United States Department of Agriculture

Minnesota Forest Ecosystem 

Vulnerability Assessment and Synthesis:

A Report from the Northwoods Climate Change 

Response Framework Project

Forest 

Service

Northern 

Research Station May 2014

General Technical 

Report NRS-133
Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies, contact:

USDA FOREST SERVICE USDA Forest Service

11 CAMPUS BLVD., SUITE 200 Publications Distribution

NEWTOWN SQUARE, PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

May 2014 Fax: 740-368-0152

Manuscript received for publication September 2013

The forests in northern Minnesota will be affected directly and indirectly by a changing climate 

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

Laurentian Mixed Forest Province of Minnesota under a range of future climates. We synthesized 

and summarized information on the contemporary landscape, provided information on past 

climate trends, and described a range of projected future climates. This information was used to 

parameterize and run multiple vegetation impact models, which provided a range of potential 

vegetative responses to climate. Finally, we brought these results before a multidisciplinary panel 

of scientists and land managers familiar with northern Minnesota forests to assess ecosystem 

vulnerability through a formal consensus-based expert elicitation process. 

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

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

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

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

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

annual temperature of 3.0 to 8.8 °F for the assessment area. Projections for precipitation indicate 

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

by scenario. We identified potential impacts on forests by incorporating these climate projections 

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

that northern boreal species such as black spruce and paper birch may fare worse under 

future conditions, but other species such as American basswood and white pine may benefit 

from projected changes in climate. Published literature on climate impacts related to wildfire, 

invasive species, and diseases also contributed to the overall determination of climate change 

vulnerability. 

We assessed vulnerability for eight forest systems in northern Minnesota: six Native Plant 

Community Systems and two managed forest systems. The basic assessment was conducted 

through a formal elicitation process of 23 science and management experts from across the 

state, who considered vulnerability in terms of potential impacts on a system and the system’s 

adaptive capacity. Acid Peatlands, Forested Rich Peatlands, and Wet Forests were determined to 

be the most vulnerable. Systems adapted to disturbance through fire and drought or flooding, 

such as Fire-Dependent Forests or Floodplain Forests, were perceived as less vulnerable to 

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

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

ABSTRACT

Cover Photo

Fall colors in northeastern Minnesota. Photo by Jack Greenlee, Superior National Forest.
Minnesota Forest Ecosystem Vulnerability 

Assessment and Synthesis: 

A Report from the Northwoods Climate Change 

Response Framework Project

Stephen Handler, Matthew J. Duveneck, Louis Iverson, Emily Peters, 

Robert M. Scheller, Kirk R. Wythers, Leslie Brandt, Patricia Butler, Maria Janowiak, 

P. Danielle Shannon, Chris Swanston, Kelly Barrett, Randy Kolka, Casey McQuiston, 

Brian Palik, Peter B. Reich, Clarence Turner, Mark White, Cheryl Adams, 

Anthony D’Amato, Suzanne Hagell, Patricia Johnson, Rosemary Johnson, 

Mike Larson, Stephen Matthews, Rebecca Montgomery, Steve Olson, 

Matthew Peters, Anantha Prasad, Jack Rajala, Jad Daley, Mae Davenport, 

Marla R. Emery, David Fehringer, Christopher L. Hoving, Gary Johnson, 

Lucinda Johnson, David Neitzel, Adena Rissman, 

Chadwick Rittenhouse, and Robert Ziel
Authors

STEPHEN HANDLER is a climate change specialist with 

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

Forest Service, 410 MacInnes Drive, Houghton, MI 49931, 

sdhandler@fs.fed.us.

MATTHEW J. DUVENECK is a postdoctoral fellow with 

Harvard Forest, Harvard University, 324 North Main Street, 

Petersham, MA 01366, mduveneck@fas.harvard.edu.

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

Forest Service, Northern Research Station, 359 Main Road, 

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

EMILY PETERS is a data analyst with Minnesota Pollution 

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

Emily.Peters@state.mn.us.

ROBERT M. SCHELLER is an assistant professor at Portland 

State University, 1719 SW 10th Avenue, Portland, OR 97207, 

rmschell@pdx.edu.

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

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

kwythers@umn.edu.

LESLIE BRANDT is a climate change specialist with the 

Northern Institute of Applied Climate Science, U.S. 

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

lbrandt@fs.fed.us.

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.

MARIA JANOWIAK is a climate change adaptation and 

carbon management scientist with the Northern Institute 

of Applied Climate Science, U.S. Forest Service, 

410 MacInnes Drive, Houghton, MI 49931, 

mjanowiak02@fs.fed.us.

P. DANIELLE SHANNON 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, 

dshannon@mtu.edu.

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

Forest Service, Northern Research Station, and director 

of the Northern Institute of Applied Climate Science, 

410 MacInnes Drive, Houghton, MI 49931, 

cswanston@fs.fed.us.

KELLY BARRETT is a wildlife biologist with the Chippewa 

National Forest, 1235 Division Street, Deer River, MN 56636, 

kbarrett@fs.fed.us.

RANDY KOLKA is a team leader and research soil scientist 

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

1831 Hwy 169 East, Grand Rapids, MN 55744, 

rkolka@fs.fed.us.

CASEY McQUISTON is a soil scientist with the Superior 

National Forest, 8901 Grand Avenue Place, Duluth, 

MN 55808, cmcquiston@fs.fed.us.

BRIAN PALIK is a team leader and research ecologist with 

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

1831 Hwy 169 East, Grand Rapids, MN 55744, 

bpalik@fs.fed.us.

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

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

preich@umn.edu.

CLARENCE TURNER is an ecologist and climate change 

specialist with the Minnesota Department of Natural 

Resources and the Minnesota Forest Resources Council, 

500 Lafayette Road, St. Paul, MN 55155, 

clarence.turner@state.mn.us.

MARK WHITE is a forest ecologist with The Nature 

Conservancy, 94 S. Lake Avenue, #308, Duluth, MN 55802, 

mark_white@tnc.org.

CHERYL ADAMS is a forest ecologist with UPM-Kymmene 

Blandin Paper Mill, 115 SW First Street, Grand Rapids, 

MN 55744, cheryl.adams@upm-kymmene.com.

ANTHONY D’AMATO is an associate professor at the 

University of Minnesota, 1530 Cleveland Avenue N., 

St. Paul, MN 55108, damato@umn.edu.

SUZANNE HAGELL is a research associate at the University 

of Wisconsin – Madison, 1630 Linden Drive, Madison, 

WI 53706, hagell@wisc.edu.

PATRICIA JOHNSON is a fire management officer with the 

Superior National Forest, 2020 W. Hwy 61, Grand Marais, 

MN 55604, pjjohnson@fs.fed.us.

ROSEMARY JOHNSON is a silviculturist with the Chippewa 

National Forest, 201 Minnesota Avenue E., Walker, 

MN 56484, rrjohnson@fs.fed.us.

MIKE LARSON is a research scientist with the Minnesota 

Department of Natural Resources, 1201 E. Hwy 2, 

Grand Rapids, MN 55744, michael.larson@state.mn.us.
STEPHEN MATTHEWS is a research assistant professor 

at Ohio State University and an ecologist with the U.S. 

Forest Service, Northern Research Station, 359 Main Road, 

Delaware, OH 43015, snmatthews@fs.fed.us.

REBECCA MONTGOMERY is an associate professor at the 

University of Minnesota, 1530 Cleveland Avenue N., St. Paul, 

MN 55108, rebeccam@umn.edu.

STEVE OLSON is a forester with the Fond Du Lac Band of 

Lake Superior Chippewa, 1720 Big Lake Road, Cloquet, 

MN 55720, steveolson@fdlrez.com.

MATTHEW PETERS is a geographic information systems 

technician with the U.S. Forest Service, Northern 

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

matthewpeters@fs.fed.us.

ANANTHA PRASAD is an ecologist with the U.S. Forest 

Service, Northern Research Station, 359 Main Road, 

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

JACK RAJALA is the chief executive officer at Rajala 

Companies, P.O. Box 23, Bigfork, MN 56628, 

jirajala@rajalacos.com.

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

660 Pennsylvania Avenue SE., Suite 401, Washington, 

DC 20003, jad.daley@tpl.org.

MAE DAVENPORT is an associate professor at the University 

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

MN 55108, mdaven@umn.edu.

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

Forest Service, Northern Research Station, 81 Carrigan Drive, 

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

DAVID FEHRINGER is the Great Lakes assistant 

regional director with The Forestland Group, LLC, 

33687 Lake Gerald Road, Toivola, MI 49965, 

dave@forestlandgroup.com.

CHRISTOPHER L. HOVING is an adaptation specialist with the 

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

Lansing, MI 48909, hovingc@michigan.gov. 

GARY JOHNSON is an extension professor at the University 

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

MN 55108, johns054@umn.edu.

LUCINDA JOHNSON is a senior research associate 

at the Natural Resources Research Institute, 

5013 Miller Trunk Highway, Duluth, MN 55811, 

ljohnson@d.umn.edu.

DAVID NEITZEL is an epidemiologist with the Minnesota 

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

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

ADENA RISSMAN is an assistant professor at the University 

of Wisconsin – Madison, 1630 Linden Drive, Madison, 

WI 53706, arrissman@wisc.edu.

CHADWICK RITTENHOUSE is an assistant research professor 

at the University of Connecticut, Wildlife and Fisheries 

Conservation Center, 1376 Storrs Road, Storrs, 

CT 06269-4087, chadwick.rittenhouse@uconn.edu.

ROBERT ZIEL (retired) was the program coordinator for the 

Lake States Fire Science Consortium, 1680 Madison Avenue, 

Wooster, OH 44691. He can be reached at 

rhziel1@gmail.com.
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CONTEXT AND SCOPE

This assessment is a fundamental component 

of the Northwoods Climate Change Response 

Framework project. The Framework is a 

collaborative, cross-boundary approach among 

scientists, managers, and landowners to incorporate 

climate change considerations into natural resource 

management. Three ecoregional Framework 

projects are underway, covering 135 million acres 

in the northeastern and midwestern United States: 

Northwoods, Central Appalachians, and Central 

Hardwoods. Each regional project interweaves four 

components: science and management partnerships, 

vulnerability assessments, adaptation resources, and 

demonstration projects. 

We designed this assessment to be a synthesis of the 

best available scientific information. Its primary goal 

is to inform forest managers in northern Minnesota, 

in addition to people who study, recreate, and live 

in these forests. As new scientific information 

arises, we will develop future versions to reflect 

that accumulated knowledge and understanding. 

Most importantly, this assessment does not make 

recommendations about how this information should 

be used. 

The scope of the assessment is terrestrial forested 

ecosystems, with a particular focus on tree 

species. Climate change will also have impacts on 

aquatic systems, wildlife, and human systems, but 

addressing these issues in depth is beyond the scope 

of this assessment. 

The large list of authors reflects the highly 

collaborative nature of this assessment. Stephen 

Handler served as the primary writer and editor of 

the assessment. Matthew Duveneck, Louis Iverson, 

Emily Peters, Robert Scheller, Kirk Wythers, and 

Peter Reich led the forest impact modeling and 

contributed writing and expertise to much of the 

assessment. All modeling teams coordinated their 

efforts impressively. Leslie Brandt, Patricia Butler, 

Maria Janowiak, Danielle Shannon, and Chris 

Swanston provided significant investment into 

the generation and coordination of content, data 

analysis and interpretation, and coordination among 

many other Climate Change Response Framework 

assessments. Kelly Barrett, Randy Kolka, Casey 

McQuiston, Brian Palik, Clarence Turner, and 

Mark White provided substantial input throughout 

the document. Cheryl Adams, Anthony D’Amato, 

Suzanne Hagell, Patricia Johnson, Rosemary 

Johnson, Mike Larson, Stephen Matthews, Rebecca 

Montgomery, Steve Olson, Matthew Peters, Anantha 

Prasad, Jack Rajala, Jad Daley, Mae Davenport, 

Marla Emery, David Fehringer, Christopher Hoving, 

Gary Johnson, Lucinda Johnson, David Neitzel, 

Adena Rissman, Chadwick Rittenhouse, and Robert 

Ziel provided input to specific chapters. 

In addition to the authors listed, many people 

made valuable contributions to the assessment. 

John Almendinger and Paul Dubuque (Minnesota 

Department of Natural Resources) provided 

information and photos regarding the Native Plant 

Communities in Minnesota, as did Lawson Gerdes 

(Minnesota County Biological Survey). George 

Preface
Host and Terry Brown (Natural Resources Research 

Institute) provided a map on the distribution of 

Native Plant Community Systems for Chapter 

1. Tara Bal and Andrew Storer (Michigan 

Technological University) provided a summary of 

ongoing research for Chapter 5. Don Rees (Chugach 

National Forest, formerly with the Chippewa 

National Forest) provided guidance for the overall 

structure of the document. 

We would especially like to thank John Pastor 

(University of Minnesota – Duluth), Mark Fulton 

(Bemidji State University), Peter Wycoff (University 

of Minnesota – Morris), and Craig Loehle (National 

Council for Air and Stream Improvement), who 

provided formal technical reviews of the assessment. 

Their thorough reviews greatly improved the quality 

of this assessment.
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

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

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

Chapter 2: Climate Change Science and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Chapter 3: Observed Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes . . . . . . . . . . . . . . . . . 67

Chapter 5: Future Climate Change Impacts on Forests ................................... 84

Chapter 6: Forest Ecosystem Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Chapter 7: Management Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

Appendix 1: Common and Scientific Names of Flora, Fauna, and Diseases . . . . . . . . . . . . . . . . . . 189

Appendix 2: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

Appendix 3: Additional Future Climate Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

Appendix 4: Supplementary Model Results ........................................... 206

Appendix 5: Vulnerability and Confidence Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

Appendix 6: Contributors to Implications Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

Contents
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Executive Summary

This assessment evaluates key ecosystem 

vulnerabilities for forest ecosystems in the 

Laurentian Mixed Forest Province in Minnesota 

across a range of future climate scenarios. 

This assessment was completed as part of the 

Northwoods Climate Change Response Framework 

project, a collaborative approach among researchers, 

managers, and land owners to incorporate climate 

change considerations into forest management. 

The assessment summarizes current conditions and 

key stressors and identifies past and projected trends 

in climate. This information is then incorporated 

into model projections of future forest change. These 

projections, along with published research and local 

knowledge and expertise, are used to identify the 

factors that contribute to the vulnerability of major 

forest systems within the assessment area over the 

next 100 years. A final chapter summarizes the 

implications of these impacts and vulnerabilities for 

forest management across the region. 

CHAPTER 1: THE CONTEMPORARY

LANDSCAPE

Summary

This chapter describes the forests and related 

ecosystems across the Laurentian Mixed Forest 

Province in Minnesota and summarizes current 

threats and management trends. This information 

lays the foundation for understanding how shifts 

in climate may contribute to changes in forest 

ecosystems, and how climate may interact with other 

stressors on the landscape.

Main Points 

● More than 85 percent of the forest land in 

Minnesota occurs within the assessment area, 

most of which is owned by private land owners. 

● Major stressors and threats to forest ecosystems 

in the region are:

• Fragmentation and land-use change

• Fire regime shifts 

• Nonnative species invasion

• Forest pests and disease

• Overbrowsing by deer

• Extreme weather events 

● Management practices during the past several 

decades have tended to favor aspen across the 

landscape and reduce species diversity and 

structural complexity. 

● The forest products industry is a major 

contributor to the region’s economy, and most 

of the forest land in the assessment area is 

managed according to at least one sustainability 

certification standard. 

CHAPTER 2: CLIMATE CHANGE 

SCIENCE AND MODELING

Summary

This chapter provides a brief background on climate 

change science, models that simulate future climate 

change, and models that project the effects of 

climate change on tree species and ecosystems. This 

chapter also describes the climate data used in this 

assessment.
Executive Summary



Main Points 

● Temperatures have been increasing at a global 

scale and across the United States over the past 

century.

● Major contributors to warming are greenhouse 

gases from fossil fuel burning, agriculture, and 

changes in land use.

CHAPTER 3: OBSERVED CLIMATE 

CHANGE 

Summary

This chapter summarizes our understanding 

of observed changes and climate trends in the 

assessment area and across the Midwest region, with 

a focus on the last century.

Main Points 

● Mean, maximum, and minimum temperatures 

have been increasing across all seasons, with 

winter temperatures warming the most rapidly. 

● The assessment area has received more 

precipitation, particularly in the summer and fall. 

● More precipitation has been delivered in heavy 

events of 3 inches or greater. 

● Snowfall has been decreasing across northern 

Minnesota, although there has been an increase in 

large winter storms. 

● Climate change has also been indicated by trends 

in lake ice, growing season length, and wildlife 

range shifts.

CHAPTER 4: PROJECTED CHANGES 

IN CLIMATE, EXTREMES, AND 

PHYSICAL PROCESSES

Summary

This chapter examines how climate may change in 

the assessment area over the next century, according 

to a range of model projections. Published scientific 

literature provides the basis for describing possible 

trends in a range of climate-driven processes, such 

as extreme weather events and snowfall. 

Main Points

● Temperature is projected to increase across all 

seasons over the next century, with dramatic 

warming projected in winter.

● Precipitation is projected to increase in winter 

and spring across a range of climate scenarios, 

but summer precipitation may decrease.

● Intense precipitation events may continue to 

become more frequent. 

● Snowfall is projected to continue to decline 

across the assessment area, with more winter 

precipitation falling as rain. 

● Soils are projected to be frozen for shorter 

periods during winter. 

CHAPTER 5: FUTURE CLIMATE 

CHANGE IMPACTS ON FORESTS

Summary

This chapter summarizes the potential impacts 

of climate change on forests in the Laurentian 

Mixed Forest Province in Minnesota, drawing on 

information from a coordinated series of model 

simulations and published research. 

Palisade Head on the North Shore of Lake Superior. Photo by 

Casey McQuiston, Superior National Forest.
Executive Summary

Community Type Vulnerability Evidence Agreement

Fire-Dependent Forest Moderate Medium Medium

Mesic Hardwood Forest Moderate Medium Medium

Floodplain Forest Low-Moderate Limited-Medium Medium

Wet Forest High Limited-Medium Medium

Forested Rich Peatland High Medium Medium-High

Acid Peatland High Medium Medium-High

Managed Aspen Moderate-High Medium High

Managed Red Pine Moderate-High Medium Medium

Table 1.—Vulnerability determinations by natural community type

Main Points

● Boreal species such as quaking aspen, paper 

birch, tamarack, and black spruce are projected 

to decrease in suitable habitat and biomass across 

the assessment area.

● Species with ranges that extend to the south such 

as American basswood, black cherry, northern 

red oak, and eastern white pine may increase 

in suitable habitat and biomass across the 

assessment area.

● Many common species in northern Minnesota 

may decline under the hotter, drier future climate 

scenario.

● Forest productivity will be influenced by a 

combination of factors such as carbon dioxide 

(CO2

) fertilization, water and nutrient availability, 

and species migration. 

● Model projections do not account for many other 

factors that may be modified by a changing 

climate, including:

• Drought stress

• Changes in hydrology and flood regime

• Wildfire frequency and severity

• Altered nutrient cycling

• Changes in invasive species, pests, and 

pathogens

CHAPTER 6: FOREST ECOSYSTEM 

VULNERABILITIES

Summary

This chapter focuses on the climate change 

vulnerability of major forest systems in the 

assessment area during the next 100 years, 

particularly on shifts in dominant species, system 

drivers, and stressors. The adaptive capacity 

of forest systems was also examined as a key 

component of overall vulnerability. Synthesis 

statements are provided to capture general trends, 

and detailed vulnerability determinations are also 

provided for eight major forest systems (Table 1). 

We consider a system to be vulnerable if it is at risk 

of a composition change leading to a new identity, 

or if the system is anticipated to suffer substantial 

declines in health or productivity.

Main Points

Potential Impacts on Drivers and Stressors

● Temperatures will increase (robust evidence, 

high agreement). All global climate models 

project that temperatures will increase with 

continued increases in atmospheric greenhouse 

gas concentrations.
Executive Summary



● Winter processes will change (robust evidence, 

high agreement). All evidence agrees that 

temperatures will increase more in winter than in 

other seasons across the assessment area, leading 

to changes in snowfall, soil frost, and other winter 

processes. 

● Growing seasons will get longer (robust 

evidence, high agreement). There is high 

agreement among information sources that 

projected temperature increases will lead to 

longer growing seasons in the assessment area.

● The amount and timing of precipitation will 

change (medium evidence, high agreement).

All global climate models agree that there will 

be changes in precipitation patterns across the 

assessment area. 

● Intense precipitation events will continue to 

become more frequent (medium evidence, 

medium agreement). There is some agreement 

that the number of heavy precipitation events will 

continue to increase in the assessment area. If 

they do increase, impacts from flooding and soil 

erosion may also become more damaging.

● Droughts will increase in duration and area 

(limited evidence, low agreement). A study 

using multiple climate models indicates that 

drought may increase in length and extent, and an 

episodic precipitation regime could mean longer 

dry periods between events. 

● Soil moisture patterns will change (medium 

evidence, high agreement), with drier soil 

conditions later in the growing season 

(medium evidence, medium agreement).

Studies show that climate change will affect 

soil moisture, but there is disagreement among 

climate and impact models on how soil moisture 

will change during the growing season. 

● Climate conditions will increase fire risks 

by the end of the century (medium evidence, 

medium agreement). Some national and global 

studies suggest that wildfire risk will increase 

in the region, but few studies have specifically 

looked at wildfire potential in the assessment 

area. 

● Many invasive species, insect pests, and 

pathogens will increase or become more 

damaging (limited evidence, high agreement).

Evidence indicates that an increase in temperature 

and greater moisture stress will lead to increases 

in these threats, but research to date has examined 

few species. 

Potential Impacts on Forests 

● Boreal species will face increasing stress 

from climate change (medium evidence, high 

agreement). Impact models agree that boreal or 

northern species will experience reduced suitable 

habitat and biomass across the assessment 

area, and that they may be less able to take 

advantage of longer growing seasons and warmer 

temperatures than temperate forest communities. 

● Southern species will be favored by climate 

change (medium evidence, high agreement).

Impact models agree that many temperate species 

will experience increasing suitable habitat and 

biomass across the assessment area, and that 

longer growing seasons and warmer temperatures 

will lead to productivity increases for temperate 

forest types. 

● Forest communities will change across the 

landscape (limited evidence, high agreement).

Although few models have specifically examined 

how communities may change, model results 

from individual species and ecological principles 

suggest that recognized forest communities may 

change in composition as well as occupied range.

● Forest productivity will increase across the 

assessment area (medium evidence, medium 

agreement). Model projections and other 

evidence support modest productivity increases 

for forests across the assessment area, although 

there is uncertainty about the effects of CO2

fertilization. It is expected that productivity will 

be reduced in localized areas. 
Executive Summary

Adaptive Capacity Factors

● Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies 

have consistently shown that diverse systems are 

more resilient to disturbance, and low-diversity 

systems have fewer options to respond to change.

● Species in fragmented landscapes will have less 

opportunity to migrate in response to climate 

change (limited evidence, high agreement). The 

dispersal ability of individual species is reduced 

in fragmented landscapes, but the future degree 

of landscape fragmentation and the potential 

for human-assisted migration are two areas of 

uncertainty. 

● Systems that are limited to particular 

environments will have less opportunity to 

migrate in response to climate change (limited 

evidence, high agreement). Despite a lack of 

published research demonstrating this concept 

in the assessment area, our current ecological 

understanding indicates that migration to new 

areas will be especially difficult for species and 

systems with narrow habitat requirements. 

● Systems that are more tolerant of disturbance 

have less risk of declining on the landscape 

(medium evidence, high agreement). Basic 

ecological theory and other evidence support 

the idea that systems that are adapted to more 

frequent disturbance will be at lower risk. 

CHAPTER 7: MANAGEMENT

IMPLICATIONS

Summary

This chapter summarizes the implications of 

potential climate change to forest management and 

planning in northern Minnesota. This chapter does 

not make recommendations as to how management 

should be adjusted to cope with these impacts, 

because impacts and responses will vary across 

ecosystems, ownerships, management objectives, 

and site-specific conditions. 

Main Points

● Plants, animals, and people that depend on forests 

may face additional challenges as the climate 

shifts. 

● Greater financial investments may be required to 

manage forests and infrastructure and to prepare 

for severe weather events. 

● Management activities such as wildfire 

suppression or recreation activities such as 

snowmobiling may need to be altered as 

temperatures and precipitation patterns change. 

● Climate change may present opportunities for 

the forest products industry, recreation, and other 

sectors if changing conditions are anticipated.


Introduction

CONTEXT

This assessment is part of a regional effort across 

the Northwoods region of Minnesota, Wisconsin, 

and Michigan called the Northwoods Climate 

Change Response Framework (Framework; www.

forestadaptation.org). The Framework project was 

initiated in 2009 in northern Wisconsin with the 

overarching goal to incorporate climate change 

considerations into forest management. To meet 

the challenges brought about by climate change, a 

team of federal and state land management agencies, 

private forest owners, conservation organizations, 

and others have come together to accomplish three 

objectives: 

● Provide a forum for people working across the 

Northwoods to effectively and efficiently share 

experiences and lessons learned. 

● Develop new user-friendly information and tools 

to help land managers factor climate change 

considerations into decisionmaking. 

● Support efforts to implement actions for 

addressing climate change impacts in the 

Northwoods. 

The Framework process is designed to work at 

multiple scales. The Northwoods Framework is 

coordinated across the region, but activities are 

generally conducted at the state level to allow for 

greater specificity. Therefore, this assessment will 

focus on northern Minnesota and will serve as a 

companion for similar assessments completed in 

northern Michigan and Wisconsin. Additionally, 

regional Framework projects are underway in the 

Central Hardwoods region (Missouri, Illinois, and 

Indiana) and the Central Appalachians region (Ohio, 

West Virginia, and Maryland). 

The Northwoods Framework is an expansion of the 

original northern Wisconsin effort, and has been 

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

Agriculture (USDA), Forest Service. Across the 

Northwoods, the project is being guided by an array 

of partners with an interest in forest management, 

including:

● Northern Institute of Applied Climate Science

● U.S. Forest Service, Eastern Region

● U.S. Forest Service, Northern Research Station

● U.S. Forest Service, Northeastern Area (State & 

Private Forestry)

● Trust for Public Land

● The Nature Conservancy

● American Forest Foundation

● Great Lakes Forest Alliance 

● Wisconsin Department of Natural Resources

● Minnesota Department of Natural Resources

● Michigan Department of Natural Resources

This assessment is designed to provide detailed 

information for forest ecosystems within Minnesota. 

Several independent efforts related to climate 

change, natural ecosystems, and human well-being 

are also occurring in the state. This assessment 

should complement similar products created for 

Minnesota and the region, and the Framework 

project will attempt to integrate corresponding 

information as well. 

This assessment bears some similarity to other 

synthesis documents about climate change science, 

such as the National Climate Assessment (draft 

report at http://ncadac.globalchange.gov/) and the 

Intergovernmental Panel on Climate Change (IPCC) 
Introduction



reports (e.g., IPCC 2007). Where appropriate, 

we refer to these larger-scale documents when 

discussing national and global changes. This 

assessment differs from these reports in many ways, 

however. This assessment was not commissioned 

by any federal government agency nor does it 

give advice or recommendations to any federal 

government agency. It also does not evaluate policy 

options or provide input into federal priorities. 

Instead, this report was developed by the authors to 

fulfill a joint need of understanding local impacts 

of climate change on forests and assessing which 

tree species and forest systems may be the most 

vulnerable in northern Minnesota. Although it was 

written to be a resource for forest managers, it is first 

and foremost a scientific document that represents 

the views of the authors.

SCOPE AND GOALS

The primary goal of this assessment is to summarize 

potential changes to the forest ecosystems of 

northern Minnesota under a range of future climate 

scenarios, and determine the vulnerability of forest 

communities to these changes during the next 

100 years. Included is a synthesis of information 

about the 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 23.3 million acres 

throughout northern Minnesota (Fig. 1). The 

assessment area boundaries are defined by the 

Laurentian Mixed Forest Province (Ecological 

Province 212) in Minnesota (Bailey 1995, Bailey 

et al. 1994). Ecological Section X in Minnesota of 

Albert’s Regional Landscape Ecosystems describes 

virtually the same area (Albert 1995). In addition 

to these ecological boundaries, we used countylevel information that most closely represented the 

assessment area when ecoregional data were not 

available, limiting our selections to the 23 counties 

that are most analogous to the assessment area 

(Box 1). 

Figure 1.—(A) Assessment area and (B) the 23 counties used to approximate the Laurentian Mixed Forest Province in 

Minnesota when county-level data were required.
Introduction

Box 1: Counties Used to Represent 

 the Assessment Area 

Aitkin 

Becker

Beltrami

Benton

Carlton

Cass

Chisago

Clearwater

Cook

Crow Wing

Hubbard

Isanti

Itasca

Kanabec

Kochiching

Lake

Lake of the Woods

Mille-Lacs

Morrison

Pine

Roseau

St. Louis

Wadena

This assessment area covers more than 85 percent 

of the forested area within Minnesota (U.S. 

Forest Service 2011). Within this landscape, 

major land owners include the State of Minnesota 

(approximately 3.4 million acres), counties 

(approximately 2.8 million acres), the Superior 

National Forest (approximately 2.0 million acres), 

and the Chippewa National Forest (approximately 

667,000 acres) (U.S. Forest Service 2011). 

Supplementary information specific to these land 

owners was used when available and relevant to 

the broader landscape. This assessment synthesizes 

information covering all of northern Minnesota in 

recognition of the area’s dispersed patterns of forest 

composition and land ownership.

ASSESSMENT CHAPTERS

This assessment contains the following chapters: 

Chapter 1: The Contemporary Landscape

describes existing conditions, providing background 

on the physical environment, ecological character, 

and broad socioeconomic dimensions of northern 

Minnesota.

Chapter 2: Climate Change Science and 

Modeling contains background on climate change 

science, projection models, and impact models. It 

also describes the techniques used in developing 

climate projections to provide context for the model 

results presented in later chapters.

Chapter 3: Observed Climate Change provides 

information on the past and current climate of the 

assessment area in northern Minnesota, summarized 

from The Nature Conservancy’s interactive 

ClimateWizard database and published literature. 

This chapter also discusses some relevant ecological 

indicators of observed climate change. 

Chapter 4: Projected Changes in Climate, 

Extremes, and Physical Processes presents 

downscaled climate change projections for the 

assessment area, including future temperature and 

precipitation data. It also includes summaries of 

other climate-related trends that have been projected 

for northern Minnesota and the Midwest region. 

Chapter 5: Future Climate Change Impacts on 

Forests summarizes impact model results that were 

prepared for this assessment. Different modeling 

approaches were used to model climate change 

impacts on forests: a species distribution model 

(Climate Change Tree Atlas), a forest simulation 

model (LANDIS-II), and a biogeochemical model 

(PnET-CN). This chapter also includes a review of 

literature about other climate-related impacts on 

forests.

Chapter 6: Forest Ecosystem Vulnerabilities

synthesizes the potential effects of climate change 

on the forested ecosystems of the Laurentian Mixed 

Forest Province and provides detailed vulnerability 

determinations for eight major forest systems.

Chapter 7: Management Implications draws 

connections from the forest vulnerability 

determinations to a wider network of related 

concerns shared by forest managers, including forest 

management, recreation, cultural resources, and 

forest-dependent wildlife. 
Chapter 1: The Contemporary Landscape

The contemporary landscape of northern Minnesota 

results from a variety of interacting factors, 

including physical, ecological, economic, and 

social conditions. This chapter provides a brief 

introduction to the assessment area in general and 

to forest ecosystems in this region in particular. 

This context is critical for interpreting information 

presented in the remainder of this assessment. The 

references cited in each section will be helpful for 

readers looking for more in-depth information on a 

particular subject. 

LANDSCAPE SETTING

Physical Environment 

This section draws information primarily from 

Bailey’s Description of the Ecoregions of the 

United States (Bailey 1995, McNab and Avers 

1994). Albert’s description of regional landscape 

ecosystems in Michigan, Minnesota, and Wisconsin 

(Albert 1995) is also used to supplement this 

information, along with the description by the 

Minnesota Department of Natural Resources (DNR) 

of the Ecological Land Classification in the state 

(Minnesota DNR 2003). 

Minnesota lies at the intersection of four Ecological 

Provinces (Fig. 2). The assessment area is the 

portion of the Laurentian Mixed Forest Province 

located in Minnesota, which can be thought of as a 

transition zone between temperate broadleaf forests 

to the south and true boreal forests to the north 

(Minnesota DNR 1999). The major prairie-forest 

border within North America bisects Minnesota and 

continues farther north into Canada and south into 

the Midwestern United States. 

Figure 2.—Ecological provinces in Minnesota, as defined 

by Bailey (1995). Modified from Minnesota Department of 

Natural Resources (1999).

Climate 

Baseline climate data from 1971 through 2000 

provide a general picture of contemporary climate 

averages for the assessment area (Table 2) 

(ClimateWizard 2012, Gibson et al. 2002). These 

averages are important to keep in mind when 

considering how the climate in the assessment area 

has changed and may continue to change. Observed 

climate trends for the 20th century are presented in 

Chapter 3, and projections of future climate trends 

are presented in Chapter 4. 

Annual precipitation averages 27.2 inches across the 

Laurentian Mixed Forest Province (ClimateWizard 
Chapter 1: The Contemporary Landscape

10

Average Mean Mean maximum Mean minimum

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

Annual 27.2 39.0 50.2 27.7

Winter (Dec.-Feb.) 2.3 9.9 20.7 -1.0

Spring (Mar.-May) 5.8 39.6 51.8 27.5

Summer (June-Aug.) 12.0 64.5 76.6 52.4

Fall (Sept.-Nov.) 7.1 41.5 51.5 31.5

Table 2.—Average climate information for the assessment area, 1971 through 2000 (ClimateWizard 2012)

2012), ranging from 21 inches along the western 

border of the province to 32 inches along the Lake 

Superior shoreline (Minnesota DNR 2003). This 

gradient from drier in the northwest to wetter in the 

east is consistent across all seasons in the assessment 

area. Summer is by far the wettest season, with 

almost 45 percent of annual precipitation falling 

from June through August (Table 2). Winter is the 

driest month on average across the assessment area. 

Annual snowfall is influenced strongly by proximity 

to Lake Superior and varies from 40 to 70 inches, 

with much higher totals possible in areas prone to 

lake-effect snow (McNab and Avers 1994). 

The mean annual temperature is 39 °F (3.9 °C) 

(ClimateWizard 2012), ranging from 34 °F (1.1 °C) 

along the northern border with Canada to 40 °F 

(4.4 °C) toward the southern end of the Province 

(Minnesota DNR 2003). Winter temperatures are 

very cold, with minimum extremes ranging from 

-30 °F (-34.4 °C) near Lake Superior to -45 °F 

(-42.8 °C) and colder to the west. The general 

pattern during the growing season within the 

assessment area is warmer and drier in the 

southwest, shifting to cooler and wetter in the 

northeast. Growing season length ranges from 80 

to 140 days, with the shortest growing seasons 

occurring toward the northern, inland portions of 

the region (McNab and Avers 1994). Compared to 

similar latitudes in the nearby states of Wisconsin 

and Michigan, Minnesota generally features a 

more continental climate with hotter summers and 

colder winters. Droughts are also more common in 

Minnesota than in Wisconsin or Michigan (Stearns 

1997b). 

Geology and Landform 

Bedrock geology in northern Minnesota commonly 

includes granite, greenstone, quartzite, iron oxides, 

metasediments, and igneous rocks. In the assessment 

area, bedrock is most commonly exposed in upland 

areas near the shoreline of Lake Superior. 

Glacial activity has shaped the terrain of northern 

Minnesota. The last glacier receded from the state 

approximately 12,000 years ago. Glaciers moved 

along the Great Lake valleys and subsequently 

spread to upland areas. Glaciers were active for 

thousands of years during the most recent Ice Age, 

so the terrain of Minnesota reflects a complicated 

pattern of glacial advance and retreat. A large glacial 

lake (Glacial Lake Agassiz) also existed along 

the northern section of the assessment area. Thin 

soils and kettle lakes are common throughout the 

assessment area, as are rolling glacial till plains 

and flat, poorly drained peatlands (Minnesota DNR 

2003). The most prominent uplands are linear ranges 

paralleling the Lake Superior shoreline and similar 

ranges farther north (McNab and Avers 1994). Many 

forest communities are associated with particular 

landforms and geologic features, and knowledge of 

the extent and pattern of these features is necessary 

when considering how climate change may shape 

forest ecosystems across the assessment area. 
Chapter 1: The Contemporary Landscape

11

Hydrology 

Water resources are an abundant and defining 

feature of the assessment area. The assessment 

area is bounded by Lake Superior to the east, and 

the Laurentian Mixed Forest Province contains 

thousands of inland lakes, including most of the 

largest lakes within the state (Minnesota DNR 

2012b). The U.S. Geological Survey (USGS) 

National Land Cover Database (NLCD) categorizes 

36.5 percent of the assessment area as wetland and 

9.3 percent as open water (USGS 2011a). 

Within the assessment area, the largest watersheds 

are the St. Louis, Rainy, Big Fork, and Crow Wing 

Rivers, along with the headwaters of the Mississippi 

River. According to USGS streamflow stations in 

northern Minnesota on the St. Louis and Big Fork 

Rivers, peak surface flow typically arrives in a 

major pulse during April, May, and June, with flows 

substantially reduced from July through September 

(Fig. 3) (USGS 2011b). During the growing season, 

the lowest streamflow typically occurs in August 

and September. Within the assessment area, less 

than 10 percent of the annual precipitation falls 

during winter (ClimateWizard 2012). Two-thirds 

of the annual precipitation generally falls during 

the growing season of May through September 

(18 inches). 

Figure 3.—Average annual streamflow for two large 

watersheds in northern Minnesota. The St. Louis River drains 

into Lake Superior and has a drainage area of 3,430 square 

miles. The Big Fork River flows north to Canada and has a 

drainage area of 1,480 square miles (U.S. Geological Survey 

2011b).

Soils 

The combination of underlying bedrock, glacial 

activity, vegetation, and climate has resulted in a 

variety of soils within this assessment area. Upland 

and wetland forest soils have developed on either 

glacial drift or bedrock (Albert 1995). Glacial till, 

as well as material deposited from lakebeds and 

rivers, is extensive throughout the area. Soils in 

the assessment area are quite variable in texture, 

chemistry, stoniness, and drainage conditions. 

Glacial deposits can be 200 to 600 feet thick in some 

locations, but bedrock outcrops are common in the 

eastern portion of the assessment area. Fine-scale 

information on soil types in each Ecological Section, 

Sub-section, and Sub-subsection is available in 

Albert’s descriptions of Regional Landscape 

Ecosystems (Albert 1995) and the U.S. Department 

of Agriculture, Natural Resources Conservation 

Service Web Soil Survey portal (websoilsurvey.nrcs.

usda.gov/app/HomePage.htm). 

Ecosystem Composition

Land Cover 

This assessment area covers 23.3 million acres 

across northern Minnesota. According to the NLCD, 

wetlands account for 36.5 percent of the overall land 

cover in this assessment area (USGS 2011a). Much 

of the terrain in northern Minnesota is composed 

of forested bogs and lowland forests, and these 

lands are categorized as wetlands in the NLCD. 

Forests account for 36.5 percent of the assessment 

area, and planted/cultivated land and water each 

account for roughly 10 percent. Only 2.6 percent of 

the assessment area is classified as developed land. 

According to data from the U.S. Forest Service, 

Forest Inventory and Analysis (FIA) Program, forest 

land covers 63 percent of the assessment area 

(14.6 million acres) (U.S. Forest Service 2011). 

The FIA figure for forest land is different from the 

NLCD figure for forests because it captures many of 

the forested wetland and planted forest stands that 

the NLCD classification may have termed “wetland” 

or “planted.” 

0

1000

2000

3000

4000

5000

6000

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

Streamflow (cubic feet/sec)

St. Louis River at

Scanlon, MN

(1908-2010)

Big Fork River at

Big Falls, MN

(1909-2010)
Chapter 1: The Contemporary Landscape

12

Figure 4 presents a map of land cover for Minnesota, 

based on a different classification system from the 

NLCD (Fry et al. 2011). This map shows the clear 

gradation from forest to nonforest cover that occurs 

along the southern and western boundary of the 

assessment area. 

Figure 4.—Land cover in Minnesota based on the U.S. Geological Survey National Land Cover Database (2006).
Chapter 1: The Contemporary Landscape

13

The original land cover in Minnesota has been 

estimated from 19th-century General Land Office 

surveys (Stearns 1997a). Estimates are that 

approximately half to two-thirds of Minnesota was 

forested (Miles et al. 2011). In general, deciduous 

forests are more commonly found in areas of more 

productive soils and coniferous forests occur in less 

favorable locations with poorer soils. Albert (1995) 

offers a more complete summary of presettlement 

vegetation throughout the assessment area. Appendix 

1 lists common and scientific names of plant, fauna, 

and other species mentioned in this assessment. 

Conifers dominated both upland and lowland forests, 

but northern hardwoods were present throughout 

the area and were dominant on 10 to 25 percent 

of the mesic sites. Sugar maple and other northern 

hardwoods were nearly absent from the northern 

and northwestern portions of the Laurentian Mixed 

Forest Province, probably because of frequent and 

intense fires, late spring frosts, and poor drainage 

conditions in the peatlands. On the uplands, jack 

pine dominated the droughty, fire-prone outwash 

plains, beach ridges, and thin soils on bedrock. 

White pine and red pine dominated pitted outwash 

and sandy moraines that burned less frequently and 

less intensely than the outwash plains. Aspen-birch 

forests occurred intermittently throughout upland 

areas. The glacial lake plains all supported extensive 

areas of swamp and peatland dominated by black 

spruce and tamarack, along with some northern 

white-cedar, balsam poplar, paper birch, and aspen. 

Current estimates are that one-third of Minnesota is 

forested. A dramatic decline in forest cover occurred 

during the late 1800s and early 1900s, when roughly 

half of the state’s forests were lost to lumbering and 

conversion to agriculture (Minnesota DNR 2010a). 

The overall extent of forest land in Minnesota has 

been stable during the past 40 years. Forest land 

in the northern half of the state declined by about 

200,000 acres from 1977 to 2008, whereas the 

southern half of the state gained about 600,000 acres 

during this period (Miles et al. 2011). According 

to MODIS data from the National Aeronautics and 

Space Administration (NASA), more than 85 percent 

of Cook, Koochiching, and Lake Counties is forest 

land, and less than 1 percent of Benton and Isanti 

Counties is forest land (Headwaters Economics 

2011). Counties with smaller percentages of forest 

land are located in the agricultural areas along the 

southern edge of the assessment area. 

Although forest is the primary land cover type in 

the assessment area, much of this forest exists as 

fragmented edges or patches (Minnesota DNR 

2010a). Most of the state’s interior forest occurs 

within the assessment area (Fig. 5). Between 1992

and 2001, northeastern Minnesota lost portions of 

interior forest. This trend continued from 2001 to 

2006, as ecological sections within the assessment 

area lost 1 to 8 percent of their interior forest land 

(Riitters and Wickham 2012). Fragmentation 

changes forest ecosystems in a variety of ways, 

such as altering microclimates, facilitating invasive 

species, and disrupting disturbance regimes (Riitters 

and Wickham 2012). Habitat fragmentation is crucial 

in the context of climate change because it may 

determine the ability of tree species to migrate and 

respond naturally to changing conditions (Iverson et 

al. 2004a, Scheller and Mladenoff 2008). 

Forest Communities 

Natural Communities

The Minnesota DNR has prepared a classification 

of Native Plant Communities (NPCs) in the 

state based on analysis of extensive field plot 

data (Minnesota DNR 2003). The authors of this 

assessment decided to use the NPC classification 

as a basis for describing forest ecosystems in this 

vulnerability assessment, a decision supported by 

partner organizations throughout Minnesota. Native 

Plant Communities are groups of native plants not 

greatly altered by modern human activity or by 

introduced organisms; they often repeat over space 

and time. They are classified in a hierarchy from 
Chapter 1: The Contemporary Landscape

14

landscape-level NPC Systems (e.g., Fire-Dependent 

Forests) to more specific NPC Classes (e.g., Central 

Dry Pine Woodland) to even more specific Types 

and Sub-types. NPCs are organized by considering 

vegetation, hydrology, landforms, soils, and natural 

disturbance regimes. 

Figure 5.—Forest fragmentation in Minnesota. No distinction is made between natural or developed edges in this analysis, 

and a forest pixel must be at least 295 feet from a nonforest pixel to be considered an interior forest. Data are from the 2006 

National Land Cover Database. Prepared by D. Meneguzzo, U.S. Forest Service.
Chapter 1: The Contemporary Landscape

15

Thirteen NPC Systems and 64 NPC Classes are 

recognized throughout the Laurentian Mixed Forest 

Province in Minnesota (Minnesota DNR 2003). 

Of these, six NPC Systems are of concern for this 

assessment because they feature substantial forest 

cover and are characterized by dominant tree species 

(Table 3). The authors of this assessment decided 

to focus on the System level for the purposes of 

determining climate change vulnerability, because 

more precise levels of the NPC hierarchy (e.g., 

Classes and Types) occur at smaller spatial scales 

than available climate information. For complete 

descriptions of associated landforms, soil types, 

disturbance regimes, and common species for all 

NPC Systems, see the Field Guide to the Native 

Plant Communities of Minnesota: the Laurentian 

Mixed Forest Province (Minnesota DNR 2003). 

Fire-Dependent Forest System

Red pine * Balsam fir * Bur oak

Jack pine * Black spruce * White spruce

Quaking aspen * Northern red oak Northern pin oak

Paper birch * Red maple Northern white-cedar

Eastern white pine * Bigtooth aspen

Mesic Hardwood Forest System

Sugar maple * Red maple * White spruce *

American basswood * Bur oak * Northern white-cedar

Paper birch * Green ash * Ironwood

Quaking aspen * Black ash * Eastern white pine

Northern red oak * Yellow birch * White oak

Floodplain Forest System

Silver maple * American elm * Eastern cottonwood *

Black ash * American basswood * Boxelder

Green ash * Black willow * Bur oak

Wet Forest System

Black ash * Black spruce * White spruce

Northern white-cedar * Paper birch Green ash

Balsam fir * Yellow birch American basswood

Balsam poplar * Quaking aspen Tamarack

Red maple * American elm

Forested Rich Peatland System

Tamarack * Balsam fir Paper birch

Black spruce * Speckled alder Eastern white pine

Northern white-cedar * Red maple White spruce

Acid Peatland System

Tamarack * Black spruce * Bog birch

Species that are characteristic of each system are marked with an asterisk (*), but these are not exhaustive species lists. 

Scientific names can be found in Appendix 1.

Table 3.—Forested Native Plant Community Systems occurring within the Laurentian Mixed Forest Province 

(Minnesota Department of Natural Resources 2003)
Chapter 1: The Contemporary Landscape

16

These NPC Systems have recently been mapped 

across the assessment area (Table 4, Fig. 6). FireDependent Forests are the most abundant NPC 

System in the assessment area, concentrated 

primarily in the northeastern portion of the state. 

Mesic Hardwood Forests are concentrated in the 

southern and central portion of the assessment area, 

with a thin band occurring along the North Shore of 

Lake Superior. Forested Rich Peatlands are mostly 

concentrated in a large area between Red Lake and 

Lake of the Woods. Distribution maps for individual 

species across the state are available from FIA data 

(Miles et al. 2011).

Native Plant Community System Acres

Fire-Dependent Forest 7,549,215

Mesic Hardwood Forest 4,745,114

Floodplain Forest 8,978

Wet Forest 1,754,886

Forested Rich Peatland 3,987,130

Acid Peatland 1,472,061

Table 4.—Acreage occupied by each of the forested 

Native Plant Community Systems within the assessment 

area (unpublished data, T. Brown and G. Host, Natural 

Resources Research Institute)

Figure 6.—Map of Native Plant Community Systems within the assessment area (version 2.6, prepared by T. Brown and 

G. Host, Natural Resources Research Institute). 
Chapter 1: The Contemporary Landscape

17

Managed Forest Systems

In addition to NPC Systems, the authors of this 

assessment decided to explicitly consider two 

managed forest systems: managed aspen and 

managed red pine forests. These two managed forest 

systems are included in this assessment for two 

reasons. Intensively managed forests, such as shortrotation aspen or planted red pine, do not conform to 

the typical site characteristics, species composition, 

structure, or successional pathways described by 

the NPC classification. Therefore, it is reasonable 

to expect that these managed forest systems will 

respond differently to future conditions than 

forests more closely approximating NPC Systems. 

Additionally, these two kinds of managed forests are 

substantial economic resources in the assessment 

area and cover a large percentage of the land area. 

The most recent FIA survey for Minnesota describes 

the profound importance of quaking aspen (Miles 

et al. 2011). Quaking aspen is the most abundant 

tree species in Minnesota, composing 30 percent 

of forest land area and 19 percent of the total livetree volume across the state. Aspen forests are most 

heavily concentrated within the Laurentian Mixed 

Forest Province. As of the 2008 FIA survey, the 

live-tree volume of aspen on forest land across 

the state was almost 3.5 billion cubic feet, triple 

the volume of the next most abundant species 

(paper birch). Aspen is also the most economically 

important tree species in Minnesota, accounting for 

53 percent of the total harvest from forest land in 

Minnesota in 2007. Aspen roundwood production is 

roughly 115 million cubic feet, more than five times 

the production of other commercially important 

species. The overall volume of aspen has declined 

in recent years, primarily due to natural succession 

and harvesting of older aspen stands. Aspen is an 

early successional, shade-intolerant species that can 

grow on a wide range of soil types and landforms 

(Burns and Honkala 1990). Because it reproduces 

heavily from root suckers and will succeed to other 

forest types in the absence of disturbance, aspen 

is typically managed in even-aged rotations from 

35 to 60 years, depending on site conditions and 

management objectives. 

Red pine is also an important managed forest type 

within Minnesota and especially throughout the 

assessment area. Red pine accounts for only 

3 percent of the forest land area across the state, but 

this species has been increasing in abundance and 

economic importance for the past several decades 

(Miles et al. 2011). Red pine has the 4th highest livetree volume in Minnesota, with more than 1 billion 

cubic feet. Red pine has the highest annual net 

growth (4.6 percent) among commercially important 

tree species in Minnesota, and also experiences the 

lowest mortality (0.3 percent). This species had the 

3rd highest roundwood production in the state in 

2007, approximately 18 million cubic feet. Red pine 

is shade-intolerant and requires a fairly precise set 

Managed aspen stand. Photo by Eli Sagor, University of 

Minnesota, used with permission.
Chapter 1: The Contemporary Landscape

18

of conditions for successful natural regeneration and 

seedling establishment (Burns and Honkala 1990). 

Managed red pine therefore relies almost exclusively 

on planting seedlings in single-species, even-aged 

stands, with rotation ages ranging from 60 to 

70 years in more intensively managed stands to 

120 years or more in long-rotation systems. Red pine 

planting typically follows extensive site preparation, 

with thinning treatments commencing 20 to 

30 years after harvest and continuing roughly 

every 15 years until final harvest. Most red pine 

plantations in Minnesota have been planted on 

sites that are suitable for this species, on sandier, 

relatively nutrient-poor soils. 

Forest Composition and Abundance

FIA Forest Types 

FIA inventory data are useful to organize forest 

land into broad forest-type groups to facilitate 

comparison among similar species (Table 5) 

(U.S. Forest Service 2011). The FIA forest-type 

categories do not perfectly align with the NPC 

Systems described above, but they are a useful 

source of information while a complete inventory of 

NPC Systems for the entire state does not exist. The 

aspen/birch forest-type group is the most common 

throughout the assessment area, covering more than 

6 million acres of forest land. The spruce/fir foresttype group is the only other category accounting 

for more than 10 percent of the forest land in the 

assessment area. 

Compared to the forest-type group distribution 

across the entire state, the assessment area contains 

a higher proportion of aspen/birch, spruce/fir, and 

the white/red/jack pine group (U.S. Forest Service 

2011). This area contains 99 percent of the spruce/

fir forest-type group found within Minnesota, and 

more than 90 percent of the aspen/birch and white/

red/jack pine groups. The oak/hickory forest-type 

group is much less common in the assessment area 

than throughout the entire state, but the Laurentian 

Mixed Forest Province still contains 47 percent of 

the state’s acres of this forest-type group. Other 

hardwood forest-type groups are present at low 

levels, such as maple/beech/birch and elm/ash/

cottonwood. Their presence reflects the area’s 

position as a transition zone between the broadleaf 

forest biome and the boreal forest biome. 

Assessment area* Minnesota (statewide)*

Forest-type group Acres Percent cover Acres Percent cover

Aspen/birch group 6,023,059 41.2 6,616,679 38.7

Spruce/fir group 3,907,439 26.8 3,931,600 23.0

Elm/ash/cottonwood group 1,149,853 7.9 1,645,652 9.6

Oak/hickory group 1,005,843 6.9 2,111,137 12.4

White/red/jack pine group 951,350 6.5 1,011,431 5.9

Maple/beech/birch group 915153 6.3 1,170,828 6.9

Oak/pine group 276,717 1.9 314,991 1.8

Other hardwoods group 190,886 1.3 237,055 1.4

Exotic softwoods group 6,036 < 0.0 8,237 < 0.0

Other eastern softwoods group 3,411 < 0.0 22,337 0.1

Exotic hardwoods group 814 < 0.0 8,752 0.1

Total 14,602,821 17,078,699

*This tally does not include nonstocked forest land.

Table 5.—Acres occupied and percent cover of different FIA forest-type groups on forest land within the assessment 

area and for the entire state (U.S. Forest Service 2011)
Chapter 1: The Contemporary Landscape

19

FIA inventories across the state in recent years 

suggest that species composition in Minnesota’s 

forests is shifting (Miles et al. 2011). Differences in 

the number of trees in poletimber and sawtimber size 

classes from 2003 to 2008 reveal some interesting 

trends. Many of the increasing species are southern 

species at the northern edge of their ranges in 

Minnesota, including eastern redcedar, boxelder, 

American elm, green ash, eastern cottonwood, black 

walnut, bur oak, silver maple, black ash, bitternut 

hickory, and red maple. Species on the decline 

include red and white oak, along with northern 

species like jack pine, quaking aspen, and paper 

birch.

Drivers of Change in Forest Ecosystems 

Past Forest Ecosystem Change 

The current status of Minnesota’s forests reflects 

a dynamic past. As the last glacial sheets receded 

between 12,000 and 14,000 years ago, species 

like spruce and tamarack were relatively quick to 

colonize the expanding northward terrain in the 

Great Lakes region (Davis and Shaw 2001, Davis et 

al. 2005, Stearns 1997a). Pollen records also reveal 

that balsam fir, jack pine, and red pine were present 

in northern Minnesota shortly thereafter, followed by 

white pine and maples around 7,000 to 5,000 years 

ago (Davis 1983, Stearns 1997a). Hemlock migrated 

westward to the eastern border of Minnesota by 

about 1,500 years before present, being limited 

from entering much of the assessment area by 

precipitation and summer temperatures (Davis et al. 

2005). 

Pollen records illustrate that species moved 

independently of one another, and that species that 

coexist today did not necessarily coexist in the 

past or respond similarly to past climate changes 

(Dickmann and Leefers 2003). Refuge location, 

seed size, germination requirements, competitive 

ability, and dispersal vectors were all important 

factors determining how species responded to this 

period of dramatic postglacial climatic change. After 

the initial colonization of the Great Lakes area, 

periodic climate fluctuations resulted in advance and 

retreat of the prairie-forest border, with grassland 

and savanna occupying most of Minnesota during a 

warm and dry period between 8,000 and 3,500 years 

before present (Dickmann and Leefers 2003, Frelich 

and Reich 2010). 

Small mining sites and settlements from the 

Woodland era (3,000 to 300 years before the present 

day) have been uncovered throughout Minnesota, 

although extensive settlement was probably limited 

in the northern forests (Stearns 1997a). In savannas 

and pine forests, Native Americans intentionally 

set fires to aid in hunting and to make travel easier. 

Other impacts were minimal, including small 

agricultural conversions and wood harvesting. 

Similarly, early French and British settlement in the 

region appears to have had little impact on forests 

until the early 1800s. Indirect impacts to forests, 

particularly forested wetlands, likely occurred due 

to hunting and trapping of keystone species such as 

beaver.

The Government Land Survey began in Minnesota 

in 1847 and initiated an era of intense logging of 

Minnesota’s forests (Stearns 1997a). White pine 

logging operations expanded along the St. Croix 

and Mississippi River corridors until peaking 

around the turn of the 20th century (Stearns 1997a). 

The expansion of railroad lines soon facilitated 

harvest of upland areas farther from waterways and 

connected timber markets to the plains. As white 

pine declined, logging shifted to other species such 

as red pine and hardwoods. Only half of Minnesota’s 

forest land remained after the boom of widespread 

lumbering and land-use conversion to agriculture 

(Miles et al. 2011). The effects of this era on native 

ecosystems are well documented, with widespread 

catastrophic wildfires, eroded and dammed streams 

and waterways, and cascading impacts on vegetation 
Chapter 1: The Contemporary Landscape

20

Box 2: Forest Carbon

Forest ecosystems around the world play a valuable 

role as carbon (C) sinks. Terrestrial C within 

forest soils, belowground biomass, dead wood, 

aboveground live biomass, and litter represents an 

enormous store of C (Birdsey et al. 2006). Terrestrial 

C stocks in the region have generally been increasing 

for the past few decades, and there is increased 

attention on the potential to manage forests to 

maximize and maintain this C store (Malmsheimer 

et al. 2011, Minnesota DNR 2010a). Carbon 

sequestration and storage in forest ecosystems 

depends on the health and function of those 

ecosystems in addition to human management, 

episodic disturbances, and forest stressors. 

Forest land within the assessment area is estimated 

to hold nearly 1.4 billion metric tons C, or roughly 

95 metric tons per acre (U.S. Forest Service 2011). 

There is little difference in the amount of C stored 

per acre (C density) across the different major 

forest ownership categories. State lands generally 

hold more C than the average (104.8 metric tons 

per acre), national forests are roughly equal to the 

average (95.9 metric tons per acre), and private 

lands hold less (88.2 metric tons per acre). The 

pattern of C allocation in forest ecosystems is 

generally consistent across ownerships. Soil organic 

C is by far the largest carbon pool, followed by 

live aboveground, litter, live belowground, and 

deadwood pools. 

Among different forest types, however, the amount 

of C stored per acre differs widely (Fig. 7). The 

spruce/fir forest-type group holds roughly 50 

percent more C per acre than any other foresttype group because forest soils in these forest 

types typically are very rich in C. Climate is one of 

the factors that dictate the size of these per-acre 

C pools. Spruce/fir forests tend to grow in colder 

areas on poorer soils, where decomposition and 

tree growth are slow, so most C is stored in the soil. 

Maple/beech/birch forests tend to grow in warmer 

areas on more productive soils, so decomposition 

rates are faster and more C is stored in living 

biomass.

Peatlands in particular are capable of storing 

hundreds of metric tons of C per acre, and northern 

Minnesota contains more peat than any other state 

outside Alaska (Minnesota DNR 2011a). These areas 

are not reflected in the FIA figures mentioned above, 

but estimates are that Minnesota’s peatlands hold 

roughly 4.25 billion metric tons of carbon (Anderson 

et al. 2008). 

Figure 7.—Average carbon density (amount of carbon stored per acre) 

of major forest-type groups within the assessment area (U.S. Forest 

Service 2011).
Chapter 1: The Contemporary Landscape

21

and wildlife communities (Frelich and Reich 1996, 

Stearns 1997a). The ecological effect of the era was 

essentially to set back the course of forest succession 

by hundreds of years for flora, fauna, and soils. 

In Minnesota, another effect was to shift forests 

to more homogeneous composition and structure, 

with a shift in dominance from conifer to broadleaf 

deciduous species (White and Host 2008). 

After the wave of logging and wildfire, the first 

State-owned forest reserve was established in 1903 

(Stearns 1997a). The Chippewa National Forest was 

established in 1908 and the Superior National Forest 

followed the next year. The Great Depression era 

brought about increased attempts at reforestation 

and fire suppression. The end of World War II 

generally coincided with the rise of the paper and 

pulp industry and the beginning of the industrial 

forestry era in Minnesota. Harvest has continued 

during the past half century, and forests in the state 

have generally been maturing during the past several 

decades. The total live-tree aboveground biomass on 

timberland in 2008 increased 5.4 percent from 2003 

and 24 percent since the 1977 FIA inventory (Miles 

et al. 2011). 

Natural Disturbance Regimes 

Natural disturbance has historically been a regular 

component of forest ecosystems in Minnesota. 

Disturbances like fire, windthrow, ice damage, and 

insect defoliation can be highly variable across 

a large landscape, influenced by climate, soils, 

landform, and vegetation. NPC Systems have 

distinct disturbance regimes, characterized in part 

by the soils, landforms, and vegetation of each 

system (Minnesota DNR 2003). The Field Guide 

to the Native Plant Communities of Minnesota: the 

Laurentian Mixed Forest Province (Minnesota DNR 

2003) contains detailed descriptions of disturbance 

regimes for each NPC System. 

White and Host (2008) estimated that within 

the Laurentian Mixed Forest Province fire was 

historically 5 to 10 times more prevalent in fireprone ecological sub-sections characterized by 

shallow, coarse-textured soils over bedrock or welldrained sands. Areas with infrequent presettlement 

fire featured loamy or clay soils and complex 

topography. Recent studies suggest that the severity 

of a single fire could be quite heterogeneous across 

local and landscape scales (Carlson et al. 2011, 

Flannigan et al. 2009, Kirschbaum and Gafvert 

2010). Presettlement fire return frequency in pine 

forests has been estimated at 20 to 150 years, 

although many natural fires were not stand-replacing 

(Stearns 1997a). Fire-return interval in presettlement 

Minnesota boreal forests has been estimated at 50 to 

100 years, but the past century of fire suppression 

has reduced this frequency (Frelich and Reich 

1995b, White and Host 2008). Frequent fires in 

boreal systems tend to favor jack pine and aspen, 

and longer fire-return intervals allow black spruce, 

balsam fir, paper birch, and white cedar to develop 

in an uneven-aged stand formed by smaller-scale 

disturbances of wind, insects, and disease. 

Windthrow events in Minnesota boreal forests tend 

to cause greater mortality among early successional 

species like aspen, red pine, and jack pine (Rich 

et al. 2007). Wind events cause less mortality for 

species like northern white-cedar and red maple, 

although older stands of all forest types have greater 

susceptibility to this type of disturbance (Rich et al. 

2007). Windthrow events appear to have been more 

frequent toward the western edge of the assessment 

area (White and Host 2008). Large-scale windthrow 

is the primary source of stand-replacing disturbance 

for hardwood forests, with a calculated return 

interval of roughly 1,200 years. Absent these largescale wind events, disturbance in hardwood forests is 

primarily small- to medium-sized gap creation from 

localized tree mortality and pest outbreaks. 
Chapter 1: The Contemporary Landscape

22

Beaver-caused flooding is also a regular, but 

localized, disturbance factor in the assessment area 

(Kirschbaum and Gafvert 2010), particularly in 

the Floodplain Forest, Wet Forest, Forested Rich 

Peatland, and Acid Peatland Systems. 

Pests and Diseases 

Several important pests and diseases are worth 

noting for forest ecosystems in this assessment 

area (Table 6). Native pests are often recurring 

and cyclic, and introduced pests and diseases pose 

unknown threats to Minnesota forests. Major forest 

pests that have exhibited significant activity within 

the assessment area during recent years include 

spruce budworm, forest tent caterpillar, and eastern 

larch beetle (Miles et al. 2007, 2011; Minnesota 

DNR 2011b). Additionally, ash decline and aspen 

decline have been significant forest health issues in 

the assessment area. 

A dramatic increase in gypsy moth trapping figures 

suggests that this forest pest will soon be established 

in Minnesota (Minnesota DNR 2011b). The emerald 

ash borer has been found in St. Paul, Minnesota, 

and threatens all species of ash (Minnesota DNR 

2011b). Black ash mortality across the state is about 

1 percent of total volume per year, but recent results 

from Michigan suggest that the mortality rate could 

increase rapidly if the emerald ash borer continues to 

spread (Miles et al. 2011, Pugh et al. 2012). 

Forest health agent Species affected Location Trend and notes

Aspen blotch miner Quaking aspen Cook, Lake, St. Louis Counties, 

especially North Shore of Lake 

Superior

Heavy populations detected, unknown 

number of acres

Birch leaf miner Paper birch Cook, Lake, and St. Louis Counties Heavy populations detected, unknown 

number of acres

Eastern larch beetle Tamarack North-central MN, primarily 

Lake of the Woods, Beltrami, 

Koochiching Counties

Mortality on 20,000 acres in 2011, rising 

trend since 2001 with a total of 120,000 

acres killed

Forest tent caterpillar Aspens, oaks, 

birches, and other 

hardwoods

Central MN, southwest portion of 

assessment area

Defoliation on more than 60,000 acres in 

2011, recent outbreak has been building 

since 2006

Jack pine budworm Jack pine NA 0 defoliated acres in 2011, sharp declines 

since 2005, expecting another outbreak in 

roughly 3 years

Larch casebearer Tamarack Northeast MN, primarily St. Louis, 

Itasca, and Aitkin Counties

11,000 acres discolored in 2011, 

substantial decline since 2009 peak

Spruce budworm Balsam fir and 

white spruce

Primarily St. Louis and Lake 

Counties 

In 2011 more than 135,000 acres of 

defoliation and more than 90,000 acres of 

mortality. First outbreak in this area since 

the 1970s, though outbreaks have been 

recorded every 10 to 15 years in MN 

Ash decline Black ash Central and northeast MN More than 25,000 acres detected in 2011, 

steadily increasing trend since 2005

Aspen decline Quaking aspen 

and paper birch

Northeast MN, primarily Lake and 

Cook Counties

57,000 acres detected in 2011, down from 

larger detections in 2005, 2008-2010

Table 6.—Major forest health issues documented within the Laurentian Mixed Forest Province as of 2011, 

consolidated from Minnesota Department of Natural Resources (2011b)
Chapter 1: The Contemporary Landscape

23

Nonnative Plant Species 

Nonnative plant species are a risk to forest 

ecosystems when they become invasive. These 

species affect forest ecosystems through direct 

competition for resources, alteration of fire or 

hydrologic conditions, disruption of natural 

succession and pollination, and other cascading 

influences. In Minnesota forests, most nonnative 

plant species are understory species. According to 

the recent FIA inventory, 67 introduced or invasive 

plant species were identified on 184 vegetation 

diversity plots throughout the state (Miles et 

al. 2011). Almost half (45 percent) of the plots 

had nonnative species. Key invasive species are 

presented in Table 7. 

Deer browse damage on a red pine seedling. Photo by Casey 

McQuiston, Superior National Forest.

Forest System References

Fire-Dependent Forest System

Insect pests such as aspen blotch miner, birch leaf miner, forest tent 

caterpillar, jack pine budworm, spruce budworm, and white pine tip 

weevil cause reduced growth or mortality of target species.

(Burns and Honkala 1990; Miles et al. 2007, 

2011; Minnesota Department of Natural 

Resources 2011b)

Aspen decline causes reduced growth, crown dieback, or mortality of 

aspen species and paper birch.

(Minnesota Department of Natural Resources 

2011b)

Diseases such as white pine blister rust, Armillaria, sirococcus and 

sphaeropsis shoot blights, and Diplodia shoot blight lead to damage and 

tree mortality.

(Burns and Honkala 1990; Miles et al. 2007, 

2011; Minnesota Department of Natural 

Resources 2011b, Munck et al. 2009; Stanosz et 

al. 2001)

Suppression of natural fire regimes has reduced structural and species 

diversity, allowed hardwood encroachment on many sites, and limited 

suitable conditions for natural regeneration.

(Cleland et al. 2001, 2004; Nowacki and Abrams 

2008; Weyenberg et al. 2004)

Hazel encroachment increases competition following disturbance and 

reduces suitable conditions for natural regeneration, particularly in 

north-central Minnesota.

(Palik and Johnson 2007, Tappeiner 1971)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species.

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

Johnson 2007, Waller and Alverson 1997, White 

2012)

(Table 7 continued on next page)

Table 7.—Summary of the major current stressors and impacts for forested Native Plant Community Systems and 

managed forests in northern Minnesota
Chapter 1: The Contemporary Landscape

24

Forest System References

Mesic Hardwood Forest System

Insect pests such as forest tent caterpillar, spruce budworm, white pine 

tip weevil, and gypsy moth cause reduced growth or mortality of target 

species.

(Burns and Honkala 1990; Miles et al. 2007, 

2011; Minnesota Department of Natural 

Resources 2011b)

Diseases such as white pine blister rust and Armillaria lead to damage 

and tree mortality. 

(Burns and Honkala 1990; Miles et al. 2007, 

2011; Minnesota Department of Natural 

Resources 2011b)

Exotic earthworms reduce forest litter, alter nutrient and water cycling, 

alter soil conditions, facilitate exotic plant species, decrease regeneration 

suitability for many forest species, and increase drought susceptibility for 

sugar maple.

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

al. 2010)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species.

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

and Nagel 2009, Waller and Alverson 1997, 

White 2012)

Invasive plants such as garlic mustard, Pennsylvania sedge, and European 

buckthorn reduce suitable conditions for natural regeneration, facilitate 

other exotic species, and alter understory plant communities.

(Heimpel et al. 2010, Minnesota Department 

of Natural Resources 2012a, Powers and Nagel 

2009)

Excessive drought causes reduced growth or mortality. (Auclair et al. 2010, Burns and Honkala 1990, 

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

Soil frost and freeze-thaw cycles damage roots and new growth, and 

may cause crown dieback or widespread decline of maple and birch 

species.

(Auclair et al. 2010, Bourque et al. 2005, Burns 

and Honkala 1990, Tierney et al. 2001)

Floodplain Forest System

Flood control and alteration of river channels have altered flood 

regimes, creating timing mismatches between high-water periods and 

seed dispersal and dormancy and lack of suitable conditions for natural 

regeneration.

(Opperman et al. 2010, Romano 2010)

Invasive plants such as reed canarygrass and European buckthorn 

reduce suitable conditions for natural regeneration, facilitate other exotic 

species, and alter understory plant communities.

(Minnesota Department of Natural Resources 

2012e)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species.

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

and Alverson 1997, White 2012)

(Table 7 continued on next page)

Table 7 (continued).
Chapter 1: The Contemporary Landscape

25

Forest System References

Wet Forest System

Ash decline has caused widespread crown dieback and reduced growth 

for black ash.

(Minnesota Department of Natural Resources 

2011b, Palik et al. 2011)

Invasive plants such as reed canarygrass and Pennsylvania sedge reduce 

suitable conditions for natural regeneration, facilitate other exotic 

species, and alter understory plant communities. 

(Benedict and Frelich 2008, Minnesota 

Department of Natural Resources 2012e, 

Powers and Nagel 2009)

Altered hydrologic regimes lead to excessive waterlogging or excessive 

drought and result in reduced growth and susceptibility to dieback and 

decline.

(Palik et al. 2011)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species (northern white-cedar in particular). 

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

and Alverson 1997, White 2012)

Insect pests such as spruce budworm cause reduced growth or mortality 

of target species.

(Burns and Honkala 1990; Miles et al. 2007, 

2011; Minnesota Department of Natural 

Resources 2011b)

Excessive drought causes reduced growth or mortality. (Minnesota Department of Natural Resources 

2003)

Forested Rich Peatland System

Raised water tables can result in tree mortality, and lowered water tables

can lead to improved tree growth but also susceptibility to drought.

(Glaser 1983, Swanson and Grigal 1991)

Road or ditch building leads to altered drainage patterns. (Glaser 1983)

Diseases such as mistletoe reduce growth and mortality of spruce 

species and tamarack.

(Baker et al. 2012, Swanson and Grigal 1991)

Insect pests such as tamarack sawfly, eastern larch beetle, and larch 

casebearer have reduced growth and caused mortality of spruce species 

and tamarack.

(Minnesota Department of Natural Resources 

2011b, Swanson and Grigal 1991)

Acid Peatland System

(see Forested Rich Peatland)

(Table 7 continued on next page)

Table 7 (continued).
Chapter 1: The Contemporary Landscape

26

Forest System References

Managed Aspen

Diseases such as hypoxylon canker and shoot blights reduce growth or 

cause mortality.

(Burns and Honkala 1990)

Aspen decline causes crown dieback or mortality. (Minnesota Department of Natural Resources 

2011b)

Insect pests such as aspen blotch miner, forest tent caterpillar, and forest 

tent caterpillar cause reduced growth or mortality.

(Burns and Honkala 1990; Miles et al. 2007, 

2011; Minnesota Department of Natural 

Resources 2011b; Worrall et al. 2013)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of aspen.

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

and Alverson 1997, White 2012)

Excessive drought causes reduced growth or mortality. (Anderegg et al. 2012, Burns and Honkala 1990, 

Worrall et al. 2013)

Exotic earthworms reduce forest litter, alter nutrient and water cycling, 

alter soil conditions, facilitate exotic plant species, decrease regeneration 

suitability for many forest species, and increase drought susceptibility.

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

al. 2010)

Managed Red Pine

Diseases such as Armillaria, sirococcus and sphaeropsis shoot blights, 

scleroderris canker, and Diplodia shoot blight reduce growth and lead to 

topkill or mortality.

(Miles et al. 2007, 2011; Minnesota Department 

of Natural Resources 2011b; Munck et al. 2009; 

Stanosz et al. 2001)

Insect pests such as bark beetles and sawflies cause damage or mortality. (Burns and Honkala 1990, Minnesota 

Department of Natural Resources 2011b)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of red pine seedlings. 

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

Johnson 2007, Waller and Alverson 1997, White 

2012)

Lack of thinning treatments results in increased competition for 

moisture, nutrients, and light, as well as increased susceptibility to 

drought. 

(Peck et al. 2012, Powers et al. 2011, Sucoff and 

Hong 1974)

Hazel encroachment increases competition following disturbance and 

reduces suitable conditions for natural regeneration, particularly in 

north-central Minnesota.

(Palik and Johnson 2007, Tappeiner 1971)

Table 7 (continued).
Chapter 1: The Contemporary Landscape

27

Current Stressors 

Each of the NPC Systems and managed forest 

types faces a particular suite of threats and stressors 

(Table 7). We define stressors as agents that tend to 

disrupt the natural functioning of forest ecosystems 

or impair their health and productivity. This 

information is collected from published literature as 

well as from local forest managers. The impacts of 

particular threats and stressors are highly dependent 

on local conditions and are not consistent across an 

area as large and diverse as the Laurentian Mixed 

Forest Province. 

These particular threats should be considered in 

addition to landscape-level threats such as forest 

fragmentation, the legacy of past management 

practices, and altered disturbance regimes. It is 

often difficult to examine the effects of just one of 

these landscape-level threats in isolation, because 

they have all interacted across the assessment area 

during the past century. Fragmentation caused 

by agricultural and urban development, forest 

management, and other factors has tended to reduce 

the ratio of interior to edge conditions in forests 

(Miles et al. 2011, Radeloff et al. 2005) The legacy 

of forest management and land use in northern 

Minnesota has been well documented, with the 

general outcomes being a transition to more earlysuccession forests with reduced structural, spatial, 

and species diversity (Friedman and Reich 2005, 

Reich et al. 2001, Schulte et al. 2007). Natural 

disturbance regimes have been disrupted by fire 

suppression in upland systems and by hydrologic 

disruption in riparian and lowland forests. Natural 

regeneration and succession of forest ecosystems 

are strongly tied to disturbance regimes, so in many 

cases alteration of these regimes has resulted in less 

regeneration of disturbance-adapted species and 

reduced landscape diversity (Nowacki and Abrams 

2008, Romano 2010). 

FIA inventories track tree mortality across the state. 

Mortality across all forest land in Minnesota was 

1.9 percent of live tree volume in 2008, which is 

substantially higher than mortality levels in previous 

assessments and higher than the mortality rates 

documented for Iowa and Wisconsin forests (Miles 

et al. 2011). Mortality is greatest in national forests, 

at 2.3 percent. It is often difficult to determine the 

cause of death for a particular tree, but leading 

factors tend to be climate, disturbance events, insects 

and disease, or a combination of these factors. 

Conservation Status

The Minnesota County Biological Survey has 

assigned conservation ranks to the 40 NPC Classes 

that occur within the Laurentian Mixed Forest 

Province (Table 8). These rankings are designed to 

categorize the risk of elimination of the community 

from Minnesota (Minnesota County Biological 

Survey 2009). The rankings range from “critically 

imperiled” (S1) to “secure, common, widespread, 

and abundant” (S5). These rankings consider 

inherent geographic ranges, the amount of potential 

range currently occupied, long-term trends, and 

other factors. 

Forest-dependent Wildlife and Plants

Minnesota forests provide important habitat for 

wildlife species. As of 2009, 5 of the 11 Minnesota 

species listed under the federal Endangered Species 

Act were forest-dependent species: the gray wolf 

(delisted in Minnesota as of 2012), Canada lynx, 

Karner blue butterfly, dwarf trout lily, and Leedy’s 

roseroot (Minnesota DNR 2010a). 

The Minnesota Wildlife Action Plan Web site (www.

dnr.state.mn.us/cwcs/index.html) lists Species 

of Greatest Conservation Need by key habitats, 

including upland and lowland deciduous and 

conifer forest habitats. Roughly two-thirds of the 
Chapter 1: The Contemporary Landscape

28

Forest System Conservation status*

Fire-Dependent Forest System

FDn12 Northern Dry-Sand Pine Woodland S2

FDn22 Northern Dry-Bedrock Pine (Oak) Woodland S2 - S3

FDn32 Northern Poor Dry-Mesic Mixed Woodland S1 - S3

FDn33 Northern Dry-Mesic Mixed Woodland S2 - S5

FDn43 Northern Mesic Mixed Forest S2 - S5

FDc12 Central Poor Dry Pine Woodland S2

FDc23 Central Dry Pine Woodland S1 - S2

FDc24 Central Rich Dry Pine Woodland S1 - S3

FDc25 Central Dry Oak-Aspen (Pine) Woodland S2

FDc34 Central Dry-Mesic Pine-Hardwood Forest S2 - S3

Mesic Hardwood Forest System

MHn35 Northern Mesic Hardwood Forest S4

MHn44 Northern Wet-Mesic Boreal 

Hardwood-Conifer Forest S2 - S4

MHn45 Northern Mesic Hardwood (Cedar) Forest S2 - S4

MHn46 Northern Wet-Mesic Hardwood Forest S4

MHn47 Northern Rich Mesic Hardwood Forest S3

MHc26 Central Dry-Mesic Oak-Aspen Forest S4

MHc36 Central Mesic Hardwood Forest (Eastern) S4

MHc37 Central Mesic Hardwood Forest (Western) S4

MHc47 Central Wet-Mesic Hardwood Forest S3

Floodplain Forest System

FFn57 Northern Terrace Forest S3

FFn67 Northern Floodplain Forest S3

FFs59 Southern Terrace Forest S1 - S3

Forest System Conservation status*

Wet Forest System

WFn53 Northern Wet Cedar Forest S3 - S4

WFn55 Northern Wet Ash Swamp S3 - S4

WFn64 Northern Very Wet Ash Swamp S4

WFs57 Southern Wet Ash Swamp S4

WFw54 Northwestern Wet Aspen Forest S4

Forested Rich Peatland System

FPn62 Northern Rich Spruce Swamp (Basin) S3

FPn63 Northern Cedar Swamp S3 - S4

FPn71 Northern Rich Spruce Swamp (Water Track) S3

FPn72 Northern Rich Tamarack Swamp (Eastern Basin) S3

FPn81 Northern Rich Tamarack Swamp (Water Track) S4

FPn82 Northern Rich Tamarack Swamp 

(Western Basin) S4 - S5

FPs63 Southern Rich Conifer Swamp S2 - S3

FPw63 Northwestern Rich Conifer Swamp S3

Acid Peatland System

APn80 Northern Spruce Bog S4

APn81 Northern Poor Conifer Swamp S4 - S5

APn90 Northern Open Bog S2 - S5

APn91 Northern Poor Fen S3 - S5

*Conservation status ranks represent the statewide 

conservation status for Native Plant Community Types 

within a given Class. S1 = critically imperiled. S2 = imperiled. 

S3 = vulnerable to extirpation. S4 = apparently secure; 

uncommon but not rare. S5 = secure, common, widespread, 

and abundant. A range of ranks are presented when 

individual Native Plant Community Types within a Class 

had different conservation status ranks. Adapted from 

Minnesota County Biological Survey (2009).

Table 8.—Native Plant Community Classes occurring within the Laurentian Mixed Forest Province

state’s animal Species of Greatest Conservation 

Need occur in the assessment area. Additionally, 

the Minnesota Rare Species Guide (www.dnr.state.

mn.us/rsg/filter_search.html) lists 234 rare species 

within the Laurentian Mixed Forest Province. The 

Minnesota County Biological Survey is responsible 

for collecting and interpreting information on 

the distribution and ecology of rare species and 

communities throughout the state. It has not yet 

completed field surveys in many of the counties 

within the assessment area, but the counties that 

have been surveyed contain many areas rated as 

Outstanding or High Biodiversity Significance 

(Minnesota County Biological Survey 2012). 

Additionally, the Forest Plans of the Superior 

National Forest and Chippewa National Forest 

include several forest-dependent wildlife species 

with particular management emphasis (Table 9) 

(Chippewa National Forest 2004, Superior National 

Forest 2004).
Chapter 1: The Contemporary Landscape

29

Superior Chippewa

Species* National Forest National Forest Habitat

Canada lynx x x Boreal forest

Gray wolf x x Variety of forested and nonforested habitats

Wood turtle x Upland and lowland forests adjacent to water

Boreal owl x Mature upland aspen and aspen/conifer forest

Great gray owl x x Mature upland forest near lowland conifer forest

Three-toed woodpecker x Mature conifer forest and dead conifer forest

Olive-sided flycatcher x x Boreal forests

Northern goshawk x x Mature upland forest

Blanding’s turtle x Forested wetlands

Four-toed salamander x Northern hardwoods

Red-shouldered hawk x Mature upland forest

Black-backed woodpecker x Mature conifer forest and dead conifer forest

Goblin fern x x Mature northern hardwoods

 *Scientific names are in Appendix 1.

Table 9.—Forest-dependent species with a particular management focus within the forest plans of the Superior 

National Forest and Chippewa National Forest (Chippewa National Forest 2004, Superior National Forest 2004)

Both mature forests and young forest habitats are 

needed to fulfill habitat requirements of forestdependent wildlife. These requirements can vary 

by season or throughout the lifespan of a species. 

Although American woodcock populations have 

declined throughout much of their range, they have 

remained relatively stable in Minnesota due to the 

abundance of young forest habitat (Minnesota DNR 

2010a). The golden-winged warbler, which has 

experienced steep declines throughout its range, 

has remained stable in the state, where an estimated 

40 percent of the global population breeds.

SOCIOECONOMIC CONDITIONS 

Forest Ownership Trends

Minnesota has the highest percentage of public 

ownership of any state in the eastern United States, 

at 56 percent (Miles et al. 2011). Minnesota also has 

the highest percentage of State-owned land 

(21 percent) and county-owned land (18 percent) of 

any state in the nation. Total federal land ownership 

is greater than 15 percent in five counties within the 

assessment area, with more than 43 percent in Lake 

County (Headwaters Economics 2011). State land 

ownership is more than 15 percent in 12 counties, 

with more than 53 percent in Koochiching County. 

Tribal land is another significant ownership category 

in the assessment area, accounting for almost 

600,000 acres in Beltrami County and more than 

100,000 acres in Clearwater and Lake of the Woods 

Counties. 

Table 10 displays the breakdown of forest land 

ownership within the assessment area and for 

Minnesota as a whole (U.S. Forest Service 2011). 

The assessment area contains 100 percent of national 

forest land in the state, 92 percent of the stateowned forest land, and 96 percent of all county and 

municipal forest land. Private land accounts for the 

biggest proportion of forest land in the assessment 

area, at more than 5.3 million acres (37.1 percent). 

Across the entire state, 75 percent of private forest 

land is owned by families, and 25 percent is in other 

private ownerships (Butler 2008). 
Chapter 1: The Contemporary Landscape

30

The breakdown of forest-type groups within these 

different ownerships is not uniform. For example, 

within the assessment area the State owns 23 percent 

of the forest land, but these lands contain 41 percent 

of the spruce/fir forest-type group. Private forest 

land contains a disproportionately high percentage 

of southern forest-type groups, and national forests 

account for a higher percentage of the white/red/jack 

pine forest type group and the oak/pine group. 

General Trends in Land Use 

In Minnesota, two major factors have been 

contributing to forest fragmentation in recent 

years: large-scale divestiture of forest industry 

land and parcelization of nonindustrial private 

forests (Minnesota DNR 2010a). Parcelization is 

the division of larger landholdings into smaller 

units. The average landholding size in Minnesota 

decreased from 39 acres in 1982 to 31 acres in 2003 

(Minnesota DNR 2010a). Although parcelization 

may not have direct impacts immediately, this 

pattern often results in consequences for forests as 

well as for forest industry (Gobster and Rickenbach 

2004, Haines et al. 2011). Long-term studies in 

northern Wisconsin have shown that parcelization 

is often a precursor to fragmentation and land-use 

change in forests (Haines et al. 2011). 

Within the assessment area, 13.1 percent of private 

land was developed as residential land as of the year 

2000 (Headwaters Economics 2011). Between 1980 

and 2000, the proportion of residential land grew 

nearly twice as fast in the assessment area as the 

national average. Beltrami County (175 percent), 

Clearwater County (190 percent), and Lake County 

(131 percent) had the highest rates of residential land 

development, and the overall rate for the assessment 

area was 59 percent. Most of this residential land 

exists in parcel sizes between 1.7 and 40 acres. 

Land-use change is projected to proceed gradually 

in northern Minnesota through 2020, as rural land 

is converted to exurban and urban development 

(Theobald 2005). Land development will be 

constrained in the assessment area due to the high 

proportion of federal, state, and county-owned 

land, so growth is projected to occur near existing 

municipalities within the southern half of the area.

Census Data on 

Human Population Patterns 

The human population within the assessment area 

is approximately 783,000 (Headwaters Economics 

2011). Population has been steadily increasing 

since 1970. The population growth in the region 

Assessment area Minnesota (statewide)

Ownership Acresa Percent cover Acresa Percent cover

Private 5,360,640 37.1 7,543,420 44.2

National forest 2,606,339 18.1 2,606,339 15.3

State 3,357,248 23.3 3,638,769 21.3

County and municipal 2,841,410 19.7 2,973,624 17.4

Other federalb 264,922 1.8 316,548 1.9

 Total forest land area (acres) 14,430,559 17,078,700

a

 Nonstocked lands are not included in the numbers presented in this table. 

b

 Includes the National Park Service, U.S. Fish and Wildlife Service, and Bureau of Land Management in the Department of the 

Interior; Department of Defense; and other federal agencies. 

Table 10.—Forest land ownership within the assessment area and for the entire state, according to FIA data 

(U.S. Forest Service 2011)
Chapter 1: The Contemporary Landscape

31

was slower than the statewide average for the same 

period (29 percent compared to 38 percent). The 

population trend has not been uniform within the 

assessment area. The fastest growing counties from 

1970 to 2009 in the assessment area were Chisago 

(186 percent) and Isanti Counties (132 percent). 

The population declined over the past 40 years in 

Koochiching, Lake, Lake of the Woods, and 

St. Louis Counties. The 2010 U.S. Census confirms 

that this trend continued during the most recent 

census period (Mackun et al. 2011). The population 

density for the assessment area remains between 

1 and 49 people per square mile, with the exception 

of Crow Wing County (Mackun et al. 2011). The 

population throughout the assessment area is also 

aging faster than the U.S. average (Headwaters 

Economics 2011). 

Economic Sectors 

Overall

Compared with Minnesota as a whole, the economy 

of the assessment area is slightly depressed. Average 

earnings per job declined 0.2 percent between 

2000 and 2009, whereas the statewide average 

rose 1.1 percent during that period (Headwaters 

Economics 2011). Per capita income for the 

assessment area increased at a faster rate than the 

statewide average during the decade, but remains 

almost 25 percent below the statewide norm. 

Unemployment in this region is also higher than 

the Minnesota average. Several counties underwent 

dramatic employment gains in the past 40 years. 

Benton, Cass, Chisago, and Isanti Counties all 

reported more than 300-percent increases in 

employment figures, and these counties also 

reported increases in personal income. 

Agriculture

Minnesota is a major agricultural state, ranking 

7th overall in the United States for total market 

value of agricultural products (U.S. Department of 

Agriculture 2007). Counties in southern Minnesota 

are much more engaged in commercial agriculture 

than the counties within the assessment area. 

Nevertheless, agriculture-related employment 

accounts for more than 8.0 percent of total 

employment in seven counties and 3.6 percent 

throughout the assessment area (Headwaters 

Economics 2011). During the 40 years between 

1970 and 2009, employment in this sector declined 

slightly for the assessment area. 

Census of Agriculture summaries are organized by 

congressional district, and Minnesota’s 8th district 

most closely matches the assessment area. The 

number of farms and the total land area in farms 

decreased by 12 percent in this district from 2002

to 2007. Within these counties in northeastern 

Minnesota, oilseed crops, grains, beef cattle, and 

aquaculture are the most common farm types. 

Additionally, this area of Minnesota is top ranked 

in the state for producing Christmas trees and 

short-rotation woody crops (U.S. Department of 

Agriculture 2007). 

Recreation and Tourism

Tourism is an $11 billion industry in Minnesota, 

and the state has above-average participation rates 

in nature-based outdoor activities (Minnesota DNR 

2010a). Hunting and wildlife watching continue 

Brook trout in northern Minnesota. Photo by Kelly 

McQuiston, Minnesota Department of Natural Resources, 

used with permission.
Chapter 1: The Contemporary Landscape

32

to contribute more than $1 billion to the state’s 

economy. Northern Minnesota relies heavily on 

the economic contributions of travel and tourism. 

Johnson and Beale (2002) identified many of the 

counties within the assessment area as “recreation 

counties,” based on employment and income 

from tourism-related industries, proximity to 

natural amenities, and other factors. Employment 

within the travel and tourism sector accounted for 

nearly 18 percent of total private employment in 

the assessment area in 2009 with seven counties 

registering 25 percent or more of total employment 

within this area (Headwaters Economics 2011). The 

assessment area includes two national forests, one 

of which contains the Boundary Waters Canoe Area 

Wilderness; the Grand Portage National Monument; 

Voyageurs National Park; many state parks and 

recreation areas; and several popular tourist 

destinations like the North Country National Scenic 

Trail and the North Shore of Lake Superior. 

Forest Products Industry

The forest products industry is also an important 

source of income and employment in Minnesota. 

The wood products and paper industries in the state 

account for more than $6 billion in economic output 

(Minnesota DNR 2010a). The economic impact of 

the forestry sector was rated “high” or “moderate” 

throughout most of the Laurentian Mixed Forest 

Province in a spatial assessment by the Minnesota 

DNR. As of 2010, almost 38,000 people were 

employed in the forest products sector statewide, 

including secondary manufacturing.

In 2009, employment in timber-related jobs 

represented about 3.4 percent of total private 

employment throughout the assessment area, or 

about 8,800 jobs (Headwaters Economics 2011). 

Employment in this sector accounts for almost 35 

percent of the private employment in Roseau 

County and 24 percent in Koochiching County. 

St. Louis, Carlton, and Itasca Counties also have 

high levels of employment in this sector. From 

1998 to 2009, employment in the forest products 

industry declined about 28 percent across the 

assessment area. This decrease was largely the result 

of declining employment in sawmills and paper 

mills. Meanwhile, employment related to growth and 

harvesting and wood products manufacturing held 

steady. Recent mill closings and national and global 

economic conditions have reduced the volume of 

timber exports from the state (Minnesota DNR 

2010a). 

An example of an important nontimber forest 

product in Minnesota is balsam fir boughs, nearly all 

of which are harvested from within the assessment 

area (Minnesota DNR 2010a). Balsam boughs 

used in the wreath industry contribute more than 

$23 million in sales annually. Most balsam bough 

harvesting occurs in St. Louis, Aitkin, Itasca, 

Cass, Lake, Koochiching, Cook, Beltrami, Lake of 

the Woods, Clearwater, Carlton, and Pine Counties. 

Mining

Mining is a major industry in northern Minnesota, 

accounting for more than $5 billion in economic 

output as of 2005. In recent years, there has been 

increasing interest in expanding mining operations 

within northeastern Minnesota. The state ranks 

first in the nation in production of iron ore and 

A variety of products made from birch bark. Photo by Eli 

Sagor, University of Minnesota, used with permission.
Chapter 1: The Contemporary Landscape

33

taconite (Minnesota DNR 2012c). The mining 

industry accounted for 1.4 percent of total private 

employment in the assessment area in 2009, almost 

all of which is related to metallic ore mining 

(Headwaters Economics 2011). Employment in 

this sector declined about 50 percent from 1998 

to 2009. Lake County and St. Louis County in 

northeastern Minnesota have the highest proportion 

of mining-related employment, close to 4 percent 

of total private employment. Taconite and iron ore 

operations are mostly confined to the Mesabi Iron 

Range in northeastern Minnesota (Minnesota DNR 

2012c). Within the assessment area, there are also 

significant operations to extract granite, horticultural 

peat, crushed stone, copper, nickel, manganese, 

and sulfur. 

Forest Harvest and Products 

As mentioned above, the forestry sector is a major 

economic contributor in Minnesota and within the 

assessment area in particular. The value of forest 

products manufactured across the state rose steadily 

through the 1980s and 1990s from just under 

$2 billion to almost $8 billion, but appears to have 

been gradually declining during the most recent 

decade (Minnesota DNR 2010a). An overall market 

slowdown underlies this trend, and the closing of 

several major mills within the assessment area has 

further affected timber markets in Minnesota. 

Industrial roundwood production in Minnesota is 

directed to a variety of major product types (Fig. 8). 

Pulpwood accounts for 77 percent of the production 

across all product categories, with saw logs 

accounting for 19 percent. Within the assessment 

area, the largest volume of net growth occurs 

on private forest land, followed by county and 

municipal land, state land, and national forests 

(U.S. Forest Service 2011). Harvest removal 

volumes follow this same pattern. 

The rate of harvest removal also differs by species 

within the assessment area (Table 11). The aspen/

birch forest-type group accounted for more than 

53 percent of the total annual harvest removals 

within the assessment area, followed by the maple/

beech/birch group at almost 12 percent. Other 

forest-type groups account for minor fractions of the 

harvest removals within the assessment area. Across 

the entire state, quaking aspen has the highest 

harvest rate at 3.4 percent removals of standing 

volume in the 2008 FIA inventory (Miles et al. 

2011).

Figure 8.—Statewide industrial roundwood production by major product category, as of 2007 (Miles et al. 2011).
Chapter 1: The Contemporary Landscape

34

The growth-to-removals ratio of live trees on forest 

land provides a simple metric for determining 

whether the withdrawals of harvesting are outpacing 

the gains of growth (Table 11). This ratio takes into 

account gross growth, mortality, and removals. 

Among ownership classes in the assessment area, the 

growth-to-removals ratio is less than 1 for the aspen/

birch and maple/beech/birch forest-type groups, 

showing that removals were generally outpacing 

growth in these forest types (U.S. Forest Service 

2011). The ratio was reversed for other major foresttype groups as many native forest types had net 

growth many times greater than annual removals. 

For all forest-type groups within the assessment 

area, the combined ratio was 1.34, which means 

overall volume on forest land is increasing. Across 

the entire state, this ratio was 1.5 as of 2008 

(Miles et al. 2011).

Forest Certification 

Forest certification programs allow a public or 

private landowner to voluntarily submit to thirdparty audits that ensure environmental, social, and 

economic best practices are followed on their lands. 

In exchange, certification programs recognize these 

landowners with a seal of approval. There is not 

a consistent set of management practices that are 

followed on all of these ownerships. The extent of 

forest certification does provide an indication of 

the amount of forest land that is being managed 

with formal management plans according to general 

principles of sustainability, with regular audits. 

The total area of certified forest land amounts to 

more than 8.4 million acres—almost half of all 

the forest land in the state (Table 12). As of 2011, 

Minnesota ranked first in the nation in acres of 

forest certified under the Forest Stewardship Council 

(FSC) standards (Pingrey 2011). Almost 5 million 

acres of state forest land are dual certified under FSC 

and the Sustainable Forestry Initiative (SFI), and 

more than 2.5 million acres of county-owned forest 

land are certified under one or more certification 

schemes (Barnard 2012). Private industrial forest 

owners manage another 740,000 acres of certified 

forests. Most forest land within the assessment area 

has been certified under one or more certification 

standards (Fig. 9). 

Forest-type group Net growth* Removals Growth:Removals 

Aspen/birch group 102,368,753 112,530,769 0.91

Elm/ash/cottonwood group 16,610,182 6,016,633 2.76

Exotic hardwoods group 52,351 - NA

Exotic softwoods group 439,979 - NA

Maple/beech/birch group 20,142,182 24,570,096 0.82

Oak/hickory group 27,223,592 15,334,528 1.78

Oak/pine group 11,148,280 1,379,715 8.08

Other -5,043,866 16,578,941 NA

Other eastern softwoods group 146,367 22,164 6.60

Other hardwoods group 823,618 7,624,027 0.11

Spruce/fir group 50,713,017 12,740,352 3.98

White/red/jack pine group 55,467,213 12,169,403 4.56

 Total 280,091,666 208,966,626 1.34

*Negative net growth indicates that a forest-type group had more mortality than gross growth during the inventory period.

Table 11.—Annual net growth and removals for the assessment area in cubic feet per year, organized by forest-type 

group (U.S. Forest Service 2011)
Chapter 1: The Contemporary Landscape

35

Certification standard*

American Tree Farm

Landowner FSC only Dual FSC/SFI SFI only System - PEFC Total

Public

Minnesota state forests 4,960,177 19,076 4,979,253

County

Beltrami 147,000 147,000

Carlton 73,000 73,000

Clearwater 90,140 90,140

Crow Wing 103,000 103,000

Koochiching 286,500 286,500

Aitken 221,657 221,657

Cass 253,494 253,494

Lake 151,216 151,216

Itasca 301,660 301,660

St. Louis 895,174 895,174

Private

Industrial

Potlatch Forest Holdings 252,217 252,217

Forest Capital Partners 298,955 298,955

UPM-Kymmene 189,385 189,385

Nonindustrial 15,433 15,433

MN Tree Farmers 225,069 225,069

Total by standard 1,195,677 5,659,817 1,402,590 225,069 8,483,153

*FSC = Forest Stewardship Council, SFI = Sustainable Forestry Initiative, PEFC = Programme for the Endorsement of Forest 

Certification schemes. Modified from Barnard (2012). 

Table 12.—Acres of forest land in Minnesota certified under three major certification schemes as of February 2011

SUMMARY

Forests are a defining feature across Minnesota, 

and particularly within the assessment area. The 

forest ecosystems in the assessment area are 

dynamic, and they have been shaped by a multitude 

of factors, including climate, geology, glaciation, 

land conversion and development, and human 

management. In addition to being the dominant land 

cover, forests are important for wildlife habitat, 

C storage, economic and cultural resources, and 

other values. The context presented in this chapter 

will be helpful for interpreting information contained 

in the chapters that follow. It may be particularly 

important to refer back to this information when 

considering information on climate change impacts 

(Chapter 5), forest ecosystem vulnerability 

(Chapter 6), and connections with other aspects 

of forest management and planning (Chapter 7).
Chapter 1: The Contemporary Landscape

36

Figure 9.—Certified forests in Minnesota, from Barnard (2012). FSC = Forest Stewardship Council. SFI = Sustainable Forestry 

Initiative. The 225,069 acres in the American Tree Farm System are not shown due to privacy concerns.
37

Chapter 2: Climate Change Science 

and Modeling

This chapter provides a brief background on climate 

change science, climate simulation models, and 

models that project the impacts of changes in climate 

on species and ecosystems. Throughout the chapter, 

boxes indicate resources to find more information 

on each topic. The resources listed are up-to-date, 

nontechnical reports based on the best available 

science. A more detailed scientific review of climate 

change science, trends, and modeling can be found 

in the Intergovernmental Panel on Climate Change 

(IPCC) Fourth Assessment Report (IPCC 2007) and 

the whitepaper contributions to the Midwest chapter 

of the 2013 National Climate Assessment (Andresen 

et al. 2012, Winkler et al. 2012). 

CLIMATE CHANGE

Climate is not the same thing as weather. Climate 

is defined as the average, long-term meteorological 

conditions and patterns for a given area. Weather, in 

contrast, is the set of the meteorological conditions 

for a given point in time in one particular place. The 

IPCC (2007) defines climate change as “a change in 

the state of the climate that can be identified (e.g., 

by using statistical tests) by changes in the mean 

and/or the variability of its properties, and that 

persists for an extended period, typically decades 

or longer.” A key finding of the IPCC in its Fourth 

Assessment Report (2007) was that “warming of the 

climate system is unequivocal.” This was the first 

Assessment Report in which the IPCC considered the 

evidence strong enough to make such a statement. In 

addition to evidence of increased global surface, air, 

and ocean temperatures, this conclusion was based 

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

data series from all continents and most oceans. 

These data showed significant changes in snow, ice, 

and frozen ground; hydrology; coastal processes; 

and terrestrial, marine, and biological systems. The 

IPCC’s Fifth Assessment Report is underway, and 

scheduled to be released in 2014. On a national 

level, the United States Global Research Program 

has released a series of reports detailing the past and 

projected changes in climate, with a comprehensive 

report (National Climate Assessment, NCA) 

scheduled to be released in 2014 (see Box 3 

for more information). 

The Warming Trend

The Earth is warming, and the rate of warming is 

increasing. Measurements from weather stations 

across the globe indicate that the global mean 

temperature has risen by 1.4 °F (0.8 °C) over the 

past 50 years, nearly twice the rate of the last 100 

years (Fig. 10) (IPCC 2007). The first 12 years in 

the 21st century rank among the warmest 14 years in 

the 133-year period of record of global temperature 

(National Oceanic and Atmospheric Administration 

[NOAA] National Climatic Data Center 2012a). 

Temperatures in the United States have risen by 2 °F 

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

year 2012 ranked as the warmest year on record in 

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

previous record year of 1998 and 3.3 °F above the 

20th-century average (NOAA National Climatic 

Data Center 2012b). 
Chapter 2: Climate Change Science and Modeling

38

Box 3: Global, National, and Regional Assessments 

Intergovernmental Panel on Climate Change

The Intergovernmental Panel on Climate Change 

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

international body for the assessment of climate 

change. It was established by the United Nations 

Environment Programme (UNEP) and the World 

Meteorological Organization (WMO) in 1988 to 

provide the world with a clear scientific view on the 

current state of knowledge in climate change and its 

potential environmental and socioeconomic impacts. 

The most recent report is available for download at 

the Web address below.

Climate Change 2007: Synthesis Report 

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

contents.html

U.S. Global Change Research Program

The U.S. Global Change Research Program (USGCRP; 

www.globalchange.gov) is a federal program that 

coordinates and integrates global change research 

across 13 government agencies to ensure that it 

most effectively and efficiently serves the nation 

and the world. Mandated by Congress in the Global 

Change Research Act of 1990, the USGCRP has since 

made the world’s largest scientific investment in the 

areas of climate science and global change research. 

It has released several national synthesis reports 

on climate change in the United States, which are 

available for download below. 

Global Change Impacts on the United States

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

nca-overview.html

Synthesis and Assessment Products

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

assessments/2004-2009-synthesis-and-assessmentproducts

National Climate Assessment

http://ncadac.globalchange.gov/

Effects of Climatic Variability and Change on Forest 

Ecosystems: a Comprehensive Science Synthesis for 

the U.S.

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

Midwest Technical Input Report for the National 

Climate Assessment (coordinated by the Great 

Lakes Integrated Science and Assessment [GLISA] 

Center)

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

Figure 10.—Trends in global temperature compared to the 1951 through 1980 mean. Data source: NASA Goddard Institute for 

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

39

Average temperature increases 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 for many 

regions of the world while the frequency of hot days 

and nights has increased (IPCC 2007). Within the 

United States, 356 all-time high temperature records 

were broken in 2012, compared to only 4 all-time 

low temperature records (NOAA National Climatic 

Data Center 2012b). There is also a strong indication 

that the frequency of heat waves and heavy 

precipitation events has increased during 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). 

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 Scientific Congress on 

Climate Change concluded that “recent observations 

show that societies and ecosystems are highly 

vulnerable to even modest levels of climate change, 

with poor nations and communities, ecosystem 

services and biodiversity particularly at risk” 

(Richardson et al. 2009). Temperature rises of more 

than 3.6 °F (2 °C) above average will be difficult 

for contemporary societies to cope with, and are 

expected to cause major societal and environmental 

disruptions through the rest of the century and 

beyond (Richardson et al. 2009). 

Scientists have been able to attribute these changes 

to human causes by using climate model simulations 

of the past, both with and without human-induced 

changes in the atmosphere, and then comparing 

those simulations to observational data. Overall, 

these studies have shown a clear human effect on 

recent changes in temperature, precipitation, and 

other climate variables due to changes in greenhouse 

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

2010). 

Chapter 3 provides specific information about 

observed climate trends for the assessment area in 

Minnesota and the surrounding region, and Chapter 

4 describes a range of anticipated future climate 

simulations.

The Greenhouse Effect

The greenhouse effect is the process by which 

certain gases in the atmosphere absorb and re-emit 

energy that would otherwise be lost into space 

(Fig. 11). This 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, including 

carbon dioxide (CO2

), methane (CH4), nitrous oxide 

(N2

O), and water vapor, contribute to the greenhouse 

effect. Water vapor is the most abundant greenhouse 

gas, but its residence time in the atmosphere is on 

the order of days as it quickly responds to changes 

in temperature and other factors. Carbon dioxide, 

CH4

, N2O, and other greenhouse gases reside in 

the atmosphere for decades to centuries. Therefore, 

these long-lived gases are the primary concern with 

respect to long-term warming. 
Chapter 2: Climate Change Science and Modeling

40

Figure 11.—Idealized model of the natural greenhouse effect. Figure courtesy of the Intergovernmental Panel on Climate 

Change (2007).

Human Influences on Greenhouse Gases

Human activities have increased CO2 , CH4 , and 

N2

O in the atmosphere since the beginning of the 

industrial era (Fig. 12), leading to an enhanced 

greenhouse effect. More CO2

 has been released 

by humans into the atmosphere than any other 

greenhouse gas. Carbon dioxide levels have been 

increasing at a rate of 1.4 parts per million (ppm) 

per year for the past 50 years (IPCC 2007), reaching 

395 ppm in January 2013 (Tans and Keeling 2013). 

In recent decades, fossil fuel burning has been 

responsible for approximately 83 to 94 percent of 

the human-induced increase in CO2 . The remaining 

6 to 17 percent of human-caused emissions has 

come primarily from deforestation of land for 

conversion to agriculture. However, increases in 

fossil fuel emissions over the past decade mean that 

the contribution from land-use changes has become 

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

2009).

Methane is responsible for roughly 14 percent 

of greenhouse gas emissions (IPCC 2007). 

Concentrations of this gas have also been increasing 

as a result of human activities, including agricultural 

production of livestock and increases in rice 

production. Livestock production contributes to CH4

emissions primarily from fermentation in the guts of 
Chapter 2: Climate Change Science and Modeling

41

Figure 12.—Concentrations 

of greenhouse gases over 

the past 2005 years, showing 

increases in concentrations 

since 1750 attributable 

to human activities in the 

industrial era. Concentration 

units are parts per million 

(ppm) or parts per billion 

(ppb), indicating the 

number of molecules of 

the greenhouse gas per 

million or billion molecules 

of air. Figure courtesy of the 

Intergovernmental Panel on 

Climate Change (2007).

cattle and other ruminants. Rice production requires 

wet conditions that are also ideal for microbial CH4

production. Other sources of CH4

 include biomass 

burning, microbial emissions from landfills, fossil 

fuel combustion, and leakage of natural gas during 

mining and distribution. 

Nitrous oxide accounts for about 8 percent of global 

greenhouse gas emissions (IPCC 2007). The primary 

human source of N2

O is agriculture. Using more 

fertilizer increases N2

O emissions from soil as soil 

microbes break down nitrogen-containing products. 

In addition, converting tropical forests to agricultural 

lands increases microbial N2

O production. Other 

sources of N2

O from human activities include nylon 

production and combustion of fossil fuels.

Humans have reduced stratospheric ozone 

through the use of chlorofluorocarbons (CFCs) 

in refrigeration, air conditioning, and other 

applications. Restrictions against the use of CFCs 

under the Montreal Protocol led to a decline in CFC 

emissions and reductions in ozone have subsequently 

slowed. After CFCs were banned, another class of 

halocarbons, hydrofluorocarbons (HFCs, also known 

as F-gases), largely replaced CFCs in refrigeration 

and air conditioning. Although HFCs do not deplete 

stratospheric ozone, many are powerful greenhouse 

gases. Currently, HFCs account for about 1 percent 

of greenhouse gas emissions (IPCC 2007).

CLIMATE MODELS

Scientists use models, which are simplified 

representations of reality, to simulate future climates. 

Models can be theoretical, mathematical, conceptual, 

or physical. General circulation models (GCMs), 

which combine complex mathematical formulas 

representing physical processes in the ocean, 

atmosphere, and land surface within large computer 

simulations, are important in climate science. They 

are used in short-term weather forecasting as well as 

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

42

General Circulation Models

General circulation models simulate physical 

processes on the Earth’s surface, oceans, and 

atmosphere through time by using mathematical 

equations in three-dimensional space. They can 

work in time steps as small as minutes or hours in 

simulations covering decades to centuries. Because 

of their high level of complexity, GCMs require 

intensive computing power, and must be run on 

immense supercomputers. 

Although climate models use highly sophisticated 

computers, limits on computing power mean that 

projections are limited to relatively coarse spatial 

scales. Instead of simulating climate for every single 

point on Earth, modelers divide the land surface, 

ocean, and atmosphere into a three-dimensional 

grid (Fig. 13). Each 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 

Figure 13.—Schematic describing climate models, which are systems of differential equations based on the basic laws of 

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

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

within each grid and evaluate interactions with neighboring points. Figure courtesy of the National Oceanic and Atmospheric 

Administration (2008).
Chapter 2: Climate Change Science and Modeling

43

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

latitudes, about the size of the northeastern quarter 

of Minnesota. These horizontal grids are stacked in 

interconnected vertical layers that simulate ocean 

depth or atmospheric thickness at increments usually 

ranging from 650 to 3,280 feet.

Several GCMs have been used in climate projections 

for the IPCC reports and elsewhere (Box 4). These 

models (in parentheses) have been developed by 

internationally renowned climate research centers 

such as NOAA’s Geophysical Fluid Dynamics 

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

the United Kingdom’s Hadley Centre (HadCM3) 

(Pope et al. 2000), and the National Center for 

Atmospheric Research (PCM) (Washington et al. 

2000). These models use slightly different grid sizes 

and ways of quantitatively representing physical 

processes. They also differ in sensitivity to changes 

in greenhouse gas concentrations, which means that 

some models will tend to project higher increases in 

temperature than others under increasing greenhouse 

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

instances, the choice of GCM can have a larger 

influence on the projected climate trends than the 

choice of greenhouse gas emissions scenario.

Box 4: More Resources on Climate Models and Emissions Scenarios

U.S. Forest Service

Climate Projections FAQ

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

U.S. Global Change Research Program

Climate Models: an Assessment of Strengths and 

Limitations

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

assessments/2004-2009-synthesis-and-assessmentproducts 

Intergovernmental Panel on Climate Change

Chapter 8: Climate Models and Their Evaluation

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

ch8.html

Special Report on Emissions Scenarios: 

Summary for Policymakers

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

php?idp=0

Great Lakes Integrated Science and Assessment 

(GLISA) Center

Midwest Technical Input Report for the National 

Climate Assessment

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

Like all models, GCMs have strengths and 

weaknesses. They are useful and reliable tools 

because they are based on well-understood physical 

processes. In general, GCM simulations of past 

climates correspond well with measured and proxybased estimates of ancient climates (Maslin and 

Austin 2012). These models are judged in part by 

their ability to accurately simulate past climate 

against proxy estimates. But GCM projections 

are not perfect (Maslin and Austin 2012). Climate 

scientists’ understanding of some climate processes 

is incomplete, and some influential climate 

processes occur at spatial scales that are too small 

to be modeled given current computing power. 

Additionally, GCM projections are impossible to 

validate perfectly, because the projections are driven 

by future conditions that have never previously 

occurred. Finally, future climate projections may 

be unable to capture the frequency of extreme 

weather events or large climate shifts. Technological 

advances in computing along with scientific 

advances in our understanding of Earth’s physical 

processes will lead to continued improvements 

in GCM projections. Projections may still have 

a considerable range of future values, however, 

because adding greater modeling complexity 
Chapter 2: Climate Change Science and Modeling

44

introduces new sources of uncertainty (Maslin and 

Austin 2012).

Emissions Scenarios

General circulation models require significant 

amounts of information to project future climates. 

Some of this information, like future greenhouse gas 

concentrations, is not known and must be estimated. 

Although human population growth, economic 

circumstances, and technological developments will 

certainly have dramatic effects on future greenhouse 

gas concentrations, these developments cannot be 

completely foreseen. One common approach for 

dealing with uncertainty about future greenhouse gas 

concentrations is to develop alternative storylines 

about how the future may unfold and then calculate 

the potential greenhouse gas concentrations for each 

storyline. The IPCC’s set of standard emissions 

scenarios is a widely accepted set of storylines 

(IPCC 2007). In GCMs, the use of different 

emissions scenarios results in different climate 

projections.

Emissions scenarios are a quantitative representation 

of alternative storylines given certain demographic, 

technological, or environmental developments. None 

of the scenarios includes any changes in national or 

international policies directed specifically at climate 

change such as the Kyoto Protocol. However, 

some of the scenarios that include a reduction in 

greenhouse gases via other means suggest what we 

could expect if these policies were implemented. Six 

different emissions scenarios are commonly used in 

model projections (Fig. 14).

The A1FI scenario is the most fossil-fuel intensive 

storyline, and thus results in the highest projected 

future greenhouse gas concentrations. GCM 

simulations using the A1FI scenario predict the most 

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. GCM simulations 

using the B1 scenario predict the least future 

warming. Although these scenarios were designed to 

describe a range of future emissions over the coming 

decades, it is important to note that the future will 

conceivably be different from any of the developed 

scenarios. It is highly improbable that future 

greenhouse gas emissions will be less than described 

by the B1 scenario even if national or international 

policies were implemented immediately. In fact, 

current emissions more closely track the greenhouse 

gas emissions of the A1FI scenario, and global 

emissions since 2000 have even exceeded the A1FI 

scenario values in some years (NOAA National 

Climatic Data Center 2012a, Raupach et al. 2007).

Figure 14.—Projected global greenhouse gas (GHG) 

emissions (in gigatons [Gt] of carbon dioxide equivalent 

per year) assuming no change in climate policies under 

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

published in the Special Report on Emissions Scenarios 

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

shaded area) of recent scenarios published since SRES. 

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

Figure courtesy of the Intergovernmental Panel on 

Climate Change (2007).
Chapter 2: Climate Change Science and Modeling

45

Downscaling

As mentioned previously, GCMs simulate climate 

conditions only for relatively large areas. To 

examine the future climate of areas within northern 

Minnesota, a smaller grid scale is useful. 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 

of the future for much smaller spatial scales. Grid 

resolution for downscaled climate projections is 

typically about 6.2 miles. Although it is useful 

to have more localized projections, downscaling 

introduces further uncertainty to the future GCM 

projections, so users are advised to pay attention 

to general trends rather than individual pixels or 

clusters of pixels. 

Statistical downscaling has several advantages 

and disadvantages (Box 5) (Daniels et al. 2012, 

Maslin and Austin 2012). It is a fairly simple 

and inexpensive way to produce smaller-scale 

projections using GCMs. One limitation is that 

downscaling assumes that past relationships 

Box 5: Model Limitations and Uncertainty

“All models are wrong, some are useful.” 

–George Box (Box and Draper 1987) 

Models are conceptual representations of reality, 

and any model output must be evaluated for its 

accuracy to simulate any biological or physical 

response or process. The overall intention is to 

provide the best information possible for land 

managers given the uncertainty and limitations 

inherent in models.

Model results are not considered standalone 

components of this vulnerability assessment because 

there are many assumptions made about the 

processes simulated by GCMs and impact models, 

uncertainty in future greenhouse gas concentrations, 

and limits on the numbers of inputs that a model 

can reliably handle. Precipitation projections 

usually have much more variability among future 

climate projections than temperature. Regions with 

complex topography contain much more diversity 

in microclimates than many models can capture. 

Many nonclimate stressors, such as insect pests or 

pathogens, can overshadow the impact of climate on 

a species or community, especially in the short term. 

Therefore, model results are best interpreted by local 

experts to identify regional caveats and limitations of 

each model, and are best considered with additional 

knowledge and experience in the forest ecosystems 

being assessed. 

We integrated fundamentally different types 

of impact models into our assessment of forest 

vulnerability to climate change. These models 

operate at different spatial scales and provide 

different kinds of information. The DISTRIB model 

projects the amount of available suitable habitat 

for a species. The LANDIS-II model projects 

changes in biomass and species distribution. The 

PnET-CN model projects ecosystem productivity. 

There are similarities between some inputs into 

these models—downscaled climate models and 

scenarios, simulation periods, and many of the 

same species—but because of the fundamental 

differences in their architecture, their results are not 

directly comparable. Their value lies in their ability to 

provide insights into how various interrelated forest 

components may respond to climate change under a 

range of possible future climates. 

Models can be useful, but they are inherently 

incomplete. For that reason, an integrated approach 

using multiple models and expert judgment is 

needed. The basic inputs, outputs, and architecture 

of each model are summarized in this chapter with 

clear descriptions of the limitations and caveats 

of each model. Limitations of these models with 

specific applicability to forest ecosystems are 

discussed in more detail in Chapter 5.
Chapter 2: Climate Change Science and Modeling

46

between modeled and observed temperature and 

precipitation will remain consistent 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 difficult 

to obtain a good downscaled estimate of future 

climate for that area. Finally, local influences on 

climate that occur at finer scales (such as land cover 

type or topography) also add to uncertainty when 

downscaling climate projections.

Another approach, dynamical downscaling, uses a 

regional climate model (RCM) embedded within 

a GCM (Daniels et al. 2012). Like GCMs, RCMs 

simulate physical processes through mathematical 

representations on a grid. However, RCMs operate 

on a finer resolution than GCMs, typically ranging 

from 15.5 to 31.0 miles, but can be as fine as 

6.2 miles or less. Thus, they can simulate the effects 

of topography, land cover, lakes, and regional 

circulation patterns that operate on smaller scales. 

As with statistical downscaling, dynamical 

downscaling has pros and cons (Daniels et al. 

2012). It is advantageous for simulating the effects 

of climate change on processes such as lake-effect 

snow or extreme weather. However, like GCMs, 

RCMs require a lot of computational power and 

they are not necessarily more accurate at projecting 

change than GCMs (Kerr 2013). Dynamically 

downscaled data are usually available only for 

one or two GCMs or scenarios, and for limited 

geographic areas. Because dynamically downscaled 

data are limited for the assessment area, we use 

statistically downscaled data in this report. 

Downscaled Climate Projections 

Used in this Assessment 

In this assessment, we report statistically 

downscaled climate projections for two GCMemissions scenario combinations: GFDL A1FI and 

PCM B1. Both models and both scenarios were 

included in the IPCC Fourth Assessment Report

(IPCC 2007). The latest version of the National 

Climate Assessment, currently in development, also 

draws on statistically downscaled data based on 

IPCC models and scenarios but uses the A2 scenario 

as an upper bound, which projects lower emissions 

compared to A1FI. The IPCC includes several other 

models, which are represented as a multi-model 

average in its assessment reports. The National 

Climate Assessment takes a similar approach in 

using a multi-model average. For this assessment, 

we instead selected two models that had relatively 

good skill at simulating climate in the eastern United 

States and that bracketed a range of temperature and 

precipitation futures. This approach gives readers 

a better understanding of the level of agreement 

among models and provides a set of alternative 

scenarios that can be used by managers in planning 

and decisionmaking. 

The National Oceanic and Atmospheric 

Administration’s GFDL model is considered 

moderately sensitive to changes in greenhouse gas 

concentrations (Delworth et al. 2006). In other 

words, any change in greenhouse gas concentration 

would lead to a change in temperature that is 

higher than some models and lower than others. 

By contrast, the National Center for Atmospheric 

Research’s model, PCM, is considered to have 

low sensitivity to greenhouse gas concentrations 

(Washington et al. 2000). As mentioned above, 

the A1FI scenario is the highest greenhouse 

gas emissions scenario used in the 2007 IPCC 

assessment, and is the most similar to current 

global trends in greenhouse gas emissions. The B1 

scenario is the lowest greenhouse gas emissions 

scenario used in the 2007 IPCC assessment, and is 

thus much lower than the trajectory for greenhouse 

gas emissions during the past decade. Therefore, 

the GFDL A1FI and PCM B1 scenarios span a large 

range of possible futures. Although both projections 

are possible, the GFDL A1FI scenario represents a 

more realistic projection of future greenhouse gas 
Chapter 2: Climate Change Science and Modeling

47

emissions and temperature increases (Raupach et al. 

2007). It is important to note that actual emissions 

and temperature increases could be lower or higher 

than these projections. 

This assessment relies on a statistically downscaled 

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

maximum, and minimum temperature and total daily 

precipitation were downscaled to an approximately 

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

uses a modified statistical asynchronous quantile 

regression method to downscale daily GCM output 

and historical climate data (Stoner et al. 2013). 

This approach is advantageous because GCM 

and historical data do not need to be temporally 

correlated, and it is much better at capturing extreme 

temperatures and precipitation events than a linear 

regression approach (Hayhoe 2010b). This is a 

different statistically downscaled data set than used 

in the National Climate Assessment, which uses a 

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

data set was chosen for several reasons. First, the 

data set covers the entire United States, and thus 

allows a consistent data set to be used in this and 

other regional vulnerability assessments. Second, 

it includes downscaled projections for the A1FI 

emissions scenario, which is the scenario that most 

closely matches current trends in global greenhouse 

gas emissions (Raupach et al. 2007). Third, the 

data set includes daily values, which are needed for 

some impact models used in this report. Finally, the 

7.5-mile grid scale was fine enough to be useful for 

informing land management decisions. 

Summarized projected climate data are shown in 

Chapter 4. To show projected changes in temperature 

and precipitation, we calculated the average daily 

mean, maximum, and minimum temperature for 

each month for three 30-year periods (2010 through 

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

monthly averages were grouped into seasonal and 

annual values. Mean monthly precipitation was also 

calculated and summed seasonally and annually for 

the same periods. We then subtracted these values 

from the corresponding 1971 through 2000 average 

to determine the departure from current climate 

conditions. Historical climate data used for the 

departure analysis were taken from ClimateWizard 

(Girvetz et al. 2009). Chapter 3 includes more 

information about the observed climate data from 

ClimateWizard. 

Importantly, the downscaled future climate 

projections were also used in each of the forest 

impact models described below. This consistency 

in future climate data allows for more effective 

comparison across different model results. The 

models also operate on grid scales that may be larger 

or smaller than the grid scale of the downscaled data 

set, and grid scales were adjusted accordingly.

New bur oak leaves emerging in spring 2013. Photo by Eli 

Sagor, University of Minnesota, used with permission.
Chapter 2: Climate Change Science and Modeling

48

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. 

Impact models use GCM projections as inputs, as 

well as information about tree species, life-history 

traits of individual species, and soil types. Many 

different models are used to simulate impacts 

on species and forest ecosystems. These models 

generally fall in one of two main categories: species 

distribution models (SDMs) and process models. 

In this assessment, we used one SDM, the Climate 

Change Tree Atlas (Prasad et al. 2007-ongoing), 

and two process models, LANDIS-II (Scheller et 

al. 2007) and PnET-CN (Aber et al. 1997). These 

models operate at different spatial scales and 

provide different kinds of information. We chose 

them because they have been used to assess climate 

change impacts on ecosystems in our geographic 

area of interest, and have stood up to rigorous peer 

review in scientific literature.

Species distribution models establish a statistical 

relationship between the distribution of a species 

or community and key attributes of its habitat. 

This relationship is used to predict how the range 

of the species will shift as climate change affects 

those attributes. These models are much less 

computationally expensive than process models, 

so they can typically provide projections for the 

suitable habitat of many species for a larger area. 

There are some caveats that users should be aware 

of when using them, however (Wiens et al. 2009). 

The models use a species’ realized niche instead 

of its fundamental niche. The realized niche is the 

actual habitat a species occupies given predation, 

disease, and competition with other species. A 

species’ fundamental niche, in contrast, is the 

habitat it could potentially occupy in the absence 

of competitors, diseases, or predators. Given 

that a species’ fundamental niche may be greater 

than its realized niche, SDMs may underestimate 

current niche size and future suitable habitat. In 

addition, species distributions in the future might be 

constrained by competition, disease, and predation in 

ways that do not currently occur. If so, SDMs could 

overestimate the amount of suitable habitat in the 

future. Furthermore, fragmentation or other physical 

barriers to migration may create obstacles for species 

otherwise poised to occupy new habitat. Therefore, 

a given species might not actually be able to enter 

the assessment area in the future, even if Tree Atlas 

projects it will gain suitable habitat. Additionally, 

SDMs like Tree Atlas do not project that existing 

trees will die if suitable habitat moves out of an area. 

Rather, this is an indication that they will be living 

farther outside their ideal range and will be exposed 

to more climate-related stress. 

In contrast to SDMs, process models such as 

LANDIS-II and PnET-CN simulate community 

and tree species dynamics based on interactive 

mathematical representations of physical and 

biological processes. Process models can simulate 

future change in tree species dispersal, succession, 

biomass, and nutrient dynamics over space and time. 

Because these models simulate spatial and temporal 

dynamics of a variety of complex processes, they 

typically require more computational power than an 

SDM. Therefore, fewer species or forest types can 

be modeled compared to an SDM. Process models 

have several assumptions and uncertainties that 

should be taken into consideration when applying 

results to management decisions. Process models 

rely on empirical and theoretical relationships that 

are specified by the modeler. Any uncertainties in 

these relationships can be compounded over time 

and space, leading to an erroneous result.
Chapter 2: Climate Change Science and Modeling

49

Although useful for projecting future changes, both 

process models and SDMs share some important 

limitations. They assume that species will not 

adapt evolutionarily to changes in climate. This 

assumption may be true for species with long 

generation times (such as trees), but some short-lived 

species may be able to adapt even while climate is 

rapidly changing. Both types of models may also 

magnify the uncertainty inherent in their input data. 

Data on the distribution of trees, site characteristics, 

and downscaled GCM projections are estimates that 

add to uncertainty. No single model can include all 

possible variables, so there are important inputs that 

may be excluded from individual models, such as 

competition from understory vegetation, herbivory, 

and pest outbreaks. Given these limitations, it is 

important for all model results to pass through a 

filter of local expertise to ensure that results match 

with reality on the ground. Chapter 6 and Appendix 

5 explain the approach used in this assessment for 

determining the vulnerability of forest ecosystems 

based on local expertise and model synthesis. 

Climate Change Tree Atlas

The Climate Change Tree Atlas (Tree Atlas) 

incorporates a diverse set of information about 

potential shifts in the distribution of tree species’ 

habitat in the eastern United States over the next 

century (Iverson et al. 2008, Prasad et al. 2007-

ongoing). Tree Atlas is actually a set of different 

models and information that work together. The 

species distribution model DISTRIB measures 

relative abundance, referred to as importance 

values, for 134 eastern tree species. Inputs include 

tree species distribution data from the U.S. Forest 

Moose in northern Minnesota. Photo by Casey McQuiston, Superior National Forest.
Chapter 2: Climate Change Science and Modeling

50

Service Forest Inventory and Analysis (FIA) 

Program and 38 predictor variables (pertaining to 

climate, soil properties, elevation, land use, and 

fragmentation), which are used to model current 

species abundance with respect to current habitat 

distributions using statistical techniques (Iverson et 

al. 2008). DISTRIB then projects future importance 

values and suitable habitat for individual tree species 

by using downscaled GCM data readjusted to a 

12-mile grid (Prasad et al. 2007-ongoing). 

Each tree species is further evaluated for additional 

factors not accounted for in the statistical models 

(Matthews et al. 2011b). These modifying 

factors (Appendix 4) are based on supplementary 

information about life-history characteristics 

such as dispersal ability or fire tolerance as well 

as information on pests and diseases that have 

been having negative effects on the species. This 

supplementary information allows us to identify 

when an individual species may do better or worse 

than model projections would suggest.

For this assessment, the DISTRIB model uses 

the GFDL A1FI and PCM B1 model-scenario 

combinations. The results provided in Chapter 5 

differ from online Tree Atlas results because they 

are specific to the assessment area and use the new 

statistically downscaled data set described above. 

Modifying factors are based on general species 

traits that are consistent across the entire range of 

a species, so the modifying factor values presented 

in the assessment are not unique for the assessment 

area. 

LANDIS-II

The LANDIS-II model is an integrated modeling 

approach for simulating landscape changes that 

is process-driven and flexible for a variety of 

applications (Scheller et al. 2007). It is based on 

earlier versions of the LANDIS model (Mladenoff 

2004). This model simulates disturbance, 

management, succession, and other processes in 

a grid-based framework that emphasizes spatial 

interactions across the landscape and among 

processes (e.g., climate change, harvesting, 

succession, fire, wind, and seed dispersal). This 

approach means that processes occur both within 

a given grid cell and between cells. LANDIS-II 

simulates age-based cohorts of individual tree 

species, rather than individual trees. It can run 

simulations for many decades and large spatial 

extents (greater than 1 million acres). Some 

processes are simulated to occur randomly based 

on probabilities and cell conditions, such as fire 

disturbance or seed dispersal. Specifically, the 

Biomass Succession (v3.1), Biomass Harvest (v2.0), 

Base Wind, and Base Fire extensions were used for 

all simulations (see www.landis-ii.org for further 

details on the options available).

Inputs to LANDIS-II include an initial conditions 

map with tree species assigned to age cohorts across 

all forested areas, soils information, and other spatial 

data. Climate change is incorporated by integrating 

specific species parameters to calculate maximum 

aboveground net primary productivity (Aber et al. 

1997) and the probability of establishment (Xu et 

al. 2009) at every time step. LANDIS-II calculates 

these parameters by using monthly maximum and 

minimum temperature, precipitation, and solar 

radiation. Other inputs include foliar nitrogen 

(N) content, maximum foliar mass area, and soil 

water-holding capacity. LANDIS-II also requires 

modelers to specify timber harvest prescriptions 

(Ravenscroft et al. 2010) and rotation periods for fire 

and wind disturbances (White and Host 2008). More 

information on the harvest prescriptions used for 

this assessment can be found in Appendix 4. Outputs 

include maps of species distribution over time and 

time series graphs for aboveground biomass by 

species and for aggregated forest types.
Chapter 2: Climate Change Science and Modeling

51

For this assessment, two future climate scenarios, 

PCM B1 and GFDL A1FI, were used to simulate a 

range in potential future climate. A current climate 

scenario was also constructed as a baseline for 

comparison. The current climate scenario was 

designed by using climate data from 1970 through 

1999 as a range of possible values. These data were 

accessed from the PRISM data set (Gibson et al. 

2002), and values were randomly sampled from 

this range for all future years of the simulation. 

The simulations used a 4.9-acre cell size and a 

150-year horizon from 2000 to 2150. The landscape 

in northeastern Minnesota covered 3.95 million 

acres of forest. This is a subset of the assessment 

area, located in the northeast portion of the state 

(Chapter 5). LANDIS-II simulations included 

21 tree species currently present within this 

landscape. Forest management practices were 

described with a business-as-usual scenario, 

presented in more detail in Appendix 4. 

PnET-CN

PnET-CN is an ecosystem-level process model that 

simulates carbon (C), water, and N dynamics in 

forests over time (Aber et al. 1997, 2001; Ollinger et 

al. 2008; Peters et al. 2013). PnET-CN accounts for 

physiological and biogeochemical feedbacks, which 

allows C, water, and N cycles to interact with each 

other. This enables PnET-CN to simulate the effects 

of water and N limitation on forest productivity. 

A strength of the PnET-CN model is its ability to 

simulate forest responses to many simultaneously 

changing environmental factors, including climate, 

N deposition, tropospheric ozone, and atmospheric 

CO2 . Although PnET-CN can be applied to large 

geographical regions, it is not a spatially dynamic 

model and cannot represent ecological processes 

such as succession or migration. PnET-CN 

assumes forest composition does not change over 

time. Rather, the utility of PnET-CN is to assess 

the physiological response of existing forests to 

projected environmental change. 

PnET-CN requires input information on climate, 

soil, and vegetation. Climate and atmospheric 

inputs include monthly air temperature, 

precipitation, photosynthetically active radiation, 

tropospheric ozone concentration, atmospheric CO2

concentration, and atmospheric N deposition rate. 

Soils are defined by their water holding capacity. 

Vegetation inputs include a suite of parameters, 

such as specific leaf area or leaf lifespan, that 

define a particular forest type. Forest types used 

by PnET-CN in this assessment are similar to FIA 

forest-type groups, such as maple/beech/birch 

(Miles et al. 2011). Output from PnET-CN includes 

many variables related to C, water, and N cycling, 

including key ecosystem processes such as net 

primary production, net ecosystem production, 

evapotranspiration, and N mineralization. Full 

information on the PnET-CN simulations used in this 

assessment, including inputs, methods, and results, 

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

For this assessment, we ran PnET-CN from 1960 to 

2100 across the Laurentian Mixed Forest Province in 

Minnesota using a grid resolution of 0.6 miles. Two 

future climate scenarios, PCM B1 and GFDL A1FI, 

were used to simulate a range in potential future 

climate and atmospheric CO2

 concentration. Current 

tropospheric ozone concentrations and N deposition 

rates (data provided by the U.S. Environmental 

Protection Agency) were held constant into the 

future. Soil water-holding capacity was defined by 

using the Natural Resources Conservation Service’s 

Soil Survey Geographic Database (Matthew Peters, 

U.S. Forest Service, personal comm.). Vegetation 

cover was defined by using a vegetation map based 

on FIA data and satellite imagery (Wilson et al. 

2012), which included six forest-type groups (maple/

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

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

did not include any harvest, fire, or wind-related 

disturbances in the simulations for this assessment.
Chapter 2: Climate Change Science and Modeling

52

SUMMARY

Temperatures have been increasing in recent 

decades at global and national scales, and the 

overwhelming majority of scientists attribute this 

change to increases in greenhouse gases from human 

activities. Even if dramatic changes are made to help 

curtail greenhouse gas emissions, these greenhouse 

gases will persist in our atmosphere for decades to 

come. Scientists can model how these increases in 

greenhouse gases may affect global temperature and 

precipitation patterns by using general circulation 

models. These large-scale climate models can be 

downscaled to finer resolution and incorporated into 

other types of models that project changes in forest 

composition and ecosystem processes to inform 

local decisions. There are inherent uncertainties 

in what the future holds, but all of these types of 

models can help us frame a range of possible futures. 

This information can then be used in combination 

with the local expertise of researchers and managers 

to provide important insights about the potential 

effects of climate change on forests.
53

Chapter 3: Observed Climate Change

Climate is the long-term weather pattern for a region 

for a period of decades. As discussed in Chapter 

1, climate is one of the principal factors that have 

determined the composition and extent of forest 

ecosystems in the Laurentian Mixed Forest Province 

over the past several thousand years. 

This chapter describes the climate trends in the 

assessment area that have been observed during 

the past century, including documented patterns 

of climate-related processes and extreme weather 

events. Ecosystems in northern Minnesota are 

already exhibiting signals that they are responding to 

shifts in temperature and precipitation. This chapter 

presents a few case studies to illustrate how the 

effects of climate change are being documented in 

ecological indicators such as growing season shifts, 

wildlife populations, fish populations, and lake ice 

timing. 

HISTORICAL TRENDS 

IN TEMPERATURE AND 

PRECIPITATION 

Substantial changes in temperature and precipitation 

have occurred in northern Minnesota over the 

past 100 years, and the rate of change appears 

to be increasing (Minnesota Department of 

Natural Resources [DNR] 2011a). We used the 

ClimateWizard Custom Analysis tool to assess the 

changes in temperature and precipitation across 

the assessment area (ClimateWizard 2012, Girvetz 

et al. 2009). Data for the tool are derived from 

PRISM (Gibson et al. 2002), which models historic 

measured point data onto a continuous 2.5-mile grid 

for the entire United States. We examined long-term 

(1901 through 2011) trends for annual, seasonal, 

and monthly temperature (mean, maximum, 

and minimum) and total precipitation within the 

assessment area. Accompanying tables and figures 

present the change during the 110-year period 

estimated from the slope of the linear trend. In the 

following text we highlight increasing or decreasing 

trends for which we have high confidence that they 

did not occur by chance. For more information 

regarding confidence in trends and the PRISM data, 

refer to Appendix 2. 

Temperature 

The mean annual temperature within the assessment 

area increased 2.2 °F (1.2 °C) from 1901 to 2011, 

and this trend has been consistent across the area 

(ClimateWizard 2012). This trend is similar to 

the rate of increase across the entire state during 

roughly the same time (Minnesota DNR 2011a). 

Mean annual temperatures fluctuated considerably 

during the 20th century, with almost 10 °F (5.6 °C) 

separating the hottest and coldest years on record 

(Fig. 15). 

Temperatures in the assessment area have increased 

across all seasons, but the rate of increase has 

varied from season to season (Table 13). The largest 

increase in mean temperature occurred during winter 

(3.7 °F, 2.1 °C), and spring mean temperatures 

increased by 2.7 °F (1.5 °C). Fall and summer mean 

temperatures have increased by smaller amounts. 

Mean minimum temperatures have increased at a 

faster rate than mean high temperatures across all 

seasons. The warming trends for the assessment area 

closely follow observed statewide trends, with the 

greatest warming increases occurring in winter low 
Chapter 3: Observed Climate Change

54

Figure 15.—Annual mean temperature within the 

assessment area from 1901 through 2011 (ClimateWizard 

2012). The blue line represents the running 5-year mean. 

The red regression line shows the trend across the entire 

period.

temperatures (Minnesota DNR 2011a). Data from 

1971 through 2000 indicate that the winter warming 

trend in recent years has been roughly 50 percent 

faster than the 20th-century trend (ClimateWizard 

2012). 

Observed temperature trends also differ by month 

within the assessment area (Fig. 16). Temperature 

increases were greatest during the winter and spring 

months, peaking in February with an increase of 

almost 6.0 °F (3.3 °C) in monthly mean temperature 

from 1901 to 2011. Mean temperature increases 

that were greater than the annual mean increase 

Season Average temperature increase Average high temperature increase Average low temperature increase

Annual 2.2 1.4 3.1

Winter (Dec.-Feb.) 3.7 2.7 4.8

Spring (Mar.-May) 2.7 2.2 3.2

Summer (June-Aug.) 1.5 0.4 2.6

Fall (Sept.-Nov.) 1.0 0.2 1.9

Table 13.—Increases in mean annual and seasonal temperature (°F) from 1901 through 2011 in the assessment area 

(ClimateWizard 2012)

occurred during January, March, May, August, and 

December. Mean temperature did not decline in 

any month during this period. Increases for mean 

minimum temperatures were larger than increases 

for mean temperature or high temperatures for all 

months. Mean minimum temperatures increased 

4.0 °F (2.2 °C) or more in January, February, and 

March. Mean high temperatures increased less than 

mean temperatures across all months, and actually 

decreased for July and October. These decreases 

were very slight in both cases.

Temperature trends differed geographically 

across the assessment area (Fig. 17). In winter, 

the greatest warming has occurred in the center 

of the assessment area, focused on an area around 

Brainerd, Grand Rapids, and Leech Lake. The North 

Shore of Lake Superior has also warmed faster than 

surrounding areas during the winter months. Spring 

temperature increases have been strongest in these 

same areas. Summer trends have been much more 

uniform across the assessment area, except for areas 

of greater warming around Lake of the Woods and 

the North Shore of Lake Superior, which appear 

to be driven mostly by increased mean minimum 

temperatures. Fall spatial trends were also similar 

to summer months, with moderate changes across 

the assessment area. As mentioned above, fall 

temperatures held essentially constant over the 

20th century. Interestingly, a slight but widespread 

cooling trend for mean maximum temperature 

occurred in the northwest portion of the assessment 

area during summer and fall.
Chapter 3: Observed Climate Change

55

Figure 16.—Change in mean monthly temperatures from 1901 through 2011 within the assessment area (ClimateWizard 

2012).

Trapper’s Creek in winter. Photo by Casey McQuiston, Superior National Forest.
Chapter 3: Observed Climate Change

56

Figure 17.—Annual and seasonal observed temperature changes from 1901 through 2011 in the assessment area. Change is 

calculated from the slope of the regression line across the timeframe (ClimateWizard 2012). Stippling indicates there is less 

than 10-percent probability that the trend could have occurred by chance alone.
Chapter 3: Observed Climate Change

57

Precipitation

From 1900 through 2011, mean annual precipitation 

increased by 4.3 inches across the assessment area 

(Table 14) (ClimateWizard 2012). A recent statewide 

assessment calculated an increase in precipitation 

of 2.7 inches during the 20th century, so it appears 

that the assessment area is getting wetter than the 

rest of Minnesota (Minnesota DNR 2011a). Several 

of the driest years on record occurred between 1910 

and 1940 for the assessment area (Fig. 18). During 

the second half of the 20th century there was great 

year-to-year variation in precipitation, but the overall 

trend appears to have been a moderate increase. 

The trend in the assessment area seems to be that 

spring, summer, and fall are getting much wetter and 

winter is getting only slightly wetter. Fall had the 

largest absolute increase in precipitation from 1901 

to 2011 (1.6 inches). Summer had the next largest 

absolute increase over this period (1.4 inches). 

Among the individual months, precipitation declined 

only in February. Mean October precipitation 

increased by 0.9 inches from 1901 to 2011, the 

largest increase among all months. Precipitation also 

increased notably in January, April, and December. 

There are also interesting geographic differences in 

observed precipitation trends across the assessment 

area (Fig. 19). Across the entire year, the greatest 

precipitation increases were observed in the 

southeastern portion of the assessment area along 

the border with Wisconsin. Precipitation increased 

by a larger amount along the North Shore of 

Lake Superior throughout all seasons than in the 

surrounding areas. Winter and spring did not differ 

much by geography as most of the area gained 1 

inch or less of precipitation during these seasons. 

Summer precipitation increased in the southeastern 

and northwestern portions of the assessment 

area, with increases of more than 2 inches near 

Lake of the Woods and in a band extending south 

from Hibbing. Two main areas of increased fall 

precipitation were located around Park Rapids and 

Duluth. 

Interactions Between 

Temperature and Precipitation

Observed temperature and precipitation trends in 

the assessment area correspond with larger regional 

climate patterns. An examination of observed 

temperature and precipitation from 1950 through 

2006 for the entire country found that areas that 

tended to get wetter during warm seasons also 

Season Average precipitation increase 

Annual 4.3

Winter (Dec.-Feb.) 0.4

Spring (Mar.-May) 0.9

Summer (June-Aug.) 1.4

Fall (Sept.-Nov.) 1.6

Table 14.—Increase in annual and seasonal precipitation 

(inches) from 1901 through 2011 in the assessment area 

(ClimateWizard 2012)

Figure 18.—Annual precipitation (inches) within the 

assessment area from 1901 through 2011 (ClimateWizard 

2012). The blue line represents the running 5-year mean. 

The red regression line shows the trend across the entire 

period.
Chapter 3: Observed Climate Change

58

Figure 19.—Annual and seasonal precipitation changes from 

1901 through 2011 in the assessment area (ClimateWizard 

2012). Change is calculated from the slope of the regression 

line across the timeframe. Stippling indicates there is less 

than 10-percent probability that the trend could have 

occurred by chance alone.

tended to have reduced high temperatures during 

those seasons (Portmann et al. 2009). Conversely, 

areas of the country that are getting drier during 

warm seasons have also shown increased high 

temperatures. For the upper Midwest, this pattern 

was most evident during summer and fall. Fall 

exhibited the smallest temperature increases within 

the assessment area (Table 13) and also the largest 

precipitation increases (Table 14). October in 

particular had the greatest increase in precipitation 

(0.9 inches), but a decreasing mean high temperature 

(-0.6 °F, -0.3 °C). A similar phenomenon occurred 

in July. The causes of this relationship between 

precipitation and high temperatures are not yet fully 

explained, but it has been proposed that cloudiness, 

evaporation of surface moisture, organic aerosols 

from forests, and air pollution may all be involved 

(Portmann et al. 2009). 

HISTORICAL TRENDS IN EXTREMES 

AND PHYSICAL PROCESSES

Although it can be very instructive to examine 

long-term means of climate and weather data, in 

many circumstances extreme events can have a 

greater impact on forest ecosystems and the human 

communities that depend on them. Weather or 

climate extremes are defined as individual weather 

events or long-term patterns that are unusual in their 

occurrence or have destructive potential (Climate 

Change Science Program [CCSP] 2008). These 

events can trigger catastrophic disturbances in forest 

ecosystems, along with significant socioeconomic 

disasters. The distribution of individual species or 

forest types is often controlled by particular climatic 

extremes. Climate change has been estimated to 

have increased the probability of several kinds of 

extreme weather events, although it is difficult to 

directly attribute one particular event to climate 

change (Coumou and Rahmstorf 2012). Extreme 

events are difficult to analyze with standard 

statistical methods, so long-term studies of weather 

and climate trends are necessary. 
Chapter 3: Observed Climate Change

59

Many physical processes important for forest 

ecosystems are also driven by climate and weather 

patterns. These factors, such as snowpack and 

soil frost, can regulate annual phenology, nutrient 

cycling, and other ecosystem dynamics. Changes 

to these physical processes can result in impacts 

and stress that might not be anticipated from mean 

climate values alone. This section presents a few 

key trends that have been observed in Minnesota or 

throughout the broader region. 

Snow and Winter Storms

Cold and snowy winters are characteristic of 

northern Minnesota, although the state experiences 

fewer snowstorms than nearby Wisconsin or 

Michigan (Changnon and Changnon 2007). There 

is a gradient of increasing winter precipitation from 

west to east across the assessment area, owing to the 

prevailing wind direction, topography, and lakeeffect snow from Lake Superior (ClimateWizard 

2012). For the assessment area, winter precipitation 

increased only 0.4 inches during the 20th century 

(Table 14, Fig. 19). 

Annual snowfall amounts have been decreasing 

between 1 and 3 percent per decade in northern 

Minnesota during the 20th century (Kunkel et al. 

2013). Regional trends indicate that snowfall is quite 

variable from year to year, but few heavy snowfall 

years have occurred in the most recent 30 years 

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

been more intense as well. From 1900 to 1990, there 

was an increase in snowstorms of 6 inches or more 

across the upper Midwest (CCSP 2008). Extreme 

low-snow years in the four-state region including 

Minnesota became less common over the 20th 

century (Kunkel et al. 2009). This trend corresponds 

with the slight increase in winter precipitation across 

the assessment area and the wider region. Longterm records from across the Great Lakes indicate 

that lake-effect snow increased gradually across the 

region during the 20th century, probably due to the 

warming of these water bodies and the decreasing 

trend in lake ice cover (Burnett et al. 2003, Kunkel 

et al. 2013). 

Soil Frost

Soil frost dynamics are important for forest 

ecosystems because soil temperatures can affect 

water infiltration rates, nutrient cycling, and tree 

growth. Research has shown that deeper snow depth 

results in shallower soil frost depth in northern 

forests, and thinner snowpack results in colder 

soil temperatures and deeper soil frost (Hardy et 

al. 2001). Long-term data indicate that winter soil 

temperatures tended to decrease during the 20th 

century across northern Michigan and northern 

Wisconsin, even as temperatures increased (Isard 

et al. 2007). Similarly, Sinha et al. (2010) found 

evidence for decreasing winter soil temperatures 

within the assessment area in recent decades. 

Therefore, even as winter temperatures have risen 

in the assessment area (Table 13), frost depth may 

have increased as snowpack conditions became more 

variable. Warmer winter air temperatures have led 

to more snowmelt in intervening periods between 

snowfall events. During the entire 20th century, 

however, there appears to have been a 12- to 

24-day decline in the annual number of soil frost 

days (Sinha et al. 2010). 

Additionally, Sinha et al. (2010) found evidence for 

one to two more freeze-thaw cycles per winter in 

northern Minnesota during the 20th century. Freezethaw cycles can damage roots of frost-intolerant tree 

species and affect the timing of nutrient release in 

forest soils (Auclair et al. 2010, Tierney et al. 2001). 

Intense Precipitation

Intense precipitation events have become more 

frequent across much of the continental United 

States (Kunkel et al. 2008). In the upper Midwest, 

there was a 50-percent increase in the frequency 
Chapter 3: Observed Climate Change

60

of days with rainfall of 4 or more inches during 

the 20th century (CCSP 2008). A recent study by 

Groisman et al. (2012) also supports this trend, 

noting that moderately heavy rainfall events 

(0.5 to 1.0 inches) became less frequent for the 

central United States, while rainfall events of at least 

1 inch became more common. Heavy precipitation 

events that used to occur only once every 12 months 

are now occurring every 9 months across the upper 

Midwest, an increase of roughly 35 percent during 

the past 60 years (Madsen and Willcox 2012). 

This trend is exemplified in Minnesota, where a 

104-percent increase was observed in rainstorms of 

3 inches or more between 1960 and 2011 (Fig. 20) 

(Saunders et al. 2012). Four rainfall events of more 

than 10 inches have occurred in Minnesota since 

2004, including the event that caused extensive 

flooding in the Duluth area in 2012 (Minnesota DNR 

2011a). Storms of this magnitude are predicted to 

occur only once every 1,000 years. Maximum daily 

rainfall for the assessment area typically occurs in 

June, July, or August (Villarini et al. 2011a).

Water running over a forest road following the June 2012 rainstorm in northeastern Minnesota. Photo by Patrick Hampston, 

Superior National Forest.

Figure 20.—Changes in the frequency of rainfall events of 

3 inches or more in Minnesota from 1961 through 2011, 

compared to the baseline years 1961 through 1990. Figure 

from Saunders et al. (2012) and used with permission of the 

authors.
Chapter 3: Observed Climate Change

61

Flooding and Streamflow

Long-term data on flooding is difficult to interpret 

because of the variety of measures used to describe 

floods. From 1961 to 1979, the National Weather 

Service reported no severe flood years in the fourstate region including Minnesota, while there were 

4 such years between 1983 and 2001 (Cartwright 

2005). Olsen et al. (1999) also found an increasing 

trend in the frequency of flood-level flows along 

the upper Mississippi River in Minnesota, but 

noted that there are several complicating factors in 

attributing this trend. In particular, anthropogenic 

land-use change over the past century has had a 

considerable influence on flooding frequency in 

the upper Midwest. Increased flood levels in the 

upper Midwest may be driven by land use practices, 

agricultural practices, and dam construction 

(Villarini et al. 2011b). Even taking these factors into 

account, however, Tomer and Schilling (2009) still 

found evidence that Midwestern watersheds have 

exhibited increased discharge during the past several 

decades. They attribute this trend to climate change. 

Extreme Temperatures

High temperatures can influence forests in a 

variety of ways, and some species and forest types 

are limited by hot growing-season temperatures. 

Extreme temperatures may also be associated with 

disturbance events like droughts and wildfire. Longterm records indicate that extreme hot weather has 

become more frequent across the Midwest during the 

second half of the 20th century (Kunkel et al. 2013, 

Perera et al. 2012). Recent heat waves have been 

characterized by very high humidity levels as well 

as high nighttime temperatures (Kunkel et al. 2013). 

Additionally, multi-day heat waves have become 

more common over the past 60 years (Perera et al. 

2012). Summer cool days have become less frequent 

during this same period. These trends correspond to 

global patterns of increasing occurrence of extreme 

hot weather and decreasing occurrence of extreme 

cool weather (Hansen et al. 2012). A study across the 

entire Midwest region found that intense cold waves 

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

recurrence threshold) have been less frequent during 

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

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

Soil Moisture and Drought 

Droughts are among the greatest stressors on 

forest ecosystems, and can often lead to secondary 

effects of insect and disease outbreaks on stressed 

trees and increased fire risk. In North America and 

the Midwest in particular, there has been a trend 

toward wetter conditions since 1950, and there is 

no detectable trend for increased drought based on 

the Palmer Drought Severity Index (Dai et al. 2004, 

Karl et al. 2009). Another study of hydrologic trends 

in the United States over the last century (1915 

through 2003) also observed reduced duration and 

severity of droughts across the upper Midwest as 

a result of increased precipitation (Andreadis and 

Lettenmaier 2006). Data from Minnesota support 

this pattern. Since 2000, there have been at least 

six periods where more than 80 percent of the state 

was rated moderately dry or worse, according to the 

U.S. Drought Monitor archives (National Drought 

Mitigation Center 2013). Nevertheless, between 

1895 and 2013, the trend in the assessment area 

has been toward slightly less common and less 

severe droughts during the growing season, with 

the years between 1920 and 1940 representing the 

most extreme droughts during the period of record 

(National Oceanic and Atmospheric Administration, 

National Climatic Data Center 2013).

Thunderstorms and Tornadoes

Strong thunderstorms occur most frequently in the 

summer months in northern Minnesota, and these 

weather events can be particularly damaging if they 

generate tornadoes. Based on long-term data from 

1896 to 1995, the assessment area in Minnesota 

averaged 20 to 35 thunderstorm days per year 

(Changnon 2003). There is a clear south-to-north 

gradient of decreasing thunderstorm frequency 

across the assessment area and the entire state. 
Chapter 3: Observed Climate Change

62

The number of tornadoes across the state has been 

increasing recently. Between 2001 and 2010 the 

mean number of tornadoes was 51 per year, up 

from only 37 tornadoes per year between 1981 and 

2010 (National Weather Service 2012). In 2010, 

Minnesota set a state record for the number of 

tornadoes occurring in one year (104), including 

48 tornadoes on a single day in June (Minnesota 

DNR 2011a). The U.S. Annual Tornado Maps from 

1950 to 2009 show that few of these tornadoes occur 

within the Laurentian Mixed Forest Province, but are 

more common in western and southern Minnesota 

(National Weather Service 2012). 

Windstorms

In warm months the assessment area occasionally 

experiences very powerful windstorms, often called 

derechos. These events can result in substantial 

windthrow disturbances, as evidenced by the 1999 

storm that passed through northern Minnesota along 

the Canadian border. This single storm blew down 

roughly 665,000 acres of forest within the Boundary 

Waters Canoe Area Wilderness and the Quetico 

Provincial Park (Price and Murphy 2002). Smallerscale wind disturbances also introduce complexity 

in forest stands throughout the region (Schulte 

and Mladenoff 2005, White and Host 2008). The 

frequency of derechos decreases with increasing 

latitude in Minnesota, and northern Minnesota has 

been roughly the northern limit for warm-season 

derecho occurrence in North America (Coniglio 

and Stensrud 2004). Our understanding of historical 

trends in derecho frequency and geographic location 

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

of the 20th century (Peterson 2000). 

Paper birch regeneration blown over in a strong windstorm in summer 2012. Photo by John Rajala, Rajala Companies, used 

with permission.
Chapter 3: Observed Climate Change

63

INDICATORS OF CLIMATE CHANGE

The following case studies present some examples 

of early indications of climate change within 

northern Minnesota ecosystems. A more extensive 

list of observed changes throughout the state is 

available in Climate Change and Renewable Energy: 

Management Foundations (Minnesota DNR 2011a).

Lake Ice

Across Minnesota, long-term records have shown 

that lake ice is breaking up earlier in the spring and 

forming later in the fall. Spring ice-out dates shifted 

earlier by 1.3 days per decade between 1965 and 

2002 (Johnson and Stefan 2006). The mean ice-out 

date appears to be advancing almost twice as rapidly 

since 1990. Observed ice-in dates in the fall changed 

even faster, shifting later by 7.5 days per decade 

from 1978 to 2002, and by 14 days per decade since 

1990. The combined effect of these trends is a longer 

ice-free period for lakes across the region and the 

assessment area. 

Regional data and model simulations support this 

trend (Kling et al. 2003, Mishra et al. 2011), as well 

as trends across the entire northern hemisphere 

(Magnuson et al. 2000). Within the three-state region 

of Minnesota, Michigan, and Wisconsin, observed 

changes in lake ice duration indicate that ice-in and 

ice-out dates have been shifting three to four times 

more rapidly since 1980 than across the 20th century 

(Kling et al. 2003). Therefore, the total duration of 

lake ice is shrinking at an accelerating rate, which 

long-term trends may underestimate. Ice cover on 

the Great Lakes is also declining substantially, with a 

mean decline of 71 percent in ice coverage between 

1973 and 2010 (Wang et al. 2012). Reduced ice 

cover exposes more of the lake’s surface to radiation, 

allowing the lake to absorb and retain more heat. 

Increased lake temperatures can contribute to shifts 

in ice formation and coverage, which can strongly 

influence near-shore climates and weather events, 

such as lake-effect snow.

Timing of the Growing Season

Changes in the temperature regime will also 

influence the seasonal timing of favorable 

temperatures for plant growth. Meteorological 

records show an earlier onset of warm temperatures 

in spring and later onset of cold temperatures 

in winter during the 20th century. This altered 

seasonality results in the overall lengthening of 

the potential growing season. There is increasing 

worldwide evidence of response of phenology, 

the timing of biological events such as leafing, 

flowering, and leaf coloring (Menzel et al. 2006, 

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

et al. 2006a). The degree to which organisms take 

advantage of a longer active growing season depends 

on the species. A global analysis of leafing and 

flowering trends shows a consistent advancement 

of 5 to 6 days per 1.8 °F (1.0 °C) of warming across 

many species and locations (Wolkovich et al. 2012).

Long-term records of plant phenology exist in 

Minnesota but are scattered and have not yet been 

systematically analyzed. A 50-year record (1941 to 

1991) from St. Paul, Minnesota, shows evidence of 

an 8-day advancement of quaking aspen leafing 

(Fig. 21) (Hodson 1991). In Wisconsin, a 61-year 

record (1936 to 1947; 1976 to 1998) shows 

7-day advancement of spring phenology across 

55 monitored plants and animals (Bradley et al. 

1999). A warming experiment in northern 

Minnesota suggests similar patterns of spring 

advancement for common forest tree species in the 

state (R. Montgomery and P. Reich, University of 

Minnesota, unpublished data). 

Wildlife Populations and Range Shifts

Changes in wildlife populations in northern 

Minnesota may be taken as further evidence of 

climate change. As temperatures have warmed 

across the state, researchers in northern Minnesota 

have witnessed the northward range expansion of 

animal species. Monitoring inventories in Voyageurs 
Chapter 3: Observed Climate Change

64

31-Mar

5-Apr

10-Apr

15-Apr

20-Apr

25-Apr

30-Apr

5-May

10-May

15-May

20-May

1940 1950 1960 1970 1980 1990 2000 2010

Year of observation

Date that aspen leaves emerged

Historic data set (A. Hodson; St. Paul, MN)

Current data

(C. Buyarski;

East Bethel, MN)

Figure 21.—Leaf-out dates for quaking aspen based on a 50-year record collected in St. Paul, Minnesota, and a 4-year record 

collected in East Bethel, Minnesota (~30 miles north of St. Paul). The St. Paul record is based on daily observations whereas 

the East Bethel record is based on weekly observations. Figure courtesy of R. Montgomery, University of Minnesota.

National Park from 1996 through 2001 recorded 

four rodent species that previously had resided south 

of the area: the white-footed mouse, rock vole, 

Franklin’s ground squirrel, and eastern gray squirrel 

(Jannett et al. 2007). It also appears that badger 

and raccoon are expanding their ranges within the 

northern part of the state. Land-use change might 

have also facilitated the northward spread of the 

raccoon and ground squirrel, but these species had 

not previously taken advantage of the land-use 

changes brought about by heavy logging in the 

early 20th century. 

Moose is an iconic species in Minnesota. The 

southern extent of the moose distribution range 

occurs within the assessment area, and evidence 

suggests that ambient temperature thresholds in 

winter and summer may be largely responsible 

for determining where moose can thrive (Lenarz 

et al. 2009, 2010). Higher winter temperatures 

improve survival of moose parasites like ticks, and 

also improve survival of white-tailed deer, which 

transmit additional parasites and diseases to moose 

(Rempel 2011). Higher summer temperatures 

exacerbate heat stress in moose and make it more 

difficult to maintain optimal body temperature. 

The northeastern Minnesota moose population has 

been on a recent downward trend, based on a recent 

examination of population demographics (Lenarz et 

al. 2010). This trend is due to a variety of factors, but 

increasing temperatures within the assessment area 

are suspected of having placed greater heat stress 

on moose in northeastern Minnesota. Minnesota 

DNR studies indicate that the population decline for 

northeastern moose has accelerated in recent years, 

with a 52-percent decline from 2010 to 2013 and a 

35-percent decline from 2012 to 2013 (Minnesota 

DNR 2013).
Chapter 3: Observed Climate Change

65

Warming temperatures have also been linked to 

the declining moose populations elsewhere in the 

region. The population in northwestern Minnesota 

has declined dramatically in the past 20 years and 

now fewer than 100 individuals remain (Lenarz et al. 

2010). Additionally, ecological modeling to describe 

the range of moose in Ontario found significant 

relationships between moose abundance and mean 

winter and summer temperatures from 1990 through 

1999 (Rempel 2011). 

Fish Populations 

and Reproductive Phenology

Cisco (also called lake herring or tullibee) is a coldwater fish native to northern Minnesota, which is the 

southern part of the species’ range (Fig. 22). Cisco 

is a common species that provides important forage 

for other fish in inland lakes such as walleye, lake 

trout, and northern pike (Fang et al. 2009, Jiang et 

al. 2012). Cold, oxygenated water is crucial for cisco 

survival. Cisco populations have been monitored in 

almost 650 Minnesota lakes since 1946 (Minnesota 

DNR 2011a). These surveys have documented a 

42-percent decline in inland lake cisco populations 

since 1975. Recent research suggests that this 

decline is the result of warming lake temperatures, 

which lead to stratification and eutrophication 

processes that reduce oxygen availability. Lakes that 

host cisco have not typically been subject to humancaused eutrophication (Minnesota DNR 2011a). 

Changes have also been observed in the yearly 

reproductive cycles of important fish species in 

Minnesota. Walleye spawning is often closely 

associated with ice-out events in rivers and lakes. 

Long-term egg collection data for Minnesota 

fisheries show that as ice-out dates have gradually 

shifted earlier into the spring, walleye spawning 

seasons have shifted earlier as well (Schneider et 

al. 2010). For most locations, egg laying appears 

to have tracked climate change relatively closely 

during the 20th century. Spawning advanced 

0.5-1.0 days for every 1.0 days of advanced ice-out. 

Potential implications of this shift could include 

Figure 22.—Distribution of 648 lakes where cisco have 

been sampled in Minnesota DNR surveys since 1948. Figure 

courtesy of Peter Jacobson, Minnesota DNR.

mismatches between egg hatch dates and prey 

availability for the larval walleye, or mismatches 

between spawning seasons and peak streamflow. 

SUMMARY

Northern Minnesota has observed several notable 

shifts in climate, climate-driven processes, and 

extreme weather events (Box 6). In general, the 

assessment area is experiencing warmer weather 

across the year, particularly with respect to mean 

minimum temperatures and winter temperatures. 

Precipitation increased during the 20th century and 

the precipitation regime has intensified, resulting in 

more large precipitation events. Characteristic winter 

conditions are diminishing, and growing seasons 

appear to be lengthening. These trends are consistent 

with regional, national, and global observations 

about anthropogenic climate change. Ecological 

indicators are beginning to reflect these changes as 

well, as evidenced by changing ranges of wildlife 

species and changing phenology. 
Chapter 3: Observed Climate Change

66

Box 6: More Information on Observed Climate Trends and Ecological Indicators

Much more information on historical climate trends 

and ecological indicators for northern Minnesota 

exists than was possible to present in this chapter. 

Interested readers will be able to find more 

information from the following resources: 

• Minnesota State Climatology Office: 

climate.umn.edu/ 

• Minnesota Department of Natural Resources 

“Climate” Web page: 

www.dnr.state.mn.us/climate/index.html 

• University of Minnesota Extension “Extreme 

Weather” Web page: 

www.extension.umn.edu/extreme-weather/

drought-fire/climatology/ 

• Minnesota Phenology Network: 

https://www.usanpn.org/mnpn/home 

• ClimateWizard:

www.climatewizard.org/ 

• National Climatic Data Center: 

www.ncdc.noaa.gov/ 
67

Chapter 4: Projected Changes in Climate, 

Extremes , and Physical Processes

This chapter describes climate projections for the 

assessment area over the 21st century, including 

projections related to patterns of extreme weather 

events and other climate-related processes. 

Temperature and precipitation projections are 

derived from downscaled climate models. Chapter 2

more fully describes the models, data sources, 

and methods used to generate these downscaled 

projections, as well as the inherent uncertainty in 

making long-term projections. We focus on two 

plausible climate scenarios for the assessment area, 

chosen to bracket a range of possible climate futures. 

Information related to future weather extremes and 

other impacts is drawn from published research. 

PROJECTED TRENDS 

IN TEMPERATURE 

AND PRECIPITATION

To represent the range of plausible climate futures 

in the assessment area, we report projected changes 

in temperature and precipitation for three 30-year 

periods in the next century (2010 through 2039, 

2040 through 2069, 2070 through 2099) (Stoner 

et al. 2013). For each of these periods, we calculated 

the average mean, maximum, and minimum 

temperature for each season and across the entire 

year. We also calculated mean annual and seasonal 

precipitation for the same periods. We use the 1971 

to 2000 average as a contemporary “baseline” to 

determine future departure from current climate 

conditions. Observed climate data for the baseline 

period are from ClimateWizard (Girvetz et al. 2009), 

based on the PRISM data set (see Chapter 3 and 

Appendix 2). 

For all climate projections, we report values for 

a combination of two general circulation models 

(GCMs) and emissions scenarios: GFDL A1FI and 

PCM B1 (see Chapter 2). The GFDL A1FI modelscenario combination projects greater changes 

in terms of future temperature increases and 

precipitation decreases, and PCM B1 projects less 

change. Although both projections are plausible, 

GFDL A1FI may be more realistic based on our 

current global greenhouse gas emissions trajectory 

(Raupach et al. 2007). The future will probably be 

different from any of the developed scenarios, so we 

encourage readers to consider the range of possible 

climate conditions in the coming decades rather than 

one particular scenario.

Temperature

The Laurentian Mixed Forest Province in Minnesota 

is projected to warm substantially during the 

21st century (Figs. 23 to 26). Compared to the 

1971 to 2000 baseline period, the average annual 

temperature is projected to increase 3.0 °F (1.7 °C) 

under the PCM B1scenario and 8.8 °F (4.9 °C) under 

the GFDL A1FI scenario. The projected temperature 

increase is not consistent across all seasons. Both 

models project that winter months (DecemberFebruary) will experience dramatic warming by the 

end of the century (PCM B1: 3.9 °F, 2.2 °C; GFDL 

A1FI: 9.8 °F, 5.4 °C), but spring months (MarchMay) will experience less warming (PCM B1: 

2.2 °F, 1.2 °C; GFDL A1FI: 5.4 °F, 3.0 °C). The 

GFDL A1FI scenario also projects an increase of 

11.4 °F (6.3 °C) in summer temperatures by the end 

of the century. Summer warming is much milder 

under the PCM B1 scenario (2.3 °F, 1.3 °C). See 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

68

Appendix 3 for a table of temperature projections 

for the assessment area, as well as maps of projected 

change in the early century (2010 through 2039) and 

mid-century (2040 through 2069). 

Figure 23.—Projected difference in mean daily temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

69

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

compared to baseline (1971 through 2000) for two climate scenarios.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

70

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

compared to baseline (1971 through 2000) for two climate scenarios.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

71

Figure 26.—Projected mean, maximum, and minimum temperatures in the assessment area averaged over 30-year periods for 

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

panels have different Y-axis values.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

72

Minimum temperatures are projected to increase 

more than maximum temperatures under both 

scenarios across nearly all seasons. Summer is 

the only exception to this trend, with increases in 

maximum temperatures projected to be slightly 

higher than increases in minimum temperatures 

under the PCM B1 scenario. By the end of the 

century, winter minimum temperatures are expected 

to increase 5.0 °F (2.8 °C) under the PCM B1 

scenario and 12.1 °F (6.7 °C) under the GFDL A1FI 

scenario. 

Temperature increases are projected to be relatively 

minor between the 1971 through 2000 baseline 

period and the 2010 through 2039 period 

(Fig. 26). Additionally, the projections under the 

two future scenarios do not diverge substantially 

until mid-century (2040 to 2069). The GFDL A1FI 

scenario leads to much larger temperature increases, 

with the greatest amount of change expected to 

occur mid-century. Alternatively, the PCM B1 

projections indicate a substantially smaller increase 

in temperature, with relatively constant increases 

during the 21st century. 

Projected temperature trends are geographically 

consistent across the assessment area (Figs. 23 to 

25). An interesting spatial pattern exists along the 

North Shore of Lake Superior. Compared to the 

assessment area as a whole, this area is expected 

to face even larger temperature increases during 

winter. This trend is apparent under both model 

scenarios. The same area, particularly Cook County, 

is projected to experience cooler or only slightly 

warmer spring and summer months. These trends 

are generally consistent for minimum and maximum 

average temperatures as well. 

Although the two climate scenarios project different 

amounts of warming, they are in agreement that 

mean, maximum, and minimum temperatures will 

increase in the assessment area for winter, spring, 

and fall. The two models display very different 

futures for summer months, with the PCM B1 

scenario showing very little warming and scattered 

areas of cooling. Conversely, the GFDL A1F1 

scenario projects most of the assessment area will 

experience warming on the order of 10 °F to 

12 °F (5.6 °C to 6.7 °C) for mean, maximum, and 

minimum summer temperatures. This is even greater 

than the large temperature increases expected in 

winter months. 

Precipitation

The two climate scenarios we chose for this 

assessment describe a range of future precipitation 

for the assessment area (Figs. 27 and 28), but it 

is important to keep in mind that other GCM and 

emissions scenario combinations could project 

values outside of this range. Substantial differences 

exist among projections of precipitation across the 

Midwest (Kunkel et al. 2013, Winkler et al. 2012). 

The PCM B1 scenario projects that the assessment 

area will receive 3.0 inches more annual rainfall at 

the end of the next century compared to the baseline 

years of 1971 through 2000. The assessment area 

may experience a slight decrease in annual rainfall 

during this same period according to the GFDL 

A1F1 scenario (-0.4 inches), with a large decrease 

occurring during the summer. See Appendix 3 for a 

table of precipitation projections for the assessment 

area, as well as maps of projected change in the 

early century (2010 through 2039) and mid-century 

(2040 through 2069).

The seasonal precipitation trends show even more 

departure between the two scenarios. In particular, 

most of the difference between these two climate 

scenarios exists in spring and summer. Under the 

PCM B1 scenario, spring months are expected 

to receive steadily increasing precipitation 

during the 21st century, with a total increase of 

1.6 inches. Summer precipitation under this scenario 

is projected to be relatively constant, with a total 

change of around 0.5 inches. The GFDL A1FI 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

73

Figure 27.—Projected difference in mean precipitation at the end of the century (2070 through 2099) compared to baseline 

(1971 through 2000) for two climate scenarios.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

74

Figure 28.—Projected trends in average precipitation in the assessment area averaged over 30-year periods for the entire year 

and by season. The 1971 through 2000 value is based on observed data from weather stations. Note that the panels have 

different Y-axis values.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

75

scenario projects a much sharper distinction between 

these seasons, with spring gaining 3.2 inches and 

summer precipitation declining by 4.8 inches. Those 

projections represent a 54-percent increase in spring 

precipitation, followed by a 40-percent decrease in 

summer precipitation. Winter and fall precipitation is 

expected to remain relatively consistent under both 

scenarios. 

Like the future temperature projections, precipitation 

across the assessment area is expected to change 

only slightly between the baseline period (1971 

through 2000) and the early part of the 21st century 

(2010 through 2039). The greatest change in 

precipitation projections under this scenario occurs 

in mid-century (2040 through 2069), driven by 

substantial declines in summer precipitation. The 

PCM B1 scenario projects gradual change across 

the 21st century. 

The PCM B1 scenario projections indicate that 

precipitation increases will occur throughout the 

assessment area, with the greatest increase in 

the northwestern portion of the assessment area. 

These increases are projected mainly in the spring 

and summer months (Fig. 27). The North Shore 

of Lake Superior and southern portion of the 

assessment area show a decrease in precipitation 

during summer and fall under this scenario. Under 

the GFDL A1FI scenario, the spatial patterns of 

projected precipitation are relatively even across the 

assessment area for all seasons (Fig. 27). The maps 

of precipitation departure from baseline conditions 

for this scenario also highlight the sharp contrast 

between projected spring increases and summer 

decreases. 

Evapotranspiration and 

Precipitation Ratios

Temperature and precipitation values are both 

important climatic factors governing forest 

ecosystems, and it is projected that both will shift 

within the assessment area in the coming century. 

A given amount of change in temperature or 

precipitation may be ecologically significant, but it 

is difficult to know how changes in one value might 

buffer or amplify changes in the other. For example, 

a given increase in temperature may not result in 

significant ecological change if precipitation also 

increases, but the same increase in temperature could 

result in a severe change if accompanied by reduced 

precipitation. As temperatures rise, the atmosphere 

is able to hold larger quantities of water, which 

causes evaporation and transpiration to increase. 

Increasing both evaporation and transpiration leads 

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

Therefore, precipitation generally needs to increase 

significantly to compensate for even moderate 

temperature increases. One way to examine the 

potential interaction between temperature and 

precipitation shifts is to look for changes in the ratio 

of evapotranspiration (ET) to precipitation (P). This 

ratio, ET:P, is essentially a metric to describe how 

completely a forest ecosystem is using the available 

water. Changes in this ratio indicate whether a forest 

is experiencing relatively drier or wetter conditions. 

We used the ecosystem model PnET-CN to calculate 

projected changes in ET:P for the assessment area, 

comparing the 1971 through 2000 baseline period 

to the years 2070 through 2099. Evapotranspiration 

is an output of the PnET-CN model, so these 

values also incorporate projected changes in forest 

productivity due to temperature and precipitation 

changes, growing season length, carbon dioxide 

(CO2

) fertilization, and other factors. Chapter 2 more 

fully describes the PnET-CN model, and further 

results from this model are presented in Chapter 5 

and Peters et al. (2013). Figure 29 displays the 

projected annual and seasonal changes in ET:P 

under both PCM B1 and GFDL A1FI. Positive 

values indicate that ET is increasing relative to 

available moisture and that ecosystems would be 

subject to more moisture stress. Conversely, negative 

values indicate that more moisture is available. It is 

important to note that PnET-CN projects major water 

savings under elevated CO2 , which is an area of 

considerable uncertainty (Peters et al. 2013).
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

76

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

the assessment area over the next century. Data from Stoner et al. (2013) and Peters et al. (2013). Positive values indicate that 

ET is increasing relative to available moisture and that forests would be experiencing more moisture stress. Negative values 

indicate that more moisture is available.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

77

Both scenarios project slightly wetter total annual 

conditions (decrease in annual ET:P) across the 

entire assessment area by the end of the century, 

indicating greater water availability to forest 

ecosystems. Spring values are mixed across the 

assessment area in both scenarios, with drier 

conditions projected under the PCM B1 scenario 

(increasing ET:P) and more pronounced moisture 

increases under the GFDL A1FI scenario (decreasing 

ET:P). Summer months display the largest departure 

between the two projected scenarios, with PCM 

B1 projecting slightly wetter conditions (slightly 

decreasing ET:P) and GFDL A1FI projecting much 

drier conditions (large increase in ET:P). This overall 

trend is consistent with the precipitation trends 

discussed above. 

The ET:P values highlight that the GFDL A1FI 

scenario may result in a much higher degree of 

moisture stress in summer months than indicated by 

precipitation values alone. The projected summer 

temperature increase of 11.4 °F (6.3 °C) results 

in higher evapotranspiration for forests across the 

assessment area, which essentially intensifies the 

projected precipitation decline. There is high spatial 

variation for spring months under this scenario, but 

the ET:P ratio for spring is also slightly positive 

under the GFDL A1FI scenario. This result means 

that in some areas evapotranspiration increases may 

outweigh the projected precipitation increase. 

Additionally, forests along the North Shore of Lake 

Superior are projected to experience greater moisture 

limitation (increasing ET:P) in spring months under 

both future climate scenarios, despite projected 

increases in precipitation. This outcome indicates 

that productivity increases and longer growing 

seasons could lead to increases in ET that outpace 

the projected increases in precipitation. 

As mentioned above, ET:P values projected by 

PnET-CN include the effects of CO2

 fertilization, 

which results in significantly higher water-use 

efficiency and lower evapotranspiration for forest 

ecosystems (Ollinger et al. 2002). Projections not 

including the effects of higher atmospheric CO2

concentrations resulted in substantially higher ET:P 

ratios for the assessment area during the growing 

season (not shown). These results suggest that 

forests could have more frequent and extreme 

moisture stress in the future if water-use efficiency 

benefits from CO2

 fertilization are less significant 

than modeled by PnET-CN. Chapter 5 includes more 

information on the potential for CO2

 fertilization 

to influence forest productivity and water-use 

efficiency. 

PROJECTED CHANGES IN 

EXTREMES AND PHYSICAL 

PROCESSES

Mean temperature, precipitation, and ET:P ratios 

are not the only climatic factors that are important 

for regulating forest ecosystems. Other examples 

include extreme weather events, soil frost, and 

snowfall. Extremes are by their very nature difficult 

to forecast and model reliably, and climate-mediated 

processes often involve several interacting factors. 

Nevertheless, the scientific community is developing 

a clearer sense of how climate change may alter 

some of those weather events and physical processes 

across the Midwest (Kunkel et al. 2013). Below, 

we present a summary of current evidence on how 

climate change may affect other climate-related 

factors in the assessment area. 

Snow and Freezing Rain

Studies have shown that across much of the 

Midwest, an increasing percentage of winter 

precipitation is being delivered as rain rather than 

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

This shift from snowfall to rainfall is strongly 

correlated to winter wet-day temperatures. As 

winter temperatures increase across the assessment 

area, it is projected that more winter precipitation 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

78

in northern Minnesota will also be delivered as 

rain (Sinha and Cherkauer 2010). Total snow water 

equivalent (the amount of water contained in the 

snowpack) is projected to decrease by roughly 40 to 

80 percent by the end of the century under a range of 

climate scenarios (Sinha and Cherkauer 2010). 

A study of neighboring Wisconsin presents several 

projected snowfall trends that may be applicable to 

the assessment area (Notaro et al. 2011). Researchers 

anticipate snowfall across Wisconsin to decline 31 to 

47 percent by the end of the century under a range of 

climate scenarios. The largest reductions may occur 

in the early and late portions of the snow season, in 

November, March, and April. Under the same range 

of climate projections, the frequency of snowfall 

days is expected to decline between 41 and 

54 percent. Finally, snow depth throughout the 

winter is expected to decline even more than 

snowfall amounts, because snow depth will also be 

reduced by warm temperatures between snowfall 

events. 

Additionally, modeling studies have projected 

that climate change will result in slightly more 

frequent freezing rain events across the assessment 

area (Lambert and Hansen 2011). The projected 

changes are slight (2.5 more events per decade), 

but this trend is consistent with the projected shift 

in winter precipitation from snowfall to rain. The 

trend of increasing lake-effect snow may continue 

in the short term while winter temperatures remain 

cold enough to produce snow (Burnett et al. 2003). 

Model simulations suggest that lake-effect snow 

may decrease by the end of the 21st century due 

to warming temperatures, and that lake-effect rain 

may become more common during winter months 

(Kunkel et al. 2002). 

Shifts in winter precipitation will generally advance 

the timing of snowmelt runoff earlier into the year. 

The ability of soils to absorb this moisture will 

depend on infiltration rates and the soil frost regime. 

If soils are able to absorb and retain more of this 

moisture, soil moisture could be higher at the outset 

of the growing season. If this moisture is instead lost 

to runoff, forests in the assessment area could enter 

the growing season with a moisture deficit. 

Soil Frost

Winter temperatures are projected to increase across 

the assessment area under both PCM B1 and GFDL 

A1FI, which would be expected to increase soil 

temperatures. Snowcover typically insulates forest 

soils, however, so reduced snowpack under climate 

change could also leave the soil surface more 

exposed to fluctuations in air temperature and result 

in deeper soil frost (Isard et al. 2007). A study that 

attempted to integrate these conflicting trends found 

that cold-season soil temperatures may increase 

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

there would be approximately 30 fewer soil frost 

days per winter on average across the assessment 

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

Cherkauer 2010). The projected trends for soil frost 

across the region are shown in Figure 30. Total frost 

depth is projected to decline by 40 percent across the 

assessment area. Also, the annual number of freezethaw cycles is not expected to change by the end of 

the century. Therefore, it appears that warmer winter 

air temperatures will more than counteract the loss 

of snow insulation and that soil frost will generally 

be reduced across the assessment area. These 

projections are generally consistent with studies 

of snowpack and soil frost in New England forests 

(Campbell et al. 2010). 

Growing Season Length

The growing season has shifted in the assessment 

area during the past century, as noted in Chapter 

3. Growing seasons are dictated by a variety of 

factors, including day length, air temperatures, 

soil temperatures, and dates of first and last frost 

(Linderholm 2006). Therefore, a variety of metrics 

can describe how growing seasons may continue to 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

79

Figure 30.—Baseline and projected number of annual soil frost days for the Midwest under a range of climate scenarios, 

from Sinha and Cherkauer (2010). Base refers to the average annual number of soil frost days, 1977 through 2006. Earlybase, mid-base, and late-base refer to the difference in mean soil frost days from the baseline period for 2010 through 2039, 

2040 through 2069, and 2070 through 2099. The A2 emissions scenario is roughly equivalent to the A1FI scenario in terms of 

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

change under a range of climate scenarios. A study 

covering the entire Midwest examined the changes 

in dates for the last spring frost and first fall frost 

under a range of climate scenarios (Wuebbles and 

Hayhoe 2004). This study projected that the growing 

season will be extended by 30 days under the B1 

emissions scenario and 70 days under the A1FI 

scenario (Fig. 31). The last spring frost dates are 

projected to shift earlier into the year by the end 

of the century at approximately the same rate that 

first fall frost dates will retreat later into the year. 

Another study across the Midwest projected that 

the assessment area in Minnesota will have 

roughly 20 fewer frost days by the middle of the 

21st century, under the A2 emissions scenario 

(Kunkel et al. 2013).

As the climatic growing season changes, not all 

species will track these changes equally with their 

own phenology. For example, if native tree species 

are adapted to respond to day length changes at their 

particular latitude for leaf-drop in the fall, they may 

not be able to extend their growing seasons later in 

the year. If invasive species or southern migrants are 

adapted to a different day length regime or to frost 

dates, they may be more able to take advantage of 

the longer climatic growing season. 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

80

Figure 31.—Changes in length of frost-free season and dates of last spring and first autumn frost over the Midwest states. 

Historical data on the frost-free season are shown from 1900 through 2000, based on observed data. Projections from 2001 

through 2099 are shown in orange for the higher A1FI scenario and blue for the lower B1 scenario. Bars show year-to-year 

variability and solid lines indicate 10-year running means. From Kling et al. (2003), modified from Wuebbles and Hayhoe 

(2004), and reprinted with the permission of the authors.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

81

Intense Precipitation

As described in Chapter 3, there is a clear trend 

toward more extreme precipitation events in 

Minnesota and throughout the Midwest (Kunkel 

et al. 2008, Saunders et al. 2012). Rainfall from 

these high-intensity events is representing a larger 

proportion of the total annual and seasonal rainfall, 

meaning that the precipitation regime is becoming 

more episodic. An assessment covering the entire 

Great Lakes region projected that the frequency 

of single-day and multi-day heavy rainfall events 

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

recent assessments across a combination of climate 

projections indicate that the entire Midwest region 

will experience 23 percent more rainfall events 

of at least 1 inch, with larger events increasing by 

progressively larger amounts (Kunkel et al. 2013). 

Other future climate projections indicate that the 

assessment area may experience 2 to 4 more days of 

extreme precipitation (95th percentile or greater) by 

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

It is important to consider this trend in combination 

with the projected increases or decreases in mean 

precipitation for the 21st century. A given increase 

or decrease in precipitation will probably not be 

distributed evenly across a season or even a month. 

Additionally, large-scale modeling efforts have 

suggested that climate change will increase the yearto-year variation of precipitation across the northern 

United States (Boer 2009). Therefore, the assessment 

area may have more extreme wet and dry years in 

the future. Further, ecological systems are not all 

equally capable of holding moisture that comes in 

the form of extreme events. Areas with shallow soils 

may not have the water holding capacity to retain 

moisture received in intense rainstorms, and areas 

with fine-textured soils might not have fast enough 

infiltration rates to absorb water from these kinds of 

storms. Therefore if rainfall becomes more episodic, 

these areas may suffer from additional drought stress 

even if overall moisture or precipitation increases. 

Landscape position will also influence the ability of 

a particular location to retain moisture from extreme 

events.

Flooding and Streamflow

High-intensity rainfall events are linked to both flash 

flooding and widespread floods, depending on soil 

saturation and stream levels at the time of the event. 

As noted in Chapter 3, there has been a trend toward 

more frequent flooding in river systems across the 

Midwest. A modeling study examining climate 

change impacts on streamflow across the region 

suggested that runoff and streamflow may shift 

substantially across northern Minnesota (Wuebbles 

et al. 2009). Researchers projected a 30-percent 

increase in winter and spring runoff under the B1 

emissions scenario and a 90-percent increase under 

the A2 emissions scenario (Fig. 32). This reflects 

an overall increase in winter precipitation, more 

frequent winter rainfall, and snowmelt events. The 

same study projected that summer runoff could vary 

between a 24-percent increase and a 16-percent 

decrease under the same climate scenarios. The 

range of fall runoff values covered slight increases 

and even larger decreases. Mean and peak flows 

in the Upper Mississippi River were projected to 

increase by roughly 30 percent under both scenarios 

by the end of the century. Further modeling studies 

for rivers across the Midwest project that summer 

low flow levels may decrease, summer high flows 

may increase, and overall flashiness may increase in 

summer months (Cherkauer and Sinha 2010). 

Temperature Extremes

In addition to projecting mean temperatures, 

downscaled daily climate data can be used to 

estimate the frequency of extreme high and low 

temperatures in the future. Studies from across the 

Midwest point to an increasing frequency of hot 

days across the assessment area, with roughly 20 

to 30 more days per year above 95 °F and a greater 

frequency of multi-day heat waves by the end of the 

century (Diffenbaugh et al. 2005, Perera et al. 2012, 

Winkler et al. 2012). Downscaled climate scenarios 

also project that the Midwest will experience 

between 25 and 38 fewer days below freezing by 

the end of the 21st century (Sinha and Cherkauer 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

82

Figure 32.—Past (1976 to 2006) seasonal cumulative runoff values and projected (2069 to 2099) changes under a range of 

climate scenarios, from Wuebbles et al. (2009). The A2 emissions scenario is roughly equivalent to the A1FI scenario in terms 

of greenhouse gas emissions, and the A1B scenario is approximately a middle range between A1FI and B1.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

83

2010), and 12 to 15 fewer days that are colder than 

the current 95th percentile cold event (Diffenbaugh 

et al. 2005). These trends are consistent with another 

assessment of the Midwest that projected the 

assessment area in Minnesota could experience 

up to 10 more days above 95 °F (35 °C) and up to 

25 fewer days below 10 °F (-12 °C) by the middle 

of the 21st century (Kunkel et al. 2013).

Thunderstorms and Windstorms

An increasing frequency of strong convective 

storms across the entire Midwest has been observed 

during recent decades (Changnon 2011a, 2011b; 

Diffenbaugh et al. 2008). It is reasonable to expect 

that this trend will continue under a warmer climate. 

Modeling studies indicate that there will be more 

days with weather conditions that support severe 

thunderstorms in the assessment area, particularly 

in summer months (Trapp et al. 2007). This pattern 

is primarily due to an increase in atmospheric 

water vapor during summer months. Modeling 

studies suggest that weather conditions in the upper 

Midwest could lead to more storms that result in 

extreme rainfall but without strong convective 

winds (Trapp et al. 2007). This concept is supported 

by other research that forecasts a decrease in the 

frequency of severe tornadoes across the states of 

Minnesota, Wisconsin, and Michigan (Lee 2012). 

The timing of tornado season may continue to shift 

under future conditions, and tornadoes may occur 

farther north in areas where they have historically 

been uncommon. 

As mentioned in Chapter 3, a general lack of 

long-term data on straight-line wind storms limits 

our understanding of the trends for these events 

(Peterson 2000). Straight-line wind storms are 

prompted by different conditions than convective 

storms such as thunderstorms and tornadoes. There 

is a great deal of inherent annual and decadal 

variability for extreme wind events, and any shift in 

these events due to climate change is expected to be 

small over the next century (Winkler et al. 2012). 

SUMMARY

Northern Minnesota is projected to experience 

profound changes in climate by the end of the 

century. Direct changes include shifts in mean 

temperature and precipitation as well as altered 

timing and extremes. Projected changes also extend 

to more indirect climate-controlled factors such as 

an increasing frequency of extreme rainstorms and 

decreased soil frost during winter. By the end of the 

21st century the climate of the Laurentian Mixed 

Forest Province in Minnesota is generally projected 

to be hotter and more variable, with more moisture 

stress towards the end of the growing season and 

less characteristic winter weather. In the next 

chapter, we examine the ecological implications of 

these anticipated changes. 
84

Chapter 5: Future Climate Change Impacts 

on Forests

In this chapter, we describe the potential effects 

of climate change on forest ecosystems in the 

assessment area over the next century. These effects 

include the direct impacts of climate change, as 

well as indirect impacts due to forest pests, invasive 

species, altered disturbance regimes, and other 

interacting factors. To gain a better understanding 

of how forests in northern Minnesota may respond 

to climate change, we rely on forest impact models 

as well as scientific literature. This information 

provides us with the foundation to assess the 

potential vulnerability of forest ecosystems in the 

Laurentian Mixed Forest Province of Minnesota 

(Chapter 6). 

MODELED PROJECTIONS 

OF FOREST CHANGE

Forest ecosystems in the assessment area may 

respond to climate change in a variety of ways. 

Potential changes include shifts in the spatial 

distribution, abundance, and productivity of 

tree species. For this assessment, we rely on a 

combination of three forest modeling efforts to 

describe these potential changes. Researchers using 

the Tree Atlas, LANDIS-II, and PnET-CN models 

contributed results to this assessment (Table 15). 

Tree Atlas uses statistical techniques to model 

changes in suitable habitat for individual species 

over broad geographic areas. LANDIS-II is a 

spatially dynamic simulation model that includes 

migration, natural disturbances, timber harvest, 

and competition to project the abundance and 

distribution of individual tree species. PnET-CN 

simulates the movement of carbon (C), water, and 

nitrogen (N) in forest ecosystems and calculates the 

productivity of aggregated forest types. No single 

model offers a perfect projection of future change, 

but each tool is valuable for a particular purpose or 

set of questions. Complementary patterns across 

models are reinforced, and differences between 

model projections provide opportunities to better 

understand the nuances of ecological responses 

given the strengths and limitations of the models. 

For a more thorough description of the different 

models, and specifically how they were applied for 

this assessment, see Chapter 2. 

These model results are best used to describe 

trends across large areas and over long time scales. 

Models are not designed to deliver precise results 

for individual forest stands or a particular year in the 

future, despite the temptation to examine particular 

data points or locations on a map. 

Importantly, all of these modeling investigations 

relied on a consistent set of future climate data. 

Research teams used the same combinations of 

general circulation models (GCMs) and emissions 

scenario combinations described in detail in Chapter 

4: GFDL A1FI and PCM B1. The GFDL A1FI 

model-scenario combination is on the higher end of 

the spectrum for future temperature increases and 

precipitation decreases, and PCM B1 represents a 

milder projection. This consistency in the climate 

data used as inputs means that the forest impact 

models are describing potential forest changes over 

the same range of plausible future climates. See 

Chapter 2 for a more complete description of GCMs 

and emissions scenarios. 
Chapter 5: Future Climate Change Impacts on Forests

85

Feature Tree Atlas LANDIS-II PnET-CN

Summary Suitable habitat distribution 

model (DISTRIB) + supplementary 

information (modifying factors)

Spatially dynamic process 

model

Ecosystem-level carbon, 

water, and nitrogen process 

model

Primary outputs for this 

assessment

Area-weighted importance values 

and modifying factors by species

Aboveground biomass by 

species and distribution 

maps by forest type

Aboveground net primary 

productivity by forest type

Analysis area Laurentian Mixed Forest Province 

in MN

Northeast portion of 

assessment area – see Fig. 36

Laurentian Mixed Forest 

Province in MN

Migration Nob Yes No

Competition, survival, 

and reproduction 

No Yes No

Forest management No Yes Nob

Disturbances No (but addressed through 

modifying factors)

Yes (fire, wind, and timber 

harvest)

Nob

Tree physiology 

feedbacks

No No Yes

Succession or 

community shifts

No Yes No

Biogeochemical 

feedbacks

No Nob Yes

Table 15.—Overview of impact models used for this assessment and the different features included in simulations of 

future conditionsa

a

 See Chapter 2 for model descriptions, parameters, and scenarios used for this assessment.

b

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

Tree Atlas

Importance values of 134 eastern tree species were 

modeled for potential habitat suitability in the 

assessment area by using the DISTRIB model, a 

component of the Tree Atlas toolset (Iverson et al. 

2008). Importance value is an index of the relative 

abundance of a species in a given community. For 

an individual 12.4-mile grid cell, the importance 

value for a species can range from 0 (not present) 

to 100 (completely covering the area). Cell-bycell importance values are then summed across 

the assessment area to reach the area-weighted 

importance value for a species, so area-weighted 

importance values can be well above 100. This 

analysis was completed for the entire assessment 

area, and 74 of the 134 species currently have or are 

projected to have suitable habitat in the area. Chapter 

2 contains more detail on the Tree Atlas methods. 

The projected change in potential suitable habitat 

for the 74 species was calculated for the years 2070 

through 2099 by comparing the GFDL A1FI and 

PCM B1 scenarios to present values (Table 16). 

Species were categorized based upon whether the 

results from the two climate-emissions scenarios 

projected an increase, decrease, or no change in 

suitable habitat compared to current conditions, or 

if the model results were mixed between scenarios. 
Chapter 5: Future Climate Change Impacts on Forests

86

Declines under Both Scenarios:

Balsam fir (-) Decrease Large Decrease

Balsam poplar Large Decrease Large Decrease

Black spruce Large Decrease Large Decrease

Mountain maple (+) Large Decrease Large Decrease

Northern white-cedar Decrease Large Decrease

Quaking aspen Decrease Large Decrease

Tamarack (-) Decrease Decrease

White spruce Decrease Decrease

No Change under Both Scenarios:

Chokecherry No Change No Change

Striped maple No Change No Change

Increases under Both Scenarios

American elm Increase Large Increase

American hornbeam Increase Large Increase

Bitternut hickory (+) Large Increase Large Increase

Black cherry (-) Large Increase Large Increase

Black oak Large Increase Large Increase

Black walnut Large Increase Large Increase

Black willow (-) Large Increase Large Increase

Boxelder (+) Increase Large Increase

Eastern cottonwood Increase Large Increase

Eastern hophornbeam (+) Increase Increase

Eastern red cedar Increase Large Increase

Eastern white pine Increase Increase

Hackberry (+) Large Increase Large Increase

Northern pin oak (+) Large Increase Large Increase

Red maple (+) Increase Increase

River birch Increase Increase

Silver maple (+) Large Increase Large Increase

Slippery elm Large Increase Large Increase

Sugar maple (+) Large Increase Increase

Swamp white oak Increase Large Increase

White ash (-) Large Increase Large Increase

White oak (+) Large Increase Large Increase

Wild plum Increase Increase

Mixed Results Between Scenarios

American basswood No Change Increase

Bigtooth aspen No Change Decrease

Black ash (-) No Change Decrease

Bur oak (+) No Change Increase

Butternut (-) No Change Large Decrease

Green ash No Change Large Increase

Jack pine No Change Decrease

Northern red oak (+) Increase No Change

Paper birch No Change Large Decrease

Peachleaf willow Large Decrease Large Increase

Pin cherry Decrease No Change

Red pine No Change Increase

Rock elm (-) No Change Increase

Yellow birch Large Increase Decrease

Species Gaining New Habitat

American beech New Habitat New Habitat

Black hickory NA New Habitat

Black locust New Habitat New Habitat

Blackgum (+) NA New Habitat

Blackjack oak (+) NA New Habitat

Chestnut oak (+) NA New Habitat

Chinquapin oak New Habitat New Habitat

Eastern hemlock (-) New Habitat New Habitat

Eastern redbud NA New Habitat

Flowering dogwood NA New Habitat

Honeylocust (+) New Habitat New Habitat

Mockernut hickory (+) NA New Habitat

Northern catalpa NA New Habitat

Ohio buckeye NA New Habitat

Osage-orange (+) New Habitat New Habitat

Pignut hickory New Habitat New Habitat

Pin oak (-) New Habitat New Habitat

Post oak (+) NA New Habitat

Red mulberry New Habitat New Habitat

Sassafras New Habitat New Habitat

Scarlet oak New Habitat New Habitat

Shagbark hickory New Habitat New Habitat

Shingle oak NA New Habitat

Sugarberry NA New Habitat

Sweet birch (-) NA New Habitat

Sweetgum NA New Habitat

Yellow-poplar (+) NA New Habitat

Species are assigned to change classes based on the 

comparison between end-of-century (2070 through 2099) 

and current figures for area-weighted importance value. 

Species with particularly high and low modifying factors 

are marked with plus (+) or minus (-) signs. See Appendix 

4 for complete results, including classification rules; model 

reliability; modification factors; and current, early-century, 

mid-century, and late-century importance values.

Table 16.—Potential changes in suitable habitat for 74 tree species in the assessment area for the PCM B1 and GFDL 

A1FI climate scenarios, from the Tree Atlas model

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

87

Further, some tree species that are currently not 

present in the assessment area were identified as 

having potential suitable habitat in the future under 

one or both scenarios. See Appendix 4 for complete 

results from the DISTRIB model, including both 

model-scenario combinations for 2010 through 

2039, 2040 through 2069, and 2070 through 2099. 

Modifying factors have also been incorporated into 

the Tree Atlas to provide additional information on 

potential forest change. Modifying factors include 

life-history traits and environmental factors that 

make a species more or less able to persist on the 

landscape (Matthews et al. 2011b). These factors 

are not explicitly included in the DISTRIB outputs, 

and are based on a review of a species’ life-history 

traits, known stressors, and other factors. Examples 

of modifying factors are drought tolerance, 

dispersal ability, shade tolerance, site specificity, 

and susceptibility to insect pests and diseases. 

Modifying factors are highly related to a species’ 

adaptive capacity (see Chapter 6). Information on 

modifying factors is included in the summary of 

projected changes in habitat (Table 16), where a plus 

(+) or minus (-) sign after a species name indicates 

that certain modifying factors could lead the species 

to do better or worse, respectively, than DISTRIB 

model results indicate. As an example, the species 

with the five highest and five lowest modifying 

factor scores are displayed in Table 17. Appendix 4

contains more information on the specific modifying 

factors for each species.

When examining these results, it is important to keep 

in mind that model reliability is generally higher for 

more common species than for rare species. When 

model reliability is low, less certainty exists for the 

model results. See Appendix 4 for specific rankings 

of model reliability for each species.

Declining Species

For the assessment area in Minnesota, 8 of the 74

modeled species are projected to undergo declines 

in suitable habitat under both the PCM B1 and 

GFDL A1FI scenarios. The projected declines in 

importance values are more severe for these species 

under GFDL A1FI than under PCM B1. This result 

is not surprising, given that the species projected to 

Species Factors that affect rating*

Highest adaptive capacity

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

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

of temperature tolerances, drought tolerant

3. Bur oak drought tolerant, fire tolerant

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

5. Osage-orange wide range of habitats

Lowest adaptive capacity

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

establishment, fire intolerant, dependent on specific hydrological regime

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

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

4. White ash emerald ash borer, drought intolerant, fire intolerant

5. Eastern hemlock hemlock wooly adelgid, drought intolerant

*See Appendix 4 for a complete listing of modifying factors for each species.

Table 17.—Species with the five highest values and five lowest values for adaptive capacity, based on Tree Atlas 

modifying factors
Chapter 5: Future Climate Change Impacts on Forests

88

lose suitable habitat are primarily boreal or northern 

species near the southern limit of their range in the 

assessment area. Most of these species including 

characteristic species such as balsam fir, quaking 

aspen, white spruce, and tamarack, are widespread 

across the landscape. Therefore, the reduction of 

suitable habitat may affect a large portion of forested 

landscape in northern Minnesota. 

Balsam fir and black spruce are projected to have 

the most dramatic proportional reductions in suitable 

habitat. Balsam fir and tamarack also have very 

low modifying factor scores, suggesting that there 

are life-history traits or biological stressors that 

may cause these species to lose even more suitable 

habitat than the model results indicate. For example, 

insect pests like the larch casebearer and larch 

sawfly in the assessment area may cause even worse 

outcomes for tamarack than projected. 

A projected reduction in suitable habitat at the end 

of the 21st century does not imply that these species 

will be extirpated or that mature, healthy trees will 

die. What this result indicates is that these species 

will be living farther outside their ideal climatic 

envelope and that these conditions may expose these 

species to greater stress. Living outside a suitable 

range also raises the risk of regeneration failure due 

to climatic factors.

Species with Mixed Results 

Between Scenarios

Fourteen of the 74 total species had mixed results 

between the two climate scenarios, with different 

combinations of projected increases, decreases, 

and no change. In some cases, the results indicate 

that a species is expected to gain more suitable 

habitat under the GFDL A1FI scenario than under 

PCM B1 (e.g., American basswood, bur oak, green 

ash, and red pine). This subset of species could be 

favored by hotter, drier conditions and is typically 

more common to the south of the assessment area. 

In other cases, the results indicate that a species 

is expected to retain more suitable habitat under 

PCM B1 (e.g., bigtooth aspen, black ash, jack pine, 

and yellow birch). This subset of species is more 

characteristically northern and generally projected 

to undergo large declines in suitable habitat under 

GFDL A1FI. Black ash has low modifying factor 

scores, suggesting that this species could lose 

more suitable habitat than the models indicate. For 

example, anticipated emerald ash borer infestations 

are expected to result in declines for black ash across 

northern Minnesota.

Increasing Species

Suitable habitat is projected to increase for 23 

species by the end of the century under both the 

PCM B1 and GFDL A1FI scenarios. Most of these 

are temperate deciduous species such as American 

elm, black cherry, sugar maple, and white oak. 

Additionally, many of these species are projected 

to gain more suitable habitat under GFDL A1FI 

than under PCM B1, suggesting that hotter, drier 

conditions in northern Minnesota may be suitable for 

an array of species. Eastern white pine and eastern 

red cedar are two conifer species projected to gain 

suitable habitat in the assessment area. 

All of the 23 species projected to increase under both 

scenarios are currently present within the Laurentian 

Mixed Forest Province. Some of these species 

occur at very low densities and may be uncommon 

throughout much of the assessment area (e.g., 

bitternut hickory, hackberry, and river birch). They 

could essentially be considered new entries to the 

assessment area. Overall, the potential for suitable 

habitat increases for temperate species raises the 

possibility of a large shift within forest communities 

in northern Minnesota. Several species common to 

the south of the assessment area may become more 

widespread, assuming higher regeneration success 

under future forest conditions. Because many of the 

species projected to lose suitable habitat are still 

expected to persist in the assessment area by the end 

of the century, forests in northern Minnesota could 
Chapter 5: Future Climate Change Impacts on Forests

89

potentially contain a higher overall diversity of 

species in the future, with a blend of temperate and 

boreal species. 

Importantly, DISTRIB results only indicate a change 

in suitable habitat, not necessarily the ability of a 

given species to migrate to newly available habitat 

and colonize successfully. A few species projected 

to gain suitable habitat, such as black cherry and 

white ash, have low modifying factor scores, which 

suggest they may be less able to take advantage of 

increasing suitable habitat. Dutch elm disease is also 

expected to limit the future increase of American 

elm. Conversely, nine of these species, including 

bitternut hickory, northern pin oak, and red maple, 

have positive modifying factors. 

Species Gaining New Habitat

DISTRIB also projects that 27 species not currently 

present will gain new suitable habitat within the 

assessment area by the end of the 21st century. 

A given species may not necessarily be able to 

migrate to newly available habitat and colonize 

successfully, however. Species not currently present 

in the assessment area would require long-distance 

migration, whether intentional or unintentional, 

to occupy suitable habitat in northern Minnesota. 

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 (Ibáñez et al. 2008). 

Most species can be expected to migrate more 

slowly than their habitats will shift (Iverson et al. 

2004a, 2004b). Of course, human-assisted migration 

is a possibility for some species and is expected to 

be tested and used during the next decades (Pedlar et 

al. 2012).

Of the 27 species in this category, 12 are projected 

to have suitable habitat under both the PCM B1 

and GFDL A1FI scenarios. Under GFDL A1FI, 15 

additional migrant species are projected to have an 

increase in suitable habitat within the assessment 

area. The list of new entry species includes several 

oak and hickory species and could also reasonably 

include some of the very rare species mentioned 

in the section above. Most of the species projected 

to enter the assessment area only under the GFDL 

A1FI scenario are from ecological provinces far 

to the south of the assessment area. Several of 

these species have high modifying factor scores, 

indicating that they possess life-history traits that 

might help them be even more tolerant of future 

climatic conditions. For example, blackjack oak is 

rated highly because it is relatively drought-tolerant, 

regenerates well after fire, and is readily established 

from seed. 

Geographic Trends

DISTRIB outputs can be visualized spatially, 

and these results can provide greater context for 

interpreting the projected changes in suitable habitat. 

Figures 33 to 35 display the changes in suitable 

habitat for three example species in three different 

change classes: quaking aspen, sugar maple, and 

white oak. These maps highlight that projected 

changes are not uniform across the assessment area, 

and that areas of suitable habitat are governed by 

soils, moisture gradients, and other factors. Quaking 

aspen appears to retain a large amount of suitable 

habitat in north-central Minnesota under the PCM 

B1 scenario, but under the GFDL A1FI scenario 

most of the suitable aspen habitat is confined to the 

northeast corner of the assessment area. Sugar maple 

is projected to gain substantial areas of suitable 

habitat along the North Shore of Lake Superior 

under both climate scenarios, and under the PCM 

B1 scenario the potential distribution of this species 

extends much farther to the west. It is possible 

that the dry summer conditions projected under 

the GFDL A1FI scenario would limit the range 

expansion of sugar maple (Chapter 4). White oak is 

virtually absent from most of the assessment area 

today, occurring only in the southern and western 
Chapter 5: Future Climate Change Impacts on Forests

90

Figure 33.—Modeled importance values for quaking aspen 

across the Laurentian Mixed Forest Province under current 

climate conditions (top) and projected for the years 2070 

through 2099 under the PCM B1 (middle) and GFDL A1FI 

(bottom) climate scenarios, from the Tree Atlas model. 

Importance values can range from 0 to 100. An importance 

value of zero (light yellow) indicates that the species is not 

present.

Figure 34.—Modeled importance values for sugar maple 

across the Laurentian Mixed Forest Province under current 

climate conditions (top) and projected for the years 2070 

through 2099 under the PCM B1 (middle) and GFDL A1FI 

(bottom) climate scenarios, from the Tree Atlas model. 

Importance values can range from 0 to 100. An importance 

value of zero (light yellow) indicates that the species is not 

present.
Chapter 5: Future Climate Change Impacts on Forests

91

portions of the assessment area. Under the PCM 

B1 scenario white oak is projected to gain suitable 

habitat in these same areas, remaining largely absent 

from the northeast portion of the assessment area. 

Under the GFDL A1FI scenario, however, white 

oak is projected to have as much habitat along the 

Canadian border as along the southern portion of the 

assessment area. Suitable habitat maps for all the 

species addressed in this assessment are available 

online at the Climate Change Tree Atlas Web site 

(Appendix 4). 

As is the case for interpreting any spatial model 

outputs, local knowledge of soils, landforms, and 

other factors is necessary to determine if particular 

sites may indeed be suitable habitat for a given 

species in the future. These maps serve only as a 

guide to broad trends. 

LANDIS-II

Results from the LANDIS-II model include 

aboveground biomass (biomass) for 21 tree species 

and distribution maps for aggregated forest type 

communities. Importantly, the LANDIS-II model 

results described in this assessment cover an 

analysis area that is smaller than the Laurentian 

Mixed Forest Province in Minnesota (Fig. 36). 

This landscape covers most of the Northern 

Superior Uplands Ecological Section and a portion 

of the Northern Minnesota Drift and Lake Plains 

Ecological Section (Minnesota Department of 

Natural Resources [DNR] 2003). This analysis 

area includes a diversity of forest types, and all 

six forested Native Plant Community Systems are 

represented within this region. Compared to the 

assessment area as a whole, the LANDIS-II analysis 

area contains a higher proportion of Fire-Dependent 

Forests and Acid Peatlands and a lower proportion 

of Mesic Hardwood Forests and Forested Rich 

Peatlands (Chapter 1, Fig. 6). Refer to Chapter 2 for 

a complete description of climate, disturbance, and 

management scenarios included in this assessment.

Figure 35.—Modeled importance values for white oak across 

the Laurentian Mixed Forest Province under current climate 

conditions (top) and projected for the years 2070 through 

2099 under the PCM B1 (middle) and GFDL A1FI (bottom) 

climate scenarios, from the Tree Atlas model. Importance 

values can range from 0 to 100. An importance value of zero 

(light yellow) indicates that the species is not present.
Chapter 5: Future Climate Change Impacts on Forests

92

Figure 36.—Analysis area modeled by LANDIS-II for this 

assessment.

Aboveground Biomass

The LANDIS-II projections are plotted for each of 

the 21 tree species over the 21st century for three 

climate scenarios (Figs. 37a and 37b). Species are 

limited to the most common tree species found 

within the LANDIS-II study area. Biomass values 

are averaged across the LANDIS-II analysis area. 

See Appendix 4 for a table of biomass values for the 

species assessed by LANDIS-II. 

The current climate scenario is useful to highlight 

trends that might be expected if the climate were 

to remain stable during the next 100 years. This 

scenario demonstrates the effect of successional 

changes, such as the continued recovery of forests 

after historical periods of intensive logging. 

Although the current climate is not projected to 

remain the same over the next century (Chapter 4), 

this climate scenario is a useful reference to judge 

the relative increases or decreases in biomass under 

the two climate change scenarios (PCM B1 and 

GFDL A1FI). All scenarios in the LANDIS-II results 

incorporate natural disturbances (i.e., fire and wind), 

as well as timber harvest.

According to recent Forest Inventory and Analysis 

(FIA) data for the assessment area, the annual net 

growth is greater than annual harvest removals 

(Chapter 1, growth-to-removal ratio for the 

assessment area = 1.34). This finding helps explain 

the increasing trends for many of the species 

under the current climate scenario. The biomass 

projections of nearly all the species modeled for this 

assessment indicate at least a short-term biomass 

increase, regardless of climate scenario. All forested 

landscapes have a degree of “landscape inertia” 

in that current trends are projected to continue 

into the near future (the next several decades). 

This momentum was built into the LANDIS-II 

simulations based on recent observed patterns of 

forest growth and regeneration, so that even species 

that are projected to eventually decline in biomass 

often show initial increases.

The species modeled by LANDIS-II can be 

organized according to the proportional changes 

relative to the current climate scenario (Table 18). 

As mentioned above, the current climate scenario 

is essentially a control scenario, and the climate 

scenarios may either increase or decrease landscapescale biomass of a species relative to that control. 

LANDIS-II simulations may indicate that a given 

species may gain biomass across the analysis area 

compared to the year 2000, even if the projected 

biomass under the future climate scenarios is less 

than the current climate scenario. White spruce 

illustrates this pattern (Fig. 37b). 

Declining Species

Five species (balsam fir, black spruce, paper birch, 

quaking aspen, and white spruce) are projected to 

decrease relative to the control scenario under both 

PCM B1 and GFDL A1FI. These are characteristic 

boreal species generally anticipated to fare poorly 
Chapter 5: Future Climate Change Impacts on Forests

93

Figure 37a.—LANDIS-II biomass projections over the 21st century for the first 11 of 21 modeled tree species under three 

climate scenarios, presented in alphabetical order. Note that the Y-axis differs by species.
Chapter 5: Future Climate Change Impacts on Forests

94

Figure 37b.—LANDIS-II biomass projections over the 21st century for the final 10 of 21 modeled tree species under three 

climate scenarios, presented in alphabetical order. Note that the Y-axis differs by species.
Chapter 5: Future Climate Change Impacts on Forests

95

Decrease - Both Scenarios

Balsam fir (↓) Quaking aspen 

Black spruce (↓) White spruce (↓)

Paper birch (↓)

Increase PCM B1, Decrease GFDL A1FI

Black ash (↓) Northern white-cedar

Jack pine Red pine

Northern red oak Yellow birch 

Decrease PCM B1, Increase GFDL A1FI

None

Small Change

Bur oak

Increase - Both Scenarios

American basswood (↑) Green ash (↑)

American elm (↑) Red maple (↑)

Bigtooth aspen (↑) Sugar maple

Black cherry (↑) White oak (↑)

Eastern white pine

*Species are grouped into change classes based on the 

proportional change between end-of-century (2100) 

biomass under the PCM B1 and GFDL A1FI scenarios and 

the current end-of-century biomass under the current 

climate scenario. Up or down arrows (↑,↓) indicate the 

proportional change under one or both climate scenarios 

was greater than 50 percent. Small Change indicates that 

both scenarios projected less than 20-percent change in 

either direction. See Appendix 4 for complete results for all 

21 species.

Table 18.—Potential changes in biomass for 21 tree 

species* in the LANDIS-II analysis area

under warmer conditions. Four out of the five 

decreaser species are projected to decline greater 

than 50 percent under one or both climate scenarios. 

The GFDL A1FI scenario resulted in larger 

biomass declines for all five of these species. The 

proportional biomass declines under GFDL A1FI are 

more than double the expected decline under PCM 

B1. Balsam fir illustrates this pattern (Fig. 37a). 

These trends indicate that climate-related shifts are 

driving the biomass projections for these species, 

and that the hotter, drier GFDL A1FI scenario 

amplifies the decline. 

Species with Mixed Results

Six species (black ash, jack pine, northern red oak, 

northern white-cedar, red pine, yellow birch) are 

projected to increase relative to the control scenario 

under PCM B1, but projected to decline under 

GFDL A1FI. Bur oak also displays this pattern, but 

the proportional changes are small enough (less 

than 20-percent increase and decrease) that this 

species was included in the small change category. 

Interestingly, no species are projected to decrease 

under PCM B1 and increase under GFDL A1FI. 

These results suggest that slightly warmer 

temperatures and slightly wetter growing seasons 

under PCM B1 might benefit these species, but that 

a more severe change to hotter temperatures, wetter 

springs, and drier summers under GFDL A1FI may 

reduce the landscape-scale biomass of these species. 

Although oaks are generally expected to be favored 

by warmer, droughtier conditions, northern red oak 

is projected to decline under GFDL A1FI. These 

biomass declines appear to be driven by reduced 

seedling establishment in the latter half of the 

21st century. Northern red oak is also considered 

substantially less drought tolerant than bur oak, 

white oak, or northern pin oak, the other oaks 

important in the region. This outcome reinforces 

the dynamic nature of LANDIS-II, which projects 

the ability of species cohorts to grow, compete, 

reproduce, and disperse across the landscape. If 

climate influences one particular phase of this 

lifecycle, biomass trends will reflect that effect.

Increasing Species

Nine species, including American basswood, 

sugar maple, and eastern white pine, are projected 

to increase relative to the control scenario under 

both PCM B1 and GFDL A1FI. These projected 

increasers are generally temperate species, or 

species with broad tolerance for temperature and 

moisture conditions. For seven species, projected 

increases were greater than 50 percent under one 

or both climate scenarios. With the exception of 
Chapter 5: Future Climate Change Impacts on Forests

96

bigtooth aspen and eastern white pine, projected 

biomass increases were proportionally larger under 

GFDL A1FI than PCM B1. This result indicates 

that many of the projected increaser species, such 

as black cherry and red maple, may be favored to a 

greater degree under GFDL A1FI. Bigtooth aspen 

and eastern white pine, conversely, are expected 

to undergo proportionally larger biomass increases 

under the milder PCM B1 scenario. It is important 

to note that these LANDIS-II simulations do not 

consider the full effects of pests and diseases. 

Dutch elm disease is expected to limit the biomass 

increases of American elm, and emerald ash borer is 

expected to limit the future increase of green ash.

Patterns over Time

For certain species that are projected to increase 

under the PCM B1 scenario, there appears to be a 

late-century plateau or decline that could indicate 

the point at which a temperature or precipitation 

threshold has been crossed. Balsam fir, green ash, 

paper birch, and white spruce all exhibit late-century 

biomass plateaus or declines under the PCM B1 

scenario. When similar “tipping points” are shown 

under the GFDL A1FI scenario, they seem to occur 

earlier, usually before the year 2050. Black ash, 

black spruce, northern white-cedar, paper birch, 

red pine, and white spruce all exhibit short-term 

biomass increases followed by long-term declines 

under the GFDL A1FI scenario. Additionally, a few 

species are projected to increase in biomass across 

the LANDIS-II analysis area at the end of the 21st 

century, particularly for the GFDL A1FI scenario. 

This seems to be the case for mesic hardwood 

species at the northern extent of their ranges, such as 

American basswood, American elm, bigtooth aspen, 

black cherry, bur oak, green ash, red maple, and 

white oak. 

The combined results for all species across the 

LANDIS-II analysis area tell an interesting story. 

For the PCM B1 scenario, biomass is expected 

to rise steadily by about 50 percent across the 

landscape, reaching a plateau around the year 2080. 

This trajectory closely tracks the combined results 

for the current climate scenario. The GFDL A1FI 

scenario projects a slight increase to the year 2050, 

followed by a slight decline to the year 2100, then 

a sharper increase to the year 2150. When viewed 

in concert with the projected declines in northern 

or boreal species, this trend appears to support 

the possibility of a landscape-level transition to a 

hardwood-dominated landscape around the year 

2100 under the GFDL A1FI scenario. It is possible 

that noticeable biomass gains for southern hardwood 

species are delayed until late-century because of lag 

times associated with projected rates of migration, 

colonization, and growth to the overstory forest 

layers. It is important to reiterate that LANDIS-II 

assesses only the 21 species listed above, and does 

not account for the possibility of new migrants to 

enter the analysis area.

Productivity

LANDIS-II simulations also require estimates of 

aboveground net primary productivity (productivity) 

as an input. For this assessment, productivity 

estimates were calculated via a version of the 

PnET model. These estimates of productivity 

used as inputs to LANDIS-II did not account 

for the potential effects of carbon dioxide (CO2 ) 

fertilization, which will be discussed in more detail 

below. Compared to the year 2000, these simulations 

indicate an overall productivity decline of roughly 

40 percent under GFDL A1FI by 2100. Alternatively, 

productivity is projected to remain fairly steady 

under PCM B1, declining by only 3 percent by the 

year 2100. Productivity is not the sole determinant 

of biomass but is an important regulator of potential 

biomass and recovery from disturbances. 

Geographic Trends

The forest-type maps indicate the potential for 

landscape-level change under the two climate 

scenarios (Fig. 38). LANDIS-II allows for species 

cohorts to migrate, compete, reproduce, and 

undergo disturbances across the landscape, and 
Chapter 5: Future Climate Change Impacts on Forests

97

Figure 38.—Projected changes in land cover over the 21st century for six aggregated forest types under three different future 

climate scenarios, from the LANDIS-II model. Note that the simulations extend to the year 2150 for the graphs as well as for 

the resulting landscape maps.

these transitions are governed by a range of factors 

including soils and landform. To create these land 

cover maps, individual locations (“cells”) in the 

LANDIS-II simulations were classified into six 

forest categories based on characteristic species 

composition. These forest types are not perfectly 

correlated with the forested Native Plant Community 

Systems, and should not be cross-walked directly to 

them (Appendix 4), in part because LANDIS-II does 

not simulate lowland forest systems. Additionally, 

LANDIS-II simulations account for only the 21 

species modeled in this assessment. Therefore, 

these maps do not represent the potential for new 

species to migrate into the landscape or the potential 

for low-abundance species to increase within the 

assessment area, as suggested by Tree Atlas results.

Under the current climate scenario, the primary trend 

is a 10- to 15-percent increase in the proportion of 

the landscape occupied by white pine and red pine 

forests and a corresponding decline in the aspenbirch forest cover type. This trend assumes higher 

regeneration success than has been historically 

observed for pine species. Under the PCM B1 

scenario these trends are accentuated, in addition 

to a 5-percent increase in northern hardwoods and 

a 20-percent decline in spruce-fir forests across the 

landscape. The overall landscape in 2150 under 

the PCM B1 scenario looks similar to the starting 

landscape and to the current climate scenario in 

2150. The noticeable differences are more red pine 

and white pine and less area of spruce-fir and aspenbirch forests. 
Chapter 5: Future Climate Change Impacts on Forests

98

The GFDL A1FI scenario results in a more dramatic 

compositional shift across the landscape as sprucefir forests are eliminated and northern hardwoods 

occupy more than 30 percent of the landscape by 

2150. Aspen-birch forests are projected to occupy 

20 percent less of the landscape, and red pine and 

white pine forests increase by roughly the same 

amount. The resulting landscape map shows that 

most of the increase in northern hardwoods will 

occur primarily along the North Shore of Lake 

Superior, with expanding white pine and red pine 

forests throughout the analysis area. 

Despite the large difference between the resulting 

forest-type maps under the two future climate 

scenarios, Figure 38 also shows the high degree of 

similarity between these projections for the first half 

of the 21st century. The LANDIS-II simulations 

under the PCM B1 and GFDL A1FI scenarios look 

nearly identical until 2050. Tree species are longlived and there will be a lag time before changes 

in climate are translated into forest changes. 

As mentioned above, community response will 

also lag due to species migration, establishment, 

competition, and reproduction. A certain amount 

of change during the first 50 years may be masked 

in these simulations because seedlings account for 

little biomass and a particular location may not 

be classified differently if the overstory remains 

intact. But the GFDL A1FI simulation between 2050 

and 2100 highlights the possibility for substantial 

landscape-level changes to occur in a short period of 

time. 

PnET-CN

The PnET-CN model projects changes in 

aboveground net primary productivity (productivity). 

Productivity is commonly used as a measure of how 

well forests are photosynthesizing and accumulating 

biomass, which is essentially a way to describe 

overall ecosystem function. In this assessment, we 

report absolute productivity as well as percentage 

changes in productivity. The PnET-CN uses 1971 

through 2000 as a baseline period, and simulates 

productivity changes from 2000 through 2099. 

Modeling simulations presented in this assessment 

are described more completely in Peters et al. 

(2013). 

For this assessment, PnET-CN results describe 

six aggregated forest types rather than individual 

species. These forest types are based on FIA foresttype groups (Miles et al. 2011), which are not 

perfectly matched with the nine forest communities 

considered in this assessment. The six FIA foresttype groups in the PnET-CN simulations are: 

aspen/birch, maple/beech/birch, oak/hickory, 

elm/ash/cottonwood, pine, and spruce/fir. These 

groups are assigned based only on tree species 

composition and they do not account for soils, 

disturbance processes, or other factors. Still, they are 

a useful way to describe broad forest categories in 

Minnesota. Figure 39 shows the distribution of these 

forest-type groups across the assessment area.

Productivity Trends

As noted above, the PnET-CN and LANDIS-II 

simulations were run under different conditions. In 

particular, the LANDIS-II simulations presented 

above do not account for the effects of elevated 

atmospheric CO2 , but the PnET-CN simulations 

discussed here consider the effects of rising CO2

on forest productivity. Under both PCM B1 and 

GFDL A1FI, PnET-CN projects that productivity 

will increase from the baseline period (1971 

through 2000) through the end of the century 

(Fig. 40). All forest-type groups show increases 

in productivity, with greater absolute and relative 

increases in deciduous broadleaf forests (aspen/

birch, oak/hickory, maple/beech/birch, and elm/

ash/cottonwood) compared to conifer forests 

(spruce/fir and pine). This trend is consistent for 

both climate scenarios. Under the PCM B1 scenario, 

the deciduous forest-type groups appear to peak 

at around 100-percent increases in productivity 

and pine and spruce/fir are projected to plateau 
Chapter 5: Future Climate Change Impacts on Forests

99

Figure 39.—Spatial distribution of the aggregated forest-type groups used in PnET-CN simulations. These forest-type groups 

remain fixed for the duration of the PnET-CN simulations.

around 40 percent. Under the GFDL A1FI scenario, 

productivity is projected to increase throughout the 

century with no apparent plateau. The deciduous 

forest-type groups exhibit increases of roughly 

200 percent across the assessment area by the 

end of the century, with pine forests increasing in 

productivity by nearly 150 percent. The spruce/fir 

forest-type group is projected to experience a smaller 

gain in productivity by the end of the century, 

reaching a 100-percent increase.

For all forest types across the assessment area, the 

PCM B1 scenario resulted in an average productivity 

increase of 68 percent compared to the baseline 

period. The absolute increase in productivity from 

baseline to end-of-century averages 362 grams 

biomass per square meter per year and ranges from 

78 to 821 grams biomass per square meter per year. 

The productivity increases projected in the GFDL 

A1FI scenario are roughly two times greater than 

the increases projected under the PCM B1 scenario. 

Under this scenario, the average relative increase in 

productivity from baseline to end-of-century is 

144 percent. The absolute increase in productivity 

from baseline to end-of-century is on average 

707 grams biomass per square meter per year and 

ranges from 7 to 1,336 grams biomass per square 

meter per year across the assessment area.
Chapter 5: Future Climate Change Impacts on Forests

100

Figure 40.—Projected trends in aboveground net primary productivity (ANPP) from PnET-CN for six aggregated forest-type 

groups under the PCM B1 and GFDL A1FI future climate scenarios. Changes in productivity are relative to the 1971 through 

2000 baseline period. Outputs have been smoothed based on a 5-year running mean.
Chapter 5: Future Climate Change Impacts on Forests

101

The main drivers of the increased forest productivity 

projected by PnET-CN are CO2

 fertilization and 

growing season length. Elevated atmospheric 

CO2

 concentrations enable trees to absorb more C 

through stomata on their leaves. As a result, water 

loss is reduced and photosynthesis is increased 

for a given amount of water use. Effects of CO2

fertilization were larger under the GFDL A1FI 

scenario than under the PCM B1 scenario. 

Warmer temperatures enhanced C uptake earlier 

in the spring and later in the fall, but C uptake was 

reduced in mid-summer due to water limitations on 

photosynthesis. Growing season length increased 

more under the GFDL A1FI scenario (1 to 2 months 

across the assessment area) than under the PCM B1 

scenario (roughly 1 month). In general, this longer 

growing season allowed forests to accumulate more 

biomass per year in the simulation. 

In separate simulations with the level of atmospheric 

CO2

 fixed at 350 parts per million (not shown in 

this assessment), climate changes alone resulted 

in minor to no change in productivity under PCM 

B1 and declines in productivity under GFDL 

A1FI by the end of the 21st century. Productivity 

was reduced in the fixed CO2

 simulations due to 

water limitations. These results closely mirror the 

LANDIS-II productivity inputs discussed above. 

PnET-CN tends to predict a larger CO2

 fertilization 

effect on productivity than other ecosystem models, 

so this effect may be a generous estimate (Medlyn 

et al. 2011). Because field studies have not directly 

tested ecosystem responses to CO2

 concentrations 

greater than 900 ppm in mature forests, it is difficult 

to recalibrate the model based on current knowledge. 

Although PnET-CN accounts for biogeochemical 

feedbacks like water and nutrient limitation, this 

model does not account for other factors that 

could reduce forest productivity. For example, the 

model does not account for competition; forest 

management; or disturbances from deer herbivory, 

wind, fire, or insect pests. Additionally, the model 

does not account for forest-type change over time; 

forest composition is essentially static through the 

100-year simulations. Therefore, it may be most 

helpful to think of these results as an indication of 

the potential ecosystem productivity response of 

existing forests to climate change. 

Geographic Trends

Productivity is projected to increase from the 

baseline period under both future climate scenarios 

throughout the assessment area (Fig. 41). Under 

the PCM B1 scenario, productivity increases are 

projected to be highest along the southern half of 

the assessment area and along the North Shore of 

Lake Superior. Productivity increases are projected 

to be lowest along the Canadian border, particularly 

north and east of Red Lake and in northern St. 

Louis County. Under the GFDL A1FI scenario, 

productivity increases are above 200 percent 

along the North Shore of Lake Superior and in 

Koochiching, Itasca, Becker, Hubbard, and Cass 

Counties. 

Geographic trends in the PnET-CN outputs appear 

as a result of a combination of factors. The forest 

type assigned to each pixel remains constant in these 

simulations, and different forest types have different 

abilities to respond to climate shifts. A variety of 

other parameters, such as soil type, nutrient status, 

and soil water-holding capacity, are also critical for 

determining whether a particular forest pixel will be 

able to successfully take advantage of climate shifts 

and translate favorable growing conditions into 

productivity increases. 

The PnET-CN simulations indicate the productivity 

of forests in the assessment area could switch from 

being temperature limited to being water limited 

by the end of the 21st century. See Chapter 4 for 

a discussion of area-wide changes in the ratio of 
Chapter 5: Future Climate Change Impacts on Forests

102

Figure 41.—Projected aboveground net primary productivity (ANPP) changes across the assessment area under the PCM B1 

and GFDL A1FI future climate scenarios, from the PnET-CN model. Productivity values for the baseline period of 1971 through 

2000 are absolute figures (top panels), and the future values for each scenario are relative percentages compared to the 

baseline period (bottom panels). Baseline values are slightly different between the two climate scenarios because of slight 

variations in the downscaled GCM data.

evapotranspiration to precipitation, which is a 

related output of the PnET-CN model. Soil waterholding capacity could play a critical role in how 

forests in the assessment area respond to future 

climate changes. In the PnET-CN simulations, areas 

with lower water holding capacity were less buffered 

from water limitation and more prone to reductions 

or smaller increases in productivity (Peters et al. 

2013). This conclusion is supported by previous 

research on the effect of climate change on southern 

boreal and northern temperate forests, which found 

that hardwood species would be more able to replace 

declining boreal species in areas with more available 

soil moisture (Pastor and Post 1988, Post and Pastor 

1996). 
Chapter 5: Future Climate Change Impacts on Forests

103

Discussion of Model Results

Agreements 

The question of how ecosystems might respond to 

future climate changes has been a topic of study 

for almost 30 years (Emanuel et al. 1985, Solomon 

1986). Studies relying on earlier estimates of future 

warming and more simplistic vegetation simulations 

outlined broad effects that have been reinforced 

by more advanced models and recent simulations. 

This corroboration particularly applies to forests in 

the northern Great Lakes region. The results of the 

modeling simulations performed for this assessment 

reinforce the concept of boreal forest decline and 

subsequent increase in more temperate broadleaf 

species and forest types, to the extent allowed by soil 

moisture (Emanuel et al. 1985, Pastor and Post 1988, 

Post and Pastor 1996, Solomon 1986). 

Despite the differences between the modeling 

approaches, Tree Atlas, LANDIS-II, and PnETCN show some strong similarities in forest change 

during the next century under a range of future 

climates. All three models indicate that characteristic 

boreal species or northern species at their southern 

range limits will face increasing climate stress. For 

example, the list of declining species for both the 

Tree Atlas and LANDIS-II models includes many 

of the same species: balsam fir, black spruce, paper 

birch, quaking aspen, and white spruce. The declines 

for these species were more substantial under GFDL 

A1FI than under PCM B1. Additionally, the PnETCN results project a weaker potential productivity 

response for spruce/fir forests compared to other 

forest types. As the climate warms through the 21st 

century, these species and forest types are projected 

to face increasing climate-related stress. Paper 

birch, in particular, was projected by Tree Atlas and 

LANDIS-II to fare reasonably well under the PCM 

B1 scenario but to decline sharply under the GFDL 

A1F1 scenario.

Moreover, both Tree Atlas and LANDIS-II tend 

to agree on which species may increase under 

climate change. American basswood, American 

elm, black cherry, eastern white pine, green ash, red 

maple, sugar maple, and white oak are all projected 

to gain suitable habitat and biomass across the 

landscape. These are mostly temperate hardwood 

species from the northern and central floristic 

regions in Minnesota (Minnesota DNR 2003), and 

it is not surprising that they would be tolerant of 

warmer year-round conditions and a slightly drier 

growing season. PnET-CN outputs also indicate that 

deciduous forest types have the potential for large 

productivity increases across the Laurentian Mixed 

Forest Province in Minnesota, and many of these 

forest types are characteristic of areas south of the 

assessment area. 

Additionally, the models suggest similar responses 

for a few species with mixed results under the two 

climate scenarios. Black ash, jack pine, northern red 

oak, and yellow birch are projected to increase under 

PCM B1 and decrease under GFDL A1FI according 

to LANDIS-II, and these species have similar 

patterns according to Tree Atlas results.

Disagreements

As mentioned above, productivity projections differ 

between the LANDIS-II and PnET-CN simulations 

used for this assessment. Results from LANDIS-II 

are driven by projections of an overall decline in 

productivity under the GFDL A1FI scenario and 

almost no change under the PCM B1 scenario. 

PnET-CN, however, projects large productivity 

increases under both scenarios, with productivity 

gains nearly twice as large under the GFDL A1FI 

scenario. This discrepancy is almost certainly 

due to the way that these models account for the 

potential CO2

 fertilization effect. As mentioned 

above, the PnET-CN simulations appear to be driven 
Chapter 5: Future Climate Change Impacts on Forests

104

mainly by the potential CO2 fertilization effect, 

and the productivity estimates used by LANDIS-II 

do not account for CO2

 fertilization. It is unclear 

how substantial this factor will be over the long 

term. Experiments with CO2

 enrichment in forests 

suggest net primary productivity will increase under 

elevated CO2 , although this response can diminish 

over time due to water or nutrient limitation and 

tree age (Norby and Zak 2011, Norby et al. 2005). 

Additionally, productivity increases under elevated 

CO2

 could be partially offset by reductions in 

productivity from warming-induced drought stress 

or the effects of future disturbances (Dieleman et al. 

2012). 

There do not appear to be any major discrepancies 

among species between the model results. Bigtooth 

aspen is projected to increase in biomass according 

to LANDIS-II, but Tree Atlas projects that this 

species may show no change under the PCM B1 

scenario and a large decrease under the GFDL A1FI 

scenario. This discrepancy might be an effect of 

the different analysis area used for the LANDIS-II 

simulations, if bigtooth aspen is projected to gain 

habitat in the far northeastern part of Minnesota and 

decline throughout the rest of the state. It might also 

reflect a difference between the assumptions of the 

two models related to disturbance or competition. 

LANDIS-II and Tree Atlas indicate different 

outcomes for red pine under GFDL A1FI, with 

LANDIS-II projecting decreasing biomass and Tree 

Atlas projecting increasing suitable habitat. The 

different analysis areas for the models could also 

contribute to this disagreement, because Tree Atlas 

projects that red pine will gain suitable habitat in 

northeast and north-central Minnesota by the end 

of the century, and LANDIS-II does not account for 

north-central Minnesota. 

Limitations

All forest impact models are only simplified 

representations of reality. No model fully considers 

the entire range of ecosystem processes, stressors, 

interactions, and future changes to forests. Each 

model leaves out processes or drivers that may be 

key drivers of change. Future uncertainty is not 

limited to climate scenarios; it is inherent in human 

interactions with forests. The contributing authors 

of this assessment generated a summary list of some 

of the factors not incorporated into these modeling 

results to facilitate further discussion. Highlights of 

this list are: 

• Human management and policy responses to 

forest changes and climate trends

• Future wildfire behavior, fire suppression, and 

ability to apply prescribed fire 

• Novel successional pathways for forest 

ecosystems

• Trends in land-use change or forest fragmentation

• Lowland or wetland forest dynamics less 

understood than upland forests

• New major insect pests or disease agents 

• Magnitude of CO2

 fertilization effect 

• Herbivory pressure in the future, particularly 

from white-tailed deer

• Extreme weather events not captured well in 

downscaled climate change or ecosystem models 

• Phenology changes and timing mismatches for 

key ecosystem processes

• Responses of understory vegetation and soil 

microorganisms and mycorrhizal associations

• Future changes in forest industry, both in 

products and in markets 

• Interactions among all these factors

Most of these factors could drive large changes in 

forests throughout the assessment area, depending 

on how much change occurs in the future. The 

potential for interactions among these factors adds 

layers of complexity and uncertainty. Despite these 

limitations, impact models are still the best tools we 

have, and they are the best way to simulate a range 

of possible climate futures. It is most helpful to 

keep the above limitations in mind when weighing 

the different projections, and to use them to inform 
Chapter 5: Future Climate Change Impacts on Forests

105

an overall assessment. The comparison among 

several different kinds of models allows for a better 

understanding of the range of possibilities. In the 

following section, we draw upon published literature 

to address other factors that may dictate how forest 

ecosystems in northern Minnesota respond to 

climate change. 

SUMMARY OF CURRENT

SCIENTIFIC KNOWLEDGE 

A growing body of scientific literature is gradually 

clarifying some of the potential ways that 

greenhouse gases and the climate change they cause 

may influence forest ecosystems (CCSP 2008, Vose 

et al. 2012). These impacts can broadly be divided 

into the direct effects of changing temperature, 

precipitation, and CO2

 levels, and the indirect effects 

of altered stressors or the development of additional 

stressors. It is also important to note that some of 

the impacts may in fact be positive or beneficial to 

native forests in the assessment area. The remainder 

of this chapter summarizes the state of scientific 

knowledge on additional direct and indirect effects 

of climate change on forests in the Laurentian Mixed 

Forest Province and the wider Great Lakes region. 

Drought Stress 

In the assessment area, the potential for more 

frequent droughts and moisture stress during the 

growing season appears to be greater under the 

GFDL A1FI climate scenario (Chapter 4). Even 

under the milder PCM B1 scenario, warmer 

temperatures may lead to increased transpiration and 

physiological stress. Even if seasonal precipitation 

increases slightly during the growing season, 

projected temperature increases may lead to net drier 

soil conditions. 

A recent study found that forests in both wet and 

dry environments around the world typically operate 

within a relatively narrow range of tolerance for 

drought conditions (Choat et al. 2012). Drought 

stress causes air bubbles to form in the xylem 

of growing trees (cavitation). Consequently the 

ability to move water is reduced, which results in 

diminished productivity or mortality, depending 

on the extent of the failure. Forest species from 

rain forests, temperate forests, and dry woodlands 

all showed a similarly low threshold for resisting 

hydraulic failure. Research indicates that drought 

length may be more important to tree mortality than 

drought severity or average dryness over a period of 

years (Gustafson and Sturtevant 2013). Furthermore, 

differences between land types can amplify or soften 

the effects of drought on tree mortality.

Some recent examples from published literature 

highlight the possibility for drought stress for 

northern Minnesota forests. A widespread aspen 

decline in northern Minnesota has been linked to 

the combined effects of a multi-year drought and 

insect defoliation (Worrall et al. 2013). Projections 

considering growing-season temperature and 

precipitation indicate that aspen could lose more 

than half its suitable habitat in the upper Great Lakes 

region by mid-century (Worrall et al. 2013). Studies 

in hybrid poplar plantations show that late-season 

moisture stress can reduce growth in the following 

growing season (Chhin 2010). During the past 

century, drought has been linked to dieback in sugar 

maple, birch species, and ash species in Maine 

(Auclair et al. 2010). In the western United States, 

prolonged drought has caused widespread mortality 

of aspen (Anderegg et al. 2012). 

Conversely, modeling in northern Wisconsin 

suggests that drought events might benefit pioneer 

forest types like aspen and birch, even though 

individuals of these species are generally drought 

intolerant (Gustafson and Sturtevant 2013). 

Additionally, elevated atmospheric CO2

 may help 

adult trees of some species like bur oak withstand 

seasonal moisture stress (Wyckoff and Bowers 

2010), and this effect may already be detectable 

across the eastern United States (Keenan et al. 
Chapter 5: Future Climate Change Impacts on Forests

106

2013). Site-level factors like stand density will 

also influence susceptibility to moisture stress, as 

high-density stands face increased competition for 

available moisture (D’Amato et al. 2011, Magruder 

et al. 2012). Drought-stressed trees typically are 

also more vulnerable to insect pests and diseases 

(Minnesota DNR 2011b). 

Windstorms

Blowdowns from windstorms can have an important 

influence on the structure and species composition 

of forests in the assessment area, whether through 

small-scale events which add complexity to the 

landscape or through stand-replacing events (Frelich 

and Reich 1995a, 1995b; White and Host 2008). 

Species composition, stand age, soils, topography, 

and a host of other factors can control how a 

given forest is physically affected by a wind event 

(Peterson 2000; Rich et al. 2007, 2010). Some 

models project an overall increase in the frequency 

of extreme wind events across the central United 

States, but any increase in blowdowns in the 

assessment area may not be outside the already high 

range of variability (Chapter 4). 

Under climate change, stand-replacing events like 

blowdowns could potentially act as a catalyst for 

more rapid ecosystem change than would occur 

through migration and competition alone. Climatic 

conditions following a major wind event in the 

future may not favor typical successional pathways, 

particularly if advance regeneration consists of 

novel species mixes. Additionally, future blowdowns 

may lead to more wildfires if climate change 

results in more frequent extreme fire weather in the 

assessment area. 

Frost and Snowfall

As discussed in Chapter 4, winter processes in the 

assessment area such as snowfall and soil frost 

may change substantially under climate change. 

Paradoxically, soil frost depth and the number of soil 

freeze-thaw events may increase in the near future as 

snowpack declines and soils are less insulated from 

cold temperatures (Hardy et al. 2001). This trend 

may already be affecting species with frost-intolerant 

root systems like sugar maple in the assessment area 

(Box 7). Northern hardwood species are generally 

shallow rooted and more vulnerable to freezing, and 

frost-related mortality in this forest type has been 

observed elsewhere in the northern United States 

(Auclair et al. 2010).

As winter temperatures increase over the 21st 

century, the average snowpack in the assessment 

area is projected to continue to decline. However, 

forest soils will be less frequently exposed to multiday periods of extreme cold, so the net effect is 

projected to be a decrease in the duration of the soil 

frost season by 1 to 2 months across the assessment 

area by 2100 (Chapter 4). Shifts in the timing of 

the soil frost season may have cascading impacts 

on a variety of ecosystem processes. Unfrozen 

soils will be better able to absorb snowmelt and 

rainfall, leading to increased infiltration (Sinha and 

Cherkauer 2010). Increased infiltration may also 

lead to increased nutrient leaching from forest soils 

if the phenology of plant communities does not 

closely track the change in soil frost (Campbell et 

al. 2010). Studies from northern hardwood forests 

in New England have shown that snowmelt and 

Red pine damaged in a strong windstorm in summer 

2012. Photo by John Rajala, Rajala Companies, used with 

permission.
Chapter 5: Future Climate Change Impacts on Forests

107

Box 7: Hardwood Decline in the Upper Great Lakes Region

Northern hardwood stands with sugar maple 

crown dieback have recently been reported in the 

upper Great Lakes region (Michigan DNR 2011). To 

investigate the cause of this dieback, researchers 

from Michigan Technological University have 

established permanent plots on industry, federal, 

and state land in the Upper Peninsula of Michigan, 

northern Wisconsin, and eastern Minnesota (Bal 

2013). Plots are located in stands dominated by 

sugar maple with varying degrees of crown dieback. 

Data collection has included assessments of full 

crown and boles, canopy density, regeneration, 

habitat, earthworm impacts to the forest floor, soil 

compaction, topography, and nutrient status of 

soil and foliage. Average dieback percentage of live 

trees at all plots varied from 15 percent in 2009 to 

approximately 7 percent in 2012. A vigorous, healthy 

sugar maple stand should have less than 10 percent 

dieback.

Analysis has indicated that sugar maple dieback 

is related to many factors, including earthworms, 

climate, and site-level nutrients. Out of all plot 

variables measured, high densities of European 

earthworms removing the forest floor in northern 

hardwood forests were the most significant factor 

related to sugar maple crown dieback. The removal 

of the duff layer exposes roots and exacerbates 

further stresses on trees. Analysis of both raw 

tree ring data and basal area growth indicates a 

significant positive relationship with total winter 

snowfall, number of days with snowcover on 

the ground, and number of days below freezing 

temperatures across the region (Fig. 42), all of which 

have been decreasing in recent decades. Tree roots 

of sugar maple and other northern hardwoods are 

generally frost intolerant, and lack of adequate 

snowcover exposes these shallow roots to freezing 

conditions. Moderate drought conditions in recent 

years, especially in the Upper Peninsula of Michigan, 

have likely further contributed to maple dieback 

and decline. Soil and foliar nutrient analysis suggest 

site-specific variations in soil nutrients may have 

predisposed trees to decline. 

Figure 42.—Average sugar maple mean annual growth ring increment (RI) 

from research plots in western Upper Michigan, northern Wisconsin, and 

eastern Minnesota (118 plots, 313 trees), average annual total number of 

days with maximum temperatures below freezing, and average annual total 

number of days with snow depth greater than 1 inch from local weather 

stations (NOAA Climatic Data Center) (Bal 2013).
Chapter 5: Future Climate Change Impacts on Forests

108

soil thawing are advancing rapidly in the spring 

and that overstory leaf-out dates are lagging behind 

(Groffman et al. 2012), so these systems are 

probably losing additional soil nutrients. 

Altered winter processes in the assessment area may 

also affect regeneration conditions for some tree 

species. Yellow birch is best able to disperse seeds 

over snow, and therefore may be impaired by less 

consistent snowpacks (Burns and Honkala 1990, 

Groffman et al. 2012). 

Hydrologic Impacts

Hydrology is tightly linked to the health and 

function of forest ecosystems, whether through 

maintenance of soil moisture during the growing 

season, seasonal flooding, creating necessary 

decomposition conditions, or other processes. 

Most Native Plant Community Systems in the 

Laurentian Mixed Forest Province are defined 

by particular soil moisture requirements for the 

seasonality and extent of saturation (Minnesota 

DNR 2003). This relationship may be particularly 

true for Forested Rich Peatlands and Acid Peatlands 

(Bridgham et al. 2008, Gorham 1991, Swanson and 

Grigal 1991). Additionally, certain species such as 

northern white-cedar and eastern cottonwood have 

particular seedbed requirements that are tightly 

linked to hydrologic conditions (Burns and Honkala 

1990, Cornett et al. 2000b). Hydrology may even 

direct forest ecosystem response in ways that 

appear counterintuitive. For example, dry years in 

Minnesota during the early part of the 20th century 

favored the establishment of savanna species like 

bur oak, but also allowed seedlings of green ash to 

successfully invade grasslands and savannas due to 

reduced competition from herbaceous plants (Ziegler 

et al. 2008).

Climate change is projected to alter hydrologic 

regimes throughout the assessment area. As 

discussed in Chapters 3 and 4, heavy precipitation 

events have been increasing across the assessment 

area during the past century and this trend is 

expected to continue. In addition to more episodic 

precipitation events, future climate scenarios project 

a wide possible range of seasonal precipitation 

and soil moisture (Chapter 4). Such variability 

may expose forest ecosystems to greater risk of 

hydrologic extremes: waterlogging and flooding 

on one hand, and moisture stress and drought on 

the other. Forests that are accustomed to seasonal 

or annual variations in water availability may be 

better able to tolerate this variability. In particular, 

Fire-Dependent Forests, Riparian Forests, and 

Wet Forest Systems are all tolerant of varying 

degrees of hydrologic fluctuation (Minnesota 

DNR 2003). Forests that depend on a more stable 

regime of soil moisture or water levels throughout 

the year or between years may be more stressed 

by hydrologic variation—particularly Mesic 

Hardwood Forests, Forested Rich Peatlands, and 

Acid Peatlands (Minnesota DNR 2003). Peatlands 

have been shown to respond in a matter of years 

to water table fluctuations of a few inches, and the 

productivity and functioning of these systems could 

be especially sensitive to the combination of water 

table variability and the direct effects of warming 

(Bridgham et al. 2008, Swanson and Grigal 1991). 

In a review of the consequences of more extreme 

precipitation regimes, Knapp et al. (2008) also 

proposed that mesic systems may be most negatively 

impacted because of increasing duration and severity 

of soil water stress. Xeric systems would generally 

be less affected by a more extreme precipitation 

regime because they already are limited by moisture 

stress and larger pulses of precipitation might afford 

them slightly longer periods of moisture. Hydric 

systems, on the other end of the spectrum, are 

already limited by anoxic conditions so longer dry 

periods between precipitation pulses might increase 

some ecosystem functions like biomass productivity. 

This conceptual framework does not incorporate 

modifiers like soil texture, root depth, and the 

particular regeneration requirements of tree species. 
Chapter 5: Future Climate Change Impacts on Forests

109

The general principles make sense, as long as soil 

moisture changes are not dramatic enough to result 

in prolonged regeneration failures.

Additionally, hydric systems like Forested Rich 

Peatlands could gradually transition to a novel 

ecosystem type given increased productivity and 

increased soil respiration. That is, these systems 

may be less stressed by a more extreme precipitation 

regime according to some measures, but they may 

still be vulnerable in terms of shifting to a new 

vegetation community. If extended drought or 

hydrologic alteration causes local water tables to 

drop and peat layers begin to decompose rapidly, 

a peatland forest of black spruce and tamarack 

could be colonized by a variety of other tree species 

(Gorham 1991). Conversely, if excessive flooding 

or hydrologic alteration causes water tables to rise, 

Forested Rich Peatlands or Acid Peatlands could 

transition to open wetland systems. These changes 

may be difficult to forecast given the uncertainty 

in future precipitation regimes and groundwater 

dynamics, but the effects could be important for C 

storage in peatland systems (Bridgham et al. 2008, 

Gorham 1991). 

Soil Erosion

As climate change continues to intensify the 

hydrologic cycle, the increase in heavy rainfall 

events is projected to continue across the assessment 

area. One of the potential impacts of this trend is 

that soil erosion rates will increase (Nearing et al. 

2004, 2005). One study of agricultural systems 

across the United States estimates that erosion rates 

could increase twice as fast as total rainfall amounts 

(Nearing et al. 2004). Most studies examining 

the effects of climate change on soil erosion have 

focused on agricultural settings, rather than forests. 

Although additional vegetative cover and root 

stabilization in forest systems may make forests 

less prone to soil erosion, not all forest soils will be 

equally protected. Reductions in vegetative cover 

due to a variety of climate-related impacts, such as 

earthworm invasion or prolonged drought, could 

increase susceptibility to erosion. Additionally, the 

projected decline in snowpack and the transition 

from snowfall to rain in winter months might make 

forest soils particularly vulnerable to erosion during 

the late fall and early spring. 

The Minnesota Department of Natural Resources 

prepared a soil erodibility map for the state, which 

accounts for soil types as well as slope (Minnesota 

DNR 2012f). Portions of the Laurentian Mixed 

Forest Province rank as moderately or highly 

vulnerable to soil erosion according to this analysis, 

particularly the Northern Superior Uplands 

Ecological Section and the western portion of the 

Minnesota Drift and Lake Plains Ecological Section 

(Fig. 43). As rainfall patterns continue to shift in the 

assessment area, these areas may be most susceptible 

to increased soil erosion. 

Figure 43.—Map of inherent soil erodibility across 

Minnesota, presented as an index value calculated based on 

soil type and slope (Minnesota DNR 2012f).

Major watersheds

High: 196

Low: 2
Chapter 5: Future Climate Change Impacts on Forests

110

Wildfire

Wildfire is an important driver for forests across 

northern Minnesota. Fire-Dependent Forests 

are obviously closely tied to wildfire dynamics, 

but fire could also become an increasing source 

of disturbance in other forest types if climatic 

shifts over the 21st century result in different 

fire behavior. The climate of an area can directly 

affect the frequency, size, and severity of fires, and 

climate also indirectly affects fire regimes through 

its influence on vegetation vigor, structure, and 

composition (Sommers et al. 2011). 

Many aspects of Minnesota’s fire regime are 

expected to be affected by changes in climate, with 

response to climate change varying over time and 

space. Authors of a review paper on climate and 

wildfire conclude that fire-related impacts may be 

more important to some ecosystems than the direct 

effects of climate change on species fitness and 

migration (Sommers et al. 2011). Fire could have 

a greater influence because it can be a catalyst for 

change in vegetation, perhaps prompting more rapid 

change than would be expected based only on the 

changes in temperature and moisture availability. As 

with wind disturbances, the potential exists for novel 

successional pathways following wildfire if climatic 

conditions, seed sources, or management decisions 

favor different forest types. 

Even if uncertainty exists for the near term, model 

simulations from around the world tend to agree that 

there will be increases in fire activity by the end of 

the 21st century under climate change (Moritz et 

al. 2012). This agreement is particularly high for 

boreal forests, temperate coniferous forests, and 

temperate broadleaf and mixed forests. These global 

assessments correspond with regional research on 

climate and wildfire. Projections for boreal forests 

in Canada estimate that there may be a 100-percent 

increase in the annual area burned by the end of 

the century, along with a 50-percent increase in 

fire frequency (Flannigan et al. 2009). Research 

on boreal forest systems in Quebec projects that 

the wildfire season may shift later into the growing 

season, with wildfire risk doubling in August (Le 

Goff et al. 2009). Future fire activity may depend 

most on the relationship between temperature, 

precipitation, and evapotranspiration. If temperature 

and evapotranspiration increases amplify the effects 

of declining precipitation or overwhelm modest 

precipitation increases, fires are expected to increase 

(Drever et al. 2009). 

Research suggests that human activities may have a 

larger influence on wildfire activity than biophysical 

drivers in some landscapes (Miranda et al. 2012). 

Land use and management decisions will be the 

primary factor that determines whether a change 

in fire risk might translate to an actual increase 

in wildfire activity. Future policies on wildfire 

Column of the Pagami Creek Fire on September 10, 2011. 

Photo by Casey McQuiston, Superior National Forest.
Chapter 5: Future Climate Change Impacts on Forests

111

suppression and prescribed fire are key sources of 

uncertainty. Complex spatial patterns of land use and 

active fire management programs also make broadscale predictions of area burned unreliable for the 

Laurentian Mixed Forest Province.

Invasive Species

As described in Chapter 1, nonnative invasive 

species are a major threat to forests in northern 

Minnesota. It is generally expected that invasive 

plants will “disproportionally benefit” under 

climate change due to more effective exploitation 

of changed environments and more aggressive 

colonization of new areas (Dukes et al. 2009). The 

potential for climate change to disrupt hydrologic 

regimes, increase soil erosion, and intensify a variety 

of other stressors certainly raises the potential for 

invasive species to exploit altered environments. As 

an example of these potential interactions, studies 

in northern Minnesota found that a combination 

of invasive earthworms and warming conditions 

could benefit exotic understory plant species 

(Eisenhauer et al. 2012). Similarly, invasive species 

may facilitate the invasion and establishment of 

other nonnative species. This may be the case with 

European earthworms and European buckthorn, 

which appear to have a co-facilitating relationship 

(Heimpel et al. 2010). Invasive species can also limit 

regeneration opportunities for native tree species. 

Forest Pests and Diseases

Under a high emissions scenario, researchers 

forecast more insect pest damage due to increased 

metabolic activity in active periods and increased 

winter survival (Dukes et al. 2009). The effect of 

climate on particular forest insects remains uncertain 

in many cases, however. Gypsy moth is limited by 

cold winter temperatures across the Midwest, and 

is anticipated to expand its range northward under 

future climate change scenarios (Frelich and Reich 

2010, Vanhanen et al. 2007). 

It is more difficult to anticipate the response of forest 

pathogens under a warmer future due to complex 

modes of infection, transmission, survival, and tree 

response (Dukes et al. 2009). A review of forest 

diseases and the potential impacts of climate change 

highlights the potential for interactions involving 

other stress agents that make trees more susceptible 

to diseases (Sturrock et al. 2011). Pathogens are 

generally expected to become more damaging in 

forests as the climate changes, because they will 

be able to adapt more quickly to new climatic 

conditions, migrate more quickly to suitable habitat, 

and reproduce at faster rates than host tree species. 

One example of a potential disease migrant to the 

assessment area could be sudden oak death, a fungal 

pathogen currently limited to the West Coast and 

southeastern United States. This disease is limited 

by cold temperatures. Risk maps for sudden oak 

death are based on 1971 to 2000 climate normal, 

however, and do not account for projected climate 

shifts (Venette and Cohen 2006). The suitability 

maps for sudden oak death based on historical 

climate data already include the North Shore of Lake 

Superior as marginally suitable habitat, along with 

all of northern Wisconsin and Michigan (Fig. 44). 

Particularly under warmer climate change scenarios, 

this disease could conceivably survive in Minnesota. 

Herbivory

As mentioned above, a change in snowfall amount 

and duration throughout the assessment area is 

expected to change the wintertime foraging behavior 

for herbivores such as moose, white-tailed deer, and 

snowshoe hare. Climate change is expected to favor 

white-tailed deer and reduce populations of moose 

throughout the assessment area (Frelich et al. 2012, 

Rempel 2011). Warmer winter temperatures and 

reduced snow depth may reduce energy requirements 

for deer, and increase access to forage during winter 

months (Wisconsin Initiative on Climate Change 

Impacts Wildlife Working Group 2011). Conversely, 

warmer temperatures appear to cause greater 

physiological stress and parasite loads in moose.
Chapter 5: Future Climate Change Impacts on Forests

112

Figure 44.—Predicted climatic suitability for establishment of sudden oak death, based on the ecoclimatic index (EI) calculated 

by Venette and Cohen (2006). Suitability is based on 1971 to 2000 climate data and estimated climate thresholds of the 

pathogen.

If deer populations increase over the 21st century, 

this herbivore could have even greater impacts on 

forest vegetation across the assessment area than it 

already has. Research has found that deer browsing 

pressure may limit the ability of forests to respond to 

climate change (Fisichelli et al. 2012). This possible 

effect is because species anticipated to expand their 

ranges northward in the assessment area, such as 

red maple, sugar maple, and red oak, are browsed 

much more heavily than boreal conifers such as 

balsam fir and white spruce. Deer herbivory may 

also favor species which are not browsed heavily, 

such as ironwood and black cherry, or invasive 

species like buckthorn or Japanese barberry. Tree 

Atlas, LANDIS-II, and PnET-CN results project that 

most mesic hardwood species and eastern white pine 

will experience gains in suitable habitat, biomass, 

and productivity in the assessment area during the 

21st century, but none of these models accounts for 

herbivory. 

Carbon Dioxide Fertilization

In addition to effects on climate, CO2 can affect 

plant productivity and species composition. Elevated 

CO2

 may enhance growth and water use efficiency 

of some species, potentially offsetting the effects 

of drier growing seasons (Ainsworth and Rogers 

2007, Norby and Zak 2011, Wang et al. 2006). 

There is already some evidence for increased forest 

growth in the eastern United States (Cole et al. 

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

if enhanced growth can be sustained (Bonan 2008, 

Foster et al. 2010). The potential for water-use 

efficiency gains to buffer against moisture deficits 
Chapter 5: Future Climate Change Impacts on Forests

113

could be particularly important for forests in the 

assessment area, given the potential for late-season 

moisture stress during the growing season. Research 

on bur oak in Minnesota indicates that this effect 

may have already improved the ability of adult trees 

to withstand seasonal moisture stress (Wyckoff and 

Bowers 2010).

As mentioned in the discussion of PnET-CN 

results, several factors might actually limit the CO2

fertilization effect. Nutrient and water availability, 

ozone pollution, and tree age and size all play 

major roles in the ability of trees to capitalize on 

CO2

 fertilization (Ainsworth and Long 2005). Fire, 

insects, disease, and management could reduce 

forest productivity in discrete locations, and longterm ecosystem transitions might also influence the 

ability of forests to take advantage of additional 

atmospheric CO2 . 

Nutrient Cycling

As air temperatures warm and precipitation patterns 

change, changes may also occur in the way nutrients 

are cycled between plants, soils, and the atmosphere. 

Changes in nutrient cycling can have important 

implications for forest productivity, which can be 

limited by nutrients such as phosphorus, calcium, 

and N. Studies across the northeastern United 

States can give some insight into potential effects of 

climate change on nutrient cycling. 

Decomposition of vegetation is carried out primarily 

by enzymes released from bacteria and fungi. These 

enzymes are sensitive to changes in temperature, 

and thus there is generally a positive effect of 

temperature on the rate of enzymatic activity as long 

as moisture is also sufficient (Brzostek and Finzi 

2012, Rustad et al. 2001). In addition to increases 

in temperature, changes in growing season, soil 

frost, soil moisture, soil pH, and the interaction 

among these factors can affect nutrient cycling 

(Campbell et al. 2009). For example, more nutrients 

may leach from forest soils as a result of earlier 

spring thaws because the onset of photosynthesis in 

plant communities may not be advancing as rapidly 

and plants are not ready to take up the products of 

overwinter decomposition (Campbell et al. 2010). 

A review of nutrient cycling and climatic factors 

for sugar maple concluded that extremes in light 

environment, temperature, precipitation, pathogen 

attack, and herbivory can induce or amplify nutrient 

imbalances (St. Claire et al. 2008). For example, 

excessive or inadequate soil moisture can limit 

nutrient acquisition by tree roots. Many studies 

have examined the effects of extended dry periods 

followed by moisture pulses on nutrient cycling 

(Borken and Matzner 2009). Although these 

moisture pulses do lead to a flush of mineral N, it is 

not sufficient to compensate for the lack of microbial 

activity during dry periods. Thus, an increase in wetdry cycles appears to lead to a reduction in nutrient 

availability for trees. These results suggest that the 

increasingly episodic precipitation regime in the 

assessment area may add further stress to forests in 

the future. 

Additionally, changes in tree species composition 

could alter the rate of N cycling in forest 

ecosystems, which would lead to further effects 

on productivity and vegetation changes. Conifer 

and oak litter contains less N compared to northern 

hardwood species, so hardwood species invading 

a spruce-fir or pine forest may create a positive 

feedback loop as their litter gradually increases 

available soil N and thereby increases their relative 

competitive advantage (Pastor and Post 1988). 

Interactions

Clearly, none of the changes described above will 

occur in isolation. Climate change has the potential 

to alter this entire suite of ecosystem processes 

and stressors, in addition to others not considered 

here. The potential for interactions among these 

impacts will be critically important in determining 

the resulting changes to forest ecosystems across the 
Chapter 5: Future Climate Change Impacts on Forests

114

assessment area. Just as there are typically multiple 

interacting drivers for individual tree mortality 

(Dietze and Moorcroft 2011), overall community 

shifts may also be prompted by a variety of factors 

(Frelich and Reich 2010). 

Recognizing the potential for these interactions 

will be necessary to accurately assess the risks that 

climate change poses to forests. Scientific research is 

beginning to clarify how biotic and abiotic stressors 

can operate in concert, but these types of studies are 

still rather rare (Gellesch et al. 2013). It has long 

been known that stressed trees are more susceptible 

to insect pests and diseases. Recent research has 

found that drought stress leads to more damaging 

forest tent caterpillar outbreaks (Babin-Fenske and 

Anand 2011). Earthworm invasion tends to create 

warmer, drier soil surface conditions with more bare 

soil in forest systems, which may favor species that 

can germinate in these conditions (Eisenhauer et al. 

2012). Earthworm invasion may also make northern 

hardwood forests more vulnerable to the effects of 

drought (Larson et al. 2010), leading to greater risk 

of disease and pest outbreak. This is simply one 

chain of interactions, and many more links could 

be drawn to phenological changes, fire seasons, and 

other climate-mediated impacts. 

SUMMARY

Climate change has the potential to affect forest 

ecosystems in a variety of ways. Some of these 

potential impacts have been investigated through 

a coordinated set of model projections. Results 

from Tree Atlas, LANDIS-II, and PnET-CN each 

contribute particular kinds of information about how 

tree species and forest ecosystems could potentially 

respond to a range of possible climate futures. These 

results generally agree that characteristic boreal or 

northern species and forest types are projected to 

experience declines in suitable habitat, landscapelevel biomass, and productivity. These model 

projections indicate that temperate species may 

perform better, raising the possibility for potentially 

large community shifts across the Laurentian Mixed 

Forest Province in Minnesota. 

Further, research on the direct and indirect impacts 

of climate change on forests highlights several 

potential drivers of change in the assessment area. 

These impacts may arise from chronic stress (e.g., 

extended drought), gradual changes (e.g., warming 

winter temperatures and declining snow levels), or 

discrete disturbance events (e.g., stand-replacing 

wildfires or insect pest outbreaks). Many of these 

factors may operate in concert, and synergistic or 

multiplying interactions may be the most difficult to 

understand and forecast. 

Human decisions will add uncertainty to the 

response of ecosystems to climate change. Future 

land management decisions will largely dictate 

how these potential changes may affect forests in 

northern Minnesota. For example, fire suppression 

policies and tactics may help determine the 

future extent and severity of wildfires across the 

assessment area, and public pressure and political 

will may determine how these decisions are made. 

These choices related to management and policy are 

beyond the scope of this assessment, but they will be 

critical in determining how forests in the assessment 

area will adapt to climate change. 
115

Chapter 6: Forest Ecosystem Vulnerabilities

This chapter describes the climate change 

vulnerability of eight major forested systems in 

the assessment area during the next century. The 

Intergovernmental Panel on Climate Change 

(IPCC) defines vulnerability as the susceptibility of 

a system to the adverse effects of climate change 

(IPCC 2007). It is a function of the potential 

impacts to a system and the adaptive capacity of 

that system to tolerate these impacts (Fig. 45). We 

consider a system to be vulnerable if it is at risk of 

changes leading to a new identity, or if the system is 

anticipated to suffer substantial declines in health or 

productivity. This broad definition of vulnerability 

is warranted because forests are valued both for 

their particular character and mix of species and 

for the services they provide. The vulnerability of 

an ecosystem to climate change is independent of 

the economic or social values associated with the 

system, and the ultimate decision of whether to 

conserve vulnerable systems or allow them to shift 

to an alternate state will depend on the individual 

objectives of land management organizations.

This chapter is organized into two sections. First, 

we present an overall synthesis of potential climate 

impacts on forests, organized according to drivers 

and stressors, ecosystem impacts, and factors that 

influence adaptive capacity. This synthesis is based 

on the current scientific consensus of published 

literature (Chapters 4 and 5). In the second section, 

we present individual vulnerability determinations 

for the six forested Native Plant Community 

Systems and two managed forest systems considered 

in this assessment. 

A SYNTHESIS OF CLIMATE CHANGE 

IMPACTS ON FOREST ECOSYSTEMS 

Potential impacts are the direct and indirect 

consequences of climate change on individual 

ecosystems. Impacts are a function of a system’s 

exposure to climate change and its sensitivity to any 

changes. Impacts could be beneficial to a system if 

the changes result in improved heath or productivity, 

a greater area occupied by the system, or a tendency 

to maintain the identity of the system. Negative 

potential impacts would tend toward declining 

health and productivity, reduced territory occupied 

by the system, or a composition shift that leads to a 

substantially different identity for the system. 

Figure 45.—Key components of vulnerability, illustrating 

the relationship among exposure, sensitivity, and adaptive 

capacity.
Chapter 6: Forest Ecosystem Vulnerabilities

116

Throughout this chapter, statements about potential 

impacts and adaptive capacity factors will be 

qualified with a confidence statement. These 

confidence statements are formatted according to a 

confidence determination diagram from the IPCC’s 

recent guidance for authors (Mastrandrea et al. 2010) 

(Fig. 46). Confidence was determined by gauging 

both the level of evidence and level of agreement 

among information sources. Evidence is robust when 

there are multiple lines of evidence, as well as an 

established theoretical understanding to support the 

vulnerability determination. Agreement refers to the 

agreement among the available sources of evidence, 

not the level of agreement among authors of this 

assessment. Agreement was rated as high if theories, 

observations, and models tended to suggest similar 

outcomes.

Potential Impacts 

on Drivers and Stressors 

Many physical and biological factors contribute to 

the state of forest ecosystems in northern Minnesota. 

Some of these factors serve as drivers, defining 

features that determine the identity of a system. 

Other factors can serve as stressors, reducing 

the health, productivity, and integrity of specific 

systems. Many factors, such as flooding or fire, may 

be drivers in one system and stressors in another. 

Moreover, some disturbances, such as flooding or 

fire, could be drivers in certain systems but could 

act as stressors if the timing or intensity of the 

disturbance changes.

Temperatures will increase (robust evidence, high 

agreement). All global climate models project that 

temperatures will increase with continued increases 

in atmospheric greenhouse gas concentrations. 

A large amount of evidence from across the globe 

shows that temperatures have been increasing and 

will continue to increase due to human activities 

(Chapter 2). Temperatures across the assessment 

area have already exhibited significant increases 

Figure 46.—Confidence determination diagram used in the 

assessment. Adapted from Mastrandrea et al. (2010).

(Chapter 3), and continued temperature increases 

are projected for the assessment area even under the 

most conservative future climate scenario 

(Chapter 4). 

Winter processes will change (robust evidence, 

high agreement). All evidence agrees that 

temperatures will increase more in winter than in 

other seasons across the assessment area, leading 

to changes in snowfall, soil frost, and other winter 

processes. 

Both climate scenarios for the assessment area 

project that winter temperatures will increase at 

a faster rate than temperatures in other seasons 

(Chapter 4). Even with projected increases in winter 

precipitation, temperature increases indicate that a 

greater proportion of moisture will be delivered as 

rainfall during this season. Combined with increased 

snowmelt from higher temperatures, the amount 

of snow on the ground is expected to decrease 

across the assessment area (Sinha and Cherkauer 

2010). In addition, northern Minnesota may have 

30 to 40 fewer days of soil frost by the end of the 

century (Sinha and Cherkauer 2010). Although 

these conditions could increase water infiltration 

into the soil and reduce runoff, they may also 
Chapter 6: Forest Ecosystem Vulnerabilities

117

lead to greater soil water losses through increased 

evapotranspiration. The decrease in snowcover 

and frozen soil is expected to affect a variety of 

ecosystem processes, including decomposition 

activity, nutrient cycling, the onset of the growing 

season, and other phenological factors. 

Growing seasons will get longer (robust evidence, 

high agreement). There is high agreement among 

information sources that projected temperature 

increases will lead to longer growing seasons in the 

assessment area.

Evidence at both global and local scales indicates 

that growing seasons have been getting longer, 

and this trend is projected to become even more 

pronounced over the next century (Chapters 3 

and 4). Longer growing seasons have the potential 

to affect the timing and duration of ecosystem and 

physiological processes across the region (Dragoni 

and Rahman 2012). As seasons shift so that spring 

arrives earlier and fall extends later into the year, 

phenology may shift for plant species that rely on 

temperature as a cue for the timing of leaf-out, 

reproductive maturation, and other developmental 

processes (Schwartz et al. 2006a, Walther et al. 

2002). Longer growing seasons could also result in 

greater growth and productivity of trees and other 

vegetation, but only if balanced by available water 

and nutrients (Chapter 5). Moreover, growing season 

might not benefit all tree species equally (Chapter 4).

The amount and timing of precipitation will 

change (medium evidence, high agreement).

All global climate models agree that there will 

be changes in precipitation patterns across the 

assessment area. 

For the climate projections used in this assessment 

(Chapter 4) and other publications, large variation 

exists for projected changes in precipitation for 

the assessment area (Kling et al. 2003, Minnesota 

Department of Natural Resources 2011a). Although 

individual model projections for the assessment area 

may differ, there is general agreement that annual 

precipitation is expected to remain consistent or 

increase slightly during the 21st century. Models 

also tend to agree that precipitation patterns between 

seasons may shift substantially (Kunkel et al. 2013). 

Precipitation increases are generally expected to be 

larger in winter and spring, which is in agreement 

with both climate scenarios presented in this 

assessment (Chapter 4). Summer precipitation is 

projected to increase slightly or decrease sharply 

(Chapter 4).

Intense precipitation events will continue to 

become more frequent (medium evidence, 

medium agreement). There is some agreement 

that the number of heavy precipitation events will 

continue to increase in the assessment area. If they 

do increase, impacts from flooding and soil erosion 

may also become more damaging. 

Heavy precipitation events have been increasing 

in number and severity in the upper Midwest in 

general and for Minnesota in particular (Groisman et 

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

and many models agree that this trend will continue 

over the next century (IPCC 2007, Kling et al. 2003, 

Kunkel et al. 2013). Most heavy precipitation events 

occur during summer in Minnesota. The magnitude 

or frequency of flooding could also potentially 

increase in the winter and spring due to increases 

in total runoff and peak stream flow during those 

times (Cherkauer and Sinha 2010). Flood risks 

will ultimately depend on local geology as well as 

future decisions regarding infrastructure and land 

use, which remain unknown. Increases in runoff 

after heavy precipitation events could also lead to an 

increase in soil erosion (Nearing et al. 2004). 

Droughts will increase in duration and area 

(limited evidence, low agreement). A study using 

multiple climate models indicates that drought 

may increase in length and extent, and an episodic 

precipitation regime could mean longer dry periods 

between events.
Chapter 6: Forest Ecosystem Vulnerabilities

118

With an increasingly episodic precipitation regime, 

it has been suggested that there may be longer 

intervals between heavy rainfall events in the future 

(Knapp et al. 2008). Studies examining a range 

of climate model projections disagree with this 

conclusion, projecting that northern Minnesota 

may experience fewer consecutive days without 

precipitation in the future (Kunkel et al. 2013). 

Overall, there is relatively low confidence in the 

projected future frequency of droughts across the 

central United States. Climate projections described 

in this assessment also highlight the possibility of 

reduced precipitation and increased moisture stress 

during summer months, particularly under the GFDL 

A1F1 scenario (Chapter 4). 

Soil moisture patterns will change (medium 

evidence, high agreement), with drier soil 

conditions later in the growing season (medium 

evidence, medium agreement). Studies show that 

climate change will affect soil moisture, but there is 

disagreement among climate and impact models on 

how soil moisture will change during the growing 

season. 

As discussed above, seasonal changes in 

precipitation are expected across the assessment 

area. Due to projected decreases in summer 

precipitation and increases in winter and spring 

precipitation, it is reasonable to expect that soil 

moisture regimes will also shift. Longer growing 

seasons and warmer temperatures may also result 

in greater evapotranspiration losses and lower 

soil-water availability later in the growing season 

(Chapter 4). Outputs from the PnET-CN model 

indicate that forests in the assessment area may 

become increasingly moisture-limited under 

climate change (Chapter 5). This may be the case 

particularly in locations where soils and landforms 

do not allow precipitation from intense events to be 

retained. Model projections differ greatly, however, 

and it is also possible that the assessment area will 

have an increase in precipitation sufficient to offset 

increases in evapotranspiration. 

Climate conditions will increase fire risks by the 

end of the century (medium evidence, medium 

agreement). Some national and global studies 

suggest that wildfire risk will increase in the region, 

but few studies have specifically looked at wildfire 

potential in the assessment area.

At a global scale, the scientific consensus is that 

fire risk will increase by 10 to 30 percent due to 

higher summer temperatures (IPCC 2007). For the 

early part of the 21st century, there is low agreement 

in this trend across climate models (Moritz et al. 

2012). By the end of the century, however, most 

models project an increase in wildfire probability, 

particularly for boreal forests, temperate coniferous 

forests, and temperate broadleaf forests. Studies 

from southern Canada also project more active 

wildfire regimes in the future (Drever et al. 2009, 

Flannigan et al. 2009, Le Goff et al. 2009). In 

addition to the direct effects of temperature and 

precipitation, increases in fuel loads from pestinduced mortality or blowdown events could 

increase fire risk, but the relationship between 

these factors can be complex (Hicke et al. 2012). 

Forest fragmentation and unknown future wildfire 

management decisions also make fire projections 

more uncertain for the assessment area. Additionally, 

we do not have clear projections of how the nature 

of the fire regimes in Minnesota may change—the 

proportion of surface fires to crown fires, for 

example.

An area burned over in 2011 during the Pagami Creek Fire 

near Forest Center, Minnesota. Photo by Jason Butcher, 

Superior National Forest.
Chapter 6: Forest Ecosystem Vulnerabilities

119

Many invasive species, insect pests, and 

pathogens will increase or become more 

damaging (limited evidence, high agreement).

Evidence indicates that an increase in temperature 

and greater moisture stress will lead to increases in 

these threats, but research to date has examined few 

species. 

Invasive species are already a persistent and 

growing stressor across much of the United States. 

Changes may exacerbate this problem, as warmer 

temperatures may allow some invasive plant 

species, insect pests, and pathogens to expand their 

ranges farther north (Dukes et al. 2009). Northern 

Minnesota may lose some of the protection offered 

by a traditionally cold climate and short growing 

season. Combinations of factors may also favor 

invasive species, such as exotic earthworms, 

and facilitation among several nonnative species 

(Chapter 5). Pests and pathogens are generally 

more damaging in stressed forests, so there is high 

potential for these agents to interact with other 

climate-mediated stressors. Unfortunately, we 

lack basic information on the climatic thresholds 

that apply to many forest pests, and our ability to 

predict the mechanisms of infection, dispersal, 

and transmission for disease agents remains low. 

Furthermore, it is not possible to predict all future 

invasive species, pests, or pathogens that may enter 

the assessment area during the 21st century. 

Potential Impacts on Forests

Shifts in drivers and stressors mentioned above 

will naturally lead to changes in forests throughout 

the assessment area during the next century. 

Indirect impacts of climate change may become 

manifest through shifts in suitable habitat, species 

composition, or function of forest ecosystems. 

Boreal species will face increasing stress 

from climate change (medium evidence, high 

agreement). Impact models agree that boreal or 

northern species will experience reduced suitable 

habitat and biomass across the assessment area, 

and that they may be less able to take advantage of 

longer growing seasons and warmer temperatures 

than temperate forest communities. 

Across northern latitudes, it is generally expected 

that warmer temperatures will be more favorable to 

species that are located at the northern extent of their 

range and less favorable to those at the southern 

extent (Parmesan and Yohe 2003). Results from 

climate impact models project a decline in suitable 

habitat and landscape-level biomass for northern 

species such as balsam fir, black spruce, tamarack, 

quaking aspen, and white spruce (Chapter 5). 

PnET-CN results also suggest that spruce/fir forests 

may have smaller productivity gains than other 

forest types across the range of anticipated climate 

futures. Boreal species may remain in areas with 

favorable soils, management, or landscape features. 

Additionally, northern species may be able to persist 

in the assessment area if competitor species are 

unable to colonize these areas (Iverson et al. 2008).

Southern species will be favored by climate 

change (medium evidence, high agreement).

Impact models agree that many temperate species 

will experience increasing suitable habitat and 

biomass across the assessment area, and that longer 

growing seasons and warmer temperatures will lead 

to productivity increases for temperate forest types. 

Model results project that species near their northern 

range limits in the assessment area will become 

more abundant and more widespread under a range 

of climate futures (Chapter 5). The list of species 

projected to increase in suitable habitat and biomass 

includes American basswood, black cherry, bur oak, 

eastern white pine, green ash, red maple, white oak, 

and a variety of minor southern species (Chapter 5). 
Chapter 6: Forest Ecosystem Vulnerabilities

120

PnET-CN outputs also indicate that deciduous 

forest types have the potential for large productivity 

increases across northern Minnesota. In addition, 

Tree Atlas results project that suitable habitat may 

become available for species not currently found in 

the assessment area (e.g., shagbark hickory, black 

locust, and post oak) by the end of the century. 

Habitat fragmentation and dispersal limitations 

could hinder the northward movement of southerly 

species, despite the increase in habitat suitability. 

Most species can be expected to migrate more 

slowly than their habitats will shift (Iverson et al. 

2004a, 2004b; McLachlan et al. 2005; Scheller and 

Mladenoff 2008). Pests and diseases such as emerald 

ash borer and Dutch elm disease are also expected to 

limit some species projected to increase.

Forest communities will change across the 

landscape (limited evidence, high agreement).

Although few models have specifically examined 

how communities may change, model results from 

individual species and ecological principles suggest 

that recognized forest communities may change in 

composition as well as occupied range.

Species will respond individually to climate change, 

which may lead to the dissolution of traditional 

community relationships (Davis et al. 2005, Root et 

al. 2003). The model results presented in Chapter 

5 raise the possibility for potentially large changes 

in forest communities across northern Minnesota. 

Generally, the models suggest that climate trends 

may favor hardwoods across the landscape after 

2050, though ecological lag times, soil conditions, 

and management decisions may slow forest 

type conversions. Conceptual models based on 

ecological principles lend support to this possibility, 

particularly along ecological transition zones 

(Frelich and Reich 2010). Modeling studies also 

project that forest communities may move across 

the assessment area (Iverson et al. 2008, Lenihan et 

al. 2008). Therefore, the Native Plant Community 

Systems and Classes described in the assessment 

area may rearrange into novel communities. 

Observed trends have suggested that forest species 

may be more prone to range contraction at southern 

limits and less able to expand ranges northward to 

track climate change (Murphy et al. 2010, Woodall 

et al. 2013, Zhu et al. 2011). Therefore possibility 

also exists for nonnative species to take advantage of 

shifting forest communities and unoccupied niches 

if native forest species are limited (Hellmann et al. 

2008). 

Forest productivity will increase across the 

assessment area (medium evidence, medium 

agreement). Model projections and other evidence 

support modest productivity increases for forests 

across the assessment area, although there is 

uncertainty about the effects of carbon dioxide 

(CO2

 ) fertilization. It is expected that productivity 

will be reduced in localized areas.

PnET-CN results and other studies on CO2

fertilization show support for general increases in 

productivity across the assessment area (Chapter 5). 

Warmer temperatures are expected to speed nutrient 

cycling and increase photosynthetic rates for most 

tree species in the assessment area. Longer growing 

seasons could also result in greater growth and 

productivity of trees and other vegetation, but only if 

sufficient water and nutrients are available 

(Chapter 5). Conversely, LANDIS-II modeling 

results for this assessment project gradual 

productivity declines under the GFDL A1FI 

scenario. LANDIS-II simulations do not include the 

possible effects of CO2

 fertilization, which could 

increase productivity. Episodic disturbances such 

as fires, wind events, droughts, and pest outbreaks 

are expected to reduce productivity in certain areas 

over different time scales. In addition, lags in 

migration of species to newly suitable habitat may 

also reduce productivity until a new equilibrium is 

reached (Pastor and Post 1988, Post and Pastor 1996, 

Solomon 1986). 
Chapter 6: Forest Ecosystem Vulnerabilities

121

Adaptive Capacity Factors

Adaptive capacity is the ability of a species or 

ecosystem to accommodate or cope with potential 

climate change impacts with minimal disruption 

(Glick et al. 2011). Below, we summarize factors 

that could reduce or increase the adaptive capacity 

of forest systems within the assessment area. Greater 

adaptive capacity tends to reduce climate change 

vulnerability, and lower adaptive capacity tends to 

increase vulnerability. 

Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies have 

consistently shown that diverse systems are more 

resilient to disturbance, and low-diversity systems 

have fewer options to respond to change.

Climate change is expected to alter the nature and 

timing of many kinds of disturbance events across 

the assessment area (Chapters 4 and 5). In general, 

species-rich communities have exhibited greater 

resilience to extreme environmental conditions and 

greater potential to recover from disturbance than 

less diverse communities (Ogden and Innes 2007; 

Tilman 1996, 1999). This relationship makes less 

diverse communities inherently more susceptible to 

future changes and stressors (Swanston et al. 2011). 

Elmqvist et al. (2003) emphasize that “response 

diversity,” or the diversity of potential responses 

of a system to environmental change, is a critical 

component of ecosystem resilience. Response 

diversity is generally reduced in less diverse 

ecological systems. Fire-Dependent Forests and 

Mesic Hardwood Forests generally support a large 

number of tree species, and therefore have many 

possible future trajectories. Acid Peatland, managed 

aspen, and managed red pine systems all have fewer 

potential options. Genetic diversity within species is 

also critical for the ability of populations to adapt to 

climate change, because species with high genetic 

variation have better odds of producing individuals 

that can withstand extreme events and adapt to 

changes over time (Hoffmann and Sgrò 2011, 

Reusch et al. 2005). 

Species in fragmented landscapes will have less 

opportunity to migrate in response to climate 

change (limited evidence, high agreement). The 

dispersal ability of individual species is reduced 

in fragmented landscapes, but the future degree 

of landscape fragmentation and the potential 

for human-assisted migration are two areas of 

uncertainty. 

Habitat fragmentation can hinder the ability of 

tree species to migrate to more suitable habitat on 

the landscape, especially if the surrounding area 

is nonforested (Ibáñez et al. 2006, Iverson et al. 

2004a). Modeling results indicate that mean centers 

of suitable habitat for tree species will migrate 

between 60 and 350 miles by the year 2100 under 

a high emissions scenario and between 30 and 

250 miles under milder climate change scenarios 

(Iverson et al. 2004a). Based on data gathered for 

seedling distributions, it has been estimated that 

many northern tree species could possibly migrate 

northward at a rate of 60 miles per century (Woodall 

et al. 2009). Other evidence indicates that natural 

migration rates could be far slower for some species 

(McLachlan et al. 2005, Murphy et al. 2010). 

Fragmentation makes this disparity even more 

challenging, because the landscape is essentially less 

permeable to migration (Jump and Peñuelas 2005, 

Scheller and Mladenoff 2008). 

Systems that are limited to particular 

environments will have less opportunity to 

migrate in response to climate change (limited 

evidence, high agreement). Despite a lack of 

published research demonstrating this concept 

in the assessment area, our current ecological 

understanding indicates that migration to new areas 

will be especially difficult for species and systems 

with narrow habitat requirements. 

Several species and forest types in northern 

Minnesota are confined to particular habitats on the 

landscape, whether through particular requirements 

for hydrologic regimes or soil types, or other 

reasons. Similar to species occurring in fragmented 
Chapter 6: Forest Ecosystem Vulnerabilities

122

landscapes, isolated species and systems face 

additional barriers to migration (Jump and Peñuelas 

2005). Widespread species may also have particular 

habitat requirements. For example, sugar maple 

is often limited to soils that are rich in nutrients 

like calcium, so this species may actually have 

less newly suitable habitat in the assessment area 

than might be projected solely from temperature 

and precipitation patterns. Floodplain Forests 

are not expected to be able to migrate to upland 

areas because many species depend on seasonal 

flood dynamics for regeneration and a competitive 

advantage. Similarly, Acid Peatlands contain a 

unique mix of species that are adapted to low 

pH values, peat soils, and particular water table 

regimes. These systems face additional challenges 

in migration compared to more-widespread species 

with broad ecological tolerances. 

Systems that are more tolerant of disturbance 

have less risk of declining on the landscape 

(medium evidence, high agreement). Basic 

ecological theory and other evidence support the 

idea that systems that are adapted to more frequent 

disturbance will be at lower risk. 

Disturbances such as wildfire, flooding, and pest 

outbreaks are expected to increase in the area 

(Chapters 4 and 5). Mesic Hardwood Forests in 

particular are adapted to gap-phase disturbances, 

with stand-replacing events occurring over hundreds 

or thousands of years. Therefore, these systems 

may be less tolerant of more frequent widespread 

disturbances. Mesic systems can create conditions 

that could buffer against fire and drought to some 

extent, but these systems are not expected to do well 

if soil moisture declines significantly (Nowacki and 

Abrams 2008). Forest systems in the assessment area 

that are more tolerant of drought, flooding, or fire 

are expected to be better able to withstand climatedriven disturbances. This principle holds true only 

to a given point, because it is also possible for 

disturbance-adapted systems to experience too much 

disruption. For example, Fire-Dependent Forests 

such as Northern Dry-Sand Pine Woodland (FDn12) 

or Northern Dry-Bedrock Pine (Oak) Woodland 

(FDn22) might cover a greater extent under drier 

conditions with more frequent fire, but these systems 

might also convert to savannas or open grasslands 

if fire becomes too frequent or drought becomes too 

severe. 

VULNERABILITY DETERMINATIONS 

FOR INDIVIDUAL FOREST SYSTEMS

Climate-induced shifts in drivers, stressors, and 

dominant tree species are expected to result in 

different impacts to forested systems within the 

assessment area. Some communities may have 

a greater capacity to adapt to these changes than 

others, whereas some may be susceptible to 

relatively minor impacts. Therefore, it is helpful 

to consider these factors for individual forest 

systems in addition to describing general principles 

related to vulnerability and adaptive capacity. 

Table 19 presents a summary of major drivers and 

stressors for each forest community covered in this 

assessment. 

The following vulnerability determinations draw on 

the information presented in previous chapters, as 

well as an expert panel assembled from a variety of 

organizations and disciplines across the assessment 

area. The 23 panelists evaluated anticipated climate 

trends for the assessment area and model projections 

(Chapter 5), in combination with their own expertise. 

For each forest system, panelists considered the 

potential impacts and adaptive capacity to assign a 

vulnerability determination and a level of confidence 

in that determination using the same confidence 

scale described above. For a complete description 

of the methods used to determine vulnerability, see 

Appendix 5.
Chapter 6: Forest Ecosystem Vulnerabilities

123

Forest system Major drivers Major stressors

Fire-Dependent Forest coarse-textured soils or shallow soils over 

bedrock, fire-return intervals 20 to 150 years.

fire suppression, insect pests and diseases, 

understory hazel competition, deer 

herbivory

Mesic Hardwood Forest mesic soils or deep impermeable layers, 

consistent moisture and nutrients, gap-phase 

disturbances with stand-replacing events 

every 400 to 2,000 years.

exotic earthworms, invasive plants, insect 

pests, diseases, freeze-thaw cycles, drought, 

deer herbivory

Floodplain Forest alluvial soils, annual or occasional floods, 

connectivity to river and water table

changes to flood regime, buckthorn and reed 

canarygrass, drought, deer herbivory

Wet Forest wet-mesic soils, saturated in spring and dry in 

summer, periodic flooding

changes to soil moisture regime, ongoing 

ash decline, invasive species, insect pests, 

drought

Forested Rich Peatland peat soils, saturated throughout growing 

season, moisture through precipitation and 

groundwater, pH greater than 5.5

changes to water table, roads and beaver 

dams, insect pests and diseases, winterburn, 

drought, deer herbivory

Acid Peatland peat soils, saturated throughout growing 

season, moisture through precipitation only, 

pH less than 5.5, nutrient-poor environments

changes to water table, roads and beaver 

dams, insect pests and diseases, winterburn, 

drought

Managed Aspen wide range of soil types and landforms, 

frequent disturbance, even-aged 

management on 35- to 60-year rotation

forest tent caterpillar and gypsy moth, 

drought, deer herbivory, hypoxylon canker, 

exotic earthworms

Managed Red Pine sandy to mesic soils, limited by high summer 

temperatures, dependent on planting for 

regeneration, even-aged management on 60- 

to 120-year rotation

Armillaria, red pine shoot blight, understory 

hazel competition, deer herbivory, bark 

beetles, drought stress in dense stands

Table 19.—Forest systems considered in this assessment, with a summary of major drivers and stressors for each 

system

Overall vulnerability determinations ranged 

from low-moderate (Floodplain Forests) to high 

(Wet Forests, Forested Rich Peatlands, and Acid 

Peatlands) (Table 20). Panelists tended to rate the 

amount of evidence as medium (between limited 

and robust) for most forest systems. Incomplete 

knowledge of future wildfire regimes, interactions 

among stressors, and precipitation regimes were 

common factors limiting this component of overall 

confidence. The ratings of agreement among 

information also tended to be in the medium range. 

Contrasting information related to precipitation 

regimes under the two climate change scenarios was 

one factor that limited the level of agreement among 

information. In general, ratings were slightly higher 

for agreement than for evidence. Evidence appears 

not to be as robust as the experts would prefer, but 

the information that is available leads them to reach 

a similar conclusion.
Chapter 6: Forest Ecosystem Vulnerabilities

124

Forest system Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Fire-Dependent Forest Negative Moderate-High Moderate Medium Medium

Mesic Hardwood Forest Moderate Moderate-High Moderate Medium Medium

Floodplain Forest Moderate-Positive Moderate Low-Moderate Limited-Medium Medium

Wet Forest Negative Low High Limited-Medium Medium

Forested Rich Peatland Negative Low High Medium Medium-High

Acid Peatland Negative Low High Medium Medium-High

Managed Aspen Moderate-Negative Moderate Moderate-High Medium High

Managed Red Pine Moderate-Negative Moderate-Low Moderate-High Medium Medium

Table 20.—Vulnerability determination summaries for the forest systems considered in this assessment

In the sections that follow, we summarize the 

climate-related impacts on drivers, stressors, and 

dominant tree species that were major contributors 

to the vulnerability determination for each forest 

system. In addition, we summarize the main factors 

contributing to the adaptive capacity of each 

community type.
Chapter 6: Forest Ecosystem Vulnerabilities

125

Fall colors in northeastern Minnesota. Photo by Jack Greenlee, Superior National Forest.
Chapter 6: Forest Ecosystem Vulnerabilities

126

Fire-Dependent Forest System

Moderate Vulnerability (medium evidence, medium agreement)

Changes to the fire regime for northern Minnesota are particularly threatening for this system, in addition 

to the loss of suitable habitat for many key species and the potential for greater pest and disease activity. 

A high tolerance for disturbance increases the adaptive capacity of this system. 

Negative Potential Impacts

Drivers: Fire-Dependent Forests are generally 

found on coarse-textured or shallow soils, and may 

be able to tolerate the projected shift toward drier 

soils during the summer months. Evidence indicates 

that wildfires may burn larger areas in northern 

Minnesota under climate change, and that the fire 

season may shift later into the growing season. 

Blowdown-causing wind events could also provide 

more fuel buildup for large fire events. Greater 

wildfire activity could benefit these forest types, but 

it is possible that too much change to the fire regime 

would hamper regeneration. 

Dominant Species: Considering the range of 

possible climate futures, most dominant species that 

make up Fire-Dependent Forests are expected to 

decline in suitable habitat and across the assessment 

area according to model projections (jack pine, 

quaking aspen, paper birch, balsam fir, and black 

spruce). The same modeling studies suggest red 

pine and white pine will remain relatively constant 

or experience slight increases across the assessment 

area, and that minor components of Fire-Dependent 

Forests like northern red oak, bur oak, and red maple 

will also increase across the assessment area. 

Stressors: Climate change is expected to intensify 

several key stressors for Fire-Dependent Forests. 

Insect pests and diseases may become more virulent 

and damaging under a warmer climate, and the 

possibility exists for new pests such as western 

bark beetles to arrive in the assessment area. The 

continued shift toward mesic species within FireDependent Forests may be encouraged by climate 

change if fire suppression activities continue and 

broadleaf species like red maple continue to spread. 

White-tailed deer populations are also anticipated 

to increase with warmer winters, so herbivory 

on preferred species may continue to hinder 

regeneration. 

Moderate-High Adaptive Capacity 

Fire-Dependent Forests are generally tolerant of 

drought and disturbances and can contain a diversity 

of species, so these forests have greater adaptive 

capacity to climate change. Additionally, these 

forests can persist on poor soils, so the possibility 

exists that Fire-Dependent Forests could “retreat” 

to favorable locations on the landscape even if 

overall conditions change. Southern portions of the 

assessment area may be more likely to shift to Mesic 

Hardwoods because fragmentation and broadleaf 

species may limit fire activity. 
Chapter 6: Forest Ecosystem Vulnerabilities

127

A northern dry-mesic mixed woodland (FDn33). Photo 

by John Almendinger, Minnesota Department of Natural 

Resources, used with permission.

A northern dry-mesic mixed woodland, red pine - white pine 

woodland, balsam fir subtype (FDn33a1). Photo by Ethan 

Perry, Minnesota Department of Natural Resources, used 

with permission.

Jack pine stand after the 2011 Pagami Creek Fire, the 

largest fire in northern Minnesota in the last century. 

Photo by Shawn Fraver, University of Minnesota, used with 

permission.
Chapter 6: Forest Ecosystem Vulnerabilities

128

Mesic Hardwood Forest System

Moderate Vulnerability (medium evidence, medium agreement)

Climate change may intensify several major stressors for this forest system, such as drought and forest 

pests. High species diversity may increase resilience to future change, and uncertainty regarding 

future moisture regimes and potential interactions between stressors limits the confidence in this 

determination. 

Moderate Potential Impacts

Drivers: Mesic Hardwood Forests depend on 

relatively moist, nutrient-rich soils and a lack of 

wildfire disturbance. The potential for climate 

change to increase the frequency of extended 

droughts poses a threat to these forests for multiple 

reasons, including increased moisture stress, 

wildfire occurrence, and susceptibility to other stress 

agents. Hardwood forests occurring on moist, rich 

soils may be buffered from short-term droughts or 

seasonal moisture stress. Warming temperatures may 

also allow this system to expand into previously 

unsuitable areas. 

Dominant Species: Model projections indicate 

that most of the dominant species that make up 

Mesic Hardwood Forests are expected to gain in 

suitable habitat and biomass across the assessment 

area (American basswood, sugar maple, red maple, 

green ash, and bur oak). Deciduous forest types are 

also projected to have large potential productivity 

increases. Paper birch and quaking aspen are 

two key species anticipated to decline across the 

assessment area, and modeling results are mixed 

for northern oak and yellow birch. Several minority 

species in this system may also increase in biomass 

and suitable habitat across the assessment area (e.g., 

eastern white pine, ironwood, American elm, white 

oak, and bitternut hickory). NPC Class MHn44 may 

be particularly vulnerable because this class contains 

boreal species such as quaking aspen, balsam fir, and 

paper birch. 

Stressors: Climate change could amplify several 

major stressors to Mesic Hardwood Forests. Forest 

tent caterpillar and other pests may cause more 

frequent and severe damage in climate-stressed 

forests, and new pests such as gypsy moth and Asian 

longhorn beetle present unknown risks. White-tailed 

deer populations may also increase with warmer 

winters, which may hinder hardwood regeneration 

as well as the northward expansion of this system. 

The potential also exists for synergistic negative 

interactions between stressors in this system, such 

as earthworms, herbivory, drought, and invasive 

species. 

Moderate-High Adaptive Capacity 

Mesic Hardwood Forests generally contain a large 

number of species, which leads to a high response 

diversity. These forests could also gain territory lost 

by other forest types under wetter or drier future 

conditions. This system contains several species 

at their northern range limits, such as sugar maple 

and northern red oak, which may benefit from 

gene flow between southern populations. Increased 

CO2

 concentrations may also increase the wateruse efficiency of some species, reducing the risk 

of moisture stress. Stands with few species and 

reduced structural diversity may have lower adaptive 

capacity. 
Chapter 6: Forest Ecosystem Vulnerabilities

129

A mature red oak stand in north-central Minnesota 

underplanted with white pine, 2012. Photo by John Rajala, 

Rajala Companies, used with permission.

A mixed hardwood stand in north-central Minnesota 

(MHn35). Photo by Stephen Handler, U.S. Forest Service.

A northern rich mesic hardwood forest (MHn47). Photo by John Almendinger, Minnesota 

Department of Natural Resources, used with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

130

Floodplain Forest System

Low-Moderate Vulnerability (limited-medium evidence, medium agreement)

Climate change is expected to affect the flow regimes in riparian systems, which will have unknown 

consequences for this system. Low agreement on future precipitation and stream flow regimes is the 

primary uncertainty for this system, in combination with a lack of research and management experience.

Moderate-Positive Potential Impacts

Drivers: Climate change has the potential to alter 

the flow regimes in riparian systems across the 

assessment area. Floodplain Forests are particularly 

adapted to withstand annual and seasonal floods. 

The regeneration requirements of several species 

within this system are also linked to these floods. 

If climate change results in shifts in the timing or 

volume of stream flows, this forest system could be 

impaired. 

Dominant Species: Under a range of possible 

climate futures, most of the dominant species 

within Floodplain Forests are expected to gain in 

suitable habitat across the assessment area (silver 

maple, American elm, American basswood, black 

willow, eastern cottonwood). LANDIS-II is not 

suited to simulate lowland forest systems, but this 

tool projects large biomass increases for American 

basswood, American elm, and green ash in upland 

areas under both climate scenarios. Elm/ash/

cottonwood forests could experience large potential 

productivity gains under a range of climate futures. 

Emerald ash borer is expected to reduce the amount 

of green and black ash in future Floodplain Forests.

Stressors: Hydrologic alteration of river systems 

through dams and river channelization has already 

had negative impacts on Floodplain Forests. 

Invasive species such as reed canarygrass and 

European buckthorn are existing threats to these 

forests, and invasive species have the potential to 

increase in abundance in the assessment area under 

climate change. White-tailed deer populations are 

expected to increase with warmer winters, which 

may hinder regeneration of this system. If the trend 

continues toward more-intense precipitation events, 

extreme floods may present risk to this system 

through excessive waterlogging and downcutting of 

riverbanks. 

Moderate Adaptive Capacity 

Floodplain Forests are adapted to periodic 

disturbances and fluctuating soil moisture, so they 

might be capable of tolerating future changes to 

the hydrologic cycle. There is a lack of knowledge 

and management history in these forests compared 

to other systems in the assessment area, so it is 

unknown if there are certain disturbance thresholds 

that are excessive or beneficial for Floodplain 

Forests. It is not expected that other forest species 

will outcompete and replace these species in riparian 

settings, so Floodplain Forests may be at low risk 

for transition to other forest types. Conversely, 

these forests are confined to floodplains and are not 

expected to expand to new territory in the future. 
Chapter 6: Forest Ecosystem Vulnerabilities

131

A southern floodplain forest (FFs68). Photo by John 

Almendinger, Minnesota Department of Natural Resources, 

used with permission.

A riparian area in northern Minnesota with a classic riparian mix of 

hardwood species. Photo by Robert Scheller, Portland State University, 

used with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

132

Wet Forest System

High Vulnerability (limited-medium evidence, medium agreement)

Ongoing ash decline and emerald ash borer present serious existing threats to this system. These stressors 

may be exacerbated by climate change impacts to the precipitation regime. Limited research and 

management history and uncertainty about future precipitation reduce confidence in this determination.

Negative Potential Impacts

Drivers: Wet Forests depend on wet-mesic soils 

with saturated conditions in the spring and dry 

conditions in the summer months. Climate change 

has the potential to alter precipitation patterns across 

the assessment area, particularly during the growing 

season. The regeneration requirements of several 

species within this system are also linked to the 

timing of these wet and dry periods. Shifts in the 

timing or amount of precipitation could disrupt the 

function of these forests. 

Dominant Species: The potential for emerald ash 

borer to spread throughout the assessment area 

presents a serious risk to black ash and green ash 

in Wet Forests. Considering the range of possible 

climate futures, most of the dominant species that 

make up Wet Forests are expected to decline in 

suitable habitat and biomass across the assessment 

area, particularly under the GFDL A1FI scenario 

(black ash, northern white-cedar, balsam fir, balsam 

poplar, and black spruce). Model projections indicate 

that red maple may become a larger component 

of this system, and that minor species within Wet 

Forests like American elm and American basswood 

will also increase across the assessment area. 

Elm/ash/cottonwood forests could experience large 

potential productivity gains under a range of climate 

futures.

Stressors: The ongoing decline in black ash in the 

assessment area already presents problems for the 

health of Wet Forests. Invasive species such as reed 

canarygrass and European buckthorn are existing 

threats to these forests, and invasive species have the 

potential to increase in abundance in the assessment 

area under climate change. White-tailed deer 

populations are expected to increase with warmer 

winters, which may hinder regeneration of northern 

white-cedar in particular. Dutch elm disease is 

expected to limit the potential increase in American 

elm. 

Low Adaptive Capacity 

Knowledge and management history of these forests 

are lacking compared to other forest systems in the 

assessment area, so we know less about how they 

function and respond to disturbance. Many species 

that exist in Wet Forests can tolerate intermittent 

wet and dry conditions, so this system might 

be adaptable to short-term floods and droughts. 

Extended droughts would cause significant damage 

to these shallow-rooted forests. Increased winter 

and spring precipitation could buffer summer 

moisture stress if excess water is retained in lowlying areas on the landscape. Additionally, Wet 

Forests often exist as large complexes of a single 

species or a few species, so they have low response 

diversity. These forests also exist as isolated pockets 

on the landscape in some areas, so they may be 

disconnected in terms of migration and gene flow. 
Chapter 6: Forest Ecosystem Vulnerabilities

133

A northern very wet ash swamp (NPC Class WFn64). Photo 

by John Almendinger, Minnesota Department of Natural 

Resources, used with permission.

A northern wet ash swamp (NPC Class WFn55). Photo 

by John Almendinger, Minnesota Department of Natural 

Resources, used with permission.

A northern wet cedar forest (NPC Class WFn53). Photo 

by John Almendinger, Minnesota Department of Natural 

Resources, used with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

134

Forested Rich Peatland System

High Vulnerability (medium evidence, medium-high agreement)

Forests in peat systems have limited tolerance to changes in water tables. Additionally, the dominant 

species in these forests are expected to decline under a range of climate futures. Low agreement on 

future precipitation trends is the primary uncertainty for this system. 

Negative Potential Impacts

Drivers: Climate change has the potential to alter 

the water tables in low-lying areas across the 

assessment area. Forested Rich Peatlands function in 

a relatively narrow window of water table conditions 

and can respond in a matter of years to water table 

changes. Higher water levels could result in a 

transition to open peatland systems, but lower water 

levels could allow other forest types to invade as 

peat layers dry and decompose. 

Dominant Species: Most species in this system are 

at the southern edge of their ranges in Minnesota, 

and therefore may not tolerate warmer conditions. 

The dominant species in Forested Rich Peatlands, 

tamarack and black spruce, are projected to undergo 

declines in suitable habitat and biomass across 

the landscape. Declines may be most severe for 

black spruce. Other minor species like balsam fir 

and paper birch are also expected to decline under 

the hotter, drier climate scenario. The assessment 

area is also approaching the southern range limit 

for sphagnum moss. Red maple, white pine, and 

speckled alder may become larger components of 

this system in the future, but it is unclear if Forested 

Rich Peatlands will maintain their inherent identity 

if that composition shift occurs. Impact models 

presented in this assessment are not designed 

specifically to address peatland systems, so results 

should be interpreted with a degree of caution. 

Stressors: Roads, beaver dams, drainage ditches, 

or other watershed modifications that change flood 

regimes or water tables are already stressors in 

some parts of the assessment area. Their effects 

may be intensified by climate change. Additionally, 

higher growing-season temperatures may increase 

evapotranspiration rates and reduce the rate of 

peat accumulation in these systems as a result of 

increasing decomposition rates. Warmer winters and 

reduced snowpack may also increase the occurrence 

of winterburn in these systems, and allow for more 

frequent outbreaks of pests such as tamarack sawfly 

and eastern larch beetle. 

Low Adaptive Capacity

Forested Rich Peatlands typically receive water 

inputs through groundwater as well as precipitation, 

so these forests may be somewhat buffered from 

seasonal or short-term moisture deficits. Increased 

winter and spring precipitation could also be retained 

in low-lying areas on the landscape and compensate 

for summer droughts. Forested Rich Peatlands are 

widely distributed across the assessment area, but 

are confined to particular hydrologic regimes, soil 

types, and landscape positions. Therefore, they are 

not expected to expand to new territory within the 

assessment area or outcompete other forest types. In 

some locations Forested Rich Peatlands occur within 

a matrix of Fire-Dependent Forests like jack pine 

systems, so they may be exposed to more frequent 

wildfire if climate change results in extended 

droughts and more active wildfire regimes in the 

assessment area. 
Chapter 6: Forest Ecosystem Vulnerabilities

135

A forested rich peatland in northern Minnesota. Photo by 

Casey McQuiston, Superior National Forest.

A northern cedar swamp (FPn63). Photo by Ethan Perry, 

Minnesota Department of Natural Resources, used with 

permission.

A northern rich tamarack swamp (FPn82). Photo by Ethan 

Perry, Minnesota Department of Natural Resources, used 

with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

136

Acid Peatland System

High Vulnerability (medium evidence, medium-high agreement)

Acid Peatlands are not resilient to changes in water tables and are not buffered by groundwater inputs. 

The dominant species in these forests are expected to decline under a range of climate futures. Future 

precipitation trends are the primary uncertainty for this system.

Negative Potential Impacts

Drivers: Acid Peatlands typically occur on perched 

water tables without connection to groundwater. 

Therefore, these systems are even more vulnerable 

to water level changes than Forested Rich Peatlands. 

Higher water levels could result in a transition 

to open peatland systems, and lower water levels 

could cause greater drought stress and mortality in 

shallow-rooted forests. 

Dominant Species: The dominant tree species in 

Acid Peatlands, black spruce and tamarack, are 

projected to have significant declines in suitable 

habitat and biomass across the landscape according 

to ecosystem models. Declines may be most severe 

for black spruce. These species are at the southern 

edge of their ranges in Minnesota, and therefore 

may not tolerate warmer conditions. The assessment 

area is the southern range limit for sphagnum moss 

as well. Acid peatlands also contain a suite of 

rare and endemic plant species that are adapted to 

acidic, nutrient-poor conditions. These associated 

species are also presumably vulnerable to changes 

in water table level and the peat substrate. Impact 

models presented in this assessment are not designed 

specifically to address peatland systems, so results 

should be interpreted with a degree of caution. 

Stressors: Roads, beaver dams, drainage ditches, 

or other watershed modifications that change flood 

regimes or water tables are already stressors in some 

parts of the assessment area. These modifications 

may be intensified by climate change. Additionally, 

higher growing-season temperatures may increase 

evapotranspiration rates and reduce the rate of 

peat accumulation in these systems as a result of 

increasing decomposition rates. Warmer winters may 

also increase the occurrence of winterburn in Acid 

Peatlands, and allow for more frequent outbreaks of 

pests like tamarack sawfly.

Low Adaptive Capacity

Acid Peatlands receive water inputs only through 

precipitation, so these systems may be particularly 

susceptible to shifts in precipitation patterns and 

droughts. Increased winter and spring precipitation 

could possibly be retained in low-lying areas on the 

landscape and compensate for summer droughts. 

Acid Peatlands are more widely distributed across 

the assessment area than Forested Rich Peatlands, 

but are typically smaller and more confined to 

particular hydrologic regimes. These systems are 

slower to recover from disturbances like fires and 

blowdown events than Forested Rich Peatlands. 

Because of their acid conditions, however, these 

forests may face less competition from other forest 

types. 
Chapter 6: Forest Ecosystem Vulnerabilities

137

A northern spruce bog (APn80). Photo by John Almendinger, Minnesota Department of 

Natural Resources, used with permission.

A tamarack-dominated northern poor conifer swamp 

(APn81b). Photo by Stephen Handler, U.S. Forest Service.

A northern poor conifer swamp (APn81). Photo by Ethan 

Perry, Minnesota Department of Natural Resources, used 

with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

138

Managed Aspen 

Moderate-High Vulnerability (medium evidence, high agreement)

Aspen is adapted to disturbance and a wide range of sites. Ecosystem models project aspen will decline 

in northern Minnesota, and the potential exists for multiple stressors to interact under climate change. 

Limited long-term experience with intensive aspen management raises uncertainty for this system. 

Moderate-Negative Potential Impacts 

Drivers: Managed aspen stands can occur on a 

range of soil types, from dry to mesic. If climate 

change results in increased moisture stress during the 

growing season, aspen on drier sites may be exposed 

to greater drought stress and mortality. Warmer 

growing-season temperatures might encourage more 

suckering after harvests. Increased wildfire activity 

could help maintain aspen across the assessment 

area. However, with frequent disturbance from 

increased wildfire, drought, or more intensive 

management, aspen could become a less successful 

competitor in the future. 

Dominant Species: Under a range of possible 

climate futures, quaking aspen is expected to have 

large declines in suitable habitat and biomass across 

the assessment area by the end of the century. 

Quaking aspen is a boreal species near the southern 

range limit in Minnesota. Model results for bigtooth 

aspen are mixed. LANDIS-II projects biomass 

increases across both climate scenarios. Tree Atlas 

projects slightly increasing suitable habitat for 

bigtooth aspen under the PCM B1 scenario and 

decreasing suitable habitat under the GFDL A1FI 

scenario. 

Stressors: Climate change is expected to intensify 

several key stressors for managed aspen. Insect pests 

such as forest tent caterpillar and gypsy moth, along 

with diseases like hypoxylon canker, may become 

damaging under a warmer climate. Earthworm 

activity in aspen sites may make these forests more 

susceptible to drought stress, and white-tailed deer 

herbivory may also increase with warmer winters. 

The possibility exists for interactions among 

multiple stressors to lead to severe impacts. For 

example, drought stress is projected to become more 

frequent under climate change, earthworm activity 

makes forest stands more susceptible to drought, and 

insect pest outbreaks are more damaging in stressed 

forests. Multiple harvests of aspen may lead to a 

decline of nutrient status or productivity on these 

sites. 

Moderate Adaptive Capacity

Aspen is adapted to disturbance and can exist on a 

wide range of soils and landforms. The ability to 

reproduce asexually is also an advantage in some 

instances, particularly for clones better adapted to 

future conditions. Past management has reduced 

species diversity and structural diversity, thereby 

lowering the adaptive capacity of aspen forest across 

the landscape. There is a limited history of shortrotation aspen management, and many questions 

about how these systems will respond over time are 

unanswered. 
Chapter 6: Forest Ecosystem Vulnerabilities

139

Aspen crowns. Photo by Eli Sagor, University of Minnesota, used with permission.

Aspen regeneration 2 months after a clearcut. Photo by Eli Sagor, University of Minnesota, 

used with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

140

Managed Red Pine

Moderate-High Vulnerability (medium evidence, medium agreement)

Red pine is tolerant of moisture stress, and ecosystem models project red pine will remain relatively 

constant across the landscape in northern Minnesota. Climate change may amplify multiple stressors and 

enable new interactions, and single-species systems are not positioned to be very resilient. 

Moderate-Negative Potential Impacts 

Drivers: Managed red pine plantations occur on 

a range of soil types, from dry to mesic. Red pine 

has typically been planted in suitable locations in 

Minnesota. Natural regeneration is very rare in 

managed red pine systems due to shoot blight and 

deer herbivory, so maintaining red pine in these 

stands depends on planting seedlings following 

harvest. Seasonal shifts in precipitation patterns may 

impair the survival of planted seedlings, particularly 

if the trend is for wetter springs and drier summers. 

Dominant Species: Modeling results for red pine 

are mixed. LANDIS-II projects that red pine will 

increase under PCM B1and decrease under GFDL 

A1FI. Tree Atlas results indicate that this species 

may maintain consistent suitable habitat under PCM 

B1 or increase under GFDL A1F1, although suitable 

habitat may shift from north-central Minnesota to 

northeast Minnesota. Particular areas may become 

more or less suitable, but changes are projected to 

be moderate across the entire assessment area. The 

natural distribution of red pine is limited by summer 

high temperatures, so this species may be more 

vulnerable in areas projected to experience more 

warming under climate change. 

Stressors: Climate change could amplify stressors 

to managed red pine plantations and result in 

new interactions among stressors. Diseases and 

insect pests are currently not responsible for much 

mortality in mature red pine stands, but they may 

become more damaging under warmer conditions. 

New agents such as annosum root rot or western 

bark beetles may also enter the assessment area 

in the future. White-tailed deer herbivory is a 

significant stressor for red pine seedlings, and deer 

populations are anticipated to increase with warmer 

winters. Competition from hazel is an obstacle to 

planting success, and competition from European 

buckthorn could also intensify under climate change. 

Wildfire is typically excluded from these stands and 

fire is not used as a management or regeneration 

tool, so it is unclear whether more frequent wildfire 

would benefit managed red pine forests. 

Moderate-Low Adaptive Capacity

Red pine is generally a drought-tolerant species, 

and thinning can further reduce moisture stress in 

managed stands. Undermanaged stands are typically 

overstocked and more susceptible to a variety of 

stressors. Red pine plantations typically have very 

low structural and species diversity, which may 

result in low resilience to future disturbances or 

changing conditions. Additionally, red pine has 

low genetic diversity as a species, so there may be 

limited possibility to favor more suited genotypes or 

for the species to evolve greater tolerance for future 

climate conditions. 
Chapter 6: Forest Ecosystem Vulnerabilities

141

Balsam fir regeneration under a red pine stand. Photo by Eli 

Sagor, University of Minnesota, used with permission.

A 1909-origin red pine stand thinned in 1950. Photo by Eli 

Sagor, University of Minnesota, used with permission.
Chapter 6: Forest Ecosystem Vulnerabilities

142

CONCLUSIONS

Forest ecosystems in northern Minnesota will be 

affected by climate change, although systems and 

species will respond individually to these changes. 

The synthesis statements in the first half of this 

chapter can be applied as rules of thumb in the 

absence of specific information about expected 

climate change impacts. Overall, we expect forest 

systems that are adapted to a narrow range of 

conditions or that contain few species to be more 

vulnerable to changing conditions. Communities 

with higher diversity that are adapted to tolerate 

a wide range of conditions and disturbances have 

a greater chance of persisting under a range of 

plausible climates. 

The vulnerability determinations for individual 

forest systems are best interpreted as broad trends 

and expectations across the assessment area. This 

assessment uses the most up-to-date information 

from the scientific literature, a coordinated set of 

modeling results and climate projections, and the 

input of a large team of local experts. Even so, 

there are limitations and unknowns that make these 

determinations imperfect. As new information 

continues to be generated on the potential impacts of 

climate change on Minnesota forests, this assessment 

should be supplemented with additional resources. 

It is essential to consider local characteristics such 

as management history, soils, topographic features, 

species composition, forest health issues, and 

recent disturbances when applying these general 

vulnerabilities to local scales. Some site-level 

factors may amplify these expected vulnerabilities, 

yet others may buffer the effects of climate change. 

Developing a clear understanding of climate-related 

vulnerabilities across relevant scales will then 

enable forest managers, landowners, planners, or 

other resource specialists to consider appropriate 

adaptation responses. This is true whether the task 

is to manage a single stand over a few years, or to 

design a long-term management plan for a large tract 

of land. 

In the following chapter, we extend the discussion 

to consider the implications of climate trends and 

forest vulnerabilities for other ecosystem services 

and resource areas that are often important for forest 

managers. 
143

Chapter 7: Management Implications

The previous chapters of this assessment have 

described observed and anticipated climate trends, 

potential impacts to forest ecosystems, and the 

climate-related vulnerability of major forest systems 

in the assessment area. This chapter takes one 

further step, by summarizing the implications of 

these climate change impacts and vulnerabilities 

for a variety of topics important to forest managers. 

Changes in climate, impacts on forests, and 

ecosystem vulnerability will combine to create both 

challenges and opportunities in forest management.

Topics were selected to encompass major resource 

areas that are priorities for public and private land 

managers. These topics, and the descriptions of 

climate change implications, are not comprehensive. 

Some topics have received less scientific attention 

or contain greater uncertainty. For some topics we 

relied on input from subject-area experts to discuss 

climate change implications (Appendix 6). Our 

goal is to provide a springboard for thinking about 

management implications of climate change and to 

connect managers to other relevant resources. When 

available, the “more information” sections provide 

links to key resources for managers to find more 

information about the impacts of climate change on 

that particular topic.

This chapter does not make recommendations as to 

how management should be adjusted to respond to 

climate impacts. We recognize that the implications 

of climate change will vary by forest type, 

ownership, and management objective. Therefore, 

we provide broad summaries rather than focusing on 

particular management issues. A separate document, 

Forest Adaptation Resources, has been developed to 

assist land managers in a decisionmaking process to 

adapt their land management to projected impacts 

(Swanston and Janowiak 2012). 

WILDLIFE 

Climate change effects on fish and wildlife species 

and their management are areas of active research, 

and the subject is summarized only briefly here. 

Minnesota’s wildlife community is the result of 

many interacting factors, including weather and 

climate. Weather and climate affect wildlife species 

directly through heat stress, snowfall, or annual 

saturation of ephemeral wetlands. Climate and 

weather also affect wildlife indirectly through 

climate-related habitat shifts, pests and diseases, 

disturbance events, and other factors. For example, 

spruce grouse occur in the assessment area because 

past climate has favored spruce regeneration and 

competition with deciduous trees. Many species in 

northern Minnesota, such as the gray jay and the 

American marten, are not common farther south. 

If conifers decrease in the assessment area, these 

wildlife species may decrease as their habitats 

change. Conversely, populations of species like 

white-tailed deer and wild turkey are hindered by 

severe winters. Less severe winters will favor those 

species. Because Minnesota forests are habitat for 

many wildlife species at the north or south edge of 

their range, even small climate-induced changes may 

have noticeable impacts. 

Wildlife species throughout the Midwest are 

responding to climate change, and many assessments 

and vulnerability analyses suggest that wildlife 

will continue to change (Hall 2012). Several tools 
Chapter 7: Management Implications

144

have been developed to help managers evaluate the 

climate change vulnerabilities of wildlife species. 

For example, the Climate Change Bird Atlas 

examines the potential for climate change to alter the 

distribution of 147 bird species across the eastern 

United States (Matthews et al. 2011a). 

More Information

• The Minnesota State Wildlife Action Plan

identifies Minnesota wildlife species, and their

habitats, that are in greatest conservation need.

Many species of greatest conservation need may

be particularly affected by climate change.

www.dnr.state.mn.us/cwcs/index.html

• Many states are working to incorporate climate

change information into their state wildlife action

plans. Voluntary guidance has been provided by

the Association of Fish and Wildlife Agencies.

www.fishwildlife.org/files/AFWA-VoluntaryGuidance-Incorporating-Climate-Change_SWAP.

pdf

• The Climate Change Bird Atlas is a companion

to the Climate Change Tree Atlas and uses

information about direct climate effects as well

as changes in habitat to project changes in bird

species distributions.

www.nrs.fs.fed.us/atlas/bird/

• Season’s End, an organization built on the

collaboration between many hunting and

conservation groups, includes many resources

on potential climate change impacts on wildlife.

http://www.cakex.org/virtual-library/784

• The Forest Service Climate Change Resource

Center provides a summary of how climate

change may affect wildlife species.

www.fs.fed.us/ccrc/topics/wildlife/

THREATENED AND 

ENDANGERED SPECIES 

As discussed in Chapter 6, it is expected that plant 

or animal species that are already rare, threatened, 

or endangered may be especially vulnerable to 

shifts in temperature and precipitation. Rare plants 

and rare plant communities often rely on very 

particular combinations of environmental and habitat 

conditions, in many cases as relict populations from 

previous climate conditions (Devall 2009, Minnesota 

Department of Natural Resources [DNR] 2012d). 

Threatened and endangered species often face 

population declines due to a variety of other factors, 

including habitat loss, competition from invasive 

species, and disease. As temperatures become 

warmer and the precipitation regime changes, 

already rare or declining species may therefore be 

among the first to experience climate-related stress. 

The limited range of rare species makes it difficult to 

model the effects of climate and climate change on 

distribution and abundance (Schwartz et al. 2006b). 

In the absence of human intervention, rare or 

threatened species may face greater extinction risks. 

Alternatively, rare species that live in habitats which 

are buffered from climate shifts (e.g., caves or other 

climatic refugia) may be able to persist. 

Minnesota’s Rare Species Guide includes 

information on Minnesota’s endangered, threatened, 

and special concern species (Minnesota DNR 

2012d). Of the 439 species listed for the entire 

state, 234 occur in the Laurentian Forest Province. 

A black bear in northern Minnesota. Photo by Josh 

Weckman, Superior National Forest.
Chapter 7: Management Implications

145

These rare species include 2 amphibians, 18 birds, 

10 fish, 2 fungi, 20 insects, 12 lichens, 8 mammals, 

2 mosses, 18 mussels, 4 reptiles, 2 spiders, and 132

vascular plants. Potential climate change impacts 

on these species have not been comprehensively 

reviewed, and the particular climate tolerances of 

many of these species are unknown. 

More Information

• Minnesota’s Rare Species Guide is produced by 

and updated by the Minnesota DNR. 

www.dnr.state.mn.us/rsg/index.html 

FIRE AND FUELS 

Climate change will influence fire and fuels 

management in the assessment area. As discussed 

above, this summary does not address the ways 

that land managers should adapt to the potential 

changes. A wide range of potential choices in 

policy, funding, and public attitude will ultimately 

define the response that makes the most sense, 

and these responses may be different for different 

organizations and landowners. 

As described in Chapter 5, weather and climate 

are major drivers of fire behavior. Across northern 

Minnesota and the Great Lakes region, the fire 

season is controlled by a combination of day length, 

weather, and fuel conditions. Typically, day length, 

cool temperatures, and wet fuels delay the onset 

of fire season until April or May. Although the 

summer months have the longest days and warmest 

temperatures, living vegetation requires extended 

dry periods of 2 weeks or more to increase fire 

ignition and spread potential. Live trees drop leaves 

and go dormant in the fall, but most forests become 

receptive to fire around the same time that short days 

and cool temperatures return. The type and condition 

of available fuels may lead to surface fires, which 

consume ground fuels, or crown fires, which burn 

across the forest canopy.

Drought can exacerbate wildfire risk during any of 

these periods, and drought is a critical precursor for 

large summer fire events. Droughts may increase 

fire potential quickly, and indicators of fire potential 

suggest that hot and dry periods of weeks rather 

than months may be sufficient to stress live fuels 

and make them more receptive to ignition and 

spread. The projected trend toward more-intense 

precipitation could raise the potential for longer 

dry intervals between rain events (Chapter 4). With 

warmer temperatures and a range of other climatedriven stressors, the potential exists for more forests 

to be receptive to wildfire throughout the growing 

season. The two climate scenarios examined in 

this assessment reveal a wide range of possible 

precipitation values (Chapter 4), so it is uncertain to 

what degree drought stress may affect forests in the 

assessment area. 

As with other parts of the country, critical fire 

weather conditions have been responsible for many 

of the major fire events across the Great Lakes 

region. Large, intensely burning fires generally 

require some combination of strong gradient winds, 

significant atmospheric instability, and dry air. The 

fires that occur in fire-prone landscapes during these 

events tend to produce the most severe fire effects. 

These events are poorly captured by modeling tools. 

Because large wildfires are driven primarily by these 

extremes, it is difficult to forecast exactly how the 

projected climate trends may translate into changes 

in fire activity. Additionally, complex interactions 

between climate change, vegetation communities, 

seasonal precipitation, and discrete fire weather 

events will dictate whether fires are manifest as 

surface fires or crown fires. This distinction has 

important consequences for forest communities and 

fire management, and our limited understanding is a 

source of major uncertainty.

Projected changes in climate could also affect the 

ability to apply prescribed fire in Minnesota. Wetter 
Chapter 7: Management Implications

146

springs could make it difficult to conduct prescribed 

burns in spring, shifting opportunities for dormantseason burning to the fall. If summer or fall becomes 

drier, burning under those conditions could involve 

greater risk and managers may be less inclined to 

implement this practice. 

More Information 

• The Lake States Fire Science Consortium 

provides fire science information to resource 

managers, landowners, and the public about the 

use, application, and effects of fire. 

lakestatesfiresci.net/index.html 

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect wildland fire in forest 

ecosystems. 

www.fs.fed.us/ccrc/topics/wildfire/ 

A prescribed burn that occurred in Canada in a portion of the 1999 blowdown area. Photo by U.S. Forest Service, Superior 

National Forest.

WATER RESOURCES 

There are many potential interactions and 

relationships between climate change, forest 

ecosystems, and water resources in Minnesota. 

Below, we outline a few examples of these potential 

implications. Water resources in the assessment area 

are influenced not only by land management but also 

by a diverse array of other management decisions 

and policies, including infrastructure planning and 

maintenance, water quality discharge permitting, 

water extraction/diversion permitting, and biological 

resource management. These layers of policy and 

management decisions complicate the picture, but 

reinforce the notion that management decisions will 

be intertwined with ecological changes in the future. 
Chapter 7: Management Implications

147

Infrastructure

Many landowners and agencies are responsible 

for managing water infrastructure such as dams, 

drainage ditches, and culverts. Specifications for 

water infrastructure are based on past climate 

patterns, and the trend of intensifying precipitation 

has placed additional strains on old and fragile 

infrastructure. The flood event in June 2012 in 

Duluth and across northern Minnesota accounted 

for more than $100 million in damage, primarily to 

roads, bridges, and private property (Passi 2012). In 

addition, this storm caused extensive damage to area 

streams as a result of landslides and stream bank 

erosion, with expected restoration costs of roughly 

$1 million per stream. 

Water Quality

Water resource managers in the assessment area 

have long been concerned about the impacts 

of multiple stressors, including the effects of 

commercial or residential development and 

climate patterns on in-stream temperature and 

increased turbidity. Several trout streams within 

the assessment area are considered “at risk,” and 

have been identified as impaired from excessive 

temperatures and targeted for total maximum daily 

load (TMDL) studies (Minnesota Pollution Control 

Agency 2013). Within forested regions of the 

assessment area, impairments due to turbidity are 

the most common. Processes leading to increased 

turbidity are particularly sensitive to climate-related 

phenomena including increased storm intensity and 

frequency, rain-on-snow events, and other trends that 

promote stream bank erosion. These events can also 

cause water quality issues by introducing excessive 

nutrients and contaminants. 

Thermal habitat in cold-water lakes and streams 

will also continue to be impaired as temperatures 

continue to warm. If conifers are replaced by 

deciduous trees or tree cover is reduced in the 

assessment area, aquatic resources will also receive 

less shade throughout the year (Blann et al. 2002). 

As ice cover is reduced on lakes in the assessment 

area, water temperatures and oxygen profiles will 

be affected the most in shallow and moderate-depth 

lakes (Fang et al. 2004a, 2004b, 2004c; Stefan et al. 

2001).

Aquatic Organisms

Aquatic organisms are expected to be affected by 

water quality changes, more-intense precipitation 

events, and other changes to the hydrology of the 

assessment area. These impacts may not occur 

equally across species or even across life stages 

for a given organism. For example, eggs and fry 

associated with gravel habitats and fine sediments 

appear to have been the life stages most affected 

by the June 2012 floods in northern Minnesota 

(D. Hendrickson, Minnesota DNR, personal comm., 

December 2012). Cold-water fish species like cisco 

are projected to be adversely affected in many lakes 

across the assessment area, though warm-water 

fish species may benefit (Fang et al. 2004a, 2004b, 

2004c; Jiang et al. 2012). 

Water temperature is generally considered to be the 

primary physical habitat suitability parameter for 

trout, and upper temperature limits seem to depend 

specifically on the duration of high temperatures 

(Wehrly et al. 2007). Ongoing work within the 

assessment area to investigate climate change 

impacts on water temperature and base flow finds 

that streams within the Lake Superior basin may be 

affected by low flow and higher stream temperatures 

in the future (Lucinda Johnson and Meijun Cai, 

Natural Resources Research Institute; and William 

Herb, University of Minnesota, personal comm., 

February 2013). Suitable habitat for brook trout 

in the southern part of the Lake Superior basin 

in Minnesota is projected to be the habitat at 

highest risk from low flows and higher summer 

temperatures. Streams in the northern portion of the 

basin may be less at risk.
Chapter 7: Management Implications

148

More Information 

• The Great Lakes Environmental Assessment and 

Mapping (GLEAM) project compiles spatial 

information regarding many threats to Great 

Lakes ecosystems, including climate change. 

www.greatlakesmapping.org/ 

• Sustaining Lakes in a Changing Environment 

(SLICE) is a project of the Minnesota DNR, 

and is a valuable resource for land managers 

interested in long-term data on biological and 

chemical changes in Minnesota lakes. 

www.dnr.state.mn.us/fisheries/slice/index.html 

FOREST PRODUCTS

The forest industry in Minnesota accounts for 

roughly $6 billion in economic activity and more 

than 35,000 jobs (Headwaters Economics 2011, 

Minnesota DNR 2010a). Information presented in 

Chapters 5 and 6 indicates that species composition 

in the assessment area may change during the 21st 

century, which could have important implications 

for the forest products industry. Major harvested 

species like quaking aspen are projected to show 

significant declines under a range of possible 

climate futures. Conversely, hardwood species like 

American basswood and northern red oak are poised 

to increase throughout the assessment area. Large 

potential shifts in commercial species availability 

may pose risks for the forest products sector if the 

shifts are rapid and the industry is unprepared. The 

forest products industry may benefit from awareness 

of anticipated climate trends and shifts in forest 

species. In many cases, forest managers can take 

actions to reduce potential risks associated with 

climate change or proactively encourage species and 

forest types anticipated to fare better under future 

conditions (Swanston and Janowiak 2012). There 

may be regional differences in forest responses, as 

well as potential opportunities for new merchantable 

species to gain suitable habitat in the assessment 

area. If the industry can adapt effectively, it is 

possible that the net effect of climate change on the 

forest products industry across the Midwest will be 

positive (Handler et al. 2012).

Overall, the effects of climate change on the forest 

products industry depend not only on ecological 

responses to the changing climate, but also on 

socioeconomic factors that will undoubtedly 

continue to change in the coming century. Major 

socioeconomic factors include national and regional 

economic policies, demand for wood products, and 

competing values for forests (Irland et al. 2001). 

Large uncertainties are associated with each of these 

factors. The forest products industry has adjusted to 

Kiln-dried paper birch lumber. This lumber shows excellent 

color from trees that grew free of major stress. Photo by 

John Rajala, Rajala Companies, used with permission.

Kiln-dried paper birch lumber with discolored heartwood. 

Stress leads to discoloration of the heartwood, lowering the 

value of the lumber. Photo by John Rajala, Rajala Companies, 

used with permission.
Chapter 7: Management Implications

149

substantial changes during the past 100 years, and 

continued responsiveness can help the sector remain 

viable. 

More Information

• The 2010 Resources Planning Act Assessment 

includes projections for forest products and other 

resources through the year 2060 and examines 

social, economic, land use, and climate change 

influences. 

www.fs.fed.us/research/rpa/ 

NONTIMBER FOREST PRODUCTS

Changes in climate will have implications for 

nontimber forest products in the assessment area and 

throughout the Laurentian Mixed Forest Province. 

Hundreds of these products are used for food, 

medicine, craft materials, and other purposes. Many 

of these will be affected by changes in temperature, 

hydrology, and species assemblages. As illustrations, 

effects of climate change on three Northwoods 

nontimber forest products with broad cultural and 

economic importance are discussed briefly here.

Natural wild rice is a Northwoods cultural keystone 

species (Minnesota DNR 2008). It is central to the 

migration story of the Anishinaabe (also known as 

Ojibwe or Chippewa), for whom wild rice is a sacred 

food and medicine. In Minnesota, an estimated 

4,000 to 5,000 individuals harvest natural wild 

rice for sale and personal use. In 2007, wild rice 

income exceeded $4,000 per person for members 

of the Leech Lake Band of Ojibwe. Wild rice 

growth and productivity are sensitive to hydrologic 

conditions including water depth and temperature. 

Although wild rice is adapted to some seasonal 

variation, it thrives in water depths of 0.5 to 3.0 

feet. Germination requires a 3- to 4-month dormant 

period in water at 35 °F or less. Wild rice seed 

does not survive prolonged drying. With regional 

and global models predicting increased heavy 

precipitation events, higher average temperatures, 

later winter onset, and earlier spring onset, the future 

of natural wild rice in the Northwoods may be at 

risk. Specific threats include:

• prolonged droughts leading to lowered water 

depths or seed desiccation, 

• flooding, particularly in the early summer 

“floating leaf” life stage, 

• shortened periods of cold water temperatures, and

• predation or displacement by species favored by 

warmer water temperatures (e.g., carp and reed 

canarygrass).

Balsam fir boughs enter regional, national, and 

international markets as wreaths, holiday greens, 

and fragrant souvenirs. The balsam bough industry 

provides seasonal employment for thousands of 

Northwoods residents and is especially important in 

rural areas where job opportunities are limited. In 

2005, the Minnesota bough industry was reported 

to be worth more than $23 million (Jacobson et al. 

2005). Models predict sharp declines in balsam fir 

biomass and suitable habitat in the assessment area, 

particularly under the hotter, drier climate scenario 

(Chapter 5). 

Hunting morel mushrooms is a passion for 

many people throughout northern Minnesota 

(Fine 2003). Annual morel festivals and sales to 

restaurants provide supplemental income for many 

people, communities, and small businesses in the 

Northwoods. Under climate change, increased fire 

frequency and severity may result in increased morel 

fruiting. In a process similar to the spike in morel 

fruiting with the massive die-off of American elms 

due to Dutch elm disease, climate-related deaths of 

associated tree species also may result in immediate 

increases in morel fruiting. However, evidence 

from the mid-Atlantic suggests such a spike would 

be followed by a decline in fruiting frequency 

(Emery and Barron 2010). In addition, because 

morel fruiting is highly responsive to temperature 

and humidity, changes in these regimes also can be 

expected to alter the timing and intensity of morel 

fruiting.
Chapter 7: Management Implications

150

FOREST MANAGEMENT

OPERATIONS

Climate variability and change present many 

challenges for forest managers who seek to maintain 

the diverse goods and services that forests provide. 

In particular, changes in winter conditions in the 

assessment area and throughout the northern Great 

Lakes region may shorten the available timeframe 

for conventional forest management operations. 

Most management in lowland areas is accomplished 

during the winter. As summarized in Chapter 4, 

climate change in northern Minnesota is projected 

to result in shorter seasons of frozen ground, more 

midwinter thaws, less snowpack, and more rain 

during winter months. Frozen ground facilitates 

timber harvest and transport, and snowpack provides 

protection for soils during harvest operations. 

Although special equipment is available to increase 

flotation on shallow snowpack or in the absence of 

snowpack, this equipment is costly. Additionally, 

a lack of frozen ground might increase the need to 

build roads to facilitate winter harvest, which would 

require additional costs compared to conventional 

practices.

Projected changes in precipitation during the 

growing season could also have important 

implications for forest management operations. 

Intense precipitation events could delay harvest 

operations in areas of poor drainage, but these events 

may be less disruptive in areas of coarse, sandy 

soils. Alternatively, summer droughts could possibly 

extend operating windows in low-lying areas or clay 

soils. Extended or severe droughts could present 

problems in sandy areas, if it becomes necessary to 

install gravel over logging roads. 

Changes in severe weather patterns could increase 

the amount of salvage harvests that are undertaken. 

Harvesting green timber allows resource managers 

to strategically achieve desired objectives and 

outcomes. Salvage harvesting following a tornado 

or derecho, by contrast, generally arises from a 

more immediate need to remove hazardous fuels 

or clear impacted forest areas. A salvage sale also 

does not garner the same amount of financial return 

as does a green timber sale opportunity. Severe 

weather response may also involve additional 

financial burden because of costs associated with 

re-establishment.

Analysis of timber harvest records in northern 

Wisconsin have identified some consequences of the 

changes in frozen ground condition (C. Rittenhouse, 

University of Connecticut; and A. Rissman, 

University of Wisconsin – Madison, unpublished 

data). In years with warm winters, there has been 

Working carefully around a crop tree during a white pine 

thinning operation with cut-to-length harvesting equipment. 

Photo by John Rajala, Rajala Companies, used with 

permission.
Chapter 7: Management Implications

151

a shift toward greater harvest of jack pine and less 

harvest of black spruce, hemlock, and red maple. 

Interviews with loggers revealed that growingseason restrictions on harvest designed to limit oak 

wilt and other diseases reduced the annual harvest 

window. Additionally, such ongoing stressors as 

overcapitalization, loan and insurance payments, 

and high fuel prices increased pressure on loggers to 

harvest year-round. Interviews with transportation 

officials revealed concerns that operating trucks 

on marginally frozen roads (or “over-weighting”) 

contributed to conflicts over roads between industry 

and local governments. Thus, climate change 

impacts on forestry operations have complex 

implications for management and governance of 

timber production, logger livelihoods, water quality, 

and transportation systems.

More Information

• The Minnesota Forest Resources Council has 

coordinated the development of site-level timber 

harvesting and forest management guidelines 

for the state. These voluntary guidelines do not 

specifically consider climate change or climate 

variability, but they can be a useful starting point 

for assessing the various ways climate change 

could impact forest management operations. 

www.frc.state.mn.us/initiatives_sitelevel_

management.html 

INFRASTRUCTURE 

ON FOREST LAND

Changes in climate and extreme weather events are 

expected to have impacts on infrastructure on forest 

lands throughout the region, such as roads, bridges, 

and culverts. Rising temperatures alone could have 

important impacts. A recent report suggests that 

heat stress may have substantial effects on surface 

transportation infrastructure in the assessment area 

(Posey 2012). Heavy precipitation events, which are 

already increasing and projected to increase further 

in the future, may overload existing infrastructure 

that was not built to that capacity. For example, 

improper location or outdated building standards 

can make older road systems particularly susceptible 

to increased rainfall events. Engineers are already 

adapting to these changes: as infrastructure is 

replaced, it is being constructed with heavier 

precipitation events in mind. This extra preparedness 

often comes at an increased cost to upgrade to higher 

standards and capacity. Extreme events may also 

require more frequent maintenance of roads and 

other infrastructure, even if designed to appropriate 

specifications. Additionally, forest managers may 

find it necessary to take additional precautions to 

prevent erosion when designing road networks or 

other infrastructure. 

As described in Chapter 4, changes in precipitation 

may also lead to seasonal changes in streamflow, 

such as higher peak flows, which could affect 

infrastructure around streams and rivers. The heavy 

rainfall event centered on Duluth in June 2012

highlighted the potential for flood-related damage 

across the assessment area. 

An increase in the frequency or intensity of wind 

storms, which may occur in the next century, could 

also increase operating and repair expenses related 

to infrastructure. For example, a large windstorm 

across north-central Minnesota in July 2012 led to 

major damage to infrastructure on the Chippewa 

Damage to a forest road after the June 2012 rainstorm 

in northeastern Minnesota. Photo by Patrick Hampston, 

Superior National Forest.
Chapter 7: Management Implications

152

National Forest and in areas surrounding Grand 

Rapids, Minnesota. As a result, roads and trails had 

to be cleared and facilities repaired. 

More Information

• A technical report summarizing climate change 

impacts on the transportation sector (including 

infrastructure) was recently released as input 

for the Midwest region for the National Climate 

Assessment. 

http://glisa.msu.edu/media/files/NCA/MTIT_

Transportation.pdf 

FOREST CARBON 

The accumulated carbon (C) pool within forest soils, 

belowground biomass, dead wood, and aboveground 

live biomass is enormous (Birdsey et al. 2006). 

Climate change and associated impacts to forest 

ecosystems may change the ability of forests in 

northern Minnesota to store C. A longer growing 

season and carbon dioxide (CO2 ) fertilization 

may lead to increased productivity and C storage 

in forests in the assessment area (Chapter 5). 

This increase could be offset by climate-related 

physical and biological disturbances (Gough et al. 

2008, Hicke et al. 2011), leading to increases in 

C storage in some areas and decreases in others. 

As long as forests are maintained as forests in the 

assessment area, a large-scale decline in C stocks 

across northern Minnesota is not expected. If forests 

convert to nonforested conditions or if C stored in 

peat soils is lost to the atmosphere, then C storage is 

reduced over much longer time scales. 

Different forest-type groups in the assessment 

area store different amounts of C (Chapter 1). On 

average, spruce/fir forests are the most C dense, but 

most of this C occurs in organic soils. Maple/beech/

birch forests generally contain the most aboveground 

C, so an increase in these species and a decline in 

spruce/fir forests may have effects on C storage in 

some areas. Modeling studies in northern Wisconsin 

examining the effects of species composition 

changes on landscape-scale C stocks, suggest that 

some forests may increase in biomass and overall 

productivity, despite declines in boreal or northern 

species (Chiang et al. 2008, Scheller and Mladenoff 

2005). 

More Information

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect forests’ ability to store C. 

www.fs.fed.us/ccrc/topics/forests-carbon/

• A recent article, A Synthesis of the Science on 

Forests and Carbon for U.S. Forests, summarizes 

the key issues related to forest management 

and C. 

www.fs.fed.us/rm/pubs_other/rmrs_2010_ryan_

m002.pdf

WILDERNESS 

The Boundary Waters Canoe Area Wilderness 

(BWCAW) covers more than 800,000 acres in 

northeastern Minnesota, administered by the 

Superior National Forest. This wilderness area is 

iconic for its sheer physical size and high levels 

of visitor use. Like other wilderness areas, the 

BWCAW has been designated for preservation 

and protection according to specific guidelines for 

management. Climate change was not anticipated 

when the BWCAW was created, and now the 

potential for extensive ecosystem change raises 

difficult questions about the future management of 

this and other wilderness areas. 

Climate change is poised to influence the BWCAW 

ecosystem in a variety of ways (Frelich and Reich 

2009). Fire seasons are expected to shift, and more 

area is projected to burn each year under climate 

change (Chapter 5). Furthermore, many of the 

characteristic boreal species in the assessment 

area are projected to decline, and invasive species 

may increase in abundance and vigor (Chapter 5). 
Chapter 7: Management Implications

153

Depending on the amount and timing of future 

precipitation, lake levels and aquatic ecosystems 

in the BWCAW could be affected as well. Weather 

and climate could also influence recreational use, 

if spring and fall seasons become more attractive 

for visits or the threat of wildfires reduces visits 

in certain months. Additionally, managers accept 

the fact that natural hazards and obstacles are 

inherently a part of the wilderness experience, 

but try to remove trees that are posing immediate 

threats to visitors. Weather-related mortality from 

storm events, drought, or insect and disease attack 

could increase the need for this activity. Weather 

conditions also affect the need for maintenance of 

the trail tread, particularly when heavy rain events 

cause excessive erosion, or when wind events uproot 

trees and leave craters in parts of the trail. 

It is difficult to anticipate how climate-related 

impacts will influence management in wilderness 

areas, because of the legal requirement for federally 

designated wilderness areas to be natural and 

untrammeled. Some arguments favor proactive 

management for the BWCAW to help create a 

“graceful transition” under climate change based on 

maintaining native tree species and natural processes 

like fire (Frelich and Reich 2009). Any changes to 

the management of federally designated wilderness 

areas would require difficult choices and a thorough 

planning process to consider potential pros and cons. 

More Information

• The Wilderness.net Climate Change Toolbox 

provides information about climate change and 

wilderness, including management guidelines and 

strategies. 

www.wilderness.net/climate

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect wilderness area management. 

www.fs.fed.us/ccrc/topics/wilderness/ 

CULTURAL RESOURCES

Certain species can hold unique cultural importance, 

often based on established uses. Changes in forest 

composition and extent may alter the presence 

or availability of culturally important species 

throughout the region. For example, Dickmann 

and Leefers (2003) compiled a list of more than 50 

tree species in Michigan that were used by several 

Native American tribes in the region. Among these, 

northern white-cedar and paper birch stand out as 

having particular importance for defining a culture 

and way of life. Under climate change, however, 

these two species are expected to decline in suitable 

habitat and biomass over the next century 

(Chapter 5).

Paper birch from Pine County, Minnesota. Photo by Eli Sagor, 

University of Minnesota, used with permission.
Chapter 7: Management Implications

154

Climate change may also present challenges for 

managers of cultural resources on public lands. 

Extreme wind events such as tornadoes and derechos 

can directly damage buildings and other structures. 

Storm-damaged cultural resources may subsequently 

be further damaged by salvage harvest operations, 

because unsafe walking conditions and low ground 

surface visibility often make it impossible to conduct 

a cultural resources inventory before the salvage 

sale. 

A change in the frequency, severity, or duration 

of heavy precipitation and flooding could affect 

cultural resources as well. Historic and prehistoric 

habitation sites are often located near lakes or 

waterways. Flood events or storm surges can result 

in increased erosion or obliteration of significant 

archaeological sites. Similarly, torrential rains can 

trigger or exacerbate erosion of cultural resources. 

Erosion from storm surges in the Great Lakes has 

already begun to wash away cultural sites within 

the Grand Portage National Monument and Apostle 

Islands National Lakeshore (Saunders et al. 2011).

More Information

• Climate Change and World Heritage: Report on 

Predicting and Managing the Impacts of Climate 

Change on World Heritage includes a list of 

climate change threats to cultural heritage sites. 

whc.unesco.org/documents/publi_wh_papers_22_

en.pdf 

RECREATION 

Forests are the centerpieces of outdoor recreation 

in the Great Lakes region (Handler et al. 2012). 

People throughout this region enjoy hunting; fishing; 

camping; wildlife watching; and exploring trails 

on foot, bicycles, skis, snowshoes, horseback, 

and off-highway vehicles, among many other 

recreational pursuits. The vulnerabilities associated 

with climate change in forests may result in shifted 

timing or participation opportunities for forest-based 

recreation (Saunders et al. 2011). Forest-based 

recreation and tourism are strongly seasonal, and 

most visits to public lands are planned during times 

when the weather is most conducive to particular 

activities. 

Projections indicate that seasonal shifts will continue 

toward shorter, milder winters and longer, hotter 

summers in the future (Chapter 4). Climate change 

generally stands to reduce opportunities for winter 

recreation in the Great Lakes region, although 

warm-weather forms of nature-based recreation 

may benefit (Dawson and Scott 2010, Jones and 

Scott 2006, Mcboyle et al. 2007). For example, 

opportunities for winter-based recreation activities 

such as cross-country skiing, snowmobiling, and 

ice fishing may be reduced due to shorter winter 

snowfall seasons (Notaro et al. 2011) and decreasing 

periods of lake ice (Kling et al. 2003, Magnuson et 

al. 2000, Mishra et al. 2011). 

Warm-weather recreation activities such as 

mountain biking, off-highway vehicle riding, and 

fishing may benefit from extended seasons in the 

Midwest (Nicholls 2012). High spring precipitation 

could increase risks of flash flooding or lead to 

unpleasant conditions for recreation, however. 

Severe storms and flash flooding might also threaten 

infrastructure such as visitor centers, campsites, 

and trails. Fall will potentially be drier, which could 

lead to reduced water levels and diminished water 

recreation opportunities. Warmer, drier conditions in 

the summer and fall may raise the risk of wildfire, 

increasing visitor safety risk and restrictions on open 

flames. Lengthening of spring and fall recreation 

seasons will also have implications for staffing, 

especially for recreation-related businesses that rely 

on student labor—which will be unavailable during 

the school year (Nicholls 2012).

Climate can also have important influences on 

hunting and fishing. The timing of certain hunts or 

fishing seasons correspond to seasonal events, which 
Chapter 7: Management Implications

155

are in part driven by climate. Waterfowl hunting 

seasons, for example, are designed to correspond to 

the times when birds are migrating south in the fall, 

an event that is projected to shift later in the year as 

temperatures warm. As mentioned above, climate 

change may also result in substantial changes in 

habitat availability and quality for wildlife and fish 

species.

More Information

• A recent report submitted for the National

Climate Assessment summarizes the impacts of

climate change on outdoor recreational tourism

across the Midwest, including the assessment

area.

http://glisa.msu.edu/media/files/NCA/MTIT_

RecTourism.pdf

• Season’s End, an organization built on the

collaboration between many hunting and

conservation groups, includes many resources on

how climate change may affect wildlife.

http://www.cakex.org/virtual-library/784

HUMAN HEALTH CONCERNS

Vector-borne diseases, such as Lyme disease and 

West Nile virus, pose an important risk to forest 

managers and visitors alike. This issue may become 

increasingly important in northern Minnesota during 

the 21st century. As an illustration of how climate 

change can influence these kinds of diseases, we 

present a synopsis of vector-borne diseases. Vectorborne diseases are transmitted by arthropod vectors 

(e.g., ticks or mosquitoes) and cycle back and forth 

between arthropod vectors and animal reservoirs—

usually mammal or bird hosts. Humans are typically 

infected incidentally when they are bitten instead of 

animal hosts.

Climate is one of many important interacting 

variables that affect people’s risk for vector-borne 

diseases in Minnesota. Climate directly affects 

physical conditions (e.g., temperature, rainfall) 

and indirectly affects biological conditions (plants, 

animals). These physical and biological conditions 

can, in turn, influence vector-borne disease risk by 

affecting the abundance and distribution of ticks or 

mosquitos, the percentage of infected vectors, the 

abundance and distribution of animal reservoirs, the 

presence of suitable habitat for these vectors, and 

human behaviors that bring them into contact with 

infected vectors.

Most arthropod vectors of disease are sensitive to 

physical conditions, such as levels of humidity, daily 

high and low temperatures, rainfall patterns, and 

winter snowpack. For instance, blacklegged ticks 

(a.k.a. “deer ticks”), which are the vector for Lyme 

disease and several other diseases, are most active 

on warm, humid days. They are most abundant 

in wooded or brushy habitats (especially mesic 

hardwoods and managed aspen) with abundant small 

mammals and deer. Projected expansion of mesic 

hardwoods with changing climate conditions may 

increase the incidence of Lyme disease and other 

tick-borne diseases if those habitats are frequently 

visited by humans (i.e., residential, occupational, or 

recreational exposures). 

More Information

• The Minnesota Department of Health Web site

has more information on vector-borne diseases in

Minnesota.

www.health.state.mn.us/divs/idepc/dtopics/

vectorborne/index.html

• The Minnesota Department of Health has a Web

site on the climate change implications for human

health.

www.health.state.mn.us/divs/climatechange/

• The Centers for Disease Control and Prevention

Climate and Health Program includes information

on a variety of subjects.

www.cdc.gov/climateandhealth/
Chapter 7: Management Implications

156

URBAN FORESTS

Climate change is expected to affect urban forests 

in the assessment area as well. Urban environments 

can pose additional stresses, such as pollution 

from vehicle exhaust, confined root environments, 

and road salts. Urban environments also cause a 

“heat island effect,” and thus warming in cities 

will be even greater than in natural communities. 

Impervious surfaces can make urban environments 

more susceptible to floods, placing flood-intolerant 

species at risk. All of these abiotic stressors can 

make urban forests more susceptible to exotic 

species invasion, and insect and pathogen attack, 

especially because a limited range of species and 

genotypes is often planted in urban areas. Urban 

settings are often where exotic insect pests are first 

introduced. 

Projected changes in climate can pose both 

challenges and opportunities for the management 

of urban forests. Shifts in temperature and changes 

in extreme events may have effects on selection 

of species for planting. Native species projected 

to decline under climate change may not tolerate 

the even more-extreme conditions presented by 

urban settings. Conversely, urban environments 

may favor heat-tolerant or drought-tolerant native 

species or new migrants (Chapter 5). Determining 

appropriate species for planting may be a challenge, 

but community foresters are already familiar 

with the practice of planting species novel to an 

area. Because of urban effects on climate, many 

community forests already contain species that are 

from planting zones south of the area or cultivars 

that tolerate a wide range of climate conditions. 

Large disturbance events may also become more 

frequent or intense in the future, necessitating 

informed responses. For example, wind events or 

pest outbreaks may be more damaging to alreadystressed trees. If leaf-out dates advance earlier in 

the spring due to climate change, community forests 

may be increasingly susceptible to early-season 

frosts or snow storms. More people and larger 

budgets may be required to handle an increase in 

the frequency or intensity of these events, which 

may become more difficult in the face of reduced 

municipal budgets and staffing. 

More Information

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect urban forests. 

www.fs.fed.us/ccrc/topics/urban-forests/ 

• British Columbia has developed an urban forestry 

climate adaptation guide that includes some 

general considerations for adapting urban forests 

to climate change. 

www.toolkit.bc.ca/Resource/Urban-ForestsClimate-Adaptation-Guide 

• The Clean Air Partnership has developed a 

climate change impact assessment and adaptation 

plan for Toronto’s urban forest. 

www.cleanairpartnership.org/pdf/climate_

change_adaptation.pdf 

FOREST-ASSOCIATED 

TOWNS AND CITIES

A human community’s ability to respond to changes 

in its environment is directed by its adaptive 

capacity—resources that can be leveraged by the 

community to monitor, anticipate, and proactively 

manage stressors and disturbances. Although impact 

models can predict ecological community responses 

to climate change, considerably less is known about 

the social and cultural impacts of climate or forest 

change and how human communities might best 

respond. Many towns and cities in the assessment 

area are intimately tied to the health and functioning 

of surrounding forests, whether for economic, 

cultural, or recreational reasons. 
Chapter 7: Management Implications

157

Every forest-associated community has particular 

conditions, capacities, and constraints that might 

make it more vulnerable or resilient to climate 

change. Moreover, the effects of climate change 

and forest impacts are not evenly distributed 

geographically or socially. Different communities 

(e.g., indigenous communities with forest-dependent 

cultural practices, tourism-dependent communities) 

and social groups within communities (e.g., 

individuals working in forest products industries) 

may be more vulnerable to these impacts and less 

able to adapt.

If resource professionals, community leaders, 

and local organizations are to help communities 

adapt, they must be able to assess community 

vulnerabilities and capacities to organize and 

engage resources. In the Great Lakes region, 

most human community vulnerability assessment 

work to date has focused on coastal communities 

(Minnesota Sea Grant 2012). However, research 

is underway in northern Minnesota to examine the 

capacity of forest-associated communities to adapt 

to ecological change (Mae Davenport, University 

of Minnesota; Marla Emery and Pam Jakes, U.S. 

Forest Service, Northern Research Station, personal 

comm.). Researchers are using a rapid assessment 

approach to investigate the social, cultural, and 

institutional characteristics and processes that affect 

a community’s ability to adapt to ecological change 

(see “More Information” below).

When planning for climate change, decisionmakers 

can consider how ecological events or changes (e.g., 

floods, droughts, wildfire, windstorms, introduced 

species, insect or pathogen outbreaks) will affect 

their communities and community members by 

asking: 

• Is access to healthy ecosystems at risk? 

• Is there a potential for resource scarcity?

• Are cultural practices or recreational 

opportunities at risk?

• Is there potential for loss of social connectedness 

or increased social or cultural conflict?

• Is there potential for disproportionate impacts to 

certain populations?

• Is there potential for human health problems 

including stress, anxiety, despair, or sense of 

powerlessness?

More Information

• The Resilience Alliance has created a workbook 

for practitioners to assess resilience of socialecological systems. 

www.resalliance.org/index.php/resilience_

assessment 

• Minnesota Sea Grant produced a community selfassessment to address climate change readiness, 

and its Web site includes several resources useful 

for communities. 

www.seagrant.umn.edu/climate/ 

• An initial report is available for a rapid 

assessment of community adaptive capacity 

conducted in Walker, Minnesota (Davenport et al. 

2013).

www.forestry.umn.edu/People/Davenport/ 

LAND ACQUISITION 

Climate change has many important implications for 

land conservation planning in northern Minnesota. 

Put most simply, climate change science can be used 

to help prioritize land conservation investments and 

help guide project design.

In terms of prioritizing specific parcels of land, it 

may be important to identify parcels that have large 

C mitigation potential. This is particularly important 

in the Laurentian Mixed Forest Province, where 

private forest lands have some of the highest stored 

C levels in the entire country. Climate change trends 

and ecosystem models can also be used to identify 

lands that have long-term potential to provide habitat 

refugia and protection for shifting water supplies. 
Chapter 7: Management Implications

158

In the design of land conservation projects, there 

are important decisions to be made about long-term 

ownership and management prescriptions attached to 

the conservation agreement. In some cases, the best 

strategy may be to leave lands in private ownership, 

and to develop conservation easement terms that 

support adaptive management by the landowner to 

address climate shifts. In other cases, perhaps where 

complex restoration or species-specific management 

is needed, it might be appropriate to seek a public 

agency owner that can provide the necessary 

financial and technical resources. 

Private nonprofits, government agencies, 

landowners, and potential funders will need 

research-based results on anticipated climate trends 

and impacts, including spatially explicit information 

on how these shifts will play out over the land. 

This science can enable effective use of funding, 

staff time, and other resources that are essential 

to advancing “climate-informed” conservation of 

forests in Minnesota, and shaping conservation 

efforts to deliver a more resilient landscape. 

PLANNING

Until recently, climate change has not played a large 

role in natural resource planning. Many federal 

and state-level land management agencies are 

now beginning to address the issue. For example, 

the Forest Service’s 2012 Planning Rule directly 

addresses the impacts and ramifications of climate 

change. In fact, climate change was among the stated 

purposes for revising the Rule (FR Vol. 77, No. 68, 

21163 & 21164). As the Superior and Chippewa 

National Forests revise their management plans in 

the coming years, they will be required to address 

the issue of climate change under the new Planning 

Rule. 

At the state level, Minnesota’s Subsection Forest 

Resource Management Plans also have not 

historically addressed climate change. However, 

the 2010 State Forest Resource Assessment and

Strategies documents both include climate change 

as an issue that could influence the long-term 

sustainability of Minnesota’s forests (Minnesota 

DNR 2010a, 2010b). 

The Minnesota Forest Resources Council also 

coordinates a landscape-level planning program 

within the state. This collaborative planning 

process bridges ownerships, forest types, and 

administrative boundaries, and relies on the input 

and expertise of volunteer landscape committees. 

The Northeast Landscape Committee is revising 

the plan for Minnesota’s “Arrowhead” region. This 

will be the first of these plans to explicitly consider 

climate change in both short-term and long-term 

management goals and objectives. 

Incorporating climate change considerations 

into natural resources planning will always be a 

complex endeavor. The uncertainties associated 

Black spruce bog at the Marcell Experimental Forest, Itasca 

County, Minnesota. Note flowering pitcher plants in the 

foreground. Photo by Deacon Kyllander, U.S. Forest Service.
Chapter 7: Management Implications

159

with planning over long time horizons are only 

compounded by climate change. Management plans 

for national forests or state agencies are typically 

written to guide management for a 10- to 15-year 

period, and it may be difficult to envision projected 

shifts in climate within this short planning horizon. 

Additionally, major storms or disturbance events are 

inherently unpredictable, and often force managers 

to deviate from planned analysis or treatment 

cycles. If climate change results in more frequent 

disturbances or unanticipated interactions among 

major stressors, managers may be hard-pressed to 

adhere to the stated goals, objectives, and priorities 

in current plans. Future land management plans may 

have to incorporate adaptive management principles 

and include built-in flexibility to address shifting 

conditions and priorities. 

More Information

• More information on the Forest Service’s 2012

Planning Rule can be found here: 

www.fs.usda.gov/planningrule 

• Minnesota’s Forest Resource Assessment and 

Strategies documents include discussions of 

climate change. 

www.forestactionplans.org/states/minnesota 

• Minnesota’s Subsection Forest Resource 

Management Planning program is explained in 

detail on the Minnesota DNR Web site. 

www.dnr.state.mn.us/forestry/subsection/index.

html 

• The Minnesota Forest Resources Council 

coordinates the development of landscapescale plans across northern Minnesota to guide 

long-term management of forest resources 

across ownerships and jurisdictions. Plans are 

in place for all six major forested landscapes in 

Minnesota. 

www.frc.state.mn.us/resources_documents_

landscape.html 

CONCLUSIONS

The breadth of the topics above highlights the 

wide range of effects climate change may have on 

forest management in northern Minnesota. It is not 

the role of this assessment to identify adaptation 

actions that should be taken to address these climaterelated risks and vulnerabilities, nor would it be 

feasible to prescribe suitable responses for all future 

circumstances. Decisions to address climate-related 

risks for forest ecosystems in northern Minnesota 

will be affected by economic, political, ecological, 

and societal factors. These factors will be specific 

to each land owner and agency, and are highly 

unpredictable. 

Confronting the challenge of climate change 

presents opportunities for managers and other 

decisionmakers to plan ahead, manage for resilient 

landscapes, and ensure that the benefits that forests 

provide are sustained into the future. Resources 

are available to help forest managers and planners 

incorporate climate change considerations into 

existing decisionmaking processes (Swanston and 

Janowiak 2012) (www.forestadaptation.org). This 

assessment will be a useful foundation for land 

managers in that process, to be further enriched by 

local knowledge and site-specific information. 
160

aerosol

a suspension of fine solid particles or liquid 

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

air pollution, and smog. Aerosols may influence 

climate by scattering and absorbing radiation, acting 

as condensation nuclei for cloud formation, or 

modifying the properties and lifetime of clouds. 

adaptive capacity

the general ability of institutions, systems, and 

individuals to moderate the risks of climate change, 

or to realize benefits, through changes in their 

characteristics or behavior. Adaptive capacity can be 

an inherent property or it could have been developed 

as a result of previous policy, planning, or design 

decisions.

agreement

the extent to which evidence is consistent in support 

of a vulnerability statement or rating (see also 

confidence, evidence). 

alluvial

referring to a deposit of clay, silt, sand, and gravel 

left by flowing streams in a river valley or delta, 

typically producing fertile soil.

asynchronous quantile regression

a type of regression used in statistical downscaling. 

Quantile regression models the relation between a 

set of predictor variables and specific percentiles (or 

quantiles) of the response variable.

Glossary 

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.

carbon dioxide (CO2) fertilization 

increased plant uptake of CO2 through 

photosynthesis in response to higher concentrations 

of atmospheric CO2 .

climate change 

a change in the state of the climate that can be 

identified (e.g., by using statistical tests) by changes 

in the mean and/or the variability of its properties, 

and that persists for an extended period, typically 

decades or longer. Climate change may be due to 

natural internal processes or external factors, or to 

persistent anthropogenic changes in the composition 

of the atmosphere or in land use. 

climate model

see general circulation model.

climate normal

the arithmetic mean of a climatological element 

computed over three consecutive decades.

community

an assemblage of plants and animals living together 

and occupying a given area.
Glossary

161

confidence

a qualitative assessment of uncertainty as determined 

through evaluation of evidence and agreement (see 

also evidence, agreement). 

convective storm

convection is a process whereby heat is transported 

vertically within the atmosphere. Convective storms 

result from a combination of convection, moisture, 

and instability. Convective storms can produce 

thunderstorms, tornadoes, hail, heavy rains, and 

straight-line winds. 

derecho

widespread and long-lived convective windstorm 

that is associated with a band of rapidly moving 

showers or thunderstorms characterized by wind 

gusts that are greater than 57 miles per hour and that 

may exceed 100 miles per hour. 

disturbance

stresses and destructive agents such as invasive 

species, diseases, and fire; changes in climate and 

serious weather events such as hurricanes and ice 

storms; pollution of the air, water, and soil; real 

estate development of forest lands; and timber 

harvest. Some of these are caused by humans, in part 

or entirely; others are not.

downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCMs); 

involves examining the statistical relationship 

between past climate data and on-the-ground 

measurements. 

driver

any natural or human-induced factor that directly or 

indirectly causes a change in an ecosystem.

dynamical downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCMs) using 

a limited-area, high-resolution model (a regional 

climate model, or RCM) driven by boundary 

conditions from a GCM to derive smaller-scale 

information.

ecological province

climatic subzones, controlled primarily by 

continental weather patterns such as length of dry 

season and duration of cold temperatures. Provinces 

are also characterized by similar soil orders and are 

evident as extensive areas of similar potential natural 

vegetation. 

ecoregion

a region characterized by a repetitive pattern of 

ecosystems associated with commonalities in climate 

and landform.

ecosystem 

a system of living organisms interacting with 

each other and their physical environment. The 

boundaries of what could be called an ecosystem 

are somewhat arbitrary, depending on the focus of 

interest or study. Thus, the extent of an ecosystem 

may range from very small spatial scales to, 

ultimately, the entire Earth. 

emissions scenario

a plausible representation of the future development 

of emissions of greenhouse gases and aerosols that 

are potentially radiatively active, based on certain 

demographic, technological, or environmental 

developments.

evapotranspiration

the sum of evaporation from the soil and 

transpiration from plants.
Glossary

162

evidence

mechanistic understanding, theory, data, models, 

or expert judgment used to determine the level of 

confidence in a vulnerability statement or rating (see 

also agreement, confidence). 

fen

a wetland fed by surface water, or groundwater, or 

both; characterized by the chemistry of the water, 

which is neutral or alkaline. 

fire-return interval

the number of years between two successive fire 

events at a specific location.

forest land

land that is at least 10 percent stocked by forest trees 

of any size, or land formerly having such tree cover, 

and not currently developed for a nonforest use.

forest type

a classification of forest vegetation based on the 

dominant species present, as well as associate 

species commonly occurring with the dominant 

species.

forest-type group 

based on FIA definitions, a combination of forest 

types that share closely associated species or site 

requirements and are generally combined for brevity 

of reporting.

fragmentation

a disruption of ecosystem or habitat connectivity, 

caused by human or natural disturbance, creating a 

mosaic of successional and developmental stages 

within or between forested tracts of varying patch 

size, isolation (distance between patches), and edge 

length.

fundamental niche

the total habitat available to a species based on 

climate, soils, and land cover type in the absence of 

competitors, diseases, or predators.

general circulation model (GCM)

numerical representation of the climate system based 

on the physical, chemical, and biological properties 

of its components, their interactions, and their 

feedback processes, and accounting for all or some 

of its known properties (also called climate model).

greenhouse effect

the rise in temperature that the Earth experiences 

because certain gases in the atmosphere (water 

vapor, carbon dioxide, nitrous oxide, and methane, 

for example) absorb and emit energy from the sun.

growing season

the period in each year when the temperature is 

favorable for plant growth.

hardwood

a dicotyledonous tree, usually broad-leaved and 

deciduous. Hardwoods can be split into soft 

hardwoods (red maple, paper birch, quaking aspen, 

and American elm) and hard hardwoods (sugar 

maple, yellow birch, black walnut, and oaks). 

hydric

pertaining to sites or habitats with abundant moisture 

throughout the year, frequently including saturation, 

ponding, or flooding.

impact

direct and indirect consequences of climate change 

on systems, particularly those that would occur 

without adaptation.

impact model

simulations of impacts on trees, animals, and 

ecosystems. It uses general circulation model 

projections as inputs, and includes additional inputs 

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

of individual species.
Glossary

163

importance value

in the Climate Change Tree Atlas model, an index 

of the relative abundance of a species in a given 

location or pixel cell (0 = least abundant, 100 = 

most abundant).

invasive species

any species that is nonnative (or alien) to the 

ecosystem under consideration and whose 

introduction causes or is likely to cause damage, 

injury, or disruption to ecosystem processes or other 

species within that ecosystem.

Kyoto Protocol

adopted at the 1997 Third Session of the Conference 

of Parties to the 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.

mesic

pertaining to sites or habitats where soil moisture is 

available to plants throughout the growing season.

model reliability score

in the Climate Change Tree Atlas model, a “trimodel” approach to assess reliability of model 

predictions for each species, classified as high, 

medium, or low.

modifying factor

in the Climate Change Tree Atlas model, 

environmental variables (e.g., site conditions, 

interspecies competition, disturbance, dispersal 

ability) that influence the way a tree may respond to 

climate change.

Native Plant Community

in the ecosystem classification system developed 

by the Minnesota Department of Natural Resources 

and referenced in this assessment, an assemblage of 

native plants that tend to recur over space and time, 

which interact with each other and their physical 

environment in ways minimally modified by exotic 

species and negative human disturbances.

parcelization

the subdivision of a single forest ownership into 

two or more ownerships. Parcelization may result in 

fragmentation if habitat is altered. 

peak flow

the maximum instantaneous discharge of a stream or 

river at a given location. 

phenology

the timing of natural events such as the date that 

migrating birds return, the first flower dates for 

plants, and the date on which a lake freezes in the 

autumn or opens in the spring. Also refers to the 

study of this subject.

prairie 

a natural community dominated by perennial grasses 

and forbs with scattered shrubs and very few trees 

(less than 10 percent canopy cover). 

process model

a model that relies on computer simulations based 

on mathematical representations of physical and 

biological processes that interact over space and 

time.

productivity 

the rate at which biomass is produced per unit area 

by any class of organisms, or the rate of energy 

utilization by organisms.
Glossary

164

projection 

a potential future evolution of a quantity or set of 

quantities, often computed with the aid of a model. 

Projections are distinguished from predictions 

in order to emphasize that projections involve 

assumptions concerning, for example, future 

socioeconomic and technological developments that 

may or may not be realized, and are therefore subject 

to substantial uncertainty. 

proxy

a figure or data source that is used as a substitute 

for another value in a calculation. Ice and sediment 

cores, tree rings, and pollen fossils are all examples 

of things that can be analyzed to infer past climate. 

The size of rings and the isotopic ratios of elements 

(e.g., oxygen, hydrogen, and carbon) in rings and 

other substrates allow scientists to infer climate and 

timing.

pulpwood

roundwood, whole-tree chips, or wood residues used 

for the production of wood pulp for making paper 

and paperboard products. 

realized niche

the portion of potential habitat a species occupies; 

usually it is less than what is available because 

of predation, disease, and competition with other 

species.

refugia

locations and habitats that support populations of 

organisms that are limited to small fragments of their 

previous geographic range.

resilience 

capacity of a system to absorb a disturbance and 

continue to develop with similar fundamental 

function, structure, identity, and feedbacks.

runoff

that part of the precipitation that appears in surface 

streams. It is the same as streamflow unaffected by 

artificial diversions or storage.

savanna

fire-maintained grasslands with open-grown, 

scattered, orchard-like trees or groupings of trees 

and shrubs. 

saw log 

a log meeting minimum standards of diameter, 

length, and defect, including logs at least 8 feet long, 

sound and straight, and with a minimum diameter 

inside bark of 6 inches for softwoods and 8 inches 

for hardwoods, or meeting other combinations of 

size and defect specified by regional standards.

scenario 

a plausible and often simplified description of 

how the future may develop, based on a coherent 

and internally consistent set of assumptions about 

driving forces and key relationships. Scenarios 

may be derived from projections, but are often 

based on additional information from other sources, 

sometimes combined with a narrative storyline (see 

also emissions scenario). 

severity

the proportion of aboveground vegetation killed and 

the degree of forest floor and soil disruption.

significant trends

least-squares regression p-values of observed 

climate trends. In this report, significant trends 

(p < 0.10) are shown by stippling on maps of 

observed climate trends. Where no stippling appears 

(p > 0.10), observed trends have a higher probability 

of being due to chance alone. 

snow water equivalent 

the amount of water contained in snowpack. It 

is a way of measuring the amount of snow while 

accounting for differences in density.
Glossary

165

snowpack

layers of accumulated snow that usually melts during 

warmer months.

softwood

a coniferous tree, usually evergreen, having needles 

or scale-like leaves.

species distribution model

a model that uses statistical relationships to project 

future change.

statistical downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCMs) by 

deriving statistical relationships between observed 

small-scale (often station-level) variables and larger- 

(GCM-) scale variables. Future values of the largescale variables obtained from GCM projections of 

future climate are then used to drive the statistical 

relationships and so estimate the smaller-scale 

details of future climate.

stratosphere

the layer of the Earth’s atmosphere which lies 

between 6 and 30 miles above the Earth.

streamflow 

discharge that occurs in a natural surface stream 

course whether or not it is diverted or regulated.

stressor 

an agent, condition, change in condition, or other 

stimulus that causes stress to an organism.

suitable habitat

in the Climate Change Tree Atlas model, the areaweighted importance value, or the product of tree 

species abundance and the number of cells with 

projected occupancy.

swamp

freshwater, woody communities with surface water 

throughout most of the year. 

timberland

forest land that is producing or capable of producing 

more than 20 cubic feet of wood per acre per year. 

topkill

death of aboveground tree stem and branches.

transpiration

liquid water phase change occurring inside plants 

with the vapor diffusing to the atmosphere.

troposphere

the lowest part of the atmosphere from the surface 

to about 6 miles in altitude in mid-latitudes, where 

clouds and weather phenomena occur.

uncertainty

an expression of the degree to which a value (such as 

the future state of the climate system) is unknown. 

Uncertainty can result from lack of information or 

from disagreement about what is known or even 

knowable. It may have many types of sources, 

from quantifiable errors in the data to ambiguously 

defined concepts or terminology, or uncertain 

projections of human behavior. Uncertainty can 

be described using quantitative measures or by 

qualitative statements.

veneer

a roundwood product from which veneer is sliced 

or sawn and that usually meets certain standards of 

minimum diameter and length, and maximum defect. 
Glossary

166

vulnerability 

the degree to which a system is susceptible to, and 

unable to cope with, adverse effects of climate 

change, including climate variability and extremes. 

Vulnerability is a function of the impacts and 

adaptive capacity of a system. For this assessment, 

a system may be considered to be vulnerable if it 

is at risk of a composition change leading to a new 

identity, or if the system is anticipated to suffer 

substantial declines in health or productivity.

weather 

the state of the atmosphere at a given time and 

place, with respect to variables such as temperature, 

moisture, wind velocity, and barometric pressure. 

windthrow 

trees uprooted or broken by wind.

woodland

highly variable natural communities with a canopy 

of trees ranging from 30- to 100-percent openness, 

a sparse understory, and a dense ground flora rich in 

grasses, sedges, and forbs.

xeric

pertaining to sites or habitats characterized by 

decidedly dry conditions.
167

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Common Name Scientific Name

white ash Fraxinus americana

black ash Fraxinus nigra

green ash Fraxinus pennsylvanica

honeylocust Gleditsia triacanthos

butternut Juglans cinerea

black walnut Juglans nigra

eastern redcedar Juniperus virginiana

tamarack Larix laricina

sweetgum Liquidambar styraciflua

yellow-poplar Liriodendron tulipifera

Osage-orange Maclura pomifera

red mulberry Morus rubra

blackgum Nyssa sylvatica

eastern hophornbeam 

(ironwood)

Ostrya virginiana

reed canarygrass Phalaris arundinacea

white spruce Picea glauca

black spruce Picea mariana

jack pine Pinus banksiana

red pine Pinus resinosa

eastern white pine Pinus strobus

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

Common Name Scientific Name

balsam fir Abies balsamea

boxelder Acer negundo

striped maple Acer pensylvanicum

red maple Acer rubrum

silver maple Acer saccharinum

sugar maple Acer saccharum

mountain maple Acer spicatum

Ohio buckeye Aesculus glabra

garlic mustard Alliaria petiolata

speckled alder Alnus incana

serviceberry Amelanchier alnifolia

Japanese barberry Berberis thunbergii

yellow birch Betula alleghaniensis

sweet birch Betula lenta

river birch Betula nigra

paper birch Betula papyrifera

bog birch Betula pumila

goblin fern Botrychium mormo

Pennsylvania sedge Carex pensylvanica

American hornbeam Carpinus caroliniana

bitternut hickory Carya cordiformis

pignut hickory Carya glabra

shagbark hickory Carya ovata

black hickory Carya texana

mockernut hickory Carya tomentosa

northern catalpa Catalpa speciosa

sugarberry Celtis laevigata

hackberry Celtis occidentalis

eastern redbud Cercis canadensis

flowering dogwood Cornus florida

hazel Corylus cornuta

dwarf trout lily Erythronium propullans

American beech Fagus grandifolia

Appendix 1. Common and Scientific Names 

of Flora, Fauna, and Diseases

Flora

(Continued on next page)
Appendix 1

190

Flora (continued)

Common Name Scientific Name

shingle oak Quercus imbricaria

bur oak Quercus macrocarpa

blackjack oak Quercus marilandica

chinquapin oak Quercus muehlenbergii

pin oak Quercus palustris

chestnut oak Quercus prinus

northern red oak Quercus rubra

post oak Quercus stellata

black oak Quercus velutina

European buckthorn Rhamnus cathartica

Leedy’s roseroot Rhodiola integrifolia ssp. leedyi

black locust Robinia pseudoacacia

Common Name Scientific Name

peachleaf willow Salix amygdaloides

black willow Salix nigra

sassafras Sassafras albidum

American mountain-ash Sorbus americana

sphagnum moss Sphagnum spp.

northern white-cedar Thuja occidentalis

American basswood Tilia americana

eastern hemlock Tsuga canadensis

American elm Ulmus americana

slippery elm Ulmus rubra

rock elm Ulmus thomasii

wild rice Zizania palustris

FAUNa

Common Name Scientific Name

northern goshawk Accipiter gentilis

boreal owl Aegolius funereus

emerald ash borer Agrilus planipennis

moose Alces alces

Asian long-horned beetle Anoplophora glabripennis

red-shouldered hawk Buteo lineatus

gray wolf Canis lupus

beaver Castor canadensis

spruce budworm Choristoneura fumiferana

jack pine budworm Choristoneura pinus pinus

larch casebearer Coleophora laricella

olive-sided flycatcher Contopus cooperi

cisco Coregonus artedi

earthworms (nonnative) Dendrobaena octaedra, 

Lumbricus rubellus, 

and L. terrestris

eastern larch beetle Dendroctonus simplex

Blanding’s turtle Emys blandingii

northern pike Esox lucius

birch leaf miner Fenusa pusilla

wood turtle Glyptemys insculpta

four-toed salamander Hemidactylium scutatum

Common Name Scientific Name

bark beetles Ips spp. and Dendroctonus spp.

blacklegged tick Ixodes scapularis

snowshoe hare Lepus americanus

gypsy moth Lymantria dispar dispar

Canada lynx Lynx canadensis

forest tent caterpillar Malacosoma disstria

American marten Martes americana

wild turkey Meleagris gallopavo

rock vole Microtus chrotorrhinus

white-tailed deer Odocoileus virginianus

gray jay Perisoreus canadensis

white-footed mouse Peromyscus leucopus

aspen blotch miner Phyllonorycter apparella

black-backed woodpecker Picoides arcticus

three-toed woodpecker Picoides tridactlyus

white pine tip weevil Pissodes strobi

Karner blue butterfly Plebejus melissa samuelis

larch sawfly Pristiophora erichsonii

raccoon Procyon lotor

brook trout Salvelinus fontinalis

lake trout Salvelinus namaycush

(Continued on next page)
Appendix 1

191

Common Name Scientific Name

great gray owl Strix nebulosa

badger Taxidea taxus

golden-winged warbler Vermivora chrysoptera

Common Name Scientific Name

scleroderris canker Gremmeniella abietina

annosum root disease Heterobasidion irregulare

hypoxylon canker Hypoxylon mammatum

morel mushroom Morchella spp.

sudden oak death Phytophthora ramorum

sirococcus shoot blight Sirococcus conigenus

sphaeropsis shoot blight Sphaeropsis sapinea

FAUNa (continued)

Common Name Scientific Name

walleye Sander vitreus

eastern gray squirrel Sciurus carolinensis

American woodcock Scolopax minor

Franklin’s ground squirrel Spermophilus franklinii

Diseases

Common Name Scientific Name

Armillaria Armillaria mellea

dwarf mistletoe Arceuthobium pusillum

lyme disease Borrelia burgdorferi

white pine blister rust Cronartium ribicola

Diplodia Diplodia pinea and 

D. scrobiculata

West Nile virus Flavivirus spp.
192

Appendix 2. Trend Analysis 

and Historical Climate Data

To examine historical trends in precipitation 

and temperature for the analysis area, we used 

the ClimateWizard Custom Analysis Tool 

(ClimateWizard 2012, Gibson et al. 2002, Girvetz 

et al. 2009). Data for ClimateWizard are derived 

from PRISM (Parameter-elevation Regressions on 

Independent Slopes Model) (Gibson et al. 2002). 

PRISM interpolates historical data from the National 

Weather Service cooperative stations, the Midwest 

Climate Data Center, and the Historical Climate 

Network, among others. Data undergo strict quality 

control procedures to check for errors in station 

measurements. The PRISM model finds linear 

relationships between these station measurements 

and local elevation by using a digital elevation 

model (digital gridded version of a topographic 

map). Temperature and precipitation are then derived 

for each pixel on a continuous 2.5-mile grid across 

the conterminous United States. The closer a station 

is to a grid cell of interest in distance and elevation, 

and the more similar it is in its proximity to coasts 

or topographic features, the higher the weight the 

station will have on the final, predicted value for that 

cell. More information on PRISM can be found at: 

www.prism.oregonstate.edu/.

This historical gridded data set is different from that 

used in the National Climate Assessment, which uses 

a new gridded historical data set (CDDv2) from the 

National Climatic Data Center (Kunkel et al. 2013). 

The new gridded data set was not publicly available 

at the time this assessment was completed, and 

therefore we cannot fully compare this new version 

with the one available through PRISM. However, 

both are based on cooperative weather station data, 

cover the period from 1895 through 2011, and have 

similar resolutions (3.1-mile vs. 2.5-mile grid). In 

addition, the overall trends reported as input into 

the National Climate Assessment are generally 

consistent with those reported in this assessment 

(Kunkel et al. 2013). 

Linear trend analysis for 1901 through 2011 was 

performed by using restricted maximum likelihood 

(REML) estimation (Girvetz et al. 2009). Restricted 

maximum likelihood methods were used for trend 

analysis of past climate for the Intergovernmental 

Panel on Climate Change Working Group 1 Report

and are considered an effective way to determine 

trends in climate data over time (Trenberth et al. 

2007). A first-order autoregression was assumed 

for the residuals, meaning that values one time step 

away from each other are assumed to be correlated. 

This method was used to examine trends for every 

2.5-mile grid cell. The slope and p-values for the 

linear trend over time were calculated annually, 

seasonally, and monthly for each climate variable, 

and then mapped. An overall trend for an area is 

based on the trend analysis of the average value for 

all grid cells within the area over time (Table 21). 

Developers of the ClimateWizard Tool advise users 

to interpret the linear trend maps in relation to the 

respective map of statistical confidence (Figs. 47 and 

48). In this case, statistical confidence is described 

by using p-values from a t-test applied to the linear 

regression. A p-value can be interpreted as the 

probability of the slope being different from zero by 

chance alone. For this assessment, p-values of less 

than 0.1 were considered to have sufficient statistical 

confidence. Areas with low statistical confidence in 

the rate of change (gray areas on the map) should be 

interpreted with caution. 
Appendix 2

193

*P-values represent the probability of observing that trend by chance alone. Boldface p-values indicate a 10-percent probability (or less) that the 

trend was due to chance alone. TMean = mean temperature, TMax = maximum temperature, TMin = minimum temperature. 

Mean Precip. Mean TMean Mean TMax Mean TMin

Month precip. change Precip. TMean change TMean TMax change TMax TMin change TMin

or season (inches) (inches) p-value* (°F) (°F) p-value* (°F) (°F) p-value* (°F) (°F) p-value*

January 0.79 0.19 0.24 5.25 2.91 0.15 16.48 1.70 0.36 -6.00 4.12 0.07

February 0.65 -0.03 0.81 10.25 5.94 0.00 22.44 5.16 0.00 -1.94 6.73 0.01

March 1.13 0.22 0.20 23.45 3.24 0.06 35.03 2.50 0.13 11.87 3.97 0.03

April 1.89 0.42 0.14 39.36 2.49 0.04 51.26 2.44 0.11 27.46 2.54 0.01

May 2.94 0.27 0.47 51.92 2.52 0.02 64.80 1.88 0.15 39.06 3.17 0.00

June 3.98 0.42 0.40 61.46 1.30 0.23 73.81 0.22 0.87 49.12 2.36 0.01

July 3.68 0.67 0.10 66.67 0.96 0.25 79.01 -0.30 0.79 54.35 2.23 0.00

August 3.46 0.27 0.51 64.26 2.25 0.01 76.46 1.33 0.16 52.07 3.17 0.00

September 3.01 0.38 0.29 55.02 1.25 0.17 66.53 0.62 0.55 43.52 1.87 0.03

October 2.13 0.93 0.08 43.52 0.43 0.69 54.13 -0.64 0.63 32.92 1.50 0.12

November 1.31 0.26 0.20 27.10 1.43 0.34 35.59 0.54 0.72 18.61 2.31 0.13

December 0.86 0.18 0.26 11.91 2.26 0.18 21.45 1.14 0.47 2.37 3.39 0.08

Annual 25.83 4.30 0.00 38.35 2.25 0.00 49.75 1.38 0.03 26.95 3.12 0.00

Winter 2.30 0.36 0.17 9.14 3.73 0.01 20.13 2.68 0.02 -1.85 4.78 0.00

Spring 5.96 0.90 0.07 38.24 2.72 0.01 50.36 2.24 0.04 26.14 3.20 0.00

Summer 11.13 1.38 0.09 64.13 1.51 0.02 76.42 0.42 0.61 51.84 2.59 0.00

Fall 6.45 1.62 0.01 41.88 1.04 0.17 52.08 0.18 0.83 31.69 1.90 0.01

Table 21.—Average annual, seasonal, and monthly values and linear trend analysis for selected climate variables 

from 1901 through 2011 for the assessment area 

In addition, because maps are developed from 

weather station observations that have been spatially 

interpolated, developers of the ClimateWizard tool 

and PRISM data set recommend that inferences 

about trends should not be made for single grid cells 

or even small clusters of grid cells. The number of 

weather stations has also changed over time, and 

station data are particularly limited before 1948, 

meaning grid cells from earlier in the century are 

based on an interpolation of fewer points than later 

in the century (Gibson et al. 2002). Therefore, 

interpretations should be based on many grid cells 

showing regional patterns of climate change with 

high statistical confidence. For those interested 

in understanding trends in climate at a particular 

location, it is best to refer to weather station data 

for the closest station in the Global Historical 

Climatology Network from the National Climatic 

Data Center (www.ncdc.noaa.gov/). 

We selected the time period 1901 through 2011 

because it was sufficiently long to capture inter- and 

intra-decadal variation in climate for the region. We 

acknowledge that different trends can be inferred 

by selecting different beginning and end points in 

the analysis. Therefore, trends should be interpreted 

based on their relative magnitude and direction, and 

the slope of any single trend should be interpreted 

with caution. 
Appendix 2

194

Figure 47.—Map of statistical confidence (p-values for the linear regression) for trends in temperature from 1901 through 

2011. Gray values represent areas of low statistical confidence.
Appendix 2

195

REFERENCES

ClimateWizard. 2012. ClimateWizard Custom 

Analysis. The Nature Conservancy, University of 

Washington, University of Southern Mississippi, 

Climate Central, and Santa Clara University. 

Available at http://climatewizardcustom.org/. 

(Accessed April 2, 2012).

Gibson, W.P.; Daly, C.; Kittel, T.; Nychka, D.; Johns, 

C.; Rosenbloom, N.; McNab, A.; Taylor, G. 2002. 

Development of a 103-year high-resolution 

climate data set for the conterminous United 

States. Proceedings of the 13th American 

Meteorological Society Conference on Applied 

Climatology: 181-183.

Girvetz, E.H.; Zganjar, C.; Raber, G.T.; Maurer, E.P.; 

Kareiva, P.; Lawler, J.J. 2009. Applied climatechange analysis: the Climate Wizard tool.

PLoS ONE. 4(12): e8320.

Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun, L.; 

Janssen, E.; Wuebbles, D.; Hilberg, S.D.; Timlin, 

M.S.; Stoeck, L.; Westcott, N.E.; Dobson, J.G. 

2013. Regional climate trends and scenarios 

for the U.S. National Climate Assessment. Part 

3. Climate of the Midwest U.S. Washington, 

DC: U.S. Department of Commerce, National 

Oceanic and Atmospheric Administration. 103 p.

Trenberth, K.E.; Jones, P.D.; Ambenje, P.; Bojariu, 

R.; Easterling, D.; Tank, A.K.; Parker, D.; 

Rahimzadeh, F.; Renwick, J.A.; Rusticucci, M.; 

Soden, B.; Zhai, P. 2007. Observations: surface 

and atmospheric climate change. In: Solomon, 

S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, 

M.; Averyt, K.B.; Tignor, M.; Miller, H.L., eds. 

Climate Change 2007: the physical science basis. 

Contribution of Working Group I to the Fourth 

Assessment Report of the Intergovernmental 

Panel on Climate Change. Cambridge, U.K. and 

New York: Cambridge University Press.

Figure 48.—Map of statistical confidence (p-values for the 

linear regression) for trends in precipitation from 1901 

through 2011. Gray values represent areas of low statistical 

confidence.
196

Appendix 3: Additional 

Future Climate Information

This appendix presents supplementary information 

to Chapter 4: tables of projected change in 

temperature and precipitation for the assessment for 

the end of the 21st century (Tables 22 and 23) and 

maps of projected change for early- and mid-century 

(Figs. 49 through 56).

Baseline temperature (°F) Temperature departure from baseline (°F)

 (1971-2000) Scenario 2010-2039 2040-2069 2070-2099

Mean

Annual 39.0 PCM B1 1.4 2.0 3.0

GFDL A1FI 2.2 6.8 8.8

Winter (Dec.-Feb.) 9.9 PCM B1 1.5 2.5 3.9

GFDL A1FI 2.8 8.6 9.8

Spring (Mar.-May) 39.6 PCM B1 0.1 1.2 2.2

GFDL A1FI 0.3 3.8 5.4

Summer (June-Aug.) 64.5 PCM B1 1.4 1.8 2.3

GFDL A1FI 3.1 8.6 11.4

Fall (Sept.-Nov.) 41.5 PCM B1 2.9 3.0 3.9

GFDL A1FI 2.9 6.5 9.1

Mean maximum

Annual 50.2 PCM B1 1.4 2.1 2.7

GFDL A1FI 1.8 6.1 7.6

Winter (Dec.-Feb.) 20.7 PCM B1 1.3 2.1 2.7

GFDL A1FI 2.2 6.9 7.5

Spring (Mar.-May) 51.8 PCM B1 -0.2 1.1 1.8

GFDL A1FI -0.7 2.2 3.3

Summer (June-Aug.) 76.6 PCM B1 2.1 2.4 3.0

GFDL A1FI 3.0 8.8 11.2

Fall (Sept.-Nov.) 51.5 PCM B1 2.9 3.1 3.9

GFDL A1FI 3.0 6.8 8.8

Mean minimum

Annual 27.7 PCM B1 1.3 1.9 3.2

GFDL A1FI 2.6 7.5 10.0

Winter (Dec.-Feb.) -1.0 PCM B1 1.7 3.0 5.0

GFDL A1FI 3.5 10.2 12.1

Spring (Mar.-May) 27.5 PCM B1 0.3 1.2 2.6

GFDL A1FI 1.3 5.4 7.4

Summer (June-Aug.) 52.4 PCM B1 0.7 1.2 1.6

GFDL A1FI 3.2 8.4 11.5

Fall (Sept.-Nov.) 31.5 PCM B1 2.8 2.8 3.9

GFDL A1FI 2.9 6.3 9.4

Table 22.—Projected changes in mean average, maximum, and minimum temperatures under two future climate 

scenarios for the assessment area over the next century
Appendix 3

197

Baseline precipitation Departure from baseline (inches)

(inches) (1971-2000) Scenario 2010-2039 2040-2069 2070-2099

Annual 27.2 PCM B1 0.3 1.2 3.0

GFDL A1FI 1.2 -2.2 -0.4

Winter (Dec.-Feb.) 2.3 PCM B1 0.6 0.7 1.0

GFDL A1FI 0.3 0.4 0.7

Spring (Mar.-May) 5.8 PCM B1 0.4 0.8 1.6

GFDL A1FI 1.6 2.2 3.2

Summer (June-Aug.) 12.1 PCM B1 -0.6 0.1 0.5

GFDL A1FI -0.5 -4.0 -4.8

Fall (Sept.-Nov.) 7.1 PCM B1 -0.2 -0.5 -0.1

GFDL A1FI -0.2 -0.8 0.6

Table 23.—Projected changes in precipitation under two future climate scenarios for the assessment area over the 

next century

Harvested logs from an aspen clearcut in Lake County, Minnesota. Photo by Casey McQuiston, Superior National Forest.
Appendix 3

198

Figure 49.—Projected difference in mean daily temperature at the beginning of the century (2010 through 2039) compared to 

baseline (1971 through 2000) for two climate scenarios.
Appendix 3

199

Figure 50.—Projected difference in mean maximum temperature at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate scenarios. 
Appendix 3

200

Figure 51.—Projected difference in mean minimum temperature at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

201

Figure 52.—Projected difference in precipitation at the beginning of the century (2010 through 2039) compared to baseline 

(1971 through 2000) for two climate scenarios.
Appendix 3

202

Figure 53.—Projected difference in mean daily temperature for the middle of the century (2040 through 2069) compared to 

baseline (1971 through 2000) for two climate scenarios.
Appendix 3

203

Figure 54.—Projected difference in mean maximum temperature for the middle of the century (2040 through 2069) compared 

to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

204

Figure 55.—Projected difference in mean minimum temperature for the middle of the century (2040 through 2069) compared 

to baseline (1971 through 2000) for two climate scenarios.
Appendix 3

205

Figure 56.—Projected difference in precipitation for the middle of the century (2040 through 2069) compared to baseline 

(1971 through 2000) for two climate scenarios.
206

Appendix 4: Supplementary Model Results

TREE ATLAS

This section provides additional model outputs for 

the 74 species considered for this assessment. Even 

more information is available online at the Climate 

Change Tree Atlas Web site (www.nrs.fs.fed.

us/atlas/tree/tree_atlas.html), including detailed 

methods, maps of changes in importance values, and 

additional statistics. Publications describing the Tree 

Atlas tools also include key definitions and methods 

descriptions (Iverson et al. 1999, 2008, 2011; 

Matthews et al. 2011). 

Changes in Suitable Habitat

Measured area-weighted importance values (IVs) 

from the U.S. Forest Service, Forest Inventory and 

Analysis (FIA) as well as modeled current (1961 

through 1990) and future IVs (2010 through 2039, 

2040 through 2069, 2070 through 2099) from 

DISTRIB were calculated for each time period. 

Initially, 134 tree species were modeled. If a species 

never had an area-weighted IV greater than 3 (FIA, 

current modeled, or future) across the region, it was 

deleted from the list because the species either has 

not had or is not projected to have habitat in the 

region or there were not enough data. Therefore, 

only a subset of all possible species is shown. 

A set of rules was established to determine change 

classes for the years 2070 through 2099, which was 

used to create tables in Chapter 5. For most species, 

the classification rules are listed in Table 24, based 

on the ratio of future IVs to current modeled IVs. 

A few exceptions applied to these general rules. 

When there was a zero in the numerator or 

denominator, a ratio could not be calculated. 

Instead, a species was classified as gaining new 

habitat if its FIA value was 0 and the future IV was 

greater than 3. A species’ habitat was considered to 

be extirpated if the future IV was 0 and FIA values 

were greater than 3. 

Special rules were created for rare species 

(Table 25). A species was considered rare if it had 

a current modeled area-weighted IV that equaled 

<10 percent of the number of 12.5-mile by 12.5-mile 

pixels in the assessment area. This would mean that 

a species was present in only 10 percent or fewer of 

the pixels across the assessment area. The change 

classes are calculated differently for these species 

because their current infrequency tends to inflate 

the percentage change that is projected. There are 

234 pixels in the Minnesota assessment area, so the 

cutoff IV for determining a rare species is 23. 

Future:Current modeled IV Class

<0.5 large decrease

0.5 to 0.8 decrease

>0.8 to <1.2 no change 

1.2 to 2.0 increase

>2 large increase

Table 24.—General classification rules used to 

determine change categories for suitable habitat for 

common tree species using the Tree Atlas DISTRIB 

model output (Current IV > 23)
Appendix 4

207

Special rules also applied to species that were known 

to be present (current FIA IV > 0) but not modeled 

as present (current modeled = 0). In these cases, the 

FIA IV was used in place of the current modeled IV 

to calculate ratios. Then, change class rules were 

applied based on the FIA IV.

Complete DISTRIB model results are displayed in 

Table 26.

Modifying Factors

Modifying factors are key life-history or 

environmental factors that may cause a species 

to occupy more or less suitable habitat than the 

model results suggest. Tables 27 and 28 describe 

the modifying factors and adaptability scores used 

in the Tree Atlas. These factors were developed by 

using a literature-based scoring system to capture 

the potential adaptability of species to changes 

in climate that cannot be adequately captured by 

DISTRIB (Matthews et al. 2011). This approach 

was used to assess the capacity for each species to 

adapt and considered nine biological traits reflecting 

innate characteristics like competition ability for 

light and edaphic specificity. Twelve disturbance 

characteristics addressed the general response of 

a species to events such as drought, insect pests, 

Future:Current modeled IV Class

<0.2 large decrease

0.2 to <0.6 decrease

0.6 to <4 no change 

4 to 8 increase

>8 large increase (not 

used when current 

modeled IV ≤3)

Table 25.—Special classification rules used to determine 

change categories for suitable habitat for rare tree 

species using the Tree Atlas DISTRIB model output 

(Current IV < 23)

and fire. This information distinguishes between 

species likely to be more tolerant (or sensitive) to 

environmental changes than the habitat models alone 

suggest. 

For each biological and disturbance factor, a species 

was scored on a scale from -3 to +3. A score of -3 

indicated a very negative response of that species to 

that factor. A score of +3 indicated a very positive 

response to that factor. To account for confidence 

in the literature about these factors, each of these 

scores was then multiplied by 0.5, 0.75, or 1, with 

0.5 indicating low confidence and 1 indicating high 

confidence. Finally the score was further weighted 

by its relevance to future projected climate change 

by multiplying it by a relevance factor. A 4 indicated 

highly relevant and a 1 indicated not highly relevant 

to climate change. Means for individual biological 

scores and disturbance scores were then calculated 

to arrive at an overall biological and disturbance 

score for the species. 

To arrive at an overall adaptability score for the 

species that could be compared across all modeled 

tree species, the mean, rescaled (0-6) values for 

biological and disturbance characteristics were 

plotted to form two sides of a right triangle; the 

hypotenuse was then a combination (disturbance and 

biological characteristics) metric, ranging from 0 to 

8.5 (Fig. 57). For this assessment, adaptability scores 

below 3.2 are considered low, and scores above 5.3 

are considered high. 

Note that modifying factors and adaptability scores 

are calculated for a species across its entire range. 

Many species may have higher or lower adaptability 

in certain areas. For example, a species with a low 

flooding tolerance may have higher adaptability in 

areas not subject to flooding. Likewise, local impacts 

of insects and disease may reduce the adaptability of 

a species in that area.
Append

ix 4

208

American basswood 635 761 Medium 736 927 771 1031 840 1034 0.97 1.22 1.01 1.36 1.1 1.36 No Change Increase

American beech 0 62 High 36 85 54 261 76 254 0.58 1.37 0.87 4.21 1.23 4.1 New Habitat New HabitatAmerican elm 505 646 Medium 587 864 666 1487 793 1921 0.91 1.34 1.03 2.3 1.23 2.97 Increase Large Increase

American hornbeam 64 73 Medium 68 118 83 219 132 222 0.93 1.62 1.14 3 1.81 3.04 Increase Large Increase

Balsam fir 1752 1767 High 1287 579 1049 101 908 59 0.73 0.33 0.59 0.06 0.51 0.03 Decrease Large Decrease

Balsam poplar 1130 1091 High 538 445 424 349 272 424 0.49 0.41 0.39 0.32 0.25 0.39 Large Decrease Large Decrease

Bigtooth aspen 307 370 High 365 366 381 296 426 249 0.99 0.99 1.03 0.8 1.15 0.67 No Change DecreaseBitternut hickory 13 26 Low 29 118 52 132 117 162 1.12 4.54 2 5.08 4.5 6.23 Large Increase Large IncreaseBlack ash 1492 1386 High 1339 1176 1268 943 1207 913 0.97 0.85 0.92 0.68 0.87 0.66 No Change Decrease

Black cherry 129 166 High 252 435 328 659 515 561 1.52 2.62 1.98 3.97 3.1 3.38 Large Increase Large IncreaseBlack hickory 0 0 High 0 0 0 28 0 107 NA NA NA Mig. NA Mig. NA New Habitat

Black locust 0 0 Low 2 22 3 270 8 461 Mig. Mig. Mig. Mig. Mig. Mig. New Habitat New Habitat

Black oak 7 62 High 117 338 217 465 317 567 1.89 5.45 3.5 7.5 5.11 9.15 Large Increase Large IncreaseBlack spruce 1617 1567 High 916 396 691 99 529 85 0.59 0.25 0.44 0.06 0.34 0.05 Large Decrease Large DecreaseBlack walnut 1 7 Medium 8 88 26 393 68 523 1.14 12.57 3.71 56.14 9.71 74.71 Large Increase Large IncreaseBlack willow 24 77 Low 83 226 108 512 191 544 1.08 2.94 1.4 6.65 2.48 7.07 Large Increase Large IncreaseBlackgum 0 0 High 0 0 0 19 0 53 NA NA NA Mig. NA Mig. NA New HabitatBlackjack oak 0 0 Medium 0 0 0 82 0 179 NA NA NA Mig. NA Mig. NA New Habitat

Boxelder 136 347 Medium 365 808 409 1124 461 1359 1.05 2.33 1.18 3.24 1.33 3.92 Increase Large Increase

Bur oak 961 1103 Medium 999 1523 1058 1636 1157 1801 0.91 1.38 0.96 1.48 1.05 1.63 No Change Increase

Butternut 29 13 Low 20 27 22 0 24 1 1.54 2.08 1.69 0 1.85 0.08 No Change Large Decrease

Chestnut oak 0 1 High 0 0 0 29 0 36 0 0 0 29 0 36 NA New HabitatChinquapin oak 0 0 Medium 0 6 1 53 2 119 NA Mig. Mig. Mig. Mig. Mig. New Habitat New Habitat

Chokecherry 168 195 Low 175 194 174 197 171 171 0.9 1 0.89 1.01 0.88 0.88 No Change No Change

Eastern cottonwood 8 81 Low 47 205 76 1047 132 1315 0.58 2.53 0.94 12.93 1.63 16.24 Increase Large Increase

Eastern hemlock 0 20 High 80 95 92 149 153 132 4 4.75 4.6 7.45 7.65 6.6 New Habitat New HabitatEastern hophornbeam 241 322 Medium 341 449 362 535 412 516 1.06 1.39 1.12 1.66 1.28 1.6 Increase Increase

Eastern red cedar 241 17 Medium 56 396 88 1819 135 2045 3.29 23.29 5.18 107 7.94 120.3 Increase Large Increase

Eastern redbud 0 0 Medium 0 1 0 45 0 95 NA Mig. NA Mig. NA Mig. NA New HabitatEastern white pine 246 293 High 334 411 351 514 412 417 1.14 1.4 1.2 1.75 1.41 1.42 Increase Increase

Flowering dogwood 0 0 High 0 0 0 82 0 114 NA NA NA Mig. NA Mig. NA New Habitat

Green ash 344 615 Medium 558 731 600 1154 651 1260 0.91 1.19 0.98 1.88 1.06 2.05 No Change Large Increase

Hackberry 4 30 Medium 48 244 80 725 143 909 1.6 8.13 2.67 24.17 4.77 30.3 Large Increase Large IncreaseHoneylocust 0 2 Low 1 46 5 318 25 468 0.5 23 2.5 159 12.5 234 New Habitat New HabitatJack pine 884 893 High 806 822 829 619 807 607 0.9 0.92 0.93 0.69 0.9 0.68 No Change DecreaseMockernut hickory 0 0 High 0 0 0 49 0 147 NA NA NA Mig. NA Mig. NA New Habitat

Mountain maple 164 151 High 113 37 77 3 46 0 0.75 0.25 0.51 0.02 0.31 0 Large Decrease Large Decrease

Northern catalpa 0 8 Low 3 3 4 5 6 53 0.38 0.38 0.5 0.63 0.75 6.63 NA New HabitatNorthern pin oak 110 108 Medium 115 235 175 214 263 224 1.07 2.18 1.62 1.98 2.44 2.07 Large Increase Large IncreaseNorthern red oak 884 924 High 1303 1817 1406 1356 1581 1090 1.41 1.97 1.52 1.47 1.71 1.18 Increase No Change

Northern white-cedar 674 720 High 552 328 452 341 451 333 0.77 0.46 0.63 0.47 0.63 0.46 Decrease Large Decrease

Ohio buckeye 0 0 Low 0 4 0 60 0 87 NA Mig. NA Mig. NA Mig. NA New Habitat

DISTRIB results

Modeled IV Future:Current Suitable Habitat

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 Change Class

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI

Table 26.—Complete DISTRIB results for the 74 tree species in the assessment area* 

(Table 26 continued on next page)
Append

ix 4 209

Osage-orange 0 13 Medium 14 43 20 106 48 189 1.08 3.31 1.54 8.15 3.69 14.54 New Habitat New HabitatPaper birch 1834 1773 High 1737 1453 1649 527 1550 406 0.98 0.82 0.93 0.3 0.87 0.23 No Change Large Decrease

Peachleaf willow 9 7 Low 1 31 0 21 0 165 0.14 4.43 0 3 0 23.57 Large Decrease Large IncreasePignut hickory 0 0 High 0 2 0 55 1 165 NA Mig. NA Mig. Mig. Mig. New Habitat New Habitat

Pin cherry 95 84 Medium 85 97 73 126 56 83 1.01 1.16 0.87 1.5 0.67 0.99 Decrease No ChangePin oak 0 2 Medium 2 2 2 40 5 108 1 1 1 20 2.5 54 New Habitat New Habitat

Post oak 0 0 High 0 0 0 299 0 587 NA NA NA Mig. NA Mig. NA New Habitat

Quaking aspen 6076 5942 High 4801 3927 4426 1703 3815 1374 0.81 0.66 0.75 0.29 0.64 0.23 Decrease Large DecreaseRed maple 862 977 High 1292 1246 1436 1523 1739 1475 1.32 1.28 1.47 1.56 1.78 1.51 Increase Increase

Red mulberry 0 8 Low 14 81 23 428 45 624 1.75 10.13 2.88 53.5 5.63 78 New Habitat New HabitatRed pine 507 557 Medium 556 595 567 735 589 726 1 1.07 1.02 1.32 1.06 1.3 No Change Increase

River birch 2 1 Low 1 18 7 6 8 7 1 18 7 6 8 7 Increase Increase

Rock elm 17 12 Low 10 8 10 37 11 52 0.83 0.67 0.83 3.08 0.92 4.33 No Change Increase

Sassafras 0 0 High 0 2 0 86 1 161 NA Mig. NA Mig. Mig. Mig. New Habitat New Habitat

Scarlet oak 0 0 High 0 2 0 18 1 77 NA Mig. NA Mig. Mig. Mig. New Habitat New HabitatShagbark hickory 0 20 Medium 25 144 60 297 113 322 1.25 7.2 3 14.85 5.65 16.1 New Habitat New HabitatShingle oak 0 0 Medium 0 1 0 34 0 50 NA Mig. NA Mig. NA Mig. NA New HabitatSilver maple 55 80 Medium 88 277 149 551 237 654 1.1 3.46 1.86 6.89 2.96 8.18 Large Increase Large Increase

Slippery elm 36 51 Medium 85 325 126 723 209 811 1.67 6.37 2.47 14.18 4.1 15.9 Large Increase Large IncreaseStriped maple 6 14 High 9 17 12 32 16 33 0.64 1.21 0.86 2.29 1.14 2.36 No Change No Change

Sugar maple 630 761 High 1169 1376 1289 1208 1519 971 1.54 1.81 1.69 1.59 2 1.28 Large Increase Increase

Sugarberry 0 4 Medium 2 2 2 7 2 42 0.5 0.5 0.5 1.75 0.5 10.5 NA New HabitatSwamp white oak 4 1 Low 1 7 1 78 2 95 1 7 1 78 2 95 Increase Large IncreaseSweet birch 0 1 High 1 0 0 28 1 32 1 0 0 28 1 32 NA New Habitat

Sweetgum 0 0 High 0 0 0 33 0 58 NA NA NA Mig. NA Mig. NA New HabitatTamarack 965 944 High 894 756 805 556 700 535 0.95 0.8 0.85 0.59 0.74 0.57 Decrease DecreaseWhite ash 40 45 High 82 123 121 427 219 417 1.82 2.73 2.69 9.49 4.87 9.27 Large Increase Large IncreaseWhite oak 93 124 High 215 525 336 713 512 715 1.73 4.23 2.71 5.75 4.13 5.77 Large Increase Large IncreaseWhite spruce 297 316 Medium 243 185 212 138 199 169 0.77 0.59 0.67 0.44 0.63 0.54 Decrease DecreaseWild plum 3 3 Low 5 10 3 134 16 213 1.67 3.33 1 44.67 5.33 71 Increase Increase

Yellow birch 95 126 High 170 149 179 84 269 79 1.35 1.18 1.42 0.67 2.14 0.63 Large Increase Decrease

Yellow-poplar 0 0 High 0 0 0 5 0 48 NA NA NA Mig. NA Mig. NA New Habitat

DISTRIB results

Modeled IV Future:Current Suitable Habitat

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 Change Class

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1FI

Table 26 (continued).*Current importance values (Current IV) are based on modeled results, and FIA IV values are calculated based on FIA inventory data. Early-century, mid-century, and late-century importance values are average values for the indicated years. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value. Species are assigned to change classes based on the comparison between end-of-century (2070-2099) and current figures for area-weighted importance value (Tables 24-25). NA = not applicable, no suitable habitat projected.Mig. = new migrant, new suitable habitat projected. 
Appendix 4

210

Model Modifying Factors* Adaptability Scores

Common Name Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

American basswood Medium COL FTK 0.3 0.2 4.6 ○

American beech High COL INS FTK -1.1 0 3.6 ○

American elm Medium ESP DISE INS -0.8 0.3 4 ○

American hornbeam Medium COL SES FTK DRO 0.6 0.6 5.1 ○

Balsam fir High COL INS FTK DRO -3 -0.4 2.7 ―

Balsam poplar High FRG VRE COL DRO 0.1 -0.6 4 ○

Bigtooth aspen High FRG DISP COL DRO FTK 1 0.2 5.1 ○

Bitternut hickory Low DRO COL 2.2 -0.8 5.6 +

Black ash High INS COL DISP DRO SES FTK ESP -1.3 -3 1.7 ―

Black cherry High DRO ESP INS FTK COL -1.6 -0.3 3 ―

Black hickory High ESP COL 1 -2.3 4.1 ○

Black locust Low COL INS 0 -0.6 3.8 ○

Black oak High DRO ESP INS DISE 0.5 0.4 4.9 ○

Black spruce High COL ESP DISP FTK INS DRO -2.1 1.2 4.3 ○

Black walnut Medium SES COL DRO 0.4 -0.8 4 ○

Black willow Low COL FTK DRO -0.3 -2.1 2.8 ―

Blackgum High COL FTK 1.5 0.8 5.9 +

Blackjack oak Medium DRO SES FRG VRE COL FTK 1.6 0.2 5.6 +

Boxelder Medium SES DISP DRO COL SES FTK 2.4 2.1 7.4 +

Bur oak Medium DRO FTK 2.8 -0.2 6.4 +

Butternut Low FTK COL DRO DISE -1.4 -1.3 2.3 ―

Chestnut oak High SES VRE ESP FTK INS DISE 1.4 1.3 6.1 +

Chinquapin oak Medium SES 1.2 -0.7 4.8 ○

Chokecherry Low COL 0.2 -0.9 3.8 ○

Eastern cottonwood Low SES INS COL DISE FTK 0.2 -0.8 3.9 ○

Eastern hemlock High COL INS DRO -1.3 -0.9 2.7 ―

Eastern hophornbeam Medium COL ESP SES 1.7 1.3 6.4 +

Eastern red cedar Medium DRO FTK COL INS 0.6 -1.5 3.9 ○

Eastern redbud Medium 0.9 0 4.9 ○

Eastern white pine High DISP DRO FTK INS -2 0.1 3.3 ○

Flowering dogwood High COL 0.1 1 5 ○

Green ash Medium INS FTK COL -0.1 -0.3 4 ○

Hackberry Medium DRO FTK 1.7 0.3 5.7 +

Honeylocust Low COL 1.9 -0.5 5.5 +

Jack pine High DRO COL INS 1.9 -1.2 5.2 ○

Mockernut hickory High FTK 1.7 -0.3 5.4 +

Mountain maple High COL VRE ESP DRO FTK 0.8 1.5 5.9 +

Northern catalpa Low COL ESP 0.9 -1.6 4.2 ○

Northern pin oak Medium DRO FTK COL 2.5 -0.6 6 +

Northern red oak High INS 1.4 0.1 5.4 +

Northern white-cedar High COL FTK -0.7 0.5 4.2 ○

Table 27.—Modifying factors for the 74 tree species in the assessment area 

(Table 27 continued on next page)
Appendix 4

211

Ohio buckeye Low COL SES FTK 0.4 -1.9 3.5 ○

Osage-orange Medium ESP ESP 2.3 0.3 6.3 +

Paper birch High FRG DISP ESP FTK COL INS DRO -1.7 0.2 3.4 ○

Peachleaf willow Low COL 0.1 -1.7 3.4 ○

Pignut hickory High ESP INS DRO 0.2 0.4 4.7 ○

Pin cherry Medium SES FRG FTK COL 0.5 -0.7 4.2 ○

Pin oak Medium FTK COL INS DISE -0.7 -1.4 2.8 ―

Post oak High DRO SES FTK COL INS DISE 2.2 -0.6 5.7 +

Quaking aspen High SES FRG ESP COL DRO FTK 0.6 0 4.7 ○

Red maple High SES ESP ESP COL DISP 3 3 8.5 +

Red mulberry Low COL DISP FTK 0.1 0.6 4.7 ○

Red pine Medium INS COL DISP 0.9 -2.4 3.9 ○

River birch Low DISP FTK COL DRO -0.5 -0.3 3.7 ○

Rock elm Low ESP ESP SES -0.2 -2.6 2.8 ―

Sassafras High COL FTK 0.5 -0.6 4.2 ○

Scarlet oak High VRE ESP ESP INS DISE FTK -0.4 0.7 4.6 ○

Shagbark hickory Medium INS FTK -0.2 0.4 4.4 ○

Shingle oak Medium ESP COL 1.3 -0.7 4.9 ○

Silver maple Medium DISP SES COL DRO FTK 0.1 1.6 5.6 +

Slippery elm Medium COL FTK DISE 0 0.7 4.8 ○

Striped maple High COL SES DRO 1 0.3 5.1 ○

Sugar maple High COL ESP 0.9 1.3 5.8 +

Sugarberry Medium COL SES FTK -0.2 0.6 4.6 ○

Swamp white oak Low 1 -0.3 4.9 ○

Sweet birch High DISP FTK COL INS DISE -1.3 -0.3 3.2 ―

Sweetgum High VRE ESP FTK COL DRO -0.4 0.2 4.1 ○

Tamarack High FTK COL INS -0.5 -1.2 3.1 ―

White ash High INS FTK COL -2 -0.5 2.7 ―

White oak High ESP ESP SES FTK INS DISE 1.7 1 6.1 +

White spruce Medium INS 0.1 -0.6 3.9 ○

Wild plum Low COL 0.5 -1.3 3.9 ○

Yellow birch High DISP FTK INS DISE -1.4 0 3.4 ○

Yellow-poplar High SES DISP ESP INP 0.1 1.3 5.3 +

*Modifying factors are key life-history or environmental factors that may cause a species to occupy more or less suitable habitat than 

the model results suggest (Matthews et al. 2011). Explanations for the modifying factor codes are displayed in Table 28. Adaptation 

factor scores below 3.2 are considered low (-), and scores above 5.3 are considered high (+).

Model Modifying Factors* Adaptability Scores

Common Name Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

Table 27 (continued).
Appendix 4

212

Table 28.—Description of Tree Atlas modifying factor codes

Code* Description (if positive) Description (if negative)

COL Tolerant of shade or limited light conditions Intolerant of shade or limited light conditions

DISE Has a high number or severity of known pathogens that 

attack the species

DISP High ability to effectively produce 

and distribute seeds

DRO Drought-tolerant Susceptible to drought

ESP Wide range of soil requirements Narrow range of soil requirements 

FRG Regenerates well after fire

FTK Resistant to fire topkill Susceptible to fire topkill

INS Has a high number or severity of insects that may attack 

the species

INP Strong negative effects of invasive plants on the species, 

either through competition for nutrients or as a pathogen

SES High ability to regenerate with seeds to maintain 

future populations

Low ability to regenerate with seeds to maintain future 

populations

VRE Capable of vegetative reproduction through stump 

sprouts or cloning

*These codes are used to describe positive or negative modifying factors in Table 27. A species was given a code if information from 

the literature suggested that it had these characteristics. See Matthews et al. (2011) for a more thorough description of these factors 

and how they were assessed.

Figure 57.—Schematic showing how biological and disturbance modifying factors are translated into an overall adaptability 

score in the Tree Atlas model.
Appendix 4

213

LANDIS-II

This section provides additional model outputs 

and methods for the model simulations developed 

for this assessment. More information is available 

online at the LANDIS-II Web site (www.landisii.org/), including detailed model descriptions and 

publications describing the LANDIS-II core model 

and extensions.

Biomass Projections

LANDIS-II outputs include biomass of individual 

species by age cohort (Table 29). For this 

assessment, we have combined values for separate 

age cohorts into a single biomass value by species. 

Forest-type Classification 

The forest-type maps presented in Chapter 5 

(Fig. 38) rely on a simple classification scheme. 

To create these land cover maps, individual 

locations (“cells”) in the LANDIS-II simulations 

were classified into six forest categories based 

on characteristic species composition. These 

classifications are based on the dominance of key 

indicator species (Table 30). The species assignment 

to groups is based on unique species within groups 

and a balance of high abundance species within 

groups. Certain species that do not contribute to the 

unique forest type dominance are subtracted from 

the dominance calculation. Species assignment 

adjustments were made based on matching the 

proportion of individual forest types found in 

regional FIA plots to the proportion of individual 

forest types found in LANDIS-II cells for the year 

2000. 

Management and Disturbance Scenarios

The simulations developed for this assessment were 

run with the Biomass Succession (v3.1), Base Fire 

(v3.0), and Base Wind (v2.0) extensions. Forest 

management was simulated by using the Biomass 

Harvest extension (v2.1), and output was delivered 

through the Biomass Output extension (v.2.0) 

(www.landis-ii.org/exts). A business-as-usual forest 

management scenario was developed for a variety 

of forest types, ownerships, and harvest methods 

through conversations with local forest management 

experts (Table 31). 

REFERENCES

Iverson, L.R.; Prasad, A.; Schwartz, M.W. 1999. 

Modeling potential future individual treespecies distributions in the eastern United 

States under a climate change scenario: a 

case study with Pinus virginiana. Ecological 

Modelling. 115(1): 77-93.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M. 2008. Estimating potential habitat for 

134 eastern US tree species under six climate 

scenarios. Forest Ecology and Management. 

254(3): 390-406.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M.P. 2011. Lessons learned while integrating 

habitat, dispersal, disturbance, and lifehistory traits into species habitat models under 

climate change. Ecosystems. 14(6): 1005-1020.

Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, 

M.P.; Rodewald, P.G. 2011. Modifying climate 

change habitat models using tree speciesspecific assessments of model uncertainty 

and life history factors. Forest Ecology and 

Management. 262(8): 1460-1472.
Appendix 4

214

American basswood 67 40 122.53 184 194 59 174 42

70 173 260 295 70 387 123

100 227 341 538 137 754 232

American elm 11 40 18 156 29 63 26 48

70 24 212 43 78 65 167

100 29 257 64 119 133 354

Balsam fir 805 40 778 97 746 -4 674 -13

70 1062 132 982 -8 533 -50

100 1114 138 996 -11 263 -76

Bigtooth aspen 84 40 91 108 115 26 93 1

70 135 160 190 41 141 4

100 191 226 301 58 272 43

Black ash 137 40 238 174 265 11 227 -5

70 250 182 278 12 177 -29

100 248 181 270 9 116 -53

Black cherry 7 40 16 229 21 34 17 8

70 28 400 41 47 38 35

100 43 617 80 86 139 222

Black spruce 506 40 663 131 575 -13 566 -15

70 651 129 536 -18 355 -45

100 642 127 447 -30 210 -67

Bur oak 57 40 104 180 114 10 100 -3

70 132 230 146 11 114 -14

100 153 266 174 14 148 -3

Green ash 7 40 18 251 23 29 20 9

70 22 303 28 27 26 18

100 23 320 31 33 37 60

Eastern white pine 108 40 366 337 441 21 380 4

70 681 628 820 20 675 -1

100 1036 955 1288 24 1047 1

Jack pine 118 40 95 80 147 55 135 43

70 98 83 146 50 106 8

100 99 84 137 38 64 -36

Northern red oak 85 40 166 196 179 8 156 -6

70 249 294 261 5 188 -25

100 328 388 336 2 254 -23

Northern white-cedar 110 40 181 165 204 13 187 3

70 192 175 212 11 147 -23

100 203 185 222 9 110 -46

Paper birch 663 40 1628 246 1398 -14 1210 -26

70 2558 386 2066 -19 904 -65

100 2638 398 1879 -29 422 -84

Table 29.—Projected aboveground biomass for 21 species assessed with the LANDIS-II model

Current Climate PCM B1 GFDL A1FI

Year 0 Biomass Biomass Change Biomass Change

biomass (grams Change (grams relative to (grams relative to

(grams per from per current per current

per square square year 0b square climate square climate

Species meter) Yeara meter) (%) meter) biomassc (%) meter) biomassc (%)

(Table 29 continued on next page)
Appendix 4

215

Quaking aspen 2515 40 2440 97 2372 -3 2114 -13

70 2198 87 2069 -6 1366 -38

100 1811 72 1651 -9 1080 -40

Red maple 163 40 255 157 353 39 323 27

70 287 177 464 61 592 106

100 343 211 679 98 1010 194

Red pine 169 40 410 243 517 26 439 7

70 649 385 814 26 500 -23

100 858 509 1096 28 535 -38

Sugar maple 252 40 242 96 249 300 244 1

70 204 81 213 4 215 5

100 150 60 174 1600 219 46

White oak 2 40 3 137 4 15 3 1

70 4 183 5 18 4 5

100 5 236 7 36 9 59

White spruce 129 40 359 278 294 18 272 -24

70 550 425 448 -19 236 -57

100 662 512 475 -28 182 -72

Yellow birch 27 40 67 243 80 20 68 3

70 97 352 117 22 81 -16

100 127 462 166 31 87 -31

a Year represents the number of years from the year 2000 (e.g., 40 = year 2040).

b Percentage change from Year 0 is calculated as the change in biomass from year 2000 (100% equals no net change). 

c Change relative to current climate biomass is calculated as the proportional change compared to the biomass under the Current 

Climate scenario for the same year. 

Table 29 (continued).

Current Climate PCM B1 GFDL A1FI

Year 0 Biomass Biomass Change Biomass Change

biomass (grams Change (grams relative to (grams relative to

(grams per from per current per current

per square square year 0b square climate square climate

Species meter) Yeara meter) (%) meter) biomassc (%) meter) biomassc (%)

Forest type Indicator species

Spruce-fir Include black spruce, balsam fir, white spruce, and northern white-cedar

Subtract sugar maple and jack pine

Oak association Include northern red oak, white oak, and northern pin oak

Subtract black spruce and white spruce

Jack pine Include jack pine and red pine

Aspen-birch Include quaking aspen, bigtooth aspen, and paper birch

Subtract sugar maple and balsam fir

Red pine-white pine Include red pine and white pine

Northern hardwoods Include sugar maple, black cherry, northern red oak, and American basswood

Subtract bigtooth aspen and quaking aspen

Table 30.—Classification rules for creating the maps based on LANDIS-II outputs (Chapter 5, Fig. 38)
Appendix 4

216

Proportion of Proportion of

landscape managed Rotation each stand Species

Prescription Landowner* each 5 years period (years) harvested planted

Aspen clearcut DNR 8% 62 95%

Counties 8% 63

USFS 6% 89

PIF 9% 56

Aspen clearcut/plant DNR 1% 357 95% white spruce

Counties 1% 357 white pine

USFS 1% 500 red pine

PIF 2% 250

Aspen clearcut PNIF PNIF 4% 139 70%

Black spruce clearcut DNR 4% 135 80%

Counties 6% 89

USFS 4% 114

PIF 6% 89

PNIF 5% 111

Jack pine clearcut DNR 7% 70 80% jack pine

Counties 7% 70 red pine

USFS 7% 70

PIF 13% 40

PNIF 8% 60

Northern hardwoods patch DNR 24% 21 75%

USFS 25% 20

PNIF 7% 75

Northern hardwoods clearcut Counties 4% 119 50%

Northern hardwoods shelterwood PNIF 3% 172 50%

PIF 7% 75

Oak shelterwood DNR 5% 111 25%

Counties 6% 89

USFS 4% 119

PIF 7% 75

PNIF 4% 119

Red pine clearcut DNR 4% 119 100% red pine

Counties 56% 89 white spruce

USFS 4% 119

PIF 4% 119

PNIF 5% 100

White pine clearcut DNR 6% 83 50% white pine

Counties 7% 71

USFS 6% 83

PIF 8% 63

PNIF 7% 71

Spruce fir clearcut DNR 4% 125 50%

Counties 7% 70

USFS 6% 79

PIF 6% 79

PNIF 5% 100

*Landowner categories are as follows: DNR = Minnesota Department of Natural Resources, USFS = U.S. Forest Service, PIF = 

private industrial forestland, PNIF = private nonindustrial forestland. 

Table 31.—Business-as-usual (BAU) forest management scenario used in the LANDIS-II model for this assessment
217

Appendix 5: Vulnerability and 

Confidence Determination

METHODS

To assess vulnerabilities to climate change for 

each natural community type, we elicited input 

from a panel of 23 experts from a variety of land 

management and research organizations across the 

assessment area (Table 32). We sought to create a 

team of panelists who would be able to contribute 

a diversity of subject area expertise, management 

history, and organizational perspectives. Most 

panelists had extensive knowledge about the 

ecology, management, and climate change impacts 

Name Organization

Cheryl Adams UPM-Kymmene

Kelly Barrett Chippewa National Forest

Leslie Brandt* U.S. Forest Service, Northern Research Station & Northern Institute of Applied Climate Science 

Anthony D’Amato University of Minnesota

Matthew Duveneck Portland State University

Suzanne Hagell Wisconsin Initiative on Climate Change Impacts

Stephen Handler* U.S. Forest Service, Northern Research Station & Northern Institute of Applied Climate Science

Louis Iverson U.S. Forest Service, Northern Research Station 

Rosemary Johnson Chippewa National Forest

Patty Johnson Superior National Forest

Randy Kolka U.S. Forest Service, Northern Research Station 

Jason Kuiken Chippewa National Forest

Mike Larson Minnesota Deparment of Natural Resources

Casey McQuiston Superior National Forest

Rebecca Montgomery University of Minnesota

Steve Olson Fond du Lac Forestry

Brian Palik U.S. Forest Service, Northern Research Station 

Emily Peters University of Minnesota & Boreal Forest and Community Resilience Project

Jack Rajala Rajala Companies

Peter Reich University of Minnesota & Boreal Forest and Community Resilience Project

Rob Scheller Portland State University

Clarence Turner Minnesota Department of Natural Resources & Minnesota Forest Resources Council

Mark White The Nature Conservancy

Kirk Wythers University of Minnesota & Boreal Forest and Community Resilience Project

*Workshop facilitators

Table 32.—Participants in the July 2012 expert panel workshop

on forests in northern Minnesota. This panel was 

assembled at an in-person workshop in Grand 

Rapids, Minnesota, in July 2012. Below, we describe 

the structured discussion process that the panel 

followed during the workshop and in subsequent 

conversations. 

Forest Systems Assessed

The authors of this assessment opted to use the 

Native Plant Community (NPC) approach to 

classifying and describing forest ecosystems within 
Appendix 5

218

the assessment area (Chapter 1). We decided that we 

had neither the necessary precision of information 

nor the time to consider the individual NPC Classes, 

and instead we focused on the broader NPC 

Systems. The six forested NPC Systems present in 

the assessment area were the focus of the expert 

panel workshop. 

For each NPC System, we extracted information 

related to the major system drivers, dominant 

species, and stressors that characterize that 

community from the Field Guide to the Native 

Plant Communities of Minnesota: the Laurentian 

Mixed Forest Province (Minnesota Department 

of Natural Resources 2003). The panel was asked 

to comment on and suggest modifications to the 

community descriptions, and those suggestions were 

incorporated into the descriptions. 

After the expert panel workshop, we decided to 

consider two additional managed forest systems 

for this vulnerability assessment: managed 

aspen and red pine systems. The rationale was to 

complement the NPC Systems approach and to 

more explicitly consider these management-driven 

forest communities within the assessment area, 

which don’t neatly fit the ecological community 

descriptions. We developed descriptions of 

drivers, dominant species, and stressors for these 

two systems based on published literature and 

conversations with a subset of the expert panel. 

Potential Impacts 

Potential impacts are the direct and indirect 

consequences of climate change on systems. Impacts 

are a function of a system’s exposure to climate 

change and its sensitivity to any changes. Impacts 

could be beneficial or harmful to a particular forest 

or ecosystem type. To examine potential impacts, 

the panel was given several sources of background 

information on past and future climate change in 

the region (summarized in Chapters 3 and 4) and 

projected impacts on dominant tree species and 

forest productivity (summarized in Chapter 5). 

The panel was directed to focus on impacts to each 

community type from the present through the end 

of the century, but more weight was given to the 

end-of-century period. The panel assessed impacts 

by considering a range of climate futures bracketed 

by two scenarios: Hadley A1FI and PCM B1 

(Box 8). Panelists were then led through a 

structured discussion process to consider this 

information for each forest community in the 

assessment. 

Box 8: A Note on Future Climate Scenarios Used in this Assessment

The Hadley A1FI model/emissions scenario 

combination was originally chosen as the “highend” scenario instead of GFDL A1FI. This scenario 

projects slightly higher temperatures and more 

summer precipitation than GFDL A1FI, but otherwise 

is fairly similar. We chose to replace the high-end 

scenario because the Hadley A1FI scenario produced 

extremely high results for northern Michigan, 

and the authors of the companion vulnerability 

assessment for that area were uncomfortable 

publishing an assessment with unreliable data. 

The Northern Institute of Applied Climate Science 

coordinated a discussion among all research teams 

conducting impact modeling for the Climate Change 

Response Framework and all groups decided it would 

be best to use GFDL A1FI as a high-end scenario 

instead. All results summarized in Chapter 6 were 

vetted with the expert panelists to ensure their 

vulnerability rankings were still consistent with GFDL 

projections. 
Appendix 5

219

Potential impacts on each community driver and 

stressor were summarized into a spreadsheet 

based on climate model projections, the published 

literature, and insights from the panelists. Impacts 

on drivers were considered positive or negative if 

they would alter system drivers in a way that would 

be more or less favorable for that community type. 

Impacts on stressors were considered negative 

if they increased the influence of that stressor or 

positive if they decreased the influence of that 

stressor on the community type. Panelists were also 

asked to consider the potential for climate change to 

facilitate new stressors in the assessment area over 

the next century. 

To assess potential impacts on dominant tree species, 

the panelists examined the results from Tree Atlas, 

LANDIS-II, and PnET-CN. They were asked to 

consider those results in addition to their knowledge 

of life-history traits and the ecology of those species. 

The panel evaluated how much the models agreed 

with each other between climate scenarios and 

across space and time. 

Finally, panelists were asked to consider the 

potential for interactions among anticipated climate 

trends, species impacts, and stressors. Input on these 

future ecosystem interactions relied primarily on 

the panelists’ expertise and judgment because there 

are not many examples of published literature on 

complex interactions, nor are future interactions 

accurately represented by ecosystem models. 

For each community type, panelists were each 

asked to identify which impacts they felt were most 

important to that system by using an individual 

worksheet (see example at the end of this appendix). 

Each panelist then determined an overall rating of 

potential impacts for each community type based on 

the summation of the impacts on drivers, stressors, 

and dominant species across a continuum from 

negative to positive.

Adaptive Capacity

Adaptive capacity is the ability of a species or 

ecosystem to accommodate or cope with potential 

climate change impacts with minimal disruption. 

Panelists discussed the adaptive capacity of each 

forest system based on their ecological knowledge 

and management experience with the community 

types in the assessment area. Adaptive capacity 

factors for each community type were delineated 

in a spreadsheet. Panelists were told to focus on 

community characteristics that would increase 

or decrease the adaptive capacity of that system. 

Factors that the panel considered included 

characteristics of dominant species within each 

community (e.g., dispersal ability, genetic diversity, 

range limits) and comprehensive community 

characteristics (e.g., functional and species diversity, 

tolerance to a variety of disturbances, distribution 

across the landscape). Rankings were based on a 

continuous spectrum, so a mid-range score would 

indicate strength in some areas and a deficit in 

others. The panelists were directed to base their 

considerations on the current condition of the system 

given past and current management regimes, with 

no consideration of potential adaptation actions that 

could take place in the future. 

Vulnerability

Vulnerability is the susceptibility of a system to the 

adverse effects of climate change. It is a function of 

its potential impacts and the adaptive capacity of the 

system. After extensive group discussion, panelists 

individually evaluated the potential impacts and 

adaptive capacity of each community type to arrive 

at a vulnerability rating. Participants were provided 

with individual worksheets and asked to list which 

impacts they felt were most important to that system 

in addition to the major factors that would contribute 

to the adaptive capacity of that system. 
Appendix 5

220

Panelists were directed to mark their rating in 

two-dimensional space on the individual worksheet 

and on a large group poster (Fig. 58a). This 

vulnerability figure required the participants to 

evaluate the degree of potential impacts related to 

climate change as well as the adaptive capacity of 

the system to tolerate those impacts (Swanston and 

Janowiak 2012). The group compared and discussed 

individual ratings, with the goal of coming to a 

group determination through consensus. In many 

cases, the group determination was at or near the 

centroid of all individual determinations. Sometimes 

the group determination deviated from the centroid 

because further discussion convinced some group 

members to alter their original response. The group 

vulnerability determination was placed into one 

of five categories (low, low-moderate, moderate, 

moderate-high, and high) based on the discussion 

and consensus within the group, as well as the 

placement of the group determination on the figure. 

For example, if a vulnerability determination was on 

the border between low and moderate and the group 

agreed that it did not completely fall into one or 

the other category, it would receive a low-moderate 

determination.

Confidence 

Panelists were also directed to give a confidence 

rating to each of their individual vulnerability 

determinations (Fig. 58b). Panelists were asked 

to evaluate the amount of evidence they felt was 

available to support their vulnerability determination 

and the level of agreement among the available 

evidence (Mastrandrea et al. 2010). Panelists 

evaluated confidence individually and as a group, in 

a similar fashion to the vulnerability determination. 

After the expert panel workshop, this structured 

discussion was repeated for the two managed 

forest systems (managed aspen and red pine) in 

a webinar and conference call with a subset of 

the expert panel. Members of this smaller group 

were asked to describe potential climate change 

impacts, interactions, and adaptive capacity for these 

two systems. They also placed their rankings for 

vulnerability and confidence on the same figures 

used in the in-person workshop. The summary 

information related to drivers, dominant species, 

stressors, and adaptive capacity was vetted with all 

of the expert panelists to ensure that this information 

Figure 58.—Figure used for (a) vulnerability determination by expert panelists, based on Swanston and Janowiak (2012) and 

(b) confidence rating among expert panelists, adapted from Mastrandrea et al. (2010).

(a) (b)
Appendix 5

221

was adequately described and that the vulnerability 

and confidence determinations reflected the 

perspective of the entire group.

Forest System Determinations

Determinations of vulnerability and confidence were 

made for eight forested systems in the assessment 

area. The vulnerability determinations described 

above, along with information and ideas put forward 

during the group discussions, were collected and 

interpreted in order to develop the vulnerability 

summary descriptions presented in Chapter 6.

Vulnerability Statements

Recurring themes and patterns that transcended 

individual forest systems were identified and 

developed into the vulnerability statements (in 

boldface) and supporting text in Chapter 6. The lead 

author developed the statements and supporting text 

based on workshop notes and literature pertinent to 

each statement. An initial confidence determination 

(evidence and agreement) was assigned based on 

the lead author’s interpretation of the amount of 

information available to support each statement and 

the extent to which the information agreed. Each 

statement and its supporting literature discussion 

were sent to the expert panel for review. 

VULNERABILITY AND 

CONFIDENCE FIGURES

For reference, figures of individual and group 

determinations for all eight forest systems 

considered in this assessment are displayed (Figs. 

59 through 66). In each figure, individual panelist 

votes are indicated with a small circle and the group 

determination is indicated with a large square. 

We do not intend for direct comparison between 

these figures because the axes represent subjective, 

qualitative scales. In some cases, the variance of 

vulnerability and confidence votes was due to a 

difference in expert opinion as well as the inherent 

difficulty of defining what is covered in the term 

“vulnerability.”

REFERENCES

Mastrandrea, M.D.; Field, C.B.; Stocker, T.F.; 

Edenhofer, O.; Ebi, K.L.; Frame, D.J.; Held, 

H.; Kriegler, E.; Mach, K.J.; Matschoss, 

P.R.; Plattner, G.-K.; Yohe, G.W.; Zwiers, 

F.W. 2010. Guidance note for lead authors 

of the IPCC Fifth Assessment Report on 

consistent treatment of uncertainties. Geneva, 

Switzerland: Intergovernmental Panel on Climate 

Change (IPCC). Available at www.ipcc.ch/

activities/activities.shtml. (Accessed 

November 6, 2013).

Minnesota Department of Natural Resources. 2003. 

Field guide to the native plant communities 

of Minnesota: the Laurentian Mixed Forest 

Province. St. Paul, MN: Ecological Land 

Classification Program, Minnesota County 

Biological Survey, and Natural Heritage and 

Nongame Research Program. 352 p.

Swanston, C.W.; Janowiak, M.K. 2012. Forest 

adaptation resources: climate change tools and 

approaches for land managers. Gen. Tech. Rep. 

NRS-87. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 121 p. Available at www.nrs.fs.fed.us/

pubs/40543. (Accessed November 5, 2013).
Appendix 5

222

Figure 59.—Fire-Dependent Forests vulnerability and confidence determinations. Circles indicate individual determinations by 

each panelist and squares indicate the group determination after consensus was reached.

Figure 60.—Mesic Hardwood Forests vulnerability and confidence determinations. Circles indicate individual determinations 

by each panelist and squares indicate the group determination after consensus was reached.
Appendix 5

223

Figure 61.—Floodplain Forests vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.

Figure 62.—Wet Forests vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.
Appendix 5

224

Figure 63.—Forested Rich Peatlands vulnerability and confidence determinations. Circles indicate individual determinations by 

each panelist and squares indicate the group determination after consensus was reached.

Figure 64.—Acid Peatlands vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.
Appendix 5

225

Figure 65.—Managed Aspen Forests vulnerability and confidence determinations. Circles indicate individual determinations. 

The group determination was reached through consensus with the remaining panelists.

Figure 66.—Managed Red Pine Forests vulnerability and confidence determinations. Circles indicate individual determinations. 

The group determination was reached through consensus with the remaining panelists.
Appendix 5

226

Example Vulnerability Determination Worksheet

Name: Ecosystem/Forest Type: 

How familiar are you with this ecosystem? (circle one)

What do you think are the greatest potential impacts to the ecosystem?

What factors do you think contribute most to the adaptive capacity of the ecosystem?

Medium

I do some management 

or research in this 

system, or have read 

a lot about it.

Low

I have some basic 

knowledge about this 

system and how it 

operates

High

I regularly do 

management or 

research in this system

(Continued on next page)
Appendix 5

227

Vulnerability Determination

Use the handout for the vulnerability determination 

process and the notes that you have taken to plot 

your assessment of vulnerability on the figure below.

Confidence Rating

Use the handout for the confidence rating process 

and the notes that you have taken to rate confidence 

using the figure below.

The ratings above are for the entire analysis area. Please note where you think potential impacts 

or adaptive capacity may vary substantially within the analysis area (e.g., forests in the eastern 

portion may be more prone to impact X).
228

Appendix 6. Contributors 

to Implications Chapter

We relied on input from several subject-area experts 

from a variety of organizations to summarize the 

management implications of climate change in 

Chapter 7 (Table 33).

Table 33.—Contributors to implications chapter

Name Organization Subject Area

Jad Daley Trust for Public Land Land acquisition

Mae Davenport University of Minnesota Forest-associated towns and cities

Marla Emery U.S. Forest Service, Northern Research Station Nontimber forest products

Dave Fehringer The Forestland Group, LLC Forest management operations & infrastructure

Chris Hoving Michigan Department of Natural Resources Wildlife

Lucinda Johnson Natural Resources Research Institute Water resources

Gary Johnson University of Minnesota Urban forests

David Neitzel Minnesota Department of Health Human health concerns

Adena Rissman University of Wisconsin-Madison Forest management operations

Chadwick Rittenhouse University of Connecticut Forest management operations

Robert Ziel Lake States Fire Science Consortium Fire and fuels
Printed on recycled paper

Handler, Stephen; Duveneck, Matthew J.; Iverson, Louis; Peters, Emily; Scheller, 

Robert M.; Wythers, Kirk R.; Brandt, Leslie; Butler, Patricia; Janowiak, Maria; 

Shannon, P. Danielle; Swanston, Chris; Barrett, Kelly; Kolka, Randy; McQuiston, 

Casey; Palik, Brian; Reich, Peter B.; Turner, Clarence; White, Mark; Adams, 

Cheryl; D’Amato, Anthony; Hagell, Suzanne; Johnson, Patricia; Johnson, 

Rosemary; Larson, Mike; Matthews, Stephen; Montgomery, Rebecca; Olson, 

Steve; Peters, Matthew; Prasad, Anantha; Rajala, Jack; Daley, Jad; Davenport, 

Mae; Emery, Marla R.; Fehringer, David; Hoving, Christopher L.; Johnson, Gary; 

Johnson, Lucinda; Neitzel, David; Rissman, Adena; Rittenhouse, Chadwick; 

Ziel, Robert. 2014. Minnesota forest ecosystem vulnerability assessment 

and synthesis: a report from the Northwoods Climate Change Response 

Framework project. Gen. Tech. Rep. NRS-133. Newtown Square, PA; U.S. 

Department of Agriculture, Forest Service, Northern Research Station. 228 p. 

Forests in northern Minnesota will be affected directly and indirectly by a changing 

climate over the next 100 years. This assessment evaluates the vulnerability of 

forest ecosystems in Minnesota’s Laurentian Mixed Forest Province to a range of 

future climates. Information on current forest conditions, observed climate trends, 

projected climate changes, and impacts to forest ecosystems was considered in 

order to draw conclusions on climate change vulnerability. Wet Forests, Forested 

Rich Peatlands, and Acid Peatlands were determined to be the most vulnerable to 

projected changes in climate, whereas Floodplain Forests, Fire-Dependent Forests, 

and Mesic Hardwood Forests were determined to be less vulnerable. Projected 

changes in climate and the associated ecosystem impacts and vulnerabilities 

will have important implications for economically valuable timber species, forestdependent wildlife and plants, recreation, and long-range planning.

Key words: climate change, vulnerability, adaptive capacity, forests, 

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

 expert elicitation, climate projections, impacts

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities 

on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, 

familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, 

or because all or part of an individual’s income is derived from any public assistance program. (Not 

all prohibited bases apply to all programs.) Persons with disabilities who require alternate 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 file a complaint of discrimination, write to USDA, 

Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call 

800-795-3272 (voice) or 202-720-6382 (TDD). USDA is an equal opportunity provider and employer.
Northern Research Station

www.nrs.fs.fed.us