests respond to climate change.

• Management will continue to be an important 

ecosystem driver (virtually certain). Management 

practices will continue to shape forests by 

infl uencing forest composition, species movement, 

and successional trajectories. 

• Many current management objectives and 

practices will face substantial challenges 

(virtually certain). Many commercially and 

economically important tree species may face 

increased stress and lowered productivity, which 

may affect the availability for some products. 

• More resources will be needed to sustain 

functioning ecosystems (virtually certain).

Impacts of climate change will increase the human 

and capital resources needed to assist regeneration 

of native species, control wildfi res, combat invasive 

species, and cope with pests and diseases. 

The analysis area for this assessment contains 11.3 

million acres of forest land in northern Wisconsin. 

These northern forests contribute signifi cantly to 

the local economy, generating billions of dollars in 

recreation- and timber-related revenue every year. The 

forests of northern Wisconsin are likely to experience 

dramatic changes during this century under a changing 

climate. Some species and forest types are particularly 

vulnerable, while others may ultimately be more 

successful. Importantly, all forests that experience 

new stressors and environmental conditions have 

the potential for decreased productivity or loss of 

forest species. Changes in forest communities will 

affect the ecosystem services they provide, such as 

clean drinking water, carbon sequestration, wildlife 

habitat, and recreational opportunities. Practicing 

long-term sustainable management and supporting 

ecosystem resilience are fundamental principles of 

forest stewardship. Applying these principles in the 

face of climate change will require both a focused 

effort to identify the ecosystems most vulnerable to 

climate change and an active dialogue about potential 

management responses to these vulnerabilities. 

A forested landscape in northern Wisconsin.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service
4
5

INTRODUCTION

The Intergovernmental Panel on Climate Change 

has documented and summarized the “unequivocal” 

evidence for climate change, incorporating information 

in their analysis from thousands of datasets spanning 

timescales of decades to millennia (IPCC 2007). 

Although the nature and severity of future climate 

change at subregional scales remains uncertain, 

there is enough information to begin assessing the 

vulnerability of species and ecosystems across a range 

of potential future climates. We defi ne vulnerability 

as “the degree to which a system is susceptible to, and 

unable to cope with, adverse effects of climate change, 

including climate variability and extremes” and 

recognize that a system’s vulnerability is related to the 

character, magnitude, and rate of climate change and 

variation that it is exposed to, as well as the system’s 

sensitivity and capacity to adapt (IPCC 2007). Some 

forests may exhibit substantial and long-term declines 

in vigor and productivity as a result of climatic 

changes; these forests may be considered vulnerable 

even if they show some resilience in community 

composition. Other forests are more clearly vulnerable 

as ecosystem function or community composition 

is severely altered. The identifi cation of vulnerable 

species and ecosystems in the near-term is a critical 

step in long-term planning. Practicing long-term 

sustainable management and supporting ecosystem 

resilience are fundamental principles of good 

stewardship. Applying these principles in the face of 

climate change will require a focused effort to identify 

the ecosystems at greatest risk and an active dialogue 

about potential management responses to the risk.

Context

This assessment is part of a larger effort in northern 

Wisconsin called the Climate Change Response 

Framework Project. The project, initiated in 2009, 

incorporates information and expertise from a wide 

variety of scientists and land managers to help identify 

specifi c challenges posed by the changing climate, as 

well as our potential responses. It was commissioned 

by the U.S. Forest Service to address the need for 

information and tools regarding climate change 

impacts and adaptation. The Chequamegon-Nicolet 

National Forest (CNNF) was identifi ed as a pilot 

landscape, and generously supplied ecological and 

management expertise to the assessment process and 

overall effort. The Wisconsin Initiative on Climate 

Change Impacts was also a major contributor of ideas 

and information to this assessment and the entire 

project. The project addresses four major questions: 

What parts of the northern Wisconsin are most 

vulnerable to the effects of climate change? This 

assessment addresses this question by compiling a 

variety of information to inform land managers in 

northern Wisconsin about the ecosystem components 

that are most vulnerable to change under a variety of 

future climate scenarios. 

What options exist to mitigate climate change?

A mitigation assessment is underway to describe 

options for increasing carbon stocks in forests and 

wood products, increasing the use of wood for 

bioenergy, and engaging in greenhouse gas markets 

and registries.
6

INTRODUCTION

How can land managers in northern Wisconsin 

work together to respond to climate change? A 

Shared Landscapes Initiative is currently underway 

to encourage local forest owners and managers 

and the public to discuss the potential ecological 

and management pressures associated with climate 

change and to evaluate opportunities for ongoing 

discussions and effective ecosystem management 

partnerships. 

How can the latest science be applied to onthe-ground activities? The project is producing 

a document called Forest Adaptation Resources 

(FAR): Climate Change Tools and Approaches for 

Land Managers. FAR includes adaption strategies 

and approaches, as well as a workbook to apply 

them in conjunction with this assessment in order 

to design place-based tactics for climate change 

adaptation. Additionally, the project established 

a climate change science roundtable to improve 

the rapid incorporation of science and monitoring 

information into management activities. The 

roundtable supports an ongoing forum for scientists 

and managers to discuss climate change science 

needs, applications of science, and monitoring 

methods.

Scope and Goals

The primary goal of this assessment is to summarize 

potential changes to the forest ecosystems of northern 

Wisconsin under a range of future climates, and 

thereby identify species and ecosystems that may be 

vulnerable. 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 18.5 million acres of northern 

Wisconsin within Ecological Province 212 (Mixed 

Laurentian Forest) of the National Hierarchical 

Framework of Ecological Units (Bailey 1995, 

ECOMAP 1993). Under this hierarchy, ecological 

units are distinguished from one another by major 

regional climatic regimes and physical geography. This 

geographic scope defi nes the analysis area used for 

much of this document (Fig. 1). We used county-level 

information that most closely represented the analysis 

area when ecoregional data were not available (Fig. 1), 

limiting our selections to the 33 counties that are most 

analogous to the area within Ecological Province 212: 

Ashland, Barron, Bayfi eld, Brown, Burnett, Chippewa, 

Clark, Door, Douglas, Florence, Forest, Iron, 

Kewaunee, Langlade, Lincoln, Manitowoc, Marathon, 

Marinette, Menominee, Oconto, Oneida, Outagamie, 

Polk, Portage, Price, Rusk, Sawyer, Shawano, Taylor, 

Vilas, Washburn, Waupaca, and Wood.

The CNNF encompasses nearly 1.5 million acres 

within the analysis area and includes all of the major 

forest types (Fig. 2). Supplementary information 

specifi c to the CNNF was used when available and 

relevant to the broader landscape. Although the CNNF 

receives some additional focus, this assessment 

synthesizes information covering all of northern 

Wisconsin in recognition of the area’s dispersed 

patterns of forest composition and land ownership.

Assessment Chapters

Chapter 1: The Contemporary Landscape describes 

existing conditions, providing background on the 

physical environment, ecological character, and social 

dimensions of northern Wisconsin. 

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: Climate Change in Northern Wisconsin

provides information on the past and current climate 

of northern Wisconsin, as well as projected changes 

provided by the Climate Working Group of the 

Wisconsin Initiative on Climate Change Impacts.
7

INTRODUCTION

Figure 1.—(A) The portion of Ecological Province 212, Mixed Laurentian Forest (Bailey 1995), within Wisconsin that served 

as the analysis area for this assessment; and (B) the 33 counties in northern Wisconsin that best approximate Ecological 

Province 212 and therefore can provide county-level information to resolve data gaps.

A B

Figure 2.—Location of the Chequamegon-Nicolet National Forest in the forested landscape of northern Wisconsin (USDA FS 

1992, USGS 2003).
8

INTRODUCTION

Chapter 4: Climate Change Effects on Forests 

summarizes results of modeling climate change effects 

on species distribution and forest ecosystem processes. 

Two different modeling approaches were used to 

model climate change impacts on forests, a species 

distribution model (the Climate Change Tree Atlas) 

and a process model (LANDIS-II).

Chapter 5: Implications for Forest Ecosystems 

synthesizes the potential effects of climate change on 

the ecosystems of northern Wisconsin and outlines 

key changes to ecosystem stressors, responses to those 

stressors, and vulnerabilities.

Morning on a lake.

Photo by Linda R. Parker, Chequamegon-Nicolet National Forest
9

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

The contemporary landscape of northern Wisconsin 

results from numerous physical, ecological, economic, 

and social factors. This chapter includes a brief 

introduction to the complex variables that make up 

Wisconsin’s northern forests and provides context for 

the modeling results and interpretations provided in 

Chapters 4 and 5.

Physical Environment

The physical environment of northern Wisconsin is the 

result of climate, soil, water, geology, landform, and 

time. The climate of the region has generally favored 

forests. It is not as cold as northern Minnesota where 

boreal forests are dominant, nor is it as dry as areas 

farther south and west, where grasslands are favored 

(Mladenoff et al. 2008). In fact, northern Wisconsin 

possesses a convergence of three major biomes; 

eastern deciduous forest in the south gives way to 

boreal forest in the north, and tallgrass prairie persists 

to the south and west, primarily in isolated remnants. 

A wide variety of landscapes support these biomes, 

further adding to the diversity of northern Wisconsin’s 

landscape. 

Climate

Signifi cant changes in climatic conditions occur 

along a well defi ned area, known as the tension zone 

(Fig. 3), that runs through central Wisconsin from the 

northwest to the southeast (Curtis 1959, WDNR 1995). 

Landscapes north of the tension zone generally have 

cooler mean annual temperatures (39 oF, 3.9 oC), 

cooler mean August high temperatures (76 to 

79 oF, 24.5 to 26.1 oC), and lower mean January low 

temperatures (-1 to 5 oF, -18 to -15 oC) than those to 

the south (Host et al. 1995, WDNR 2009a). There is 

also variation within the analysis area; temperatures in 

the southwest are warmer than in the northwest.

Winters in northern Wisconsin are long and somewhat 

severe, with extremely cold temperatures possible. 

There are usually 100 to 140 frost-free days per 

year, and this relatively short growing season has a 

strong infl uence on the type of vegetation that can be 

sustained. Minimum winter temperature is a critical 

factor controlling the distribution of plant species 

(Host et al. 1995). 

Across the north, the average annual amount of 

precipitation ranges from 29 to 32 inches (WDNR 

1999). Snow cover typically averages 140 days, 

Figure 3.—Approximate location of the tension zone in 

Wisconsin (Curtis 1959, WDNR 1995). Reproduced with 

permission from the Wisconsin Department of Natural 

Resources.
10

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

compared to 65 days in southern Wisconsin (NOAA 

2006). A signifi cant lake-effect along Lakes Superior 

and Michigan produces temperatures that are warmer 

in autumn and cooler in the spring than inland areas 

at the same latitude. Moisture-laden air from Lake 

Superior often rises rapidly over nearby uplands, 

resulting in heavy precipitation (Albert et al. 1986). 

Northern Iron County typically receives 100 inches of 

snow a year, more than any other area in Wisconsin.

Chapter 3 provides more details on historical climate, 

current climate trends, and projected climatic trends 

for northern Wisconsin.

Geology and Landform

Bedrock geology in northern Wisconsin is largely 

formed by Precambrian rock that is more than 600 

million years old (Ostrum 1981). Bedrock outcrops 

are relatively uncommon, although they can be 

biologically signifi cant for a number of rare plants 

(such as at the Penokee-Gogebic Range). The surface 

geology is the result of Pleistocene glaciations. 

Glacial ice modifi ed the land surface as it retreated 

over bedrock of igneous rock, sedimentary rock, 

limestone, and sandstone; leaving behind a wide 

variety of landforms, huge deposits of glacial debris, 

and the depressions which would become lakes and 

wetlands (Stearns 1987). The most prominent glacial 

landforms are moraines, till plains, outwash plains, 

drainage channels, drumlin fi elds, eskers, ice-walled 

lake plains, extinct glacial lakes, and kettle lakes. 

Glacial activity also created a globally signifi cant 

concentration of lakes and an abundance of wetlands 

(WDNR 2009a).

Soils

Glacial deposits of coarse outwash sands, fi ne-textured 

clays, and tills serve as the parent material for the soils 

in of northern Wisconsin. These soils, by comparison, 

are less productive than soils found in southern 

Wisconsin. Hole (1968) described and mapped the soil 

regions of Wisconsin. Clay soils largely line the shores 

of Lake Superior. A large peninsula of sandy outwash 

soils extends from Grantsburg in Burnett County 

through the Bayfi eld County Peninsula. Other large 

areas of outwash sands are found in Vilas, Oneida, 

Marinette, and eastern Florence Counties. Rich silty 

soils dominate portions of Sawyer, Price, Rusk, Taylor 

and Marathon Counties. Loamy soils are widespread 

throughout the area, but are especially common in the 

northeast counties of Forest and Langlade.

Soils on the Chequamegon-Nicolet National 

Forest—Medium-productivity sandy loam soils are 

widespread on the CNNF; they cover 34 percent of 

the land base. Wetland and organic soils (28 percent), 

highly productive silt loams (22 percent), and low 

productivity sand-dominated soils (16 percent) 

cover the remainder (USDA FS 1998d). The depth 

of sediments over bedrock ranges from 0 to 393 feet 

and averages more than 48 feet. Change in elevation 

exceeds 300 meters and varies by landform. Generally, 

the topography is level to rolling with 5 to 20 percent 

slopes, with some areas hilly to very steep (greater 

than 35 percent).

Wet mineral and organic soils occur on about 424,000 

acres (28 percent of the Forest). These wetlands, in 

varying sizes and shapes, are scattered throughout 

the landscape but primarily concentrated at lower 

elevations in old glacial drainage ways and kettles. 

Wetland soils, which vary from 1 to 32 percent of 

individual Landtype Associations, are primarily 

acid-to-neutral peats and mucks that formed from the 

remains of woody and herbaceous plants (USDA FS 

1998d).
11

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Hydrology 

Climate interacts with the landscape to create a typical 

hydrologic response for any given area. In northern 

Wisconsin, this response can be divided into three 

broad hydrologic regimes: surface, groundwater, 

and mixed. Climate is the primary determinant of 

the seasonal pattern and total amount of runoff (a 

combination of snowfall, snowmelt, rainfall, and 

evapotranspiration). The state receives an average of 

32 inches of precipitation annually, with 12 inches 

going to streamfl ow or groundwater and 20 inches 

lost to evapotranspiration. The typical hydrologic 

pattern is seasonal (Fig. 4). Winter is a low-fl ow period 

with most water stored as snow or ice, and spring 

is a period of high fl ows resulting from snowmelt, 

rainfall, and high antecedent moisture conditions. 

Basefl ows decline in summer as a result of high 

evapotranspiration losses, lessened by occasional 

runoff from rainstorms. Autumn is similar to summer 

but with higher basefl ows resulting from reduced 

evapotranspiration and higher antecedent moisture 

conditions in the watershed.

At a more local level, differences in hydrologic 

regime primarily stem from watershed characteristics 

such as landform, soil, geology, vegetation, and land 

use. Surface runoff regimes are most responsive to 

rainfall and snowmelt events, with maximum runoff 

in watersheds where limited storage capacity results in 

a relatively rapid transmission of water into streams. 

Surface runoff watersheds have the lowest basefl ows 

per unit of drainage area (0.05 to 0.25 cubic feet per 

second per square mile [cfsm]) and higher peak fl ows 

associated with both snowmelt and stormfl ow runoff. 

Surface runoff watersheds tend to have one or more of 

the following characteristics: lacustrine clay deposits, 

fi ne textured glacial till, shallow soils over bedrock, 

fragipans, other fi ne textured or heavy soils, and peat 

bogs. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Average Flow (cfsm)

Groundwater Mixed Surface

Figure 4.—Typical patterns and levels for three streamfl ow regimes in northern Wisconsin.
12

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Groundwater runoff regimes are least responsive to 

surface runoff from storms or snowmelt and have high, 

stable basefl ows (0.5 to 1.0 cfsm) fed by a continuous 

supply of groundwater. Watersheds with groundwater 

runoff regimes generally have coarse textured glacial 

outwash or till that favors groundwater recharge 

and fen-type wetlands that discharge groundwater. 

They also tend to have suffi cient elevational relief to 

produce a steady fl ow of groundwater from uplands 

to streams. Mixed runoff watersheds typically have 

landform and soil characteristics that are intermediate 

between the surface and groundwater regimes, and 

basefl ows ranging from 0.25 to 0.5 cfsm. 

Flood fl ows in northern Wisconsin 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 infi ltration capacity. Exceptions include: the 

clay plains along Lakes Superior and Michigan which 

have low infi ltration capacity, less storage, and steep 

slopes in some locations; the Penokee-Gogebic range 

with shallow soils over bedrock and steeper slopes; 

and areas of fi ne textured till with low infi ltration 

capacity. Across much of northern Wisconsin, 100-

year fl oods (1.0 percent chance of being exceeded in 

any given year) typically range from 6 to 33 cfsm and 

2-year fl oods (66.7 percent chance of being exceeded 

in any given year) from 2.6 to 12.7 cfsm (Walker and 

Krug 2003). In those watersheds with higher fl ood 

fl ow rates, 100-year fl oods tend to range from 40 

to 220 cfsm and 2-year fl oods from 13 to 40 cfsm. 

Annual fl ood peaks in northern Wisconsin are caused 

by both snowmelt and rainfall runoff in about equal 

proportions. 

Ecosystem Composition

Northern Wisconsin’s position at the intersection of the 

eastern deciduous forest biome and the boreal forest 

biome creates a complex and somewhat unique set of 

ecological conditions. A major change in vegetation 

occurs along the tension zone (Fig. 4), where the open 

landscape of the south (once prairie and oak savanna 

but now predominantly agricultural lands) transitions 

into the mixed deciduous-coniferous forests of the 

north (WDNR 2009a). Although still dominated by 

forest, the northern landscape also contains substantial 

components of agricultural land, wetlands, and other 

land uses (Fig. 5; WDNR 1998a). 

The northern forest’s native tree species are primarily 

hardwoods (such as quaking aspen, and sugar and 

red maple) with a smaller proportion of softwoods 

(such as eastern hemlock, balsam fi r, and pines), but 

differences in landform, soils, and natural and human 

disturbances produce a great deal of variation across 

the entire landscape. Beech and eastern hemlock are 

at the western edge of their range in Wisconsin, and 

many species are at the southern extent of their range 

in Wisconsin, including jack pine, balsam fi r, yellow 

birch, black spruce, and white spruce. 

Natural Communities

A natural community is an assemblage of plant 

and animal species living together in a specifi c 

habitat and location (WDNR 2009b). It is a broader 

classifi cation than forest type, which is described 

later in this chapter. Communities may be named for 

their dominant plant species (such as pine barrens), 

a prominent environmental feature (such as Great 

Lakes dune or dry cliff), or some combination of 

these factors. The dominant communities of northern 

Wisconsin can be generally put into one of four 

categories: northern forest, barrens, wetlands, and 

aquatic.

Northern forest communities—Forest communities 

of the north range from northern dry forest on sandy 

outwash landforms to forested wetlands, which 

include black spruce and northern hardwood swamps. 

Developing on the loamy soils between these extremes 

is a mesic forest that forms the backdrop for the 

northern landscape. Before European settlement, 

northern hardwood forest covered the largest acreage 

of any Wisconsin vegetation type. Today’s second-
13

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

46%

21%

17%

8%

5%

1% 1% 1%

Forest

Agriculture

Wetland

Grassland

Open water

Urban

Shrubland

Barren

Figure 5.—Land cover in northern Wisconsin (WDNR 1998a).

growth forest is still extensive, although structurally 

and compositionally altered. Sugar maple is dominant 

or co-dominant in most stands. Eastern hemlock and 

yellow birch, once co-dominants, occur more rarely.

Barrens communities—Barrens are plant communities 

that occur on sandy soils and are dominated by 

grasses, low shrubs, small trees, and scattered large 

trees. Inclusions of variously sized and aged forest 

stands (such as mature red pine, mature oak, and aspen 

groves) and numerous wetlands are typical of most 

pine and oak barrens. The most common tree within 

pine barrens is jack pine, but red pine may also be 

present, especially in Bayfi eld County. Hill’s oak and 

bur oak may be present as scrub or as a scattering of 

larger trees. Understories are composed of grasses, 

sedges, and forbs, many of them associated with dry 

prairies. Plants of the heath family, such as blueberries 

and bearberry, and shrubs such as prairie willow, 

hazelnut, and redroot, are often prominent members of 

the barrens fl ora.

Wetland communities—Wetland communities have a 

common characteristic: their soil, or other substrate, 

is periodically saturated with or covered by water. A 

wetland is defi ned in the Wisconsin Statutes as “an 

area where water is at, near, or above the land surface 

long enough to be capable of supporting aquatic or 

hydrophytic vegetation and which has soils indicative 

of wet conditions” (WDNR 2009b). Included in the 

wetland community classifi cation are black spruce 

swamps, fl oodplain forests, northern hardwood 

swamps, northern wet forests and tamarack swamps.

Aquatic communities—Although aquatic communities 

are beyond the scope of this assessment, Wisconsin 
14

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

has a large and diverse aquatic resource that supports 

numerous species, communities, ecological processes, 

and human uses. In addition, many terrestrial species 

and processes are dependent on neighboring aquatic 

systems. 

Forest Composition and Abundance

Many forest types are present in the 11.3 million 

acres of forest land in the analysis area (FIA 2010). 

Organizations defi ne these forests using different 

classifi cation systems. This assessment uses two 

classifi cation systems: one designed by the USDA 

Forest Service Forest Inventory and Analysis 

(FIA) program, and another system of species and 

species associations used by CNNF. Estimates of 

forest characteristics by acres, type of ownership, 

and volume of timber by forest-type group were 

determined using the FIA data, which are derived 

from permanent plots across the United States 

(Miles 2010). On the scale of northern Wisconsin, 

these data reasonably refl ect the forests that are 

present. However, when studying smaller areas, it is 

possible to provide data using forest types, which are 

more specifi c to this region. Using the same forest 

classifi cation system as the CNNF also facilitates ease 

of communication and data sharing. Even so, these 

systems use similar groupings, though by different 

names. For example, the FIA maple/beech/birch foresttype group is largely synonymous with the CNNF 

northern hardwood forest type (though in Wisconsin, 

beech is present only in the eastern counties along 

Lake Michigan), and the FIA elm/ash/cottonwood 

forest-type group encompasses the CNNF lowland 

hardwood forest type. 

Across northern Wisconsin, maple/beech/birch (3.2 

million acres), aspen/birch (2.8 million acres), oak/

hickory (1.7 million acres), and spruce/fi r (1.5 million 

acres) forest-type groups are most abundant (Table 1; 

Miles 2010). The pine and lowland hardwoods foresttype groups are less common. 

Forest Types on the Chequamegon-Nicolet National 

Forest—The CNNF is primarily forested (85 percent) 

but also includes open areas (9 percent upland and 4 

percent lowland) and water features (2 percent). Of the 

forested acreage, northern hardwood and aspen forests 

cover nearly 60 percent of the land base followed by 

lowland conifer and red pine forest (Fig. 6). Lowland 

conifer forests cover 32 percent of forested land in 

the CNNF compared to only 11 percent on privately 

owned forest lands (P.E. Pingrey, unpublished data). 

Changes in Forest Ecosystems

Profound changes to northern ecosystems occurred 

between the 1850s and the early 1930s. Logging of 

eastern white pine began as early as the 1830s and 

peaked at the end of the century. The amount and 

extent of slash left after logging fueled intense and 

catastrophic fi res across most of northern Wisconsin. 

By the 1930s nearly all of the primary forest had been 

harvested or burned (WDNR 2009a). Clearcutting, 

slash burning, and stream and river modifi cations 

during the logging era, combined with repeated cutting 

and the suppression of natural disturbances, may have 

resulted in long-term changes in the ecosystems of 

northern Wisconsin (USDA FS 1998b). While pioneer 

species represented little of the northern forest before 

European settlement, a single pioneer community, the 

aspen/birch forest-type group, currently occupies about 

25 percent of the area (Miles 2010). 

Although the average age of long-lived tree species 

continued to increase from 1983 to 1996, the area 

occupied by stands more than 100 years old continued 

to decrease from already low amounts (Schmidt 

1997, Spencer et al. 1988). Frelich and Reich (1996) 

estimated that the current acreage (911,799 acres) 

of primary (unlogged) forest in 1996 in Minnesota, 

Wisconsin, and Michigan represented 1.1 percent of its 

1850 abundance (80,769,511 ac). 
15

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Forest-type group Percent cover Characteristic species (dominants and associates)

Maple/beech/birch 28 Sugar maple Basswood Quaking aspen

 (Acer saccharum) (Tilia americana) (Populus tremuloides)

 American beech White ash Yellow birch

 (Fagus grandifolia) (Fraxinus americana) (Betula alleghaniensis)

 Red maple Red oak 

 (Acer rubrum) (Quercus rubra)

Aspen/birch 25 Quaking aspen Red maple 

 (Populus tremuloides) (Acer rubrum)

Big-tooth aspen Balsam fi r 

 (Populus grandidentata) (Abies balsamea)

 Paper birch

 (Betula papyrifera)

Oak/hickory 13 Red oak Red maple Black oak 

 (Quercus rubra) (Acer rubrum) (Quercus velutina)

White oak Quaking aspen Bur oak 

 (Quercus alba) (Populus tremuloides) (Quercus macrocarpa)

Northern pin oak White pine Black cherry

 (Quercus ellipsoidalis) (Pinus strobus) (Prunus serotina)

Spruce/fi r 12 White spruce Northern white-cedar 

 (Picea glauca) (Thuja occidentalis)

 Balsam fi r Tamarack 

 (Abies balsamea) (Larix laricina)

 Black spruce 

 (Picea mariana)

Elm/ash/cottonwood 9 Black ash American elm 

 (Fraxinus nigra) (Ulmus americana)

Green ash Silver maple 

 (Fraxinus pennsylvanica) (Acer saccharinum)

 Red maple 

 (Acer rubrum)

White/red/jack pine 8 White pine Red oak Quaking aspen 

 (Pinus strobus) (Quercus rubra) (Populus tremuloides)

Red pine Northern pin oak Big-tooth aspen 

 (Pinus resinosa) (Quercus ellipsoidalis) (Populus grandidentata)

Jack pine Red maple 

 (Pinus banksiana) (Acer rubrum)

Oak/pine 3 Red oak White ash Sugar maple 

 (Quercus rubra) (Fraxinus americana) (Acer saccharum)

White pine Basswood 

 (Pinus strobus) (Tilia americana)

Red maple Big-tooth aspen 

 (Acer rubrum) (Populus grandidentata)

Other 1 Includes mixed species stands not easily categorized and areas that cannot support 

forest cover because of rocks, water, and other impediments to stocking

Table 1.—Forest type groups of northern Wisconsin.
16

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

34%

25%

14%

8%

3%

3%

3%

3%

2% 2% 2% 1%

Northern hardwoods

Aspen

Lowland conifer

Red pine

Oak

Lowland hardwoods

Spruce

Jack pine

Paper birch

Balsamfir

White pine

Hemlock

Figure 6.—Area of forest types in the Chequamegon-Nicolet National Forest in 2007 (M.A. Theisen, unpublished data).

Other changes to forest composition have occurred. 

Pine plantations have largely replaced pine barrens, 

and fi re suppression has also allowed some 

successional advance of pine barrens to pine forests 

(WDNR 1995). The decline of pine barrens has 

resulted in species viability problems for several 

savannah-associated fauna, such as sharp-tailed grouse 

(Temple 1989) and upland sandpiper (Robbins 1997). 

Jack pine, aspen, oak, and maple forests have largely 

replaced white and red pine forests. 

Strip cutting, beaver fl ooding, and deer herbivory have 

led to declines in northern white-cedar. For example, 

only 1.5 percent of the Chequamegon land base of 

the CNNF is typed as cedar, and less than 0.9 percent 

of the entire Forest contains cedar that is at least 100 

years old (USDA FS 1998c). Following disturbance, 

cedar typically converts to alder, which does not 

support the same diversity of species. Northern 

white-cedar swamps are among the most fragile plant 

communities in the CNNF, containing many rare 

orchid species, such as ram’s head lady’s slipper and 

calypso.

Forests and landscape patterns in northern Wisconsin 

are heavily infl uenced by land ownership and use. The 

largest patch sizes of forest are found on public lands, 

whereas the smallest patches are present on private 

lands. However, even public lands have relatively 

few large patches (those over 100 acres in size) of 

continuous cover types, which increases the diffi culty 

of monitoring and managing forest ecosystems. 

Across northern Wisconsin, large forested patches 

only cover about 13 percent of the landscape. Most 

lands are patchworks of small parcels, and some forest 

types have less than 10 percent of their acreage in 

large patches (USDA FS 1998a). Large patches of 

upland conifers, wetlands, and forested wetlands are 

especially scarce relative to other vegetation types in 

northern Wisconsin (USDA FS 1998a).
17

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Natural disturbance regimes—Severe wind and fi re 

events are the primary natural disturbances responsible 

for the vegetation patterning in northern Wisconsin. 

Before European settlement, wind events such as 

gales, derechos, and tornadoes occurred in all forest 

types and were more frequent and affected more area 

than fi res (Schulte and Mladenoff 2005). Fire events 

were more likely to occur in fi re-dependant forest 

types, such as jack, red, and white pine forests. The 

role of fi re in the natural disturbance regime of mesic 

upland hardwood forests was minimal. The return 

intervals of stand-replacing fi res 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 

pre-settlement times (Cleland et al. 2008). 

Windthrow events vary in extent and in the degree 

of tree mortality that they cause, in part, because 

of differences in susceptibility among species and 

age classes (Rich et al. 2007). Return intervals 

for windthrow events such as derechos 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 infl uenced the vegetation structure of 

northern Wisconsin (WDNR 2007). Before European 

settlement, outbreaks were caused by native species. 

For example, outbreaks of jack pine budworm and 

spruce budworm have been an important agent of 

mortality for their host species. Historically, wildfi res 

commonly occurred in areas of high mortality 

following such outbreaks. More recently, insect and 

disease outbreaks have occurred at an increasing 

frequency as a consequence of the increasing rate of 

introduction and establishment of nonnative insects 

and disease agents. Emerald ash borer, a beetle 

that can cause near complete mortality of ash tree 

populations throughout the eastern United States, 

has been confi rmed in several Wisconsin locations. 

Outbreaks of the nonnative gypsy moth have caused 

oak mortality and fungal diseases such as oak wilt and 

butternut canker, resulting in tree mortality on smaller 

scales. European earthworms are discussed less often, 

but may have wide-ranging and profound effects 

on northern forests, which have not experienced 

earthworm activity for millennia (Bohlen et al. 2004). 

Climatic changes, such as prolonged heat and drought 

stress, can lead to increases in incidence or severity of 

insect and disease outbreaks, although it may also slow 

the spread of pests such as earthworms.

Invasive plant species—Nonnative plant species 

have become an increasing concern across northern 

Wisconsin because of their potential to outcompete 

native species and impact 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). The 

Wisconsin Department of Natural Resources recently 

completed a statewide classifi cation of invasive 

species in Wisconsin (WDNR 2009c), and in northern 

Wisconsin, several cooperative weed management 

areas have been established to control invasive plant 

species across political boundaries. The CNNF, like 

other land managers in northern Wisconsin, is actively 

combating the spread of nonnative, invasive plants 

with integrated pest management tools that include 

prescribed fi re, mechanical treatments, and herbicide 

application (Table 2).

Current vulnerabilities—The current forest 

ecosystems of northern Wisconsin continue to be 

exposed to a wide range of natural and human-caused 

threats. Individual threats or interactions among threats 

increase the vulnerability of these systems to declines 

in productivity or abundance (Table 3), many of which 

are likely to be exacerbated by climate change.
18

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Table 2.—Nonnative plant species targeted for eradication on the Chequamegon-Nicolet National Forest.

Woody shrubs

 Buckthorn (Rhamnus cathartica & frangula)

 Asiatic honeysuckles (Lonicera sp.)

 Japanese barberry (Berberis thunbergii)

 Siberian pea shrub (Caragana arborescens)

 Oriental bittersweet vine (Celastrus orbiculatus)

 Autumn olive (Elaeagnus umbellata)

Grasses

 Reed canarygrass (Phalaris arundinacea)

 Common reed (Phragmites australis)

Composites

 Canada thistle (Cirsium arvense) 

 European marsh thistle, swamp thistle (Cirsium palustre) 

 Spotted knapweed (Centaurea biebersteinii)

 Bull thistle (Cirsium vulgare)

Aquatic Plants

 Curly pondweed (Potamogeton crispus)

 Eurasian water milfoil (Myriophyllum spicatum)

Other herbaceous plants

 Garlic mustard (Alliaria petiolata)

 Leafy spurge (Euphorbia esula)

 Purple loosestrife (Lythrum salicaria)

 Japanese knotweed (Polygonum cuspidatum)

 Bishop’s goutweed (Aegopodium podagraria)

 Wild parsnip (Pastinaca sativa)

 Common mullein (Verbascum thapsus)

 Brittle-stem hemp-nettle (Galeopsis tetrahit)

 Forget-me-not (Myosotis arvensis and scorpoides)

Extent Current threats Vulnerability to threats

All forests in northern Wisconsin Landscape fragmentation; increasing 

density of roads and housing1,2,3

Management leading to changes in 

forest composition4,5,6

Changes in natural disturbance regimes 

and landscape processes2,7

Invasive species3,8

Disease agents3,9

Drought3

Carbon dioxide and ozone pollution

Loss of characteristic, repeating 

landscape patterns5

Less shade and humidity; warmer patch 

interiors1,3

Over-dominance of maple and loss of 

rare and uncommon species4,3,5,10

Ecological simplifi cation of structures 

and age classes6,8,11,12

Severe loss of forest cover7

Loss of forests older than 100 years13

Decline of associated rare species7

Decline of associated wildlife 

species14,15

Decreased productivity16,17

Aspen: Dominated by quaking aspen, 

bigtooth aspen, or balsam poplar. Some 

stands may have co-dominant tree 

species such as balsam fi r or white 

spruce.

Ozone pollution16,18

Partial harvest damage to residual 

stand19

Repeated clearcut harvests19

Fire suppression in natural systems22

Gypsy moth and tent caterpillar19

Hypoxylon canker and white trunk rot19

Reduced short-term productivity 

following ozone exposure16,17,20

Reduced vigor of clones after multiple 

harvests on sandy soils, or compacted 

fi ne soils19

Decline in conifer component and 

decreased habitat for dependent 

wildlife21

Low diversity in structure and species 

composition19,22

Reduced productivity or adult mortality 

from insects, disease, or interaction of 

insect and disease agents19

Table 3.—Summary of current threats and vulnerabilities for forests in northern Wisconsin.
19

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Table 3 (continued).—Summary of current threats and vulnerabilities for forests in northern Wisconsin.

 Extent Current threats Vulnerability to threats

Balsam fi r: Dominated by balsam fi r; 

some stands may include a component 

of quaking aspen or paper birch.

Armillaria root disease and balsam fi r 

bark beetle3

Drought, heart rot, and butt rot19

Reduced growth rates and increased 

mortality19

Decline in associated species, such as 

white spruce19

Hemlock: Dominated by eastern 

hemlock. Yellow birch and sugar maple 

are sometimes co-dominant.

Rarity relative to pre-European 

abundance11

Habitat fragmentation

Habitat loss or conversion to hardwoods

Management resulting in loss of needledominated forest fl oor23,24

Deer browse

Drought

Remaining stands isolated and few24

Loss of microsite conditions for seedling 

establishment, such as nurse logs, bare 

mineral soil, and moisture23,24

Regeneration failure and adult mortality 

resulting from drought23,24

Seedling and sapling mortality resulting 

from deer browse23,24

Reduction of long-lived softwoods 

resulting in negative effects on several 

bird species13

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 fi re regimes19

Jack pine budworm, pine bark beetle, 

and pine tussock moth19

Conversion to red pine

Concentration of trees in early age 

classes; reduced structural and species 

diversity3,19

Decline of jack pine resulting from 

budworm attacks5

Challenges to maintaining bird species 

reliant on young jack pine25

Defoliation, reduced growth, and 

mortality19

Lowland conifer: Stands in low-lying 

sites that 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 co-dominant in some 

stands.

Road development; drought19

Flooding of lowlands by beavers13

Deer browse19,26,37

Invasive plants such as glossy 

buckthorn, European swamp thistle, 

and dwarf mistletoe19

Larch sawfl y and larch casebearer19

Altered hydrology resulting in 

unfavorable soil conditions19

Conversion to tag alder following 

disturbance, resulting in loss of cedarassociated rare and endangered 

understory plants and habitat for 

endemic boreal birds13,27

Regeneration failure and adult mortality 

of cedar resulting from a combination of 

deer browse, competition, fl ooding, and 

other disturbances19,26,28,37

Reduced growth or adult mortality 

of tamarack resulting from sawfl y 

defoliation or windthrow19

Lowland hardwood: Stands in lowlying sites that are dominated primarily 

by black ash, red maple, American elm, 

or a mixture of these species.

Drought; human caused changes in 

hydrology

Deer browse19

Sedge invasion after disturbance

Emerald ash borer

Dutch elm disease

Loss of suitable soil conditions, 

resulting in seedling and sapling 

mortality19

Potential for loss of all ash species from 

emerald ash borer infestation

Loss of elm if Dutch elm disease 

interacts with other stressors

(Table 3 continued on next page.)
20

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Northern hardwood: Stands 

composed largely of sugar and red 

maple. Eastern hemlock, yellow birch, 

basswood, red oak, and black cherry 

are also likely to be found in varying 

amounts depending on site conditions.

Management practices that promote 

over-dominance of maple6

Biomass management that promotes 

younger and smaller diameter trees3

Drought, fl ooding, and late spring 

frosts3,19

Emerald as borer, birch leaf miner, 

gypsy moth, forest tent caterpillar, other 

insect pests, and earthworms10,19,29,30

Beech bark disease, cankers, 

ash yellows, rots, and other forest 

diseases31,32

Japanese barberry, garlic mustard, and 

bush honeysuckle19,38

Deer browse33

Interaction of multiple stressors3,4,10

Loss of ashes, eastern hemlock, beech 

and other species3,6,31

Reduced structural and tree species 

diversity3,6,19

Few logs for cavity trees, snags, and 

coarse woody debris3

Drought leading to drying of ephemeral 

ponds and habitat loss for associated 

species15,34

Degradation of the soil organic layer, 

soil structure, and soil carbon storage 

from earthworm activity10,29,35

Physical damage to seedlings and 

understory plants from earthworm 

activity and deer browse10,33

Homogenization and degradation of the 

understory from deer and invasive or 

nonnative species4,10,33

Regeneration failure for co-dominant 

tree species, especially eastern 

hemlock and yellow birch4,19,26

Decline of ground-nesting birds, and 

other associated plant and animal 

species14,19

Oak: Dominated by one or more oak 

species. Aspen, eastern white pine, and 

other species may be co-dominant in 

some stands.

Fire suppression8

Drought3,36

Deer browse26

Gypsy moth, oak wilt, and two-lined 

chestnut borer19

Ozone and sulfer dioxide pollution

Regeneration failure resulting from 

shade-tolerant species competition8

Oak decline on mesic and dry-mesic 

sites

Defoliation resulting in secondary 

infections and mortality19

Stunted individuals that will not recover 

from ozone or sulfur dioxide pollution19

Interactions of drought, pests, and 

deer browse could result in widespread 

mortality3,26

Paper birch: Dominated by paper birch 

sometimes with components of aspen 

or balsam fi r.

Fire suppression

Deer browse19

Birch leaf miner and birch dieback

Lack of site preparation normally 

provided by fi re11

Failure to re-sprout after deer browse19

Regeneration failure resulting from 

interspecies competition19

Mortality if drought and other stressors 

interact19

Table 3 (continued).—Summary of current threats and vulnerabilities for forests in northern Wisconsin.

 Extent Current threats Vulnerability to threats
21

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

1

Gonzalez-Abraham et al. 2007, 2Radeloff et al. 2005, 3WDNR 2010a, 4Powers and Nagel 2009, 5Radeloff et al. 1999, 6

Crow et al. 2002, 7Canham and Loucks 1984, 8Nowacki et al. 1990, 9Schwingle 2010, 10Bohlen et al. 2004, 11Schulte et al. 

2007, 12Rooney et al. 2004, 13USDA FS 2000, 14Martin et al. 2009, 15WDNR 1995, 16Pregitzer et al. 2008, 17Karnosky et al. 

2003a, 18Karnosky et al. 2003b, 19WDNR 2010c, 20Karnosky et al. 2005, 21Zollner et al. 2008, 22Cleland et al.2001, 23Rooney 

et al. 2000, 24Mladenoff & Stearns 1993, 25Donner et al. 2009, 26Alverson et al. 1988, 27USDA FS 1998c, 28Heitzman et al. 1999, 29Hale et al. 2008, 30DATCP 2010, 31WDNR 2010b, 32WCF 2009, 33Rooney and Waller 2003, 34WDNR 2005, 35Gundale 2002, 36Rogers et al. 2008, 37Rooney et al. 2002, 38WDNR 2004.

Table 3 (continued).—Summary of current threats and vulnerabilities for forests in northern Wisconsin.

 Extent Current threats Vulnerability to threats

Red pine: Dominated by red pine. 

Some stands have an oak component 

in the understory and sometimes as a 

co-dominant.

Plantation management13

Fire suppression5

Drought and high surface soil 

temperatures19

Pine tussock moth, red pine sawfl y, red 

pine midge, and shoot blights19

Increased stress on stands not naturally 

suited to site conditions13

Reduced diversity in planted stands; 

natural plant and animal assemblages 

are not present13

Seedling mortality5,19

Reduced growth, topkill, tree mortality19

Spruce: Generally dominated by white 

spruce (occasionally black spruce or 

Norway spruce). Some white spruce 

stands may have co-dominant tree 

species such as balsam fi r or quaking 

aspen.

Spruce decline

Spruce budworm, especially in 

overmature and overstocked stands19

Defoliation, reduced growth, and 

mortality19,27

Loss of associated species, including 

a unique assemblage of largely boreal 

birds13

Loss of spruce grouse habitat15

White pine: Dominated by eastern 

white pine. Some stands may include 

a component of eastern hemlock or 

northern red oak and white ash.

Rarity on the landscape relative to 

pre-European settlement

Fire suppression

White pine blister rust and white pine tip 

weevil19

Conversion to hardwoods5

Loss of supercanopy habitat for hawks, 

great blue herons, osprey, and bald 

eagles19

Stem deformity and tree mortality19

Fishermen on Kawaguesaga Lake in northern Wisconsin.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service
22

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Wildlife 

Northern Wisconsin 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 state, including woodland caribou, bison, 

and wolverine. Others were lost but have been 

reintroduced, including the gray wolf, elk, and 

American marten. The gray wolf population in 

Wisconsin has grown from approximately 25 in 1980 

to approximately 650 in the winter of 2008-2009 

(Wydeven et al. 2009), with the majority of packs 

in located in the northern forests. A reintroduction 

of 25 elk in 1995 into the Clam Lake area (Ashland 

County) has not experienced the same level of success; 

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 northern Wisconsin in 

the early 1900s following widespread logging but 

successfully reintroduced in the 1950s and 1960s with 

rapid expansion of the population in the 1980s (Kohn 

et al. 1993). Trapping of fi sher in Wisconsin began in 

1985 and continues today. American marten, likewise, 

were extirpated from northern Wisconsin following 

the logging era but reintroduction efforts have not 

been as successful. The species remains protected 

from trapping in Wisconsin despite population 

sizes suffi cient to allow harvesting in neighboring 

Minnesota and Michigan (Williams et al. 2007). 

White-tailed deer are perhaps the wildlife species 

most identifi ed with northern Wisconsin. Deer hunting 

is a strong tradition throughout the state (Willging 

2008) and many hunters travel to northern Wisconsin 

each autumn. The 2008 post-hunt white-tailed deer 

population in the Northern Forest region of Wisconsin 

was estimated at 270,000 animals, the lowest it has 

been in 15 years (Rolley 2009; Fig. 7). The long-term 

goal is to maintain a population of 270,000 animals, 

which is 70 percent of the carrying capacity (WDNR 

1998b). 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). 

Bald eagles favor super-canopy eastern white pines in 

the vicinity of fi sh-laden rivers and lakes for nesting 

sites. The bald eagle population in northern Wisconsin, 

and throughout the state, has steadily increased since 

the late 1960s following the ban on DDT. Statewide, 

the bald eagle population has increased from 108 

active nests in 1973 to 1,142 nests in 2008 (Eckstein et 

al. 2008). Nearly 900 of those nests are present in the 

northern counties of Wisconsin. 

Ruffed grouse are a common resident bird throughout 

all but the southeastern corner of Wisconsin (Robbins 

1991). Ruffed grouse occurred statewide at the time of 

European settlement and were thought to be common 

in most areas, but relatively less common in the virgin 

conifer-hardwood forests of the north (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).

Beaver, which were found throughout Wisconsin 

before 1800, were trapped heavily during European 

settlement and their statewide population had been 

reduced to 500 by 1900. Restricted trapping and 

favorable habitat changes resulted in a rapidly 

growing beaver population, and from 1940 to 1960 

the population may have exceeded the historical level 

(Knudsen 1963). Beaver populations in the early 1950s 

were estimated at 120,000 to 170,000 in northern 

Wisconsin, and 50,000 in southern Wisconsin. Beaver 
23

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

populations grew to more than 200,000 from 1980 

to 1987, then steadily declined to 170,000 in 1990 

and 115,500 in 1992 (Kohn and Ashbrenner 1993). 

Beaver population estimates have continued to decline 

throughout northern Wisconsin, with only 45,000 

reported in a 2008 aerial survey of the northern third 

of the state, a decrease of more than 50 percent since 

1995 (Rolley et al. 2008).

Brook trout is the only trout native to streams in 

northern Wisconsin although nonnative brown 

trout and rainbow trout can be found in lakes and 

stream systems throughout northern Wisconsin. The 

introduction of nonnative trout, habitat alteration 

by beaver and by humans, and heavy fi shing 

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, and this dependence may make the 

species particularly vulnerable to a warming climate.

Breeding bird surveys conducted throughout the 

CNNF have recorded the presence of approximately 

175 species over roughly 20 years (Etterson et al. 

2007, Howe and Roberts 2005). Both positive and 

negative trends in abundance have been observed 

for some of those species. Declines are attributed to 

loss or fragmentation of mature forest, loss of habitat 

in wintering areas, mortality during migration, and 

pesticide use on wintering grounds. Asynchrony 

between nesting of migratory birds and the emergence 

of their food source (such as insects) because of 

climate change may further contribute to declining 

trends (Cotton 2003). Species associated with early 

successional, wetland, and shrub habitats (such 

as red-winged blackbird, common yellowthroat, 

mourning warbler, and brown thrasher) are also 

showing declines. Decline of pine barrens has resulted 

0

100

200

300

400

500

600

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

Post-hunt deer popula

Ɵon (thousands)

Year

GOAL

Figure 7.—Post-hunt deer populations from 1981 to 2009 in the Northern Forest Deer Management Region. Figure modifi ed 

from Rolley (2009) and reproduced with permission of the Wisconsin Department of Natural Resources.
24

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

in species viability problems for several savannahassociated fauna, such as sharp-tailed grouse (Temple 

1989) and upland sandpiper (Robbins 1997). Jack 

pine, aspen, oak, and maple forests have largely 

replaced white and red pine forests. Decline in mixed 

coniferous-deciduous forest has resulted in declining 

populations of birds associated with long-lived 

conifers. Of the 42 conifer-dependent bird species 

inhabiting the northern forests, 31 are rare (Green 

1995).

Rare Elements 

The Wisconsin Natural Heritage Inventory Working 

List contains species known or suspected to be rare 

in the state (WNHI 2009). It includes species legally 

designated as “Endangered” or “Threatened” as well 

as species in the advisory “Special Concern” category. 

Because of the convergence of three major biomes 

and the variability in landforms and climate, the 

state supports over 2,000 native vascular plants, 

about 680 vertebrate animals, and as many as 

65,000 invertebrates (WNHI 2009). Most recently, 

the Kirtland’s warbler, a species that is listed as 

endangered by the Federal government, has been 

documented breeding in young jack pine forests in 

central and northern Wisconsin. Eighty-six species on 

the Chequamegon-Nicolet National Forest are on the 

Regional Forester Sensitive Species list (Appendix 

2) as well as State lists of threatened and endangered 

species or species of special concern. An additional 13 

at-risk species have a high potential for occurrence on 

the Forest.

Additionally, several communities in northern 

Wisconsin have Natural Heritage rankings of 

“vulnerable globally” or rarer, indicating fewer than 

100 occurrences of these communities globally: 

boreal forest, northern dry forest, northern wet-mesic 

forest, and pine barrens. For example, pine barrens 

are ranked as an imperiled community in Wisconsin 

and are also imperiled globally. Several butterfl y 

species that depend on pine barrens for habitat are also 

rare, including the Federally endangered Karner blue 

butterfl y. The State endangered northern blue butterfl y 

and its State endangered plant host, dwarf bilberry, 

occur on pine barren remnants on the Nicolet land base 

of the CNNF (USDA FS 1998a).

Forest Ownership and Management

Many types of landowners are present in the forestdominated landscape of northern Wisconsin (Fig. 8). 

Within the analysis area, approximately 64 percent of 

forested land is privately-owned, with families and 

individuals owning 77 percent private forest lands 

(Miles 2010). Public forests are managed by Federal 

(13 percent), State (6 percent), and local (17 percent) 

governments.

Forest Ownership Trends

Patterns of forest ownership in northern Wisconsin 

are changing. While forest land acreage has remained 

constant over the past decade, the number of forest 

landowners has increased and the average size 

of forest ownerships has decreased (Rickenbach 

and Steele 2006). This is similar to the statewide 

trend, where the number of non-industrial private 

landowners increased by 20 percent from 1984 to 

1997, and again by 25 percent from 1997 to 2009 

(WDNR 2009d). From 1997 to 2006, the average 

size of privately owned parcels shrunk from 37 to 

28 acres. The number of parcels less than 10 acres 

nearly doubled, and the amount of land in parcels over 

100 acres decreased (WDNR 2009d). The Wisconsin 

Department of Natural Resources has recognized this 

shift in ownership patterns as problematic for forest 

management. Smaller parcels are managed with less 

effi ciency than larger parcels in terms of writing 

and executing management plans (WDNR 2009d). 

Another signifi cant trend is the decrease in the amount 

of industrially-owned forest. From 2002 to 2008, the 

amount of land owned by forest product companies 

fell from 62 percent of private lands to 24 percent, 

largely through sales of these lands to Real Estate 

Investment Trusts (REITs) and Timber Investment 

Management Organizations (TIMOs) which are 

considered non-industrial landowners (WDNR 2010a). 
25

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Some variations in forest composition in the region 

are refl ected in forest landownership. For example, 

public lands in the analysis area have higher 

proportions of aspen than lands under private 

ownership (Miles 2010), and the CNNF and Stateowned forests have higher percentages of upland 

conifers (27 percent and 30 percent, respectively) 

than private lands (18 percent). However, overall 

differences in forest composition among forest 

ownerships are not substantial, suggesting that 

management among various landowners has been 

relatively similar (Miles 2010; Fig. 9). 

Forest Management Programs 

for Private Landowners

Two programs provide tax relief to qualifi ed 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 program was started, and the last participant 

contracts will expire in 2034 (WDOR 2009). Within 

49%

17%

13%

10%

6%

4%

1%

Individual

Local (county, municipal, etc.)

U.S. Forest Service

Corporate

State of Wisconsin

NaƟve American (Indian)

Other

Figure 8.—Area of forest land by ownership in northern Wisconsin (Miles 2010).

the analysis 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, WDNR 2010a). 

The Managed Forest Law, which has been revised a 

number of times, is designed to increase land retention 

and minimize fragmentation of parcels by reducing 

property taxes for participating forest landowners. 

Landowners with 10 acres or more may qualify, 

provided they incorporate broad ecosystem objectives 

into management and develop and implement 

comprehensive Forest Stewardship Plans. These plans 

qualify as Federal Forest Stewardship Plans and are 

a prerequisite for Federal cost-sharing assistance. 

Timber harvesting is permitted, offering owners a 

forest-based income stream that reduces pressure to 

generate revenue by other means, such as livestock 

grazing or development (P.E. Pingrey, personal 

communication). There were 2,124,698 acres in the 

analysis area participating in the MFL program in 
26

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Figure 9.—Area of forest land by forest-type group and ownership in northern Wisconsin (Miles 2010).

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Maple/beech/birch group

Aspen/birch group

Oak/hickory group

Spruce/fir group

White/red/jack pine group

Elm/ash/co

Ʃonwood group

Oak/pine group

Nonstocked

Other hardwoods group

Exo

Ɵc so

Ōwoods group

Private State and local

Other Federal NaƟonal forest

Acres (millions)
27

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

2003, which represents nearly all of the acres enrolled 

statewide (WDNR 2003). In the last decade, the 

number of enrollments has more than doubled, and 

more management plans are being written for small 

parcels (WDNR 2010a). Although public access is 

often allowed on enrolled lands, many of the newly 

enrolled owners are not permitting public access 

(WCF 2006). 

Forest Certifi cation

Forest certifi cation is a process designed to ensure 

that forest products originate from forests that are 

sustainably managed. In Wisconsin, forest lands are 

certifi ed through the Forest Stewardship Council, the 

Sustainable Forestry Initiative, and the American Tree 

Farm System. Statewide, about 45 percent (7,028,795 

acres) of Wisconsin’s forest land is certifi ed, and 

about 70 percent of certifi ed lands are dual-certifi ed 

through two of these programs (Table 4). Almost half 

of private forest land (44.3 percent) and over half 

(55.7 percent) of the public lands in Wisconsin are 

certifi ed (WCF 2006). In 2005, the Managed Forest 

Law program received third-party forest certifi cation 

under the American Tree Farm System, making it the 

largest group-certifi cation program usable by private 

landowners in North America (WCF 2006). 

Certifi cation standard

 Forest Sustainable American

 Stewardship Forestry Tree Farm Dual certifi ed Dual certifi ed

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

State Forests 517,734 

Other State lands 57,225 1,023,453 

County Forests 165,953 723,834 1,464,959 

Managed Forest Law (MFL) program 2,239,205

Plum Creek Timber Company, Inc. 282,096 

Stora Enso Oyi 5,411 

Institute for Agriculture and Trade Policy 2,690 

Potlatch Corporation 68,862 

Menominee Tribal Enterprises 220,000 

CF/FIA Holding, LLC 62,945 

Traditional (Non-MFL) Tree Farms 194,427 

Total by standard 520,450 1,063,155 194,427 3,011,557 2,239,205

Table 4.—Wisconsin certifi ed acres as of November 2009 (P. E. Pingrey, unpublished data).

Forest Harvest

Within the analysis area, there is a total of 14.7 

billion cubic feet of growing stock volume capable of 

producing forest products (Miles 2010). Forest types 

dominated by hardwood species contain 78.5 percent 

of this volume, and softwood forest types contain the 

remaining 21.5 percent. The amount of wood in the 

forests of northern Wisconsin is increasing because 

tree growth is outpacing the amount being harvested. 

From 2004 to 2008, average annual growth of growing 

stock in the analysis area exceeded annual harvests and 

other removals by almost 160 million cubic feet. 

The CNNF has 2.2 billion cubic feet of growing 

stock volume (Miles 2010). Forest types dominated 

by hardwood species contain about 75 percent of 

this volume, and softwood forest types contain the 

remaining 25 percent. Average annual growth of 

growing stock exceeded annual harvests and other 

removals by approximately 34.7 million cubic 

feet from 2005 to 2008 (Miles 2010). The volume 

of harvested hardwood sawtimber and hardwood 

pulpwood has decreased since the mid-1980s 

(Fig. 10; M.A. Theisen, unpublished data). Softwood 

sawtimber is generally on the rise, but the volume of 

softwood pulpwood harvested has remained steady. 
28

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Figure 10.—Annual volume of (A) sawtimber and (B) pulpwood harvested on the Chequamegon-Nicolet National Forest from 

1986 to 2009 (M.A. Theisen, unpublished data).

A

B

0

20

40

60

80

100

120

Volume (million board feet)

SoŌwood pulpwood Hardwood pulpwood

0

5

10

15

20

25

Volume (million board feet)

SoŌwood sawƟmber Hardwood sawƟmber
29

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

In 2009, harvested sawtimber was 90 percent 

softwoods (11.3 million board feet) and 10 percent 

hardwoods (1.0 million board feet). Pulpwood 

harvested in 2009 was 60 percent hardwoods (34.0 

million board feet) and 40 percent softwoods (23.6 

million board feet; M.A. Theisen, unpublished data).

Socioeconomic Conditions

Approximately 5.7 million people lived in Wisconsin 

in 2009 (WDOA 2009a), about 1.5 million (27 

percent) of whom resided in the 33 northern counties 

(WDOA 2009a). The population in northern Wisconsin 

increased 0.5 percent from 2008 to 2009, and 6.9 

percent since 2000, which is slightly higher than the 

statewide rate. As the region becomes a more popular 

retirement destination, the median population age in 

northern Wisconsin is increasing (WDNR 2006).

According to the 2000 U.S. Census data for wage 

earners in northern Wisconsin counties, 28 percent 

earned less than $25,000 per year, 33 percent earned 

$25,000 to $49,000, and 22 percent earned $50,000 

to $75,000. Only 16 percent had incomes greater 

than $75,000 per year in 2000 (USCB 2009). Thirty 

of the northern counties had poverty rates exceeding 

10 percent from 2005 to 2007 (Isaacs and Smeeding 

2009). The data from 2007 were the most recent 

county-level data available, but it is possible that 

poverty rates have risen with the recent increases in 

unemployment rates. The Wisconsin Department of 

Work Development estimated that unemployment 

rates in the northern counties have nearly doubled 

from 2008 (3.4 to 8.6 percent) to September of 2009 

(6.7 to 17.7 percent). Unemployment rates during the 

previous year ranged from 3.4 to 8.6 percent (WDWD 

2009). 

The U.S. Census counted 386,234 families living in 

the northern counties in 2000, while the total estimated 

number of housing units was 688,291 (WDOA 2009b). 

Since 2000, the number of housing units has increased 

by an average of 1.3 percent per year (WDOA 2009b). 

Statewide, about 6 percent of housing units were used 

for seasonal, recreational, or occasional purposes, 

compared to 14.3 percent in northern Wisconsin. 

In Bayfi eld, Burnett, Florence, Forest, and Sawyer 

counties, 40 to 50 percent of housing units were 

seasonal; Vilas County had more seasonal (56 percent) 

than primary units (WDOA 2009b). 

Throughout Wisconsin, and particularly in northern 

Wisconsin, timber, recreation, and agriculture are 

signifi cant sectors of the economy. 

Forest Products Industry

The forest products industry, much of which is 

supported by the heavily forested land base, is 

extremely important in northern Wisconsin. Although 

the total forest payroll and forest industry employment 

have been declining, forest industry in Wisconsin still 

generated 9 percent ($13.8 billion) of the total value 

of all manufacturing statewide and employed more 

than 68,000 people with wages totaling more than $3 

billion per year (WDNR 2010a).

Paper and paperboard manufacturing consumes 52.8 

percent of all roundwood (hardwood and softwood), 

which refl ects the regional importance of this industry. 

Of the $20.5 billion in wood products shipped in 

2006, 67 percent came from the paper sector (WDNR 

2010a). Further, despite recent mill closures, the paper 

sector still contributes 9 percent ($13.8 billion) of the 

total value of all manufacturing shipments statewide. 

Sawlogs are second, followed by composite products, 

fuelwood, veneer logs, and other products (WDNR 

2010a). 

Northern Wisconsin contains much of the 

infrastructure for the state’s forest industry. Of the 

state’s 273 sawmills, 171 are located in northern 

Wisconsin, as are all 13 veneer mills, the only 

excelsior mill, and most of the chip, pulp, and postand-pole mills. However, only 40 percent of the 

particleboard mills are in northern Wisconsin (UWM 

2006). 
30

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

Total wood product output for the state has declined in 

value since 2000, a peak year for the wood products 

industry (WDNR 2010a). Although the numbers 

for the most recent harvest years have not yet been 

analyzed, this trend is expected to continue as a result 

of the depressed global economy and competition 

from other wood-producing countries. New biomass 

technologies and markets, greenhouse gas legislation, 

and high oil barrel prices are expected to increase 

the value of wood products as the economy recovers 

(WDNR 2010a). In 2003, nearly 361 million cubic 

feet of products were produced from forests across 

Wisconsin (Perry et al. 2008). These products were 

primarily composed of pulpwood and composites (70 

percent) and saw log products (27 percent; Perry et 

al. 2008). More softwood is being used as saw logs in 

2007 and less in pulpwood, a trend attributed to the 

maturation of softwood stands, especially red pine 

plantations harvested at larger diameters (WDNR 

2010a). Conversely, as hardwood saw log resources 

become less available, smaller diameter hardwood is 

making up a majority of the wood used for pulpwood 

and composite products (WDNR 2010a).

Recreation

Recreation is economically signifi cant throughout 

Wisconsin and is estimated to generate approximately 

$2.5 billion dollars a year through travel- and 

equipment-related revenue (WDNR 2010a). 

Recreation pressure on public lands is expected to 

increase as parcelization continues to decrease the 

amount of land open to the public (WDNR 2010a). 

Approximately 5.8 million acres of public land was 

available for recreation in 2006 (WDNR 2006). 

Recreation and tourism are important to the overall 

economy of northern Wisconsin, which offers a range 

of experiences to its residents and visitors, from 

solitude and nature study to ATV (all-terrain vehicle) 

riding opportunities. Northern and rural counties are 

dependent on resource-based tourism (Stynes 1997). 

Popular activities in northern Wisconsin include 

fi shing, camping, boating, golf, hunting, hiking, and 

bird watching. Many recreational opportunities are 

centered around water features (WDNR 2006). 

Hunting remains popular in northern Wisconsin, but 

the number of hunters has been dropping as part of a 

30-year national trend. In the past decade, participation 

in some recreational activities such as biking, crosscountry skiing, and ATV riding has been increasing. 

ATV use, in particular, continues to grow rapidly in 

northern Wisconsin (WDNR 2006). Snowmobiling 

continues to be 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).

Agriculture

Agriculture is an important industry with over 15 

million acres of farmland throughout Wisconsin. In 

2007, agriculture contributed about 9 percent ($20.2 

billion) to Wisconsin’s total income. The majority of 

this comes from the agricultural processing sectors 

such as dairy, which contributed $26.5 billion of total 

industrial output in 2007 (Deller and Williams 2009). 

About 10 percent of the state’s workforce is employed 

in the agricultural sector. 

In the analysis area, 20 percent of total land area 

is used for agriculture, with most of these lands 

concentrated in the southern part of the region 

(WDNR 1998a). Most of this acreage is dedicated to 

the production of hay and corn. Soybeans, wheat, oats, 

and barley are also common crops. Wisconsin is also 

the top producer of cranberries in the country, with 

14,000 of the 17,700 acres of cranberry bogs located 

in the northern counties. (USDA NASS 2009, WDNR 

1998a).
31

CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

This chapter provides a brief background on climate 

change science, climate simulation models, and 

climate change impact models. Throughout the 

chapter, boxes indicate resources to fi nd more 

information on each topic. The resources listed are 

up-to-date nontechnical reports based on the best 

available science and are available for free on the 

Internet. A more detailed scientifi c review of climate 

change science, trends, and modeling can be found 

in the Intergovernmental Panel on Climate Change 

(IPCC) Fourth Assessment Report (2007). 

Climate Change

Climate is defi ned as the average, long-term 

meteorological conditions and patterns for a given 

area. Weather, in contrast, is set of the meteorological 

conditions for a given point in time in one particular 

place. The IPCC (2007) defi nes climate change as “...a 

change in the state of the climate that can be identifi ed 

(e.g. 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 fi nding of the IPCC in its Fourth Assessment 

Report (2007) was that “warming of the climate 

system is unequivocal;” this was the fi rst 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 

signifi cant changes in snow, ice, and frozen ground; 

hydrology; coastal processes; and terrestrial, marine, 

and biological systems. 

The Warming Trend

Global climate is warming, and the rate of warming 

is increasing (Fig. 11). Independent 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 (IPCC 2007). Temperatures in the 

United States have risen by 2 °F (1.1 °C) in the last 50 

years (Karl et al. 2009). The decade beginning January 

More Resources on Climate Change

Intergovernmental Panel on Climate Change

 Climate Change 2007: Synthesis Report. 

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

Union of Concerned Scientists and Ecological Society of America 

 Confronting Climate Change in the Great Lakes Region. 

 http://ucsusa.org/assets/documents/global_warming/greatlakes_fi nal.pdf

U.S. Global Change Research Program

 Global Change Impacts on the United States. 

 www.globalchange.gov/publications/reports/scientifi c-assessments/us-impacts

Plants in the understory of a 

northern Wisconsin forest.

Photo by Linda R. Parker, Chequamegon-Nicolet National Forest
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

32

2000 and ending December 2009 was the warmest 

decade since 1880, and 2009 was ranked as the second 

warmest year on record (Hansen et al. 2010). 

Average temperature increases of the last 50 years 

are simplifications of a more complex pattern of 

regional and seasonal climate 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 2007). There is also a strong indication that 

the frequency of heat waves and heavy precipitation 

events has increased over this period, with new records 

for both heat and precipitation in areas of the United 

States and Canada in 2007 (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 2007). 

Figure 11.—Rates of global temperature increase from 1961 to 1990. Figure courtesy of the Intergovernmental Panel on 

Climate Change (IPCC 2007).

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 synthesis report of the International 

Scientifi c 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. Temperature rises above 3.6 °F 

(2 °C) will be diffi cult for contemporary societies 

to cope with, and are likely to cause major societal 

and environmental disruptions through the rest of the 

century and beyond” (Richardson et al. 2009). 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

33

Chapter 3 provides specific information about recent 

climate trends and future climate simulations for 

Wisconsin.

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. 12). 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 greenhouse gases in 

the atmosphere contribute to the greenhouse effect, 

including carbon dioxide, methane, nitrous oxide, 

and water vapor. Water vapor is the most abundant 

greenhouse gas, but is very responsive to driving 

factors such as temperature; its residence time in the 

atmosphere is on the order of days. Other greenhouse 

gases, including carbon dioxide, methane, and nitrous 

oxide, are likely to reside in the atmosphere for 

decades to centuries after initial introduction. Addition 

of these long-lived gases to the atmosphere will thus 

cause long-term warming. A warmer atmosphere is 

able to hold more water vapor, potentially leading to 

higher annual precipitation in some places, but also 

further increasing warming. 

Figure 12.—An idealized model of the natural greenhouse effect. Figure courtesy of the Intergovernmental Panel on Climate 

Change (IPCC 2007).
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

34

Human Infl uences on Greenhouse Gases 

Human activities have increased carbon dioxide, 

methane, and nitrous oxide since the beginning of 

the industrial era (Fig. 13), leading to an enhanced 

greenhouse effect. Of all greenhouse gases, human 

activities have clearly had the strongest impact on 

carbon dioxide concentrations in the atmosphere. 

Carbon dioxide levels have been increasing at a 

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

past 50 years (IPCC 2007), reaching 385 ppm in 

2009. In recent decades, fossil fuel burning has 

been responsible for approximately 80 percent of 

the human-induced increase in carbon dioxide. The 

remaining 20 percent of human-induced 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 concentrations have also been increasing 

from human activities. Increased agricultural 

production of livestock and increases in rice 

production are responsible for much of the humaninduced increase in methane. The guts of cattle and 

other ruminants contain microbes that release the gas. 

Rice production requires wet conditions that are also 

ideal for microbial methane production. Other sources 

of methane include biomass burning, microbial 

emissions from landfi lls, fossil fuel combustion, and 

leakage of natural gas during mining and distribution. 

Figure 13.—Concentrations of greenhouse gases 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 the Intergovernmental Panel on 

Climate Change (IPCC 2007).
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

35

Sources of nitrous oxide are less well-understood than 

methane and carbon dioxide, but the consensus based 

on current science is that the primary human source of 

nitrous oxide is from agricultural activities. Increased 

fertilizer use on agricultural fields is responsible for 

increases in emissions from soil, which is released 

when soil microbes break down nitrogen-containing 

products. In addition to increased fertilizer use, the 

conversion of tropical forests to agricultural lands can 

lead to increases in microbial nitrous oxide production. 

Other sources of production include nylon production 

and combustion of fossil fuels.

Ozone, in addition to providing protection from 

the sun’s ultraviolet (UV) rays, is a naturally 

occurring greenhouse gas. Interception of harmful 

UV rays by ozone in the upper atmosphere, or 

stratosphere, is benefi cial to plants and animals. 

Human emissions of ozone-depleting substances 

such as chlorofl uorocarbons (CFCs) led to decreases 

in stratospheric ozone, which decreased both UV 

protection from the sun and ozone-induced warming. 

Regulation under the Montreal Protocol led to a 

decline in CFC emissions and reductions in ozone 

have subsequently slowed. Vehicle exhaust can lead 

to the production of tropospheric ozone, the ozone 

responsible for smog and increased local warming in 

urban areas. 

Halocarbons, which include CFCs, are a class of 

chemical compounds that contain carbon and a 

halide such as fl uorine, chlorine, or bromine. After 

CFCs were banned, another class of halocarbons, 

hydrofl uorocarbons (HFCs, also known as “Fgases”), was used as a replacement for CFCs in 

refrigeration and air conditioning. HFCs do not 

deplete stratospheric ozone, but many are powerful 

greenhouse gases.

In addition to greenhouse gases, particulate matter 

in the atmosphere can also infl uence climate. These 

particles are known as aerosols, meaning suspensions 

of solids in the air (not to be confused with pressurized 

sprays). Naturally occurring aerosols form from 

volcanic activity and forest fi res. Human additions 

to atmospheric aerosols come from vehicle exhaust, 

burning coal and other fossil fuels, and burning wood. 

Some of these particles refl ect radiation, while others 

absorb it, so changes in aerosol concentration can 

have a cooling effect or a warming effect. There is still 

some uncertainty about how aerosols will affect future 

climate, but the scientifi c consensus is that the effects 

will not outweigh the warming effects of greenhouse 

gases (Fig. 14). 

Climate Models

Although we have estimated climate of the distant past 

using proxies and have measured climate directly in 

the recent past, we cannot “measure” future climate. 

Scientists instead use models to simulate future 

climate. These models are judged in part by their 

ability to accurately simulate past climate against 

proxy estimates. A model is a simplifi ed representation 

of reality. Models can be theoretical, mathematical, 

conceptual, or physical. The most important models 

used in climate science are general circulation 

models, which combine complex mathematical 

formulas representing physical processes in the ocean, 

atmosphere, and land surface within large computer 

simulations. 

General Circulation Models

Mathematical models that simulate the general 

circulation of the atmosphere or oceans are called 

general circulation models (GCMs). GCMs simulate 

physical processes on the Earth’s surface, oceans, 

and atmosphere through time using mathematical 

equations in three-dimensional space. GCMs can 

work in time steps as small as minutes or hours in 

simulations covering decades to centuries. Because of 

their high level of complexity, GCMs require intensive 

computing power, and must be run on immense 

supercomputers. 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

36

Figure 14.—The amount of warming infl uence (red bars) or cooling infl uence (blue bars) that different factors have had 

on climate during the industrial age (from about 1750 to the present); results are in watts per square meter, longer bars 

indicate greater infl uence on climate, and bracketed lines indicate estimated range of uncertainty. Figure courtesy of the 

Intergovernmental Panel on Climate Change (IPCC 2007).
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

37

Figure 15.—Schematic describing climate models, which are systems of differential equations based on the basic laws of 

physics, fl uid 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 the National Oceanic and Atmospheric 

Administration (NOAA 2008).

Although climate models use highly sophisticated 

computers, limits on computing power mean that 

projections are limited to relatively coarse spatial 

scales. Instead of simulating climate for every single 

point on Earth, modelers divide the land surface, 

ocean, and atmosphere into a three-dimensional grid 

(Fig. 15). Each square or “cell” within the grid is 

treated as an individual unit, and able to interact 

with adjacent cells. Although each model is slightly 

different, each square in the grid is usually between 2 

and 3 degrees latitude and longitude, or for the middle 

latitudes, about the size of the northeastern quarter 

of Wisconsin. These horizontal grids are stacked in 

interconnected vertical layers that simulate ocean 

depth or atmospheric thickness at increments usually 

ranging from 656 to 3280 feet (200 to 1000 meters). 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

38

Several research groups from around the world have 

developed GCMs that have been used in climate 

projections for the IPCC reports and elsewhere. GCMs 

have been developed by internationally renowned 

climate research centers such as NOAA’s Geophysical 

Fluid Dynamics Laboratory (GFDL CM2; Delworth 

et al. 2006), the UK’s Hadley Center (HadCM3; 

Pope 2000), and the National Center for Atmospheric 

Research (PCM; Washington et al. 2000), among 

others. 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. 

GCMs, like all models, have strengths and 

weaknesses. In general, they are useful and reliable 

tools because they are based on well-understood 

physical processes and have been successful at 

projecting climate and weather conditions. GCM 

simulations can also be run for past climate, and 

output from these simulations generally correspond 

well with proxy-based estimates of ancient climates 

and actual historical measurements of recent climates. 

GCM projections are not perfect, however. Sources 

of error in model output include incomplete scientifi c 

understanding of some climate processes, and the 

fact that some infl uential climate processes occur at 

spatial scales that are too small to be modeled with 

current computing power. Technological advances in 

the computing industry along with scientifi c advances 

in our understanding of Earth’s physical processes will 

lead to continued improvements in GCM projections. 

Emissions scenarios

Modelers must specify inputs into their GCM in order 

to perform a projection of future climatic conditions. 

Some of these inputs, like future greenhouse gas 

concentrations, are 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, they depend on social 

developments that cannot be completely foreseen. One 

common approach for dealing with uncertainty about 

future greenhouse gas concentrations is to develop 

alternative storylines about how the future may 

unfold and then calculate the potential greenhouse gas 

concentrations for each alternative storyline. The IPCC 

has established a set of standard emissions scenarios 

that represent a range of different storylines (IPCC 

2007). When these scenarios are input into GCMs, 

differences in climate projections can be compared for 

each emissions scenario. 

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 

More Resources on Climate Models and Emissions Scenarios

U.S. Global Change Research Program

 Climate Models: an Assessment of Strengths and Limitations.

 www.globalchange.gov/publications/reports/scientifi c-assessments/saps/sap3-1

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. 

 www.ipcc.ch/ipccreports/sres/emission/index.php?idp=0
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

39

Figure 16.—Global greenhouse gas emissions (in gigatons of carbon dioxide equivalent per year) assuming no change 

in climate policies: six scenarios (B1, A1T, B2, A1B, A2, and A1FI) originally published in the Special Report on Emissions 

Scenarios (SRES; IPCC 2000), 80th percentile range (gray shaded area) of recent scenarios published since SRES, and 

dashed lines showing the full range of post-SRES scenarios. Figure courtesy of the Intergovernmental Panel on Climate 

Change (IPCC 2007).

national or international polices directed specifi cally at 

climate change such as the Kyoto Protocol. However, 

some of the scenarios that include a reduction in 

greenhouse gases via other means give a hint at what 

we could expect if these policies were implemented. 

Six different emissions scenarios are commonly 

used in model projections for reports such as the 

IPCC Fourth Assessment Report (Fig. 16). The A1FI 

scenario is the most fossil-fuel intensive, and thus 

results in the highest projected future greenhouse 

gas concentrations and GCM simulations with the 

highest predicted future warming. On the other end 

of the spectrum, the B1 scenario represents a future 

where alternative energies are developed and there 

is a decreasing reliance on fossil fuels, resulting in 

the lowest rise in greenhouse gas concentrations and 

GCM simulations with the lowest increase in global 

temperature. It is important to note that the future 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

40

will likely be different from any of the developed 

scenarios. However, these scenarios were designed to 

encompass a likely range of future emissions over the 

coming decades. It is highly unlikely that we would 

see future greenhouse gas emissions below the B1 

scenario even if national or international policies were 

implemented. It is notable that current trends match or 

exceed the emissions of the A1FI scenario.

Downscaling

As mentioned previously, GCMs are only able to 

simulate climate conditions for relatively large areas, 

such as continents or subcontinental regions. However, 

to examine the future climate of areas within northern 

Wisconsin, a smaller grid scale is needed. One method 

of projecting climate on smaller spatial scales is to 

use statistical downscaling, a technique by which 

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 for much smaller 

spatial scales. Resolution for downscaled climate 

projections is typically around one-tenth to one-eighth 

degree latitude and longitude, about 39 to 58 square 

miles (100 to 150 square kilometers).

Statistical downscaling has several advantages and 

disadvantages. Although it is a relatively simple 

and inexpensive way of overcoming computing 

limitations for smaller-scale projections using GCMs. 

downscaling assumes that past relationships between 

modeled and observed temperature and precipitation 

will hold true under future change. This assumption 

may or may not be true. Another limitation is that 

downscaling depends on local climatological data. 

If there is no weather station in the area of interest, 

it may be diffi cult to obtain a good downscaled 

estimate of future climate for that area. Finally, local 

infl uences on climate that occur at fi ner scales (such 

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

aerosol concentrations) also add to uncertainty when 

downscaling climate projections. 

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 (Fig. 17). 

Impact models use GCM projections as inputs, as well 

as information about tree species, life history traits 

of individual species, and soil types (Appendix 3). 

There are a number of different models that are used 

to simulate impacts on species and ecosystems. These 

models generally fall in one of two main categories: 

species distribution models and process models. In this 

assessment, we used one species distribution model, 

the Climate Change Tree Atlas (Prasad et al. 2007), 

and one process model, LANDIS-II (Scheller et al. 

2007). These models operate at different spatial scales 

and provide somewhat different kinds of information. 

We chose them because they have both been used 

to assess climate change impacts on ecosystems in 

our geographic area of interest, and have stood up to 

rigorous peer review in scientifi c literature. 

Climate Change Tree Atlas

The Climate Change Tree Atlas (hereafter referred 

to as Tree Atlas) is a series of mapped projections 

derived from an ensemble of statistical models called 

species distribution models (Appendices 3 and 4). 

Species distribution models describe the environment 

encompassing the current distribution of a species or 

ecosystem, then map its distribution under projected 

climate change for a given point in the future. These 

models do not require intense computational power, 

allowing economical projections of future ranges for 

many species over large geographic areas. 

The Tree Atlas uses future climate projections from 

GCMs that are downscaled to a 12-by-12 mile (20-

by-20 kilometer) grid scale (Prasad et al. 2007). The 

GCMs that were used as inputs were GFDL CM2.1, 

HadCM3, PCM, and an ensemble average of the 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

41

three GCMs. This assessment presents four emissions 

scenarios and climate model combinations: (1) the 

HadCM3 model projections under the high emissions 

scenario (HadHi) as the most sensitive to projected 

changes in the concentrations of greenhouse gases; 

(2) the PCM model under the low emissions scenario 

(PCMLo) to represent the case with the least warming; 

and the averaged output from HADCM3, PCM, and 

GFDL models under (3) high (GCM3AvgHi) and 

(4) low emissions scenarios (GCM3AvgLo).

Distribution of 134 eastern tree species, from FIA data, 

was used to derive importance values (measures of 

relative abundance) for each species. In addition, 38 

environmental variables (7 climate, 9 soil classes, 12 

soil characteristics, 5 landscape and fragmentation, and 

5 elevation) were used to statistically model current 

species abundance with respect to current habitat 

distributions. The Tree Atlas uses several advanced 

statistical techniques to identify predictor variables 

for habitat, then uses downscaled GCM data to project 

potential shifts in suitable habitat in the future for 

the northeastern United States (Iverson et al. 2008b, 

Prasad et al. 2007). 

Species distribution models are very useful for 

obtaining regional projections for many species over 

a large area, but they do have some assumptions and 

uncertainties that should be carefully considered when 

using them to help inform management decisions. 

In a recent review, Wiens et al. (2009) highlighted 

some of these assumptions and uncertainties, which 

are summarized here. One characteristic of SDMs is 

that they use a species’ realized niche instead of its 

fundamental niche. The realized niche is the actual 

habitat a species occupies because of predation, 

disease, and competition with other species. A species’ 

fundamental niche, in contrast, is what habitat it 

would occupy based on climate, soils, and land cover 

type if it had no competitors, diseases, or predators. 

Given that the conditions under which a species 

could exist (fundamental niche) may be greater than 

what is currently observed in nature (realized niche), 

SDMs may underestimate niche size. In addition, new 

habitat might be constrained by competition, disease, 

and predation in ways that do not currently occur, 

so SDMs using current relationships could under- or 

overestimate future realized niche sizes. 

Species distribution models also assume that a species 

is in equilibrium (i.e., all suitable habitat is currently 

occupied). Past disturbances or barriers to migration 

may mean that not all space that can be occupied is 

currently occupied. Disturbance limitations can also 

persist into the future. Therefore, future projected 

ranges may be limited by habitat fragmentation and 

a species’ own ability to disperse. Finally, the models 

do not assume that species will adapt evolutionarily to 

changes in climate. This may be true for species with 

long generation times, but some short-lived species 

may be able to adapt even while climate is rapidly 

changing. The Tree Atlas is addressing the disturbance 

Figure 17.—Steps in the development of climate impact models using projections from general circulation models (GCMs).

1. Emission 

scenarios

developed and 

used as inputs

2. Model 

projections 

are run using 

GCMs

3. GCM 

projections are 

downscaled

to a smaller 

grid scale

4. Downscaled 

GCM data 

and other 

information are 

used as inputs 

into impact 

models
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

42

issues by utilizing more sophisticated statistical 

models, and the migration issues by coupling the 

statistical model with a simulation model called SHIFT

(Iverson et al. 2004a,b).

In addition to the ecological assumptions of SDMs, 

there are also other factors that add to uncertainty of 

these projections. The SDMs are structured around 

statistical models that inherently include uncertainty. 

They also rely on data input from existing vegetation 

distribution, site characteristics, and downscaled GCM 

projections, each of which have their own uncertainties 

and assumptions. For example, the FIA plots upon 

which Tree Atlas is based are available only on the 

scale of one sample plot per approximately 6,000 

acres. This means that the Tree Atlas cannot inform 

managers of what the species distribution will be on 

an individual stand, because the spatial resolution of 

the modeled distributions cannot exceed the spatial 

resolution of FIA data. At a broader scale, however, 

they can help managers understand what species may 

be more sensitive to changes in future climate.

LANDIS-II 

LANDIS-II is a process model. In contrast to 

species distribution models, which use statistical 

relationships to project future change, LANDISII and other process models simulate ecosystem 

processes based on mathematical representations 

of physical and biological processes. LANDIS-II is 

a spatially and temporally dynamic process model, 

meaning it allows processes to interact across space 

and time. Consequently, LANDIS-II is very useful for 

simulating ecosystem responses to novel conditions 

such as climate change. LANDIS-II simulates 

processes such as succession within a cell and allows 

interaction between cells, such as tree species seed 

dispersal. Various natural disturbance types (wind, 

fi re, and insects) and forest management approaches 

can be specifi ed. Some processes—including 

specifi ed disturbances (such as wind or fi re) or seed 

dispersal—are simulated to occur randomly, based 

Climate Data Used 

in This Assessment

In Chapter 3, we use downscaled climate 

projections provided by the Climate Working 

Group of the Wisconsin Initiative on Climate 

Change Impacts (WICCI). WICCI is a collaborative effort of the Wisconsin Department 

of Natural Resources, the University of 

Wisconsin, and other organizations to assess 

climate change impacts on multiple sectors. 

This dataset represents the most recent and 

complete downscaled climate projections for the 

state. The WICCI dataset uses an ensemble 

average of 14 general circulation models 

(GCMs). When various models tend to agree, 

using an ensemble average adds confi dence to 

projections. However, ensemble averages can 

be problematic when models vary widely, such 

as the projections for summer precipitation in 

Wisconsin. Of the three emissions scenarios 

used by WICCI, the results using highest (A2) 

and lowest (B1) emissions are presented in 

Chapter 3. 

In contrast, modeling for vegetation impacts in 

Chapter 4 was completed before downscaling 

was completed for WICCI, and therefore the 

impact models had to rely on alternate sources 

of downscaled data that covered the appropriate 

spatial extent. Instead of using only an ensemble 

average, these models use a few selected 

GCMs to examine model-to-model variation. 

The Climate Change Tree Atlas uses three 

GCMs (which are also included in the ensemble 

average used by WICCI) as well as an ensemble 

of the three GCMs. The Climate Change 

Tree Atlas uses the A1FI emissions scenario 

instead of A2 for the highest emissions scenario 

(Fig. 16). LANDIS-II uses only one emissions 

scenario, which is halfway between the lower B1 

and the higher A1FI and A2 scenarios (IS92a). 

The GCMs and scenario used in LANDIS-II 

are slightly older versions of those used in the 

ensemble average developed by WICCI, but the 

results are still comparable. 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

43

on probabilities and cell conditions. Seed dispersal 

and establishment of new trees is allowed to occur 

based on the probability of a seed reaching a cell 

within the species seed dispersal distance that has 

conditions suitable for germination and establishment. 

Since there are many potential outcomes, dynamic 

models must be simulated a number of times and 

averaged. A key benefi t of dynamic models is that 

processes are simulated and interact on a much more 

fundamental level, and may thus be most responsive to 

environmental changes such as climate.

LANDIS-II operates on a fi ner spatial scale than 

the Tree Atlas, with grid scales between 33 feet 

(10 meters) for plot-scale dynamics and 0.6 mile 

(1 kilometer) for regional dynamics, and no inherent 

limitation on cell size. A grid scale of approximately 

10 acres (4 hectares) was used for this assessment 

(Scheller et al. 2005, Scheller and Mladenoff 2008), 

with simulations run over approximately 5,800 square 

miles (15,000 square kilometers) in northwestern 

Wisconsin. LANDIS-II uses fewer tree species than 

the Tree Atlas (23 versus 134), which is partly because 

fewer tree species occupy this smaller area. Like the 

Tree Atlas, LANDIS-II relies partially on FIA data 

for inputs, but other higher resolution forest cover 

classifi cations may be used if available. As additional 

inputs, LANDIS-II divides the landscape into units 

called land types or ecoregions. The model may be 

run at variable time-steps, such as 1, 3, 5, or 10 years, 

depending on the questions being addressed and the 

spatial scale. 

For simulating climate change in northern Wisconsin, 

LANDIS-II has been run using two GCMs: CGCM1, 

the Canadian Climate Center model v.1 (Flato et al. 

2000) and HadCM2, an earlier version of the Hadley 

Centre model (Johns et al. 1997). LANDIS-II currently 

uses one emissions scenario, IS92a, an earlier version 

of emissions scenarios developed by IPCC that 

represents emissions midway between A1FI and B1. 

Models are simulated for each GCM input while also 

incorporating disturbance regimes from 1990 to 2090. 

LANDIS-II also has several assumptions and 

uncertainties that should be taken into consideration 

when applying results to management decisions. 

Process models such as LANDIS-II rely on empirical 

relationships that are specifi ed by the modeler. Any 

uncertainties in these relationships can be compounded 

over time and space, leading to an erroneous result. 

In addition, like species distribution models, process 

models also rely on GCM projections and species 

distribution data that add additional uncertainty. 

LANDIS-II has simulates many spatial and temporal 

interactions, which requires more computational power 

than a species distribution model. This also means that 

simulation time and cost computational cost increases 

with the number of cells in a landscape, so larger grid 

sizes are typically used to effi ciently simulate larger 

landscapes. Finally, LANDIS-II can be used to develop 

projections of wildlife distributions, but does not 

simulate the distributions directly. Instead, LANDIS-II 

simulates changes in habitat and is then coupled with 

species habitat models for particular wildlife species to 

produce projections of their distributions. 

Fall colors.

Photo by Linda R. Parker, Chequamegon-Nicolet National Forest
44

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

This chapter provides information on the past and 

current climate of northern Wisconsin and on the 

projected effects of climate change. Current climate 

data and downscaled projections were developed 

by the Climate Working Group of the Wisconsin 

Initiative on Climate Change Impacts (WICCI), a 

collaborative effort of the Wisconsin Department of 

Natural Resources, the University of Wisconsin, and 

other organizations to assess climate change impacts 

in Wisconsin. Chapter 2 provides more information on 

the techniques used to develop climate change model 

projections. 

Past Climate Trends and Averages 

The climate of northern Wisconsin has changed 

drastically in the past. The climate is now in a state of 

accelerated change, which is expected to continue into 

the future.

Post-glacial Climatic and Vegetation Trends

The climate of northern Wisconsin has fl uctuated since 

the retreat of Wisconsin’s vast glaciers about 10,000 

years ago. At that time, permafrost, poor drainage, and 

wet soil led to conditions that are similar to northern 

boreal regions today. The landscape was a mosaic of 

tundra-like open areas and forested areas that persisted 

until about 8,000 years ago (Mladenoff et al. 2008). 

During the period approximately 7,000 to 4,000 years 

ago, Wisconsin experienced its warmest and driest 

period since glaciation, and prairies were common 

farther east than they are currently found (Mladenoff 

et al. 2008). Shorter cycles of warming and cooling 

have occurred since that time, but the climate has 

generally remained stable over the last 3,000 years 

(Davis et al. 1993). For example, Wisconsin was 

relatively warm from 900 to 1300 A.D. and then 

experienced a cooling period over the next six 

centuries. Temperatures have risen gradually over the 

last 70 years, and more dramatically over the last 30 

years (Mladenoff et al. 2008, Stephens et al. 2009). 

Average temperatures are now only 1.8 °F (1 °C) 

cooler than the hottest period in Wisconsin’s postglacial history (Mladenoff et al. 2008).

The relatively static climate over the last 3,000 

years has provided stability for vegetation and forest 

composition. Today’s forest species were present in 

northern Wisconsin and surrounding regions since the 

beginning of this period. Eastern hemlock reached 

its northwestern limits in Wisconsin during this 

period. Lake water levels and moisture levels have 

not changed (Webb et al. 1993). Native American 

populations are credited with greatly infl uencing 

vegetation until European populations began to 

displace them around 200 years ago. The “preEuropean” landscapes that the U.S. General Land 

Offi ce mapped from 1832 to 1865 showed abundant 

oak savannahs, oak woodlands, and prairies to the west 

and south. Forests of eastern hemlock, sugar maple, 

and yellow birch dominated the north; pine, aspenbirch, and spruce-fi r forests fl ourished in the cooler, 

drier conditions present in the northeast (Mladenoff 

et al. 2008). 

Climatic Conditions from 1950 to Present

Average air temperatures for Wisconsin have increased 

about 1.1 °F (0.6 °C) from 1950 to 2006 (WICCI 

2009). This warming trend is most dramatic in the 

northwestern portion of the state (Fig. 18), where the 

annual average temperature has increased by 2.0 to 

2.5 °F (1.1 to 1.4 °C). Winter temperatures have 
45

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

increased by 2.5 °F (1.4 °C) statewide and 3.5 

to 4.5 °F (1.9 to 2.5 °C) in the northwest. Spring 

temperatures have increased by 1.7 °F (0.9 °C) 

statewide and 2.5 to 3.5 °F (1.4-1.9 °C) in the 

northwest. Summer temperatures show the least 

amount of warming, with a statewide increase of 

0.5 °F (0.3 °C), although more substantial temperature 

increases of 1.5 to 2.0 °F (0.8 to 1.1 °C) have been 

measured in the central northwest. Autumn daytime 

temperatures in Wisconsin have exhibited a cooling 

trend (-0.6 °F; -0.3 °C), especially in the northeast 

and far southwest portions of the state where average 

temperatures have dropped by 1.5 °F (0.8 °C; 

Kucharik et al. 2010).

Precipitation patterns have also changed (Fig. 19). 

Statewide average rainfall increased by 3.1 inches 

(78.7 mm) from 1950 to 2006 (WICCI 2009). Most 

of the increase has been concentrated in southern and 

western Wisconsin, with increases ranging from 2 to 

4 inches (50 to 100 mm) since 1950 (Kucharik et al. 

2010). Northern Wisconsin, however, has become 

drier, with the annual average decreasing by 0.8 to 

2.4 inches (20 to 60 mm) during this time period 

(Kucharik et al. 2010). The intensity and frequency of 

precipitation events are increasing across the state and 

the entire Midwest, which has experienced a doubling 

of heavy downpours compared to a century ago (Karl 

et al. 2009) and a 50 percent increase in the frequency 

of days with more than 4 inches (101.6 mm) of 

precipitation. Events on the all-time record rainfall list 

are quickly being replaced by record rainfalls of the 

past 20 years. Two record-breaking fl oods occurred in 

the Midwest in 1993 and 2008 (Karl et al. 2009).

Ice cover on the Great Lakes is directly related to the 

number and intensity of below-freezing days. The 

average date of ice breakup for Wisconsin lakes has 

become signifi cantly earlier, especially for smaller 

lakes (Robertson et al. 1992). An increase in air 

temperature of 1.8 °F (1 °C) shortens ice duration by 

more than 10 days (Robertson et al. 1992). Magnuson 

et al. (2000) demonstrated a similar trend on other 

small lakes in North America using at least 100 years 

of data. Over a 100 year period, North American lakes 

break up 1.3 days earlier every decade, and accelerated 

warming since 1950 is refl ected by a decrease in ice 

cover of 6.4 days per decade.

Figure 18.—Observed changes in temperature (°F) from 1950 to 2006 during the winter (A) and summer (B) seasons. Figure 

courtesy of the Wisconsin Initiative on Climate Change Impacts (WICCI 2009).

A B
46

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

Recent Observations of a Changing Wisconsin Climate

Information gathered from weather stations across Wisconsin since 1950 indicate that the state has been 

warming (Kucharik et al. 2010, WICCI 2009):

▪ Nighttime low temperatures have increased by 1.1 to 4.0 °F (0.6 to 2.2 °C) and are warming faster than 

daytime temperatures by 0.5 to 1.1 °F (0.3 to 0.6 °C), especially in the summer.

▪ Days with a minimum temperature below 0 °F (-18 °C) are much less frequent (5 fewer days per year in 

southern Wisconsin and 12 to 18 fewer days per year in the northwest).

▪ Little change has occurred in the number of hot days with a maximum temperature greater than 90 °F 

(30 °C).

▪ The date of the last spring freeze is 6 to 20 days earlier across most of the state. The average onset 

date of spring is 4 and 12 days earlier in central, western, and southern Wisconsin.

▪ The date of the fi rst autumn freeze is 3 to 18 days later across much of the state, with the greatest 

change in central and northwest Wisconsin. The southwestern and eastern borders show no change or 

earlier autumn freezes.

▪ The growing season length has increased by 5 to 20 days across the state, with the greatest change in 

central and northwestern Wisconsin.

▪ When defi ned by climatic variables, the tension zone has shifted to the north and northeast by 9 to 12 

miles (15 to 20 kilometers).

Figure 19.—Observed changes in precipitation (inches) from 1950 to 2006 during the winter (A) and summer (B) seasons. 

Figure courtesy of the Wisconsin Initiative on Climate Change Impacts (WICCI 2009).

A B
47

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

Projected changes

Projections of future climate from downscaled 

general circulation models indicate dramatic changes 

in climate over the next century. Temperature and 

precipitation are projected to change, and many 

of these changes will vary seasonally. Changes in 

temperature and precipitation will also lead to changes 

in snow and ice cover, soil moisture, lake levels, and 

streamfl ow. Chapter 2 contains a primer on how these 

models work. 

Temperature 

Scientists agree with greater than 90 percent certainty 

that the global climate will become warmer over 

the next 100 years (IPCC 2007). This warming will 

translate into local warming in northern Wisconsin 

during the 21st century. 

Data from WICCI suggest that mean annual 

temperatures in northern Wisconsin will increase by an 

average of about 10.5 °F (5.8 °C) over the next century 

under the high (A2) emissions scenario, and by 

about 6.5 °F (3.6 °C) under the lower (B1) emissions 

scenario (WICCI 2009). Winter averages are expected 

to increase by a slightly greater degree than summer 

averages. Temperatures during winter are projected to 

increase by 11.3 to 13.5 °F (6.3 to 7.5 °C) under the 

high emissions scenario and by 7.5 to 9.0 °F (4.2 to 

5.0 °C) under the low emissions scenario (Fig. 20). 

The eastern portion of the state is projected to warm on 

the lower end of that range, while the western portion 

is projected to experience warming on the higher end 

of the range. During summer, average temperatures are 

expected to increase by 9.0 to10.5 °F (5.0 to 5.8 °C) 

under high emissions, and by 5.3 to 6.0 °F (2.9 to 

3.3 °C) under low emissions (Fig. 21). Greater summer 

warming is projected in the northernmost inland part 

of the state, and less near Lake Superior and Lake 

Michigan. 

Figure 20.—Projected changes in average winter temperature (°F) from 1980 to 2090 under a low (A) and high (B) emissions 

scenario. Figure courtesy of the Wisconsin Initiative on Climate Change Impacts (WICCI 2009).

A B
48

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

Figure 21.—Projected changes in average summer temperature (°F) from 1980 to 2090 under a low (A) and high (B) 

emissions scenario. Figure courtesy of the Wisconsin Initiative on Climate Change Impacts (WICCI 2009).

A B

During both summer and winter, daily low 

temperatures are projected to increase to a greater 

extent than daily high temperatures (WICCI 2009). 

During winter months, the daily high temperature 

is projected to increase by about 10.5 °F (5.8 °C) 

under the high emissions scenario and 7.0 °F 

(3.9 °C) under the low emissions scenarios. The daily 

low temperature in winter, in contrast, is projected to 

increase by about 14.0 °F (7.8 °C) under the high and 

9.0 °F (5.0 °C) under the low emissions scenarios. 

During summer months, daily lows are projected to 

increase by about 10.3 °F (5.7 °C) under the high and 

6.0 °F (3.3 °C) under the low emissions scenarios. 

Summer highs are projected to increase by about 

9.0 °F (5.0 °C) under the high emissions scenario 

and 5.3 °F (2.9 °C) under the low emissions scenario. 

According to projections from WICCI, the frequency 

of extremely hot and cold days and nights will change 

in northern Wisconsin over the next century. The 

annual frequency of extreme heat events—above 90 

°F (32 °C)—is projected to increase by 24 to 44 days 

under the high emissions scenario, double the increase 

of 12 to 24 days under low emissions. The annual 

frequency of days above 100 °F (37.8 °C) is projected 

to increase by 5 to 11 days under the high emissions 

scenario and 1 to 3 days under the low emissions 

scenario. The frequency of extremely cold days and 

nights, in contrast, is projected to decrease. Days 

below 20 °F (-6.7 °C) are projected to decrease by 20 

to 24 days under high emissions and 12 to 20 days 

under low emissions. Nights below 0 °F (-18 °C) are 

projected to decrease by 24 to 33 nights under the high 

emissions scenario and 18 to 24 nights under the low 

emissions scenario. 

Growing season length in northern Wisconsin is also 

projected to increase by 48 to 56 days under the high 

emissions scenario and 24 to 32 days under the low 

emissions scenario (WICCI 2009). The date of the fi rst 

autumn freeze is projected to be 22 to 28 days later 

under the high emissions scenario and 14 to 18 days 

later under the low emissions scenario. The date of 

the last spring freeze is projected to be equally early: 

15 to 18 days earlier under the low emissions scenario 

and 24 to 30 days earlier under the high emissions 

scenario.
49

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

Is the Climate Cooling?

Recent controversy has arisen concerning 

trends of global temperatures from 1998 to 

2009. Lines plotted through specifi c subsets of 

years during this period appear to have negative 

or fl at 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 2007). 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 fl at or even negative, including 

a long, level period from the 1940s to the 

1970s. Nonetheless, the overall trend has been 

positive, and recent decades are clearly warmer 

than preceding decades (Chapter 2, Fig. 10). 

In fact, the NASA Goddard Institute for Space 

Studies (2010) has recently ranked the decade 

from January 2000 to December 2009 as the 

warmest on record. Information from multiple 

years, datasets, 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. 

The Penokee-Gogebic Range of northern Wisconsin.

Photo by Linda R. Parker, Chequamegon-Nicolet National Forest

Precipitation

Global mean precipitation, evaporation, and water 

vapor are expected to increase, with locally divergent 

results (IPCC 2007). In general, areas of the globe 

that currently receive higher levels of precipitation are 

projected to experience an increase in precipitation, 

while areas of the globe that receive lower levels of 

precipitation are projected to experience a decrease 

in precipitation (IPCC 2007). However, there is 

discrepancy among models regarding regional 

and seasonal precipitation patterns, resulting in 

less confi dence in the magnitude and direction of 

future changes in precipitation patterns compared to 

temperature changes. 

For northern Wisconsin, the models generally agree 

that average annual precipitation will increase by the 

end of the 21st century. Average annual precipitation 

is projected to increase by 1.5 to 2.0 inches under the 

low emissions scenario and 2.25 to 3.0 inches under 

the high emissions scenario. However, precipitation 

increases will not be uniform, and not all seasons 

will see an increase in precipitation. There is high 

consensus among the models that precipitation will 

increase during the winter, spring, and autumn (Fig. 

22). Winter precipitation is projected to increase by 0.5 

to 0.75 inches under the low emissions scenario and 

0.75 to 1.25 inches under the high emissions scenario 

(Fig. 23). There is more disagreement among models 

regarding summer precipitation (Fig. 22). The GFDL 

model, for example, projects a dramatic decrease in 

summer precipitation while the PCM model projects 

an increase. The average of all models indicates that 

summer precipitation may decrease slightly, but it is 

important to note that this projection has a high level 

of uncertainty (Fig. 24). 

Heavy precipitation events are likely to become more 

frequent, especially in areas that experience lake-effect 

precipitation (WICCI 2009). The number of days per 

decade where greater than one inch of precipitation 
50

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

Figure 22.—Projected changes in average monthly precipitation (inches) for Wisconsin from 1980 to 2090 under a low (A) and 

high (B) emissions scenario. Each line represents a different general circulation model. Below each month is the percentage 

of the models showing an increase in precipitation. Figure courtesy of the Wisconsin Initiative on Climate Change Impacts 

(WICCI 2009).

Figure 23.—Projected changes in average winter precipitation (inches) for Wisconsin from 1980 to 2090 under a low (A) and 

high (B) emissions scenario. Figure courtesy of the Wisconsin Initiative on Climate Change Impacts (WICCI 2009).

A B

A B
51

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

falls in a single day is expected to increase by 12 to 

14 days under the high emissions scenario and by 6 to 

10 days under the low emissions scenario. Frequency 

of extremely heavy precipitation events (more than 2 

inches) is projected to increase as well: 4 to 5 days per 

decade under the high emissions scenario and 2 to 3 

days per decade under the low emissions scenario. 

The amount of precipitation that falls as rain versus 

snow is also projected to change (Fig. 25). Lakeeffect precipitation is expected to continue and could 

even increase because of the decreased extent and 

duration of ice cover over Lake Superior (Kunkel et 

al. 2002). As mentioned earlier, winter precipitation 

Figure 24.—Projected changes in average summer precipitation (inches) for Wisconsin from 1980 to 2090 under a low (A) and 

high (B) emissions scenario. Figure courtesy of the Wisconsin Initiative on Climate Change Impacts (WICCI 2009).

A B

is projected to increase for northern Wisconsin. In 

the short-term this could lead to an increase in snow 

for northern Wisconsin, but warming by the end of 

the century could cause some to fall as rain instead 

of snow (Kunkel et al. 2002, Wuebbles and Hayhoe 

2004). At any point in the year, the probability of any 

particular precipitation event falling in a frozen form 

will likely decrease under both emissions scenarios 

(WICCI 2009). In addition, the fi rst snowfall is 

projected to be later, and the last snowfall is projected 

to be earlier. Even areas that maintain constant or even 

increased snowfall could still experience a decrease in 

snow depth if winter temperatures prevent snow from 

packing (Kling et al. 2003). 
52

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

Figure 25.—Projected changes in the probability that precipitation is frozen in Rhinelander, WI, in 1980 (modern) and 2090 

(future) under a low (A) and high (B) emissions scenario. The red line indicates a 50% chance of either rain or snow under 

current climate (solid line) and future climate (dashed line) conditions. Figure courtesy of the Wisconsin Initiative on Climate 

Change Impacts (WICCI 2009).

A B

Hydrological Implications

Changes in temperature and precipitation will affect 

the hydrology of northern Wisconsin. Decreases in 

snow depth and earlier onset of spring will most 

likely lead to an earlier peak streamfl ow (Karl et al. 

2009, Kling et al. 2003). Earlier peak fl ows coupled 

with increases in spring precipitation are projected 

to lead to increases in spring runoff (Wuebbles et al. 

2009) and increased risk of spring fl oods (Kling et 

al. 2003). However, by summer, stream levels could 

be dramatically reduced from increased evaporation 

from higher temperatures, especially if there are 

also reductions in summer precipitation. Higher 

temperatures and reduced summer streamfl ow could 

lead to drying of small streams (Karl et al. 2009) or 

higher stream temperatures (Kling et al. 2003). Using a 

hydrological model, Wuebbles et al. (2009) examined 

the impacts of future climate change on the upper 

Mississippi River Basin, including the Chippewa 

and Wisconsin River watersheds. Their models 

indicate that by the end of the century, runoff in these 

watersheds is expected to increase in winter and 

spring and decrease in autumn under both emissions 

scenarios. In summer, impacts on runoff depend on 

the emissions scenario: runoff is projected to decrease 

under the high emissions scenario, but increase 

slightly under the low emissions scenario. For both 

watersheds, peak and mean fl ow levels are projected to 

decrease slightly or stay the same in the short term (30 

years), but increase by 10 to 20 percent by the end of 

the century under both emissions scenarios.

Soil moisture is also projected to change in northern 

Wisconsin over the 21st century. Evapotranspiration, 

the sum of evaporation from soil and transpiration 

from plants, is projected to increase (Wuebbles and 

Hayhoe 2004), potentially leading to a decrease 

in soil moisture during the summer months when 

temperatures are high, plants are actively transpiring, 
53

CHAPTER 3: CLIMATE CHANGE IN NORTHERN WISCONSIN

and precipitation may be reduced. Current estimates 

based on climate models suggest that summer and 

autumn soil moisture could decrease by up to 30 

percent (Wuebbles and Hayhoe 2004). In contrast, 

winter and spring soil moisture is projected to increase 

by up to 80 percent as a result of seasonal precipitation 

increases (Wuebbles and Hayhoe 2004). 

Increased evaporation resulting from increases in 

temperature would likely cause a lowering of lake 

levels (Karl et al. 2009). Lake levels are projected 

to peak earlier in the year in response to earlier 

snow melt and higher precipitation in the winter 

and spring, and these peak levels may be lower than 

levels observed during the past century (Wuebbles 

and Hayhoe 2004). Increased temperatures will lead 

to decreases in the duration, thickness, and extent of 

ice cover during the winter months (Wuebbles and 

Hayhoe 2004). 

Climate change may also impact lakes through 

changes in the timing of thermal stratifi cation (Kling 

et al. 2003). Stratifi cation is a phenomenon that occurs 

during warmer months when the sun’s rays warm the 

lake surface but cannot reach (or warm) the water at 

the bottom. Differences in density of water between 

the warm surface and the cool bottom keep the layers 

from mixing, and the lake becomes stratifi ed. Warmer 

temperatures and longer periods where lakes remain 

unfrozen may cause lakes to remain stratifi ed for 

longer periods, which could lead to oxygen depletion 

at deeper levels of the water column.

A small waterfall in the forest.

Photo by Tom Matthiae, Chequamegon-Nicolet National Forest
54

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Climate change will alter the forested ecosystems 

of northern Wisconsin. The most visible changes 

may initially be in forest health and vigor, although 

fundamental changes in ecosystem function, 

structure, and species distribution will likely become 

increasingly evident. Other important physical and 

biological characteristics of ecosystems, such as 

soil and water quality, carbon storage and nutrient 

cycling, and wildlife habitat type and quality, may not 

be as readily apparent (Anderson et al. 1976, Ice and 

Stednick 2004, Waring and Schlesinger 1985). 

This chapter describes the effects of climate change on 

some of these forest ecosystem characteristics, with 

an emphasis on the expected changes in tree species 

abundance and composition. Two different modeling 

approaches were used to simulate climate change 

impacts on forests, a species distribution model (The 

Tree Atlas) and a process model (LANDIS-II), and 

the results from these two modeling approaches are 

summarized. 

Climate Change and Forests

Climate change is expected to impact both entire 

forest ecosystems and the individual tree species that 

compose forest communities. Projected increases 

in annual mean temperature, increased variability 

in precipitation and soil moisture balance, and 

increases in atmospheric carbon dioxide will have 

direct and indirect effects on multiple, interconnected 

ecosystem components and drivers. In Wisconsin 

and the Midwest, climate change is expected to 

result in increased likelihood of periodic drought 

conditions, fl ash fl ooding, and extreme weather events 

(UCS 2009). Disturbances are expected to increase, 

including severe wind events (Frelich and Reich 

2010). Within forests, the climatological changes 

and increased disturbance will interact with natural 

processes to increase the intensity and duration of 

stress. For example, the combined effects of drought 

conditions, longer growing seasons, and increased 

temperatures will likely intensify fi re regimes 

(frequency, severity, and season length) and increase 

insect infestation (frequency, duration, and extent; 

Soja et al. 2007). Additionally, a number of vital soil 

processes will be altered, including nutrient cycling, 

carbon storage, decomposition, and water storage 

and recharge. Importantly, all of these new stressors 

will occur in addition to existing stressors, such as 

pathogens and insects, competition from invasive 

species, and ozone and other pollution. Cascading 

interactions of stressors in these systems threaten 

to reduce productivity further and can even cause 

mortality as trees become less vigorous and resilient 

(Aber et al. 2001). 

Trees and other plant species have responded to past 

climate change in a number of ways and at a number 

of spatial scales (Ritchie 1986). The ranges of tree 

species in eastern North America have generally 

shifted northward as the climate has warmed over 

the past 14,000+ years since the last ice age (Davis 

1981, Delcourt and Delcourt 1987, Webb et al. 

1987). Evidence is mounting that tree species, along 

with many other organisms, are currently moving 

northward (Parmesan and Yohe 2003, Woodall et al. 

2009) and upward in elevation (Lee et al. 2005), with 

some species migrating at very high rates (Woodall 

et al. 2009). Such shifts are likely to continue and 

even accelerate in the coming decades in Wisconsin 

as habitat suitability changes, resulting in declines in 
55

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

species abundance or productivity, or even the loss of 

entire species from certain areas. Past disturbances, 

habitat fragmentation or loss, and management 

practices already prevent species from occupying all 

suitable habitat within their ranges. If climate change 

were to further decrease the suitable habitat of major 

species in conjunction with these other effects, then 

overall forest abundance or density would decrease 

and forest productivity declines would be exacerbated 

(Aber et al. 2001, Hanson and Weltzin 2000).

New conditions may be suitable for other species, 

however, leading to successional shifts and changing 

forest composition over time. Elevated carbon dioxide 

may actually enhance the growth of some species 

(Norby et al. 2005) and increase their water use 

effi ciency (Ainsworth and Rogers 2007), potentially 

offsetting the effects of drier growing seasons. There 

is already some evidence for increased forest growth 

globally (Bonan 2008) and in the eastern United States 

(Cole et al. 2010, McMahon et al. 2010), but the issue 

question is still under active discussion (Bonan 2008, 

Foster et al. 2010) and it remains unclear if long-term 

enhanced growth can be sustained. Nutrient and water 

availability, ozone pollution, and tree age and size all 

play major roles in the ability of trees to capitalize 

on carbon dioxide fertilization (Ainsworth and Long 

2005). Ecosystem community shifts may take place 

as some species are genetically better able to take 

advantage of carbon dioxide fertilization than others 

(Souza et al. 2010).

Other community shifts may result from plant 

migration. It is possible that some species that 

are currently considerably distant from northern 

Wisconsin will migrate into the region, even as 

resident species disappear. However, it is important to 

note that migration of new species is constrained by a 

number of factors, including seed dispersal dynamics 

and landscape fragmentation, and it is expected that 

species will not be able to migrate northward without 

substantially lagging behind changes in climate 

(Iverson et al. 2004a,b; Scheller and Mladenoff 2008). 

The Climate Change Tree Atlas

The Climate Change Tree Atlas is one of two 

approaches that were used to understand the potential 

impacts of climate change on forests in northern 

Wisconsin (Chapter 2, Appendix 4; Iverson et al. 

2008a,b). The Tree Atlas uses an ensemble of species 

distribution models to examine the features that 

contribute to a tree species’ current habitat and then 

project where similar habitat conditions are likely to 

occur in the future. The Tree Atlas does not predict 

where species will be present in the future, but rather 

where suitable habitat for individual tree species may 

be present. 

For this assessment, the Tree Atlas examined the 

location of potential suitable habitat for 73 tree species 

that currently are present in northern Wisconsin or 

are predicted to have suitable habitat in the future. 

The results of this analysis show that climate change 

will likely lead to changes in the suitable habitat 

of many common tree species. The ways in which 

tree species will actually respond to climate change, 

however, is infl uenced by a number of “modifying 

factors” including site conditions, competition from 

other species, landscape connectivity, the degree of 

disturbance, and the ability of species to disperse, 

many of which cannot be modeled directly by the 

Tree Atlas. To fi ll these gaps, we used additional 

information from the research literature and examined 

the individual models to assess the unique ways in 

which individual species may respond to climate 

change. When practical, the contributions of these 

modifying factors have been included, with more 

details presented in Appendix 4.

Of 134 species modeled in the eastern United States 

by the Tree Atlas (Prasad et al. 2007), 73 are of 

interest for this assessment because they currently 

have or are projected to have suitable habitat under 

future conditions in northern Wisconsin. A number 

of projections of future climate were used in the 

modeling, and results are reported using a low 
56

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

emissions scenario (B1) and a high emissions scenario 

(A1FI). Based on potential changes in suitable habitat, 

these 73 species were grouped into eight classes 

(Table 5) that describe the potential for their suitable 

habitat to expand, shrink, or remain constant by the 

end of the 21st century, relative to current FIA data 

(Iverson et al. 2008b). The possibilities observed 

ranged from species extirpation to long distance 

migration of species. 

These classes also provide a relative assessment of 

species’ vulnerability to climate change. Of the 73 

species, 21 species show some potential to increase 

under climate change, 19 show potential to decrease, 

Table 5.—Eight classes of potential changes in suitable habitat for 73 tree species under climate change 

based on results from the Climate Change Tree Atlas.

Extirpated: One species is currently in the analysis area, but all suitable habitat disappears by 2100

 Mountain maple

Large Decline: Twelve species show large declines in suitable habitat, especially under the high emissions scenarios

 Balsam fi r Eastern hemlock Sugar maple

Bigtooth aspen Northern white-cedar Tamarack

Black ash Paper birch White spruce

Black spruce Quaking aspen Yellow birch

Small Decrease: Six species show smaller declines, mostly apparent in the high emissions scenarios

Balsam poplar Eastern white pine Red maple

 Butternut Jack pine Rock elm

No Change: Six species show roughly similar suitable habitat now and in the future

American basswood Green ash Northern red oak

Chokecherry Northern pin oak Red pine

Small Increase: Four species have an increased amount of suitable habitat in the future as compared to current, especially 

under the higher emissions scenarios

American elm Eastern hophornbeam White ash

 American hornbeam

Large Increase: Seventeen species have much higher estimates of suitable habitat in the future as compared to current, 

especially with the higher emissions scenarios 

 American beech Boxelder Shagbark hickory

Bitternut hickory Bur oak Silver maple

Black cherry Eastern cottonwood Slippery elm

Black oak Eastern red cedar Swamp white oak

Black walnut Hackberry White oak

Black willow Osage orange

New Entry under High and Low Emissions Scenarios: Eleven species have never or very rarely been detected in the 

analysis area during FIA sampling, but show potential suitable habitat entering the region, even under the low emission 

scenarios

Black locust Ohio buckeye River birch

Flowering dogwood Pignut hickory Sassafras

Honey locust Pin oak Yellow poplar

Mockernut hickory Red mulberry

New Entry under High Emissions Scenarios: Sixteen species have never or very rarely been detected in the analysis area 

during FIA sampling, but show potential suitable habitat entering the region under high emissions

Black hickory Eastern redbud Scarlet oak

Blackgum Northern catalpa Shingle oak

Blackjack oak Peachleaf willow Sugarberry

 Chestnut oak Pecan Sycamore

Chinkapin oak Post oak Wild plum

 Common persimmon
57

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

6 show little or no change, and another 27 species 

show the potential for some level of new suitable 

habitat entering the region under at least the high 

emissions scenario.

Loss of Suitable Habitat

In the Tree Atlas analysis, mountain maple was the 

only species to fall into this class. Mountain maple 

is a predominantly northern species, and in northern 

Wisconsin it is typically found in the understory of 

northern hardwoods and other mesic forest types. 

Data from FIA (Miles 2010) sampling indicates that 

the species is very uncommon now. Loss of suitable 

habitat was projected under both low and high 

emissions scenarios. Further analysis of the Tree Atlas 

indicates that the model for mountain maple has a high 

degree of reliability, and that the species is probably 

quite sensitive to temperature changes. However, 

examination of both the disturbance and biological 

modifying factors suggests that the species could do 

somewhat better than the models suggest.

Large Declines in Suitable Habitat

The Tree Atlas identifi ed 12 species that are projected 

to have large declines in potential suitable habitat: 

black spruce, balsam fi r, northern white-cedar, yellow 

birch, paper birch, quaking aspen, white spruce, 

eastern hemlock, sugar maple, black ash, tamarack, 

and bigtooth aspen. Many of these species are very 

common and important in the forests of northern 

Wisconsin. Additionally, partly due to the high 

frequency of these species in the region, all but one, 

white spruce, have high model reliability scores. 

Black spruce—Black spruce is second only to 

mountain maple in projected suitable habitat decreases. 

The model predicts suitable habitat for black spruce 

being reduced to 13 percent of its current habitat under 

the low emissions scenario and 8 percent under the 

high emissions scenario. Black spruce also appears 

to be highly sensitive to the disturbances that are 

expected to increase in a warmer climate—especially 

drought, insect pests, and fi re—suggesting the species 

may do worse than indicated by the habitat modeling. 

Although somewhat benefi ted by biological factors, 

especially shade tolerance, black spruce may be the 

most at risk among all species in northern Wisconsin. 

Balsam fi r—Although balsam fi r is similar to black 

spruce in its sensitivity to disturbance and climatic 

factors and is found in many of the same habitats, our 

assessment shows it may be slightly more resilient. 

The model predicts suitable habitat for balsam fi r 

being reduced to 18 percent of current habitat under 

the low emissions scenarios and 9 percent under the 

high emissions scenario. 

Northern white-cedar—Northern white-cedar is 

projected to have substantial reductions in suitable 

habitat, with habitat at the end of the century being 

reduced to 37 percent of current habitat under the 

high emissions scenario and 23 percent under the low 

emissions scenario. 

Quaking aspen—Quaking aspen currently has the 

highest importance value (a measure of relative 

abundance) in all of northern Wisconsin and is found 

throughout the region. Although quaking aspen is 

projected to remain widespread, habitat is projected 

to decline to 45 percent of current habitat under the 

low emissions scenario and 17 percent under the 

high emissions scenario. Several modifi cation factors 

indicate that this species may do better than the habitat 

model predicts, including its ability to persist over 

large areas, broad temperature tolerances, cloning 

ability, and resilience to disturbance. 

Paper birch—Currently ranked as fourth in 

importance value, paper birch suitable habitat is 

projected to decrease to 42 percent of current habitat 

under the low emissions scenario and 13 percent under 

the high emissions scenario. This projected loss may 

be exacerbated by negative response to drought and 

fi re topkill. 
58

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Bigtooth aspen—Bigtooth aspen has a projected 

habitat response that is greatly dependent on the 

emissions scenario. Under the low emissions scenario, 

a decline to 80 percent of current habitat is projected; 

whereas under the high emissions scenario, habitat is 

projected to decline to 25 percent of current habitat. 

This difference in response is caused by a greater 

northward shift in habitat that creates a greater loss of 

occupancy under higher emissions. Further analysis 

of the model indicates that the cooler and wetter 

environments may contain the habitats most likely 

to remain suitable for bigtooth aspen over the next 

century. 

Sugar maple—Currently ranked third in importance 

value, sugar maple is found across the forests of 

northern Wisconsin. Although suitable habitat is 

projected to decline to 62 percent of current habitat 

under the low emissions scenario and to 31 percent 

under the high emissions scenario, sugar maple is 

likely to persist in areas where it is currently found 

(Fig. 26). Sugar maple has many characteristics, such 

as high shade tolerance and fewer disease and insect 

pests relative to other species, that indicate that this 

species may do better than projected in the habitat 

suitability models. 

Yellow birch—Yellow birch habitat is projected to 

decline to 38 percent of current habitat under the low 

emissions and to 15 percent under the high emissions 

scenario (Fig. 26). The species’ high dispersal ability 

may be a benefi t under climate change, yet sensitivity 

to regional climate, fi re topkill, and insect pests 

suggest that the species may fare worse than projected 

by the model. 

Eastern hemlock—The projected changes in suitable 

habitat for eastern hemlock vary substantially, with 

declines to 17 percent of its current habitat under the 

high emissions scenario and to 59 percent under the 

low emissions scenario. Along with the model results, 

consideration also has to be given to its susceptibility 

to hemlock wooly adelgid, a nonnative insect that 

is currently limited by cold winter temperatures but 

may be able to invade and persist in the projected 

warmer climate (Paradis et al. 2008). Combined with 

the species’ negative response to drought, the adelgid 

(if introduced) may push the species’ decline beyond 

what was projected in the habitat model. Eastern 

hemlock is not a valuable timber species, but it has 

signifi cant ecological and aesthetic value. This species 

often grows in the lower, wetter areas along streams, 

and thus provides cooler habitats for many other 

organisms. 

White spruce—White spruce habitat is projected 

to decline to 52 percent of current under the low 

emissions scenario and 39 percent under the high 

emissions scenario. Although the habitat model for 

white spruce had only moderate reliability (compared 

to high reliability for all other species projected 

to experience large habitat declines), additional 

examination of the species’ susceptibility to climate, 

disturbance, and other factors provide good evidence 

for a likely negative habitat response to climate 

change. 

Black ash—Black ash suitable habitat is projected 

to decline to 69 percent of current under the low 

emissions scenario and 52 percent under the high 

emissions scenario. Despite the loss of suitable 

habitat, there is little projected change in the extent 

of the species’ occupancy, meaning that areas that 

are currently occupied are likely to remain occupied 

but at a lower level. The habitat model results for any 

ash species also need to be considered relative to the 

species’ high susceptibility to the nonnative emerald 

ash borer. 

Tamarack—The model predicts suitable habitat for 

tamarack being reduced to 74 percent of current 

habitat under the low emissions scenario and to 53 

percent under the high emissions scenario. However, 

tamarack is also projected to largely retain its 

occupancy despite these decreases. Unlike the other 

species in this class, tamarack appears to be more 

infl uenced by soil variables than by climatic variables. 

This resilience to changes in temperature is likely to 

support occupancy. 
59

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Chequamegon/Nicolet NF

Ecoregion -212

Importance Value

0

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Figure 26.—Potential habitat changes for two species projected to have large declines in suitable habitat—sugar maple and 

paper birch—showing current suitable habitat (top) and projected suitable habitat under the low (middle) and high (bottom) 

scenarios of climate change; importance values indicate relative abundance of a species in a given community (0 = rare or not 

present, 50 = most abundant).
60

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Small Declines in Suitable Habitat

This group consists of six species that vary greatly in 

current importance values, reliability of models, and 

response between emissions scenarios: balsam poplar, 

rock elm, jack pine, red maple, eastern white pine, and 

butternut (Appendix 4). In general, the species show 

little or no change under the low emissions scenario, 

but under high emissions, suitable habitat is reduced. 

The one exception is balsam poplar, which shows a 

reduction in suitable habitat under the low emissions 

scenario but no change under the high emissions 

scenario. In this section, three of the six species are 

discussed (all three with highly reliable models). 

Jack pine—Suitable habitat is reduced to 81 percent 

of current habitat under the low emissions scenario 

and to 66 percent under the high emissions scenario. 

The habitat models for jack pine indicate possible 

expansion of area, but not necessarily abundance, if 

the soils can support it. Further analysis of the models 

and of the species indicate that the species is not 

particularly controlled by temperature variables and 

may be well adapted in a slightly, but not extremely, 

warmer climate. The expansion of jack pine may be 

limited more by soil-related conditions, such as soil 

texture and drainage. This species may even be better 

adapted to projected climate changes because of its 

positive response to the disturbance regimes (primarily 

fi re) predicted to occur more frequently. 

Red maple—Red maple is one of the most adaptable 

tree species in North America and is expanding 

greatly in the eastern United States. Consequently, the 

modeled reduction in suitable habitat may not refl ect 

the ability of this species to persist under climate 

change. The habitat model projects reductions in 

suitable habitat to 86 percent of current habitat under 

the low emissions scenario and to 55 percent under 

the high emissions scenario, even though there is no 

projected change in the species’ occupancy of suitable 

habitat. Considering the species’ formidable resilience 

to disturbances and its wide tolerance of soil types, 

moisture conditions, and pH levels, red maple appears 

to be at a greater advantage than all other species and 

is likely to do much better than modeled. Indeed, this 

species may even continue to expand geographically 

under most scenarios of climate change. Abrams 

(1998) hypothesizes that until the early 1800s, red 

maple was limited primarily by frequent fi res set by 

Native Americans and early European settlers, and 

then by various forces in the wake of widespread 

deforestation. With the cessation of fi re, a general 

mesophication of the eastern United States has allowed 

red maple to fl ourish. The Tree Atlas, however, 

projects that under the most severe regimes, higher 

temperatures and lower precipitation may cause a 

cessation of the mesophication in some areas, resulting 

in a reversal of recent red maple expansion (Nowacki 

and Abrams 2008). 

Eastern white pine—No real change in suitable 

habitat is expected under the low emissions scenario, 

but the models show a severe reduction under the high 

emissions scenario to 32 percent of current habitat. 

The model shows relatively high sensitivity of the 

species to higher temperatures and lower precipitation. 

Eastern white pine is also quite sensitive to disturbance 

factors, especially drought, disease, insects, and fi re 

topkill. Should these factors increase substantially 

under warming, the models may underestimate the 

extent of suitable habitat reduction.

No Change in Suitable Habitat

This class is made up of six species that, on average 

across the low and high emissions scenarios, show 

little (less than 20 percent) change in suitable habitat 

(Appendix 4). Four of the six models have medium 

reliability (only one has high reliability), so a 

signifi cant amount of the variation can be attributed to 

model error. Nonetheless, the group contains several 

species of large signifi cance, including green ash, 

northern pin oak, northern red oak, and basswood. 
61

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Green ash—According to the habitat models, suitable 

habitat for green ash is expected to decrease to 75 

percent of current habitat under the low emissions 

scenario and increase to 123 percent of current habitat 

under the high emissions scenario. However, the 

model results may have limited value for this species 

because of its susceptibility to the nonnative emerald 

ash borer. 

Northern pin oak—Suitable habitat for northern pin 

oak is also projected to increase slightly under the low 

emissions scenario and decrease slightly under the 

high emissions scenario. The area occupied, however, 

could expand somewhat under both scenarios because 

of high adaptability to disturbances, a benefi t that is 

somewhat offset by sensitivity to January temperatures 

and other climatic drivers. 

American basswood—Currently found throughout 

northern Wisconsin, American basswood suitable 

habitat is projected to increase to 124 percent of 

current habitat under the low emissions scenario, but 

shows no change under the high emissions scenario. 

Northern red oak—Northern red oak is found across 

most of northern Wisconsin, both now and into the 

future under any scenario. The model for red oak, 

which has high reliability, projects an increase in 

suitable habitat under the low emissions scenario and a 

slight decrease under the high emissions scenario. 

Small Increase in Suitable Habitat

This class contains only four species, all of which are 

common throughout northern Wisconsin: American 

elm, eastern hophornbeam, American hornbeam, and 

white ash. 

American elm—American elm suitable habitat is 

projected to increase to 1.7 times current habitat 

under the low emissions scenario and to 2.2 times 

current habitat under the high emissions scenario. 

However, the species is sensitive to disturbance 

factors, especially Dutch elm disease, suggesting 

that the species may fare worse than models predict. 

The development of disease-resistant varieties may 

also change the dynamics of this species in the future 

(Merkle et al. 2007). 

Eastern hophornbeam—Eastern hophornbeam is a 

widespread but minor species in northern Wisconsin. 

The models show increases in suitable habitat, 

especially under the high emissions scenario. 

White ash—White ash, like other ash species 

discussed earlier, is an exceptional case because 

the habitat modeling does not take into account the 

disastrous implications of emerald ash borer on 

the future of the species. In addition to this pest, 

sensitivity to other factors like fi re topkill, disease, and 

pollution will contribute to its vulnerability to decline 

in the future. 

Trees changing colors along a lake edge.

Photo used with permission of Patricia R. Butler, Northern Institute of Applied Climate Science
62

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Large Increase in Suitable Habitat

Model results identifi ed 17 species that are projected 

to increase dramatically in both spatial extent and 

in importance value, especially under the higher 

emissions scenarios. However, only three of these 

species (white oak, black oak, American beech) have 

high model reliability scores, so the interpretation of 

the suitability scores must proceed with caution. Many 

of the species that are projected to have large increases 

are oaks and hickories. 

Bur oak—Suitable habitat is projected to increase 

to 1.7 times current habitat under the low emissions 

scenario and to 2.3 times current habitat under the high 

emissions scenario. The model for bur oak has medium 

reliability and the range expansion to all of the 

analysis area under the high emissions scenario should 

be viewed with moderate confi dence. Bur oak has 

characteristics that may be benefi cial under projected 

conditions; it is one of the most drought-tolerant oak 

species and is favored by periodic fi res, which may 

increase under warming and drought conditions. 

Black oak—Black oak is projected to have a large 

increase in suitable habitat (Fig. 27) under both the 

low emissions scenario (fourfold increase) and the 

high emissions scenario (sixfold increase). 

White oak—White oak suitable habitat is projected 

to increase to more than 2.7 times current habitat 

under both emissions scenarios. Although the models 

indicate that this species will generally fare well under 

both emissions scenarios, some locations in northern 

Wisconsin may become too dry for the species under 

the high emissions scenario. 

Shagbark hickory—Shagbark hickory suitable habitat 

is projected to increase to 8 times current habitat under 

both scenarios. Its potential for occupancy expands 

to the entire analysis area under the high emissions 

scenario, and has medium model reliability. Shagbark 

hickory can tolerate a wide range of conditions—

although it grows best under humid conditions and 

does not tolerate fi re very well—and is susceptible to 

many diseases and pests. 

Bitternut hickory—Bitternut hickory has low model 

reliability and so its projected expansion into all of 

northern Wisconsin under the high emissions scenario 

and its 3-fold increase in suitable habitat under both 

emissions scenarios should be viewed cautiously. 

Bitternut hickory has thin bark and is more susceptible 

to fi res than oaks; however, it tolerates drought and 

other disturbances well, partly because of its vigorous 

sprouting ability. 

Swamp white oak—Swamp white oak also has low 

model reliability so its projected range expansion and 

twofold increase in suitable habitat under the low 

emissions scenario and sevenfold increase under the 

high emissions scenario should be viewed cautiously. 

Although swamp white oak is not particularly driven 

by climatic factors, the model shows that January 

temperatures and growing season precipitation 

infl uence its suitable habitat. It is moderately resistant 

to fi re, and like most oaks it is tolerant of a wide range 

of conditions. 

New Suitable Habitat under Low 

and High Emissions Scenarios

Conditions are predicted to become suitable for 11 

species by the end of 21st century under the low 

emissions scenario, with even greater increases in 

suitable habitat for these species under the high 

emissions scenario. Model reliability varies widely, 

so this rating should be carefully considered when 

interpreting the results. Additionally, it is important to 

consider that while suitable habitat may be present for 

these species in the future, the ability for these species 

to become established in the region is dependent 

on a number of factors, including the dispersal and 

establishment ability of the species (Iverson et al. 

2004a). Many of the tree species that are projected to 

have new habitat are currently present in hardwood 

forests farther south. 
63

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

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Black Oak Black Cherry

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Figure 27.—Potential habitat changes for two species projected to have large increases in suitable habitat—black oak and 

black cherry—showing current suitable habitat (top) and projected suitable habitat under the low (middle) and high (bottom) 

scenarios of climate change; importance values indicate relative abundance of a species in a given community (0 = rare or 

not present, 50 = most abundant).
64

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Pignut hickory—Substantial amounts of suitable 

habitat for pignut hickory may be present in northern 

Wisconsin by the end of the century, especially under 

the high emissions scenario. Model reliability for 

pignut hickory is high, and the species now exists in 

temperature and precipitation regimes similar to those 

projected for the analysis area under both emissions 

scenarios, especially the low emissions scenario. 

Mockernut hickory—Mockernut hickory shows 

potential expansion of habitat into the region, with 

substantial amounts of suitable habitat appearing 

under the high emissions scenario and less under the 

low emissions scenario. Because the species shows 

very positive response to disturbances—particularly 

drought, wind, and disease— it may do better than 

modeled. 

Flowering dogwood—Flowering dogwood habitat 

is modeled, with moderate reliability, to behave 

similarly to hickory species (above) in area and habitat 

quality. Although the species is not expected to have 

large increases in importance value, suitable habit is 

projected to expand to include 70 percent of the region 

under the high emissions scenario and 15 percent 

under the low emissions scenario. 

Yellow-poplar—A large increase in the extent of 

suitable habitat is projected under the high emissions 

scenario (to about 40 percent of northern Wisconsin) 

but only a very small increase is projected under the 

low emissions scenario (Fig. 28). 

Sassafras—Sassafras is modeled to have suitable 

habitat increase to occupy almost 80 percent of 

northern Wisconsin under the high emissions scenario 

and 30 percent under the low emissions scenario. 

New Suitable Habitat 

under the High Emissions Scenario Only

This group of 16 species is characterized by little or 

no current occupancy in northern Wisconsin, but the 

models indicate that suitable habitat could appear 

by the end of the century under the high emissions 

scenario. 

Blackgum—Blackgum shows some range expansion 

into roughly 20 percent of northern Wisconsin under 

the high emissions scenario, but with a very low level 

of suitable habitat within that area. The model has high 

reliability, but the species could do even better than 

modeled given its positive response to disturbance and 

biological factors. 

Blackjack oak—Habitat for blackjack oak is projected 

to appear throughout most of northern Wisconsin 

under the high emissions scenario only. Although this 

model has medium reliability, it should be treated with 

caution since the species’ current range is more than 

340 miles (550 kilometers) to the south. 

Sycamore—Suitable habitat is projected to increase 

greatly under the high emissions scenario (to 80 

percent of northern Wisconsin) but to only a minor 

extent under the low emissions scenario. The species’ 

current range boundary is approximately 110 miles 

(180 kilometers) from the nearest part of the analysis 

area.
65

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

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Importance Value

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Figure 28.—Potential habitat changes for two species for which suitable habitat is projected to expand into northern Wisconsin 

under both emissions scenarios—yellow-poplar (left column) and red mulberry (right column)—showing current suitable habitat 

(top) and projected suitable habitat under the low (middle) and high (bottom) scenarios; importance values indicate relative 

abundance of a species in a given community (0 = rare or not present, 50 = most abundant).
66

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

LANDIS-II 

The LANDIS-II model was also used to understand 

how forests in northern Wisconsin may be affected 

by climate change. LANDIS-II is a process model 

that examines the relationships among physical and 

biological processes and uses these relationships to 

understand potential ecosystem changes (Chapter 2;

Scheller et al. 2005, Scheller and Mladenoff 2008). 

This chapter summarizes LANDIS-II model 

predictions for 3.7 million acres, approximately 20 

percent of the northern Wisconsin landscape 

(Fig. 29). The model incorporates tree species life 

history characteristics, dispersal characteristics, and 

landscape patterns to simulate changes in tree biomass 

and distribution in response to disturbances (wind 

damage and tree harvest) and climate change. 

Tree Species Responses

The LANDIS-II simulations reported here include 

climate change scenarios plus a control scenario that 

maintains a climate similar to the period from 1960 

to 1990. Using the model to simulate forest processes 

over the next 100 to 200 years under both constant 

climate and changing climate helps identify where 

climate change will have the greatest impacts. The 

forest community changed very little during the 

control simulation (no climate change) when the level 

of disturbance from wind and forest management 

were held constant. In contrast, excluding wind and 

management disturbances from the control simulation 

yielded a dramatic benefi t for late-successional 

species. 

The climate change scenarios used were based on 

two general circulation models (GCMs): the second 

generation Hadley Centre for Climate (HadCM2) 

model and the fi rst generation Canadian Climate 

Center (CGCM1) model. Both GCMs used the 

IPCC emissions scenario IS92A, which assumes that 

emission levels at the end of the 21st century will be 

between the low and high scenarios used in the Tree 

Atlas. Of the two GCMs, the HadCM2 model has 

Figure 29.—Area modeled by LANDIS-II (Scheller et al. 

2005, Scheller and Mladenoff 2008).

a more moderate warming response to greenhouse 

gases. It was originally developed in the late 1990s, 

but is now considered too unresponsive given greater 

understanding of climate change mechanisms and 

processes. For this reason, the results presented in this 

section will largely focus on the results of the CGCM1 

model and where both models yield similar results. 

Both climate change scenarios (IS92a with CGCM1 or 

HadCM2) projected overall increases in aboveground 

biomass, with or without disturbance, although there 

was high variation within the region of interest and 

among species. Both scenarios projected substantial 

changes in tree species composition under climate 

change, with fi ve species (balsam fi r, paper birch, 

white spruce, jack pine, and red pine) extirpated 

largely because of failure to establish (Table 6). Also 

projected was a general increase of earlier successional 

species tolerant of warmer and drier climate (such as 

oaks, hickories, and bigtooth aspen). 
67

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

For some species, the presence or absence of 

disturbance contributed to changes in biomass. For 

example, sugar maple biomass showed a greater 

increase with no disturbance, whereas basswood, 

bigtooth aspen, red maple, and red oak fared better 

when disturbances from wind and forest management 

were included in the model. Overall, the greatest 

changes in composition occurred when disturbance 

was not included in the model. This is because many 

important northern Wisconsin species are shade 

intolerant and can only germinate under the increased 

sunlight that follows a disturbance. 

Many of the projected changes may depend on 

changes in site conditions. Sites that are currently too 

wet for some mesic species, such as sugar maple and 

basswood, may become more suitable for these species 

under warmer and drier future conditions. 

Results from the model suggest that forest 

management will remain a strong driver of forest 

composition in the near future (approximately 50 

years) despite projected climatic changes. This is 

because harvesting helps maintain species diversity 

by retaining open niches for many of the dominant, 

shade-intolerant tree species. However, by the end 

of the 21st century, climate change is expected to 

become a more important driver and interact more 

strongly with management, landscape fragmentation, 

and natural disturbance to suppress forest productivity. 

The result is that many currently common “northern” 

species may decline, and landscape fragmentation may 

constrain seed dispersal and impede the movement of 

other species into newly available niches. Some of the 

species limited in this way were white ash, bitternut 

hickory, white oak, black oak, and bur oak.

Synthesis of Model Results

The use of two very different models, the Climate 

Change Tree Atlas and LANDIS-II, to model the 

effects of climate change on tree species’ dominance 

and habitat suitability in northern Wisconsin provides 

a rich set of information for further evaluation of 

potential responses. The models have fundamentally 

different architecture, but they include many of 

the same tree species and their results show many 

similarities. One of the most profound changes 

projected by both analyses is the tendency of many 

northern and boreal species at the southern edge of 

their current range to decrease in abundance or extent 

as their current habitat becomes less suitable and 

reestablishment becomes more diffi cult. Both models 

projected high likelihood of habitat declines for 

balsam fi r, paper birch and white spruce. 

Other species also showed high potential for declines 

in both models: red pine, jack pine, northern whitecedar, quaking aspen, and yellow birch. However, 

even though models may project species decline or 

even extirpation from northern Wisconsin, the actual 

response of these species to climate change remains 

highly uncertain. Smaller areas of suitable habitat 

may be retained on the landscape even for the most 

vulnerable species, creating refugia. Additionally, 

declines will not necessarily be uniform, but may 

instead be partially offset by enhanced or new habitat. 

Depending on the spatial distribution of these changes, 

some species will be more likely to re-establish at 

different locations within the region. 

Table 6.—Modeling results for LANDIS-II 

for northern Wisconsin tree species based 

upon effects of climate change, disturbance 

(management and wind), and fragmentationconstrained seed dispersal.

Severe decline or loss Likely decline

White spruce Northern white-cedar

Balsam fi r Quaking aspen

Red pine Yellow birch

Jack pine Red maple

Paper birch Red oak

Current species Southern species

 that possibly increase limited by dispersal

Sugar maple White oak

Basswood Bur oak

Bigtooth aspen Black oak

White ash Bitternut hickory
68

CHAPTER 4: CLIMATE CHANGE EFFECTS ON FORESTS

Both models are also in agreement that a number of 

tree species have the potential to increase in abundance 

and extent under climate change. Some of these—such 

as bur oak, black oak, and bitternut hickory—currently 

exist in the region and may experience more favorable 

habitat under future conditions. Additionally, the 

Tree Atlas projections suggest that some species 

not currently found in northern Wisconsin may gain 

suitable habitat. Successful colonization of new 

habitats depends on the ability of a species to reach a 

new location and then become established. Dispersal 

and establishment, in turn, depend on highly complex 

and interrelated biological and physical processes. 

Therefore, the movement of new tree species into an 

area will often lag substantially behind the decline of 

existing species (Iverson et al. 2004a).

In some instances, the two models suggested different 

outcomes for the same tree species. These include 

sugar maple, bigtooth aspen, and red oak. The 

Tree Atlas projects substantial declines in suitable 

habitat for sugar maple and bigtooth aspen, whereas 

LANDIS-II classifi es them as increasing species. 

However, the LANDIS-II results incorporate some of 

the Tree Atlas modifying factors, which indicate that 

both species may do somewhat better than modeled 

by the Tree Atlas. LANDIS-II classifi es red oak as 

a “likely decline” species, whereas the Tree Atlas 

predicts “no change” in habitat suitability. It is likely 

that the differences in outcomes may be explained 

in part by the use of different GCMs and emissions 

scenarios, and in part by the different emphases in 

modeling approaches. LANDIS-II is a process model 

that includes stochastic dynamics such as harvesting, 

disturbance, and fragmentation effects on species 

movement; whereas the Tree Atlas approach uses a 

statistical model with modifying factors. A more indepth comparison of the two models may help explain 

how the different model assumptions and model 

processes affect predictions.

Lastly, it is important to emphasize that even the 

highest quality models have limitations in predicting 

how climate change will affect ecosystems. Not every 

complexity can be modeled for natural systems. 

For example, as we have seen in the recent past, 

an unexpected introduction of pests or disease can 

rapidly change the system dynamics with implications 

that outweigh any direct or indirect climate change 

impacts. Also, neither of the models used in this 

assessment explicitly incorporate the ecosystem 

feedbacks of carbon dioxide fertilization. However, 

the strength of using these models, especially when 

multiple approaches are used, is their ability to 

offer insights into how various interrelated forest 

components may respond to climate change within an 

established range of uncertainty. 

A wetland in northern Wisconsin.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service
69

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

The current and anticipated rates of climate change are 

unprecedented, with potentially severe consequences 

for many ecosystems in northern Wisconsin. This 

region has experienced dramatic climate changes 

in the past, which have shaped the landscape and 

contributed to the current species composition of 

forests. Additionally, human intervention has further 

transformed the landscape both before and since 

European settlement. Today’s forests are therefore a 

legacy of long-term climatic changes and more than 

a century of management. Although there is some 

uncertainty about the magnitude of change in the 

future climate, there is high confi dence that warmer 

temperatures and shifting precipitation patterns 

will infl uence forest ecosystems. The exceptional, 

increasingly rapid rate of climate change may place 

many ecosystems at risk, challenging both forests and 

forest management to adapt.

The following statements represent our assessment 

of potential ecosystem responses and vulnerabilities 

to a range of future climatic changes as presented 

in Chapters 3 and 4. Though the trends are already 

established for many of these responses and 

vulnerabilities, the climate scenarios evaluated are 

targeted to year 2100. Each assessment statement 

is followed by our qualitative view of its likelihood 

of occurring, using specifi c language (Fig. 30) 

established by the Intergovernmental Panel on 

Climate Change (IPCC 2005). 

Figure 30.—Language for describing confi dence in assessment fi ndings based on terminology used by the Intergovernmental 

Panel on Climate Change (Backlund et al. 2008, IPCC 2005).

Degree of Likelihood

Exceptionally 

Unlikely

Very 

Unlikely Unlikely

About

as Likely

as Not

Likely Very 

Likely

Virtually 

Certain
70

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Shifting Stressors 

Climate change may relieve some stressors, while 

exacerbating others. Warmer temperatures and 

shifting precipitation patterns are expected to strongly 

infl uence ecosystem drivers. 

Temperatures will increase (virtually certain) 

Annual increases in temperature represent the 

broadest possible stressor, strongly infl uencing other 

stressors and ecosystem responses. Even the most 

conservative models and scenarios project an increase 

in average temperature for northern Wisconsin 

(IPCC 2007, WICCI 2009). These increases are 

projected to be greatest in the winter, with daily lows 

more affected than daily highs (Chapter 3). Warmer 

annual averages have already been shown to affect 

the structure and function of ecosystems by directly 

or indirectly altering hydrology, soil conditions, 

and nutrient cycling. These changes further affect 

species phenology, range and distribution, community 

composition, and complex ecosystem dynamics 

(Huntington et al. 2009, Walther et al. 2002). Warmer 

winter temperatures and longer growing seasons 

could benefi t some species, but these changes may 

not synchronize with other ecosystem responses and 

climate variables and may ultimately result in a net 

negative effect. For example, day length will not 

change, so the ultimate effect on various species is 

not clear. Summer temperatures are also expected to 

continue rising, leading to reduced water availability 

and increased drought stress and fi re risk, especially in 

late summer (Huntington 2008). 

Growing seasons will get longer 

(virtually certain) 

There is already evidence of growing seasons 

lengthening throughout much of Wisconsin over the 

last several decades, and this trend is virtually certain 

to continue. Temperature increases and longer growing 

seasons will affect ecosystem and physiological 

processes (Bradley et al. 1999, Campbell et al. 

2009). Earlier springs and longer growing seasons 

will likely translate into shifts in the phenology of 

plant species that rely on temperature as a cue for 

the timing of reproductive maturation and other 

developmental processes (Schwartz et al. 2006a, 

Walther et al. 2002). Shifts in phenology have already 

been observed for a number of temperate species and 

can be linked to a warming climate (Bradley et al. 

1999, Richardson et al. 2006, Schwartz et al. 2006a). 

Longer growing seasons could also result in greater 

growth and productivity of vegetation (Drake et al. 

1997). However, productivity will largely depend on 

the amount of moisture and nutrients that are available 

to plants throughout the growing season. Although, 

carbon dioxide increases in the atmosphere have been 

shown to reduce the rate of water lost from plants 

through evapotranspiration (Gedney et al. 2006), there 

will likely be a net increase in evapotranspiration in 

response to higher temperatures and a longer growing 

season (Huntington 2008, IPCC 2007). A longer 

growing season is thus likely to create greater water 

demand, which will cause plant stress if the demand is 

not synchronized with adequate water supply.

The nature and timing of precipitation will 

change (virtually certain)

It is virtually certain that future precipitation 

patterns in northern Wisconsin will be substantially 

different from today, but projections of precipitation 

differ considerably on the seasonal distribution and 

magnitude of these changes (IPCC 2007, WICCI 

2009). Many of these differences arise because 

certain fundamental components of climate in 

northern Wisconsin are inherently diffi cult to model: 

lake-effect snow, changes in snow frequency and 

amount, and frequency of events such as severe 

summer thunderstorms. Some of this uncertainty 

will be resolved with better coupling of land-wateratmosphere components in medium-scale models, 

but there will always be annual variation due to the 

stochastic nature of these events (NRC 2008). The 

projected overall increase in annual precipitation in 

northern Wisconsin includes a net increase in some 

seasons, and a net decrease in others. Most models 
71

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

project that northern Wisconsin will experience 

an increase in precipitation in the late winter or 

early spring, but there is a trend toward reduced 

precipitation in the late summer and early autumn 

(Hayhoe et al. 2007, Plummer et al. 2006, WICCI 

2009). These changes can have considerable effects 

on hydrology and groundwater recharge, and could 

lead to increased drought stress in the late summer 

(Huntington et al. 2009). Signifi cant increases in lakeeffect snow during the 20th century have been detected 

in the Great Lakes region, and those increases may 

be the result of warmer surface waters and decreased 

ice cover on the Great Lakes (Burnett et al. 2003). 

Continued temperature increases in late winter will 

likely cause more winter precipitation to fall as rain. 

However, it is still unclear exactly to what extent air 

temperature, weather patterns, and other factors will 

interact to change precipitation events from snow to 

rain in the coming decades. 

Soil moisture patterns will change (virtually 

certain), with drier soil conditions later in the 

growing season (likely)

The reduction of late summer rain suggested 

by several models, coupled with increases in 

evapotranspiration from increased temperatures, will 

likely lead to decreases in soil moisture later in the 

growing season (Dai et al. 2004). Even if there are 

increases in precipitation in the summer, as a few 

models suggest, increases in evapotranspiration will 

likely lead to lower soil water availability (Hayhoe et 

al. 2007, Huntington et al. 2004, Ollinger et al. 2008). 

This may interact with a longer growing season to 

exacerbate overall stress on the system. In addition, a 

shift from snow to rain in the winter could cause more 

winter precipitation to runoff rather than be stored 

in the snowpack, resulting in less water stored in the 

soil, and contributing to drier soils later in the summer 

(Huntington et al. 2009). Drier soils, especially forest 

fl oors, may also increase susceptibility to wildfi res 

(Dale et al. 2001). Just as dry soils may be problematic 

in warm temperatures, wet soils may present a 

problem if exposed to freezing temperatures. Soils 

covered by deep snowpack early in the season can be 

insulated from severely cold temperatures and remain 

unfrozen throughout the winter (Decker et al. 2003). 

If higher winter temperatures reduce the snowpack, 

the resulting soil freezing could be damaging to fi ne 

roots of trees, such as sugar maple, that are vulnerable 

to rapid freeze-thaw changes (Mohan et al. 2009). 

Increases in the frequency and extent of soil frost 

could also infl uence soil water movement and reduce 

groundwater recharge, also potentially increasing 

surface runoff (Hardy et al. 2001, Zhao and Gray 

1999). Changes in soil moisture patterns will vary 

widely by season and landscape position, but increases 

in the length and severity of late summer drought will 

likely have the most widespread effects.

The frequency, size, and severity of natural 

disturbances will change across the 

landscape (very likely)

Climate change may accelerate the frequency or 

increase the severity of disturbances, such as drought, 

catastrophic winds, ice storms, rainstorms, wildfi res, 

and fl oods (Campbell et al. 2009, Dale et al. 2001, 

Dukes et al. 2009, Hanson and Weltzin 2000, Mohan 

et al. 2009, Peterson 2000), and indeed, evidence 

continues to mount that disturbance events are 

increasing in frequency (e.g., Westerling 2006 ) and 

intensity (e.g., Min et al. 2011). Changes in these 

various disturbance regimes, with their ability to 

fundamentally alter ecosystems, may have the most 

obvious and even drastic effects on northern Wisconsin 

forests. Some of these disturbances may also interact 

to increase system susceptibility to other disturbances. 

Increased annual temperatures can increase the 

incidence of drought and tree mortality, potentially 

increasing the incidence of downed and dead wood 

and contributing to increased frequency, extent, and 

severity of wildfi res (Huntington et al. 2009). In one 

study of boreal forests, the number of acres burned 

was estimated to double by the end of the 21st century 

(Flannigan et al. 2009). With fi res occurring more 

frequently, attempts to control wildfi res will likely 

become less successful (Flannigan et al. 2009). 
72

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

In areas where fi re is rare, wind may be the most 

infl uential disturbance type in forests and may 

increase in severity and occurrence as the atmospheric 

conditions leading to high winds become more 

common (Berz 1993, Peterson 2000) and return 

intervals for severe wind events shorten (Frelich and 

Reich 2010). Wind damage can shift a system into 

smaller tree size-class distributions as larger trees 

suffer more bole breakage, leaving smaller trees as 

survivors (Peterson 2000). Succession may be set 

back if sprouts of damaged trees reclaim the canopy, 

or altered altogether if understory species shift the 

composition toward late seral species (Peterson 2000). 

Ice storms could also increase in frequency with 

slightly warmer temperatures, causing precipitation 

to fall as freezing rain instead of snow (NRC 2008). 

More winter precipitation could further increase 

the frequency of ice storms. Ice damage can range 

from light to fatal, depending on the amount and 

area covered and on stand history, composition, 

and structure (Dale et al. 2001, Rhoads et al. 2002). 

Increased frequency and intensity of rainstorms will 

also increase the risk of fl ooding or erosion in many 

areas. 

Pests and diseases will increase or become 

more severe (very likely)

The combination of increased stress on native species 

and longer grower seasons can benefi t nonnative and 

invasive fl ora and fauna. Warming tends to increase 

the rate of insect development, allowing some pests to 

complete a second lifecycle in a single growing season 

(Ayres and Lomabardo 2000, Dukes et al. 2009). 

When these accelerated life cycles are combined with 

drought stress, pests can hasten changes in ecosystem 

composition (Weltzin et al. 2003, Williamson 1999). 

The spatial extent of pest damage may also increase. 

Some pests and diseases that are currently moderated 

by very cold winter temperatures may better survive 

warmer winters, increasing their population and 

expanding their range (Dukes et al. 2009, Karl et 

al. 2009). For example, hemlock wooly adelgid is 

currently limited in its northern expansion by its 

sensitivity to low winter temperatures and could 

expand further north as temperatures increase 

(Paradis et al. 2008). Emerald ash borer’s range 

in the United States is not known to be limited by 

current temperatures, but a longer growing season 

could increase the rate of reproduction and the rate of 

host mortality if larvae are active later into autumn 

(Poland and McCullough 2006). Pathogens such as 

Diplodia pinea, found in red pine, may also become 

problematic. Water stress may become more frequent 

and severe in red pine as habitat suitability shifts, 

resulting in more frequent infections, with consequent 

loss of productivity or even mortality (Munck et al. 

2009, Peterson 1997, Stanosz et al. 2001). 

Nonnative invasive plants would also experience 

changes in distribution and habitat suitability (Sher 

and Hyatt 1999), but many of these plants are already 

particularly adept at competing for resources and 

establishing after disturbances. With increases in forest 

stress and disturbances, invasives may exacerbate 

climate impacts on community composition, 

potentially even creating monocultures of nondesirable species (Sher and Hyatt 1999, Weltzin et al. 

2003). 

Ecosystem Response to Shifting 

Stressors

Shifts in stressors will likely lead to changes in forest 

community composition and ecosystem function. 

These changes may not be experienced similarly 

across northern Wisconsin, but instead will impact 

individual forest types and locations differently as a 

function of site conditions, species compositions, and 

management histories (Table 7). 
73

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Extent

Potential threats 

from climatic changes Potential vulnerabilities to threats

Responses that may 

accommodate change

All forests in 

northern Wisconsin

Warmer temperatures

Longer growing seasons

Altered precipitation regimes

Drier soils during summer

Decline of associated rare species

Decline of associated wildlife 

species

Increased threats from insects, 

diseases, and invasive plants 

Altered disturbance regimes may 

lead to changes in successional 

trajectories

Many common tree species are 

projected to have reduced habitat 

suitability 

Species with wider ecological 

amplitude, especially those 

suited to warmer and drier 

conditions, may be more 

resilient to changes

Aspen Increased medium- and largescale disturbancesDecline of quaking aspen 

abundance or productivity

Low within-stand diversity may 

increase risk of substantial aspen 

declines 

Medium-scale disturbances 

may not adequately allow for 

reestablishment

Lack of genetic diversity within 

clones may be a disadvantage

Reliance on large-scale and 

stand-replacing disturbances 

may buffer some impacts

Wide ecological amplitude 

and clonal nature of aspen 

may increase resilience

Balsam fi r Habitat suitability may be 

substantially decreased

Forest is less resilient to 

disturbances

Increased competition with shadetolerant species, such as red maple

Cooler or wetter site 

conditions or microclimates 

may be present and serve as 

refugia

Hemlock More summer storms and 

wind events may lead to 

shifts in prevailing natural 

disturbance regimes

Acceleration of current decline 

Drier conditions and increased 

disturbances may exacerbate 

current regeneration limitations 

from dispersal, competition, and 

browsing

Static ecosystem is less resilient to 

disturbance

Forest may fare better on the 

edges of streams, lakes, and 

wetlands

Jack pine Increased risk of fi re 

occurrence

Decline in productivity, especially on 

very dry sites

Fire may benefi t jack 

pine establishment and 

competitiveness

Table 7.—Climate change-related threats, vulnerabilities, and responses for forests in northern 

Wisconsin.

(Table 7 continued on next page.)
74

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Extent

Potential threats 

from climatic changes Potential vulnerabilities to threats

Responses that may 

accommodate change

Lowland conifer Altered hydrology and 

precipitation patterns may 

lead to reduced duration of 

soil saturation or ponding

Increased risk of fi re 

occurrence in dried organic 

soils

Habitat suitability may be 

substantially decreased

Reduced soil moisture or saturation 

may cause declines in hydrophytic 

tree species

Static ecosystem is less resilient to 

disturbance

Non-peatland sites may fare 

better

Cooler or wetter site 

conditions or microclimates 

may be present and serve as 

refugia

Lowland hardwood Altered hydrology and 

precipitation patterns may 

lead to reduced duration of 

soil saturation or ponding

Black ash habitat suitability may be 

substantially decreased

Low within-stand diversity may 

increase risk if black ash declines 

substantially

Drier conditions may lead to 

increased competition from other 

tree and plant species 

Emerald ash borer may interact with 

other stressors to cause widespread 

mortality

Decline of black ash may be 

buffered where other species 

are present, such as red 

maple

Cooler or wetter site 

conditions or microclimates 

may be present and serve as 

refugia

Northern hardwood More summer storms and 

wind events may alter 

prevailing natural disturbance 

regimes

Increased root damage from 

altered freeze-thaw cycles

Decline of sugar maple productivity, 

especially on drier sites

Increased disturbances may 

accelerate current decline of 

eastern hemlock and yellow birch

Drying of ephemeral ponds may 

increase stress on associated 

species

Decline of some species 

may be buffered where 

tree species composition is 

diverse

Oak Decline in productivity, especially on 

very dry sites

Warmer and drier conditions 

may favor oak species on a 

variety of sites

Oak species not currently 

present may expand into 

new areas, but extent is 

limited by dispersal, winter 

temperatures, competition, 

and browsing

Paper Birch Increased fi re and wind 

disturbance

Increased disturbances may 

accelerate current decline 

Wind or other medium-scale 

disturbances may not adequately 

allow for reestablishment

Fire may create conditions 

needed for paper birch 

establishment

(Table 7 continued on next page.)

Table 7 (continued).—Climate change-related threats, vulnerabilities, and responses for forests in 

northern Wisconsin.
75

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Extent

Potential threats 

from climatic changes Potential vulnerabilities to threats

Responses that may 

accommodate change

Red pine Increased risk of fi re 

occurrence

Low within-stand diversity may 

increase risk of substantial declines 

Younger stands may be vulnerable 

to pests that are currently present in 

warmer locations, especially under 

drought conditions

Increased competition from some 

deciduous species, such as red 

maple and red oak

Fire may benefi t red 

pine establishment and 

competitiveness

Mature stands at appropriate 

densities are often less 

susceptible to pests

Competition from some 

species, such as beaked 

hazelnut and balsam fi r, may 

decrease

Spruce Habitat suitability may be 

substantially decreased for white 

spruce and several associated 

species 

Drier soils may affect shallow-rooted 

white spruce

Interactions among pests, drought, 

and other stressors may exacerbate 

current declines

White pine Decline on drier sites due to 

drought intolerance

Super-canopy structure may 

increase individual tree mortality

Increased competition from some 

associated species, such as red oak

White pine can persist on a 

range of sites in the absence 

of severe drought

Paper birch trees.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service

Table 7 (continued).—Climate change-related threats, vulnerabilities, and responses for forests in 

northern Wisconsin.
76

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Suitable habitat for many tree species will 

move northward (virtually certain)

Winter temperature can be a limiting factor for many 

plant species (USDA 2003), so the distribution and 

occurrence of plants, including trees, will likely 

change as a result of warmer annual and winter 

temperatures. In general, trees that are at the species’ 

range boundary are more likely to be infl uenced by 

climate change. Warmer temperatures will be more 

favorable to trees that are located at the northern 

extent of their range and less favorable to those 

in the southern extent (Parmesan and Yohe 2003). 

Northern species may persist, with declining vigor, in 

some southern portions of their range if competitors 

from farther south are unable to colonize these areas 

(Iverson 2008a,b). The result could be reductions in 

overall forest biomass as northern species decline 

and southern species are slow to arrive (Scheller and 

Mladenoff 2007). Catastrophic disturbances, such as 

wildfi re, could facilitate establishment of colonizing 

species from the south if environmental conditions 

promote germination and vigor of their seeds, but may 

also have the potential to completely destroy an area’s 

ability to maintain forest cover at any scale (Camill 

and Clark 2000). Habitat fragmentation and the limited 

dispersal ability of seeds could hinder the northward 

movement of the more southerly species despite the 

increase in habitat suitability (Ibanez et al. 2008, 

Scheller and Mladenoff 2007).

Many of the current dominant tree species 

will decline (likely)

Many of the current icons of the northern forests 

are likely to decline on the landscape. Balsam fi r, 

white spruce, paper birch, and quaking aspen are all 

projected to decrease in abundance as their suitable 

habitat decreases. Declines could occur in different 

life stages, depending on the species. For example, 

some species may experience a decline in seed set, 

some could see declines in successful germination 

or establishment, and others could fi nd it diffi cult to 

grow into maturity (Ibanez et al. 2007, 2008). Mature 

trees may initially fare better than young trees, a 

short-term effect if the species as a whole is unable 

to grow into maturity and reproduce (Ibanez et al. 

2008). The models used in this assessment provided 

different indications for some species. For example, 

sugar maple showed an increase during the LANDISII simulations, but a loss of suitable habitat when 

modeled with Tree Atlas, although the Tree Atlas 

modifying factors suggested that changes in regional 

climate or disturbance may lessen the severity of the 

calculated decrease in habitat. 

Forest succession will change, making future 

trajectories unclear (very likely)

The dynamic combination of ecosystem stressors, 

especially as they relate to habitat shifts, may lead to 

the dissolution of traditional community relationships 

(Davis et al. 2005, Root et al. 2003, Webb and Bartlein 

1992). This may cause diffi culties in predicting 

the next successional stage based on long-standing 

defi nitions of seral stages. Novel climate regimes that 

have not occurred in recent millennia may produce 

forest communities that have no known analog (Root 

et al. 2003). Even if habitat suitability of species 

is known, further predictions of future ecosystem 

community composition are greatly complicated 

by unknowns associated with these novel climates 

(Iverson et al. 2008a,b) and lag times in migration 

(Davis 1989). 

Most species can be expected to migrate more slowly 

than their habitats will shift (Iverson et al. 2004a,b). 

If native species are slow or unable to colonize newly 

suitable habitats, nonnative invasives may increase 

in abundance and range, or become introduced to fi ll 

the niche (Hellman et al. 2008). A potential decline in 

important associated species, such as pollinators and 

mycorrhizae, could further hinder the colonization of 

new areas by native species (Clark 1998). 

Interactions of multiple stressors will reduce 

forest productivity (likely)

The combination of changes in climate; increased 

intensity, severity, and extent of disturbances; slowed 

arrival of better adapted southern species; and changes 

in evapotranspiration and soil moisture will likely 
77

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

result in declining growth rates and forest yields 

(Aber et al. 2001, Hanson and Weltzin 2000). Any 

disturbance that weakens tree vigor can make that 

tree more susceptible to other disturbances. Droughtstressed plants, for example, may become more 

susceptible to fi re, insect pests, diseases, and other 

disturbances (Huntington 2009, Peterson 2000). The 

cumulative damages may increase the likelihood 

of mortality, such as Diplodia root blight mortality 

in drought-stressed red pine (Stanosz et al. 2001). 

Some factors may partially offset reductions in 

growth. Recent evidence suggests that carbon dioxide 

fertilization has enhanced forest growth in the eastern 

United States (McMahon et al. 2010); however, these 

benefi ts can be diminished by pollution, site quality, 

species limitations, and ultimately by plant maturity 

(Norby et al. 2005).

Ecosystem Vulnerabilities

Certain species, communities, and ecosystems may be 

particularly vulnerable to severe declines in abundance 

or may be lost entirely from the landscape. These 

particularly vulnerable components have a few shared 

characteristics. 

Risk will be greater in low diversity 

ecosystems (very likely)

Species-rich communities have exhibited greater 

resilience to extreme environmental conditions and 

greater potential to recover from disturbance (Tilman 

1996, 1999). Conversely, ecosystems that have low 

species diversity or low functional diversity (occupy 

the same niche) will be more threatened by climate 

change (Peterson et al. 1998; Walker 1992, 1999). 

A forested landscape in northern Wisconsin.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service
78

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Ecosystems currently dominated almost entirely by 

a single species may experience severe degradation 

if that species declines. Within northern hardwood 

ecosystems, for example, stands dominated by sugar 

maple may be more vulnerable than those containing 

larger components of red maple, red oak, basswood, 

and other associates that offer a wider range of 

moisture tolerance. 

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 

resilient to disturbance (Reusch et al. 2005). It is 

unclear, however, how changing climate stressors 

will affect contemporary plant community genetics. 

The effects of climate change on tree reproductive 

processes may result in lower reproductive success 

and decreases in population size and genetic diversity. 

Fragmentation and deforestation also increase the 

potential for inbreeding and reduced genetic diversity 

(Bawa and Dayanandan 1998). Species with low 

genetic diversity, especially plantation monocultures, 

are often intolerant of disturbances and more 

susceptible to insect outbreaks, exacerbating the 

vulnerability of those species to climate change (Noss 

2001). In natural systems, low genetic diversity may 

contribute to species- or community-level declines; 

for plantation species, more intensive management 

practices may mitigate such effects. 

Disturbance will destabilize static ecosystems 

(very likely)

Systems that are less resilient to disturbances 

may be particularly vulnerable as a wide range of 

natural disturbances increase, including drought, 

wind events, fi re, ice storms, and wind storms. For 

example, lowland hardwood, lowland conifer, northern 

hardwood, and hemlock forests are not fi re-adapted. 

They may experience an increase in fi re frequency or 

severity under drier conditions, making them unable 

to recover to their previous state (Dale et al. 2001). An 

increase in fi re could also pave the way for more fi readapted species such as jack pine or oak. 

Climate change will exacerbate problems for 

species already in decline (very likely)

Model results in this assessment indicate that climate 

change is projected to reduce habitat suitability for 

many species that are already experiencing declines. 

Eastern hemlock and yellow birch are currently 

declining in northern Wisconsin from natural or 

human-induced causes (Rooney et al. 2000, Zhu et 

al. 2000), and recruitment of northern white-cedar is 

very low (Forester et al. 2008). Historic mid-Holocene 

declines of eastern hemlock and white spruce have 

coincided with periods of climate warming and 

drought in the past, and those declines have been 

attributed mainly to insect pests (Bhiry and Filion 

1996). More recently, logging in the mid-to-late 

1800s resulted in slash fi res that destroyed seeds and 

seedlings, and the drought-sensitive hemlock has 

been unable to recover in affected areas (Mladenoff 

et al. 2008). The replacement of hemlock by sugar 

maple and other hardwoods has further decreased the 

habitat suitability for establishment of its seedlings; 

this, combined with deer browsing and periods of 

drought, has slowed recruitment within hemlock 

stands to levels that are too low to maintain the cover 

type (Rooney et al. 2000). Climate change is expected 

to exacerbate this loss through increases in drought 

incidence and reductions in summer soil moisture, 

further reducing seedling and sapling recruitment and 

the vigor of mature trees (Rooney et al. 2000). 

Additionally, intolerance to injury during winter 

freeze-thaw events is thought to play a signifi cant 

role in yellow birch decline. Absence of snow cover 

increases the vulnerability of the shallow roots to 

freezing and the stem xylem to cavitation. Warmer 

winters and longer winter thaws may lead to increased 

damage in yellow birch and result in reduced spring 

shoot growth (Zhu et al. 2000).
79

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Resilience will be weakened in fragmented 

ecosystems (very likely)

Smaller and more isolated forest fragments may be 

unable to adapt as easily as larger continuous areas 

of forest. Smaller patch sizes support less diversity of 

species and genetic material, reducing species ability 

to adapt to a changing climate. Habitat fragmentation 

can hinder the ability of trees to migrate to more 

suitable habitat on the landscape, especially if the 

surrounding area is nonforested (Iverson et al. 2004a,b; 

Noss 2001). Modeling results in this assessment and 

elsewhere indicate that trees would need to migrate 

at rates of hundreds of feet to several miles per year 

to keep pace with the changes in climate that are 

projected to occur over the next century (Iverson and 

Prasad 2002, Petit et al. 2008). This rate of migration 

may be unattainable through natural means, even in 

the absence of fragmentation (Davis and Shaw 2001, 

McLachlan et al. 2005). 

Altered hydrology will jeopardize lowland 

forests (very likely)

Lowland forests rely on saturated soils, which 

may place them at great risk from higher rates of 

evapotranspiration during warmer, drier summers. 

If changes in precipitation regimes result in less 

water availability later in the growing season, altered 

hydrology could leave these sites vulnerable to drying 

and extreme stress. Lowland conifer sites often have 

peaty organic soils. If these sites occasionally dry out, 

they may be vulnerable to catastrophic wildfi res. The 

combination of lower-than-average rainfall and high 

summer temperatures in a Wisconsin peatland resulted 

in such a fi re in 1930, the effects of which included 

nutrient changes, aspen invasion, and increased soil 

pH (Vogl 1969). Within lowland conifer systems, 

perched bogs fed primarily by surface runoff may be 

the most vulnerable to frequent drying and subsequent 

changes in plant community. Minerotrophic fens, 

however, may be less vulnerable to drying because 

they receive a continuous supply of groundwater 

(Siegel 1988). Lowland hardwood forests rely on 

spring runoff, which may be altered if peak discharge 

comes earlier in the year. These sites are dominated 

by black ash, which is at risk for being eradicated by 

the emerald ash borer. The loss of ash may also alter 

hydrology, possibly by raising the water table via 

reduced evapotranspiration. Loss of tree cover could 

facilitate take over by grass species and the consequent 

loss of lowland hardwood forests. 

Changes in habitat will disproportionately 

affect boreal species (virtually certain) 

Many northern Wisconsin species that are currently 

found at the southern extent of their range are 

projected to decline (Chapter 4). Projected decreases in 

potential suitable habitat are especially signifi cant for 

many boreal species, including paper birch, balsam fi r, 

black spruce, and white spruce. This decline assumes 

that the southern extent of boreal species is driven 

by temperature, but the southern range may also be 

controlled by competition with better-adapted southern 

species (Graham et al. 1990).

Ecosystems that are Better Able to 

Accommodate Change

It is important to note that often the opposite 

characteristics of what makes systems 

vulnerable to change might make certain 

species, communities, and ecosystems more 

accommodating to change. Ecosystems better 

able to accommodate change may include the 

following: 

• Species that are currently increasing 

• Species with a wider ecological range of 

tolerances 

• Species with greater genetic diversity 

• Species and ecosystems adapted to 

disturbances

• Species and ecosystems adapted to 

warmer, drier climates

• Species in the middle or northern extent of 

their range

• Diverse communities and species

• Habitats within larger, contiguous blocks
80

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

Further reductions in habitat will impact 

threatened, endangered, and rare species

(virtually certain)

Species are threatened, endangered, or rare because 

of specifi c habitat requirements and relative inability 

to cope with change (Rabinowitz 1981). These same 

characteristics may make them more vulnerable than 

other species to changes in climate and subsequent 

changes in disturbance regimes, soil moisture, pests, 

and hydrology. Population extinctions of rare and 

endangered species have already been linked to recent 

climate change (McLaughlin et al. 2002). Further 

changes in climate will likely have even greater 

effects. Because these species are rare, there are often 

insuffi cient data to model future suitable habitats. 

For example, there are a number of rare species that 

are associated with ephemeral ponds, such as the 

red-shouldered hawk (WDNR 2005). It is hard to 

predict where, if anywhere, these ponds will exist in 

the future, and how these species may migrate to new 

suitable areas. 

Ecosystem changes will have signifi cant 

effects on wildlife (very likely)

If forest tree communities change, wildlife species 

that rely on them may also be vulnerable (Rodenhouse 

et al. 2009). Climate change could adversely affect 

pine warblers, spruce grouse, and other bird species 

that rely on specifi c forest tree species or habitat 

characteristics. Results from statistical models 

highlighted in the Climate Change Bird Atlas suggest 

range shifts for the majority of bird species even under 

a low emissions scenario (Matthews et al. 2004). 

Ephemeral ponds will likely dry faster in summer so 

their number, area and depth will likely decline in the 

future. Amphibians (salamanders and some frogs) that 

require longer time to mature may be most vulnerable 

(Rodenhouse et al. 2009), but birds may be impacted 

as well (WDNR 2005). Species that rely on specialized 

food sources or specifi c tree species may be vulnerable 

if requisite resources decline. Wildlife species with a 

wider range of suitable habitat and food sources, such 

as white-tailed deer or gray squirrels, may fare better. 

Management Implications 

The fundamental challenge facing forest managers 

in the coming decades is how to sustain ecosystem 

function, products, and services even as forests are 

adapting to climate change. 

Management will continue to be an important 

ecosystem driver (virtually certain)

Forests in northern Wisconsin range from 

intensively managed to relatively untouched, yet 

no forest is completely outside the infl uence of 

management. Many contemporary forests, such as 

red pine plantations, would not exist without human 

intervention and management. In addition, active 

management is maintaining systems—such as jack 

pine, aspen, and white spruce—in locations where they 

might otherwise be much less abundant. The infl uence 

of management will likely be even greater with 

changes in habitat suitability. For example, LANDIS-II 

results show that harvesting may be a more important 

driver of forest species composition than climate 

change over the next 40 to 50 years, especially because 

harvesting can slow species movement by removing 

competing tree species before they reproduce 

(Scheller and Mladenoff 2005). Management can 

also reset successional trajectories or create artifi cial 

disturbances. In northern Wisconsin, old growth 

forest is exceedingly rare and statewide decreases 

in forests over 100 years old have been recorded as 

recently as the 1983 to 1996 time period (WDNR 

2009a). Management factors need to be considered in 

conjunction with changes in climate, hydrology, and 

natural disturbance regimes. 

Many current management objectives and 

practices will face substantial challenges 

(virtually certain)

Some management objectives and strategies may need 

to be reconsidered as a result of changing conditions 

on the landscape (Baron et al. 2008, Heller and 

Zavaleta 2009, Joyce et al. 2009, Millar et al. 2007). 

Many commercially and economically important tree 

species—such as sugar maple and white spruce—may 
81

CHAPTER 5: IMPLICATIONS FOR FOREST ECOSYSTEMS

face increased stress and decreased productivity, 

which could affect their availability for some products. 

Markets and industries may need to be reexamined to 

favor new species that grow better under an altered 

climate (Irland et al. 2001, Kirilenko and Sedjo 2007). 

Conservation of certain threatened and endangered 

species will become more challenging as their current 

habitats become less suitable. If atypical climate 

regimes develop, current silvicultural understanding 

of species responses to management may need to be 

re-evaluated, since current knowledge is based on past 

climate regimes and observed species interactions. 

More resources will be needed to sustain 

functioning ecosystems (virtually certain)

Shifts in management objectives and practices will 

place increasing demands on human and capital 

resources (Baron et al. 2008). For example, native 

species that could previously regenerate unassisted 

may need to be artifi cially regenerated. Increased 

planting may be necessary to facilitate colonization 

and establishment on new sites or supplement natural 

regeneration efforts. Management to control fi re, 

nonnative invasive species, diseases, and pests may 

need to increase as well. All of these increasing 

demands will place additional burdens on already 

over-taxed budgets and personnel at all levels of 

government. Research is needed to quantify the 

costs of these additional actions, which may be less 

expensive than taking no action in the long run.

Summary

The forests of northern Wisconsin are likely to 

experience dramatic changes during this century under 

a changing climate. Some species and forest types are 

particularly vulnerable, while others may ultimately 

be more successful. Importantly, all forests that 

experience new stressors and environmental conditions 

have the potential for decreased productivity or loss 

of forest species. Changes in forest communities will 

affect the ecosystem services they provide, such as 

clean drinking water, carbon sequestration, wildlife 

habitat, and recreational opportunities. Practicing 

long-term sustainable management and supporting 

ecosystem resilience are fundamental principles of 

forest stewardship. Applying these principles in the 

face of climate change will require both a focused 

effort to identify the ecosystems most vulnerable to 

climate change and an active dialogue about potential 

management responses to these vulnerabilities. 

Young tamarack trees in a wetland.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service
82

GLOSSARY

Adaptation: Adjustment in natural or human systems 

in response to actual or expected climatic stimuli 

or their effects, which moderates harm or exploits 

benefi cial opportunities (IPCC 2007).

Aerosol: A suspension of fi ne solid particles or liquid 

droplets in a gas, such as smoke, oceanic haze, air 

pollution, and smog. Aerosols may infl uence climate 

by either scattering and absorbing radiation; or by 

acting as condensation nuclei for cloud formation or 

modifying the properties and lifetime of clouds 

(IPCC 2007, King et al. 2007). 

Analysis area: The portion of Wisconsin that falls 

within Ecological Province 212 (Mixed Laurentian 

Forest) of the National Hierarchical Framework of 

Ecological Units (Bailey 1995, ECOMAP 1993). 

When data was not available for this analysis 

area, data were selected for the 33 counties most 

analogous to Province 212: Ashland, Barron, Bayfi eld, 

Brown, Burnett, Chippewa, Clark, Door, Douglas, 

Florence, Forest, Iron, Kewaunee, Langlade, Lincoln, 

Manitowoc, Marathon, Marinette, Menominee, 

Oconto, Oneida, Outagamie, Polk, Portage, Price, 

Rusk, Sawyer, Shawano, Taylor, Vilas, Washburn, 

Waupaca, and Wood.

Bagging Trees: This statistical technique begins with 

a ‘regression tree’ approach, but recognizes that part of 

the output error in using a single regression tree comes 

from the specifi c selection of an original dataset. The 

bagging trees method uses another statistical technique 

called ‘bootstrapping’ to create several similar 

datasets. Regression trees are then produced from 

these new datasets and results are averaged.

Barrens: Plant communities that occur on sandy soils 

and are dominated by grasses, low shrubs, small trees, 

and scattered large trees (Curtis 1959).

Basefl ow: The condition of only groundwater 

providing the entire fl ow of a stream (during most of 

the year, streamfl ow is composed of both groundwater 

discharge and land surface runoff). 

Biomass: The mass of living organic matter (plant 

and animal) in an ecosystem; biomass also refers 

to 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 (King et al. 2007).

Biome: A regional ecosystem with a distinct 

assemblage of vegetation, animals, microbes, and 

physical environment often refl ecting a certain climate 

and soil.

Boreal forest: Found only between 50-55 and 

65-70 degrees latitude of the Northern Hemisphere, 

the boreal forests are adapted to cool northern 

temperatures and low rainfall (below 500mm; 

FAO 2001). 

Carbon dioxide fertilization: Increased plant uptake 

of carbon dioxide through photosynthesis in response 

to higher concentrations of atmospheric carbon dioxide 

(Norby et al. 2005).

Cavitation: Percent loss of hydraulic conductivity, or 

the process by which xylem vessels become air-fi lled, 

resulting in reduced water transport (Zhu et al. 2000). 

Composite products: Roundwood logs, bolts, and 

chips used in the manufacture of reconstituted wood 

products (chip board, fl ake board, oriented strand 

board (OSB), engineered lumber, etc.). Principal 

species used in composite products include aspen, 

jack pine, and birch.
83

GLOSSARY

Conifer: Any gymnosperm tree or shrub of the 

phylum Coniferophyta, typically bearing cones and 

evergreen leaves. The group includes the pines, 

spruces, fi rs, larches, yews, junipers, cedars, cypresses, 

and sequoias.

Derecho: Widespread and long-lived windstorm that 

is associated with a band of rapidly moving showers or 

thunderstorms.

Downscaling: A method for obtaining high-resolution 

climate or climate change information from relatively 

coarse-resolution global climate models; involves 

examining the statistical relationship between past 

climate data and on-the-ground measurements. 

Disturbance: Stresses and destructive agents such as 

invasive species, diseases, and fi re; 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.

Driver: Any natural or human-induced factor that 

directly or indirectly causes a change in an ecosystem 

(Carpenter et al. 2006).

Eastern deciduous forest: Dominated by deciduous 

trees such as oaks, maples, beech, hickories, and 

birches that drop their leaves. Softwoods do live in 

this forest, but they are rarely dominant. This forest 

develops under cold winters (but not as cold as the 

boreal region to the north), and annual rainfall is 

higher in this forest than anywhere else in north 

America except for the subtropical and tropical areas 

to the south.

Ecological processes: Processes fundamental to the 

functioning of a healthy and sustainable ecosystem, 

usually involving the transfer of energy and substances 

from one medium or trophic level to another.

Ecoregion: Repetitive pattern of ecosystems 

associated with commonalities in soil and landform 

that characterize that larger region.

Edaphic: Of or pertaining to soil characteristics.

Emissions scenario: A plausible representation of 

the future development of emissions of greenhouse 

gases and aerosols that are potentially radiatively 

active, based on certain demographic, technological, 

or environmental developments (IPCC 2007, 

King et al. 2007).

Ensemble average: The average value of a large 

number of output values from a climate model; a way 

to address some of the uncertainties in the system. 

Evapotranspiration: The sum of evaporation and 

plant transpiration from the Earth’s land surface to 

atmosphere.

Excelsior: A product made of wood slivers, cut from 

logs, mainly used in packaging, for the cooling pads in 

home evaporative cooling systems known as swamp 

coolers, for erosion control mats, or as a raw material 

for the production of other products such as bonded 

wood wool boards.

Extirpation: Local extinction or loss of a species from 

an area.

Fen: A peat-forming wetland fed by sources other 

than precipitation, such as drainage from surrounding 

mineral soils or groundwater movement. 

Fire-return interval: The number of years between 

two successive fi re events at a specifi c location.

Forest type: A classifi cation of forest land based on 

the dominant species present, as well as associate 

species commonly occurring with the dominant 

species (Perry et al. 2004).

Forest type group: Based on FIA defi nitions, 

a combination of forest types that share closely 

associated species or site requirements and are 

generally combined for brevity of reporting (Perry 

et al. 2004).

Fragipan: A natural subsurface horizon with very 

low organic matter, high bulk density, is slowly or 

very slowly permeable to water, is considered root 

restricting, and usually has few to many bleached, 

roughly vertical planes which are faces of coarse or 

very coarse polyhedrons or prisms. A fragipan has hard 

or very hard consistence (seemingly cemented) when 

dry but shows a moderate to weak brittleness when 

moist (SAF 2008).
84

GLOSSARY

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 (cumulative length of patch edges).

Functional diversity: The value, range, and relative 

abundance of functional traits in a given ecosystem 

(Diaz et al. 2007).

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.

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 

weather and temperature is right for plants and crops 

to grow.

Growing stock: A classifi cation of timber inventory 

that includes live trees of commercial species 

meeting specifi ed standards of quality or vigor. When 

associated with volume, this includes only trees 5.0 

inches in diameter at breast height and larger (Perry 

et al. 2004).

Habitat: Those parts of the environment (aquatic, 

terrestrial, and atmospheric) often typifi ed by a 

dominant plant form or physical characteristic, on 

which an organism depends, directly or indirectly, 

in order to carry out its life processes.

White pine along the edge of a wetland.

Photo by Maria K. Janowiak, Northern Institute of Applied Climate Science and U.S. Forest Service
85

GLOSSARY

Hardwood: A dicotyledonous tree, usually broadleaved 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 walnuts, and oaks (Perry et al. 

2004). 

Hydraulic gradient: The slope of the water, or the 

drop in pressure head per length in the direction of 

streamfl ow (SAF 2008).

Hydrophytic vegetation: Plant life that occurs in 

areas where the frequency and duration of inundation 

or soil saturation produce permanently or periodically 

saturated soils of suffi cient duration to exert a 

controlling infl uence on the plant species present.

Impact model: Simulations of impacts on trees, 

animals, and ecosystems; these models use GCM 

projections as inputs, and include additional inputs 

such as tree species, soil types, and life history traits 

of individual species.

Importance value: An index of the relative abundance 

of a species in a given community (0 = least abundant, 

50 = most abundant).

Industry-owned forest: Land owned by forest 

product companies that harvest and market timber.

Intensity: Amount of precipitation falling per unit of 

time.

Kyoto Protocol: Adopted at the 1997 Third Session 

of the Conference of Parties to the UN 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 

(IPCC 2007).

Lacustrine clay deposits: Pertaining to or formed in a 

lake; usually softer than adjacent tills and often show 

clear horizontal parting of silt or fi ne sand.

Landtype Association: The characterization of 

landscapes from Sections and Subsections into 

further refi ned units that describe the composition of 

landscapes by similarities and repeatable patterns in 

landforms, their dominant formative processes, and 

underlying geologic materials.

Mesic: Characterized as requiring a moderate amount 

of water.

Mesophication: “Whereby microenvironmental 

conditions (cool, damp, and shaded conditions; less 

fl ammable fuel beds) continually improve for shadetolerant mesophytic species and deteriorate for shadeintolerant, fi re-adapted species” (Nowacki and Abrams 

2008).

Minerotrophic: Groundwater-fed; areas infl uenced 

by groundwater that has been in contact with soil 

or bedrock and is richer in mineral nutrients than 

rainwater. 

Model error: Uncertainty caused by a lack of 

complete understanding of some climate processes, or 

by the inability of models to pick up small-scale but 

infl uential climate processes.

Model reliability score: For the Tree Atlas: a 

‘tri-model’ approach to assess reliability of model 

predictions for each species, classifi ed as high, 

medium, or low depending on the assessment 

of the stability of the bagged trees and the R2

 in 

RandomForest (Iverson et al. 2009).

Modifying factor: Environmental variables (i.e., site 

conditions, interspecies competition, disturbance, 

dispersal ability, etc.) that infl uence the way a tree 

may respond to climate change.

Nonindustrial private landowners: An ownership 

class of private lands where the owner does not 

operate wood-using plants (Perry et al. 2004).

Northern hardwoods: Forest group with wet-mesic 

to dry-mesic soils, medium to high soil nutrient level, 

and supporting trees species such as sugar maple 

(dominant), basswood, eastern hemlock, yellow birch, 

eastern hophornbeam, red maple, and white ash.

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 fl ow: The maximum instantaneous discharge of a 

stream or river at a given location. 
86

GLOSSARY

Pioneer species: a plant capable of invading bare sites 

(e.g., newly exposed soil) and persisting there until 

supplanted by successional species, or any new arrival 

in the early stages of succession.

Plasticity: The ability of an organism to change 

its characteristics (gene expression or behavior) in 

response to changes in the environment.

Phenology: The study of the timing of natural events 

such as the date that migrating birds return, the fi rst 

fl ower dates for plants, and the date on which a lake 

freezes in the autumn or opens in the spring.

Projection: A model-derived estimate of future 

climate, and the pathway leading to it.

Process model: A model that relies on computer 

simulations based on mathematical representations of 

physical and biological processes that interact over 

space and time.

Proxy: 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 (Perry et al. 

2004). Principal pulpwood species are aspen, maples, 

jack pine, red pine, birch, and fi r. 

RandomForests: The RandomForest is a statistical 

technique similar to Bagging Trees in that it also uses 

bootstrapping to construct multiple regression trees. 

The difference is that each tree is produced with a 

random subset of predictors. Typically, 500 to 2,000 

trees are produced and the results are aggregated by 

averaging. This technique eliminates the possibility of 

overfi tting data.

Real Estate Investment Trust (REIT): Considered a 

nonindustrial landowner.

Realized niche: The portion of potential habitat a 

species occupies; usually it less than what is available 

because of predation, disease, and competition with 

other species.

Recharge: The natural process of movement 

of rainwater from land areas or streams through 

permeable soils into water-holding rocks that provide 

underground storage (i.e., aquifers).

Refugia: Locations and habitats that support 

populations of organisms that are limited to small 

fragments of their previous geographic range.

Resilience: The ability of a social or ecological system 

to absorb disturbances while retaining the same basic 

structure and ways of functioning, the capacity for 

self-organization, and the capacity to adapt to stress 

and change (IPCC 2007).

Roundwood: Logs, bolts, and other round timber 

generated from harvesting trees for industrial or 

consumer use (Perry et al. 2004). 

Runoff: That part of the precipitation that appears 

in surface streams. It is the same as streamfl ow 

unaffected by artifi cial diversions or storage.

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 specifi ed by regional standards 

(Perry et al. 2004). 

Sawtimber: A live tree of commercial species 

containing at least a 12-foot saw log or two 

noncontiguous 8-foot or longer saw logs, and meeting 

specifi cations for form; softwoods must be at least 

9 inches, and hardwoods must be at least 11 inches, 

respectively, in diameter outside the bark (Perry et al. 

2004). 
87

GLOSSARY

Scenario: A coherent, internally consistent and 

plausible description of a possible future state of the 

world. It is not a forecast; rather, each scenario is 

one alternative image of how the future can unfold. 

A projection may serve as the raw material for a 

scenario, but scenarios often require additional 

information (IPCC 2007).

Severity: The proportion of aboveground vegetation 

killed and the degree of forest fl oor and soil disruption 

(Oliver and Larson 1996).

Softwood: A coniferous tree, usually evergreen, 

having needles or scale-like leaves (Perry et al. 2004).

Snowpack: Layers of accumulated snow that usually 

melt during warmer months.

Species distribution model: A model that uses 

statistical relationships to project future change.

Stochastic: Patterns resulting from random effects.

Stratosphere: The layer of the Earth’s atmosphere 

which lies between 10 and 50 kilometers above the 

Earth.

Streamfl ow: Discharge that occurs in a natural 

surface stream course whether or not it is diverted or 

regulated.

Stormfl ow: Runoff that occurs due to a heavy 

precipitation event.

Succession: The gradual supplanting of one 

community of plants by another. Early successional 

or “pioneer” species typically produce a great 

quantity of seeds and that are capable to germinate 

and grow under direct sunlight. Once they have 

produced a closed canopy, the lack of direct sunlight 

makes it diffi cult for seedlings to develop. It is then 

the opportunity for shade “tolerant” species to get 

established. When these pioneers die, the shade 

tolerants will replace them. The shade tolerant 

species are capable of growing under the canopy, and 

therefore, in the absence of catastrophes, will stay to 

become the late successional species. 

Super-canopy: A level of canopy formed above the 

main upper canopy, such as the highest canopy of 

eastern white pine.

Tension zone: A transitional band that corresponds 

to a number of climatic factors. Vegetation north and 

south of the tension zone refl ect habitat conditions as a 

result of climatic differences.

Threat: A source of danger or harm.

Till: Unstratifi ed soil deposited by a glacier; consists 

of sand and clay and gravel and boulders mixed 

together.

Timber Investment Management Organization 

(TIMO): Considered a nonindustrial landowner.

Troposphere: The lowest part of the atmosphere from 

the surface to about 10 kilometers in altitude in midlatitudes (ranging from 9 kilometers in high latitudes 

to 16 kilometers in the tropics on average) where 

clouds and “weather” phenomena occur.

Topkill: Death of above-ground tree stem and 

branches.

Uncertainty: A term used to describe the range of 

possible values around a best estimate, sometimes 

expressed in terms of probability or likelihood 

(King et al. 2007).

Veneer: A roundwood product from which veneer is 

sliced or sawn and that usually meets certain standards 

of minimum diameter, length, and maximum defect. 

Principal veneer species are maple, aspen, birch, oaks, 

and black walnut.

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 character, 

magnitude, and rate of climate change and variation 

to which a system is exposed, its sensitivity, and its 

adaptive capacity (IPCC 2007).
88

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105

APPENDIX 1

Flora

Common Name Scientifi c Name

balsam fi r Abies balsamea

boxelder Acer negundo

red maple Acer rubrum

silver maple Acer saccharinum

sugar maple Acer saccharum

mountain maple Acer spicatum

ohio buckeye Aesculus glabra

speckled alder Alnus incana

yellow birch Betula alleghaniensis

sweetbirch Betula lenta

river birch Betula nigra

paper birch Betula papyrifera

calypso Calypso bulbosa

American hornbeam Carpinus caroliniana

bitternut hickory Carya cordiformis

pignut hickory Carya glabra

pecan Carya illinoensis

shellbark hickory Carya laciniosa

shagbark hickory Carya ovata

black hickory Carya texana

mockernut hickory Carya tomentosa

northern catalpa Catalpa speciosa

redroot Ceanothus herbaceus

sugarberry Celtis laevigata

hackberry Celtis occidentalis

eastern redbud Cercis canadensis

fl owering dogwood Cornus fl orida

hazelnut Corylus americana

ram’s head lady’s slipper Cypripedium arietinum

common persimmon Diospyros virginiana

American beech Fagus grandifolia

white ash Fraxinus americana

black ash Fraxinus nigra

green ash Fraxinus pennsylvanica

honey locust Gleditsia triacanthos

butternut Juglans cinerea

black walnut Juglans nigra

eastern red cedar Juniperus virginiana

tamarack Larix laricina

yellow poplar Liriodendron tulipifera

osage orange Maclura pomifera

red mulberry Morus rubra

blackgum Nyssa sylvatica

eastern hophornbeam Ostrya virginiana

white spruce Picea glauca

black spruce Picea mariana

jack pine Pinus banksiana

red pine Pinus resinosa

eastern white pine Pinus strobus

sycamore Platanus occidentalis

Common Name Scientifi c Name

balsam poplar Populus balsamifera

eastern cottonwood Populus deltoides

bigtooth aspen Populus grandidentata

quaking aspen Populus tremuloides

wild plum Prunus americana

pin cherry Prunus pensylvanica

black cherry Prunus serotina

chokecherry Prunus virginiana

white oak Quercus alba

swamp white oak Quercus bicolor

scarlet oak Quercus coccinea

northern pin oak Quercus ellipsoidalis

shingle oak Quercus imbricaria

bur oak Quercus macrocarpa

blackjack oak Quercus marilandica

chinkapin oak Quercus muehlenbergii

pin oak Quercus palustris

chestnut oak Quercus prinus

northern red oak Quercus rubra

post oak Quercus stellata

black oak Quercus velutina

black locust Robinia pseudoacacia

peachleaf willow Salix amygdaloides

prairie willow Salix humilis

black willow Salix nigra

sassafras Sassafras albidum

northern whitecedar Thuja occidentalis

American basswood Tilia americana

eastern hemlock Tsuga canadensis

American elm Ulmus americana

slippery elm Ulmus rubra

rock elm Ulmus thomasii

Fauna

red-winged blackbird Agelaius phoeniceus

great blue heron Ardea herodias

upland sandpiper Bartramia longicauda

ruffed grouse Bonasa umbellus

beaver Castor canadensis

common yellowthroat Geothlypis trichas

bald eagle Haliaetus leucocephalus

American marten Martes americana

fi shers Martes pennanti

white-tailed deer Odocoileus virginianus

mourning warbler Oporornis spp.

osprey Pandion haliaetus

brook trout Salvelinus fontinalis

brown thrasher Toxostoma rufum

sharp-tailed grouse Tympanuchus phasianellus

Table A1-1. Common and scientifi c names of regional fl ora and fauna.
106

APPENDIX 2

Species Rank and Status1

Federally-listed Species (Endangered, Threatened)

Fassett’s locoweed (Oxytropis campestris var. chartacea) G5,S2,SE,FT

Kirtland’s warbler (Dendroica kirtlandii) G1,S1,SC,FE

Regional Forester Sensitive Species (RFSS)

Animals

Northern goshawk (Accipiter gentiles) G5,S2B,S2N,SC

Lake sturgeon (Acipenser fulvenscens) G3G4,S3,SC

Le Conte’s sparrow (Ammodramus leconteii) G4,S2B,SC

Upland sandpiper (Bartramia longicauda) G5,S2B,SC

Red-shouldered hawk (Buteo lineatus) G5,S3S4B,ST

Gray wolf (Canis lupus) G4,S2,ST

Swainson’s thrush (Catharus ustulatus) G5,S2B,SC

Black tern (Chlidonias niger) G4,S3B,SC

Trumpeter swan (Cygnus buccinator) G4,S1B,SE

Cerulean warbler (Dendroica cerulean) G4,S2S3B,ST

Spruce grouse (Falcipennis canadensis) G5,S1S2B,ST

Wood turtle (Glyptemys insculpta) G4,S3,ST

Green-faced clubtail (Gomphus viridifrons) G3,S3,SC

Bald eagle (Haliaeetus leucocephalus) G4,S3B,SC

Henry’s elfi n butterfl y (Incisalia henrici) G5,S2,SC

Northern blue butterfl y (Lycaeides idas nabokovi) G5,S1,SE

American marten (Martes americana) G5,S3,SE

Greater redhorse (Moxostoma valenciennesi) G4,S2S3,ST

Pugnose shiner (Notropis anogenus) G3,S2S3,ST

Chryxus arctic (Oeneis chryxus) G5,S2,SC

Extra-striped snaketail (Ophiogomphus anomalus) G3,S1,SE

Pygmy snaketail (Ophiogomphus howei) G3,S3,ST

Connecticut warbler (Oporornis agilis) G4,S3B,SC

Tawny crescent spot (Phyciodes batesii) G4,S3,SC

Black-backed woodpecker (Picoides arcticus) G5,S2B,SC

West Virginia white (Pieris virginiensis) G3G4,S2,SC

Zebra clubtail (Stylurus scudderi) G3G4,S3,SC

Sharp-tailed grouse (Tympanuchus phasianellus) G4,S2B,SC

Ellipse mussel (Venustaconcha ellipsiformis) G3G4,S2,ST

Plants

Round-leaved orchis (Amerorchis rotundifolia) G5,S2,ST

Missouri rock cress (Arabis missouriensis var. deamii) G4G5,S2,SC

Green spleenwort (Asplenium trichomanes-ramosum) G4,S1,SE

Alpine milk vetch (Astragalus alpinus) G5,S1,SE

Mingan’s moonwort (Botrychium minganense) G4,S2,SC

Table A2-1. Rare species on the Chequamegon-Nicolet National Forest.
107

APPENDIX 2

Regional Forester Sensitive Species (RFSS) (continued)

Plants

Goblin fern (Botrychium mormo) G3,S3,SE

Blunt-lobed grapefern (Botrychium oneidense) G4,S2,SC

Ternate grapefern (Botrychium rugulosum) G3,S2,SC

Northern water-starwort (Callitriche hermaphroditica) G5,S2,SC

Calypso orchid (Calypso bulbosa) G5,S3,ST

Stoloniferous sedge (Carex assiniboinensis) G5,S1,SC

Rocky Mountain sedge (Carex backii) G4,S1,SC

Crawe’s sedge (Carex crawei) G5,S3,SC

Northern bog sedge (Carex gynocrates) G5,S3,SC

Livid sedge (Carex livida var. radicaulis) G5,S2,SC

Michaux’s sedge (Carex michauxiana) G5,S2,ST

Many-headed sedge (Carex sychnocephala) G4,S2,SC

Sheathed sedge (Carex vaginata) G5,S3,SC

Spineless hornwort (Ceratophyllum echinatum) G4,S2,SC

Northern wild comfrey (Cynoglossum virginianum var. boreale) G5

Ram’s head lady’s slipper (Cypripedium arietinum) G3,S2,ST

Glade fern (Diplazium pycnocarpon) G5,S2,SC

Spreading woodfern (Dryopteris expansa) G5,S2,SC

Male fern (Dryopteris fi lix-mas) G5,S1,SC

Fragrant fern (Dryopteris fragrans var. remotiuscula) G5,S3,SC

Capitate spikerush (Eleocharis olivacea) G5,S2,SC

Few-fl owered spikerush (Eleocharis quinquefl ora) G5,S2,SC

Marsh willow-herb (Epilobium palustre) G5,S3,SC

Marsh horsetail (Equisetum palustre) G5,S2,SC

Russet cotton-grass (Eriophorum chamissonis) G5,S2,SC

Large-leaved avens (Geum macrophyllum var. macrophyllum) G5,S1,SC

Fir clubmoss (Huperzia selago) G5,S2, SC

Butternut (Juglans cinerea) G4,S3

Bog (moor) rush (Juncus stygius) G5,S1,SE

Large-fl owered ground cherry (Leucophysalis grandifl ora) G3?,S1,SC

American shore-grass (Littorella unifl ora) G5,S2,SC

White adder’s mouth (Malaxis brachypoda) G4,S3,SC

Large-leaved sandwort (Moehringia macrophylla) G4,S1,SE

Farwell’s water milfoil (Myriophyllum farwellii) G5,S3,SC

Canada mountain-ricegrass (Oryzopsis canadensis) G5,S1,SC

American ginseng (Panax quinquefolius) G4,S4

Marsh grass-of-parnassus (Parnassia palustris) G5,S2,ST

Bog bluegrass (Poa paludigena) G3,S3,ST

Western Jacob’s ladder (Polemonium occidentale var. lacustre) G5,S1,SE

Braun’s holly fern (Polystichum braunii) G5,S3,ST

Algae-like pondweed (Potamogeton confervoides) G4,S2,ST

Hill’s pondweed (Potamogeton hillii) G3,S1,SC

Lesser wintergreen (Pyrola minor) G5,S1,SE

Small yellow water crowfoot (Ranunculus gmelinii var. hookeri) G5,S2,SE

Brown beak-sedge (Rhynchospora fusca) G4G5,S2,SC

White mandarin (Streptopus amplexifolius) G5,S3,SC

Heart-leaved foamfl ower (Tiarella cordifolia) G5,S1,SE

Dwarf bilberry (Vaccinium cespitosum) G5,S2,SE

Marsh valerian (Valeriana uliginosa) G4G5,S2,ST

Table A2-1 (continued). Rare species on the Chequamegon-Nicolet National Forest.
108

APPENDIX 2

Table A2-1 (continued). Rare species on the Chequamegon-Nicolet National Forest.

Regional Forester Sensitive Species (RFSS) (continued)

Non-Vascular Plants

Ash-lowland lichen (Caloplaca parvula) G1,N1

Methuselah’s beard lichen (Usnea longissima) G3,N2

Likely to Occur Regional Forester Sensitive Species

Animals

Bullhead mussel (Plethobasus cyphyus) G3,S1,SE

Forcipate emerald dragonfl y (Somatochlora forcipata) G5,S2S3,SC

Plants

Large toothwort (Cardamine maxima) G5,S1,SC

Shore sedge (Carex lenticularis) G5,S2,ST

Fairy bells (Disporum hookeri) G4G5

Engelmann’s spike-rush (Eleocharis engelmannii) G4?,S1,SC

Auricled twayblade (Listera auriculata) G3,S1,SE

Broad-leaved twayblade (Listera convallarioides) G5,S1,ST

Arrow-leaved sweet colt’s-foot (Petasites sagittatus) G5,S3,ST

Pale-green orchid (Platanthera fl ava var. herbiola) G4,S2,ST

Spotted pondweed (Potamogeton pulcher) G5,S1,SE

Giant pinedrops (Pterospora andromeda) G5,S1,SE

Lapland buttercup (Ranunculus lapponicus) G5,S1,SE

1

Rank and Status Codes:

Global Element Rank

G1 - Critically imperiled globally

G2 - Imperiled globally

G3 - Vulnerable globally

G4 - Apparently secure globally 

G5 - Secure globally

? - Inexact numeric rank

Federal Status

FT – Federally threatened

FE – Federally endangered

State (Subnational) Element Rank

S1 - Critically imperiled

S2 - Imperiled

S3 - Vulnerable

S4 - Apparently secure

SC - Special Concern

SA - Accidental

SH - Historical occurrence

S#B - Long-distance migrant, breeding status

S#N - Long-distance migrant, non-breeding status

State Status

SE - State endangered

ST - State threatened

SC - State special concern

Source for ranking defi nitions: WNHI 2009
109

APPENDIX 3

Figure A3-1.—Inputs and geographic information system (GIS) outputs to the DISTRIB model that was used to develop the 

Climate Change Tree Atlas from general circulation models (GCMs), U.S. Forest Service Forest Inventory and Analysis (FIA) 

data, and other data; source: L. Iverson.

Outputs

Importance Value

Maps for 

Importance 134 tree Species

Outputs

Importance Values

for 134 Tree

Species

(Response Variables)

FIA

Current

p

Forest Type

Maps

p

Values

(Response Variables)

DISTRIB

Model

DISTRIB

Predicted Species Richness

Maps

Random Forest

• Climate

• Soil

Current

Ranked Species

Maps

DISTRIB GCM Climate

• Elevation

• Land-use

• Landscape

(Predictor Variables)

Tables

Indices of N-S

Predicted

Future

HadCM3, PCM, GFDL 

GCM Climate

Variables

Swap

(Predictor Variables)

Shifts (High & Low emissions)
110

APPENDIX 3

Figure A3-2.—Inputs to LANDIS-II; source: D. Mladenoff.
111

APPENDIX 4. TREE SPECIES VULNERABILITY WITHIN 

NORTHERN WISCONSIN FORESTS TO CLIMATE CHANGES1

In this Appendix, we go into far more technical detail 

than Chapter 4 about the modeling methods and the 

results of this assessment. Becoming familiar with the 

methodology and rating schemes will allow readers 

to compare species’ potential to change under various 

scenarios of climate change.

Wisconsin has been realizing a recent warming, 

a trend that is projected to increase at a level that 

is being determined largely by human decisions 

and actions. A warming climate will impact forest 

ecosystems and the humans who work and play 

in them. Climate change is expected to shift the 

suitable habitat for tree species generally northward. 

Obviously, individual trees cannot get up and walk 

to more suitable habitats, so the trees currently or 

potentially in habitats that become increasingly 

unsuitable for them would become stressed, resulting 

in increased mortality, declining regeneration success, 

and dwindling populations. Catastrophic events, 

such as severe storms or fi res, are also more probable 

under most scenarios of climate change. Such events 

could precipitate rapid species turnover against the 

stressed trees in favor of the more suitable species. 

The increasing stress would also make some species 

more susceptible to other stressors, including droughts, 

fl oods, pathogens, insects, invasive plants and animals, 

and atmospheric deposition. Tree mortality could rise 

due to these secondary impacts, but it may be diffi cult 

to attribute the changes directly to a changing climate. 

Conversely, habitat that becomes less suitable for one 

suite of species would likely become more suitable for 

1

 Louis Iverson, Anantha Prasad, Stephen Matthews, and 

Matthew Peters, Northern Research Station, Delaware, OH

others. Some of those species are already present in 

northern (or southern) Wisconsin, and their numbers 

would likely increase as they experience greater 

regeneration success and survival. Some other species, 

which are presently growing far south, may have 

newly suitable growing conditions appear in northern 

Wisconsin. Whether species would become established 

in these newly suitable areas depends on natural or 

artifi cial migration rates and the species’ specifi c 

requirements for establishment and growth. Healthy 

debate and research is needed to evaluate whether 

assisted migration for newly suitable species should be 

pursued, and if so, how it can be effectively done and 

where (Hoegh-Guldberg et al. 2008, Vitt et al. 2010). 

Climate change is also likely to change the 

productivity of the forest ecosystems. If species 

become stressed because they are no longer well suited 

to their climate, their growth rates will likely decline. 

However, the warmer temperatures, longer growing 

seasons, and higher levels of carbon dioxide will 

compensate in some ways and lead to faster growth 

rates for some species. 

The ranges of tree species in eastern North America 

have generally shifted northward as the climate has 

warmed over the past 14,000+ years since the last ice 

age (Davis 1981, Delcourt and Delcourt 1981, Webb 

et al. 1987). Evidence is mounting that tree species, 

along with many other organisms, are continuing 

this northward movement, some at very high rates 

(Hoegh-Guldberg et al. 2008). Evidence is growing 

of wide-spread tree mortality that can be attributed to 

drier and hotter conditions, often predisposing forests 

to insect pest outbreaks, such as the mountain pine 

beetle in western North America (Allen et al. 2010). 
112

APPENDIX 4

Habitats for individual species have always, and will 

continue to, shift independently and at different rates, 

resulting in changing forest community compositions 

over time (Webb 1992). Such shifts are likely to occur 

in the coming decades in Wisconsin, so that suitable 

habitat will decline to different degrees among some 

species, while others will increase to various degrees. 

Some species will retain the same habitat, while others 

could completely disappear from northern Wisconsin. 

While it is likely that habitat will become suitable for 

some species not currently found in the State, it is less 

clear how rapidly—or even whether—those species 

will migrate into the region without active human 

intervention (Higgins and Harte 2006). Studies on 

six eastern United States species showed that, at the 

estimated maximum migration rate for the Holocene 

period (50 km per century in fully forested conditions, 

Davis 1981), less than 15 percent of the newly suitable 

habitat has even a small possibility of being colonized 

within 100 years (Iverson et al. 2004). 

The relatively rapid nature of the projected climate 

shifts, along with the limits on the rate at which trees 

can migrate over a landscape, especially in the current 

and future fragmented state of forests, constrain 

the rate of ‘natural’ migration. We are currently 

incorporating this constraint for numerous tree species 

with a spatially explicit cellular model (SHIFT) that 

calculates colonization probabilities for each cell 

based on habitat quality, abundance of the species, and 

distance between colonized and uncolonized cells.

The work presented here is based on modeling the 

primary individual tree species of northern Wisconsin. 

We briefl y describe the methods we used and then 

present an evaluation of potential tree species changes, 

along with several other interpretive measures that 

complement the models, to assist in further evaluation 

of vulnerabilities and potential management options.

Methods

The region of study is approximately the northern 

third of Wisconsin, defi ned by Ecological Province 

212 (Laurentian Mixed Forest Province) that occurs 

in Wisconsin, hereafter referred to as Wisconsin 212 

(Introduction, Fig. 1). The Chequamegon-Nicolet 

National Forest (CNNF) is in this area, intermixed 

with much State, local, and private forests. These 

forests are rather fragmented and young, as the area 

was mostly cutover prior to the CNNF beginning 

forest management in the early 1930s.

Each species is modeled separately using a statistical 

approach based on where the species now exists 

in relation to many soil, climate, and landscape 

features. For species information on a total of 

134 tree species, we used U.S. Forest Service 

Forest Inventory and Analysis (FIA) data to build 

importance value estimates for each species. We use 

38 environmental variables—7 climate, 9 soil-class, 

12 soil-characteristic, 5 landscape and fragmentation, 

and 5 elevation variables—to model current species 

abundance with respect to the environment. Then, 

current climate variables (1960 to 1990) are swapped 

with the future climates (~2070 to 2100) according 

to several global circulation models (GCMs) and 

emissions scenarios. We used two emissions scenarios 

developed for the Intergovernmental Panel on Climate 

Change (IPCC): a high level of emissions (A1FI) that 

assumes a continued high rate of fossil fuel emissions 

to 2050, and a lower emissions scenario (B2) that 

assumes a rapid conversion to conservation of energy 

and reduced reliance on fossil fuels (Nakicenovic et al. 

2000). 

The scenarios are also based on the output from 

three different GCMs: the HadleyCM3 model (Pope 

2000), the GFDL or Geophysical Fluid Dynamics 

Laboratory model (Delworth et al. 2006), and the PCM 

or Parallel Climate Model (Washington et al. 2000). 

For reporting, we present the HadleyCM3 model 

projections under the high emissions scenario (HadHi) 
113

APPENDIX 4

as the most sensitive to projected changes in the 

concentrations of greenhouse gases in the atmosphere 

(an extreme warming case), the PCM model under the 

low emissions scenario (PCMLo) to represent the least 

warming, and the averaged output from these models 

that produced an average high emissions scenario 

(GCM3AvgHi) and an average low-emissions scenario 

(GCM3AvgLo). 

This is a statistical-empirical approach using decisiontree ensembles to model and to predict changes 

in the distribution of potential habitats for future 

climates (Prasad et al. 2006, Iverson et al. 2008b). 

Because our data were nonlinear and nonparametric 

with numerous hidden interactions, they violated 

most statistical assumptions; traditional parametric 

statistical approaches would have poorly captured 

the complex patterns we were modeling. The newer 

machine-learning, data-driven approaches using 

decision-tree ensembles were more appropriate for 

predictions and for providing valuable insights into 

the important factors infl uencing species distributions. 

Specifi cally we developed and used a ‘tri-model’ 

approach: RandomForest (1000 decision trees with 

re-sampled data and randomized subset of predictors) 

for predictions, Bagging Trees (averaging of 30 

decision-trees with resampling) for assessing the 

stability among individual decision-trees, and a single 

decision tree to assess the main variables affecting 

the distribution if the stability among trees proved 

satisfactory (Prasad et al. 2006). Further details have 

been published on the methods for deriving suitable 

habitat for the eastern United States (Iverson et al. 

2008b) or for a particular region—the Northeastern 

U.S. (Iverson et al. 2008b). The results were estimates 

of the potential future suitable habitat; note they do 

not estimate the actual distribution of species, only the 

likely suitable habitat of each species.

We have also been developing several metrics to help 

interpret and further evaluate the results obtained from 

the suitable habitat modeling. First, because some 

species are more reliably modeled than others, we 

provide an assessment of the reliability of each model. 

For example, species with highly restricted ranges and 

low sample size often produce less satisfactory models 

compared to more common species (Schwartz et al. 

2006). There are therefore quite large differences in the 

reliability of the predictions among species. For each 

species, the ‘tri-model’ approach allowed us to assess 

the reliability of the model predictions, classifi ed as 

high, medium or low depending on the assessment 

of the stability of the bagged trees and the R2

 in 

RandomForest. If the model reliability of a species 

was high, we could use a single decision-tree to map 

the important predictors infl uencing the distribution 

geographically. This high rating occurred for 55 of 

the 134 tree species in our models. Even if the model 

reliability was medium or low, RandomForest predicts 

better without overfi tting due to its inherent strengths 

compared to a single decision-tree (Cutler et al. 2007).

Second, we provide a literature assessment of 

modifying factors. No model, statistical or otherwise, 

can include all the biological or disturbance factors 

that may infl uence a species’ response to climate 

change. We address some of the uncertainty 

among the models for nine biological and 12 

disturbance modifi cation factors that infl uence 

species distributions. These factors can modify 

the interpretation of the original model results by 

A wetland in northern Wisconsin.

Photo by Linda R. Parker, Chequamegon-Nicolet National Forest
114

APPENDIX 4

increasing or decreasing potential future importance of 

a species. Each species is given a default score based 

on the literature, and can be changed by managers as 

they consider local conditions for each of the factors. 

With knowledge of site-specifi c processes, managers 

may be better suited to interpret the models after 

considering the modifi cation factors. They can then 

use these factors to modify the interpretation of the 

potential suitable habitat models. The goal of this 

effort is to provide information on the distribution of 

species under climate change that accounts for the 

natural processes that infl uence the fi nal distribution. 

This approach encourages decision makers to be 

actively involved in managing tree habitats under 

projected future climatic conditions. 

Another important consideration for model 

interpretation is to identify the predictor variables 

that have the most infl uence on the species, and 

whether they are occurring at the broadest extent of 

the species range or exerting infl uence on species at 

a more local scale. This not only helps to understand 

how the models are working but also provides insights 

to managers on the factors that control species 

distributions. Therefore, we partition the variable 

importance scores into the two scale categories based 

on the pattern of variable node location within the 

intermediate steps of the RandomForest algorithm. To 

statistically distinguish the very important biological 

question of whether a variable is only marginally 

important at the broadest extent vs. very important 

at a local scale (these two situations could have very 

similar variable importance scores), we examine the 

RandomForest ensemble structure for an assessment 

of where (what node) and with what strength these 

variables are entering the model. The node location 

is summarized over all trees and for each variable 

where it occurs. Then we weight the position by the 

frequency of occupancies at the location to determine 

an average fi rst position of the variable. The weighted 

node location in many ways represents an inverse 

relationship to the variable importance value; when 

these two metrics are combined it is possible to 

evaluate the relative contribution of the variables 

to the initial splits of the data (thus refl ecting more 

regional partitioning). This allows us to partition the 

total variable importance (weighted by the number of 

variables in a class) for these regional variables into 

climate, soil, elevation, and landscape components. 

The second group of variables is then designated as 

being beyond the fi rst cut, up to the median weighted 

node location or where they most frequently occur 

no further then the 15th node split. These variables 

represent the more locally infl uencing variables. 

For those species that are predicted to have new 

suitable habitat appear in northern Wisconsin, we 

provide an assessment on how far each may have 

to travel to reach the southern border of Wisconsin 

212. This entailed a statistical approximation of the 

current species boundary, followed by an estimation 

of the Euclidean distance to the nearest edge of the 

Wisconsin 212 boundary.

Each of 134 tree species was evaluated for presence in 

Wisconsin 212, either now or potentially in the future 

(with a few very minor and poorly modeled species 

eliminated). The above metrics were sorted into 3 to 

5 classes, with a 0 class, 1 to 2 negative class, and 1 to 

2 positive class (examples for Biological Factors: 

BF--, BF-, BF0, BF+, BF++; and for Model 

Reliability: MR-, MR0, MR+). 

Finally, we are beginning to evaluate selected species 

for their potential migration from where they exist 

currently to where they may be able to colonize over 

the next 100 years. This helps us understand not only 

how the suitable habitat may move, but also how far 

the species may move across the fragmented habitats 

of southern Wisconsin. We calibrate movement at the 

approximate (generous) migration rate of 50 km per 

century, according to paleoecological data from the 

Holocene period (Davis 1981). Details of the method 

(although under revision now) are presented in several 

publications (Iverson and Schwartz 1999, Iverson et al. 

2004, Schwartz et al. 2006).
115

APPENDIX 4

Guide to Interpreting Species Data

In this section, we explain the tabulated data more 

fully to assist in understanding and interpreting the 

results that are presented in this Appendix, believing 

that a few minutes of investment to understand the 

acronyms, layout, and metrics will allow a much richer 

interpretation for all the species involved.

Climate Change Models and Scenarios 

The temperatures and precipitation estimates for the 

Wisconsin 212 area are shown in Fig. A4-1. Growing 

season conditions over three time intervals this century 

show increasing temperatures and fairly constant 

precipitation, thereby creating expected additional 

physiological stress on biota near the end of the 

growing seasons.

Current: 30-year averaged climate (1960 to 1990)

PCMlo: Parallel Climate Model (mildest of all models 

we tested, meaning least sensitive to carbon dioxide), 

with low emissions scenario (B2, conservation of 

energy adopted worldwide). In all the models and 

scenarios, we model trees based on the climate 

projected for 2070 to 2100.

GCM3lo: average of the three GCM (PCM, GFDL, 

and Had) outputs using low emissions (B2).

GCM3hi: average of the GCM outputs using high 

emissions (A1FI, the current fossil fuel-intensive 

trajectory continues for next few decades).

Hadhi: Hadley CM3 model (the harshest of all 

models we tested, and therefore the most sensitive 

to atmospheric carbon dioxide), with high emissions 

scenario (A1FI).

Modifi cation Factors (MODFACs) 

These are factors that need to be evaluated and 

understood in conjunction with the RandomForest 

outputs of potential species habitat change.

HL: habitat under the low emissions scenario, this 

is ranking of a species as to its potential to increase 

or decrease in the future under low carbon dioxide 

emissions and is simply a classifi cation of the 

(future : current) suitable habitat ratios into HL++ or 

HL+ (large or medium increase in habitat) to HL0 

(less than 10 percent change in habitat either way) to 

HL-- or HL- (large or medium decrease in habitat), 

HLM (migration of habitat into Wisconsin 212), and 

HLE (extirpation of habitat from Wisconsin 212. 

Figure A4-1.—Estimates for May to September growing season (A) temperature (°F) and (B) precipitation currently and for 

three time intervals in this century, according to PCMlo (mild scenario, light gray lines) and Hadhi scenario (harsh scenario, 

black lines).

A B
116

APPENDIX 4

HH: habitat under the high emissions scenario, this 

is ranking of a species as to its potential to increase 

or decrease in the future under high carbon dioxide 

emissions, similar to the HL description.

MR: model reliability, this is an assessment of the 

reliability of the model, ranging from poor (MR-) to 

medium (MR0) to high (MR+).

BF: biological factors, this is an average score of 

9 biological traits for each species, as scaled and 

ranked from -3 (strong negative infl uence on modeled 

outcomes from climate change) to +3 (strong positive 

infl uence on modeled outcomes). The nine traits are 

shown in Figure A4-2.

DF: disturbance factors, like BF, this is an average 

score of 12 disturbances (Fig. A4-2), and the capacity 

of a species to withstand them (a scaled score of -3 

indicates the species cannot tolerate the disturbance 

very well and a +3 indicates high tolerance). The 

expectation is that several disturbance factors will 

increase under climate change.

RC: regional climate, this score is the proportion 

of total variable importance (38 variables in the 

RandomForest modeling), for regional variables, 

attributable to climate variables.

LC: local variables, this score relates to the proportion 

of total variable importance, for local variables, that 

can be attributed to climate.

Figure A4-2.—Modifying factors considered in the analysis of white ash, including nine biological factors and 12 disturbance 

factors.
117

APPENDIX 4

MD: migration distance, this score only applies to 

species that currently do not exist in Wisconsin 212 

but may have habitat appear there by 2100. The 

number following the MD is a rounded off Euclidian 

distance (nearest 100 km) from the current range 

boundary to the southern boundary of Wisconsin 212. 

NA (not available) indicates we cannot calculate the 

MD.

Occupancy

The total study area consists of 225 20 x 20 km cells 

within the Wisconsin 212 area that were modeled to 

be of suitable habitat for the species cells. Data are 

reported for Current, PCMlo, GCM3lo, GCM3hi, and 

Hadhi.

Area Weighted Importance Values (AWIV) 

For each of the 225 cells in Wisconsin 212, the average 

importance value was calculated and then summed 

across all cells to obtain the area weighted importance 

value for each species, and for each scenario (Current, 

PCMlo, GCM3lo, GCM3hi, and Hadhi). Importance 

values are relative scores (ranging from 0 to 100) 

based on basal area and number of stems in inventory 

plots, with 100 indicating the presence of a only a 

single species. Theoretically, if only one species was 

found everywhere, the maximum AWIV would be 

calculated as: 100 importance value average x 225 

cells = 22500. Of course in reality, no species comes 

anywhere close to this theoretical maximum. 

Future: Current Ratios

The ratio of AWIV under a future climate scenario 

to current. Thus, a score >1 indicates an increase in 

overall suitable habitat in the future (~2100), whereas 

a score < 1 indicates a decrease.

Niche Plot Scores

We plotted the current annual temperature and 

precipitation profi les for the eastern United States 

and the current distribution (by importance value) 

for each species within this profi le. We also 

overplotted three polygons, with black representing 

the climate of Wisconsin 212 now, blue representing 

the future under the PCMlo mild scenario, and red 

representing the future under the Hadhi harsh scenario 

(Fig. A4-3). Thus, the niche plot gives a quick view of 

whether a species currently exists in analog climates 

in the United States and whether it will have similar 

temperature/precipitation habitat in the future in 

Wisconsin 212. Of course, this analysis does not 

account for the capacity to adapt to new conditions, or 

the genetic variations contained within the species. In 

Table A4-3, we have calculated the percentage of the 

three polygons that the species occupies, allowing a 

quick comparison of a species’ future versus current 

temperature/precipitation conditions in Wisconsin 212.

Change Class

Each of 73 species has been assigned to one of eight 

classes, based on vulnerability to potential change 

under climate change. These are the primary classes 

determined by the RandomForest model of potential 

change in suitable habitat. Within these classes, we 

also present an overall assessment of the MODFACs 

adjustment to ranking interpretations. Species Model 

Results

Of 134 species modeled in the eastern United States, 

73 were of interest for Wisconsin 212. Using the 

estimates of potential changes in suitable habitat, we 

sorted the species according to their potential to gain, 

lose, remain unchanged, or enter into Wisconsin 212 

from outside. We classify them into eight classes of 

vulnerability to climate change, ranging from most 

vulnerable to least, followed by the number of species 

in each class:
118

APPENDIX 4

Figure A4-3.—Niche plot showing importance value scores according to current annual temperature and precipitation patterns 

in the eastern United States; black spruce represented, with the black, blue, and red polygons representing the Wisconsin 212 

(Bailey 1995) climate now, in 2100 with PCMlo, and in 2100 with Hadhi, respectively.

1. Extirpated (Extirp): These species are in 

Wisconsin 212 currently, but all suitable habitat 

disappears by 2100. (1 species)

2. Large Decline (LgDec): These species show large 

declines in suitable habitat, especially under the 

high emissions scenarios. (12 species)

3. Small Decrease (SmDec): These species show 

smaller declines, mostly apparent in the high 

emissions scenarios. (6 species)

4. No Change (NoChg): These species show roughly 

similar suitable habitat now and in the future. (6 

species)

5. Small Increase (SmInc): These species have 

more suitable habitat in the future as compared to 

current, especially with the higher emissions. (4 

species)

6. Large Increase (LgInc): These species have much 

more suitable habitat in the future as compared to 

current, especially with the higher emissions. (17 

species)
119

APPENDIX 4

7. New Entry Both (NewEntBoth): These species 

have not currently been detected via FIA sampling 

in Wisconsin 212 (or very rarely so), but show 

potential suitable habitat entering the region, even 

under the low emissions scenarios. (11 species)

8. New Entry High (NewEntHi): These species have 

not currently been detected via FIA sampling 

in Wisconsin 212 (or very rarely so), but show 

potential suitable habitat entering the region, 

especially under higher emissions. (16 species)

Results for each of 73 are presented in Tables A4-1 

through A4-3. Figures A4-4 through A4-10 compare 

vulnerability of species within groups 2 through 

8 using (future : current) suitable habitat ratios; 

they also show the role of modifying factors in the 

interpretations using modifying factor scores. Of 

the 73 species, 21 show some potential to increase 

under climate change (classes 5-6), 19 show potential 

decreases (classes 1-3), 6 show little or no change, and 

27 species have some level of modeled suitable habitat 

enter Wisconsin 212 under at least the high emissions 

scenario (classes 7-8). Because Wisconsin 212 nearly 

borders the northern boundary of the United States 

and many species farther south are available to 

move northward, habitat for all but one species (the 

very uncommon mountain maple, Acer spicatum) 

are projected to be retained to some degree under 

most scenarios. So overall, the models predict that 

overall tree species richness in Wisconsin 212 could 

potentially benefi t. 

As one considers individual species, however, the 

picture shows major losses of some key species 

that are currently very important for the region. The 

following paragraphs describe selected species for 

each class in an attempt augment the modeled suitable 

habitat scores with information about model reliability 

(MR), disturbance factors (DF), biological factors 

(BF), regional climate (RC), local climate (LC), and, 

where appropriate, migration distance (MD). Codes 

are provided along with the split points in Table A4-1. 

Tables A4-2 and A4-3 present the specifi c raw and 

coded data for each species, we will not cite the tables 

whenever we refer to one of them in the following 

interpretations; we expect the reader to track the 

species on each table when interpreting. We cannot 

present each variable here, due to space restrictions, 

but we attempt to present the primary variables driving 

a particular score when appropriate. Our web site does 

carry much of the information for each species, along 

with many accompanying maps (Prasad et al. 2007).

It is important to note that when we say “suitable 

habitat” it is referring to the “area weighted 

importance value”, or the product of species 

abundance (importance value) times the number 

of cells with projected occupancy. This measure of 

suitable habitat is more comprehensive than simply 

area occupied, as it considers also the abundance of the 

species. When we refer to the amount of land projected 

to be occupied, we use the terms “percent occupancy” 

or “occurrence of species in analysis area”. 

Table A4-1.—Table of Climate Change Tree Atlas codes used in Tables A4-2 and A4-3.

Levels

Code Description -- - 0 + ++

HL* Habitat Change - Low Emissions F:C <0.4 0.4 to 0.8 0.8 to 1.2 1.2 to 2 > 2

HH* Habitat Change - High Emissions F:C <0.4 0.4 to 0.8 0.8 to 1.2 1.2 to 2 > 2

DF Disturbance Factors -3.0 to -1.5 -1.5 to -0.5 -0.5 to 0.5 0.5 to 1.5 1.5 to 3.0

BF Biological Factors -3.0 to -1.5 -1.5 to -0.5 -0.5 to 0.5 0.5 to 1.5 1.5 to 3.0

RC Regional Climate 0 to 20% 20 to 40% 40 to 60% 60 to 80% 80 to 100%

LC Local Climate 0 to 20% 20 to 40% 40 to 60% 60 to 80% 80 to 100%

*Note: For HL and HH, there are also possibilities for the species to disappear completely (E=extirpated) or newly enter the region 

(M=migrated)
120

APPENDIX 4

319 Mountain maple High 22 14 0 0 0 0 1 0 0 0 0 0.00 0.00 0.00 0.00 HLE HHE Extirpated 95 Black spruce High 128 179 67 49 38 41 434 75 42 36 37 0.17 0.10 0.08 0.09 HL-- HH-- Lg. Dec. 12 Balsam fi r High 151 179 103 95 78 79 999 235 132 89 93 0.24 0.13 0.09 0.09 HL-- HH-- Lg. Dec.Table A4-2.—Climate Change Tree Atlas results for 73 species: occurrence, area-weighted importance value, and suitable habitat ratio (future : current).375 Paper birch High 213 224 201 199 170 173 1225 567 471 150 159 0.46 0.38 0.12 0.13 HL- HH-- Lg. Dec.371 Yellow birch High 162 197 138 101 70 67 358 170 104 56 54 0.48 0.29 0.16 0.15 HL-- HH-- Lg. Dec.261 Eastern hemlock High 126 162 111 77 38 38 233 176 99 39 39 0.76 0.42 0.17 0.17 HL- HH-- Lg. Dec.746 Quaking aspen High 216 225 219 218 218 219 2928 1322 1283 482 499 0.45 0.44 0.16 0.17 HL- HH-- Lg. Dec.241 Northern white-cedar High 134 197 155 145 124 126 577 258 173 134 135 0.45 0.30 0.23 0.23 HL-- HH-- Lg. Dec.743 Bigtooth aspen High 195 223 222 211 155 142 499 458 344 131 118 0.92 0.69 0.26 0.24 HL0 HH-- Lg. Dec.318 Sugar maple High 201 223 225 225 225 225 2022 1324 1159 655 605 0.65 0.57 0.32 0.30 HL- HH-- Lg. Dec. 94 White spruce Medium 119 146 127 125 100 101 203 103 109 78 79 0.51 0.54 0.39 0.39 HL- HH-- Lg. Dec.543 Black ash High 198 225 223 223 216 215 856 603 580 438 445 0.70 0.68 0.51 0.52 HL- HH- Lg. Dec. 71 Tamarack High 142 212 191 193 184 183 415 323 293 218 221 0.78 0.71 0.53 0.53 HL- HH- Lg. Dec.601 Butternut Low 42 27 47 66 0 0 17 37 55 0 0 2.13 3.17 0.00 0.00 HL++ HHE Sm. Dec.129 Eastern white pine High 184 224 216 213 141 150 452 463 546 148 142 1.02 1.21 0.33 0.32 HL0 HH-- Sm. Dec.316 Red maple High 224 225 225 225 225 225 2065 1888 1673 1172 1103 0.91 0.81 0.57 0.53 HL0 HH- Sm. Dec.977 Rock elm Low 53 50 37 39 35 38 32 24 26 19 17 0.75 0.81 0.59 0.52 HL- HH- Sm. Dec.105 Jack pine High 95 175 187 202 191 202 596 470 500 372 409 0.79 0.84 0.62 0.69 HL0 HH- Sm. Dec.741 Balsam poplar High 64 93 45 52 73 127 81 28 37 58 114 0.34 0.45 0.72 1.40 HL-- HH0 Sm. Dec.763 Chokecherry Low 69 96 105 144 21 26 65 77 118 4 7 1.18 1.80 0.07 0.10 HL+ HH-- No Change125 Red pine Medium 140 213 219 213 190 193 473 512 543 311 309 1.08 1.15 0.66 0.65 HL0 HH- No Change833 Northern red oak High 198 224 225 225 225 225 833 1069 1237 738 665 1.28 1.48 0.89 0.80 HL+ HH0 No Change809 Northern pin oak Medium 83 134 170 192 188 191 236 249 307 216 222 1.05 1.30 0.92 0.94 HL0 HH0 No Change951 American basswood Medium 196 225 225 225 225 225 631 713 847 691 661 1.13 1.34 1.10 1.05 HL+ HH0 No Change544 Green ash Medium 95 168 162 179 215 217 416 283 341 502 521 0.68 0.82 1.21 1.25 HL- HH+ No Change541 White ash High 168 209 223 225 225 225 282 443 405 443 431 1.57 1.44 1.57 1.53 HL+ HH+ Sm. Inc.701 Eastern hophornbeam Medium 144 223 224 225 225 224 294 325 412 457 477 1.11 1.40 1.55 1.62 HL+ HH+ Sm. Inc.391 American hornbeam Medium 84 118 171 173 197 197 109 158 163 174 175 1.45 1.49 1.60 1.60 HL+ HH+ Sm. Inc.972 American elm Low 204 224 225 225 225 225 733 1166 1293 1633 1599 1.59 1.76 2.23 2.18 HL+ HH++ Sm. Inc.762 Black cherry High 177 222 225 225 225 225 362 1054 981 550 418 2.91 2.71 1.52 1.16 HL++ HH+ Lg. Inc.823 Bur oak Medium 98 167 215 225 225 225 358 545 645 796 870 1.52 1.80 2.22 2.43 HL+ HH++ Lg. Inc.531 American beech High 32 60 179 162 163 157 66 212 185 155 151 3.23 2.83 2.37 2.31 HL++ HH++ Lg. Inc.802 White oak High 93 141 225 225 225 225 328 850 992 929 862 2.59 3.02 2.83 2.63 HL++ HH++ Lg. Inc.402 Bitternut hickory Low 53 95 166 201 225 225 81 212 285 302 302 2.63 3.53 3.74 3.74 HL++ HH++ Lg. Inc.837 Black oak High 38 104 225 225 225 225 162 610 698 995 998 3.76 4.30 6.13 6.15 HL++ HH++ Lg. Inc.313 Boxelder Medium 14 100 189 225 225 225 127 298 430 721 847 2.35 3.39 5.69 6.68 HL++ HH++ Lg. Inc.804 Swamp white oak Low 18 18 43 58 116 116 12 21 32 88 83 1.72 2.61 7.27 6.84 HL++ HH++ Lg. Inc.407 Shagbark hickory Medium 5 64 163 197 225 225 59 404 524 495 467 6.89 8.94 8.45 7.96 HL++ HH++ Lg. Inc.317 Silver maple Medium 31 102 179 222 225 225 80 385 593 682 698 4.81 7.42 8.52 8.73 HL++ HH++ Lg. Inc.922 Black willow Low 31 58 194 222 225 225 44 194 310 440 465 4.41 7.03 9.98 10.55 HL++ HH++ Lg. Inc.975 Slippery elm Medium 41 62 177 224 225 225 38 314 461 537 555 8.35 12.27 14.29 14.76 HL++ HH++ Lg. Inc.742 Eastern cottonwood Low 9 45 156 217 225 225 23 140 287 551 534 5.95 12.22 23.47 22.73 HL++ HH++ Lg. Inc.641 Osage orange Medium 0 7 38 84 218 225 4 22 64 216 219 5.49 16.09 53.90 54.79 HL++ HH++ Lg. Inc. 68 Eastern red cedar Medium 5 18 166 223 225 225 16 287 782 1376 1330 18.18 49.51 87.11 84.25 HL++ HH++ Lg. Inc.602 Black walnut Medium 2 8 161 220 225 225 2 226 389 612 603 130.83 224.93 353.61 348.33 HL++ HH++ Lg. Inc.462 Hackberry Medium 2 10 118 196 225 225 0 210 428 732 744 604.04 1232.37 2105.60 2141.55 HL++ HH++ Lg. Inc.

Occurrence of Species in Analysis Area Area-weighted Importance Value Future : Current Suitable Habitat Suitable Habitat

 Low High Low High Low High

FIA Model Emissions Emissions Emissions Emissions Emissions Emissions Low High ChangeNo. Common Name Reliability FIA Current PCM Avg. Avg. HAD Current PCM Avg. Avg. HAD PCM Avg. Avg. HAD Emissions Emissions Class
121

APPENDIX 4

Table A4-2 (continued).—Climate Change Tree Atlas results for 73 species: occurrence, area-weighted importance value, and suitable habitat ratio (future : current).830 Pin oak Medium 0 6 41 61 154 158 0 23 42 169 166 NA NA NA NA HLM HHM New-Both331 Ohio buckeye Low 0 0 13 60 144 94 0 5 42 115 67 NA NA NA NA HLM HHM New-Both373 River birch Low 3 3 20 67 108 141 0 10 45 77 110 NA NA NA NA HLM HHM New-Both901 Black locust Low 2 4 122 198 225 225 0 104 187 469 550 NA NA NA NA HLM HHM New-Both931 Sassafras High 0 1 50 85 177 179 0 24 51 172 176 NA NA NA NA HLM HHM New-Both403 Pignut hickory High 0 1 62 87 189 198 0 44 57 171 178 NA NA NA NA HLM HHM New-Both682 Red mulberry Low 1 0 93 180 225 225 0 84 280 679 711 NA NA NA NA HLM HHM New-Both491 Flowering dogwood High 0 0 31 83 160 155 0 11 55 166 162 NA NA NA NA HLM HHM New-Both552 Honey locust Low 0 2 67 140 225 225 0 49 189 482 493 NA NA NA NA HLM HHM New-Both621 Yellow-poplar High 0 0 15 10 95 103 0 4 2 64 73 NA NA NA NA HLM HHM New-Both409 Mockernut hickory High 0 1 26 44 152 167 0 7 21 135 146 NA NA NA NA HLM HHM New-Both404 Pecan Low 0 0 0 0 50 58 0 0 0 25 32 NA NA NA NA HLE HHM New-High921 Peachleaf willow Low 5 0 0 0 27 50 0 0 0 34 63 NA NA NA NA HLE HHM New-High408 Black hickory High 0 0 0 1 152 161 0 0 0 149 166 NA NA NA NA HLE HHM New-High766 Wild plum Low 2 4 5 21 193 195 0 0 4 157 161 NA NA NA NA HLM HHM New-High452 Northern catalpa Low 0 2 0 0 11 30 0 0 0 5 18 NA NA NA NA HLE HHM New-High806 Scarlet oak High 0 0 4 7 76 76 0 1 0 51 51 NA NA NA NA HLM HHM New-High731 Sycamore Medium 0 0 7 7 171 190 0 1 1 141 157 NA NA NA NA HLM HHM New-High461 Sugarberry Medium 0 0 0 0 44 67 0 0 0 26 37 NA NA NA NA HLE HHM New-High826 Chinkapin oak Medium 0 0 6 35 225 225 0 0 16 211 223 NA NA NA NA HLM HHM New-High817 Shingle oak Medium 0 1 2 37 152 154 0 0 21 138 143 NA NA NA NA HLM HHM New-High471 Eastern redbud Medium 0 0 1 27 218 225 0 0 9 231 238 NA NA NA NA HLM HHM New-High835 Post oak High 0 0 0 10 225 225 0 0 1 648 761 NA NA NA NA HLM HHM New-High824 Blackjack oak Medium 0 0 0 1 218 221 0 0 0 244 316 NA NA NA NA HLE HHM New-High521 Common persimmon Medium 0 0 0 0 30 79 0 0 0 11 45 NA NA NA NA HLE HHM New-High693 Blackgum High 0 2 3 4 46 47 0 0 0 24 25 NA NA NA NA HLE HHM New-High832 Chestnut oak High 0 0 0 3 24 26 0 0 0 14 16 NA NA NA NA HLE HHM New-High

Occurrence of Species in Analysis Area Area-weighted Importance Value Future : Current Suitable Habitat Suitable Habitat

 Low High Low High Low High

FIA Model Emissions Emissions Emissions Emissions Emissions Emissions Low High ChangeNo. Common Name Reliability FIA Current PCM Avg. Avg. HAD Current PCM Avg. Avg. HAD PCM Avg. Avg. HAD Emissions Emissions Class
122

APPENDIX 4319 Mountain maple High 0.1 0.79 DF+ 1.48 BF+ 100 3 RC++ 22.5 LC- 81.3 36.7 11.1 Extirpated

 95 Black spruce High 2 -2.14 DF-- 1.24 BF+ 100 2 RC++ 40.8 LC- 87.5 33.3 0 Lg. Dec. 12 Balsam fi r High 4.9 -3 DF-- -0.35 BF0 83.5 4 RC++ 59.5 LC-- 93.8 56.7 0 Lg. Dec.FIA 375 Paper birch High 6.6 -1.72 DF-- 0.18 BF0 100 3 RC++ 60.6 LC-- 100 83.3 0 Lg. Dec.371 Yellow birch High 1.8 -1.38 DF- -0.04 BF0 85.3 4 RC++ 15.1 LC-- 100 90 14.8 Lg. Dec.261 Eastern hemlock High 1.3 -1.34 DF- -0.88 BF- 62.3 6 RC+ 15.9 LC0 90.6 80 40.7 Lg. Dec.746 Quaking aspen High 15 0.55 DF+ 0.01 BF0 100 4 RC++ 55.8 LC0 100 93.3 11.1 Lg. Dec.241 Northern white-cedar High 3.2 -0.69 DF- 0.49 BF0 71.3 3 RC+ 28.4 LC- 100 66.7 0 Lg. Dec.743 Bigtooth aspen High 2.6 1 DF+ 0.16 BF0 100 2 RC++ 65.9 LC- 100 96.7 25.9 Lg. Dec.318 Sugar maple High 10.1 0.86 DF+ 1.33 BF+ 59.5 3 RC0 32.3 LC0 100 100 40.7 Lg. Dec. 94 White spruce Medium 1 0.07 DF0 -0.61 BF- 100 3 RC++ 36.4 LC-- 84.4 66.7 0 Lg. Dec.543 Black ash High 4.3 -1.31 DF- -3 BF-- 62.8 3 RC+ 32.7 LC0 100 90 18.5 Lg. Dec. 71 Tamarack High 1.9 -0.48 DF0 -1.24 BF- 36.6 3 RC- 9.7 LC+ 93.8 70 3.7 Lg. Dec.601 Butternut Low 0.2 -1.41 DF- -1.27 BF- 66.2 3 RC+ 30.4 LC- 71.9 76.7 37 Sm. Dec.129 Eastern white pine High 2.4 -1.97 DF-- 0.13 BF0 46.2 5 RC0 30.1 LC- 96.9 93.3 51.9 Sm. Dec.316 Red maple High 11.1 3 DF++ 3 BF++ 26.5 3 RC- 27.1 LC0 100 100 51.9 Sm. Dec.977 Rock elm Low 0.3 -0.21 DF0 -2.61 BF-- 34.7 2 RC- 21.5 LC0 59.4 46.7 29.6 Sm. Dec.105 Jack pine High 3 1.86 DF++ -1.24 BF- 63.5 2 RC+ 30.2 LC+ 87.5 60 0 Sm. Dec.741 Balsam poplar High 0.4 0.14 DF0 -0.59 BF- 100 2 RC++ 39.3 LC- 93.8 53.3 0 Sm. Dec.763 Chokecherry Low 0.4 0.17 DF0 -0.86 BF- 100 3 RC++ 49.1 LC- 93.8 83.3 37 No Change125 Red pine Medium 2.2 -0.03 DF0 -2.64 BF-- 76.9 3 RC+ 76.6 LC- 93.8 83.3 22.2 No Change833 Northern red oak High 3.8 1.38 DF+ 0.13 BF0 79.1 4 RC+ 49.8 LC- 96.9 96.7 74.1 No Change809 Northern pin oak Medium 1.5 2.52 DF++ -0.57 BF- 100 2 RC++ 45.8 LC- 78.1 70 14.8 No Change951 American basswood Medium 3.3 0.31 DF0 0.16 BF0 100 3 RC++ 25.2 LC-- 100 100 66.7 No Change544 Green ash Medium 2 -0.14 DF0 -0.25 BF0 38.3 4 RC- 31.6 LC0 96.9 93.3 96.3 No Change541 White ash High 1.4 -2 DF-- -0.54 BF- 100 3 RC++ 46.9 LC- 93.8 100 59.3 Sm. Inc.701 Eastern hophornbeam Medium 1.3 1.72 DF++ 1.29 BF+ 47.9 2 RC0 42.5 LC0 100 100 55.6 Sm. Inc.391 American hornbeam Medium 0.6 0.55 DF+ 0.61 BF+ 0 2 58 LC- 87.5 93.3 33.3 Sm. Inc.972 American elm Low 3.8 -0.79 DF- 0.3 BF0 80.2 4 RC++ 30 LC- 100 100 96.3 Sm. Inc.762 Black cherry High 1.5 -1.55 DF-- -0.33 BF0 80.2 4 RC++ 43.1 LC- 100 100 66.7 Lg. Inc.823 Bur oak Medium 1.1 2.76 DF++ -0.16 BF0 48.6 4 RC0 33.8 LC- 78.1 73.3 92.6 Lg. Inc.531 American beech High 0.4 -1.14 DF- 0.04 BF0 30.5 3 RC- 18.2 LC0 78.1 96.7 29.6 Lg. Inc.802 White oak High 1.5 1.66 DF++ 1 BF+ 60.7 7 RC+ 30.6 LC0 71.9 90 55.6 Lg. Inc.402 Bitternut hickory Low 0.4 2.17 DF++ -0.83 BF- 100 3 RC++ 43.7 LC- 68.8 83.3 88.9 Lg. Inc.837 Black oak High 0.6 0.52 DF+ 0.42 BF0 0 1 33.9 LC0 68.8 83.3 70.4 Lg. Inc.313 Boxelder Medium 0.2 2.38 DF++ 2.06 BF++ 35.3 3 RC- 16.9 LC0 62.5 80 88.9 Lg. Inc.804 Swamp white oak Low 0.1 1.03 DF+ -0.3 BF0 58.9 2 RC0 31.4 LC- 46.9 76.7 40.7 Lg. Inc.407 Shagbark hickory Medium 0.1 -0.21 DF0 0.37 BF0 80 6 RC+ 29.1 LC0 31.3 80 59.3 Lg. Inc.317 Silver maple Medium 0.3 0.14 DF0 1.63 BF++ 62.5 6 RC+ 24.9 LC0 78.1 80 74.1 Lg. Inc.922 Black willow Low 0.2 -0.31 DF0 -2.13 BF-- 100 2 RC++ 49.3 LC- 68.8 80 85.2 Lg. Inc.975 Slippery elm Medium 0.2 0.03 DF0 0.69 BF+ 37.8 4 RC- 26.2 LC0 81.3 90 92.6 Lg. Inc.742 Eastern cottonwood Low 0.1 0.21 DF0 -0.76 BF- 41.6 3 RC0 45.5 LC-- 53.1 76.7 88.9 Lg. Inc.641 Osage orange Medium 0 2.31 DF++ 0.33 BF0 40.2 3 RC0 35.4 LC0 0 26.7 96.3 Lg. Inc. 68 Eastern red cedar Medium 0.1 0.55 DF+ -1.48 BF- 100 2 RC++ 46.6 LC- 46.9 73.3 85.2 Lg. Inc.602 Black walnut Medium 0 0.34 DF0 -0.83 BF- 84.1 4 RC++ 41.4 LC0 43.8 76.7 96.3 Lg. Inc.462 Hackberry Medium 0 1.66 DF++ 0.3 BF0 29.9 3 RC- 22.5 LC+ 43.8 70 96.3 Lg. Inc.

Model Percent of Disturbance Biological Regional Climate Local Climate Niche Plots Migration Change

No. Common Name Reliability AWIV in 212 Scale Factors Scale Factors Scale # Variables Factors Scale Factors Current PCMlo HADHi Distance ClassTable A4-3.—Climate Change Tree Atlas results for 73 species: modifying factors, niche plot values, and migration distance.
123

APPENDIX 4

830 Pin oak Medium 0 -0.66 DF- -1.39 BF- 80.4 4 RC++ 20.2 LC- 15.6 46.7 37 New-Both331 Ohio buckeye Low 0 0.38 DF0 -1.92 BF-- 0 1 56.4 LC0 3.1 23.3 33.3 400 km New-Both373 River birch Low 0 -0.45 DF0 -0.33 BF0 0 4 39.4 LC0 28.1 46.7 33.3 New-Both901 Black locust Low 0 0 DF0 -0.59 BF- 55.4 3 RC0 30.5 LC0 25 66.7 59.3 New-Both931 Sassafras High 0 0.48 DF0 -0.64 BF- 53.2 4 RC0 30.5 LC0 18.8 60 40.7 200 km New-Both403 Pignut hickory High 0 0.21 DF0 0.4 BF0 33.9 3 RC- 13.9 LC+ 12.5 73.3 51.9 200 km New-Both682 Red mulberry Low 0 0.07 DF0 0.59 BF+ 52.4 4 RC0 17.1 LC+ 25 66.7 96.3 100 km New-Both491 Flowering dogwood High 0 0.07 DF0 0.95 BF+ 46.2 7 RC0 16.5 LC- 21.9 60 51.9 200 km New-Both552 Honey locust Low 0 1.9 DF++ -0.54 BF- 32.4 3 RC- 30.5 LC0 9.4 50 96.3 200 km New-Both621 Yellow-poplar High 0 0.14 DF0 1.25 BF+ 55.9 6 RC0 21.2 LC- 0 23.3 44.4 200 km New-Both409 Mockernut hickory High 0 1.69 DF++ -0.28 BF0 46.7 5 RC0 20.6 LC0 12.5 73.3 55.6 200 km New-Both404 Pecan Low 0 -1.24 DF- -1.7 BF-- 0 2 29.9 LC- 0 0 14.8 1100 km New-High921 Peachleaf willow Low 0 0.14 DF0 -1.65 BF-- 999 0 7.9 LC+ 28.1 16.7 14.8 400 km New-High408 Black hickory High 0 1.03 DF+ -2.28 BF-- 40.3 3 RC0 33.1 LC0 0 0 18.5 600 km New-High766 Wild plum Low 0 0.48 DF0 -1.34 BF- 0 1 33.2 LC0 34.4 53.3 18.5 New-High452 Northern catalpa Low 0 0.93 DF+ -1.58 BF-- 0 1 33.9 LC-- 0 16.7 37 1100 km New-High806 Scarlet oak High 0 -0.34 DF0 0.71 BF+ 0 3 24.7 LC+ 6.3 36.7 48.1 600 km New-High731 Sycamore Medium 0 1.28 DF+ -0.9 BF- 68.2 2 RC+ 33.7 LC0 0 36.7 85.2 200 km New-High461 Sugarberry Medium 0 -0.17 DF0 0.64 BF+ 0 2 35.9 LC- 0 0 18.5 1100 km New-High826 Chinkapin oak Medium 0 1.17 DF+ -0.66 BF- 37.1 3 RC- 43 LC- 3.1 33.3 77.8 400 km New-High817 Shingle oak Medium 0 1.31 DF+ -0.73 BF- 0 2 34.9 LC+ 0 10 37 400 km New-High471 Eastern redbud Medium 0 0.9 DF+ -0.04 BF0 100 3 RC++ 83.7 LC-- 0 3.3 85.2 400 km New-High835 Post oak High 0 2.17 DF++ -0.59 BF- 78.7 5 RC+ 64.7 LC- 0 3.3 48.1 600 km New-High824 Blackjack oak Medium 0 1.55 DF++ 0.2 BF0 66.2 3 RC+ 62.1 LC- 0 0 51.9 600 km New-High521 Common persimmon Medium 0 1.17 DF+ 0.95 BF+ 100 3 RC++ 31.9 LC0 0 0 25.9 600 km New-High693 Blackgum High 0 1.45 DF+ 0.83 BF+ 39.9 3 RC- 39.3 LC- 6.3 30 51.9 400 km New-HighFIA 832 Chestnut oak High 0 1.38 DF+ 1.29 BF+ 17.4 6 RC-- 36.2 LC0 15.6 53.3 48.1 600 km New-High

Model Percent of Disturbance Biological Regional Climate Local Climate Niche Plots Migration Change

No. Common Name Reliability AWIV in 212 Scale Factors Scale Factors Scale # Variables Factors Scale Factors Current PCMlo HADHi Distance ClassTable A4-3 (continued).—Climate Change Tree Atlas results for 73 species: modifying factors, niche plot values, and migration distance.
124

APPENDIX 4

Class 1: Extirpated

Only one species, mountain maple, Acer spicatum, 

fell into this class, and it is very rare now. The metrics 

of HLE, HLE, MR+, DF+, BF+, RC++, LC- indicate 

that the model for the species was good and that both 

low and high emissions scenarios are projected to 

extirpate the habitat. However, both the disturbance 

and biological MODFACs show the species could do 

somewhat better than the models suggest. Conversely, 

the species is highly connected to climatic variables 

as regional drivers (growing season temperatures), 

indicating the species is probably quite sensitive to 

temperature changes.

Figure A4-4.—(Future : Current) suitable habitat ratios and modifi cation factors for Class 2 (large decline) species, sorted by 

decreasing disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species 

with MODFACs < 0 may do worse than modeled.

Class 2: Large Decline

This class has 12 species that are currently very 

common and important in the region, but show large 

potential declines in suitable habitat (Fig. A4-4). All 

currently exist in at least 146 of the 225 (65 percent) 

20 x 20 km pixels in Wisconsin 212; and all but one 

species, white spruce, have high model reliability 

scores. 

Black spruce—Black spruce, Picea mariana, has 

the most serious projected decline in suitable habitat, 

reduced to 10 percent of current for the average of 

low emissions scenarios and 8 percent for the average 
125

APPENDIX 4

high emissions scenarios. The niche plot (Fig. A4-3) 

clearly shows no current black spruce occupation in 

any of the HadHi niche and only a third of the PCMLo 

niche. Black spruce also appears to be highly sensitive 

to the disturbance factors (DF--) that would increase 

in a warmed climate, especially drought, insect pests, 

and fi re topkill. That, coupled with high regional 

sensitivity to temperature variables (RC++), indicates 

the species may do worse than indicated by the niche 

modeling. Although somewhat benefi ted by biological 

factors (BF+), especially shade tolerance, this species 

may be the most at risk among all (besides mountain 

maple) in the region. 

Balsam fi r—Balsam fi r, Abies balsamea, has traits 

similar to black spruce as far as sensitivity to climate 

change, but our assessment shows it may be slightly 

more resilient. It is found in the same habitats and 

in the same parts of Wisconsin 212, and the niche 

modeling shows a reduction of suitable habitat to 

13 percent of current for the average low emissions 

scenarios and 9 percent for the high emissions 

scenarios. Area of habitat is not projected to be 

reduced as much, however. For example, the number 

of cells projected to remain with habitat is almost 

twice (79 versus 41 out of 225 cells) for the fi r over 

the spruce under the HadHi emissions scenario. The 

other scores of DF--, BF0, RC++, and LC-- again 

show the extra sensitivity to disturbance factors 

(especially insect pests, drought, and fi re topkill) and 

regional climate factors (growing season and July 

temperatures), again indicating that the species may do 

worse than the models project. The niche plot clearly 

shows that the species does not now exist in any 

location under temperatures projected under HadHi.

Northern white-cedar—Northern white-cedar, Thuja 

occidentalis, is projected have its suitable habitat 

reduced to 30 percent of current for average low 

emissions and 23 percent for high emissions. The 

niche plot shows no current occupancy in the future 

HadHi temperature and precipitation regimes, and 

about two-thirds occupancy in the PCMLo niche. The 

other metrics, DF-, BF0, RC+, LC-, indicate no serious 

issues relative to the other factors not considered in the 

niche models, indicating relatively high confi dence.

Yellow birch—Yellow birch, Betula alleghaniensis, 

is projected to have its suitable habitat reduced to 

29 percent of current for the low emissions scenarios 

and 16 percent for the high emissions scenarios. The 

biological factors on the whole are neutral (BF0) 

but the high dispersal ability may be a benefi t under 

climate change, given that the climate niche within 

the region is at the periphery of the species tolerance 

zone. The other factors (DF-, RC++, LC--), like 

those from the habitat model (HL--, HH--), suggest 

further potential restrictions for this species under 

climate change, and that it may not do even as well as 

projected. The species model indicates a strong climate 

infl uence at the regional scale (RC++) and that climate 

is less of a driver at local scales (LC--). In addition, a 

more negative trend in response to disturbance (DF-) is 

infl uenced by sensitivity to fi re topkill and insect pests. 

Paper birch—Paper birch, Betula papyrifera, is the 

fourth highest species in terms of current modeled 

importance value, and Wisconsin 212 accounts for 

6.6 percent of the total summed importance value for 

the species across the entire eastern United States. It 

is projected to have its suitable habitat substantially 

reduced to 38 percent of current for the average 

low emissions scenarios and 12 percent for the high 

emissions scenarios. This projected loss may be 

exacerbated by negative response to drought and fi re 

topkill, thus infl uencing the negative disturbance score 

(DF--). The other metrics do not strongly suggest 

further climate change stress to the species, with BF0 

and LC--; and while RC++ on the surface would raise 

concerns, the model is primarily driven by the local 

scale variables.

Quaking aspen—Quaking aspen, Populus 

tremuloides, has the highest current modeled 

importance value and occupies all 225 cells in 

Wisconsin 212. The suitable habitat is projected 
126

APPENDIX 4

to change little in terms of the number of cells 

(minimum of 218 cells), but the quality of the habitat 

is projected to decline to 44 percent of current for 

low the emissions scenarios and 16 percent for the 

high emissions scenarios. The strong regional climate 

infl uence (RF++, to a lesser extent local, LC0) and 

shift in climatic niche space points further to a pattern 

of reduced habitat quality but not occupancy within the 

region. The species characteristics and these metrics 

suggest that the species may be able to do better under 

climate change than the habitat model projects, with a 

more positive response to disturbance (DF+) because 

of its broad temperature tolerances (after all it is found 

throughout Canada and much of the western United 

States), and balancing by biological factors (BF0). 

White spruce—White spruce, Picea glauca, is 

projected to decline in suitable habitat to 54 percent 

of current for low emissions scenarios and 39 percent 

for the low emissions scenarios. The species model is 

driven by a strong regional climate infl uence (RC++), 

with little local climate infl uence (LC--). Under the 

highest emissions scenario, HadHi, the temperature 

and precipitation niche would have no contemporary 

habitat for the species, further highlighting the species 

potential risk to climate change impacts. The species 

is less sensitive to disturbance factors (DF0), but has a 

more negative biological profi le (BF-). It is important 

to reiterate the lower model reliability (MR0) for this 

species when interpreting the species results, but the 

overall negative metrics provide good evidence of 

likely negative habitat response to climate change.

Eastern hemlock—Eastern hemlock, Tsuga 

canadensis, projected suitable habitat declines to 

17 percent of current under the high emissions 

scenarios and to 42 percent of current under the 

low emissions scenarios. This species is moderately 

climate driven at regional scales (RC+, LC0) with 

temperature serving as the primary determinant. 

Perhaps most signifi cant to persistence over the next 

century is its sensitivity to disturbance and biological 

factors (DF-, BF-). The major negative factor now is 

its susceptibility to hemlock wooly adelgid, and this, 

combined with negative response to drought, will 

likely push losses beyond those projected. Eastern 

hemlock is not a valuable timber species, but it has 

signifi cant ecological and aesthetic value. Eastern 

hemlocks often grow in the lower, wetter areas along 

streams, and thus provide cooler habitats for many 

other organisms. 

Sugar maple—Sugar maple, Acer saccharum, has 

the third highest weighted importance value (suitable 

habitat) and is found throughout Wisconsin 212. It also 

accounts for just over 10 percent of the total summed 

importance values across the eastern United States, so 

it is an extremely important species. While the species 

is projected to decline in suitable habitat to 57 percent 

of current under the average low emissions scenarios 

and 32 percent under the high emissions scenarios, the 

species is projected to continue to exist in all 225 cells 

for all models. The species model utilized a variety of 

predictors and is not highly driven by climate factors 

alone at both regional and local scales (RC0, LC0). 

The other metrics suggest many characteristics that 

may benefi t the species under climate change (DF+, 

BF+), most notably its positive score related to its light 

competition and its few disease and insect predators. 

Thus, the species may do better than projected in our 

suitable habitat models. However, the niche plot shows 

that the species does not now exist in temperature/

precipitation regimes that would occur for nearly 60 

percent of the region under the most severe model 

tested (HadHi).

Black ash—Black ash, Fraxinus nigra, despite little 

projected change in occupancy within the region (from 

225 cells to a minimum of 215 cells), the weighted 

importance value is projected to decline to 68 percent 

of its current habitat for the emissions scenario and 

51 percent for the high emissions scenario. Like many 

species in this class, the climatic niche overlap within 

the region shows a shift out of much of the species 

range under the highest emissions scenario (HadHi), 

which is also corroborated by a regional climate 
127

APPENDIX 4

infl uence (RC+, LC0). The species’ susceptibility to 

emerald ash borer, of course, is a major limitation to its 

persistence and is refl ected in a negative disturbance 

score (DF-). In addition, the strong negative biological 

factor score (BF--), resulting in part from limited 

dispersal and seedling establishment, puts this species 

at a much greater risk in the future than the climate 

change habitat models alone would suggest.

Tamarack—Tamarack, Larix laricina, is projected to 

remain a part of Wisconsin 212 with little change in 

the extent, but with declines in suitable habitat to 71 

percent of current for the low emissions scenarios and 

53 percent for the high emissions scenarios. Unlike 

the other species in this class, it shows more local 

climate drivers (LC+, RC-) and the species model is 

most infl uenced by soil variables. This lack of regional 

sensitivity to climate likely accounts for the stability 

of occupancy despite the fact that the climatic niche 

moves substantially above its present temperature 

zone under HadHi. The other metrics do not strongly 

indicate greater risk under climate change (DF0, BF-), 

but the species negative competitive score for light 

may restrict establishment into new areas.

Bigtooth aspen—Bigtooth aspen, Populus. 

grandidentata, has a projected suitable habitat 

response that greatly depends on the emissions 

scenarios. Under average low emissions, a modest 

decline to 69 percent of current habitat is projected, 

compared to 26 percent under the high emissions 

scenario. These changes are brought about by a greater 

shift northward, thus loss of occupancy, with higher 

emissions (from 223 cells to a minimum of 142 cells 

under HadHi). This pattern is refl ected by a strong 

regional climate infl uence (RC++, LC-), with the 

regional climate niche maps indicating that the coolerwetter areas are the habitats most likely to remain in 

suitable habitat. The species has a positive disturbance 

profi le (DF+) resulting in part from relatively lower 

susceptibility to insect pests. The biological factors do 

not indicate further susceptibility (BC0), and the total 

weight of evidence suggests that the habitat models 

likely provide a good place to begin considering the 

potential changes for this species within the region.

A forested landscape in northern Wisconsin.

Photo by Tom Matthiae, Chequamegon-Nicolet National Forest
128

APPENDIX 4

Class 3: Small Decline

This group consists of six species that vary greatly 

in current importance, in quality of models, and in 

variation in response between emissions scenarios 

(Fig. A4-5). In general, the species show little or 

no change at the low emission level, but under high 

emissions, the habitat is reduced. The exception is 

Balsam poplar (Populus balsamifera), which shows 

a reduction under the low emissions scenerio but no 

change under the high emissions scenario. We discuss 

three of the six species here (all three with highly 

reliable models).

Jack pine—Jack pine, Pinus banksiana, models show 

some expansion of potentially occupied cells, though 

it is not certain all these new cells can edaphically 

support jack pine. However, the overall suitable 

habitat is reduced to 84 percent of current for the 

low emissions scenarios and 62 percent for the high 

emissions scenarios. The temperature/precipitation 

niche plot clearly shows that under low emissions, the 

temperatures and precipitation can host the species 

but not so for high emission outcomes. The metrics of 

DF++, BF-, RC+, LC+ could indicate that the species 

Figure A4-5.—(Future : Current) suitable habitat ratios and modifi cation factors for Class 3 (small decline) species, sorted by 

decreasing disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species 

with MODFACs < 0 may do worse than modeled.

will do better under the predicted disturbance regimes; 

and it is not particularly controlled by temperature 

variables (growing season precipitation is the only 

important climate variable, and that at the local 

scale) and thus may do just fi ne in a slightly, but not 

extremely, warmed climate. 

Red maple—Red maple, Acer rubrum, is one of the 

most adaptable species on the planet and is expanding 

greatly in the eastern United States. Consequently, 

we have always been uneasy with estimates of 

reduced suitable habitat according to our models; for 

Wisconsin 212, we model suitable habitat reductions 

to 81 (low emissions) and 57 percent (high emissions) 

of current size even though we model no loss in pixels 

with habitat. However, when we added the MODFAC 

information, we see the maximum advantages, 

compared to all 134 species, for both disturbance 

(DF++) and biological (BF++) factors. So this species 

will likely do much better than modeled. In fact, based 

on other evidence, the species may continue to fl ourish 

regardless of the climate changes underway. Abrams 

(1998) describes red maple as a “supergeneralist” that 

has been expanding greatly throughout much of the 

eastern United States in recent decades and thrives 
129

APPENDIX 4

in a wide variety of circumstances. He hypothesizes 

that until recently the species was limited primarily by 

frequent fi res, often set by Native Americans and early 

European settlers. One caveat, however, to the idea 

that the species will continue to expand even under a 

changed climate: the niche plot shows that under the 

most severe regime modeled, HadHi, only 52 percent 

of the higher temperature, lower precipitation zones 

modeled currently host red maple. Perhaps a ceasing 

of the ‘mesophication’ of some zones of the eastern 

United States could result in a reversal of red maple 

expansion as seen in recent decades (Nowacki and 

Abrams 2008). 

Eastern white pine—Eastern white pine, Pinus 

strobus, shows even a slight increase in suitable 

habitat under the average low emissions scenarios, 

but a severe reduction in suitable habitat (to only 33 

percent of current) under the high emissions scenarios. 

The niche plot also shows empty zones in the niche 

space under HadHi, again implicating sensitivity 

to higher temperatures and lower precipitation. It 

is also quite sensitive to disturbance factors (DF--), 

especially drought, disease, insects, and fi re topkill, 

indicating that the models may underestimate the 

Figure A4-6.—(Future : Current) suitable habitat ratios and modifi cation factors for Class 4 (no change) species, sorted by 

decreasing disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species 

with MODFACs < 0 may do worse than modeled.

extent of reduction if these stressors increased much 

under warming. The other metrics (BF0, RC0, LC-) 

do not show any real trends. In any event, the models 

show the large differential depending on the amount 

of carbon we humans emit to the atmosphere over the 

next decades. 

Class 4: No Change

This class is made up of six species that, on average 

across the low versus high emissions scenarios, show 

little change either way (Fig. A4-6). Four of the six 

models have medium reliability, and only one with 

high reliability, so a signifi cant amount of the variation 

can be attributed to model error. Nonetheless, the 

group contains several species of large signifi cance, 

including green ash, red pine, northern pin oak, 

northern red oak, and basswood.

Green ash—The models predict that green ash, 

Fraxinus pennsylvanica, would decrease slightly 

(82 percent of current suitable habitat) under the low 

emissions scenarios, and increase slightly (121 percent 

of current) under the high emissions scenarios. The 

other metrics show no real modifi cations to the models 
130

APPENDIX 4

(DF0, BF0, RC-, LC0), and the niche plot shows that 

the species can grow well in future temperature and 

precipitation regimes. However, this species obviously 

breaks down our scoring system, in that the emerald 

ash borer has and will likely continue to reduce the 

species to a very low level over the next decades 

(Poland and McCullough 2006). The model has only 

medium model reliability, which reduces the reliability 

of the outcomes; in addition, the disturbance factor 

score is unable to account for the severe role of the 

beetle as averaged among 12 disturbance factors. It 

is unlikely that special management will be needed 

for climate change, but rather to try to minimize the 

devastation from the emerald ash borer. 

Northern pin oak—Northern pin oak, Quercus 

ellipsoidalis, is another species that is projected to 

increase slightly in suitable habitat under the low 

emissions scenarios, and decrease slightly under the 

high emissions scenarios. The area occupied, however, 

could expand somewhat under both emissions 

scenarios. According to the niche plot, nearly all of 

the HadHi niche space is not currently hosting pin 

oak. The species shows a great deal of adaptability 

to disturbances (DF++) so that it may do better than 

modeled, but that benefi t is somewhat canceled by its 

sensitivity to climatic drivers as indicated by the RC++ 

rating (with January temperature ranking highest).

American basswood—American basswood, Tilia 

americana, is a species currently found throughout 

Wisconsin 212, and is modeled to continue to occupy 

the entire region and increase slightly in suitable 

habitat under the average low emissions scenarios, 

while remaining stable under the high emissions 

scenario. The niche plot shows adequate habitat in 

the region in the future, based on where the species 

exists now. The other metrics (DF0, BF0, RC++, LC--) 

show, on average, little sensitivity to disturbance or 

biological factors, but relatively high regional and low 

local sensitivity to climate variables. 

Northern red oak—Northern red oak, Quercus rubra

(HL+, HH0, DF+, BF0, RC+, LC-), has high model 

reliability, almost entirely covers Wisconsin 212 

both now and into the future under any scenario, and 

shows an increase in suitable habitat under the low 

emissions scenarios and a slight decrease under the 

high emissions scenarios. Our models indicate some 

regional control by average temperature; however 

locally, soil conditions seem to be more dominant. It 

is particularly sensitive to oak wilt and oak decline 

but can withstand periodic fi res quite well, sprouting 

vigorously. The niche maps show that the habitats 

for northern red oak are favorable under the low 

emissions scenario but may be restricted under the 

high emissions scenario as it hits its upper range of 

temperature.

Class 5: Small Increase

This class of species has only four species (American 

elm, eastern hophornbeam, American hornbeam, and 

white ash), all of which are found quite commonly in 

nearly all pixels of Wisconsin 212 (Fig. A4-7). Only 

white ash has a high model reliability rating, and it of 

course suffers from the threat of destruction from the 

emerald ash borer. 

American elm—American elm, Ulmus americana

(HL+, HH++, DF-, BF0, RC++, LC-), is found 

in almost every pixel and has suitability models 

that show increases in suitable habitat to 1.8 (low 

emissions) and 2.2 (high emissions) times the current 

habitat. The niche plot analysis shows that the species 

currently grows well in habitats that will likely be 

prominent by year 2100 under either low or high 

emissions. However, it is sensitive to disturbance 

factors, especially Dutch elm disease, and is highly 

related to temperature variables at the regional level 

so that model projections are likely overestimating 

suitable habitat in the future. 
131

APPENDIX 4

Eastern hophornbeam—Eastern hophornbeam, 

Ostrya virginiana (HL+, HH++, DF++, BF+, RC0, 

LC0), is a widespread but minor species in Wisconsin 

212 (and elsewhere in the eastern United States). 

The models show increases in suitable habitat, 

especially under the high emissions scenarios. The 

niche plot shows plenty of current occupation in 

temperature/precipitation regimes of PCMLo, but 

the higher temperatures of HadHi niche space host 

eastern hophornbeam only 55 percent of the time. 

The disturbance factor scores (DF++), and to some 

degree also the biological factor scores (BF+), indicate 

that the species has resilience to these stresses and 

thus may be able to handle the majority of conditions 

projected under climate change.

White ash—White ash, Fraxinus americana

(HL+, HH+, DF--, BF-, RC++, LC-), like green ash 

discussed earlier, is an exceptional case because the 

RandomForest modeling does not take into account 

the disastrous implications of emerald ash borer on 

the future of the species. The disturbance factor class 

is DF--, indicating that not only emerald ash borer 

but also factors like fi re topkill, disease, and pollution 

contribute to its vulnerability to decline in the future. 

The niche plot analysis indicates that the white ash 

currently grows fi ne in temperature/precipitation 

regimes of both PCMLo and HadHi. 

Class 6: Large Increase

This class represents a relatively large group (n=17) of 

species that all are projected to increase dramatically 

in both extent and in suitable habitat, especially under 

the higher emissions scenarios (Fig. A4-8). Most 

expand to every pixel in Wisconsin 212 under the high 

emissions scenarios. However, we stress that only four 

of the 17 species (black cherry, white oak, black oak, 

American beech) have high model reliability scores 

so that interpretation of the suitability scores must 

proceed with caution.

Black cherry—Black cherry, Prunus serotina

(DF--, BF0, RC++, LC-), has high model reliability 

and shows a trend in suitable habitat in Wisconsin 

212 with a large increase under the low emissions 

scenarios, and then a reduction again as the bulk of 

the habitat moves through and north of Wisconsin 212 

(Fig. 27 in main document). Occupancy remains at 

100 percent under all scenarios. This species is quite 

vulnerable to disturbances, however, so the model 

outputs may underestimate the climate change impacts 

on black cherry.

Bur oak—Bur oak, Quercus macrocarpa (HL+, 

HH++, DF++, BF0, RC0, LC-), has medium model 

reliability so that expansion to the entire range under 

Figure A4-7.—(Future : Current) suitable habitat ratios and modifi cation factors for Class 5 (small increase) species, sorted by 

decreasing disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species 

with MODFACs < 0 may do worse than modeled.
132

APPENDIX 4

Figure A4-8.—(Future : Current) suitable habitat ratios and modifi cation factors for Class 6 (large increase) species, sorted by 

decreasing disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species 

with MODFACs < 0 may do worse than modeled.
133

APPENDIX 4

high emissions scenario should be viewed with 

moderate confi dence. This species shows an expansion 

of suitable habitat of 1.8 times with the low emissions 

scenarios and 2.2 times with the high emissions 

scenarios. The model also indicates that bur oak is 

not very climatically sensitive, at the regional or local 

scales. Bur oaks are quite resistant to disturbance 

factors, being one of the most drought tolerant of oaks. 

It is favored by periodic fi res that may increase under 

warming and drought conditions. It is only moderately 

affected by diseases. The niche map for bur oak 

shows that under both emissions scenarios, the species 

currently grows in temperature/precipitation habitats 

projected for Wisconsin 212. 

Black oak—Black oak, Quercus velutina (HL++, 

HH++, DF+, BF0, LC0), has high model reliability 

lending confi dence to predictions of the expansion 

to the entire range, and the large increase in suitable 

habitat under both the low (4-fold increase) and high 

(6-fold increase) emissions scenarios (Fig. 27 in main 

document). It is not much of a climatically driven 

species regionally (the regional statistic RC could not 

be calculated) or locally (LC0). Black oak is not very 

drought tolerant compared to other oaks and is more 

susceptible to fi re topkill. It is moderately affected by 

diseases as it can succumb to successive defoliations 

by gypsy moth. The niche map shows that black oaks 

would likely handle higher future temperatures (low 

and high emissions scenarios) with even potentially 

increased habitat abundance.

White oak—White oak, Quercus alba (HL++, 

HH++, DF++, BF+, RC+, LC0), also has high model 

reliability. It shows expansion to the entire Wisconsin 

212 and increases in suitable habitat by over 2.8 times 

for both the low and high emissions scenarios. We 

expect it to be somewhat climatically driven regionally 

and less so locally, mainly by growing season and July 

temperature variables. Since white oak is a generalist 

and well distributed throughout its entire range, 

disturbance and biological factors should not affect it 

much, although it is quite susceptible to gypsy moth 

and several diseases. The niche map analysis shows 

white oaks currently fl ourishing at the higher future 

temperatures (both emissions scenarios), but under the 

high emissions scenario, Wisconsin 212 may become 

too dry in some locations. 

Swamp white oak—Swamp white oak, Quercus 

bicolor (HL++, HH++, DF+, BF0, RC0, LC-), has low 

model reliability; hence the range expansion and 2-fold 

(low emissions) to 7-fold (high emissions) increase 

in suitable habitat predicted by our models should be 

viewed cautiously. Although it is not a particularly a 

climate driven species, January temperatures infl uence 

the regional habitat, and growing season precipitation 

infl uences the local habitat. It is moderately resistant 

to fi re. Like most oaks, it is tolerant of a wide range of 

conditions. The niche map for swamp white oak shows 

little current growth in the temperature/precipitation 

regimes of the HadHi scenario; this could indicate 

some potential restrictions to migration should those 

conditions be realized. But again, the model is of poor 

reliability. 

Shagbark hickory—Shagbark hickory, Carya 

ovata (DF0, BF0, RC+, LC0), has medium model 

reliability and expands to the entire range under 

the high emissions scenarios and almost so under 

the low emissions scenarios, and over an 8-fold 

increase in suitable habitat under both scenarios. Both 

precipitation and temperature affect the distribution 

regionally although less so locally. The niche diagram 

shows habitats increasing under both low and high 

emissions, although the high emissions scenario can 

stretch its temperature limit. Shagbark hickory can 

tolerate a wide range of conditions although it grows 

best under humid conditions. It does not tolerate fi re 

very well and is susceptible to many diseases and 

pests. Overall, MODFACs are generally not signifi cant 

for this species.
134

APPENDIX 4

Bitternut hickory—Bitternut hickory, Carya 

cordiformis (DF++, BF-, RC++, LC-), has poor 

model reliability and so its expansion, under higher 

emissions, into all of Wisconsin 212 should be viewed 

cautiously. It also shows more than a 3-fold increase 

in summed suitable habitat for either scenario. It is 

regionally driven strongly by precipitation although 

locally less so. Bitternut hickory is thin barked and 

more susceptible to fi res than are oaks; however, it 

tolerates drought and other disturbances well, partly 

because of its vigorous sprouting ability. The niche 

diagram shows that it can increase its habitat under 

conditions of low and high emissions, although the 

latter can stretch its temperature limit. 

Figure A4-9.—(Future : Current) suitable habitat ratios and modifi cation factors (not possible to calculate for species presently 

missing) and modifi cation factors for Class 7 (new entry) species under low or high emissions scenarios, sorted by decreasing 

disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species with 

MODFACs < 0 may do worse than modeled.

Class 7: New Entry under Low and High 

Emissions Scenarios

This is a group of 11 species that are modeled, under 

both low and high emissions scenarios, to have 

suitable habitat appear by the end of the century 

(Fig. A4-9). For all species, the higher emissions 

scenarios results in more suitable habitat as compared 

with the lower emissions scenarios. Model reliability 

varies widely, so that this should be carefully 

considered when interpreting the results. The species 

of class 7 and 8 would be candidates for study should 

assisted migration or corridor management be planned 

for northern Wisconsin.
135

APPENDIX 4

Pignut hickory—Pignut hickory, Carya glabra (DF0, 

BF0, RC-, LC+, MD200), shows that suitable habitat 

for this species could migrate into and substantially 

cover Wisconsin 212 by the end of the century, 

especially under the high emissions scenario. Model 

reliability is high. The niche map analysis clearly 

show that the species now exists in the temperature/

precipitation regimes projected for both emissions 

scenarios, but especially the low emissions scenario. 

None of the MODFACs show particularly substantial 

issues on the models either positively or negatively 

so that planning can begin with the suitable habitats 

as mapped. The estimate of MD200 indicates that 

migration distance needed for the current range 

boundary to approach the boundary of Wisconsin 212 

is less than 200 km; we calculate it to be about 160 

km.

Mockernut hickory—Mockernut hickory, Carya 

tomentosa (DF++, BF0, RC0, LC0, MD200), shows 

potential expansion into Wisconsin 212 in a manner 

very similar to pignut hickory. Under the average 

high emissions scenarios, it would have suitable 

habitat in about 150 of the 225 cells, and the summed 

suitable habitat for either mockernut or pignut would 

approximate that of the resident white spruce or 

boxelder. Under low emissions, mockernut habitat 

would be very rare. Because the species shows very 

positive response to disturbance factors (DF++)—

particularly drought, wind, and disease—and is not 

particularly disadvantaged by other factors, it may do 

better than modeled. The estimated distance needed to 

migrate to reach Wisconsin 212 is about 170 km. 

Flowering dogwood—Flowering dogwood, Cornus 

fl orida (DF0, BF+, RC0, LC-, MD200), is another 

species that is modeled to mirror the hickories in 

area and habitat quality—a substantial expansion in 

occupancy from nothing to 70 percent of the region 

under the high emissions scenario, but only to about 

15 percent under the low emissions scenario. The 

modifying factors for the species are not substantial 

in either a positive or negative direction, the model 

reliability is good, and the niche map shows a 

reasonable amount of temperature and precipitation 

habitat in future compared to current occupancy 

outside the Wisconsin 212 region. Our estimates 

suggest the species would have to migrate about 180 

km to reach the border of Wisconsin 212.

Yellow-poplar—Yellow-poplar, Liriodendron 

tulipifera (DF0, BF+, RC0, LC-, MD200), is a species 

of high model reliability showing some migration 

of low-level suitable habitat into Wisconsin 212, 

especially under the high emissions scenarios, to an 

occupancy of about 40 percent of the region (Fig. 28 

in main document). The MODFACs are generally 

moderate, indicating no serious modifi cations to 

the mapped models may be necessary. We estimate 

roughly a migration of 200 km to reach the border of 

Wisconsin 212.

Red mulberry—Red mulberry, Morus rubra (DF0, 

BF+, RC0, LC+, MD100), shows an increasing trend 

in habitat with warmer scenarios (Fig. 28 in main 

document). Though the model reliability is poor, the 

niche models and the habitat models agree that the 

warmer scenarios tend to push the habitat northward 

into Wisconsin 212. However, the habitat did not need 

to move far, as it currently bounds the southern edge 

of the region. MODFACs indicate it may do slightly 

better than modeled.

Sassafras—Sassafras, Sassafras albidum (DF0, BF-, 

RC0, LC0, MD200), behaves like the other species 

narrated for this class – this time the occupancy could 

get up to almost 80 percent of the region under the 

high emissions scenarios, and about 30 percent under 

the low scenarios. Again, the model reliability is high 

and the MODFACs do not register a high level of 

modifi cation is needed to the modeled outputs. We 

estimate the species needs to travel only 120 km to 

reach the border of Wisconsin 212.
136

APPENDIX 4

Class 8: New Entry under the High 

Emissions Scenario

This group of 16 species is characterized by little or no 

current occupancy within Wisconsin 212; the models 

indicate that suitable habitat could appear, but only 

under the fairly drastic higher emissions scenarios 

Figure A4-10.—(Future : Current) suitable habitat ratios and modifi cation factors (not possible to calculate for species presently 

missing) and modifi cation factors for Class 8 (new entry) species under only harshest scenarios of high emissions, sorted by 

decreasing disturbance modifi cation scores, such that species with MODFACs > 0 may do better than modeled, while species 

with MODFACs < 0 may do worse than modeled.

(Fig. A4-10). Nonetheless, it must be remembered 

that the current pathway of global atmospheric carbon 

dioxide is aligned much closer to the higher emissions, 

and even higher that those projected here (Canadell et 

al. 2007).
137

APPENDIX 4

Blackgum—Blackgum, Nyssa sylvatica (DF+, BF+, 

RC-, LC-, MD400), a model with high reliability, 

shows some expansion of suitable habitat to roughly 

20 percent of Wisconsin 212 under the high emissions 

scenarios, but to a very low level of suitable habitat. 

Both disturbance and biological factors are positive for 

the species, indicating it could do better than modeled. 

We estimate that the current range boundary for the 

species is about 230 km from the southern edge of 

Wisconsin 212. The niche plot analysis shows that the 

species currently resides in many of the temperature/

precipitation habitats projected for both scenarios.

Black jack oak—Black jack oak, Quercus 

marilandica (MR0, DF++, BF0, RC+, LC-, MD600), 

has medium model reliability and a current range well 

south (over 550 km) of the Wisconsin 212 border. 

However, under only the high emissions scenarios, 

the models show a widespread migration of suitable 

habitat, to nearly the entire region. Regionally it is 

mainly affected by precipitation, while locally soil 

factors are more important than climate. It is highly 

drought tolerant and can adapt to periodic fi res. The 

niche map clearly shows that the habitat can become 

suitable under high emission conditions of future 

climates, and that its range may not be limited much 

by precipitation since Little’s range boundaries (1971) 

from farther west extends habitat to more arid regions. 

Nevertheless, because of a less reliable model and the 

long distances involved, the model for this species 

should be treated with caution.

Sycamore—Sycamore, Platanus occidentalis (DF+, 

BF-, RC+, LC0, MD200), is a species that is projected 

to expand far into Wisconsin 212 (to 76 percent of the 

region) under the average high emissions scenarios 

but hardly any (3 percent) under lower emissions. 

The model is scored as medium model reliability. 

The niche plot shows that the HadHi temperature/

precipitation regime fi ts squarely in the zone of high 

sycamore abundance. MODFACs tend to balance out. 

The species current boundary is approximately 180 km 

from the closest position on Wisconsin 212.

Forest canopy.

Photo by Tom Matthiae, Chequamegon-Nicolet National Forest
138

APPENDIX 4

Potential Migration Model Results for 

Black Oak (Preliminary)

A preliminary set of SHIFT runs gives us some idea of 

the potential of black oak to colonize into Wisconsin 

212 over the next century (Fig. A4-11). Because 

suitable habitat for this species is modeled to be 

suffi cient in Wisconsin 212 over this period 

(Figs. A4-9 and A4-11), the species should have 

limited physical barriers to migration. The preliminary 

colonization probability map shows at least some 

probability of colonization to roughly 100 km from 

the current boundary of black oak. 

Figure A4-11.—(A) Forested (purple) and nonforested (tan) areas in Wisconsin, showing fragmented nature of the forest, along 

with region Wisconsin 212 (green line) and the northern boundary of black oak according to Little’s maps (Little 1971); and 

(B) current importance value (relative abundance of a species in a given community) of black oak, and probability of potential 

colonization northward over 100 years.

A

B
139

APPENDIX 4

Summary

Our evaluations suggest that 73 of 134 tree species 

need to be considered with respect to potential 

impacts of climate change. Of these 73, we see 

the entire range of possibilities, from extirpation 

to long distance migration, as represented by our 

eight classes of species. For the fi rst time, we have 

included many sources of ancillary information 

from literature surveys to inform the calculation of 

modifying factor scores, dissection of the individual 

RandomForest models to assess regional and local 

variable importances, and estimates of distances from 

the current occupied polygons to the closest edge of 

Wisconsin 212. We also rely on the ‘model reliability’ 

rating to give some indication of confi dence in the 

model outputs. 

The work of Woodall et al. (2009) has helped lend 

confi dence in the overall validity of these models. 

They studied seedling numbers versus biomass of 

larger trees for 40 species that we also have modeled, 

for many thousands of FIA points throughout the 

eastern United States. They plotted both seedling 

numbers and tree biomass against latitude to show 

whether there is a difference in optimal or peak 

latitude between the two measures. They found a 

tendency for northward migration for a number of 

them; and for 37 of the 40 species, their results were 

congruent with our models.

There are still many assumptions and qualifi ers 

associated with this work. Some of them are unique 

to empirical statistical models, but most are common 

to any modeling that requires estimation into an 

unknown future. We however attempt to overcome 

some traditional limitations associated with statistical 

models by using FIA abundance data with a trimodel approach using the latest decision-tree based, 

data-mining approach that uncovers the nonlinear, 

high-dimensional nature of the data which gives 

more accurate predictions. With the addition of the 

MODFACs and other attributes, we attempt to allow at 

least a qualitative modifi cation of the RandomForest 

outputs to account for factors that cannot be 

appropriately modeled. This twin approach begins 

to address the problems and uncertainties associated 

with on-the-ground decisionmaking, leading to better 

management.

Our group aims to continue this work in evaluating 

potential migration under various scenarios, land use 

confi gurations, and with other species

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142

Abbreviations and Acronyms

ATFS American Tree Farm System

ATV All-Terrain Vehicle

CCSP Climate Change Science Program

CFC Chlorofl uorocarbon

CNNF Chequamegon-Nicolet National Forest

COF Council of Forestry

FCL Forest Crop Law

FIA Forest Inventory and Analysis (USDA Forest Service)

FSC Forest Stewardship Council

GCM General Circulation Model

HFC Hydrofl ourocarbon

IPCC Intergovernmental Panel on Climate Change

MFL Managed Forest Law

NASA National Aeronautics and Space Administration

NOAA National Oceanic and Atmospheric Administration

NRC Natural Resource Council

RFSS Regional Forester Sensitive Species 

SDM Species Distribution Model

SFI Sustainable Forestry Initiative

SRES Special Report on Emissions Scenarios (IPCC 2000)

UCS Union of Concerned Scientists

USCB United States Census Bureau

USDA FS United States Department of Agriculture, Forest Service 

USDA NASS United States Department of Agriculture, National Agricultural Statistics Service

USGCRP United States Global Climate Research Program

UV Ultraviolet

UWM University of Wisconsin-Madison

WDNR Wisconsin Department of Natural Resources

WDOA Wisconsin Department of Administration

WDOR Wisconsin Department of Revenue

WDWD Wisconsin Department of Work Development

WICCI Wisconsin Initiative on Climate Change Impacts

WISCLAND Wisconsin Initiative for Statewide Cooperation on Landscape Analysis and Data

WMO World Meteorological Organization

WNHI Wisconsin Natural Heritage Inventory

Units of Measure

cfsm cubic feet per second per square mile

GtCO2

eq gigatons of carbon dioxide equivalent

ppb parts per billion

ppm parts per million

°F degrees Fahrenheit

°C degrees Celsius
The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and 

activities on the basis of race, color, national origin, sex, religion, age, disability, political beliefs, 

sexual orientation, and marital or family status. (Not all prohibited bases apply to all programs.) 

Persons with disabilities who require alternative means for communication of program information 

(Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 202-720-2600 

(voice and TDD). To fi le a complaint of discrimination, write USDA, Director, Offi ce of Civil Rights, 

Room 326-W, Whitten Building, 1400 Independence Avenue SW, Washington, DC 20250-9410 or 

call 202-720-5964 (voice and TDD). USDA is an equal opportunity provider and employer.

Printed on recycled paper

Swanston, Chris; Janowiak, Maria; Iverson, Louis; Parker, Linda; Mladenoff, David; 

Brandt, Leslie; Butler, Patricia; St. Pierre, Matt; Prasad, Anantha; Matthews, 

Stephen; Peters, Matthew; Higgins, Dale; Dorland, Avery. 2011. Ecosystem 

vulnerability assessment and synthesis: a report from the Climate Change 

Response Framework Project in northern Wisconsin. Gen. Tech. Rep. 

NRS-82. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 142 p.

The forests of northern Wisconsin will likely experience dramatic changes over the 

next 100 years as a result of climate change. This assessment evaluates key forest 

ecosystem vulnerabilities to climate change across northern Wisconsin under a 

range of future climate scenarios. Warmer temperatures and shifting precipitation 

patterns are expected to infl uence ecosystem drivers and increase stressors, 

including more frequent disturbances and increased amount or severity of pests 

and diseases. Forest ecosystems will continue to adapt to changing conditions. 

Identifying vulnerable species and forests can help landowners, managers, 

regulators, and policymakers establish priorities for management and monitoring.

KEY WORDS: Climate change, forests, vulnerability, Wisconsin, tree species

 distributions, Climate Change Atlas, LANDIS-II
www.nrs.fs.fed.us