Forest

Service

Michigan Forest Ecosystem 

Vulnerability Assessment and Synthesis:

A Report from the Northwoods Climate Change 

Response Framework Project

United States Department of Agriculture

Northern

Research Station March 2014

General Technical

Report NRS-129
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

March 2014 Fax: 740-368-0152

Manuscript received for publication August 2013

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

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

the eastern Upper Peninsula and northern Lower Peninsula of Michigan under a range of 

future climates. We synthesized and summarized information on the contemporary landscape, 

provided information on past climate trends, and described a range of projected future climates. 

This information was used to parameterize and run multiple vegetation impact models, which 

provided a range of potential vegetative responses to climate. Finally, we brought these results 

before a multidisciplinary panel of scientists and land managers familiar with Michigan 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 over the past century 

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

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

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

annual temperature of 2.2 to 8.1 °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 elm and white oak 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 nine forest communities in the assessment area, 

which were a combination of U.S. Department of Agriculture, Forest Service Forest Inventory and 

Analysis program forest types and Michigan Natural Features Inventory natural communities. 

The basic assessment was conducted through a formal elicitation process of 27 science and 

management experts from across the state, who considered vulnerability both in terms of 

potential impacts on a system and in terms of the system’s adaptive capacity. Upland spruce-fir, 

jack pine, lowland conifers, and red pine-white pine forest communities were determined to 

be the most vulnerable. Barrens and lowland/riparian hardwood communities were perceived 

as less vulnerable to projected changes in climate. These projected changes in climate and the 

associated impacts and vulnerabilities will have important implications for economically valuable 

timber species, forest-dependent wildlife and plants, recreation, and long-range planning. 

ABSTRACT

Cover Photo

Douglas Lake at the University of Michigan Biological Station. Photo by Stephen Handler, 

U.S. Forest Service.
Michigan 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, Amy Clark Eagle, Joshua G. Cohen, 

Rich Corner, Peter B. Reich, Tim Baker, Sophan Chhin, Eric Clark, David Fehringer, 

Jon Fosgitt, James Gries, Christine Hall, Kimberly R. Hall, Robert Heyd, 

Christopher L. Hoving, Ines Ibáñez, Don Kuhr, Stephen Matthews, 

Jennifer Muladore, Knute Nadelhoffer, David Neumann, Matthew Peters, 

Anantha Prasad, Matt Sands, Randy Swaty, Leiloni Wonch, Jad Daley, 

Mae Davenport, Marla R. Emery, 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 Ph.D. candidate at Portland 

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

duveneck@pdx.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 forest ecologist for the University of 

Minnesota’s Institute on the Environment, 1954 Buford 

Avenue, St. Paul, MN 55108, ebpeters@umn.edu.

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.

AMY CLARK EAGLE is the Biodiversity and Conservation 

program leader with the Michigan Department of Natural 

Resources, P.O. Box 30028, Lansing, MI 48909, 

eaglea@michigan.gov.

JOSHUA G. COHEN is the lead ecologist with Michigan 

Natural Features Inventory, 530 West Allegan Street, 

Lansing, MI 48933, CohenJ@michigan.gov.

RICH CORNER is the Watershed, Soils, and Botany program 

manager with the Huron-Manistee National Forests, 

1755 South Mitchell Street, Cadillac, MI 49601, 

racorner@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.

TIM BAKER is the forest silviculturist with the Hiawatha 

National Forest, 820 Rains Drive, Gladstone, MI 49837, 

tbaker@fs.fed.us.

SOPHAN CHHIN is an assistant professor at Michigan State 

University, 480 Wilson Road, Room 126, East Lansing, 

MI 48824-1222, chhin@msu.edu.

ERIC CLARK is a biologist with the Sault Ste. Marie Tribe of 

Chippewa Indians, 523 Ashmun Street, Sault Ste. Marie, 

MI 49783, eclark@saulttribe.net.

DAVID FEHRINGER is the Great Lakes assistant regional 

director with The Forestland Group, LLC, 33687 Lake Gerald 

Road, Toivola, MI 49965, dave@forestlandgroup.com.

JON FOSGITT is a forest management specialist with 

Compass Land Consultants, Inc., E5539 Woodland Avenue, 

AuTrain, MI 49806, jon@compasslandconsultants.com.

JAMES GRIES is the Soils, Watershed, and Ecology program 

manager with the Hiawatha National Forest, 820 Rains 

Drive, Gladstone, MI 49837, jgries@fs.fed.us.

CHRISTINE HALL is the director of Land Resources with 

The Nature Conservancy, 101 South Front Street, Suite 105, 

Marquette, MI 49855, chall@tnc.org.

KIMBERLY R. HALL is the Great Lakes climate change 

ecologist with The Nature Conservancy, 101 East Grand 

River, Lansing, MI 48906, kimberly_hall@tnc.org.

ROBERT HEYD is a Forest Pest Management program 

manager with the Michigan Department of Natural 

Resources, 1990 US 41 South, Marquette, MI 49855, 

heydr@michigan.gov.

CHRISTOPHER L. HOVING is an adaptation specialist with the 

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

Lansing, MI 48909, hovingc@michigan.gov.

INES IBÁÑEZ is an assistant professor at the University of 

Michigan, 440 Church Street, DANA Building, Ann Arbor, 

MI 48109, iibanez@umich.edu. 
DON KUHR is a regional timber management specialist with 

the Michigan Department of Natural Resources, 5100 M-28, 

Newberry, MI 49868, kuhrd@michigan.gov.

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.

JENNIFER MULADORE is an ecologist with Huron Pines, 

4241 Old US 27 South, Suite 2, Gaylord, MI 49735, 

Jennifer@huronpines.org.

KNUTE NADELHOFFER is a professor with the University 

of Michigan and director of the University of Michigan 

Biological Station, 830 North University, Ann Arbor, 

MI 48109, knute@umich.edu.

DAVID NEUMANN is a silviculturist with the Michigan 

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

MI 48909, neumannd@michigan.gov.

MATTHEW PETERS is a geographic information systems 

technician with the U.S. Forest Service, Northern 

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

matthewppeters@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.

MATT SANDS is a silviculturist with the Huron-Manistee 

National Forests, 650 North Michigan Avenue, Baldwin, 

MI 49304, msands@fs.fed.us.

RANDY SWATY is the LANDFIRE ecologist with The Nature 

Conservancy, 101 South Front Street, Suite 105, Marquette, 

MI 49855, rswaty@tnc.org.

LEILONI WONCH is a biologist with the Sault Ste. Marie Tribe 

of Chippewa Indians, 523 Ashmun Street, Sault Ste. Marie, 

MI 49783, lwonch@saulttribe.net.

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.

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 North 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 is the program coordinator for the Lake States 

Fire Science Consortium, 1680 Madison Avenue, Wooster, 

OH 44691, ziel.4@osu.edu. 
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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 Michigan, 

in addition to people who study, recreate, and live 

in these forests. As new scientific information 

arises, we will develop future versions to reflect 

that accumulated knowledge and understanding. 

Most importantly, this assessment does not make 

recommendations about how this information should 

be used. 

The scope of the assessment is terrestrial forested 

ecosystems, with a particular focus on tree 

species. 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 R. 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. Amy Clark Eagle, Joshua Cohen, and 

Rich Corner provided substantial input throughout 

the document. Tim Baker, Sophan Chhin, Eric Clark, 

David Fehringer, Jon Fosgitt, James Gries, Christine 

Hall, Kimberly R. Hall, Robert Heyd, Christopher L. 

Hoving, Ines Ibáñez, Don Kuhr, Stephen Matthews, 

Jennifer Muladore, Knute Nadelhoffer, David 

Neumann, Matthew Peters, Anantha Prasad, Matt 

Sands, Randy Swaty, Leiloni Wonch, Jad Daley, 

Mae Davenport, Marla R. Emery, 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. David 

Maercklein (Hiawatha National Forest) helped 

determine the overall structure and content of the 

assessment. Andrew Burton, Tara Bal, and Andrew 

Storer (Michigan Technological University) all 

Preface
provided summaries of ongoing research. Scott 

Robbins (Michigan Forest Products Council) 

provided data on certified forest land in Michigan.

We would especially like to thank Dan Kashian 

(Wayne State University), Jessica Miesel (Michigan 

State University), and Doug Pearsall (The Nature 

Conservancy), 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 3: Observed Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes . . . . . . . . . . . . . . . . . 68

Chapter 5: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Chapter 6: Ecosystem Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Chapter 7: Management Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Appendix 1: Species Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

Appendix 2: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Appendix 3: Additional Future Climate Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

Appendix 4: Supplementary Model Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Appendix 5: Vulnerability and Confidence Determination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

Appendix 6: Contributors to Implications Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

Contents
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This assessment evaluates key vulnerabilities for 

forest ecosystems in northern Michigan across a 

range of future climate scenarios. This assessment 

was completed as part of the Northwoods 

Climate Change Response Framework project, 

a collaborative approach among researchers, 

managers, and landowners to incorporate climate 

change considerations into forest management. 

The assessment summarizes current conditions and 

key stressors and identifies past and projected trends 

in climate. This information is then incorporated 

into model projections of future forest change. These 

projections, along with published research, local 

knowledge, and expertise, are used to identify the 

factors that contribute to the vulnerability of major 

forest systems within the assessment area during 

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 major forest communities 

across Albert’s Ecological Sections VII and VIII 

and summarizes current threats and management 

trends. This information lays the foundation for 

understanding how shifts in climate may contribute 

to changes in forests, and how climate may interact 

with other stressors on the landscape.

Main Points

● More than 70 percent of the forest land in 

Michigan occurs within the assessment area, most 

of which is owned by private landowners. 

● Major stressors and threats to forests in the region 

include:

• Fragmentation and land-use change

• Fire regime shifts 

• Nonnative species invasion

• Forest pests and disease

• Overbrowsing by deer

● Management practices over the past century have 

tended to favor early successional stages of many 

forest types across the landscape and reduce 

species diversity and structural complexity. 

● A major ecological transition zone for both 

terrestrial and aquatic systems occurs between 

the northern and southern halves of Michigan’s 

Lower Peninsula.

● The forest products industry is a major 

contributor to the region’s economy.

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 forest 

communities. This chapter also describes the climate 

data used in this assessment.

Executive Summary
Executive Summary



Main Points

● Temperatures have been increasing at a global 

scale and across the United States during 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 and spring temperatures showing the most 

rapid warming. 

● 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. 

● Annual snowfall has been increasing slightly 

across northern Michigan, and the number of 

large winter storms has increased. 

● Climate change has also been indicated by trends 

in lake ice, growing season length, and wildlife 

range shifts.

CHAPTER 4: PROJECTED CHANGES 

IN CLIMATE, 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 during 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 than in the past. 

● Snowfall is projected to decline across the 

assessment area by the end of the 21st century, 

with more winter precipitation falling as rain. 

Large cedar in a lowland conifer forest. Photo by Bradford 

Slaughter, Michigan Natural Features Inventory, used with 

permission.
Executive Summary

● 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 northern Michigan, 

drawing on information from a coordinated series 

of model simulations and published research. 

Main Points

● Boreal species such as quaking aspen, paper 

birch, tamarack, jack pine, and black spruce are 

projected to experience reduced suitable habitat 

and biomass across the assessment area.

● Species with ranges that extend to the south such 

as American basswood, black cherry, and white 

oak may experience increased suitable habitat and 

biomass across the assessment area.

● Many common species in northern Michigan 

may decline under the hotter, drier future climate 

scenario.

● Forest productivity will be influenced by a 

combination of factors such as carbon dioxide 

fertilization, water and nutrient availability, local 

disturbances, 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 vulnerability of major 

forest systems in the assessment area to climate 

change, with an emphasis 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. 

Detailed vulnerability determinations are also 

provided for nine 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 acreage, health, or productivity. 

Forest system Vulnerability Evidence Agreement

Upland spruce-fir High Medium-High Medium-High

Jack pine High-Moderate Medium Medium-High

Red pine-white pine High-Moderate Limited-Medium Medium

Lowland conifers High-Moderate Medium Medium

Aspen-birch Moderate Medium Medium

Northern hardwoods Moderate Medium Medium

Lowland-riparian hardwoods Moderate Medium Low-Medium

Oak associations Low-Moderate Medium Medium

Barrens Low-Moderate Limited-Medium Medium

Table 1.—Vulnerability determinations by forest system
Executive Summary



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.

● 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 may 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 suitable habitat and 

biomass will increase for many temperate species 

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.
Executive Summary

● Forest productivity will increase across the 

assessment area (medium evidence, medium 

agreement). Some 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. 

Adaptive Capacity Factors

● Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies 

have consistently shown that more-diverse 

systems are more resilient to disturbance, and 

low-diversity systems 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 particularly difficult for species and 

systems with narrow habitat requirements. 

● Systems that are more tolerant of disturbance 

have less risk of declining on the landscape 

(medium evidence, high agreement). Basic 

ecological theory and other evidence support 

the idea that systems 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 Michigan. This chapter does 

not make recommendations as to how management 

should be adjusted to cope with these impacts, 

because impacts and responses will vary across 

forest types, 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. 

● Information on anticipated climate change 

impacts may allow the forest products industry, 

recreation, and other sectors to adapt and 

capitalize on potential new opportunities.

A MODIS satelite image of the Great Lakes on a cloudless 

summer day. Image by Jeff Schmaltz, NASA.


Introduction

CONTEXT

This assessment is part of a regional effort across 

the Northwoods region of Minnesota, Wisconsin, 

and Michigan called the Northwoods Climate 

Change Response Framework (Framework; www.

forestadaptation.org). The Framework project 

was initiated in 2009 in northern Wisconsin 

with the overarching goal of helping managers 

incorporate climate change considerations into 

forest management. To meet the challenges brought 

about by climate change, a team of federal and state 

land management agencies, private forest owners, 

conservation organizations, and others have come 

together to accomplish three objectives: 

1. Provide a forum for people working across the 

Northwoods to effectively and efficiently share 

experiences and lessons learned. 

2. Develop new user-friendly information and tools 

to help land managers factor climate change 

considerations into decisionmaking. 

3. Support efforts to implement actions for 

addressing climate change impacts in the 

Northwoods. 

The Framework process is designed to work at 

multiple scales. The Northwoods Framework is 

coordinated across the region, but activities are 

generally conducted at the state level to allow for 

greater specificity. Therefore, this assessment will 

focus on the eastern Upper Peninsula and northern 

Lower Peninsula of Michigan and will serve as a 

companion for similar assessments completed in 

the rest of the Northwoods. 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 the 

assessment area. Our primary focus is on 

major forest vegetation communities. Several 

independent efforts related to climate change, 

natural communities, and human well-being are 

also occurring in the state. This assessment should 

complement similar products created for Michigan 

and the region, and the Framework project will 

attempt to integrate corresponding information as 

well.
Introduction

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) 

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 Michigan. Although it was 

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

and foremost a scientific document that represents 

the views of the authors.

SCOPE AND GOALS

The primary goal of this assessment is to summarize 

potential changes to the forest ecosystems of 

northern Michigan under a range of future climate 

scenarios and determine the vulnerability of forest 

communities to these changes over 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 16.6 million acres 

throughout Michigan’s eastern Upper Peninsula and 

northern Lower Peninsula (Fig. 1). The assessment 

area boundaries are defined by Ecological Sections 

VII and VIII in Michigan (northern Lower 

Peninsula and eastern Upper Peninsula), as defined 

by Albert (1995). More generally, this area falls 

within Ecological Province 212 (Mixed Laurentian 

Forest) of the National Hierarchical Framework 

of Ecological Units (Bailey 1995, McNab et al. 

2007), which is the overall focus of the Northwoods 

Framework. We used county-level information that 

most closely represented the assessment area when 

eco-regional data were not available, limiting our 

selections to the 40 counties that are most analogous 

to the area within Ecological Sections VII and 

VIII (Box 1). The western Upper Peninsula was 

not included in this assessment because that area 

is being described in combination with northern 

Wisconsin in a companion vulnerability assessment 

under the Northwoods Framework. 

This assessment area covers 70.2 percent of 

the forested land within Michigan (U.S. Forest 

Service 2011). Within this landscape, major land 

ownerships include the Hiawatha National Forest 

(approx. 880,000 acres), Huron-Manistee National 

Forests (approx. 980,000 acres), and the Michigan 

Department of Natural Resources (approx. 3,260,000 

acres in the assessment area) (U.S. Forest Service 

Figure 1.—Ecological Sections VII and VIII in Michigan 

(green shading), and the 40 counties that were used to 

approximate the assessment area when county-level data 

were required. Modified from Albert (1995).
Introduction

Box 1: Counties Used to Represent 

 the Assessment Area 

Alcona

Alpena

Antrim

Arenac

Benzie

Charlevoix

Cheboygan

Clare

Crawford

Emmet

Gladwin

Grand Traverse

Iosco

Isabella

Kalkaska

Lake

Leelanau

Manistee

Mason

Mecosta

Missaukee

Montcalm

Montmorency

Muskegon

Newaygo

Oceana

Ogemaw

Osceola

Oscoda

Otsego

Presque Isle

Roscommon

Wexford

Lower Peninsula: Ecological Section VII

Alger 

Chippewa 

Delta 

Luce 

Upper Peninsula: Ecological Section VIII 

Mackinac 

Menominee 

Schoolcraft 

2011). Supplementary information specific to these 

landowners was used when available and relevant to 

the broader landscape. This assessment synthesizes 

information covering all of northeastern Michigan in 

recognition of the area’s complex 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 

Michigan.

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 Michigan, summarized 

from The Nature Conservancy’s interactive 

ClimateWizard database and published literature. 

This chapter also discusses 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 Michigan and the Midwest region. 

Chapter 5: Future Climate Change Impacts 

on Forests summarizes impact model results that 

were prepared for this assessment. Three modeling 

approaches were used to model climate change 

impacts on forests: a species distribution model 

(Climate Change Tree Atlas), a forest simulation 

model (LANDIS-II), and a biogeochemical model 

(PnET-CN). This chapter also includes a 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 assessment area and 

provides detailed vulnerability determinations for 

nine major forest communities.

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 Michigan results 

from a variety of interacting factors, including 

physical, ecological, and socioeconomic conditions. 

This chapter provides a brief introduction to 

northern Michigan in general and the forest 

ecosystems within the assessment area 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 

Albert’s description of Regional Landscape 

Ecosystems in Michigan, Minnesota, and Wisconsin 

(Albert 1995) unless otherwise noted. Ecological 

Section VII (Northern Lacustrine-Influenced Lower 

Michigan, Michigan’s northern Lower Peninsula) 

and Ecological Section VIII (Northern LacustrineInfluenced Upper Michigan and Wisconsin, 

Michigan’s eastern Upper Peninsula) define the 

boundary for this assessment (Fig. 2). Ecological 

sections have distinctive natural conditions based on 

differences in climate, geology, glacial landforms, 

and soils. The following descriptions summarize 

information for Ecological Sections VII and VIII. 

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. 

The proximity of the Great Lakes is a fundamental 

factor that shapes the climate of the assessment area. 

Albert (1995) describes the profound lake influence 

as generally moderating temperatures compared to 

areas of similar latitude in the neighboring states 

of Minnesota and Wisconsin. Northern Michigan 

is generally cooler in the summer and warmer in 

the winter than would be expected at its latitude, 

although this trend depends on proximity to the 

surrounding Great Lakes. On average, the eastern 

Upper Peninsula is colder than the northern Lower 

Peninsula across all seasons, although the interior 

Figure 2.—Assessment area, defined by Ecological Sections 

VII and VIII in Michigan. Modified from Albert (1995).
Chapter 1: The Contemporary Landscape

10

northern Lower Peninsula experiences shorter 

growing seasons and more continental winters than 

the rest of the state. Areas along the Great Lakes 

shorelines are generally warmer than interior areas. 

The growing season ranges from 70 days toward the 

interior of the Lower Peninsula to 160 days along 

the Lake Michigan coastline in the Upper Peninsula. 

Extreme minimum temperature ranges from 

-48.5 °F (-44.7 °C) toward the interior of the Lower 

Peninsula to -26 °F (-32.2 °C) along the Lake Huron 

coast (Albert 1995). 

The assessment area also receives substantial lakeeffect snow during the wintertime along the Great 

Lakes coastlines, which means that precipitation is 

generally more evenly distributed throughout the 

year when compared to neighboring states. Winter 

and spring each account for roughly 20 percent 

of annual precipitation, and summer and fall each 

contribute roughly 30 percent (ClimateWizard 

2012). Average annual precipitation ranges from 28 

to 34 inches throughout the assessment area, with a 

gradient of wetter to drier from the southwest to the 

northeast across the Lower Peninsula (Albert 1995, 

ClimateWizard 2012). Snowfall averages between 

40 inches per year along the eastern edge of the 

Lower Peninsula and 140 inches per year along the 

Upper Peninsula’s Lake Superior coastline (Albert 

1995). 

Geology and Landform 

In Ecological Section VII, bedrock geology of the 

assessment area includes limestone, shale, dolomite, 

sandstone, and gypsum, along with occasional 

coal deposits. Ecological Section VIII is underlain 

with sandstone along the Lake Superior coast and 

sandstone and dolomite inland. Bedrock is exposed 

as lakeshore cliffs, bedrock lakeshores, and cobble 

shores along the shores of the Great Lakes. Inland 

bedrock systems within the assessment area include 

alvar and bedrock glades. County-level geological 

summaries are available for detailed information 

across the state (Apple and Reeves 2007). A map of 

bedrock geology for Michigan is available through 

the Michigan Department of Environmental Quality 

(Michigan Department of Environmental Quality 

2013). 

Glacial activity has shaped the soils and terrain 

of northern Michigan. The last glacier receded 

approximately 13,000 years ago from the Lower 

Peninsula, and 10,000 years ago from the Upper 

Peninsula (Millar 1940). Glaciers were active for 

thousands of years during the most recent Ice Age, 

so the terrain of Michigan reflects a complicated 

pattern of glacial advance and retreat. Till plains, 

moraines, and outwash plains occur throughout the 

assessment area, and glacial lake plains exist along 

the Great Lakes shorelines (Jerome 2006). 

Average Mean Mean maximum Mean minimum

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

Annual 31.6 43.2 53.5 33.0

Winter (Dec. - Feb.) 5.5 20.5 28.9 12.1

Spring (Mar. - May) 7.2 41.0 52.2 29.8

Summer (June - Aug.) 9.7 64.9 76.8 52.9

Fall (Sept. - Nov.) 9.2 46.3 55.7 36.8

Table 2.—Average climate information for the assessment area, 1971 through 2000 (ClimateWizard 2012)
Chapter 1: The Contemporary Landscape

11

Hydrology 

Michigan is bounded by four Great Lakes, with 

more than 11,000 inland lakes and 36,000 miles of 

rivers and streams. Its proximity to Lake Superior, 

Lake Michigan, and Lake Huron shapes the climate 

regimes and seasonal weather patterns within the 

assessment area, as mentioned above. The National 

Land Cover Database (NLCD) categorizes more 

than 26 percent of the assessment area as wetland 

and almost 4 percent as open water (U.S. Geological 

Survey 2011a).

Chase and others (1991) summarize the hydrologic 

regime across the entire state. Precipitation averages 

1.5 to 2.0 inches per month from December to 

March and 3.0 to 4.0 inches per month from May 

to September. Data from 1971 through 2000 for the 

assessment area confirm this pattern (Table 2) 

(ClimateWizard 2012). Historically, surface 

water supplies have been continually replenished 

throughout the year. Humidity is generally moderate, 

so evaporation is slow. Severe droughts have been 

infrequent, as have severe floods. Floods usually 

occur in late winter or early spring with warming 

temperatures and rainfall on frozen or saturated 

ground. 

Within the assessment area, the largest watersheds 

are the Escanaba, Manistique, Tahquamenon, 

Cheboygan, Au Sable, Thunder Bay, Manistee, 

Muskegon, and Tittabawasee Rivers (Clark 1999). 

According to U.S. Geological Survey streamflow 

stations on major rivers in the assessment area, peak 

surface flow typically arrives in a major pulse during 

April and May, with flows substantially reduced 

from June through August (U.S. Geological Survey 

2011b). August typically has the lowest streamflow 

during the growing season. 

Rivers and streams in Michigan have been 

categorized based on catchment area, July mean 

water temperature, and diagnostic fish species (Zorn 

et al. 2008). Nearly all of the streams and rivers 

in the assessment area are cold, cold transitional, 

or warm transitional, which means they have an 

average July water temperature less than 70 °F 

(21.0 °C) (Fig. 3). At the southern boundary of the 

assessment there is a relatively abrupt change to 

warm streams and rivers. 

Soils 

The combination of underlying bedrock and glacial 

activity has resulted in clearly different soils 

between the Upper and Lower Peninsulas. In the 

northern Lower Peninsula, soils are generally deep 

and sandy, with sandy end moraines, sandy glacial 

lake plains, and sandy ground moraines. Soils in 

this section are mostly well drained, except along 

streams and rivers. Glacial drift can be 1,000 feet 

thick, with large hills and rolling topography (Albert 

1995, Apple and Reeves 2007). In this half of the 

assessment area, bedrock is exposed only along the 

shorelines or in close proximity to the shore. 

In the eastern Upper Peninsula, soils are sands 

or clays within the lake plains and are generally 

wetter, and bedrock is closer to the surface. Lakebottom sand or clay deposits occur in combination 

with organic soils in the eastern half of the Upper 

Peninsula (Albert 1995, Apple and Reeves 2007). 

Along the lake plains, forested dune fields are 

commonly interspersed with bogs and wetlands 

(Arbogast et al. 2002). 

Fine-scale information on soil types is available 

in Albert’s descriptions of Regional Landscape 

Ecosystems (Albert 1995), and the USDA Natural 

Resources Conservation Service Web Soil Survey 

portal (http://websoilsurvey.nrcs.usda.gov/app/

HomePage.htm).
Chapter 1: The Contemporary Landscape

12

Figure 3.—River classification map based on catchment area, July mean water temperature, and diagnostic fish species. 

Cold = July mean water temperature is less than 63.5 °F (17.5 °C), Cold-transitional = July mean water temperature is between 

63.5 °F (17.5 °C) and 67 °F (19.5 °C), Warm-transitional = July mean water temperature is between 67 °F (19.5 °C) and 70 °F 

(21.0 °C), Warm = July mean water temperature is greater than 70 °F (21.0 °C). Stream = Segment catchment area is less than 

80 square miles, Small river = Segment catchment area is between 80 and 300 square miles, Large river = Segment catchment 

area is greater than 300 square miles. Figure from Zorn et al. (2008) and used with the permission of the authors.
Chapter 1: The Contemporary Landscape

13

Land Cover

This assessment covers 16.6 million acres 

throughout the eastern Upper Peninsula and northern 

Lower Peninsula of Michigan (Fig. 2). According 

to the NLCD, wetlands account for 26.3 percent 

of the overall land cover in the assessment area 

(Table 3) (U.S. Geological Survey 2011a). Much 

of the terrain in the assessment area is occupied by 

peatlands and lowland forests, which are categorized 

as wetland in the NLCD. According to data from the 

U.S. Forest Service Forest Inventory and Analysis 

(FIA) program, forest land covers 70.3 percent 

of the assessment area (11.6 million acres) (U.S. 

Forest Service 2011). The FIA estimate of forest 

land is higher than that of the NLCD because FIA 

captures many of the forested wetland and planted 

forest stands that the NLCD may have classified as 

“wetland” or “planted.”

Figure 4 presents a map of land cover for Michigan, 

based on a classification system different from the 

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

gradation from forest to nonforest cover that occurs 

along the southern boundary of the assessment area. 

The original land cover in Michigan has been 

estimated from General Land Office surveys that 

occurred from 1815 to 1860 and from notes of early 

European settlers (Comer et al. 1995). Estimates 

are that approximately 95 percent of Michigan’s 

vegetation was forest (Dickmann and Leefers 2003, 

Sparhawk and Brush 1929). Bailey (1995) defines 

the Laurentian Mixed Forest Province (Province 

212) as a transitional zone between boreal zones 

to the north and broadleaf deciduous zones to 

the south. Albert (1995) offers a more complete 

summary of presettlement vegetation throughout the 

assessment area. Tables 20 through 22 in Appendix 

1 list common and scientific names of plant, fauna, 

and other species mentioned in this assessment. 

Northern hardwoods were common on finer-textured 

soils common to moraines and drumlin fields. 

Forests of white pine and red pine were located on 

sandier soils in narrow outwash channels and on 

moraines at the edges of outwash plains, where fires 

were relatively common but less frequent or severe 

than on outwash plains. Jack pine and northern 

pin oak dominated sandier soils on flat, droughty 

outwash plains, particularly in the northern Lower 

Peninsula. Northern grasslands were also present in 

areas associated with jack pine barrens. In southern 

portions of the northern Lower Peninsula, forests 

transitioned to oak-pine barrens and dry sand prairie 

in outwash areas. Conifer and hardwood-conifer 

swamps covered large portions of glacial lake plains, 

but also occurred along drainages throughout the 

section. Peatlands also existed in sandy lake plains. 

On clay lake plains, forests were a diverse mix 

of conifer and hardwood species, including white 

spruce, balsam fir, white pine, eastern hemlock, 

quaking aspen, balsam poplar, and red maple. 

Current estimates are that 53 percent of Michigan 

is forested (Price 2010). Data from FIA inventories 

indicate that following a decline in forest cover 

from 1955 to 1980, forest cover has been slowly 

increasing within the assessment area (Pugh et al. 

2012). The trend has continued to the most recent 

FIA inventories. Forest land in the eastern Upper 

Peninsula increased 2.2 percent from 2004 to 2009, 

while the percentage of forest land in the northern 

Lower Peninsula remained nearly constant (Pugh et 

al. 2012). Alger County has the largest percentage 

Table 3.—Land cover classifications within the 

assessment area (U.S. Geological Survey 2011a)

Land cover class Cover (%)

Forest 44.3

Wetlands 26.3

Planted/cultivated 10.3

Herbaceous 6.8

Developed 6.0

Water 3.6

Shrubland 2.1

Barren 0.5
Chapter 1: The Contemporary Landscape

14

of forest area at 88 percent, and Isabella County has 

only 6 percent forest land (Headwaters Economics 

2011). Counties with smaller percentages of forest 

land are located in the agricultural areas along the 

southern edge and the northwestern quarter of the 

Lower Peninsula.

Figure 4.—Land cover in Michigan based on the U.S. Geological Survey National Land Cover Database (2006).

Although forest land is the primary land cover 

type in the assessment area, much of this forest 

land exists as fragmented edges or patches (Pugh 

et al. 2009) (Fig. 5). Very little interior forest 

(minimum 5.6 acres or at least 200 feet from a forest 

edge) remains in the northern Lower Peninsula, 
Chapter 1: The Contemporary Landscape

15

Figure 5.—Forest fragmentation in Michigan, from Pugh et al. (2009). In that assessment 30-meter (98-foot) pixels were 

classified as “interior forest” if they had a continuous forest canopy and were part of a minimum of 5.6 acres or at least 

200 feet from the nearest forest edge.
Chapter 1: The Contemporary Landscape

16

and the remaining interior forest in the eastern 

Upper Peninsula is mostly within the Hiawatha 

National Forest and state forest boundaries. 

This fragmentation is due to a variety of factors, 

including agriculture, urban development, and forest 

management practices. 

Forest Communities 

For the purposes of this assessment, we rely on a 

combination of data sources to categorize forest 

communities in northern Michigan. The resulting 

forest systems are a combination of FIA forest-type 

groups and natural communities described by the 

Michigan Natural Features Inventory (MNFI; 

Table 4). For complete descriptions of the MNFI 

natural communities, including associated 

landforms, soil types, disturbance regimes, and 

common species, see Natural Communities of 

Michigan: Classification and Description (Kost et 

al. 2007) and related natural community abstracts. 

Box 2 provides more information on FIA forest-type 

groups and MNFI natural communities. 

Other forested natural communities present in 

northern Michigan but not considered explicitly 

in this assessment include muskeg communities 

and wooded dune and swale complexes. These 

communities and others are described in detail in 

Natural Communities of Michigan: Classification 

and Description (Kost et al. 2007) and the associated 

MNFI community abstracts. 

White pine and red pine forest. Photo by Joshua Cohen, Michigan Natural Features Inventory, used with permission.
Chapter 1: The Contemporary Landscape

17

Forest system Major drivers Characteristic species*

Related natural 

communities or 

associations

Upland 

spruce-fir

thin nutrient-poor soils or glacial lake plains, 

high snowfall areas with short growing seasons, 

moderated climate in lake-effect areas, 

catastrophic disturbances from fire, wind, and 

pests

balsam fir, white spruce, eastern white 

pine, red maple, northern white-cedar, 

paper birch, quaking aspen, eastern 

hemlock

Boreal forest

Jack pine 

(including 

pine-oak)

coarse-textured soils, upland areas, droughttolerant, fire-return intervals 50 to 250 years, 

requires scarification or fire for regeneration, 

favored by cold temperatures

jack pine, red pine, eastern white pine, 

red maple, northern red oak, northern 

pin oak, black oak

Dry northern 

forest

Red pinewhite pinesandy to dry-mesic soils, limited by high summer 

temperatures, dependent on disturbance for 

regeneration, red pine regeneration primarily 

through planting, favored by drought, fire-return 

intervals 50 to 250 years

red pine, eastern white pine, jack 

pine, red maple, black cherry, bigtooth 

aspen, black oak, eastern hemlock, 

northern red oak

Dry northern 

forest

Dry-mesic 

northern forest

Lowland 

conifers

peat or mineral soils, low landscape positions, 

saturated throughout growing season, 

groundwater seepage, windthrow events, standreplacing fire on long cycles, limited by drought

northern white-cedar, black spruce, 

tamarack, balsam fir, paper birch, 

quaking aspen, eastern hemlock, 

white spruce

Poor conifer 

swamp

Rich conifer 

swamp

Hardwoodconifer swamp

Aspen-birch gradient of soil types and landforms, frequent 

disturbance or management, limited by warm 

temperatures and moisture stress

quaking aspen, bigtooth aspen, red 

maple, northern white-cedar, paper 

birch, balsam fir, balsam poplar

Aspen 

association

Northern 

hardwoods

mesic soils or deep impermeable layers, consistent 

moisture and nutrients, gap-phase disturbances 

with stand-replacing events every 400 to 2,000 

years

sugar maple, red maple, American 

beech, American basswood, eastern 

hemlock, northern red oak, white ash, 

black cherry, northern white-cedar, 

yellow birch, balsam fir, eastern white 

pine, white spruce

Mesic northern 

forest

Lowlandriparian 

hardwoods

alluvial soils or impermeable clay lenses, nutrientrich soils, seasonally or annually inundated or 

saturated, connectivity to river or water table, tipup mounds and periodic dry conditions important 

for regeneration

red maple, northern white-cedar, silver 

maple, black ash, green ash, quaking 

aspen, balsam fir, paper birch, eastern 

hemlock, yellow birch, American elm, 

balsam poplar, American basswood, 

sycamore, swamp white oak, hackberry, 

black willow, white ash, eastern 

cottonwood

Hardwoodconifer swamp

Floodplain forest

Northern 

hardwood 

swamp

Oak 

associations

sandy to dry-mesic soils, limited by cold 

temperatures, dependent on disturbance for 

regeneration, drought-tolerant, fire-return intervals 

50 to 250 years or longer

northern red oak, white oak, black oak, 

northern pin oak, eastern white pine, 

red pine, quaking aspen

Oak association

Barrens coarse-textured soils, excessively drained and 

drought-prone, fire-return intervals 1 to 50 years, 

canopy cover only 5 to 25 percent, subject to cold 

temperatures and frost pockets

jack pine, black oak, northern pin oak, 

white oak, red pine, eastern white pine

Oak-pine barrens

Oak barrens

Pine barrens

Table 4.—Forest systems considered in this assessment, with a summary of major drivers and species for each system 

(Cohen 2000a, 2000b, 2001, 2002a, 2002b, 2006, 2007; Comer 1996; Courteau et al. 2006; Kost 2002; Kost et al. 2007; 

Slaughter et al. 2007; Tepley et al. 2004; Weber et al. 2006, 2007)

*The list of characteristic species is not exhaustive, and composition may differ substantially from site to site across the assessment area. Scientific 

names are in Appendix 1.
Chapter 1: The Contemporary Landscape

18

Box 2: Forest Types and Natural Communities

In this assessment, we use two different sources 

of data for classifying forest systems: FIA foresttype groups and MNFI natural communities. These 

classification systems are used for different reasons 

and convey different types of information. Although 

there are some general relationships between the 

two systems, they are organized differently enough 

that one cannot be substituted for the other. Both 

types of information are relevant to this assessment, 

so we use both classification systems.

FIA classifications describe existing vegetation, 

and only for vegetated areas dominated by trees 

(i.e., forests). Forest-type groups are defined as 

a combination of forest types that share closely 

associated species or site requirements (e.g., 

maple/beech/birch). Forest types are a classification 

of forest land based upon and named for the 

dominant tree species (e.g., aspen). There are 

several advantages to the FIA classification system. 

The FIA system measures tree species composition 

on a set of systematic plots across the country and 

uses that information to provide area estimates for 

each forest type, making it a good way of estimating 

what is currently on the landscape and the relative 

abundance of different forest types. It does not, 

however, make any inferences about what vegetation 

was historically on the landscape and does not 

distinguish between naturally occurring and humaninfluenced conditions. Something that is classified 

as “forest land” by FIA may have been historically a 

prairie, glade, woodland, or savanna. Likewise, areas 

dominated by tree species that are not native to 

the area would still be assigned to a forest type and 

forest-type group based on dominant species. Foresttype groups have been mapped for Michigan (Fig. 6) 

(Pugh et al. 2009, 2012). 

Figure 6.—Distribution of FIA forest-type groups in Michigan as of 

2005, from Pugh et al. (2009).
Chapter 1: The Contemporary Landscape

19

Box 2: Forest Types and Natural Communities (continued)

By contrast, natural communities are defined as 

an assemblage of interacting plants, animals, and 

other organisms that repeatedly occurs across the 

landscape under similar environmental conditions 

(Kost et al. 2007). The Michigan Natural Features 

Inventory has prepared a classification of natural 

communities in Michigan, mapped representative 

occurrences of these communities across the 

state, and created distribution maps of the 

natural communities across the state by using the 

ecoregional framework of ecological subsections 

and sub-subsections (Albert et al. 2008, Kost et 

al. 2007). It recognizes 77 natural communities, 

many of which occur within the assessment 

area. See Figure 7 for an example of the natural 

community distribution maps (Albert et al. 2008). 

The advantage to the natural community system is 

that it is based on ecological relationships between 

native organisms and their physical environment. 

Natural communities describe existing vegetation 

in remaining natural forest areas, so they present a 

picture of vegetation that may have been present 

if the landscape had been left unaltered by human 

intervention. The disadvantage to using natural 

community classifications is that they have not yet 

been quantified spatially across the state. 

Figure 7.—Distribution of the boreal forest natural community, from 

Albert et al. (2008). This is an example of a series of similar distribution 

maps for other natural communities described by the Michigan Natural 

Features Inventory.
Chapter 1: The Contemporary Landscape

20

Forest Composition and Abundance

FIA Forest-type Groups

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 groups do 

not perfectly align with the forest systems described 

above, but FIA data are useful for describing forest 

abundance, growth, and other metrics. There are 

more than 11.6 million acres of forest land in the 

assessment area, which includes a variety of forest 

types (U.S. Forest Service 2011). 

Within the assessment area maple/beech/birch is the 

most common forest-type group, followed by aspen/

birch, spruce/fir, white pine/red pine/jack pine, and 

oak/hickory. Compared to the state as a whole, 

the assessment area contains similar proportions 

of these forest-type groups. The percentage cover 

of each forest-type group differs by less than 

5 percent between the assessment area and the entire 

state. Aspen/birch, spruce/fir, and white/red/jack 

pine are slightly more common in the assessment 

area than across the state, and elm/ash/cottonwood, 

maple/beech/birch, and oak/hickory are slightly less 

common in the assessment area. Forest-type group 

distribution differs by region, however. More than 

half (51 percent) of all the aspen in the state occurs 

in the northern Lower Peninsula, along with most of 

the state’s pine- and oak-dominated forests (Pugh 

et al. 2012). More than half of all the northern whitecedar in the state can be found in the eastern Upper 

Peninsula (Pugh et al. 2012). 

Assessment area Michigan (statewide)

Forest-type group Acres Cover (%) Acres Cover (%)

Aspen/birch 2,203,228 18.9 3,210,171 16.2

Douglas-fir 2,378 <0.0 2,378 <0.0

Elm/ash/cottonwood 866,201 7.4 2,055,440 10.4

Exotic hardwoods — — 11,860 0.1

Exotic softwoods 105,295 0.9 172,952 0.9

Fir-spruce/mountain hemlock 9,048 0.1 13,652 0.1

Maple/beech/birch 2,934,672 25.2 5,838,968 29.5

Oak/gum/cypress — — 12,133 0.1

Oak/hickory 1,515,636 13.0 3,275,139 16.5

Oak/pine 465,311 4.0 589,520 3.0

Other eastern softwoods — — 3,888 <0.0

Other hardwoods 73,851 0.6 125,618 0.6

Spruce/fir 1,816,651 15.6 2,530,814 12.8

White/red/jack pine 1,653,396 14.2 1,980,580 10.0

Total 11,645,667 19,823,113

Table 5.—Acres occupied and cover percentages of 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

21

Box 3: 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, Price 2010). Carbon sequestration and 

storage in forest ecosystems depends on the health 

and function of those ecosystems in addition to 

human management, episodic disturbances, and 

forest stressors. 

Forest land within the assessment area is estimated 

to hold nearly 1.1 billion metric tons C, or roughly 

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

There is only a little difference in the amount of C 

stored per acre across the major forest ownership 

categories. National Park Service lands generally 

hold more C than the average (105.8 metric tons per 

acre), and private lands, national forests, and state 

forests are roughly equal to the average. County and 

municipal lands tend to hold less C than the average 

for the assessment area (89.8 metric tons per acre). 

The pattern of C allocation in forest ecosystems 

is relatively consistent across ownerships. Soil 

organic C is by far the largest C pool, followed by 

aboveground live biomass, litter, live belowground 

biomass, and deadwood pools. 

Among different forest-type groups, however, the 

amount of C stored per acre differs widely (Fig. 8). 

Of all the forest-type groups, spruce/fir holds the 

most C per acre, mainly in the soil organic C pool. 

Maple/beech/birch forests tend to store the most 

aboveground C compared to other forest-type 

groups. 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 is slow and tree growth is 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.

Figure 8.—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

22

Drivers of Change in Forests 

Past Forest Ecosystem Change

The status of Michigan’s forests reflects a dynamic 

past. Pollen records indicate that as the last glacial 

ice sheets receded between 10,000 and 12,000 years 

ago, species like spruce and tamarack were relatively 

quick to colonize the expanding northward habitat 

in the Great Lakes region (Davis and Shaw 2001, 

Dickmann and Leefers 2003, Stearns 1997). Pollen 

records also reveal that balsam fir, jack pine, and 

red pine were present in northern Lower Michigan 

shortly thereafter, followed by white pine around 

9,000-10,000 years ago. Oak species also expanded 

their ranges northward, albeit at a slower pace, 

reaching the northern Lower Peninsula roughly 

9,000 years before the present. Maples appear to 

be more recent migrants into northern Michigan, 

arriving around 6,000 years ago, followed by 

hemlock (Davis 1983, Stearns 1997). 

Pollen records that reveal the range expansions 

of these species also indicate 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 (Davis 

1983). Refuge location, seed size, germination 

requirements, competitive ability, and dispersal 

vectors were all important factors determining 

how species responded to this period of dramatic 

climatic change. Following the initial colonization 

of the Great Lakes area after glacial retreat, periodic 

fluctuations in climate resulted in advance and 

retreat of the prairie-forest border, with grassland 

and savanna entering Michigan on occasion 

(Dickmann and Leefers 2003). 

Small mining sites and settlements from the 

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

day) have been uncovered throughout Michigan, 

although extensive settlement was limited in the 

northern forests (Sparhawk and Brush 1929, Stearns 

1997). In the eastern Upper Peninsula, settlements 

were typically restricted to flat areas next to 

Great Lakes shorelines (Silbernagel et al. 1997). 

In savannas and pine forests, Native Americans 

intentionally set fires to aid in hunting and to make 

travel easier (Stearns 1997). 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. 

Abandoned field below mixed white pine and hardwood stand in the northern Lower Peninsula. Photo by Matthew Duveneck, 

Portland State University, used with permission.
Chapter 1: The Contemporary Landscape

23

The Government Land Survey began in Michigan 

in 1815 and initiated an era of intense logging of 

Michigan forests. White pine logging operations 

expanded along river corridors until peaking 

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

The expansion of railroad lines soon facilitated 

logging in upland areas farther from waterways. 

As white pine declined, logging shifted to other 

species such as hemlock and hardwoods. By 1910, 

virtually all of the merchantable timber in the state 

had been harvested. The effects of this logging 

boom are well documented, including widespread 

catastrophic wildfires, eroded and dammed streams 

and waterways, and cascading impacts on vegetation 

and wildlife communities (Dickmann and Leefers 

2003, Frelich and Reich 1996, Sparhawk and Brush 

1929, Stearns 1997). Contemporary observers 

estimated that 92 percent of original forests had 

been harvested or destroyed by wildfires, and that 25 

percent of the original standing timber was wasted in 

the lumbering operation or destroyed by subsequent 

fires (Pugh et al. 2009, Sparhawk and Brush 1929). 

The ecological effect of the era was that the region 

was converted from a complex mosaic of forest 

types and successional stages to primarily early 

successional forests. 

After the wave of logging and wildfire, the first 

State-owned forests were established in 1903, 

and the Huron National Forest was established in 

1904 (Stearns 1997). The Hiawatha and Manistee 

National Forests were created in 1931. 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 Michigan. Forests in Michigan have 

generally been recovering and maturing during the 

past 80 years (Pugh et al. 2009). Early successional 

species such as aspen and paper birch became much 

more prevalent after this period of intense landscape 

disturbance, but these forest types have reached their 

maximum acreage and are now declining (Pugh et 

al. 2012). Wildfire suppression over the past century 

has hastened the conversion of early successional 

forest types to more mesic forest types.

Natural Disturbance Regimes 

Natural disturbance has historically been a regular 

component of forest ecosystems in Michigan. 

Disturbances like fire, windthrow, ice damage, 

and insect defoliation can be highly variable 

across a large landscape, influenced by climate, 

soils, landform, and vegetation. Forest systems 

have distinct disturbance regimes, characterized 

in part by the soils, landforms, and vegetation. 

Natural Communities of Michigan: Classification 

and Description contains detailed descriptions 

of disturbance regimes for each forested natural 

community in Michigan (Kost et al. 2007), and 

Table 4 summarizes many of the major natural 

disturbances characteristic of the forest systems 

in this assessment. 

Pests and Diseases

Native pests are often recurring and cyclic, and 

introduced pests and diseases pose unknown threats 

to Michigan’s forests. There are indications that 

a few of the most threatening pests and diseases 

such as the emerald ash borer (EAB) are having 

accelerating impacts. In just 5 years between the 

most recent FIA assessments in Michigan (2004 to 

2009), the ash mortality rate increased substantially 

as EAB expanded its range in the state (fourfold 

increase for white ash, sevenfold increase for green 

ash) (Pugh et al. 2012). During this same period, 

American beech had a fivefold increase in mortality, 

which can be mostly attributed to beech bark disease 

(Pugh et al. 2012). Nonnative species are one of 

the primary concerns for private landowners in 

Michigan today (Pugh et al. 2009). Major insect 

pests are summarized for the forest systems in the 

assessment area in Table 6. 
Chapter 1: The Contemporary Landscape

24

Forest system References

Upland spruce-fir

Insect pests such as spruce budworm and balsam fir bark beetle cause 

reduced growth or mortality of target species.

(Cohen 2007; Michigan Department of Natural 

Resources 2011; Pugh et al. 2009. 2012)

Excessive drought causes reduced growth or mortality. (Cornett et al. 2000, Hanson and Weltzin 2000)

Deer and moose herbivory results in reduced growth and mortality of 

seedling and saplings of target browse species, particularly for northern 

white-cedar.

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

1997, White 2012)

Invasive plants such as glossy buckthorn and Japanese barberry reduce 

suitable conditions for natural regeneration, facilitate other exotic species, 

and alter understory plant communities.

(Cohen 2007)

Jack pine (including pine-oak)

Suppression of natural fire regimes has reduced structural and species 

diversity, allowed hardwood encroachment on many sites, and limited 

suitable conditions for natural regeneration.

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

Abrams 2008)

Insect pests such as jack pine budworm, white pine tip weevil, and bark 

beetles cause reduced growth or mortality of target species. 

(Michigan Department of Natural Resources 2011; 

Pugh et al. 2009, 2012)

Diseases such as Armillaria and scleroderis lead to damage and mortality. (Burns and Honkala 1990)

Limited ability to apply prescribed fire makes it difficult to simulate natural 

fire regimes.

(Cohen 2002b, Nowacki and Abrams 2008)

Red pine-white pine

Suppression of natural fire regimes has reduced structural and species 

diversity, allowed mesophytic encroachment on many sites, and limited 

suitable conditions for natural regeneration.

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

and Abrams 2008)

Limited ability to apply prescribed fire makes it difficult to simulate natural 

fire regimes.

(Cohen 2002a, 2002b; Nowacki and Abrams 2008)

Insect pests such as jack pine budworm, white pine tip weevil, and 

redheaded pine sawfly cause reduced growth or mortality of target species.

(Michigan Department of Natural Resources 2011; 

Munck et al. 2009; Pugh et al. 2009, 2012; Stanosz 

et al. 2001)

Diseases such as white pine blister rust, shoot blights (Diplodia, sirrococcus, 

and sphaeropsis), and Armillaria lead to damage and mortality.

(Burns and Honkala 1990)

Deer herbivory results in reduced growth and mortality of seedling and 

saplings of target browse species.

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

Alverson 1997, White 2012)

(Table 6 continued on next page)

Table 6.—Major current stressors and impacts for forest systems in northern Michigan
Chapter 1: The Contemporary Landscape

25

Forest system References

Lowland conifers

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

lead to improved tree growth but also susceptibility to drought.

(Cohen 2006, Kost 2002, Swanson and Grigal 1991)

Road or ditch building leads to altered drainage patterns. (Cohen 2006, Kost 2002, Swanson and Grigal 1991)

Insect pests such as tamarack sawfly, larch case bearer, eastern larch beetle, 

and spruce budworm cause reduced growth or mortality of target species.

(Cohen 2006; Kost 2002; Michigan Department of 

Natural Resources 2011; Pugh et al. 2009, 2012)

Diseases such as dwarf mistletoe lead to damage and mortality. (Baker et al. 2012)

Invasive plants such as mutiflora rose and European buckthorn reduce 

suitable conditions for natural regeneration, facilitate other exotic species, 

and alter understory plant communities.

(Pugh et al. 2009, 2012)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species, particularly northern white-cedar.

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

Alverson 1997, White 2012)

Management practices may remove coarse woody debris or reduce species 

diversity.

(Cohen 2006, Kost 2002) 

Excessive drought causes reduced growth or mortality. (Cornett et al. 2000, Hanson and Weltzin 2000, 

Kost 2002, Swanson and Grigal 1991)

Aspen-birch

Suppression of natural fire regimes has allowed succession to other forest 

types, and limited suitable conditions for natural regeneration.

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

Insect pests such as forest tent caterpillar, birch leaf miner, bronze birch 

borer, and gypsy moth cause reduced growth or mortality of target species.

(Michigan Department of Natural Resources 2011; 

Pugh et al. 2009, 2012)

Excessive drought causes reduced growth or mortality. (Auclair et al. 2010, Cornett et al. 2000, Hanson 

and Weltzin 2000, Worrall et al. 2013)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species.

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

Alverson 1997, Weber et al. 2006, White 2012)

Diseases such as hypoxylon canker lead to damage and mortality. (Burns and Honkala 1990, Weber et al. 2006)

Northern hardwoods

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

alter soil conditions, facilitate exotic plant species, decrease regeneration 

suitability for many forest species, and increase drought susceptibility for 

sugar maple.

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

Invasive plants such as garlic mustard and European buckthorn reduce 

suitable conditions for natural regeneration, facilitate other exotic species, 

and alter understory plant communities.

(Powers and Nagel 2009; Pugh et al. 2009, 2012)

Insect pests such as emerald ash borer, forest tent caterpillar, and white pine 

tip weevil cause reduced growth or mortality of target species.

(Michigan Department of Natural Resources 2011; 

Pugh et al. 2009, 2012)

Diseases such as beech bark disease, white pine blister rust, and Armillaria

lead to damage and mortality.

(Michigan Department of Natural Resources 2011; 

Pugh et al. 2009, 2012)

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

cause crown dieback or widespread decline of maple and birch species.

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

al. 2001)

Excessive drought causes reduced growth or mortality. (Auclair et al. 2010, Hanson and Weltzin 2000)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species.

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

Nagel 2009, Waller and Alverson 1997, 

White 2012)

Management practices may remove coarse woody debris or reduce species 

diversity.

(Cohen 2000a, Powers and Nagel 2009)

(Table 6 continued on next page)

Table 6 (continued)
Chapter 1: The Contemporary Landscape

26

Forest system References

Lowland-riparian hardwoods

Altered hydrologic regimes lead to excessive waterlogging or excessive 

drought and result in reduced growth, lack of suitable conditions for 

regeneration, and susceptibility to dieback and decline.

(Opperman et al. 2010, Romano 2010, Slaughter et 

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

Ash decline causes reduced growth, crown dieback, or mortality of ash 

species.

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

et al. 2004, Weber et al. 2007)

Invasive plants such as reed canarygrass, Japanese barberry, multiflora rose, 

and European buckthorn reduce suitable conditions for natural regeneration, 

facilitate other exotic species, and alter understory plant communities.

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

et al. 2007)

Insect pests such as emerald ash borer and gypsy moth cause reduced 

growth or mortality of target species. 

(Michigan Department of Natural Resources 2011; 

Pugh et al. 2009, 2012; Slaughter et al. 2007; 

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

Diseases such as Dutch elm disease lead to damage and mortality disease. (Pugh et al. 2009, 2012)

Excessive drought causes reduced growth or mortality. (Hanson and Weltzin 2000, Slaughter et al. 2007)

Deer herbivory results in reduced growth and mortality of seedlings and 

saplings of target browse species.

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

Alverson 1997, White 2012)

Oak associations

Suppression of natural fire regimes has reduced structural and species 

diversity, allowed hardwood encroachment on many sites, and limited 

suitable conditions for natural regeneration.

(Cleland et al. 2004, Courteau et al. 2006, Nowacki 

and Abrams 2008)

Limited ability to apply prescribed fire makes it difficult to simulate natural 

fire regimes.

(Courteau et al. 2006, Nowacki and Abrams 2008)

Oak wilt and oak decline cause reduced growth, crown dieback, or mortality 

of oak species.

(Courteau et al. 2006; Michigan Department of 

Natural Resources 2011; Pugh et al. 2009, 2012)

Deer and rabbit herbivory results in reduced growth and mortality of 

seedlings and saplings of target browse species.

(Côté et al. 2004, Courteau et al. 2006, Waller and 

Alverson 1997, White 2012)

Invasive plants such as garlic mustard and European buckthorn reduce 

suitable conditions for natural regeneration, facilitate other exotic species, 

and alter understory plant communities.

(Pugh et al. 2009, 2012)

Excessive drought causes reduced growth or mortality. (Hanson and Weltzin 2000)

Soil frost damages roots and new growth, and may cause crown dieback or 

widespread decline of oak species.

(Burns and Honkala 1990)

Barrens

Suppression of natural fire regimes has reduced structural and species 

diversity, allowed hardwood encroachment on many sites, and limited 

suitable conditions for natural regeneration.

(Cleland et al. 2004; Cohen 2000b, 2001; Comer 

1996; Nowacki and Abrams 2008)

Insect pests such as jack pine budworm cause reduced growth or mortality of 

target species.

(Michigan Department of Natural Resources 2011; 

Pugh et al. 2009, 2012)

Oak wilt and oak decline cause reduced growth, crown dieback, or mortality 

of oak species.

(Courteau et al. 2006; Michigan Department of 

Natural Resources 2011; Pugh et al. 2009, 2012)

Limited ability to apply prescribed fire makes it difficult to simulate natural 

fire regimes.

(Cohen 2000b, 2001; Comber 1996; Nowacki and 

Abrams 2008)

Invasive plants such as spotted knapweed, leafy spurge, and autumn olive 

reduce suitable conditions for natural regeneration, facilitate other exotic 

species, and alter understory plant communities.

(Cohen 2000b, 2001; Comer 1996; Pugh et al. 

2009, 2012)

Table 6 (continued)
Chapter 1: The Contemporary Landscape

27

Nonnative Plant Species 

Nonnative plant species are a risk to forests when 

they become invasive. These species affect forest 

communities through direct competition for 

resources and alteration of fire, hydrology, or soil. 

In Michigan, most nonnative plant species are 

understory species. Nonnatives are usually more 

pronounced in fragmented settings (Pugh et al. 

2009). As mentioned above, forests in the northern 

Lower Peninsula are much more fragmented than 

forests in the eastern Upper Peninsula. Figure 9 

displays information on the relative proportion and 

cover of nonnative species in forest plots across 

the region. Plots in the northern Lower Peninsula 

generally had higher nonnative species occurrence 

and extent than plots in the eastern Upper Peninsula. 

Key invasive species are presented in Table 6.

Current Stressors 

Each of the forest systems addressed in this 

assessment faces a particular suite of threats and 

stressors (Table 6). We define stressors as agents 

Figure 9.—Frequency and cover of nonnative species, as a percentage of all species sampled in standard FIA plots and 

Vegetation Diversity and Structure Indicator plots from 2001 through 2003, from Pugh et al. (2009).
Chapter 1: The Contemporary Landscape

28

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 very 

dependent on local conditions and are not consistent 

across an area as large and diverse as the assessment 

area. 

These particular threats should be considered in 

addition to landscape-level threats such as forest 

fragmentation, the legacy of past management 

practices, and altered disturbance regimes. It is often 

difficult to examine the effects of just one of these 

landscape-level threats in isolation, because they all 

have 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 (Pugh et al. 

2012, Radeloff et al. 2005). The legacy of forest 

management and land use in northern Michigan 

has been well documented; general outcomes are 

a transition to more early successional forests with 

reduced structural, spatial, and species diversity 

(Dickmann and Leefers 2003, Sparhawk and Brush 

1929). The disruption of natural disturbance regimes 

has included fire suppression in upland systems as 

well as hydrologic disruption in riparian and lowland 

forests. Natural regeneration and succession of 

forests are strongly tied to disturbance regimes, so 

in many cases alteration of disturbance regimes has 

resulted in less regeneration of disturbance-adapted 

species and reduced landscape diversity (Kost et al. 

2007, Nowacki and Abrams 2008, Romano 2010).

Conservation Status

The Michigan Natural Features Inventory has 

assigned conservation ranks to the natural 

communities that occur within the state, and 

NatureServe has assigned global conservation ranks 

to natural communities (Table 7). These rankings 

are designed to categorize the risk of elimination of 

the community from Michigan and around the world 

(Kost et al. 2007, NatureServe 2013). The rankings 

range from “critically imperiled” to “secure, 

common, and widespread.” These rankings consider 

inherent geographic ranges, the amount of potential 

range currently occupied, long-term trends, and 

other factors. 

Forest-dependent Wildlife and Plants

Forests are important for many wildlife and 

understory plant species in Michigan. The Michigan 

State Wildlife Action Plan identifies wildlife species, 

and their habitats, that are in greatest conservation 

need (Eagle et al. 2005). The Forest Plans of the 

Hiawatha and Huron-Manistee National Forests 

include several forest-dependent wildlife species 

with particular management emphasis. Examples of 

these species are displayed in Table 8. Additionally, 

Michigan’s Special Animals and Michigan’s Special 

Plants include information on Michigan’s 665 

endangered, threatened, and special concern species 

(MNFI 2013a, 2013b). Many of these species occur 

in the assessment area, including 189 species in 

the eastern Upper Peninsula and 233 species in the 

northern Lower Peninsula.

SOCIOECONOMIC CONDITIONS 

Forest Ownership Trends

Most forest land in the assessment area is privately 

owned, which matches the statewide pattern 

(Table 9, Fig. 10). The State of Michigan is the 

next largest landowner in the assessment area, 

with 28 percent. More than 75 percent of all 

Michigan’s State-owned forest land occurs within 

the assessment area. 

The breakdown of forest-type groups within these 

different ownerships is not uniform. Proportionally, 

private lands contain almost twice as much maple/

beech/birch and elm/ash/cottonwood forests as state 

or national forests. By contrast, national forests 

contain proportionally more white pine/red pine/

jack pine forests than the other ownerships. The 

breakdown of aspen/birch, spruce/fir, and remaining 

FIA forest-type groups is more even among the 

major ownership categories.
Chapter 1: The Contemporary Landscape

29

Forest system Related natural communities Conservation status*

Upland spruce-fir Boreal forest State: vulnerable 

Global: unrankable

Jack pine (including pine-oak) Dry northern forest State: vulnerable 

Global: vulnerable

Red pine-white pine Dry northern forest State: vulnerable 

Global: vulnerable

Dry-mesic northern forest State: vulnerable 

Global: apparently secure

Lowland conifers Poor conifer swamp State: apparently secure

Global: apparently secure

Rich conifer swamp State: vulnerable 

Global: apparently secure

Hardwood-conifer swamp State: vulnerable 

Global: apparently secure

Aspen-birch Aspen association NA 

Northern hardwoods Mesic northern forest State: vulnerable 

Global: apparently secure

Lowland-riparian hardwoods Hardwood-conifer swamp State: vulnerable 

Global: apparently secure

Floodplain forest State: vulnerable 

Global: vulnerable

Northern hardwood swamp State: vulnerable 

Global: apparently secure

Oak associations Oak association NA 

Barrens Oak-pine barrens State: imperiled

Global: vulnerable

Oak barrens State: critically imperiled

Global: imperiled

Pine barrens State: imperiled

Global: vulnerable

Table 7.—Forest systems considered in this assessment, related natural communities, and state and global 

conservation status ranks (modified from Kost et al. 2007)

* Critically imperiled: at very high risk of extinction due to extreme rarity, very steep declines, or other factors. Vulnerable: 

at moderate risk of extinction due to a restricted range, relatively few occurrences, recent and widespread declines, or other 

factors. Apparently secure: uncommon but not rare; some cause for long-term concern due to declines or other factors. Unrankable: 

currently unrankable due to lack of information or substantially conflicting information about status or trends. NA: not considered in 

Kost et al. (2007).
Chapter 1: The Contemporary Landscape

30

Table 8.—Some forest-dependent wildlife species and associated habitats identified in Michigan’s State Wildlife 

Action Plan (Eagle et al. 2005) or the Forest Plans of the Hiawatha and Huron-Manistee National Forests (Hiawatha 

National Forest [HNF] 2006, Huron-Manistee National Forest [HMNF] 2006)

Species* DNR HNF HMNF Associated habitats and important features

American marten X X X lowland conifer, mesic conifer, dry conifer, forest 

opening, bog, snag/tree cavity, down woody debris

American woodcock X idle/old field, pasture, row crop, lowland shrub, 

upland shrub, lowland hardwood, mesic hardwood, 

dry hardwood, lowland conifer, forest opening, river/

stream/riparian floodplain corridor, forest edges, late 

successional forest, down woody debris

Black-backed woodpecker X X mature conifer forests

Canada lynx X X heavy snowfall areas, lowland shrub, upland shrub, 

lowland conifer, mesic conifer, dry conifer, bog, swamp, 

large contiguous natural landscape, down woody debris

Eastern massasauga X X aquatic and terrestrial habitats, northern Lower 

Peninsula

Golden-winged warbler X idle/old field, lowland shrub, upland shrub, lowland 

hardwood, mesic hardwood, forest opening, bog, 

swamp, forest edges

Indiana bat X X large tree cavities, oak/hickory forests, elm, ash, and 

cottonwood species, forest opening

Karner blue butterfly X X savanna, lupine understory

Kirtland’s warbler X X X savanna, upland shrub, young jack pine, forest opening, 

large contiguous natural landscape

Moose X lowland shrub, lowland hardwood, lowland conifer, 

forest opening, inland emergent wetland, inland 

lake, river/stream/riparian floodplain corridor, large 

contiguous natural landscape

Northern goshawk X X X savanna, lowland hardwood, mesic hardwood, dry 

hardwood, lowland conifer, mesic conifer, dry conifer, 

forest opening, suburban/small town, large contiguous 

natural landscape, late successional forest

Red-headed woodpecker X prairie, idle/old field, row crop, savanna, lowland 

hardwood, mesic hardwood, dry hardwood, dry conifer, 

forest opening, swamp, river/stream/riparian/floodplain 

corridor, edge, snag/cavity, late successional forest, 

down woody debris

Red-shouldered hawk X X X lowland shrub, lowland hardwood, mesic hardwood, dry 

hardwood, lowland conifer, mesic conifer, dry conifer, 

forest opening, inland emergent wetland, submergent 

wetland, ephemeral wetland, swamp, river/stream/

riparian/floodplain corridor, coastal emergent wetland, 

edge, snag/cavity, large contiguous natural landscape, 

late successional forest

*Scientific names are in Table 21 in Appendix 1.
Chapter 1: The Contemporary Landscape

31

Species* DNR HNF HMNF Associated habitats and important features

Ruffed grouse X X young and mature aspen

Sharp-tailed grouse X X prairie, idle field, pasture, savanna, lowland shrub, 

upland shrub, dry conifer, forest opening, bog, inland 

emergent wetland, fen, ephemeral wetland, river/

stream/riparian floodplain corridor, snag/cavity

Snowshoe hare X lowland shrub, lowland hardwood, mesic hardwood, dry 

hardwood, lowland conifer, mesic conifer, dry conifer, 

bog, swamp, forest edges, dense understory, down 

woody debris

Spruce grouse X conifer forests in northern Lower Peninsula and eastern 

Upper Peninsula, low berries (especially blueberry), early 

successional spruce-jack pine forest 

Table 8 (continued)

*Scientific names are in Table 21 in Appendix 1.

a

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

b

 Includes National Park Service, Fish and Wildlife Service, Department of Defense, Bureau of Land Management, and other federal 

agencies.

Table 9.—Forest land acreage for major ownership classifications within the assessment area and for the entire state, 

according to FIA data (U.S. Forest Service 2011)

Assessment area Michigan (statewide)

Ownership Acresa Cover (%) Acresa Cover (%)

Private 6,391,864 54.9 12,406,958 62.0

State 3,258,428 28.0 4,199,896 21.0

National forest 1,743,590 15.0 2,688,094 13.4

Other federalb 162,309 1.4 320,325 1.6

County and municipal 89,475 0.8 388,197 1.9

Total forest land area (acres) 11,645,666 20,003,470

An eastern massasauga, Michigan’s only 

poisonous snake. Photo by U.S. Forest Service, 

Huron-Manistee National Forest. 
Chapter 1: The Contemporary Landscape

32

Figure 10.—Land ownership across Michigan in 2009, from Pugh et al. (2012).
Chapter 1: The Contemporary Landscape

33

General Trends in Land Use 

In Michigan, two major factors have contributed 

to forest fragmentation in recent years: large-scale 

divestiture of forest industry land and parcelization 

of nonindustrial private forests (Froese et al. 2007, 

Price 2010). Parcelization is the division of larger 

landholdings into smaller units. In the Upper 

Peninsula, more than 1 million acres of forest land 

transferred from traditional industrial ownership 

to large-scale forest investment companies in 2005 

and 2006 (Froese et al. 2007). These ownership 

changes may make certain forest land, such as 

lakeshore property, more subject to parcelization 

and development. Although parcelization may not 

immediately result in direct impacts, this pattern 

often has consequences for forests as well as for 

the 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, 35.6 percent of all 

private land is developed as residential land 

(Headwaters Economics 2011). Residential 

development within the assessment area proceeded 

faster than the U.S. average between 1980 and 

2000, with the large majority of this change as 

exurban development (lot size between 1.7 and 40.0 

acres). For the counties included in this assessment, 

residential acreage increased overall by 46.2 percent. 

Six counties, all in the northern Lower Peninsula, 

had more than 100-percent increases in residential 

acreage. Benzie County had the greatest increase, 

at 165 percent. The smallest increases occurred in 

counties with sizeable federal land holdings (Alger, 

Delta, Luce, Chippewa, Schoolcraft, and Iosco 

Counties) (Headwaters Economics 2011). 

Land-use change is projected to proceed according 

to the same pattern in northern Michigan through 

the year 2020, with conversion of rural land to 

exurban and urban development (Theobald 2005). 

Land development will be constrained in the eastern 

Upper Peninsula due to the high proportion of 

federal, state, and county-owned land, but much 

faster development is projected to occur near 

existing municipalities within the northern Lower 

Peninsula.

Human Population Patterns 

The 2010 population for counties included in 

this assessment was roughly 1.2 million people, 

compared to the total state population of 9.8 million 

people. Counties in northern lower Michigan have 

generally had small but growing populations since 

the 1960s, whereas counties in the eastern half of 

the Upper Peninsula have maintained steady or 

slightly declining populations during the past 50 

years (Headwaters Economics 2011). The overall 

population growth for the assessment area from 

1990 to 2008 was 13.8 percent, compared to the 

overall U.S. growth of 21.9 percent (Headwaters 

Economics 2011). In the eastern Upper Peninsula, 

all of the counties lost between 1 and 9,999 

residents between the 2000 and 2010 Census. In 

the northern Lower Peninsula, the trend is divided 

between northeastern counties, nearly all of 

which experienced slight population declines, and 

northwestern counties, which nearly all experienced 

slight population increases (Mackun et al. 2011). 

All of these absolute changes are between 0 and 10 

percent of the overall population for the counties in 

the assessment area. The population density for the 

assessment area remains between 1 and 49 people 

per square mile, with the exception of a few counties 

along the southern edge of the assessment area and 

four counties neighboring Traverse City, which have 

higher densities (Mackun et al. 2011). 

A socioeconomic study of Michigan’s national 

forests offers a view of the proximity of human 
Chapter 1: The Contemporary Landscape

34

populations to the forested areas of the state.1 In the 

year 2000, the Hiawatha National Forest had roughly 

400,000 U.S. residents living within 60 miles of the 

Forest boundary, and the Huron-Manistee National 

Forest had more than 2.4 million people within 

60 miles. Therefore, even though the assessment 

area hosts only a small human population, demand 

is high for access and resources, especially toward 

the southern edge of the assessment area.

Economic Sectors

Overall

Compared with Michigan as a whole, the economy 

of the assessment area is slightly depressed. 

As of 2000, median household incomes in the 

assessment area were 18 to 24 percent below the 

statewide median (see footnote 1). The proportion of 

households receiving Social Security or retirement 

income was higher than the state average. From 

1970 to 2009, personal income for counties within 

the assessment area grew 130 percent, compared 

to the U.S. average of 164 percent (Headwaters 

Economics 2011). As of 2010, unemployment in the 

assessment area was 13 percent, compared to the 

U.S. average of 10 percent (Headwaters Economics 

2011). Per capita income in the assessment area was 

less than 75 percent of the U.S. average (Headwaters 

Economics 2011). 

Agriculture

Michigan is a major agricultural state, ranking 15th 

overall in the United States for total market value 

of agricultural products (Michigan Department of 

Agriculture 2009). Counties in southern Michigan 

are engaged in commercial agriculture more than the 

1

 Leefers, L.; Potter-Witter, K.; McDonough, M. 2003. 

Social and economic assessment for the Michigan 

national forests. Report submitted to Hiawatha, HuronManistee, and Ottawa National Forests on July 25, 2003. 

On file with L. Leefers, Michigan State University, East 

Lansing, MI.

counties within the assessment area. The Michigan 

Department of Agriculture and Rural Development 

Food and Agricultural Systems Profiles includes 

information by districts within the state and by 

individual counties (Michigan Department of 

Agriculture 2009). Within the northern Lower 

Peninsula, major products are potatoes, maple 

syrup, sugar beets, cattle, beans, tart cherries, sweet 

cherries, Christmas trees, grapes, asparagus, apples, 

and oats. Agricultural land accounts for 10 to 

40 percent of the land area in the agricultural 

districts in the northern Lower Peninsula. The entire 

Upper Peninsula is a single agricultural district, 

which ranks last in the state in agricultural revenues, 

and only 4.9 percent of this area is in agricultural 

production. The western portion of the northern 

Lower Peninsula along the shore of Lake Michigan 

is a popular location for fruit orchards, which are 

particularly concentrated around Grand Traverse 

Bay. 

Farm employment accounts for 3.1 percent of 

total jobs for counties within the assessment 

area, with the highest percentages occurring 

in Osceola and Missaukee Counties (both at 

11.1 percent) (Headwaters Economics 2011). 

In 2009, employment in this sector amounted to 

17,554 jobs. Within the assessment area, agriculture 

is slightly less profitable compared to the United 

States as a whole (Headwaters Economics 2011). 

Forest Industry

The forest products industry is also a regionally 

important source of income and employment in 

Michigan. The wood products and paper industries 

in Michigan account for almost $8 billion in 

economic output (Price 2010, Pugh et al. 2012). In 

Alger and Luce Counties the forest products industry 

contributed about 50 percent of the total economic 

output (Leefers 2007). In Delta, Menominee, and 

Schoolcraft Counties, this sector represented about 

30 percent of total economic output.
Chapter 1: The Contemporary Landscape

35

Within the assessment area, the forest products 

sector generated 2.2 percent of total jobs in 2009 

(6,678 jobs) (Headwaters Economics 2011). For 

seven counties, employment in this sector accounts 

for greater than 10 percent of total employment, 

with the greatest percentage in Alger County 

(30.7 percent). From 1998 to 2009, employment 

related to the forest products industry declined by 

33.5 percent for counties within the assessment 

area. According to the Michigan’s Forests 2009

assessment from FIA, there were 201 active woodusing mills in Michigan as of 2008, which is 

about 50 percent of the number of mills that were 

operating in the state in 1990 (Pugh et al. 2012). 

Most of the reduction came from smaller sawmills 

processing less than 5 million board feet per year. 

Travel and Tourism

Travel, tourism, and outdoor recreation are major 

activities in northern Michigan, contributing greatly 

to the economy and local character of the area. The 

assessment area includes two national forests, two 

national lakeshores, many state parks and recreation 

areas, and several popular tourist destinations like 

Mackinac Island and Grand Traverse Bay. Michigan 

has more than 900 public and private campgrounds, 

most of which are in forested areas (Dickmann and 

Leefers 2003). More than 935,000 people hunt in 

Michigan each year, the most of any state in the 

country (Dickmann and Leefers 2003). Hunting is 

just one form of outdoor recreation, but it reflects 

the high levels of outdoor recreation in the state. 

In 2009, Mackinac County had the highest share of 

employment in sectors related to travel and tourism, 

with 37.2 percent of the jobs in the county occurring 

within this sector. Seven of the 40 counties in this 

assessment area had more than 25 percent of total 

employment in this sector. The average for the entire 

assessment area was 18.6 percent, which is higher 

than the U.S. average. Within the assessment area, 

travel and tourism accounted for 57,354 jobs, which 

was a 6.2-percent decline from 1998 (Headwaters 

Economics 2011). 

Oil and Gas Production

Fossil fuel extraction is also an important economic 

activity within northern Michigan, and the state 

ranks high nationally in natural gas extraction. 

Michigan has more than 13,000 oil wells and 

more than 12,000 gas wells, most of which are 

located in the northern Lower Peninsula (Michigan 

Department of Environmental Quality 2013). 

The majority of these wells have been developed 

in large clusters since 1970, with high densities 

occurring in Manistee, Grand Traverse, Kalkaska, 

Antrim, Otsego, Montmorency, and Alpena Counties 

(Michigan Department of Environmental Quality 

2013). No active wells exist in the Upper Peninsula. 

Oil and gas extraction accounts for less than 

1 percent of the total employment in all the counties 

within this assessment area, with the exceptions of 

Crawford (2.1 percent), Grand Traverse 

(1.2 percent), Isabella (1.7 percent), Mason 

(1.8 percent), Montmorency (1.0 percent), Osceola 

(3.0 percent), Otsego (3.1 percent), and Roscommon 

(1.1 percent). Kalkaska County is an outlier in the 

region, with 17.7 percent of employment associated 

with oil and gas exploration. From 1998 to 2009, 

employment in this sector held virtually constant 

throughout the assessment area. 

Forest Harvest and Products 

As mentioned above, the forestry sector is a major 

economic contributor in Michigan and within the 

assessment area in particular. In the northern Lower 

Peninsula, 121 million cubic feet of roundwood were 

harvested in 2008, and the eastern Upper Peninsula 

generated about 67 million cubic feet (Pugh et al. 

2012). More than half the total harvested roundwood 

was used as pulpwood and around 30 percent was 

used as saw logs, with the remainder being diverted 

to a variety of uses. Pulpwood production peaked in 

1994 and has been gradually declining during recent 

years. 
Chapter 1: The Contemporary Landscape

36

Annual net growth of growing stock differs by area 

within Michigan. The eastern Upper Peninsula has 

a significantly lower growth rate (2.5 percent) than 

the northern Lower Peninsula (4.0 percent) (Pugh 

et al. 2009). 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. This ratio takes 

into account gross growth, mortality, and removals. 

Across all ownership classes in the assessment area, 

the growth-to-removals ratio was 2.2 for the most 

recent FIA inventory period, meaning that growth 

generally was twice the volume of harvest removals 

in the assessment area (U.S. Forest Service 2011). 

The ratio was highest for national forests (5.6), and 

lowest for state lands (1.8), indicating higher levels 

of harvest or mortality, or both, on state lands. 

The FIA removals data also show the proportion 

of species harvested in the assessment area 

(Table 10) (U.S. Forest Service 2011). Among the 

major forest-type groups, white/red/jack pine had the 

highest growth-to-removals ratio (4.4), and aspen/

birch, elm/ash/cottonwood, oak/pine, and spruce/fir 

were all above the ratio for the assessment area as a 

whole. Maple/beech/birch had the lowest growth-toremovals ratio (1.3), probably because this foresttype group accounted for more than 40 percent 

of the harvest removals by volume for the entire 

assessment area. Aspen/birch was the second-most 

harvested forest-type group, with almost 19 percent 

of the total harvest removals.

Forest Certification

Forest certification programs allow a public or 

private landowner to voluntarily submit to third 

party audits that ensure environmental, social, 

and economic best practices on enrolled 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. 

More than 6 million acres of Michigan’s forest land 

is certified according to one or more certification 

standards, or almost a third of all forest land in 

the state (Table 11). As of 2011, Michigan ranked 

4th among all states for most forest land certified 

under the Forest Stewardship Council (FSC) system 

(Pingrey 2011). 

Forest-type group Net growth (cubic feet per year) Removals (cubic feet per year) Growth:Removals

Maple/beech/birch 103,000,708 76,307,693 1.3

Aspen/birch 83,503,219 34,109,196 2.4

White/red/jack pine 79,700,938 18,171,055 4.4

Oak/hickory 54,563,028 28,138,632 1.9

Spruce/fir 38,974,616 10,652,799 3.7

Elm/ash/cottonwood 20,866,885 5,977,897 3.5

Oak/pine 15,502,014 5,727,044 2.7

Exotic softwoods 4,805,622 708,079 6.8

Total 402,005,489 180,156,771 2.2

Table 10.—Annual net growth and removals and the ratio of growth to removals for major forest-type groups in the 

assessment area, organized according to net annual growth (U.S. Forest Service 2011)
Chapter 1: The Contemporary Landscape

37

Certification standarda

Dual Tree Farm - ATFS

Landowner FSC only FSC/SFI SFI only PEFC Group Total

Michigan state forests 4,200,000

Plum Creek 588,000

GMO/AFM 440,045

The Forest Land Group 350,000

Molpus 96,764

JM Longyear 72,660

Keweenaw 161,263

Traditional (non-MFL group) tree farms 93,153b

 

Group tree farms 142,214

The Nature Conservancy 24,000

Total by standard 607,923 4,200,000 1,124,809 93,153 142,214 6,168,099

Table 11.—Acres of certified forest land in Michigan under four common certification standards as of December 2011

a

 FSC: Forest Stewardship Council; SFI: Sustainable Forestry Initiative; PEFC: Programme for the Endorsement of Forest Certification 

schemes; ATFS Group: American Tree Farm System Group Certification; GMO/AFM: GMO Threshold Timber Corporation forest 

land enrolled in American Forest Management’s certification program; MFL: Managed Forest Law (Scott Robbins, Michigan Forest 

Products Council, pers. commun.; Christine Hall, The Nature Conservancy, pers. commun., Aug. 8, 2013). 

b

 This figure accounts only for tree farms that have been certified within the past 5 years, in order to remain conservative. The actual 

figure is possibly much higher, on the order of 695,000 acres (S. Robbins, pers. commun., December 6, 2011). 

SUMMARY

Forests are a defining feature across Michigan, 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).
38

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 for 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. 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. The United States Global Research Program 

has released a series of reports detailing the past 

and projected changes in climate at a national level, 

with a comprehensive report (National Climate 

Assessment, NCA) scheduled to be released in 2014

(see Box 4 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. 11) (IPCC 2007). The first 12 years 

of 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 2.0 °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 (1.9 °C) above the 20th-century 

average (NOAA National Climatic Data Center 

2012b). 

Average temperature increases are simplifications 

of a more complex pattern of regional and seasonal 

climate changes. For example, the frequency of cold 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

39

Box 4: Global, NaƟ onal, and Regional Assessments 

Intergovernmental Panel on Climate Change

The Intergovernmental Panel on Climate Change 

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

internaƟ onal body for the assessment of climate 

change. It was established by the United NaƟ ons 

Environment Programme (UNEP) and the World 

Meteorological OrganizaƟ on (WMO) in 1988 to 

provide the world with a clear scienƟfi c view on the 

current state of knowledge in climate change and its 

potenƟ al 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/publicaƟ ons_and_data/ar4/syr/en/

contents.html

U.S. Global Change Research Program

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

globalchange.gov) is a federal program that 

coordinates and integrates global change research 

across 13 government agencies to ensure that it 

most eff ecƟ vely and effi ciently serves the naƟ on 

and the world. Mandated by Congress in the Global 

Change Research Act of 1990, the USGCRP has since 

made the world’s largest scienƟfi c investment in the 

areas of climate science and global change research. 

It has released several naƟ onal 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

library.globalchange.gov/products/assessments/

2004-2009-synthesis-and-assessment-products

NaƟ onal Climate Assessment

hƩ p://ncadac.globalchange.gov/

Eff ects of ClimaƟ c Variability and Change on Forest 

Ecosystems: a Comprehensive Science Synthesis for 

the U.S.

hƩ p://www.treesearch.fs.fed.us/pubs/42610

Midwest Technical Input Report for the NaƟ onal 

Climate Assessment (coordinated by the Great 

Lakes Integrated Science and Assessment [GLISA] 

Center)

glisa.msu.edu/great_lakes_climate/nca.php 

Figure 11.—Trends in global temperature compared to the 1951 through 1980 mean. Data source: NASA Goddard InsƟ tute for 

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

40

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. Additionally, 

an average change of a few degrees means that 

some areas of the globe may experience much more 

change, while other areas experience very little 

change. The synthesis report of the International 

Scientific Congress on Climate Change concluded 

that “recent observations show that societies and 

ecosystems are highly vulnerable to even modest 

levels of climate change, with poor nations and 

communities, ecosystem services and biodiversity 

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

Temperature increases of more than 3.6 °F (2.0 °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 Michigan 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 reemit energy that would otherwise be lost into 

space (Fig. 12). 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. Contributing to the greenhouse effect 

are several naturally occurring greenhouse gases 

in the atmosphere, such as carbon dioxide (CO2

), 

methane (CH4

), nitrous oxide (N2O), and water 

vapor. 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.

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. 13), 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 
Chapter 2: Climate Change Science and Modeling

41

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

Change (2007).

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 is a contributor to 

CH4

 emissions, primarily from fermentation in the 

guts of 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.
Chapter 2: Climate Change Science and Modeling

42

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 also 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. These 

models are used in short-term weather forecasting as 

well as long-term climate projections.

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. 

Figure 13.—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).
Chapter 2: Climate Change Science and Modeling

43

Figure 14.—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).

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. 14). 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 2° and 

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

about the size of the assessment area in Michigan. 

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.
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

44

Several GCMs have been used in climate projections 

for the IPCC reports and elsewhere (Box 5). These 

models have been developed by internationally 

renowned climate research centers such as 

NOAA’s Geophysical Fluid Dynamics Laboratory 

(GFDL CM2) (Delworth et al. 2006), the United 

Kingdom’s Hadley Centre (HadCM3) (Pope et al. 

2000), and the National Center for Atmospheric 

Research (PCM) (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 

infl uence on the projected climate trends than the 

choice of greenhouse gas emissions scenario. 

Like all models, GCMs have strengths and 

weaknesses (Box 6). 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 

proxy-based 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 infl uential 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 scientifi c 

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 

introduces new sources of uncertainty (Maslin and 

Austin 2012).

Box 5: More Resources on Climate Models and Emissions Scenarios

U.S. Forest Service

Climate ProjecƟ ons FAQ

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

U.S. Global Change Research Program

Climate Models: an Assessment of Strengths and 

LimitaƟ ons

hƩ p://library.globalchange.gov/products/

assessments/2004-2009-synthesis-and-assessmentproducts 

Intergovernmental Panel on Climate Change

Chapter 8: Climate Models and Their EvaluaƟ on

www.ipcc.ch/publicaƟ ons_and_data/ar4/wg1/en/

ch8.html

Special Report on Emissions Scenarios: 

Summary for Policymakers

hƩ p://www.ipcc.ch/ipccreports/sres/emission/index.

php?idp=0

Great Lakes Integrated Science and Assessment 

(GLISA) Center

Midwest Technical Input Report for the NaƟ onal 

Climate Assessment

glisa.msu.edu/great_lakes_climate/nca.php 
Chapter 2: Climate Change Science and Modeling

45

Box 6: 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.

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 polices directed specifically 

at greenhouse gas mitigation such as the Kyoto 

Protocol. However, some of the scenarios that 
Chapter 2: Climate Change Science and Modeling

46

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. 15).

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 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 could 

conceivably be different from any of the developed 

scenarios. It is highly improbable that future 

greenhouse gas emissions will be less than described 

by the B1 scenario even if national or international 

policies were implemented immediately. In fact, 

current emissions more closely track the greenhouse 

gas emissions of the A1FI scenario, and global 

emissions since 2000 have even exceeded the A1FI 

scenario values in some years (NOAA National 

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

Downscaling

As mentioned previously, GCMs simulate climate 

conditions only for relatively large areas. To 

examine the future climate of areas within northern 

Michigan, 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 

Figure 15.—Projected global greenhouse gas 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 (IPCC 2007).

localized projections, users must consider that 

downscaling introduces further uncertainty to the 

future GCM projections, so it is important to pay 

attention to general trends rather than individual 

pixels or clusters of pixels.

Statistical downscaling has several advantages 

and disadvantages (Daniels et al. 2012, Maslin 

and Austin 2012) (Box 6). It is a relatively simple 

and inexpensive way to produce smaller-scale 

projections using GCMs. One limitation is that 

downscaling assumes that past relationships 

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 
Chapter 2: Climate Change Science and Modeling

47

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). Therefore, 

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 GCM-emissions 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, 

which is 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 Assessment includes 

Douglas Lake at the University of Michigan Biological Station. Photo by Stephen Handler, U.S. Forest Service.
Chapter 2: Climate Change Science and Modeling

48

several other models, which are represented as a 

multi-model average in its 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 

trends in greenhouse gas emissions globally. 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 

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. 
Chapter 2: Climate Change Science and Modeling

49

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.

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 into 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 forests in our 

geographic area of interest, and have stood up to 

rigorous peer review in scientific literature.

Species distribution models establish a statistical 

relationship between the 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, 

A Kirtland’s warbler in the northern Lower Peninsula of 

Michigan. Photo by U.S. Forest Service, Huron-Manistee 

National Forest. 

so they can typically provide projections for the 

suitable habitat of many species over a larger area. 

There are some caveats that users should be aware 

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

These models use a species’ realized niche instead 

of its fundamental niche. The realized niche is the 

actual habitat a species occupies given 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 
Chapter 2: Climate Change Science and Modeling

50

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, these models indicate that trees 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 a 

species distribution model. 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.

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, 

Planted jack pine seedling near Newberry, Michigan. Photo by Stephen Handler, U.S. Forest Service. 
Chapter 2: Climate Change Science and Modeling

51

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 during 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 

Service Forest Inventory and Analysis (FIA) 

program and environmental variables (pertaining 

to climate, soil properties, elevation, land use, and 

fragmentation), which are used to model current 

species abundance with respect to current habitat 

distributions by using statistical techniques. 

DISTRIB then projects future importance values 

and suitable habitat for individual tree species using 

downscaled GCM data readjusted to a 12-mile grid 

(Prasad et al. 2007-ongoing). 

Additionally, 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 to the assessment area.

LANDIS-II

The LANDIS-II model is an integrated modeling 

approach for simulating landscape changes that 

is process-driven and flexible to 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).
Chapter 2: Climate Change Science and Modeling

52

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.

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 grid and a 150-year time 

horizon from the year 2000 to 2150. The landscape 

in Michigan covered 6.4 million acres of forest 

across the northern Lower Peninsula, defined by 

Ecological Section VII (Albert 1995). LANDIS-II 

simulations included 26 common tree species within 

this landscape (Chapter 5). Forest management 

practices were described with a business-as-usual 

scenario, described in more detail in Appendix 4. 

PnET-CN

The PnET-CN model 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). This model 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 over time 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 

(Pugh et al. 2012). 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 
Chapter 2: Climate Change Science and Modeling

53

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 assessment area in Michigan 

with 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 National Resources Conservation Service’s 

Soil Survey Geographic Database (Matthew Peters, 

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 PnET-CN 

can account for discrete disturbance events, we 

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

disturbances for this assessment.

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.

Horseback riders on the Hiawatha National Forest. Photo by U.S. Forest Service, Hiawatha National Forest.
54

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 

communities in northern Michigan during the past 

several thousand years. 

This chapter describes the climate trends in the 

assessment area that have been observed over the 

past century, including documented patterns of 

climate-related processes and extreme weather 

events. Ecosystems in northern Michigan are already 

exhibiting signals that they are responding to shifts 

in temperature and precipitation. This chapter 

presents a few case studies to illustrate the effects 

of climate change on ecological indicators such as 

growing season length, wildlife populations, fish 

populations, and lake ice formation. 

HISTORICAL TRENDS 

IN TEMPERATURE 

AND PRECIPITATION 

Substantial changes in temperature and precipitation 

have been observed during the past 100 years. 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 

historical measured point data onto a continuous 

2.5-mile grid over the entire United States. We 

examined long-term (1901 through 2011) trends for 

annual, seasonal, and monthly temperature (mean, 

maximum, and minimum) and total precipitation 

within the assessment area. Accompanying tables 

and figures present the change over the 110-year 

period estimated from the slope of the linear trend. 

In the following text we highlight increasing or 

decreasing trends which have high probability 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 across the assessment 

area increased 1.7 °F (1.0 °C) between 1901 and 

2011 (ClimateWizard 2012). Average annual 

temperatures fluctuated considerably during the 

20th century, with almost 9 °F (5 °C) separating the 

hottest and coldest years on record (Fig. 16). 

Figure 16.—Annual mean temperature (°F) 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

55

Temperatures in the assessment area have increased 

across all seasons, but the magnitude of increase has 

varied from season to season (Table 12). The largest 

increase in average temperature occurred in winter 

(2.7 °F, 1.5 °C), and spring average temperatures 

increased by 2.6 °F (1.4 °C). Fall and summer mean 

temperatures have increased by smaller amounts. 

Average low temperatures increased at a faster 

rate than average high temperatures in winter and 

summer, but this pattern was reversed in spring 

and fall. The warming trends for the assessment 

area closely follow observed regional trends, which 

also show the greatest increases in winter low 

temperatures (Climate Change Science Program 

[CCSP] 2008). Data from 1971 through 2000 

indicate that the winter warming trend in recent 

years has been roughly three times faster than the 

20th-century trend (ClimateWizard 2012). 

Observed temperature trends also vary by month 

within the assessment area (Fig. 17). Temperature 

increases were greatest during the winter and spring 

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

Annual 1.7 1.6 1.8

Winter (Dec.-Feb.) 2.7 2.2 3.2

Spring (Mar.-May) 2.6 3.1 2.1

Summer (June-Aug.) 1.2 0.8 1.6

Fall (Sept.-Nov.) 0.4 0.5 0.3

Table 12.—Increase in mean annual and seasonal temperatures (°F) from 1901 through 2011 in the assessment area 

(ClimateWizard 2012)

Figure 17.—Change in mean monthly temperatures (°F) from 1901 through 2011 within the assessment area (ClimateWizard 

2012).
Chapter 3: Observed Climate Change

56

months, peaking in February with an increase of 

5.1 °F (2.9 °C) in monthly mean temperature from 

1901 through 2011. March, April, May, August, 

and December also warmed more than the annual 

mean increase. October was the only month when 

average temperature declined during this period, 

although this decline was very slight (-0.2 °F, 

-0.1 °C). Records for individual months correspond 

with the seasonal trends for minimum and maximum 

temperature increases. For example, average low 

temperatures increased by greater amounts than 

average high temperatures for all winter and 

summer months. 

Temperature trends varied geographically across 

the assessment area (Fig. 18). In winter, the increase 

in average temperature was between 2 and 5 °F 

(1.1 and 2.8 °C) across most of the assessment area, 

with areas along the Great Lakes coasts generally 

warming faster than interior areas. The greatest 

winter warming occurred throughout Alger, Delta, 

and Schoolcraft Counties in the Upper Peninsula, 

and Cheboygan and Presque Isle Counties in the 

northern Lower Peninsula. Spring temperature 

patterns were similar, with the largest average 

temperature increases observed in the Munising area 

in the Upper Peninsula. Summer temperatures also 

increased the most in the central Upper Peninsula 

in Alger, Delta, and Schoolcraft Counties. Summer 

temperature trends were more moderate across the 

northern Lower Peninsula, with increases in the 

eastern half of the peninsula. Fall spatial trends 

were similar to summer months, with increases 

and decreases of less than 1 °F (0.6 °C) across 

the assessment area. As mentioned above, fall 

temperatures held essentially constant during the 

20th century. A slight but widespread cooling trend 

was observed in summer and fall for mean average 

and mean minimum temperature in an area around 

Wexford and Newaygo Counties. This decline was 

more pronounced during fall months.

Fall colors on the Hiawatha National Forest. Photo by Autumn Jauck, Hiawatha National Forest.
Chapter 3: Observed Climate Change

57

Figure 18.—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

58

Precipitation

From 1900 to 2011, mean annual precipitation 

increased by 4.9 inches across the assessment area 

(Table 13) (ClimateWizard 2012). The time series of 

annual precipitation for the assessment area displays 

a consistent upward trend despite wide year-to-year 

variability (Fig. 19). From 1950 to 2011, 7 years 

with greater than 35 inches of precipitation occurred 

in the assessment area, but from 1901 to 1950 the 

area never received this much precipitation. 

The trend in the assessment area seems to be that 

summer and fall are getting much wetter, and winter 

and spring are getting only slightly wetter. The 

largest absolute increase in measured precipitation 

from 1901 through 2011 occurred in summer and 

fall (1.7 inches each). Compared to the 1971 through 

2000 baseline period, the precipitation increases in 

these seasons were also proportionally larger than 

in the other seasons. Winter and spring exhibited 

smaller precipitation increases, less than 1 inch 

during each season. With regard to the trends in 

individual months from 1901 through 2011, slight 

declines in precipitation occurred only in February 

and May, although these declines were less than 

0.2 inches for both months. Average precipitation in 

April, August, and October increased by more than 

0.8 inches from 1901 through 2011. 

There were also geographic differences in observed 

precipitation trends across the assessment area 

(Fig. 20). Across the entire year, the greatest 

precipitation increases occurred in an area bounded 

by Manistee, Oceana, Mecosta, and Wexford 

Counties. Precipitation increased between 6 and 

10 inches in this area, which consistently received 

the largest precipitation increases across all seasons. 

The central portion of the Upper Peninsula and 

the eastern half of the Lower Peninsula received 

smaller precipitation increases throughout the year. 

In summer and winter, precipitation decreased less 

than 1 inch in the central Upper Peninsula from 1901 

through 2011.

Interactions between 

Temperature and Precipitation

Observed temperature and precipitation trends 

in the assessment area correspond with larger 

regional climate patterns. Observed temperature 

and precipitation trends from 1950 through 2006

for the entire country show that areas that tended to 

get wetter during warm seasons also tended to have 

reduced high temperatures during those seasons 

Season Average precipitation increase 

Annual 4.9

Winter (Dec.-Feb.) 0.7

Spring (Mar.-May) 0.9

Summer (June-Aug.) 1.7

Fall (Sept.-Nov.) 1.7

Table 13.—Increase in annual and seasonal precipitation 

(inches) from 1901 through 2011 in the assessment area 

(ClimateWizard 2012)

Figure 19.—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

59

Figure 20.—Annual and seasonal precipitation changes 

(inches) 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.

(Portmann et al. 2009). Conversely, areas of the 

country that are getting drier during warm seasons 

are also getting warmer. For the upper Midwest, this 

pattern was most evident during summer and fall. 

Within the assessment area, summer and fall 

exhibited the smallest temperature increases within 

the assessment area (Table 12) and also the largest 

precipitation increases (Table 13). In these seasons, 

the largest average precipitation increases (Fig. 20) 

and the largest declines in average temperatures 

(Fig. 18) occurred in the southwestern portion of 

the assessment area. 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 longterm means of climate and weather data, in many 

circumstances extreme events can have a greater 

impact on forests 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 (CCSP 2008). These events 

can trigger catastrophic disturbances in forests, 

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 likelihood 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. 

Many physical processes important for forest 

ecosystems are also driven by climate and weather 

patterns. These processes, 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 
Chapter 3: Observed Climate Change

60

and stress that might not be anticipated from mean 

climate values alone. This section presents a few 

key trends that have been observed in Michigan or 

throughout the broader region. 

Snow and Winter Storms

Cold and snowy winters are characteristic of 

northern Michigan. The state experiences more 

snowstorms than nearby Wisconsin or Minnesota, 

and both the Upper and Lower Peninsulas have 

significant lake-effect snowfall (Changnon and 

Changnon 2007). Among all the eastern states, 

Michigan ranked second in the number of 

6-inch snowstorms in the 20th century (Changnon 

and Changnon 2007). There are clear winter 

precipitation gradients across the assessment 

area, decreasing from north to south in the eastern 

Upper Peninsula and decreasing from west to east 

in the northern Lower Peninsula (ClimateWizard 

2012). These trends are dictated by the prevailing 

wind direction, topography, and lake-effect snow 

from Lake Superior and Lake Michigan. For the 

assessment area, winter precipitation increased 0.7 

inches during the 20th century, and the most notable 

increase occurred in the southwestern corner of the 

assessment area (Table 13, Fig. 20).

Lake-effect snowfall over Michigan. Image courtesy of the SeaWiFS Project, NASA/Goddard Space Flight Center, and 

ORBIMAGE.
Chapter 3: Observed Climate Change

61

Annual snowfall amounts have been increasing 

between 1 and 6 percent per decade in northern 

Michigan over 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, however. From 1900 to 

1990, there was an increase in snowstorms of 

6 inches or more across the upper Midwest (CCSP

2008). The four-state region including Michigan 

had a decreasing trend of extreme low-snow years 

during the 20th century (Kunkel et al. 2009), which 

corresponds with the slight increase in winter 

precipitation across the assessment area and the 

wider region. Long-term records from across the 

Great Lakes indicate that lake-effect snow increased 

gradually across the region during the 20th century 

due to the warming of these water bodies and the 

decreasing trend in lake-ice cover (Burnett et al. 

2003, Kunkel et al. 2013). When the Great Lakes 

are free of ice during the winter, cold continental air 

masses can pick up warm water vapor and deposit 

this moisture as snowfall. This effect is reduced as 

Great Lakes are increasingly covered with ice. 

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 decreased over the 20th century across 

northern Michigan and northern Wisconsin, even as 

temperatures increased (Isard et al. 2007). A similar 

study also found evidence for decreasing winter 

soil temperatures within the assessment area in 

recent decades (Sinha et al. 2010). Therefore, even 

as winter temperatures have risen in the assessment 

area (Table 12), frost depth may have increased as 

snowpack conditions became more variable. Warmer 

winter air temperatures have led to more snowmelt 

between snowfall events. 

During the entire 20th century, trends in the duration 

and timing of soil frost have been different for the 

Upper and Lower Peninsulas of Michigan. There 

appears to have been a 12- to 24-day decline in 

the annual number of soil frost days across most 

of the northern Lower Peninsula, whereas there 

has been a corresponding increase in the annual 

number of soil frost days in the eastern Upper 

Peninsula. These findings suggest that reductions in 

winter precipitation in the eastern Upper Peninsula 

have exposed the soil to more prolonged cold 

temperatures, even as winter temperatures have 

risen. The increased winter precipitation in the 

northern Lower Peninsula may also have resulted in 

more snowfall to insulate soils from frost in this part 

of the assessment area. Compared to the early 20th 

century, one to two more freeze-thaw cycles occur 

per winter across the assessment area. These events 

indicate more variable winter conditions (Sinha et 

al. 2010). Freeze-thaw 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 of 

days with rainfall of 4 inches or more during the 

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

central United States also supports this trend, noting 

that moderately heavy rainfall events (0.5 to 

1.0 inches) became less frequent while rainfall 

events of at least 1 inch became more common 

(Groisman et al. 2012). Heavy precipitation events 

that used to occur only once every 12 months are 
Chapter 3: Observed Climate Change

62

now occurring every 9 months across the upper 

Midwest, an increase of roughly 35 percent over the 

past 60 years (Madsen and Willcox 2012). 

This trend is exemplified in Michigan, where a 

180-percent increase in rainstorms of 3 inches or 

more occurred between 1960 and 2011 (Fig. 21) 

(Saunders et al. 2012). The northern Lower 

Peninsula in particular had a larger increase in heavy 

rainfall events than surrounding states, with 50- to 

100-percent increases in the frequency of large 

precipitation events from 1931 to 1996 (Kunkel et 

al. 1999). A change in heavy precipitation events has 

not been observed in the Upper Peninsula during this 

timeframe. 

Flooding and Streamflow

Long-term data on flooding is difficult to interpret 

because of the variety of measures used to describe 

Figure 21.—Changes in the frequency of rainfall events 

of 3 inches or more in Michigan 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.

floods. From 1961 to 1979, the National Weather 

Service reported no severe flood years in the fourstate region including Michigan, and there were 

4 such years between 1983 and 2001 (Cartwright 

2005). There are several complicating factors in 

explaining 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 peaks in the 

upper Midwest may be driven by land use practices, 

agricultural practices, and dam construction 

(Villarini et al. 2011). After accounting for these 

factors, however, Midwestern watersheds still 

exhibited increased discharge during the past several 

decades and this trend has been attributed to climate 

change (Tomer and Schilling 2009). 

Extreme Temperatures

High temperatures can influence forests in a variety 

of ways, and some tree species are limited by hot 

growing-season temperatures. Extreme temperatures 

may also be associated with disturbance events 

like droughts and wildfire. Long-term records 

indicate that extreme hot weather has become 

more frequent across the Midwest region over 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 during the past 60 years (Perera et al. 

2012). Summer cool days have become less frequent 

over 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 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).
Chapter 3: Observed Climate Change

63

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 

Midwestern United States 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 the past century indicate that drought 

trends have been diverging between the Upper and 

Lower Peninsulas in Michigan. Between 1895 and 

2013, the trend in the northern Lower Peninsula 

has been toward slightly less common and less 

severe droughts during the growing season, with 

the years between 1910 and 1940 representing the 

most extreme droughts during the period of record 

(National Oceanic and Atmospheric Administration 

National Climatic Data Center 2013). Conversely, 

droughts have occurred more frequently in the 

eastern Upper Peninsula, particularly since 

1980. The western Upper Peninsula (outside the 

assessment area) has also undergone frequent and 

multi-year droughts in the past 30 years (National 

Drought Mitigation Center 2013). 

Thunderstorms and Tornadoes

Strong thunderstorms occur most frequently in the 

summer months in northern Michigan, and these 

weather events can be particularly damaging if 

they generate tornadoes. Based on long-term data 

from 1896 through 1995, the assessment area in 

Michigan averaged 25 to 35 thunderstorm days per 

year (Changnon 2003). There is a clear south-tonorth gradient of decreasing thunderstorm frequency 

across the assessment area and the entire state. 

An average of 17 tornadoes occurred in Michigan 

each year from 1981 through 2010, and tornado 

frequency appears to have remained stable in recent 

decades (National Weather Service 2012). The U.S. 

Annual Tornado Maps from 1950 to 2009 show 

that very few of these tornadoes occurred within 

the assessment area in northern Michigan, with 

only occasional occurrences in the northern Lower 

Peninsula and exceedingly rare tornadoes in the 

eastern Upper Peninsula (National Weather Service 

2012). Michigan made national news in March 2012

for an unusual outbreak of tornadoes across the 

Lower Peninsula, setting new records for the earliest 

observed tornadoes across the state (Erdman 2012). 

These events were also some of the northernmost 

tornadoes ever observed for March. 

Windstorms

In warm months of the year the assessment area 

occasionally experiences very powerful windstorms, 

often called derechos. These events can result 

in substantial windthrow disturbances. A recent 

example from the upper Midwest was the 1999 

storm that passed through neighboring 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). 

Smaller-scale 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 Michigan, and these events are 

quite rare in the eastern Upper Peninsula (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). 
Chapter 3: Observed Climate Change

64

ECOLOGICAL INDICATORS 

OF CLIMATE CHANGE

The following case studies present some examples of 

early indications of climate change within northern 

Michigan. This list is by no means comprehensive, 

but is intended to highlight a few of the ways 

that shifts in temperature, precipitation, and other 

factors may be influencing natural communities in 

Michigan. A list of suggested resources for further 

reading is provided at the end of this chapter. 

Lake Ice

Across Michigan and the entire Great Lakes region, 

long-term records have shown that lake ice is 

breaking up earlier in the spring and forming later 

in the fall (Fig. 22). The combined effect of these 

trends is a longer ice-free period for lakes across the 

region and the assessment area. 

A long-term simulation of historical lake ice 

trends across Michigan, Minnesota, and Wisconsin 

Figure 22.—Lake ice trends from across the Great Lakes region. Figure from Kling et al. (2003) and used with permission of the 

Union of Concerned Scientists.
Chapter 3: Observed Climate Change

65

estimated that breakup dates are occurring earlier 

and freeze dates are occurring later, both by about 

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

assessment area showed more gradual changes 

than southern portions of the state, and Michigan 

in particular showed more change than other states. 

These state and regional patterns of lake ice duration 

correspond with observed trends across the entire 

northern hemisphere (Johnson and Stefan 2006, 

Kling et al. 2003, Magnuson et al. 2000). Within 

the region of Michigan, Minnesota, and Wisconsin, 

observed changes in lake ice duration indicate 

that breakup and freeze dates have been shifting 

three to four times more rapidly since 1980 than 

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

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 an average decrease 

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 nearshore climates and weather events, such as lakeeffect snow.

Tree Phenology and Growth

Certain aspects in the annual life cycle of trees 

are governed by seasonal cues that are relatively 

constant from year to year, like day length. Other 

aspects are controlled by cues that can vary 

substantially from year to year, like temperature. 

For sugar maple, a common northern hardwood 

species across the assessment area, leaf expansion 

in the spring is triggered by temperature. Rather 

than sending out new leaves on the first warm day, 

sugar maples adjust the date of leaf-out based on 

aggregated temperature. Growing degree days, an 

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

the progress of sugar maple leaf expansion and 

development. Leaf expansion marks the beginning of 

a tree’s growing season, and growing season length 

can help dictate how much trees grow over time. 

Researchers have been measuring leaf phenology 

and tree growth in sugar maple stands across 

a latitudinal gradient in northern Michigan for 

more than 20 years (Fig. 23) (Burton et al. 1996). 

Annual growing season across the gradient of study 

sites differs by about 3 weeks, being longer in the 

southern portion of the assessment area where 

annual temperatures are warmer by more than 

5 °F (2.8 °C). Mean annual temperatures increased 

by 2.3 °F (1.3 °C) across the study sites from 1989 

through 2009 (A.J. Burton, Michigan Technological 

University, unpublished data). This change had a 

corresponding influence on growing season length, 

which increased by an average of 11.5 days over 

the study period. The growing season seems to be 

shifting earlier into the spring for sugar maple, as 

leaf-out dates advanced more rapidly than leaf-fall 

dates were delayed (Fig. 24). Tree growth increased 

at each of the study sites in the Lower Peninsula, 

Figure 23.—Map of four long-term study sites (A, B, C, 

D) tracking phenology in hardwood forests in northern 

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

Publishing or its licensors.

A

B

C

D

N
Chapter 3: Observed Climate Change

66

Figure 24.—Leaf phenology trends across four northern hardwood study sites in Michigan. Leaf duration is the number of days 

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

with an average 26-percent gain in aboveground 

biomass. The study site in the Upper Peninsula 

appears to have been limited by late-season droughts 

for most of the years from 2001 through 2009, and 

therefore sugar maple in this location was unable to 

take advantage of the extended growing season.

These phenology and growth trends highlight the 

influence that shifting temperatures can have on 

northern Michigan forests. This work also shows 

that forest productivity may be limited by moisture 

availability, even as temperatures rise and growing 

seasons lengthen. 

Small Mammal Populations 

and Range Shifts

Changes in wildlife populations observed in northern 

Michigan also may be taken as further evidence 

of climate change. Small mammals like mice, 

voles, and chipmunks are important actors in forest 

communities. They are dispersers of seeds and fungi; 

prey for many animals; and consumers of seeds, 

eggs, and insects. Therefore, these small animals 

have cascading influences throughout forests they 

inhabit. 
Chapter 3: Observed Climate Change

67

A study of small mammal populations across 

northern Michigan documented large range shifts 

among nine species that have northern or southern 

range limits within the assessment area (Myers et 

al. 2009). Southerly species such as white-footed 

mice, eastern chipmunks, southern flying squirrels, 

and common opossums were found to be expanding 

their ranges northward across the assessment 

area since 1980, in some cases by as much as 

150 miles. Northern species such as woodland 

deer mice, southern red-backed voles, woodland 

jumping mice, least chipmunks, and northern flying 

squirrels declined in relative abundance across the 

assessment area. In many cases, the proportion of 

southern mammal species appears to have increased 

substantially during the 20th century, replacing their 

northern counterparts in some locations. 

Similar studies have also documented a rapid 

northward range expansion for southern small 

mammals in Ontario (Garroway et al. 2011) and 

Minnesota (Jannett et al. 2007). The steadily 

warming climate across the assessment area seems 

to be an underlying cause for these changes in 

wildlife distribution and abundance. A change in 

Box 7: More Information on Observed Climate Trends and Ecological Indicators

Much more information on historical climate trends 

and ecological indicators for northern Michigan 

exists than was possible to present in this chapter. 

Interested readers will be able to find more 

information from the following resources: 

• Michigan State Climatology Office: 

climate.geo.msu.edu/ 

• Michigan Department of Environmental 

Quality “Climate Change and Global Warming” 

Web page: 

www.michigan.gov/deq/0,4561,7-135-50990-

--,00.html 

• National Phenology Network: 

www.usanpn.org/ 

• ClimateWizard: 

www.climatewizard.org/ 

• Great Lakes Integrated Science and 

Assessments Center: 

glisa.msu.edu/ 

• National Climatic Data Center: 

www.ncdc.noaa.gov/ 

small mammal populations has the potential to affect 

the function and composition of forest communities 

throughout northern Michigan. 

SUMMARY

Several notable shifts have been observed in climate, 

climate-driven processes, and extreme weather 

events within the assessment area. In general, the 

assessment area is experiencing warmer weather 

across all seasons, particularly with respect to 

winter and summer temperatures. Precipitation has 

increased over the 20th century and the precipitation 

regime has intensified, resulting in more large 

precipitation events. Characteristic winter conditions 

are diminishing, and growing seasons have been 

lengthening. These trends are consistent with 

regional, national, and global observations related to 

anthropogenic climate change. Ecological indicators 

are beginning to reflect these changes as well, as 

evidenced by changing ranges of wildlife species 

and changing phenology. For more information on 

observed climate trends, see the resources suggested 

in Box 7.
68

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 during the next century (2010 through 2039, 

2040 through 2069, 2070 through 2099) (Hayhoe 

2010a, 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 through 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 dataset 

(see Chapter 3 and Appendix 2). 

For all climate projections, we report values for 

two general circulation model (GCM)-emissions 

scenario combinations: GFDL A1FI and PCM B1 

(see Chapter 2). The GFDL A1FI model-scenario 

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 over the coming decades rather than 

one particular scenario.

Temperature

The assessment area in Michigan is projected to 

warm substantially during the 21st century (Figs. 25 

through 28). Compared to the 1971 through 2000 

baseline period, the average annual temperature 

is projected to increase 2.2 °F (1.2 °C) under the 

PCM B1scenario and 8.1 °F (4.5 °C) under the 

GFDL A1FI scenario by the end of the century. The 

projected temperature increase is not consistent 

across all seasons. Both models project that winter 

months (December through February) will show 

dramatic warming by the end of the century (PCM 

B1: 2.5 °F, 1.4 °C; GFDL A1FI: 7.3 °F, 4.1 °C), but 

spring months (March through May) will experience 

less warming (PCM B1: 1.7 °F, 0.9 °C; GFDL 

A1FI: 6.0 °F, 3.3 °C). The GFDL A1FI scenario also 

projects an increase of 11.2 °F (6.2 °C) in summer 

temperatures by the end of the century. Summer 

warming is much milder under PCM B1 (2.2 °F, 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

69

1.2 °C). See 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 25.—Projected difference in mean daily temperature (°F) 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 26.—Projected difference in mean daily maximum temperature (°F) 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 27.—Projected difference in mean daily minimum temperature (°F) 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

72

Figure 28.—Projected mean, maximum, and minimum temperatures (°F) 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.

Minimum temperatures are projected to increase 

more than maximum temperatures under both 

scenarios across nearly all seasons. Summer is the 

exception to this trend, with maximum temperature 

increases projected to be slightly greater than the 

projected increases in minimum temperatures under 

both scenarios. By the end of the century, winter 

minimum temperatures are expected to increase 

3.5 °F (1.9 °C) under PCM B1 and 9.0 °F (5.0 °C) 

under GFDL A1FI. 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

73

Temperature increases are projected to be relatively 

minor between the 1971 through 2000 baseline 

period and the 2010 through 2039 period (Fig. 28). 

Spring, in particular, is projected to show shortterm decreases under both scenarios for average 

and maximum mean temperatures. These initial 

decreases are generally reversed by mid-century. 

Additionally, the projections under the two future 

scenarios do not diverge substantially until midcentury. The GFDL A1FI scenario leads to much 

larger temperature increases, with the greatest 

amount of change expected to occur mid-century. 

Alternatively, PCM B1 projections indicate a 

substantially smaller increase in temperature, with 

relatively constant increases during the 21st century. 

Interesting spatial patterns occur within the future 

temperature projections across the assessment 

area (Figs. 25 through 27). Projections of mean 

temperature change are relatively consistent across 

the assessment area under both scenarios. The 

central Upper Peninsula, near the western boundary 

of the assessment area is projected to show reduced 

spring maximum temperatures by about 2 °F 

(1.1 °C) compared to the surrounding area under 

PCM B1. This regional difference does not appear 

in the GFDL A1FI projections. Compared to the 

assessment area as a whole, this area is also expected 

to experience larger minimum temperature increases 

across all seasons. In the northern Lower Peninsula, 

minimum temperatures are generally projected to 

increase more rapidly in the interior areas compared 

to the coastal areas. 

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 across all seasons. 

The two models display the most difference 

for summer months, with the PCM B1 scenario 

projecting very little warming and the GFDL A1F1 

scenario projecting most of the assessment area will 

warm between 10 and 12 °F (5.6 and 6.7 °C).

Precipitation

The two climate scenarios we chose for this 

assessment describe a range of future precipitation 

for the assessment area (Figs. 29 and 30), 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). 

For the assessment area in Michigan, the PCM 

B1 scenario projects that the assessment area will 

receive 2.7 inches more annual precipitation at the 

end of the next century compared to the baseline 

years of 1971 through 2000. The GFDL A1F1 

scenario projects an annual precipitation increase of 

around 1 inch for this same period. See Appendix 3 

for a table of precipitation projections for the 

assessment area, as well as maps of projected 

change in the early century and mid-century.

The seasonal precipitation trends show even more 

departure between the two scenarios. In particular, 

most of the difference between these two climate 

scenarios exists in spring and summer. Under the 

PCM B1 scenario, spring months are projected 

to receive 0.8 inches more precipitation over the 

21st century, with all of the increase coming by 

mid-century. Summer precipitation under this 

scenario is projected to increase around 1 inch by 

mid-century. The GFDL A1FI scenario projects a 

much sharper distinction between these seasons, 

with spring precipitation gaining 2.7 inches and 

summer precipitation declining by 3.8 inches. 

Those projections represent a 37-percent increase 

from baseline spring precipitation, followed by 

a 39-percent decrease from baseline summer 

precipitation. Winter precipitation is expected to 

increase slightly for both scenarios. Fall precipitation 

is expected to decline slightly by the end of the 

century under the PCM B1 scenario (-0.3 inches), 

and fluctuate during the century under GFDL A1FI.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

74

Figure 29.—Projected difference in mean precipitation (inches) at the end of the century (2070 through 2099) compared to 

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

75

Figure 30.—Projected trends in average precipitation (inches) 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

76

Similar to the future temperature projections, 

precipitation across the assessment area is expected 

to change only slightly between the baseline period 

and the early century. The greatest change in 

precipitation projections under GFDL A1FI occur 

mid-century, driven by a 3-inch decline in summer 

precipitation. The PCM B1 scenario also shows 

larger changes by mid-century than by the end of 

the century, as precipitation in spring increases by 

1.0 inches and in summer by 1.5 inches. 

The projected shifts in precipitation are not 

distributed evenly across the assessment area 

(Fig. 29). The PCM B1 scenario projects that 

precipitation increases will occur mostly in the 

western half of the northern Lower Peninsula, 

and that these increases will be delivered across 

winter, spring, and summer. The central Upper 

Peninsula is expected to decline in precipitation 

during summer, and the eastern Lower Peninsula 

is projected to receive less rainfall during spring. 

Precipitation declines in fall are widespread across 

the assessment area under PCM B1. The spatial 

patterns of projected precipitation are more even 

across the assessment area under GFDL A1FI 

(Fig. 29). Winter, spring, and fall increases are 

distributed evenly across the assessment area. 

During summer months, the central Upper Peninsula 

and southern and central Lower Peninsula are 

projected to have the most severe precipitation 

decreases (4 to 6 inches). The maps of precipitation 

departure from baseline conditions for the GFDL 

A1FI 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 

continue to change within the assessment area 

over the 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 increase, the atmosphere is 

able to hold larger quantities of water, which 

causes evaporation and transpiration to increase. 

Increasing evaporation and transpiration both lead 

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

Therefore, precipitation generally needs to increase 

significantly to compensate for even moderate 

temperature increases. One way to examine the 

potential interaction between temperature and 

precipitation shifts is to consider changes in the 

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

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

how completely a forest 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 PnET-CN, so these values also 

incorporate projected changes in forest productivity 

due to changes in temperature and precipitation, 

growing season length, CO2

 fertilization, and other 

factors. Chapter 2 more fully describes the PnETCN model, and further results from this model 

are presented in Chapter 5. Figure 31 displays the 

projected annual and seasonal changes in ET:P for 

both PCM B1 and GFDL A1FI. Positive values 

indicate that ET is increasing relative to available 

moisture and that forests would be undergoing 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.
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

77

Figure 31.—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

78

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 forests. 

Spring values are mixed across the assessment 

area under both scenarios, with drier conditions 

projected under PCM B1 (increasing ET:P) and 

more pronounced moisture increases under GFDL 

A1FI (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 increase 

in summer temperatures of 11.2 °F (6.2 °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 overall ET:P ratio for spring is also slightly 

positive under GFDL A1FI. This result means that 

in some areas evapotranspiration increases may 

outweigh the projected precipitation increase.

Additionally, forests across the assessment area are 

projected to be more moisture limited (increasing 

ET:P) in spring months under PCM B1, 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. This 

pattern is strongest in the eastern Lower Peninsula 

and central Upper Peninsula. 

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 

communities (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 these weather events and physical processes 

(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 with 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

79

in northern Michigan 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 between 40 and 

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 percent under a low climate scenario and 

47 percent under a high climate scenario by the end 

of the century. The largest reductions occurred 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. 

Areas that typically receive lake-effect snow may 

receive increased snow during the early part of the 

21st century while winter temperatures remain cold 

enough and ice cover on the Great Lakes continues 

to decrease (Burnett et al. 2003, Wright et al. 2013). 

As temperatures continue to warm through the 21st 

century, the potential exists for increasingly warm 

winter temperatures to negate the effect of decreased 

lake ice, leading to lake-effect rain events (Kunkel 

et al. 2002). 

Additionally, modeling studies have projected that 

climate change will result in less frequent freezing 

rain events across the assessment area (Lambert 

and Hansen 2011). The projected decreases were 

slight (2.5 to 10 fewer events per decade over the 

21st century), but this trend corresponds with the 

projected increase in wintertime temperatures and 

the overall shift from snowfall to rain. 

Shifts in winter precipitation and temperature 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. Snow cover 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 opposing trends found 

that cold-season soil temperatures may increase 

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

would be approximately 30 to 50 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 32. Total frost 

depth is projected to decline by 40 to 80 percent 

across the assessment area. Therefore, it appears 

that warmer winter air temperatures may more than 

counteract the loss of snow insulation, and that soil 

frost will generally be reduced across the assessment 

area. The northern Lower Peninsula is also projected 

to have two to three more freeze-thaw cycles 

per winter by the end of the century (Sinha and 

Cherkauer 2010). These projections are generally 

consistent with studies of snowpack and soil frost in 

New England forests (Campbell et al. 2010). 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

80

Figure 32.—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.

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 

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 by the end of the century (Fig. 33). The last 

spring frost dates are projected to shift earlier into 

the year at approximately the same rate that first fall 

frost dates will retreat later into the year. Another 

study across the Midwest region projected that the 

assessment area in Michigan will have 22 to 25 

fewer frost days by the middle of the 21st century, 

under the A2 emissions scenario (Kunkel et al. 

2013). The A2 emissions scenario is comparable to 

the A1FI scenario presented in this chapter, although 

it has a more gradual emissions increase through the 

21st century (Chapter 2).
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

81

Figure 33.—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.

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

82

Intense Precipitation

As described in Chapter 3, there is a clear trend 

toward more extreme precipitation events in 

Michigan 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 suggest that the Midwest will experience 

23 percent more rainfall events of at least 1 inch by 

2100, with larger events increasing by progressively 

larger amounts (Kunkel et al. 2013). Other future 

climate projections indicate that the assessment area 

may have 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 over the 21st century. A given 

increase or decrease in precipitation may not be 

distributed evenly across a season or even a month. 

Additionally, large-scale modeling efforts have 

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 effects on streamflow across the Midwest 

projected that runoff and streamflow may shift 

substantially across the assessment area in Michigan 

(Cherkauer and Sinha 2010). Researchers project 

that total winter runoff values may increase by more 

than 100 percent across the assessment area by the 

end of the 21st century, with the most dramatic 

increases occurring in the eastern Upper Peninsula 

as a result of winter melt events and winter rain 

(Fig. 34). Spring total runoff is also projected to 

increase across a range of climate scenarios, and fall 

total runoff is projected to decline by 8 to 32 percent 

across the assessment area. Additionally, summer 

low flow levels may decrease even further, summer 

high flows may increase, and overall flashiness may 

increase in summer (Cherkauer and Sinha 2010). 

Temperature Extremes

In addition to projecting mean temperatures, 

downscaled daily climate data can also 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 

20-30 more days per year above 95 °F (35 °C) 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 
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes

83

Figure 34.—Past (1977 to 2006) seasonal cumulative runoff values and projected (2070 through 2099) changes (percent) 

under a range of climate scenarios (Cherkauer and Sinha 2010). 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

84

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

Cherkauer 2010), and 12 to 15 fewer days that 

are colder than the current 95th percentile cold 

event (Diffenbaugh et al. 2005). These trends are 

consistent with another assessment covering the 

entire Midwest, which projected that northern 

Michigan could have up to 10 more days above 

95 °F (35 °C) and 15 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 has been observed across the entire Midwest 

in recent decades (Changnon 2011a, 2011b; 

Diffenbaugh et al. 2008). 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 also 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 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 projected to be 

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

SUMMARY 

The assessment area is projected to undergo 

profound changes in climate by the end of the 

century. Direct changes include shifts in mean 

temperature and precipitation as well as altered 

timing and extremes. Projected changes also extend 

to more indirect climate-controlled factors such as 

an increasing frequency of extreme rainstorms and 

decreased soil frost during winter. In general, by 

the end of the 21st century northern Michigan is 

projected to experience a climate that is hotter and 

more variable, with more moisture stress towards 

the end of the growing season and less characteristic 

winter weather. In the next chapter, we examine the 

ecological implications of these anticipated changes 

for forest ecosystems. 
85

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 during 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 Michigan 

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 assessment area (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 Climate Change Tree Atlas, LANDIS-II, 

and PnET-CN models contributed results to this 

assessment (Table 14). 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 two combinations of 

general circulation models (GCMs) and emissions 

scenarios 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 for 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

86

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 Albert’s Ecological Sections 

VII & VIII (full assessment 

area)

Albert’s Ecological Section VII 

(northern lower MI)

Albert’s Ecological Sections 

VII & VIII (full assessment 

area)

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 14.—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 DISTRIB, 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 75 of the 134 species 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 75 species was calculated for the years 2070 

through 2099 by comparing the GFDL A1FI and 

PCM B1 scenarios to present values (Table 15). 

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. 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. 
Chapter 5: Future Climate Change Impacts on Forests

87

Common name PCM B1 GFDL A1FI

Declines under Both Scenarios

Balsam fir (-) Decrease Large Decrease

Balsam poplar Decrease Decrease

Black spruce Large Decrease Large Decrease

Jack pine Decrease Large Decrease

Mountain maple (+) Large Decrease Large Decrease

Northern white-cedar Decrease Large Decrease

Paper birch Decrease Large Decrease

Quaking aspen Decrease Large Decrease

Tamarack (-) Decrease Large Decrease

White spruce Large Decrease Large Decrease

Mixed Results Between Scenarios

American basswood No Change Increase

American beech Increase No Change

Bigtooth aspen No Change Large Decrease

Bitternut hickory (+) No Change Large Increase

Black ash (-) No Change Decrease

Black cherry (-) Increase No Change

Boxelder (+) Decrease Large Increase

Bur oak (+) Decrease Large Increase

Chestnut oak (+) No Change Increase

Chokecherry No Change Large Decrease

Eastern cottonwood No Change Large Increase

Eastern hemlock (-) No Change Large Decrease

Eastern hophornbeam (+) No Change Increase

Eastern white pine No Change Large Decrease

Green ash Decrease Increase

Northern pin oak (+) No Change Large Increase

Northern red oak (+) Increase No Change

Ohio buckeye No Change Increase

Peachleaf willow Large Decrease No Change

Pin cherry No Change Large Decrease

Pin oak (-) No Change Large Increase

Red maple (+) No Change Decrease

Red pine No Change Large Decrease

River birch Decrease Increase

Rock elm (-) Decrease Increase

Striped maple No Change Decrease

Sugar maple (+) No Change Large Decrease

Swamp white oak No Change Large Increase

Sweet birch (-) No Change Increase

Yellow birch No Change Large Decrease

Common name PCM B1 GFDL A1FI

Increases under Both Scenarios

American elm Increase Large Increase

American hornbeam Increase Large Increase

Black locust Large Increase Large Increase

Black oak Increase Large Increase

Black walnut Large Increase Large Increase

Black willow (-) Increase Large Increase

Blackgum (+) Increase Large Increase

Eastern redcedar Increase Large Increase

Flowering dogwood Increase Large Increase

Honeylocust (+) Increase Increase

Sassafras Large Increase Large Increase

Scarlet oak Increase Increase

Shagbark hickory Large Increase Large Increase

Silver maple (+) Increase Large Increase

Slippery elm Increase Large Increase

Sycamore Increase Increase

White ash (-) Increase Increase

White oak (+) Increase Large Increase

Species Gaining New Habitat

Black hickory NA New Habitat

Blackjack oak (+) NA New Habitat

Chinquapin oak NA New Habitat

Common persimmon (+) NA New Habitat

Eastern redbud NA New Habitat

Hackberry (+) New Habitat New Habitat

Mockernut hickory (+) New Habitat New Habitat

Osage-orange (+) New Habitat New Habitat

Pawpaw NA New Habitat

Pignut hickory New Habitat New Habitat

Post oak (+) NA New Habitat

Red mulberry New Habitat New Habitat

Shellbark hickory NA New Habitat

Shingle oak NA New Habitat

Sugarberry NA New Habitat

Wild plum NA New Habitat

Yellow-poplar (+) New Habitat New Habitat

Table 15.—Potential changes in suitable habitat for 75 tree species in the assessment area for the PCM B1 and GFDL 

A1FI climate scenarios, from the Tree Atlas model

Species are assigned to change classes based on the comparison 

between end-of-century (2070-2099) 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, and current, early-century, 

mid-century, and late-century importance values.
Chapter 5: Future Climate Change Impacts on Forests

88

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 15), where a plus (+) or 

minus (-) sign after a species name indicates that 

certain modifying factors could lead it 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 16. 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 Michigan, 10 of the 75 

modeled species are projected to decline 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. Many of the 

species projected to decline are boreal or northern 

species near the southern limit of their range 

in the assessment area. Many of these species 

are widespread across the landscape, including 

characteristic species such as balsam fir, black 

spruce, jack pine, quaking aspen, and tamarack. 

Therefore, the reduction of suitable habitat for 

these species may affect a large portion of forested 

landscape in northern Michigan. Black spruce and 

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

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

4. Eastern hemlock hemlock wooly adelgid, drought intolerant

5. Rock elm narrow range of soil requirements, low ability to regenerate from seed

Table 16.—Species with the five highest and five lowest adaptive capacity potential, based on Tree Atlas modifying 

factorsa

a See Appendix 4 for a complete listing of modifying factors for each species.
Chapter 5: Future Climate Change Impacts on Forests

89

white spruce are projected to have the most dramatic 

reductions in suitable habitat. 

Balsam fir, black ash, paper birch, and tamarack also 

have 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. Appendix 4

contains more information on the specific modifying 

factors for each species.

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

Thirty of the 75 total species are projected to have 

mixed results between the two climate scenarios, 

with different combinations of 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 northern pin oak). 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 many species are expected to gain suitable 

habitat or remain stable under PCM B1 and decline 

under GFDL A1FI (e.g., American beech, bigtooth 

aspen, black ash, eastern hemlock, eastern white 

pine, red maple, sugar maple, and yellow birch). 

This subset of species is more characteristically 

northern and generally projected to undergo large 

declines in suitable habitat under GFDL A1FI. This 

outcome suggests that there may be a climate-driven 

“tipping point” somewhere within this range of 

future climates for these species; beyond a certain 

amount of change in temperature and precipitation, 

suitable habitat is projected to decline in the 

assessment area. 

Some of the species with mixed results occur at 

very low densities (e.g., chokecherry and swamp 

white oak) and could essentially be considered 

new entries to the assessment area. A few common 

species such as black ash and eastern hemlock 

have low modifying factor scores, suggesting that 

these species could lose more suitable habitat than 

the model indicates. Anticipated emerald ash borer 

infestations are expected to result in declines for 

black ash across northern Michigan. For American 

beech, the continued threat of beech bark disease 

also presents future challenges for this species. 

Conversely, bitternut hickory, boxelder, bur oak, 

northern red oak, northern pin oak, red maple, and 

sugar maple all have high modifying factor scores. 

Oak wilt may still be a future concern for oak 

species in Michigan. Appendix 4 contains more 

information on the specific modifying factors for 

each species.

Increasing Species

Suitable habitat is projected to increase for 

18 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 black oak, 

black walnut, white ash, and white oak. Additionally, 

most of these species are projected to gain more 

suitable habitat under GFDL A1FI than under PCM 

B1, suggesting that hotter conditions at the end of 

the century in northern Michigan may be suitable for 

an array of southern species. 

All of the species in this category are already 

present within northern Michigan. Several of 

these species occur at very low densities 
Chapter 5: Future Climate Change Impacts on Forests

90

(e.g., black locust, shagbark hickory, and sycamore) 

and could essentially be considered new entries to 

the assessment area. Overall, the potential increases 

in suitable habitat for temperate species raise the 

possibility of a large shift within forest communities 

in the assessment area. 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 Michigan 

potentially could contain a higher overall diversity 

of species in the future, with a blend of temperate 

and boreal species. 

Importantly, DISTRIB results indicate only 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 white ash and 

black willow, have low modifying factor scores 

which suggest that they may be less able to take 

advantage of increasing suitable habitat. White ash is 

threatened by emerald ash borer, so it is not expected 

to fully occupy its projected suitable habitat across 

the assessment area. Similarly, Dutch elm disease 

will continue to limit American elm. This category 

also contains several species with high modifying 

factor scores, such as white oak, silver maple, and 

honey locust.

Species Gaining New Habitat

DISTRIB also projects that 17 species not already 

present will gain new suitable habitat within the 

assessment area by the end of the 21st century. 

A given species will 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 the assessment area. 

Because the Great Lakes and the Straits of Mackinac 

present substantial barriers to migration, southern 

species may be even less able to occupy suitable 

habitat in the eastern Upper Peninsula. Habitat 

fragmentation and the limited dispersal ability of 

seeds could also hinder the northward movement of 

the more southerly species, despite the increase in 

habitat suitability (Ibáñez et al. 2008). 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 become tested and 

used during the next decades (Pedlar et al. 2012). 

Of the 17 species in this category, only 6 are 

projected to gain new suitable habitat under both 

climate scenarios (hackberry, mockernut hickory, 

Osage-orange, pignut hickory, red mulberry, and 

yellow-poplar). Under the GFDL A1FI scenario, 

11 additional migrant species are projected to have 

an increase in suitable habitat within the assessment 

area, including 4 additional oak species and 2 more 

hickory species. Most of the species projected to 

gain suitable habitat in the assessment area only 

under the GFDL A1FI scenario are from ecological 

provinces far to the south. Seven 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. None 

of the species in this category have low modifying 

factor scores. 

Geographic Trends

DISTRIB outputs can be visualized spatially, 

and these results can provide greater context for 

interpreting the projected changes in suitable 

habitat. Figures 35 through 37 display the changes 
Chapter 5: Future Climate Change Impacts on Forests

91

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 the assessment area 

under PCM B1, particularly in the eastern half of 

the Lower Peninsula. Under GFDL A1FI most of the 

suitable aspen habitat is confined to the far northeast 

corner of the eastern Upper Peninsula. Sugar maple 

is projected to lose areas of suitable habitat along the 

southern boundary of the assessment area under both 

climate scenarios, and under PCM B1 the potential 

distribution of this species is nearly equal to the 

current distribution. White oak is virtually absent 

from most of the assessment area today, occurring 

only sporadically in the northern Lower Peninsula. 

Under PCM B1 white oak is projected to gain 

suitable habitat throughout the Lower Peninsula, 

remaining largely absent from the eastern Upper 

Peninsula. Under GFDL A1FI, however, most 

suitable habitat for white oak is projected to occur 

within the eastern Upper Peninsula. 

As mentioned above, DISTRIB results indicate 

only a change in suitable habitat, not necessarily 

the ability of a given species to migrate to newly 

available habitat. A species with large seeds like 

white oak may have a smaller likelihood of naturally 

migrating across northern Wisconsin or across the 

Straits of Mackinac. 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 of broad trends.

Figure 35.—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.
Chapter 5: Future Climate Change Impacts on Forests

92

Figure 37.—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.

Figure 36.—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

93

LANDIS-II

Results from LANDIS-II include aboveground 

biomass (biomass) for 26 tree species and 

distribution maps for aggregated forest type 

communities. Importantly, the LANDIS-II model 

results described in this assessment cover only the 

northern Lower Peninsula (Ecological Section VII) 

(Fig. 38). The LANDIS-II analysis area includes 

the full diversity of forest types considered in this 

assessment, but the results are not assumed to 

represent the eastern Upper Peninsula. LANDIS-II is 

a computationally expensive model, and the authors 

contributing model results for this assessment were 

required to choose a smaller subset of the overall 

assessment area in order to complete the simulations 

(Chapter 2). 

Figure 38.—Analysis area modeled by LANDIS-II for this assessment.

Aboveground Biomass

The LANDIS-II projections are plotted for each 

of 26 tree species over the 21st century for three 

climate scenarios (Fig. 39a and 39b). 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 all 

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 highlights the effect of successional 

changes, such as the continued recovery of forests 

following historical periods of intensive logging. 
Chapter 5: Future Climate Change Impacts on Forests

94

Figure 39a.—LANDIS-II biomass projections over the 21st century for the first 13 of 26 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

Figure 39b.—LANDIS-II biomass projections over the 21st century for the final 13 of 26 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

96

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) inventory data for the assessment area, the 

annual net growth is more than double the harvest 

removals (Chapter 1: growth-to-removal ratio 

for the assessment area = 2.23). The ratio is even 

greater in the northern Lower Peninsula (Pugh et 

al. 2012). 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 recent 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 26 species modeled by LANDIS-II can be 

organized according to the proportional changes 

relative to the current climate scenario (Table 17). 

As mentioned above, the current climate scenario 

is essentially a control scenario, and the climate 

scenarios may either increase or decrease the 

landscape-scale 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. Red pine illustrates 

this pattern (Fig. 39b). 

Decrease - Both Scenarios

Balsam fir (↓) Northern pin oak (↓)

Balsam poplar (↓) Northern white-cedar

Black ash (↓) Paper birch (↓)

Black spruce (↓) Red pine

Eastern hemlock White spruce (↓)

Jack pine (↓) Yellow birch (↓)

Increase PCM B1, Decrease GFDL A1FI

American basswood (↓) Eastern white pine

American beech Green ash

Big toothed aspen Northern red oak

Black cherry Quaking aspen (↓)

Black oak Red maple

Decrease PCM B1, Increase GFDL A1FI

None

Small Change

White oak

Increase - Both Scenarios

American elm White ash

Sugar maple

Table 17.—Potential changes in biomass for 26 tree 

species in the LANDIS-II analysis areaa

a 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 end-ofcentury biomass under the current climate scenario. Up or 

down arrows (↑, ↓) indicate that 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 26 species.

Declining Species

Twelve species are projected to decrease relative 

to the control scenario under both GFDL A1FI and 

PCM B1. These species include characteristic boreal 

species (e.g., balsam fir, black spruce, jack pine) 

as well as temperate species such as northern pin 

oak and black ash. For 10 out of the 12 decreaser 

species, projected declines were greater than 
Chapter 5: Future Climate Change Impacts on Forests

97

50 percent under one or both climate scenarios. The 

GFDL A1FI scenario resulted in larger biomass 

declines for all 12 of these species. In many cases, 

the proportional biomass decline under GFDL A1FI 

is double the expected decline under PCM B1. Jack 

pine illustrates this pattern (Fig. 39a). These trends 

indicate that climate-related shifts are driving the 

biomass projections for these species, and that 

the hotter, drier scenario amplifies the decline. 

Although oaks are generally expected to be favored 

by warmer, droughty conditions, northern pin oak 

is projected to decline under both future climate 

scenarios. Reduced seedling establishment under 

climate change is the main driver affecting the 

projected decline for this species.

Species with Mixed Results

Ten species are projected to increase relative to the 

control scenario under PCM B1, but projected to 

decline under GFDL A1FI. This list mostly includes 

temperate or northern hardwood species, such as 

American basswood, black cherry, eastern white 

pine, and northern red oak. White 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. 

White pine illustrates this pattern (Fig. 39b). This 

scenario also reduces the seedling establishment 

of northern red oak by 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 biomass decline 

projected for black oak appears to be driven by 

reduced aboveground net primary productivity under 

GFDL A1FI. This outcome reinforces the dynamic 

nature of the LANDIS-II model, 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

Three species are projected to increase relative to 

the control scenario under both PCM B1 and GFDL 

A1FI (American elm, sugar maple, and white ash). 

For all species and climate scenarios, projected 

increases were less than 50 percent above the current 

climate scenario. Additionally, the proportional 

biomass increases for these species is larger under 

PCM B1 than GFDL A1FI, as illustrated by the 

projection of sugar maple (Fig. 39b). Importantly, 

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 may limit the 

future increase of white ash. 

Patterns over Time

For many species that are projected to increase 

under PCM B1, 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, balsam poplar, black ash, 

eastern hemlock, green ash, paper birch, sugar maple 

and northern white-cedar all exhibit mid- or latecentury biomass declines under PCM B1. When 

similar “tipping points” are evidenced under GFDL 

A1FI they seem to occur earlier, usually before 

2050. Yellow birch illustrates this pattern (Fig. 39b). 

In these instances, the current climate trend line also 

increases initially, implying that the species had 

already been increasing across the northern Lower 

Peninsula before 2000. Trees take decades to grow, 

reproduce, and experience mortality, so climate-
Chapter 5: Future Climate Change Impacts on Forests

98

driven declines would take 20-50 years to appear in 

landscape-scale biomass figures.

The combined results for all species across the 

LANDIS-II analysis area tell an interesting story. 

For PCM B1, biomass across the northern Lower 

Peninsula is expected to increase quickly until 2075, 

more than doubling in that time. For the remainder 

of the simulation, the combined biomass of these 

26 species more or less reaches a plateau. This 

trajectory closely tracks the combined results for the 

current climate scenario. The GFDL A1FI scenario 

projects a drastically different outcome, with an 

initial increase to 2040, followed by a steady decline 

until 2150. This combined result reflects the steep 

projected declines in boreal species, as well as in 

several mesic species. It appears that the few species 

projected to do well under GFDL A1FI may not be 

able to compensate for the overall biomass declines 

across the landscape. It is important to reiterate that 

LANDIS-II assesses only the 26 species listed in 

Table 17, and does not account for the potential of 

new migrants to enter the northern Lower Peninsula 

or for uncommon species to become more common. 

Productivity

Simulations in LANDIS-II 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 

54 percent under GFDL A1FI by 2100. Alternatively, 

productivity is projected to remain fairly steady 

under PCM B1, declining by only 4 percent by 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. 40). LANDIS-II allows for species 

cohorts to migrate, compete, reproduce, and 

undergo disturbances across the landscape, and 

these transitions are governed by a range of factors 

including soils and landform. To create these 

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

the LANDIS-II simulations were classified into 

six forest types based on characteristic species 

composition. These forest types are not perfectly 

correlated with the forest systems addressed in this 

assessment, and should not be cross-walked directly 

(Appendix 4). Because the LANDIS-II model does 

not effectively simulate lowland forest types or 

nonforested systems, the lowland conifer, lowland 

and riparian hardwoods, and barrens forest systems 

are not included in this classification. Additionally, 

LANDIS-II simulations account only for the 

26 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 gradual 35-percent increase in the proportion 

of the landscape occupied by white pine- and red 

pine-dominated forests. This increase is matched by 

slight declines of the aspen-birch, oak, and spruce-fir 

forest types. 

Under PCM B1, these trends are accentuated. 

A 10-percent increase in area occupied by northern 

hardwoods occurs across the landscape, and red 

pine-white pine forests also increase by roughly 

20 percent. The northern Lower Peninsula in 2150 

under PCM B1 looks like a hardwood-dominated 

landscape, particularly in the northwest portion of 

the Lower Peninsula area that currently contains 

most of the northern hardwood type. The virtual 
Chapter 5: Future Climate Change Impacts on Forests

99

Figure 40.—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 2150 for the graphs as well as for the 

resulting landscape maps.

absence of spruce-fir is also a noticeable change 

across the area, compared to the year 2000. 

The GFDL A1FI scenario results in a similar 

compositional change across the landscape. Sprucefir and aspen-birch forests are projected to decline 

more rapidly under this scenario, and northern 

hardwoods occupy more than 30 percent of the 

landscape by 2150. Red pine-white pine forests 

increase by roughly the same amount as under PCM 

B1, and oak forests maintain roughly the same 

proportion of overall land cover through time. The 

resulting landscape map of forest types looks similar 

to the projection for PCM B1. This forest-type map 

does not account for the differences in biomass 

described above. 

Despite the large difference between the resulting 

forest-type maps under the two future climate 

scenarios, Figure 40 also shows the high degree of 

similarity between these projections for the first 

half of the 21st century. This is partially due to 
Chapter 5: Future Climate Change Impacts on Forests

100

the simplified classification system used for the 

map, and the limited number of species used in the 

simulation. Tree species are relatively long-lived and 

there will be a lag time before changes in climate are 

translated into forest changes. As mentioned above, 

there will also be lags in community response 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 difference 

between the GFDL A1FI, PCM B1, and current 

climate simulations highlights the possibility for 

substantial landscape-level changes to occur under 

the two future climate scenarios. 

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 relative 

percentage changes in productivity. The PnET-CN 

uses 1971 through 2000 as a baseline period, and 

simulates productivity changes from 2000 to 2099. 

For this assessment, PnET-CN results describe 

six aggregated forest classifications rather than 

individual species. These classifications are based 

on FIA forest-type groups (Pugh et al. 2012), which 

are not perfectly matched with the nine forest 

communities considered in this assessment. The six 

FIA forest-type 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 

Michigan. Figure 41 shows the distribution of these 

forest-type groups across the assessment area.

Figure 41.—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.
Chapter 5: Future Climate Change Impacts on Forests

101

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. 42). All 

forest-type groups show increases in productivity, 

with greater absolute and relative increases in 

deciduous groups (aspen/birch, oak/hickory, maple/

beech/birch, and elm/ash/cottonwood) compared to 

conifer forest-type groups (spruce/fir and pine). This 

trend is consistent for both climate scenarios. Under 

PCM B1, deciduous forest-type groups appear to 

peak around an 80-percent increase in productivity 

and the pine and spruce/fir groups are projected to 

plateau around 40 percent. 

Figure 42.—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

102

Under GFDL A1FI, productivity is projected to 

increase throughout the century with no apparent 

plateau. The four deciduous forest-type groups 

exhibit increases of roughly 180 percent across 

the assessment area by the end of the century, with 

pine forests increasing in productivity by nearly 

150 percent. The spruce/fir group is projected to 

show a smaller gain in productivity by the end of the 

century, not quite reaching a 100-percent increase.

For all forest-type groups across the assessment 

area, PCM B1 resulted in an average productivity 

increase of 64 percent compared to the baseline 

period. The absolute increase in productivity from 

baseline to end-of-century averages 332 grams 

biomass per square meter per year and ranges from 

109 to 589 grams biomass per square meter per year. 

The productivity increases projected in GFDL A1FI 

are roughly two times greater than the increases 

projected under PCM B1. Under this scenario, 

the average relative increase in productivity from 

baseline to end-of-century is 138 percent. The 

absolute increase in productivity from baseline to 

end-of-century is on average 712 grams biomass 

per square meter per year and ranges from 225 

to 1493 grams biomass per square meter per year 

across the assessment area.

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. CO2

 fertilization effects 

were larger under GFDL A1FI than under PCM B1. 

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 GFDL A1FI (1 to 2 months across the 

assessment area) than under PCM B1 (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 do 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 incorporates 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 reflect 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. 43). Productivity increases are relatively 

consistent throughout the assessment area under the 

PCM B1 scenario, with a concentration of slightly 

higher productivity increases around Munising in 
Chapter 5: Future Climate Change Impacts on Forests

103

Figure 43.—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.

Alger County and along the southern boundary of 

the assessment area. Under the GFDL A1FI scenario, 

productivity increases are above 150 percent across 

most of the eastern Upper Peninsula. Productivity 

increases are slightly less in the Lower Peninsula, 

averaging approximately 100 percent. 

Geographic trends in the PnET-CN outputs appear as 

a result of a combination of factors. The forest-type 

group assigned to each pixel remains constant in 

these simulations, and different forests 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. These factors could explain 

why maple/beech/birch forests in the northern Lower 

Peninsula show less of a productivity response under 

the GFDL A1FI scenario than such forests in the 

eastern Upper Peninsula. 
Chapter 5: Future Climate Change Impacts on Forests

104

The PnET-CN simulations indicate the productivity 

of forests in the assessment area could switch 

from being temperature limited to water limited 

by the end of the 21st century. See Chapter 4 for 

a discussion of area-wide changes in the ratio of 

evapotranspiration to precipitation, which is a related 

output of the PnET-CN model. Soil water-holding 

capacity could play a critical role in determining 

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 were more able to replace 

declining boreal species in areas with more available 

soil moisture (Pastor and Post 1988, Post and Pastor 

1996).

Discussion of Model Results

Agreements 

The question of how ecosystems might respond to 

future climate changes has been a topic of study for 

close to 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 is particularly true for 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 

PnET-CN show some strong similarities in forest 

change during the next century under a range 

of future climates. All three models agree 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, balsam poplar, black spruce, jack 

pine, northern white-cedar, paper birch, and white 

spruce. The declines for these species were more 

substantial under GFDL A1FI than under PCM B1. 

Additionally, the PnET-CN results project a weaker 

potential productivity response for spruce/fir forests 

compared to other forests. As the climate warms 

through the 21st century, these species and forest 

types are projected to face increasing climate-related 

stress. 

Moreover, both Tree Atlas and LANDIS-II tend to 

agree that many species may increase or remain 

stable under PCM B1 and decrease under GFDL 

A1FI. American beech, bigtooth aspen, black cherry, 

eastern white pine, northern red oak, and red maple 

all follow this general pattern. These results support 

the idea that GFDL A1FI represents a future climate 

that is beyond the tolerance of these species. These 

results also suggest that many temperate species 

present in northern Michigan could tolerate a mild 

degree of warming and increased precipitation, as 

represented by the PCM B1 scenario. 

Tree Atlas and LANDIS-II also agree that American 

elm and white ash will increase under a range of 

future climates. These are temperate hardwood 

species, and it is not surprising that they would 

be tolerant of warmer year-round conditions. 

Outputs from PnET-CN also indicate that elm/ash/

cottonwood forests have the potential for large 

productivity increases across the assessment area 

in Michigan. 

Disagreements

As mentioned above, productivity projections differ 

between the LANDIS-II and PnET-CN simulations 
Chapter 5: Future Climate Change Impacts on Forests

105

used for this assessment. Results from LANDIS-II 

are driven by projections of an overall decline 

in productivity under GFDL A1FI and almost no 

change under PCM B1. According to PnET-CN, 

however, large productivity increases are possible 

under both scenarios, with productivity gains nearly 

twice as large under GFDL A1FI. 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 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 are a few notable disagreements between 

the species-level Tree Atlas and LANDIS-II model 

results. LANDIS-II results suggest that northern pin 

oak will decline under both climate scenarios, due to 

the low probability of seedling establishment starting 

around the year 2050. Neither Tree Atlas (without 

modifying factors) nor PnET-CN incorporates this 

factor. LANDIS-II also projects that black oak 

will decline under GFDL A1FI due to reduced 

productivity. Tree Atlas projects that black oak will 

gain suitable habitat under GFDL A1FI, however, 

and PnET-CN projects that oak/hickory forests 

will experience increasing productivity under both 

climate scenarios. Again, these differences are 

probably related to the assumptions of each model 

and may be influenced by the additional factors 

incorporated into each model—nutrient feedbacks 

and CO2

 fertilization in the case of PnET-CN 

and disturbances and management in the case of 

LANDIS-II. 

Sugar maple is projected to increase in biomass 

according to the LANDIS-II projections, but Tree 

Atlas results indicate that this species will have 

less suitable habitat across the assessment area by 

the year 2100. Additionally, Tree Atlas projects 

that American basswood would gain more suitable 

habitat under GFDL A1FI than PCM B1, yet 

LANDIS-II simulations indicate that this species 

would decline under GFDL A1FI relative to PCM 

B1. The same discrepancy is true for green ash. 

These differences might be an effect of the different 

analysis area used for the LANDIS-II simulations, 

if suitable habitat changes in the eastern Upper 

Peninsula are not reflected in the LANDIS-II results. 

In addition, the LANDIS-II simulations include 

business-as-usual disturbance that may have a strong 

influence on long-term projections. 

As mentioned above, Tree Atlas results project 

suitable habitat, not whether species will be 

physically present. Northern pin oak, black oak, 

sugar maple, basswood, and green ash all appear to 

have a short-term biomass increase before declining 

under GFDL A1FI, which suggests that they may 

face climate-related thresholds in the northern 

Lower Peninsula that are not reflected in Tree Atlas 

calculations of suitable habitat. 

PnET-CN results suggest that maple/beech/birch 

forests will perform better than other forest types 

across the range of climate futures, particularly 

under GFDL A1FI. Tree Atlas projects a decrease in 

red maple suitable habitat under GFDL A1FI, which 

is surprising. This species has the most positive 

modifying factors among all the species assessed by 

Tree Atlas, however, so it is expected that red maple 

will do better than the model suggests in northern 

Michigan. 
Chapter 5: Future Climate Change Impacts on Forests

106

Limitations

All 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 

ecosystem processes or defining features 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 summarized 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

• Changes in regeneration ecology of species

• Indirect effects of competitor species

• Changes in competitive ability of species

• Trends in land-use change or forest fragmentation

• Dynamics in lowland or wetland forests 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 impact models 

• Phenology changes and timing mismatches for 

key ecosystem processes

• Responses of understory vegetation, 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 additional 

layers of complexity and uncertainty. Despite these 

limitations, impact models are still the best tools 

available, 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 

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 Michigan respond to climate 

change. 

The Hughes Lake Fire in 2006. Photo by U.S. Forest Service, 

Huron-Manistee National Forest.
Chapter 5: Future Climate Change Impacts on Forests

107

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. 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 assessment area 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 due to changes in the ratio of 

evapotranspiration to precipitation (Chapter 4). 

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 diminished, which results 

in productivity declines 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.

Few recent examples from published literature 

highlight the possibility for drought stress for 

northern Michigan forests. Studies in hybrid poplar 

plantations show that late-season moisture stress 

can reduce growth in the following growing season 

(Chhin 2010). A recent 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). 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 trembling 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. 

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). Additionally, drought-stressed trees 

are typically more vulnerable to insect pests and 

diseases (Dukes et al. 2009, Michigan Department of 

Natural Resources 2011). 
Chapter 5: Future Climate Change Impacts on Forests

108

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 particular forest is physically affected by a 

given wind event (Peterson 2000; Rich et al. 2007, 

2010). Some models project an overall increase in 

the frequency of extreme wind events across the 

central United States, but it is not projected that any 

increase in blowdowns in the assessment area will 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 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 8). Northern hardwood species are generally 

shallowrooted 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 

soil thawing are advancing rapidly in the spring 

and that overstory leaf-out dates are lagging behind 

(Groffman et al. 2012), so these systems may lose 

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. Many 

forest systems in the assessment area have particular 

soil moisture requirements for the seasonality and 

extent of saturation. Additionally, certain species 
Chapter 5: Future Climate Change Impacts on Forests

109

Box 8: Hardwood Decline in the Upper Great Lakes Region

Northern hardwood stands with sugar maple crown 

dieback have recently been reported in the upper 

Great Lakes region (Michigan Department of Natural 

Resources 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, 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. 44), 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 44.—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

110

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. 2000). 

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 

also project a wide possible range of seasonal 

precipitation and soil moisture (Chapter 4). Such 

variability may expose forests 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, riparian 

and lowland hardwoods and aspen-birch forests 

are all tolerant of varying degrees of hydrologic 

fluctuation. Barrens, jack pine, and red and white 

pine forests are adapted to periodic moisture stress 

and drought. Forests that depend on a more stable 

regime of soil moisture or water levels throughout 

the year or between years may be more stressed 

by hydrologic variation—particularly northern 

hardwoods and lowland conifers. Peatlands have 

been shown to respond in a matter of years to 

Forested coastline and cliffs on Grand Island in the eastern Upper Peninsula. Photo by Autumn Jauck, Hiawatha National 

Forest.
Chapter 5: Future Climate Change Impacts on Forests

111

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 

affected 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. 

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 lowland conifers 

could gradually transition to a novel community 

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, lowland conifer 

forests 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 from 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 northern Lower 

Peninsula is projected to undergo two to three more 

freeze-thaw cycles by the end of the century (Sinha 

and Cherkauer 2010), and these events can also lead 

to greater erosion from forest soils.

Wildfire

Wildfire is an important driver for forests across 

the assessment area in Michigan. Barrens and jack 

pine, mixed pine, and aspen-birch forests are often 

tied to wildfire dynamics, and these communities 

are often associated with particular regimes for 

different kinds of fire, such as surface fires or crown 

fires. Fire could also become an increasing source 

of disturbance in other forest types if climatic 

shifts over the 21st century result in different fire 
Chapter 5: Future Climate Change Impacts on Forests

112

behavior. The climate of an area can directly affect 

the frequency, size and severity of fires; it also 

indirectly affects fire regimes through its influence 

on vegetation vigor, structure, and composition 

(Sommers et al. 2011). 

The response of Michigan’s fire regime to climate 

change will vary 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 fire activity will increase by the end of the 

21st century under climate change (Moritz et al. 

2012). This agreement is particularly high for 

boreal forests, temperate coniferous forests, and 

temperate broadleaf and mixed forests. These global 

assessments correspond with more local research on 

climate and wildfire. Projections for boreal forests 

in Canada estimate that there may be a 100-percent 

increase in the annual area burned by the end of 

the century, along with a 50-percent increase in 

fire frequency (Flannigan et al. 2009). Research 

on boreal forest systems in Quebec projects that 

the wildfire season may shift later into the growing 

season, 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. Key sources of uncertainty 

are future policies on wildfire suppression and 

prescribed fire. Complex spatial patterns of land use 

and active fire management programs also make 

broad-scale predictions of area burned unreliable for 

northern Michigan.

Invasive Species

As described in Chapter 1, nonnative invasive 

species are a major threat to forests in northern 

Michigan. 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 could increase the opportunity 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. 
Chapter 5: Future Climate Change Impacts on Forests

113

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). 

Hemlock wooly adelgid is limited by winter low 

temperatures of -10 to -15 °F (-24 to -26 °C) 

(Michigan Department of Natural Resources 2011). 

Current risk maps for this pest are based on average 

winter minimum temperatures (Fig. 45), but these 

maps do not account for the rapid rise in winter 

minimum temperatures that is projected across a 

range of climate scenarios (Chapter 4). 

It is more difficult to anticipate the response of 

forest pathogens under a warmer future because 

of 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 is sudden oak death, 

a fungal pathogen currently limited to the West 

Coast and southeastern United States. This disease is 

limited by cold temperatures. Current risk maps for 

sudden oak death are based on the climate normal 

for 1971 through 2000, however, and do not account 

for projected climate shifts (Venette and Cohen 

2006). The suitability maps for sudden oak death 

Figure 45.—Risk map for hemlock wooly adelgid (HWA) 

invasion in Michigan, based on observed average winter 

minimum temperatures, from Michigan Department of 

Natural Resources (2012). This map does not consider the 

projected increase in winter temperatures under climate 

change.

based on historical climate data already include 

all of Michigan as marginally suitable habitat. 

Particularly under warmer climate change scenarios, 

it is imaginable that this disease could survive in 

Michigan. 

Herbivory

As mentioned above, changes in snowfall amount 

and duration throughout the assessment area are 

projected to affect 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, 

Hoving et al. 2013, Rempel 2011). Warmer winter 

temperatures and reduced snow depth may lower the 

energy requirements for deer, and increase access to 

forage during winter months (Wisconsin Initiative 

on Climate Change Impacts Wildlife Working Group 
Chapter 5: Future Climate Change Impacts on Forests

114

2011). Conversely, warmer temperatures appear to 

cause greater physiological stress and parasite loads 

in moose. 

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). Tree 

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 project that most mesic hardwood species 

and eastern white pine will gain 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 the effects of CO2 on climate, the 

gas itself 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, 

A whitetailed deer fawn. Photo by Christopher Hoving, Michigan Department of Natural Resources, used with permission. 
Chapter 5: Future Climate Change Impacts on Forests

115

Wang et al. 2006). There is already some evidence 

for increased forest growth in the eastern United 

States (Cole et al. 2010, McMahon et al. 2010), but it 

remains unclear if enhanced growth can be sustained 

(Bonan 2008, Foster et al. 2010). The potential for 

water-use efficiency gains to buffer against moisture 

deficits could be particularly important for forests 

in the assessment area, given the potential for lateseason 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 this 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 community 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. 

Alterations in nutrient cycling 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, and precipitation, as well 

as 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). 
Chapter 5: Future Climate Change Impacts on Forests

116

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 critical in determining the resulting changes 

to forest ecosystems across the assessment area. Just 

as there are typically multiple interacting drivers 

for individual tree mortality (Dietze and Moorcroft 

2011), overall community shifts will 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. For example, 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. The earthworm example 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. The model 

results from Tree Atlas, LANDIS-II, and PnET-CN 

each contribute particular kinds of information 

about how tree species and forest communities 

could potentially respond to a range of possible 

climate futures. Generally, these model projections 

agree that characteristic boreal or northern species 

and forest types may undergo declines in suitable 

habitat, landscape-level biomass, and productivity. 

These model projections indicate that temperate 

species may perform better, raising the possibility 

for potentially large community shifts across the 

assessment area in Michigan. 

Furthermore, 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., standreplacing 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 Michigan. 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. 
117

Chapter 6: Ecosystem Vulnerabilities

This chapter describes the climate change 

vulnerability of nine major forest 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. 46). 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 nine forest systems considered in this 

assessment. 

A SYNTHESIS OF CLIMATE CHANGE 

IMPACTS ON FOREST ECOSYSTEMS 

Potential impacts are the direct and indirect effects 

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. 

Throughout this chapter, statements about potential 

impacts and adaptive capacity factors will be 

qualified with a confidence statement. These 

Figure 46.—Key components of vulnerability, illustrating 

the relationship among exposure, sensitivity, and adaptive 

capacity.
Chapter 6: Ecosystem Vulnerabilities

118

confidence statements are formatted according to a 

confidence determination diagram from the IPCC’s 

recent guidance for authors (Mastrandrea et al. 2010) 

(Fig. 47). Confidence was determined by gauging 

both the level of evidence and level of agreement 

among information sources. Evidence is robust when 

multiple lines of evidence are available 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 Michigan. 

Some of these factors serve as drivers, or 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 

(Chapter 3), and continued temperature increases 

are projected for the assessment area even under 

the most conservative future climate scenario 

(Chapter 4). 

Figure 47.—Confidence determination diagram used in the 

assessment. Adapted from Mastrandrea et al. (2010).

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 Michigan may have 30 to 

40 fewer days of soil frost by the end of the century 

and more freeze-thaw events (Sinha and Cherkauer 

2010). This decrease in snow cover 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. Warmer winters could also 

result in increased survival for insect pests. 
Chapter 6: Ecosystem Vulnerabilities

119

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, an extended 

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, Winkler et 

al. 2012). 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 Michigan in particular (Groisman et al. 2012, 

Kunkel et al. 2008, Saunders et al. 2012), and many 

models agree that this trend will continue during 

the next century (IPCC 2007, Kling et al. 2003, 

Kunkel et al. 2013). Most heavy precipitation events 

occur during summer in Michigan. 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 

following 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.

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 Michigan 

may experience fewer consecutive days without 

precipitation in the future (Kunkel et al. 2013). 
Chapter 6: Ecosystem Vulnerabilities

120

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 for 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 condition may 

be particularly true in locations where soils and 

landforms do not allow retention of precipitation 

from intense events. The northern Lower Peninsula 

generally has coarse, sandy soils, which may offer 

limited water storage capacity. There is a lot of 

variation among model projections, however, and 

it is also possible that the assessment area will 

experience an increase in precipitation sufficient to 

offset increases in evapotranspiration (Winkler et al. 

2012).

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 21st 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 Michigan may change—the 

proportion of surface fires to crown fires, for 

example. 

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 may lead to increases in 

these threats, but research to date has examined few 

species. 
Chapter 6: Ecosystem Vulnerabilities

121

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 Michigan 

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 over 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, 

white spruce, tamarack, jack pine, northern whitecedar, and paper birch (Chapter 5). PnET-CN results 

also suggest that spruce-fir forests may be less able 

to experience productivity gains than other forest 

types across the range of anticipated climate futures. 

These northern species may persist in the assessment 

area throughout the 21st century, but with declining 

vigor. Boreal species may remain in areas with 

favorable soils, management, or landscape features. 

Additionally, boreal species may be able to persist in 

the assessment area if competitor species are unable 

to colonize these areas (Iverson et al. 2008).

Southern species will be favored by climate 

change (medium evidence, high agreement).

Impact models agree that suitable habitat and 

biomass will increase for many temperate species 

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 may become 

more abundant and more widespread under a range 

of climate futures (Chapter 5). Species projected to 

increase in suitable habitat in the assessment area 

include American basswood, black cherry, green ash, 

white ash, white oak, and a variety of minor southern 

species (Chapter 5). PnET-CN outputs also indicate 

that deciduous forest types have the potential 

for large productivity increases across northern 

Michigan. In addition, Tree Atlas results project that 
Chapter 6: Ecosystem Vulnerabilities

122

suitable habitat may become available for species 

not currently found in the assessment area (e.g., 

mockernut hickory, honeylocust, and post oak) by 

the end of the century. Conversely, LANDIS-II 

projects that many temperate and southern species 

may experience biomass declines under the GFDL 

A1FI scenario. This projection indicates that many 

potential increasers may in fact be limited under 

hotter, drier conditions. 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 of potentially large changes 

in forest communities across northern Michigan. 

Generally, the models indicate that climate trends 

may favor hardwoods across the landscape after 

2050, though ecological lag times and management 

decisions may slow forest-type conversions. 

Douglas Lake at the University of Michigan Biological Station. Photo by Stephen Handler, U.S. Forest Service.
Chapter 6: Ecosystem Vulnerabilities

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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). Thus, 

the forest systems described in the assessment area 

may shift and 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. 2012). 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). Some 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. 

Results from PnET-CN 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. Simulations in LANDIS-II do not include 

the possible effects of CO2

 fertilization, which could 

increase productivity. Episodic disturbances such 

as fires, wind events, droughts, and pest outbreaks 

may reduce productivity in certain areas over 

different time scales. In addition, lags in 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).

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 enhance or reduce 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 more-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). Consequently, less diverse 

communities are 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. Northern hardwood forests 

generally support a large number of tree species and 

therefore have many possible future trajectories, 

but aspen-birch forests have fewer potential paths. 

Genetic diversity within species is also critical 

for the ability of populations to adapt to climate 

change, because species with high genetic variation 
Chapter 6: Ecosystem Vulnerabilities

124

have better odds of producing individuals that can 

withstand extreme events and adapt to changes over 

time (Reusch et al. 2005). 

Species in fragmented landscapes will have less 

opportunity to migrate in response to climate 

change (limited evidence, high agreement). The 

dispersal ability of individual species is reduced 

in fragmented landscapes, but the future degree 

of landscape fragmentation and the potential 

for human-assisted migration are two areas of 

uncertainty. 

Habitat fragmentation can hinder the ability of 

tree species to migrate to more suitable habitat on 

the landscape, especially if the surrounding area 

is not forested (Ibáñez et al. 2006, Iverson et al. 

2004a). Modeling results indicate that mean centers 

of suitable habitat for tree species will migrate 

between 60 and 350 miles by the year 2100 under 

a high emissions scenario and between 30 and 

250 miles under milder climate change scenarios 

(Iverson et al. 2004a). Based on gathered data of 

seedling distributions, it has been estimated that 

many northern tree species could possibly migrate 

northward at a rate of 60 miles per century (Woodall 

et al. 2009), and 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 migrating species (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 particularly difficult for species and systems 

with narrow habitat requirements.

Several species and forest types in northern 

Michigan are confined to particular habitats on the 

landscape, whether through particular requirements 

for hydrologic regimes or soil types, or other 

reasons. Similar to species occurring in fragmented 

landscapes, isolated species and systems face 

additional barriers to migration (Jump and Peñuelas 

2005). More-widespread species may also have 

particular habitat requirements. For example, 

sugar maple is often limited to soils that are rich in 

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. Riparian forests are not 

expected to migrate to upland areas because many 

species depend on seasonal flood dynamics for 

regeneration and a competitive advantage. Similarly, 

lowland conifer systems often contain a unique 

mix of species that are adapted to low pH values, 

peat soils, and particular water table regimes. These 

systems face additional challenges in migration 

compared to more-widespread species with broad 

ecological tolerances. 

Systems that are more tolerant of disturbance 

have less risk of declining on the landscape 

(medium evidence, high agreement). Basic 

ecological theory and other evidence support 

the idea that systems adapted to more frequent 

disturbance will be at lower risk. 

Disturbances such as wildfire, flooding, and pest 

outbreaks are expected to increase in the assessment 

area (Chapters 4 and 5). Northern hardwoods in 

particular are adapted to gap-phase disturbances, 

with stand-replacing events occurring over hundreds 

or thousands of years. If climate changes the nature 

of disturbance events in these systems so that standreplacing events become more frequent, northern 

hardwoods may suffer. Mesic systems can create 

conditions that could buffer against fire and drought 

to some extent, but these systems may not do well 

if soil moisture declines significantly (Nowacki and 
Chapter 6: Ecosystem Vulnerabilities

125

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 

climate-driven disturbances. This principle holds 

true only to a given point, because it is also possible 

for disturbance-adapted systems to undergo too 

much disruption. For example, jack pine systems 

might cover a greater extent under drier conditions 

with more frequent fire, but these systems might 

also convert to barrens or open grasslands if drought 

becomes too severe or fire becomes too frequent. 

Similarly, more-frequent surface fires could benefit 

red pine and white pine forests, but a shift to a 

crown-fire regime could reduce these species.

VULNERABILITY DETERMINATIONS 

FOR INDIVIDUAL FOREST SYSTEMS

Climate-induced shifts in drivers, stressors, and 

dominant tree species will result in different impacts 

to forested systems within the assessment area. 

Some communities may have a greater capacity to 

adapt to these changes than others, whereas some 

may be susceptible to relatively minor impacts. 

Therefore, it is helpful to consider these factors for 

individual forest systems, in addition to describing 

general principles related to vulnerability and 

adaptive capacity. Table 18 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 27 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.

Overall vulnerability determinations ranged from 

moderate-low (oak associations and barrens) to 

high (upland spruce-fir) (Table 19). Impacts were 

rated as being most negative for upland spruce-fir 

and lowland conifers, and most positive for barrens. 

Adaptive capacity was rated lowest for upland 

spruce-fir, and highest for northern hardwoods, oak 

associations, and barrens. 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 limited 

this component of overall confidence. The ratings 

of agreement among information also tended to be 

in the medium range. Contrasting information about 

precipitation regimes under the high and low climate 

change scenarios was one factor that limited the 

level of agreement among information. The way that 

forest communities were organized and described for 

this assessment also limited the agreement in some 

instances. 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.

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 system.
Chapter 6: Ecosystem Vulnerabilities

126

Forest system Major drivers Major stressors

Upland spruce-fir favored in thin nutrient-poor soils or glacial lake 

plains, high snowfall areas with short growing 

seasons, favored by moderated climate in lake-effect 

areas, catastrophic disturbances from fire, wind, and 

pests

spruce budworm and other pests, drought, 

deer and moose herbivory

Jack pine 

(including pine-oak)

coarse-textured soils, upland areas, droughttolerant, fire-return intervals 50 to 250 years, 

requires scarification or fire for regeneration, 

favored by cold temperatures

fire suppression or exclusion, insect 

pests and diseases, difficulty of applying 

prescribed fire

Red pine-white pine sandy to dry-mesic soils, limited by high summer 

temperatures, dependent on disturbance for 

regeneration, red pine regeneration primarily 

through planting, favored by drought, fire-return 

intervals 50 to 250 years

fire suppression or exclusion, difficulty 

of applying prescribed fire, hardwood 

encroachment, insect pests and diseases, 

deer herbivory

Lowland conifers peat or mineral soils, low landscape positions, 

groundwater seepage, saturated throughout 

growing season, windthrow events, stand-replacing 

fire on long cycles, limited by drought

changes to water table, roads and beaver 

dams, insect pests and diseases, deer 

herbivory, loss of coarse woody debris, 

drought and stand-replacing fire

Aspen-birch gradient of soil types and landforms, frequent 

disturbance or management, limited by warm 

temperatures and moisture stress

fire suppression, forest tent caterpillar 

and gypsy moth, drought, deer herbivory, 

hypoxylon canker

Northern hardwoods 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, management that 

removes coarse woody debris or reduces 

diversity

Lowland-riparian 

hardwoods

alluvial soils or impermeable clay lens, nutrientrich soils, seasonally or annually inundated or 

saturated, connectivity to river or water table, tip-up 

mounds and periodic dry conditions important for 

regeneration

changes to soil moisture regime, ongoing 

ash decline, invasive species, insect pests 

and disease, drought, deer herbivory

Oak associations sandy to dry-mesic soils, limited by cold 

temperatures, dependent on disturbance for 

regeneration, drought tolerant, fire-return intervals 

50 to 250 years or longer

fire suppression or exclusion, difficulty of 

applying prescribed fire, insect pests and 

diseases, deer and rabbit herbivory, exotic 

species, severe drought, root frost

Barrens coarse-textured and excessively drained soils, 

overlapping or short-interval fires, canopy cover only 

5 to 25 percent, favored by cold temperatures and 

frost pockets

fire suppression or exclusion, insect 

pests and diseases, difficulty of 

applying prescribed fire, competition 

from herbaceous species and woody 

encroachment, invasive species

Table 18.—Forest systems considered in this assessment, with a summary of major drivers and stressors for each 

system (Cohen 2000a, 2000b, 2001, 2002a, 2002b, 2006, 2007; Comer 1996; Kost 2002; Kost et al. 2007; Slaughter 

et al. 2007; Tepley et al. 2004; Weber et al. 2006, 2007)
Chapter 6: Ecosystem Vulnerabilities

127

Forest system Potential impacts Adaptive capacity Vulnerability Evidence Agreement

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

Jack pine (including pine-oak) Moderate Moderate-Low High-Moderate Medium Medium-High

Red pine-white pine Moderate Moderate-Low High-Moderate Limited-Medium Medium

Lowland conifers Negative Moderate-Low High-Moderate Medium Medium

Aspen-birch Moderate-Negative Moderate Moderate Medium Medium

Northern hardwoods Moderate Moderate-High Moderate Medium Medium

Lowland-riparian hardwoods Moderate-Negative Moderate Moderate Medium Low-Medium

Oak associations Moderate Moderate-High Low-Moderate Medium Medium

Barrens Moderate-Positive Moderate-High Low-Moderate Limited-Medium Medium

Table 19.—Vulnerability determination summaries for the forest systems considered in this assessment

Cross-country ski trails in the eastern Upper Peninsula. Photo by U.S. Forest Service, Hiawatha National Forest.
Chapter 6: Ecosystem Vulnerabilities

128

Upland Spruce-Fir 

High Vulnerability (medium-high evidence, medium-high agreement) 

The boreal species within upland spruce-fir forests are not projected to tolerate warmer temperatures, 

increased competition from other forest types, and more-active forest pests. These forests are generally 

restricted to lake-effect areas on the landscape and are not well-equipped to adapt to climate change. 

Negative Potential Impacts

Drivers: Within the assessment area, upland 

spruce-fir forests are typically confined to areas 

with shorter growing seasons and lake-effect snow 

and fog. Water temperatures of the Great Lakes are 

increasing faster than air temperatures, so lake-effect 

fog may become less common during the growing 

season. Uncertainty exists for snowfall levels in 

the assessment area, particularly for lake-effect 

snow, but it is projected that snowfall will decline 

by the end of the century. Additionally, several 

species in this system are limited by high growingseason temperatures, so projected warming in the 

assessment area may exceed the physiological limits 

of this forest type.

Dominant Species: Considering the range of 

possible climate futures, most dominant species that 

constitute upland spruce-fir forests are projected to 

decline in suitable habitat and biomass across the 

assessment area (balsam fir, northern white-cedar, 

white spruce, and paper birch). These are boreal 

species near their southern range limits. The same 

modeling studies offer mixed results for red maple, 

quaking aspen, and white pine, but these species are 

all generally projected to fare worse under the hotter, 

drier GFDL AFF1 scenario. Spruce-fir forests may 

be less able to take advantage of warmer conditions 

and longer growing seasons for productivity 

increases. 

Stressors: Insect pests like spruce budworm 

may become more active and damaging under a 

warmer climate. Prolonged droughts and warmer 

temperatures expected under climate change will 

also be particularly stressful for this forest type. 

White-tailed deer populations are also anticipated 

to increase with warmer winters, so herbivory 

on preferred species may continue to hinder 

regeneration for certain species like northern whitecedar. Conversely, non-palatable boreal conifers may 

benefit from reduced competition if deer herbivory 

prevents hardwood expansion into these sites.

Low Adaptive Capacity 

Upland spruce-fir forests can persist on sandy, 

nutrient-poor soils, so they may be able to tolerate 

short-term moisture stress. Several of these species 

produce seed and regenerate well after disturbance 

such as fire. These forests have relatively low 

diversity, however, which leads to fewer possible 

future trajectories (lower response diversity). Boreal 

forests are also spatially confined in the assessment 

area based on lake-effect conditions, and they are 

not expected to colonize new areas under anticipated 

future conditions. Hardwood forests may be able to 

invade sites occupied by boreal forests as growing 

seasons lengthen, but poor soils and comparably 

colder temperatures may limit the ability of some 

hardwoods to outcompete boreal conifers. 
Chapter 6: Ecosystem Vulnerabilities

129

A spruce plantation with an aspen component. Photo by 

Maria Janowiak, U.S. Forest Service.

A mature upland spruce-fir forest. Photo by Joshua Cohen, 

Michigan Natural Features Inventory, used with permission.

A previously burned upland spruce-fir forest. Photo by 

Joshua Cohen, Michigan Natural Features Inventory, used 

with permission.
Chapter 6: Ecosystem Vulnerabilities

130

Jack Pine (including Pine-Oak) 

High-Moderate Vulnerability (medium evidence, medium-high agreement) 

Impact models project declines in suitable habitat and biomass for jack pine, and the potential exists for 

increased hardwood competition and greater pest and disease activity. A high tolerance for disturbance 

and the current management emphasis increase the adaptive capacity of this system. 

Moderate Potential Impacts

Drivers: Jack pine forests are generally found on 

sites with coarse-textured soils, and may be able 

to tolerate the projected soil moisture decreases 

during the summer months. Studies project that 

wildfires may burn larger areas in northern Michigan 

under climate change. Severe wind events or pest 

outbreaks could also provide more fuel buildup for 

large fire events. Greater wildfire activity could 

be positive for these forest types, but too much 

change to the fire regime might hamper regeneration 

and cause these forests to shift to barrens. Jack 

pine forests are also found in frost pocket areas, 

and future warmer temperatures may allow frostintolerant species like oaks to invade these sites. 

Dominant Species: Considering the range of 

possible future climates, jack pine is expected 

to decline in suitable habitat and biomass across 

the assessment area over the next 100 years. This 

species is at the southern extent of its range in 

Michigan. Red pine is also projected to decline in 

biomass and suitable habitat, particularly under 

the hotter, drier GFDL A1FI scenario. Projections 

are mixed for eastern white pine, northern red oak, 

northern pin oak, and red maple, but these species 

are generally expected to decline under GFDL 

A1FI. Pine forests may be able to take advantage of 

warmer conditions and longer growing seasons, if 

CO2

 fertilization does in fact boost long-term wateruse efficiency and productivity. 

Stressors: Insect pests like jack pine budworm 

and diseases like scleroderris canker may become 

more damaging under a warmer climate, and the 

possibility exists for new pests such as western bark 

beetles to arrive in the assessment area. The window 

of opportunity for applying prescribed fire to jack 

pine forests may shift under future climate change, 

but it is unclear if it would expand the potential to 

use fire as a management tool. The potential also 

exists for warmer temperatures to accelerate litter 

layer decomposition in these forests, leading to 

lower water-holding capacity and greater moisture 

stress. These conditions could prompt a shift to 

barrens systems in some locations. 

Low-Moderate Adaptive Capacity 

Jack pine forests are tolerant of drought and 

disturbances and thus have greater adaptive capacity 

to climate change. These forests can persist on poor 

soils and, under future drier conditions, may gain 

territory that is currently more mesic. Contemporary 

forest management practices, including Kirtland’s 

warbler management, favors jack pine forests in 

certain areas on the landscape, including heavily 

managed plantations. Overall, low species diversity 

gives this forest type few alternatives if conditions 

shift beyond tolerable limits.
Chapter 6: Ecosystem Vulnerabilities

131

Bigtooth aspen in a young jack pine stand. Photo by 

Christopher Hoving, Michigan Department of Natural 

Resources, used with permission.

Jack pine forest. Photo by Joshua Cohen, Michigan Natural Features Inventory, used with 

permission.

Fire scars in a jack pine stand. Photo by Christopher Hoving, 

Michigan Department of Natural Resources, used with 

permission.
Chapter 6: Ecosystem Vulnerabilities

132

Red Pine-White Pine 

High-Moderate Vulnerability (limited-medium evidence, medium agreement) 

A major threat to red pine and white pine forests is the potential for greater pest and disease activity, 

along with the potential for interactions among stressors. Tolerance for drought and disturbance 

increases the adaptive capacity of these forests, and the future fire regime is a primary uncertainty. 

Moderate Potential Impacts

Drivers: More frequent moisture stress or more 

extensive droughts could benefit red pine and white 

pine (RP-WP) forests on mesic soils, but could 

impair stands on the driest sites. Moisture stress 

could favor jack pine or northern pin oak on already 

marginal RP-WP sites. Increased surface fires could 

be a positive influence for these forest types, but 

a shift to more frequent crown fires could hamper 

regeneration and favor jack pine. Management has 

maintained red pine across much of the assessment 

area, and regeneration of this species usually relies 

on planted seedlings. Seasonal shifts in precipitation 

patterns, particularly the trend toward wetter springs 

and drier summers, may impair the survival of 

planted seedlings.

Dominant Species: Considering the range of 

possible climate futures, models project declines in 

suitable habitat and biomass for red pine across the 

assessment area. Projections are mixed for white 

pine, but models indicate that this species will fare 

worse under the hotter, drier GFDL A1FI scenario. 

Pine forests may be able to increase productivity 

with warmer conditions and longer growing seasons, 

but this depends on the potential effects of CO2

fertilization. Minor components of RP-WP forests 

like northern red oak, black oak, and red maple 

also have mixed projections, although jack pine is 

anticipated to decline. 

Stressors: Insect pests and diseases such as white 

pine tip weevil and red pine shoot blight may 

become more virulent and damaging under a warmer 

climate. The continued shift toward mesic species 

in these forests may continue if fire suppression 

activities remain constant and broadleaf species like 

red maple and black cherry increase under climate 

change. With the anticipated increase in white-tailed 

deer populations resulting from warmer winters, 

herbivory on preferred species may continue to 

hinder regeneration. Red pine in particular may be 

limited by warm temperatures, so projected warming 

in the assessment area may exceed the physiological 

limits of this species.

Moderate-Low Adaptive Capacity 

Red pine and white pine forests are generally 

tolerant of drought and disturbances, which lends 

these forests greater adaptive capacity to climate 

change. This forest type could also expand to new 

favorable locations on the landscape if overall 

conditions result in increased drying. Thus, RP-WP 

forests could gain territory in mesic aspen-birch, 

oak, or northern hardwood sites. Low species 

diversity is a drawback for this forest type in 

general. White pine can tolerate a wider range of 

soil and moisture conditions than can red pine, and 

red pine has relatively low genetic diversity. Natural 

regeneration of red pine is often limited following 

harvest, particularly in the southern portion of the 

assessment area.
Chapter 6: Ecosystem Vulnerabilities

133

Mature stand of red pine. Photo by Joshua Cohen, Michigan 

Natural Features Inventory, used with permission.

Red pine stand at the University of Michigan Biological 

Station. Photo by Stephen Handler, U.S. Forest Service.

Prescribed fire in a white pine stand. Photo by Christopher 

Hoving, Michigan Department of Natural Resources, used 

with permission.
Chapter 6: Ecosystem Vulnerabilities

134

Lowland Conifers 

High-Moderate Vulnerability (medium evidence, medium agreement) 

Lowland conifer forests 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 and groundwater levels are the primary uncertainties for this forest type. 

Negative Potential Impacts

Drivers: Climate change has the potential to alter 

the precipitation patterns and soil moisture regimes 

in low-lying areas across the assessment area. 

Lowland conifer forests function in a relatively 

narrow window of water table conditions, although 

larger lowlands may be able to withstand a wider 

range of conditions than small, isolated depressions. 

Sphagnum moss, the primary source of peat in these 

systems, may be susceptible to warmer conditions. 

Lowlands may occupy potential refugia sites if they 

remain cooler longer than upland areas across the 

landscape, but this benefit may be temporary.

Dominant Species: Most lowland conifer species 

are near southern range limits in Michigan, and may 

not tolerate warmer conditions. Balsam fir, black 

spruce, eastern hemlock, northern white-cedar, paper 

birch, tamarack, and quaking aspen are projected to 

experience significant declines in suitable habitat 

and biomass across the landscape. The same 

modeling studies offer mixed results for eastern 

white pine, but nearly all conifers are projected to 

fare worse under GFDL A1FI. Spruce-fir forests may 

be less able to increase productivity under future 

conditions than other forest types. These forests 

may not maintain their identity if dominant species 

decline and water tables change. Impact models 

presented in this assessment are not well-suited 

specifically to address lowland forests, so results 

should be interpreted with a degree of caution.

Stressors: Roads and other watershed modifications 

are already harming lowland conifer forests in some 

parts of the assessment area. Water table impacts 

may be intensified as the hydrologic cycle becomes 

more episodic. Warmer growing seasons may 

increase evapotranspiration rates and reduce the rate 

of peat accumulation in these forests, and peat layers 

may begin to erode as decomposition rates increase. 

Warmer winters may also increase the occurrence 

of winter-burn in lowland conifer forests, and allow 

for more frequent outbreaks of pests like tamarack 

sawfly and spruce budworm. Deer herbivory limits 

regeneration of northern white-cedar in particular. 

Low-Moderate Adaptive Capacity

Lowland conifer forests that are connected to 

groundwater may be less vulnerable to seasonal or 

short-term moisture deficits. Low-lying areas on 

the landscape might also be protected from summer 

droughts if increased winter and spring precipitation 

is retained. Prolonged droughts, however, would be 

harmful to this forest type. Lowland conifer forests 

are not expected to expand to new territory within 

the assessment area or outcompete other forest 

types, but acid or alkaline soil conditions may make 

them less suitable for invasive species or competing 

forest types. Additionally, black spruce is capable of 

reproducing asexually in lowland systems. 
Chapter 6: Ecosystem Vulnerabilities

135

Lowland conifer forest. Photo by Joshua Cohen, Michigan 

Natural Features Inventory, used with permission.

Complex structure in a lowland conifer forest. Photo by 

Joshua Cohen, Michigan Natural Features Inventory, used 

with permission. 

Northern white-cedar in riparian area. Photo by Matthew Duveneck, Portland State 

University, used with permission. 
Chapter 6: Ecosystem Vulnerabilities

136

Aspen-Birch 

Moderate Vulnerability (medium evidence, medium agreement) 

Impact models project substantial declines for aspen and birch in northern Michigan, and the potential 

exists for multiple stressors to interact under climate change—particularly drought and forest pests. But 

these forests are a management priority, are adapted to disturbance, and exist on a wide range of sites. 

Moderate-Negative Potential Impacts 

Drivers: If climate change results in increased 

moisture stress during the growing season, drier 

sites will be exposed to greater drought stress and 

mortality. Warmer growing-season temperatures 

might encourage more suckering after disturbance, 

but projected temperatures in the assessment area 

may be beyond the physiological limits of aspen and 

birch species. Disturbance from wildfire, blowdown 

events, or continued management could benefit this 

forest type, but it is unknown whether aspen-birch 

forests would continue to persist if stand-replacing 

disturbances become much more frequent. 

Dominant Species: Under a range of possible 

climate futures, balsam poplar, bigtooth aspen, 

and paper birch are expected to decline in suitable 

habitat and biomass across the assessment area. 

Balsam poplar and paper birch are near their 

southern range limits in Michigan. Models show 

mixed results for quaking aspen under the PCM B1 

scenario, but they project that quaking aspen will 

decline substantially under GFDL A1FI. If future 

climate change is less extreme than the GFDL A1FI 

scenario, the possibility exists for this species to fare 

better across the assessment area. 

Stressors: Climate change is expected to intensify 

several key stressors for aspen-birch forests. Insect 

pests such as forest tent caterpillar and gypsy moth 

may become more damaging under a warmer 

climate, along with diseases like hypoxylon canker. 

White-tailed deer herbivory may also increase 

with warmer winters. The possibility exists for 

interactions among multiple stressors to lead to 

more-severe climate change impacts. For example, 

overlapping drought, earthworm activity, and insect 

pest outbreaks could have negative synergistic 

effects. Multiple harvests of aspen may also lead 

to a decline of nutrient status or productivity on 

these sites, so interactions may occur between 

management actions and climate-driven stressors. 

Moderate Adaptive Capacity 

Aspen and birch are adapted to disturbance and 

capable of persisting at low densities until openings 

appear. These species also exist on a wide range 

of soils and landforms. The ability to reproduce 

clonally is also an advantage in some instances, 

particularly for clones with higher tolerance for 

warmer conditions or droughts. This forest type 

is a management priority for many landowners. 

The aspen-birch forest type may be vulnerable to 

declines across the landscape, even if aspen and 

birch species still occur sporadically in the future. 

This forest type also has low species diversity, 

which may reduce adaptability to future changing 

conditions. 
Chapter 6: Ecosystem Vulnerabilities

137

Aspen in the northern Lower Peninsula. Photo by Catherine 

Salm, Huron-Manistee National Forest.

A young aspen stand. Photo by Maria Janowiak, U.S. Forest 

Service.

A stand of paper birch in autumn. Photo by Autumn Jauck, U.S. Forest Service, 

Hiawatha National Forest.
Chapter 6: Ecosystem Vulnerabilities

138

Northern Hardwoods 

Moderate Vulnerability (medium evidence, medium agreement) 

Climate change may intensify several major stressors for northern hardwoods, such as drought, invasive 

species, and forest pests. High species diversity may increase resilience to future change. Uncertainty 

regarding future moisture regimes and potential interactions between stressors limits the confidence in 

this determination. 

Moderate Potential Impacts

Drivers: Climate change poses a threat to 

these forests for multiple reasons, including 

increased moisture stress, freeze-thaw events, and 

susceptibility to other stress agents. Hardwoods on 

moist, rich soils may be buffered from short-term 

droughts or seasonal moisture stress. Climate change 

may also alter the gap-phase dynamics that enable 

the regeneration of many shade-tolerant species if 

blowdowns, pest outbreaks, or wildfires become 

more frequent or widespread. 

Dominant Species: Models projections are mixed 

for many common species that make up northern 

hardwood forests. American basswood, green ash, 

and sugar maple are generally projected to increase 

under PCM B1, but models disagree on whether 

these species will increase or decrease under GFDL 

A1FI. American beech, black cherry, eastern white 

pine, northern red oak, and red maple are projected 

to increase under PCM B1 and decrease under 

GFDL A1FI. Eastern hemlock, yellow birch, and 

northern white-cedar are projected to decline in 

biomass and suitable habitat across a range of 

climate scenarios. Deciduous forest systems may 

be more able to increase productivity across a 

range of climate scenarios than coniferous forest 

systems, however. Emerald ash borer and Dutch elm 

disease are expected to continue to limit ash and elm 

species.

Stressors: Climate change may amplify several 

major stressors to northern hardwoods, particularly 

for stands on marginal soils. The potential for 

more freeze-thaw events could exacerbate ongoing 

hardwood dieback in the assessment area. Beech 

bark disease, white pine blister rust, and other 

diseases could become more active and virulent 

under a warmer climate. Forest tent caterpillar, 

gypsy moth, and other pests may cause more 

frequent and severe damage in climate-stressed 

forests, and new pests such as Asian longhorn beetle 

present unknown risks. White-tailed deer herbivory 

may also increase if deer populations grow with 

warmer winters. Unanticipated interactions may 

also occur between stressors, such as earthworms, 

drought, and invasive species. 

Moderate-High Adaptive Capacity 

Northern hardwoods usually contain many species 

and exist on a range of soil types and landforms, 

which leads to a high response diversity (many 

potential future trajectories). Hardwood 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, which may benefit from gene flow from 

southern populations. Increased CO2

 concentrations 

may also increase the water-use efficiency of some 

species, reducing the risk of moisture stress. Sites 

dominated by a single species like sugar maple are 

more susceptible to future stressors, however, as are 

stands with reduced structural diversity. 
Chapter 6: Ecosystem Vulnerabilities

139

A snag in a mixed pine-hardwood forest. Photo by Stephen 

Handler, U.S. Forest Service.

A mature northern hardwood forest in the Upper Peninsula. 

Photo by Joshua Cohen, Michigan Natural Features 

Inventory, used with permission.

Canopy gap in a northern hardwood forest. Photo by Joshua 

Cohen, Michigan Natural Features Inventory, used with 

permission.

A mixed hardwood forest in the northern Lower Peninsula. 

Photo by Stephen Handler, U.S. Forest Service.
Chapter 6: Ecosystem Vulnerabilities

140

Lowland and Riparian Hardwoods 

Moderate Vulnerability (medium evidence, low-medium agreement) 

Climate change is projected to alter the water regimes in riparian and lowland systems, and may amplify 

the effects of insect pests and invasive species. High diversity and the presence of southern species raise 

the adaptability of these forests. The future precipitation regime is the primary uncertainty. 

Moderate-Negative Potential Impacts 

Drivers: Climate change has the potential to 

alter the hydrologic regimes in riparian systems 

and lowlands across the assessment area. These 

hardwood forests are particularly adapted to 

annual and seasonal fluxes in water tables, and the 

regeneration requirements of several species within 

this forest type are linked to these cycles. Shifts in 

the timing or amount of precipitation could disrupt 

the function of these forests. 

Dominant Species: Under a range of climate 

futures, many lowland and riparian hardwood 

species (American elm, black willow, Eastern 

cottonwood, green ash, silver maple, swamp white 

oak, and white ash) are expected to gain suitable 

habitat across the assessment area. Sycamore and 

hackberry are two southern species expected to 

gain new suitable habitat in the assessment area. 

Elm/ash/cottonwood forests could have large 

potential productivity gains under a range of climate 

scenarios. Species expected to decline include 

northern white-cedar, black ash, balsam fir, yellow 

birch, and paper birch. Pests and diseases such 

as emerald ash borer and Dutch elm disease are 

expected to limit some species that models project 

to increase.

Stressors: Invasive species such as reed 

canarygrass, Japanese barberry, and buckthorn 

species are existing threats to these forests, and 

invasive species have the potential to increase in 

abundance in the assessment area under climate 

change. Emerald ash borer is expected to reduce or 

eliminate ash species in lowland hardwood forests 

in the future. Gypsy moth and other forest pests 

may also be more damaging in climate-stressed 

forests. White-tailed deer populations are expected 

to increase with warmer winters, which may hinder 

regeneration of certain species in these forests. The 

trend toward more intense and variable precipitation 

events may present risks to this system through 

excessive waterlogging or prolonged droughts. 

Moderate Adaptive Capacity 

There is a lack of management history in these 

forests compared to other forest types in the 

assessment area, so management experience is 

limited. Many species in riparian and lowland forests 

can tolerate intermittent wet and dry conditions, and 

they can tolerate periodic floods and moisture stress. 

Extended droughts would cause significant damage 

to shallow-rooted species, but increased winter and 

spring precipitation could buffer summer droughts 

in low-lying areas on the landscape. These forests 

are rather diverse, so they have many possible future 

trajectories under changing conditions. Riparian 

forests tend to contain more southern species than 

lowland forests, so they may be less vulnerable to 

future conditions in the assessment area. 
Chapter 6: Ecosystem Vulnerabilities

141

A floodplain hardwood forest on a tributary of the 

Muskegon River. Photo by Joshua Cohen, Michigan Natural 

Features Inventory, used with permission. 

Lowland hardwoods on the Huron-Manistee National Forest. 

Photo by U.S. Forest Service, Huron-Manistee National 

Forest.

A lowland hardwood forest. Photo by Joshua Cohen, 

Michigan Natural Features Inventory, used with permission. 
Chapter 6: Ecosystem Vulnerabilities

142

Oak Associations 

Low-Moderate Vulnerability (medium evidence, medium agreement) 

Oaks are relatively tolerant of drought and warmer temperatures. The presence of white oak and black 

oak increases the adaptive capacity of this system in the northern Lower Peninsula, but the rarity of this 

forest type makes it more vulnerable in the eastern Upper Peninsula. 

Moderate Potential Impacts

Drivers: Oak associations occur on sandy to 

loamy soils. Oaks are relatively drought tolerant 

and may endure more variable precipitation 

under climate change. This community is limited 

by cold temperatures in the assessment area, so 

warming may allow oaks to expand into previously 

unsuitable areas. Past management and wildfire 

suppression allowed oak associations to expand 

into barrens and pine forests, but continued fire 

suppression is allowing mesic species like red maple 

to invade these stands. Therefore, climate change 

influences on the wildfire regime and ability to 

apply prescribed fire will have great consequence 

for oak associations. More frequent crown fires 

may encourage a shift to pine forests and barrens, 

but a continued lack of surface fires may promote 

hardwood forests. 

Dominant Species: Models project that white 

oak may gain suitable habitat and biomass in the 

assessment area, though results are mixed for 

northern red oak, northern pin oak, and black 

oak. Most oaks are near northern range limits in 

the assessment area, so they may gain territory 

under projected warming. Several new oak 

species, including bur oak, pin oak, scarlet oak, 

and post oak, may gain suitable habitat across the 

assessment area. Natural migration and expansion 

of these oak species to the eastern Upper Peninsula 

may be limited by barriers like the Great Lakes 

and fragmentation in the southern portion of the 

assessment area. 

Stressors: Climate change could amplify several 

stressors to oak associations. Forest tent caterpillar 

and gypsy moth may cause more frequent and severe 

damage under climate change, and new pests such 

as Asian longhorn beetle present unknown risks. 

Stressed forests may also be more susceptible to oak 

wilt and oak decline. White-tailed deer populations 

may also increase with warmer winters, which 

may hinder regeneration as well as the expansion 

of this forest type. Invasive species like buckthorn, 

honeysuckle, and garlic mustard can also impair 

regeneration, and are poised to increase under 

climate change. 

Moderate-High Adaptive Capacity 

Oak associations are generally expected to fare well 

under climate change, and the diversity of these 

forests (species as well as genetic diversity) leads to 

a high response diversity, or many possible future 

trajectories. These forests could gain territory lost 

by other forest types under drier future conditions, 

particularly in the northern Lower Peninsula. Species 

at their northern range limits may also benefit from 

gene flow between southern populations. The oakdominated cover type may suffer from increased 

competition with more mesic hardwoods if fire 

suppression continues. 
Chapter 6: Ecosystem Vulnerabilities

143

An oak-dominated forest. Photo by U.S. Forest Service, 

Huron-Manistee National Forest.

Using a drip torch to apply a prescribed burn in an oak 

forest. Photo by Christopher Hoving, Michigan Department 

of Natural Resources, used with permission. 

Northern red oak seedlings in sandy 

soil. Photo by Matthew Duveneck, 

Portland State University, used with 

permission.
Chapter 6: Ecosystem Vulnerabilities

144

Barrens 

Low-Moderate Vulnerability (limited-medium evidence, medium agreement) 

Barrens may be well-adapted to tolerate warmer temperatures and more episodic precipitation in the 

assessment area. Increased wildfire activity might also benefit this system, but excessive wildfire could 

shift barrens to open grassland systems. 

Moderate-Positive Potential Impacts

Drivers: Barrens typically occur on excessively 

drained, nutrient-poor sands. Infrequently, barrens 

occur on thin soils over bedrock. Climate changeinduced moisture stress or extended droughts may 

favor this system. In the assessment area, barrens 

are typically maintained by management activities, 

prescribed fire, or wildfire. Therefore, barrens could 

benefit if climate change increases the frequency 

or severity of wildfire in xeric areas across the 

assessment area. Too much fire, however, could 

result in conversion to open grassland systems. 

Conversely, warmer temperatures might allow 

species like oaks to invade barrens maintained by 

frost pockets. 

Dominant Species: Models do not explicitly 

consider the habitat suitability and performance of 

tree species in open systems, so these results should 

be interpreted with caution. Considering the range of 

possible climate futures, jack pine and red pine are 

expected to decline in suitable habitat and biomass 

across the assessment area. White oak is projected to 

fare well under a range of climate futures. The same 

modeling studies offer mixed results for other minor 

species (northern pin oak, black oak, and eastern 

white pine) occurring in barrens systems. 

Stressors: Fire suppression has contributed to 

woody encroachment and an increased presence 

of invasive species in many barrens systems, and 

climate change could pose an additional threat if 

the window to apply prescribed fires is reduced. 

Invasive species such as leafy spurge, spotted 

knapweed, and St. John’s wort are existing threats 

to these systems, and invasive species have the 

potential to increase in abundance in the assessment 

area under climate change. White-tailed deer 

herbivory is less of a threat to barrens, and may 

actually help promote open canopy conditions. 

Similarly, insect pest outbreaks from jack pine 

budworm or western pine beetles might favor 

barrens systems across the landscape. 

Moderate-High Adaptive Capacity 

The strong management presence in barrens 

systems is expected to continue, so there is a better 

chance that managed barrens will persist across 

the assessment area. Other dry forest types may 

convert to barrens if extended droughts cause 

prolonged regeneration failures or if stand-replacing 

wildfires increase in frequency, so this system may 

gain territory under climate change. Barrens are 

also expected to tolerate episodic precipitation and 

warmer temperatures. These systems require only 

minor tree cover, and many of the species that can 

occur in these systems are expected to remain stable 

across the landscape. 
Chapter 6: Ecosystem Vulnerabilities

145

An oak-pine savanna. Photo by Christopher Hoving, 

Michigan Department of Natural Resources, used with 

permission. 

A pine barren in the northern Lower Peninsula. Photo by 

Joshua Cohen, Michigan Natural Features Inventory, used 

with permission.

Lupine in a savanna in the northern Lower Peninsula. Photo by Heather Keough, U.S. Forest 

Service, Huron-Manistee National Forest.
Chapter 6: Ecosystem Vulnerabilities

146

CONCLUSIONS

Forest ecosystems in northern Michigan will be 

affected by climate change, although systems and 

species will respond to these changes individually. 

The synthesis statements in the first half of this 

chapter can be applied as general rules of thumb 

when specific information about expected climate 

change impacts is lacking. Overall, we expect 

forest systems that are adapted to a narrow range of 

conditions or that contain few species to be more 

vulnerable to changing conditions. Communities 

with higher diversity that are adapted to tolerate a 

wide range of conditions and disturbances have a 

greater chance to persist under a range of plausible 

climates. 

The vulnerability determinations for individual 

forest systems are best interpreted as broad trends 

and expectations across the assessment area. 

This assessment makes use of the most up-todate information from the scientific literature, a 

coordinated set of 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 Michigan 

forests, this assessment should be supplemented with 

additional resources. 

It is essential to consider local characteristics such 

as past management history, soils, topographic 

features, species composition, forest health issues, 

and recent disturbances when applying these general 

vulnerabilities to local scales. Some site-level 

factors may amplify these expected vulnerabilities, 

yet others may buffer the effects of climate change. 

Developing a clear understanding of 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. 
147

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. A 

more thorough assessment can be found in Changing 

Climate, Changing Wildlife: A Vulnerability 

Assessment of 400 Species of Greatest Conservation 

Need and Game Species in Michigan (Hoving et 

al. 2013). Michigan’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 Michigan, 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, species like white-tailed deer 

and wild turkey populations are hindered by severe 

winters. A decline in the frequency of severe winters 

will favor those species. Because Michigan forests 

are habitat for many wildlife species at the north 

or south edge of their range, even small climateinduced changes may have noticeable impacts. 
Chapter 7: Management Implications

148

Wildlife species throughout the Midwest are 

responding to climate change, and several 

assessments and vulnerability analyses suggest that 

wildlife will continue to change (Hall 2012). Several 

tools have been developed to help managers evaluate 

the climate change vulnerabilities of wildlife 

species. For example, the Climate Change Bird Atlas 

examines the potential for climate change to alter the 

distribution of 147 bird species across the eastern 

United States (Matthews et al. 2011a). 

More Information

• The Michigan State Wildlife Action Plan

identifies wildlife species, and their habitats, that

are in greatest conservation need. Many species

of greatest conservation need may be particularly

affected by climate change:

http://www.michigan.gov/dnr/0,4570,7-153-

10370_30909---,00.html

• Changing Climate, Changing Wildlife, a new

report from the Michigan Department of Natural

Resources – Wildlife Division, assesses how

climate change may affect 400 wildlife species in

the state:

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

• Many states are working to incorporate climate

change information into their state wildlife action

plans. 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, a collaboration of many hunting

and conservation organizations, includes many

resources on how climate change may affect

wildlife:

http://www.cakex.org/virtual-library/784

• The Forest Service’s 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). 

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 

A ruffed grouse in the eastern Upper Peninsula. Photo by 

U.S. Forest Service, Hiawatha National Forest.
Chapter 7: Management Implications

149

model the effects of climate and climate change on 

distribution and abundance (Schwartz et al. 2006b). 

In the absence of human intervention, rare or 

threatened species may face greater extinction risks. 

Alternatively, rare species that live in habitats that 

are buffered from climate shifts (e.g., caves or other 

climatic refugia) may be able to persist. 

Michigan’s Special Animals and Michigan’s 

Special Plants include information on the state’s 

665 endangered, threatened, and special concern 

species (Michigan Natural Features Inventory 

2013a, 2013b). Many of these species occur in the 

assessment area: 189 species in the eastern Upper 

Peninsula and 233 species in the northern Lower 

Peninsula. There has not been a comprehensive 

review of the potential for climate change to affect 

all of these species, and the particular climate 

tolerances of many of them are unknown. The new 

report from the Michigan Department of Natural 

Resources, Changing Climate, Changing Wildlife, 

addresses implications for many of these species 

(Hoving et al. 2013). 

More Information

• Michigan’s Rare Species Explorer is produced 

and updated by the Michigan Natural Features 

Inventory: mnfi.anr.msu.edu/explorer/search.cfm 

• Changing Climate, Changing Wildlife, a new 

report from the Michigan Department of Natural 

Resources – Wildlife Division, assesses how 

climate change may affect 400 wildlife species in 

the state: 

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

FIRE AND FUELS 

Climate change is expected to have implications for 

fire and fuels management in the assessment area. 

As discussed above, this summary does not address 

the ways that land managers should adapt to the 

potential changes. A wide range of possible 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 land owners. 

As described in Chapter 5, weather and climate 

are major drivers of fire behavior. Across northern 

Michigan 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 the 

potential for fire ignition and spread. Live trees drop 

leaves and go dormant in the fall, but most forests 

become receptive to fire around the same time that 

short days and cool temperatures return. The type 

and condition of available fuels may lead to surface 

fires, which consume ground fuels, or crown fires, 

which burn across the forest canopy. 

Drought periods can exacerbate wildfire risk during 

any of these periods, and drought is a critical 

precursor of 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 susceptible 

to ignition and spread. The projected trend toward 

more-intense precipitation could raise the potential 

for longer dry intervals between rain events 

(Chapter 4). Combined with warmer temperatures 

and a range of other climate-driven stressors, the 

potential exists for more forests to be prone to 

wildfire throughout the growing season. The two 

climate scenarios examined in this assessment 

reveal a wide range of possible precipitation values 

(Chapter 4), so it is uncertain to what degree drought 

stress may harm forests in the assessment area. 
Chapter 7: Management Implications

150

As with other parts of the country, critical fire 

weather conditions have been responsible for many 

of the major fire events across the Great Lakes 

region. Large, intensely burning fires generally 

require a combination of strong gradient winds, 

significant atmospheric instability, and dry air. The 

fires that occur in fire-prone landscapes during 

these weather events tend to produce the most 

severe fire effects. Fire-weather events are poorly 

captured by modeling tools. Because large wildfires 

are driven primarily by these fire weather events, 

it is difficult to forecast exactly how the projected 

climate trends may translate into changes in fire 

activity. Additionally, complex interactions between 

climate change, vegetation communities, seasonal 

precipitation, and discrete fire weather events will 

dictate whether fires are manifested as surface 

fires or crown fires. This distinction has important 

consequences for forest communities and fire 

management, and the limits of our understanding 

are a major uncertainty. 

Logging area burned in the Duck Lake Fire in May 2012. Photo by Stephen Handler, U.S. Forest Service.

Projected changes in climate could also affect the 

ability to apply prescribed fire in Michigan. Wetter 

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’s 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/ 
Chapter 7: Management Implications

151

WATER RESOURCES 

There are many potential interactions and 

relationships between climate change, forest 

ecosystems, and water resources in Michigan. 

Below, we outline a few examples of these potential 

implications. In addition to reflecting land-use 

decisions, water resources in the assessment area 

are influenced by a diverse array of management 

decisions and policies, including infrastructure 

planning and maintenance, water quality discharge 

permitting, water extraction/diversion permitting, 

and biological resource management. These layers 

of policy and management decisions complicate the 

picture, but reinforce the notion that management 

decisions will be intertwined with ecological 

changes in the future. 

Infrastructure

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. As a recent regional example, the 

flood event in June 2012 in Duluth and across 

northern Minnesota caused more than $100 million 

in damage, primarily to roads, bridges, and private 

property (Passi 2012). In addition, associated 

landslides and stream bank erosion extensively 

damaged area streams; restoration costs are 

estimated at $1 million per stream. 

Water Quality

Water resource managers in the assessment area have 

long been concerned with the impacts of multiple 

stressors, including the effects of commercial or 

residential development and climate patterns on 

in-stream temperature and increased turbidity (water 

cloudiness). The cold and cold-transitional rivers and 

streams within the assessment area (Chapter 1) may 

be increasingly at risk due to warming temperatures 

and altered hydrologic regimes. Impairments due 

to turbidity are also common for rivers and lakes in 

northern Michigan. Processes leading to increased 

turbidity are particularly sensitive to climate-related 

phenomena, such as increased storm intensity and 

frequency, rain-on-snow events, and other trends 

that promote stream bank erosion. These events can 

also diminish water quality by introducing excessive 

nutrients and contaminants. 

Thermal habitat in cold-water lakes and streams 

may 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 may 

be more affected in shallow and moderate-depth 

lakes (Fang et al. 2004a, 2004b, 2004c; Stefan et al. 

2001). Ongoing work within Lake Superior Basin 

to investigate climate change impacts on water 

temperature and base flow finds that streams may 

be affected by higher stream temperatures and low 

flow in the future (Lucinda Johnson and Meijun Cai, 

Natural Resources Research Institute; William Herb, 

University of Minnesota, personal comm., February 

2013).

Fish

Fish and other aquatic organisms are projected 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 evenly across species or even across 

life stages for a given organism. For example, 

fish eggs and fry associated with gravel habitats 

and fine sediments, as opposed to other habitats, 

appear to have been most affected by the June 2012 

floods in northern Minnesota (personal comm., 

D. Hendrickson, Minnesota Department of Natural 
Chapter 7: Management Implications

152

Resources, December 2012). Water temperature is 

generally considered to be the primary parameter 

for physical habitat suitability for trout, and upper 

temperature limits seem to depend specifically 

on the duration of high temperatures (Wehrly 

et al. 2007). The new report from the Michigan 

Department of Natural Resources – Wildlife 

Division, Changing Climate, Changing Wildlife, 

assesses how climate change may affect fish species 

in the state (Hoving et al. 2013). Compared to other 

taxa, fish are among the most vulnerable animals in 

Michigan. More than 80 percent of the fish species 

were assessed as vulnerable to climate change. 

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/ 

• Changing Climate, Changing Wildlife, a new 

report from the Michigan Dept. of Natural 

Resources – Wildlife Division, assesses how 

climate change may affect fish species in the 

state: 

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

FOREST PRODUCTS

In Michigan, the forest industry accounts for 

roughly $8 billion in economic activity (Price 

2010). Information presented in Chapters 5 

and 6 indicates that species composition in the 

assessment area is projected to change during 

the 21st century, which could have important 

implications for the forest products industry. Major 

harvested species like quaking aspen are projected 

to decline significantly under a range of possible 

climate futures. Conversely, hardwood species like 

American basswood and northern red oak may 

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, how climate change will affect the forest 

products industry depends not only on ecological 

responses to the changing climate, but also on 

socioeconomic factors that will undoubtedly 

continue to change over the coming century. Major 

socioeconomic factors include national and regional 

economic policies, demand for wood products, and 

Harvest operations in a white pine stand. Photo by U.S. 

Forest Service, Huron-Manistee National Forest.
Chapter 7: Management Implications

153

competing values for forests (Irland et al. 2001). 

Great uncertainty is associated with each of these 

factors. The forest products industry has adjusted to 

substantial changes during the past 100 years, and 

continued responsiveness can help the sector remain 

viable. 

More Information

• The 2010 Resources Planning Act Assessment 

presents 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 Great Lakes region. Hundreds 

of these products are used for food, medicine, craft 

materials, and other purposes. Many of these will 

be affected by changes in temperature, hydrology, 

and species assemblages. As illustrations, effects 

of climate change on two Northwoods nontimber 

forest products with broad cultural and economic 

importance are discussed briefly here.

Natural wild rice is a Northwoods cultural keystone 

species (Minnesota Department of Natural 

Resources 2008). It is central to the migration 

story of the Anishinaabe (also known as Ojibwe, 

Chippewa, or Odawa), for whom wild rice is a 

sacred food and medicine. Wild rice growth and 

productivity are sensitive to hydrologic conditions 

such as water depth and temperature. Although wild 

rice is adapted to some seasonal variation, it thrives 

in water depths of 0.5 to 3 feet. Germination requires 

a 3- to 4-month 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, or both

• flooding, particularly in the early summer 

“floating leaf” life stage 

• shortened periods of cold water temperatures

• predation and/or displacement by species favored 

by warmer water temperatures (e.g., carp and 

reed canarygrass).

Another example of a valuable nontimber 

forest product of the Northwoods is morel 

mushrooms, which people throughout northern 

Michigan passionately hunt (Fine 2003). Annual 

morel festivals and sales to restaurants provide 

supplemental income for many people, communities, 

and small businesses. 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 from Dutch elm disease, climaterelated 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.

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. 
Chapter 7: Management Implications

154

Most management in lowland areas is accomplished 

during the winter. As summarized in Chapter 4, 

climate change in northern Michigan 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 

drive up costs compared to conventional practices.

Projected changes in precipitation during the 

growing season could also have important 

implications for forest management operations. 

Intense precipitation events could delay harvest 

operations in areas of poor drainage, but these events 

may be less disruptive in areas 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 number 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 

affected forest areas. A salvage sale also does not 

garner the same amount of financial return as does a 

green timber sale opportunity.

Analysis of timber harvest records in northern 

Wisconsin has identified some consequences of the 

changes in frozen ground condition (C. Rittenhouse 

and A. Rissman, unpublished data). In years with 

warm winters, there has been a shift toward greater 

harvest of jack pine and less harvest of black spruce, 

hemlock, and red maple. Interviews with loggers 

revealed that growing-season 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 Michigan Society of American Foresters 

has produced 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 influence forest management operations: 

michigansaf.org/Business/MSAFguide-2010/1-

MainPage.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 has not been built to that capacity. For example, 

improper location or outdated building standards 

can make older road systems particularly susceptible 
Chapter 7: Management Implications

155

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 

are projected to result in seasonal changes in 

streamflow, such as higher peak flows, which could 

affect infrastructure around streams and rivers. An 

increase in the frequency or intensity of windstorms, 

which may occur over the next century, could also 

increase operating and repair expenses related to 

infrastructure. 

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: 

glisa.msu.edu/docs/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 Michigan 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 

Woods road through a northern hardwood forest. Photo by U.S. Forest Service, Hiawatha National Forest.
Chapter 7: Management Implications

156

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 recover following a disturbance, total 

C losses may be negligible over the long term. 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 affect 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). The LANDIS-II model results presented in 

Chapter 5 raise the possibility that existing species 

in northern Michigan may decline in landscape-level 

biomass across the assessment area under the GFDL 

A1FI climate scenario, but these projections do not 

account for increases in other species or the potential 

effects of CO2

 fertilization. As long as forests are 

maintained as forests in the assessment area, a largescale decline in C stocks across northern Michigan is 

not expected.

More Information

• The Forest Service’s 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 

There are a handful of federal wilderness areas in 

the assessment area, including the Seney Wilderness, 

Beaver Basin Wilderness, and the Delirium 

Wilderness. Climate change was not anticipated 

when the Wilderness Act was created, and now the 

potential for extensive ecosystem change raises 

difficult questions about the future management of 

these and other wilderness areas. 

Climate change is poised to influence the forest 

ecosystems in the assessment area in a variety of 

ways. Fire seasons are expected to shift, and more 

area is projected to burn each year under climate 

change (Chapter 5). Additionally, many of the 

characteristic boreal species in the assessment 

area are projected to decline, and invasive species 

may increase in abundance and vigor (Chapter 5). 

Depending on the amount and timing of future 

precipitation, lake levels and aquatic ecosystems 

in the assessment area could be affected as 

well. Weather and climate could also influence 

recreational use, if spring and fall seasons become 

more attractive for visits or the threat of wildfires 

reduces visits in certain months. Furthermore, 

managers accept the fact that natural hazards and 

obstacles are inherently a part of the wilderness 

experience, but they try to remove trees that 

are posing immediate threats to visitors. Tree 

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 more-proactive 

management for wilderness areas to help create a 
Chapter 7: Management Implications

157

“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 complex choices and a thorough 

planning process to consider potential pros and cons. 

More Information

• The Wilderness.net Climate Change Toolbox 

offers information about climate change and 

wilderness, including management guidelines and 

strategies: www.wilderness.net/climate. 

• The Forest Service’s 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 are 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).

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. Historical and prehistoric 

habitation sites are often located near lakes or 

waterways. Flood events or storm surges can result 

in increased erosion or obliteration of significant 

archaeological sites. Similarly, torrential rains can 

trigger or exacerbate erosion of cultural resources. 

Erosion from storm surges in the Great Lakes has 

already begun to wash away cultural sites within 

the 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 centerpiece 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 
Chapter 7: Management Implications

158

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). However, it is possible 

that areas prone to lake-effect snow may have more 

snowfall in the short term. 

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 for flash flooding or lead to unpleasant 

conditions for recreation, however. Severe storms 

and flash flooding might also threaten infrastructure 

such as visitor centers, campsites, and trails. Fall 

will potentially be drier, which could lead to 

reduced water levels, and hence diminished water 

recreation opportunities. Warmer, drier conditions in 

the summer and fall may raise the risk of wildfire, 

increasing visitor safety risk and restrictions on open 

flames. Lengthening of spring and fall recreation 

seasons will also have implications for staffing, 

especially for recreation-related businesses that rely 

on student labor—which will be unavailable during 

the school year (Nicholls 2012).

Climate can also have important influences on 

hunting and fishing. The timing of certain hunts or 

fishing seasons correspond to seasonal events, which 

are in part driven by climate. Waterfowl hunting 

seasons, for example, are designed to correspond to 

the times when birds are migrating south in the fall, 

an event that is expected to shift later in the year as 

temperatures warm. As mentioned above, climate 

change may also result in substantial changes in 

habitat availability and quality for wildlife and fish 

species. In a recent assessment of climate change 

vulnerability for wildlife species in Michigan, game 

species were generally rated as less vulnerable than 

Species of Greatest Conservation Need (Hoving 

et al. 2013), but nearly 20 percent of game species 

were rated as moderately, highly, or extremely 

vulnerable to climate change. 

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:

glisa.msu.edu/docs/NCA/MTIT_RecTourism.pdf

• Season’s End, a collaboration of many hunting

and conservation organizations, includes many

resources on how climate change may affect

wildlife:

http://www.cakex.org/virtual-library/784

• Changing Climate, Changing Wildlife, a new

report from the Michigan Department of Natural

Resources – Wildlife Division, assesses how

climate change may affect 400 wildlife species in

the state, including many fish and game species:

www.michigan.gov/documents/dnr/3564_

Climate_Vulnerability_Division_Report_

4.24.13_418644_7.pdf

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 Michigan 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.
Chapter 7: Management Implications

159

Climate is one of many important interacting 

variables that affect people’s risk for vector-borne 

diseases in Michigan. Climate directly affects 

physical conditions (e.g., temperature, rainfall) 

and indirectly affects biologic conditions (plants, 

animals). These physical and biologic conditions 

can, in turn, influence vector-borne disease risk by 

altering the abundance and distribution of ticks or 

mosquitoes, 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 Michigan Emerging Disease Issues Web site 

has more information on Lyme disease: 

www.michigan.gov/emergingdiseases 

• The Minnesota Vector-borne Diseases Web site 

has more information on vector-borne diseases: 

www.health.state.mn.us/divs/idepc/dtopics/

vectorborne/index.html 

• The Michigan Department of Community Health 

has a Web site on the climate change implications 

for human health: 

www.michigan.gov/mdch 

• The Centers for Disease Control and Prevention 

Climate and Health Program includes information 

on a variety of subjects: 

www.cdc.gov/climateandhealth/ 

URBAN FORESTS

Climate change is expected to affect urban forests in 

the assessment area as well. Urban environments can 

pose additional stresses to trees, such as pollution 

from vehicle exhaust, confined root environments, 

and road salts. Urban environments also cause a 

“heat island effect,” and thus warming in cities 

may be even greater than in natural communities. 

Impervious surfaces can make urban environments 

more susceptible to floods, placing flood-intolerant 

species at risk. 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. 
Chapter 7: Management Implications

160

Large disturbance events may also become more 

frequent or intense in the future, necessitating 

informed decisions in response. For example, wind 

events or pest outbreaks may be more damaging 

to already-stressed trees. If leaf-out dates advance 

earlier in the spring due to climate change, 

community forests may be increasingly susceptible 

to early-season frosts or snow storms. More people 

and larger budgets may be required to handle an 

increase in the frequency or intensity of these events, 

which may become more difficult in the face of 

reduced municipal budgets and staffing. 

More Information

• The Forest Service’s 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. 

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). Local examples of communitybased climate change assessment and adaptation are 

underway in Michigan (see below).

When planning for climate change, decisionmakers 

can consider how ecological events or changes (e.g., 

floods, droughts, wildfire, windstorms, introduced 

species, insect or pathogen outbreaks) will affect 

their communities and community members by 

asking: 

• 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?
Chapter 7: Management Implications

161

• 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 

• Michigan Sea Grant produced a community selfassessment to address climate change readiness, 

and its Web site includes several resources useful 

for communities: 

www.miseagrant.umich.edu/ 

• The Superior Watershed Partnership and Lake 

Trust has prepared a Lake Superior Climate 

Adaptation, Mitigation, and Implementation 

Plan focused on communities within Michigan’s 

Upper Peninsula: 

www.superiorwatersheds.org/images/ 

climate-jan.pdf 

• Alger and Delta Counties, in Michigan’s 

Upper Peninsula, have prepared communitybased climate change adaptation plans through 

participation in the Model Forest Policy 

Program’s Climate Solutions University: 

www.mfpp.org/csu/ 

LAND ACQUISITION 

Climate change has many important implications for 

land conservation planning in northern Michigan. 

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 upper Great Lakes region, where 

private forestlands have some of the highest stored 

C levels in the entire country. Climate change trends 

and ecosystems models can also be used to identify 

lands that have long-term potential to provide habitat 

refugia and protection for shifting water supplies. 

In the design of land conservation projects, there 

are important decisions to be made about long-term 

ownership and management prescriptions attached to 

the conservation agreement. In some cases, the best 

strategy may be to leave lands in private ownership, 

and to develop conservation easement terms that 

support adaptive management by the landowner to 

address climate shifts. In other cases, perhaps where 

complex restoration or species-specific management 

is needed, 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 Michigan, 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 Hiawatha 

and Huron-Manistee National Forests revise their 

management plans in the coming years, they will 

be required to address climate change under the 
Chapter 7: Management Implications

162

new Planning Rule. Similarly, Michigan’s State 

Forest Management Plans have not historically 

addressed the issue. The 2010 State Forest Resource 

Assessment and Strategy document points to climate 

change as a potential influence on the long-term 

sustainability of Michigan’s forests (Price 2010). 

Incorporating climate change considerations into 

natural resources planning will always be a complex 

endeavor. The uncertainties associated with planning 

over long time horizons are only compounded with 

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 

• Michigan’s Forest Resource Assessment and 

Strategies documents include discussions of 

climate change: 

www.forestactionplans.org/states/michigan 

• Michigan’s State Forest Planning program, along 

with the Regional State Forest Management 

Planning program, is explained on the Michigan 

Department of Natural Resources Web site. 

The recently released Regional State Forest 

Management Plans consider climate change 

explicitly: 

www.michigan.gov/dnr/0,1607,7-153-30301_

30505---,00.html 

www.michigan.gov/dnr/0,4570,7-153-30301_

30505_62551---,00.html 

CONCLUSIONS

The breadth of the topics above highlights the 

wide range of effects climate change may have on 

forest management in northern Michigan. 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 Michigan 

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. 
163

Glossary

aerosol

a suspension of fine solid particles or liquid 

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

air pollution, and smog. Aerosols may influence 

climate by 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.

biomass

the mass of living organic matter (plant and 

animal) in an ecosystem; also organic matter 

(living and dead) available on a renewable basis 

for use as a fuel; biomass includes trees and plants 

(both terrestrial and aquatic), agricultural crops 

and wastes, wood and wood wastes, forest and 

mill residues, animal wastes, livestock operation 

residues, and some municipal and industrial wastes.

carbon dioxide (CO2) fertilization 

increased plant uptake of CO2 through 

photosynthesis in response to higher concentrations 

of atmospheric CO2 .

climate change 

a change in the state of the climate that can be 

identified (e.g., by using statistical tests) by changes 

in the mean and/or the variability of its properties, 

and that persists for an extended period, typically 

decades or longer. Climate change may be due to 

natural internal processes or external factors, or to 

persistent anthropogenic changes in the composition 

of the atmosphere or in land use. 

climate model

see general circulation model.

climate normal

the arithmetic mean of a climatological element 

computed over three consecutive decades. 

community

an assemblage of plants and animals living together 

and occupying a given area.
Glossary

164

confidence

a qualitative assessment of uncertainty as determined 

through evaluation of evidence and agreement (see 

also evidence, agreement). 

convective storm

convection is a process whereby heat is transported 

vertically within the atmosphere. Convective storms 

result from a combination of convection, moisture, 

and instability. Convective storms can produce 

thunderstorms, tornadoes, hail, heavy rains, and 

straight-line winds. 

derecho

widespread and long-lived convective windstorm 

that is associated with a band of rapidly moving 

showers or thunderstorms characterized by wind 

gusts that are greater than 57 miles per hour and that 

may exceed 100 miles per hour. 

disturbance

stresses and destructive agents such as invasive 

species, diseases, and fire; changes in climate and 

serious weather events such as hurricanes and ice 

storms; pollution of the air, water, and soil; real 

estate development of forest lands; and timber 

harvest. Some of these are caused by humans, in part 

or entirely; others are not.

downscaling

methods for obtaining high-resolution climate or 

climate change information from coarse-resolution 

general circulation models. 

driver

any natural or human-induced factor that directly or 

indirectly causes a change in an ecosystem.

dynamical downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCM) 

by using a limited-area, high-resolution model 

(a regional climate model, or RCM) driven by 

boundary conditions from a GCM to derive smallerscale information.

ecoregion

a region characterized by a repetitive pattern of 

ecosystems associated with commonalities in climate 

and landform.

ecological province

climatic subzones, controlled primarily by 

continental weather patterns such as length of dry 

season and duration of cold temperatures. Provinces 

are also characterized by similar soil orders and are 

evident as extensive areas of similar potential natural 

vegetation. 

ecosystem 

a volumetric unit of the earth’s surface that includes 

air (climate), land (landform, soil, water), and biota. 

Ecosystems are defined by land area, and contain all 

the interactions between living organisms and their 

physical environment. 

emissions scenario

a plausible representation of the future development 

of emissions of greenhouse gases and aerosols 

that are potentially radiatively active, based on 

demographic, technological, or environmental 

developments.

evapotranspiration

the sum of evaporation from the soil and 

transpiration from plants.
Glossary

165

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). 

fire-return interval

the number of years between two successive fire 

events at a specific location.

forest land

land that is at least 10 percent stocked by forest trees 

of any size, or land formerly having such tree cover, 

and not currently developed for a nonforest use.

forest type

a classification of forest vegetation based on the 

dominant species present, as well as associate 

species commonly occurring with the dominant 

species.

forest-type group 

based on FIA definitions, a combination of forest 

types that share closely associated species or site 

requirements and are generally combined for brevity 

of reporting.

fragmentation

a disruption of ecosystem or habitat connectivity, 

caused by human or natural disturbance, creating a 

mosaic of successional and developmental stages 

within or between forested tracts of varying patch 

size, isolation (distance between patches), and edge 

length.

fundamental niche

the total habitat available to a species based on 

climate, soils, and land cover type in the absence of 

competitors, diseases, or predators.

general circulation model (GCM)

numerical representation of the climate system based 

on the physical, chemical, and biological properties 

of its components, their interactions, and their 

feedback processes, and accounting for all or some 

of its known properties (also called climate model).

greenhouse effect

the rise in temperature that the Earth experiences 

because certain gases in the atmosphere (water 

vapor, carbon dioxide, nitrous oxide, and methane, 

for example) absorb and emit energy from the sun.

growing season

the period in each year when the temperature is 

favorable for plant growth.

hardwood

a dicotyledonous tree, usually broad-leaved and 

deciduous. Hardwoods can be split into soft 

hardwoods (red maple, paper birch, quaking aspen 

and American elm) and hard hardwoods (sugar 

maple, yellow birch, black walnut, and oaks). 

hydric

referring to sites or habitats with abundant moisture 

throughout the year, frequently including saturation, 

ponding, or flooding.

impact

the direct and indirect consequences of climate 

change on systems, particularly those that would 

occur without adaptation.

impact model

a model simulating impacts on trees, animals, 

and ecosystems. It uses general circulation model 

projections as inputs, and includes additional inputs 

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

of individual species.
Glossary

166

importance value

in the Climate Change Tree Atlas model, an index 

of the relative abundance of a species in a given 

location or pixel cell (0 = least abundant, 100 = most 

abundant).

invasive species

any species that is nonnative (or alien) to the 

ecosystem under consideration and whose 

introduction causes or is likely to cause damage, 

injury, or disruption to ecosystem processes or other 

species within that ecosystem.

Kyoto Protocol

adopted at the 1997 Third Session of the Conference 

of Parties to the U.N. Framework Convention on 

Climate Change in Kyoto, Japan; it contains legally 

binding commitments to reduce anthropogenic 

greenhouse gas emissions by at least 5 percent below 

1990 levels in the period 2008-2012.

mesic

referring to sites or habitats where soil moisture is 

available to plants throughout the growing season.

model reliability score

in the Climate Change Tree Atlas model, a “trimodel” approach to assess reliability of model 

predictions for each species, classified as high, 

medium, or low.

modifying factor

in the Climate Change Tree Atlas model, 

environmental variables (e.g., site conditions, 

interspecies competition, disturbance, dispersal 

ability) that influence the way a tree may respond to 

climate change.

parcelization

the subdivision of a single forest ownership into 

two or more ownerships. Parcelization may result 

in fragmentation if habitat is altered under new 

ownership. 

peak flow

the maximum instantaneous discharge of a stream or 

river at a given location. 

phenology

the timing of natural events such as the date that 

migrating birds return, the first flower dates for 

plants, and the date on which a lake freezes in the 

autumn or opens in the spring. Also refers to the 

study of this subject. 

prairie 

a natural community dominated by perennial grasses 

and forbs with scattered shrubs and very few trees 

(less than 10 percent canopy cover). 

process model

a model that relies on computer simulations based 

on mathematical representations of physical and 

biological processes that interact over space and 

time.

productivity 

the rate at which biomass is produced per unit area 

by any class of organisms, or the rate of energy 

utilization by organisms.

projection 

a potential future evolution of a quantity or set of 

quantities, often computed with the aid of a model. 

Projections are distinguished from predictions 

in order to emphasize that projections involve 

assumptions concerning, for example, future 

socioeconomic and technological developments that 

may or may not be realized, and are therefore subject 

to substantial uncertainty. 

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.
Glossary

167

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.

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 trend

in this report, least-squares regression p-values of 

observed climate trends are significant when p<0.10. 

For trends where p>0.10, observed trends have a 

higher probability of being due to chance alone. 

softwood

a coniferous tree, usually evergreen, having needles 

or scale-like leaves.

snow water equivalent 

the amount of water contained in snowpack. It 

is a way of measuring the amount of snow while 

accounting for differences in density.

snowpack

layers of accumulated snow that usually melts during 

warmer months.

species distribution model

a model that uses statistical relationships to project 

future change.

statistical downscaling

a method for obtaining high-resolution climate or 

climate change information from relatively coarseresolution general circulation models (GCMs) by 

deriving statistical relationships between observed 

small-scale (often station-level) variables and 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.
Glossary

168

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 

in excess of 20 cubic feet per acre per year of wood. 

transpiration

liquid water phase change occurring inside plants 

with the vapor diffusing to the atmosphere.

troposphere

the lowest part of the atmosphere from the surface 

to about 6 miles in altitude in mid-latitudes (ranging 

on average from 5 miles in high latitudes to 9 miles 

in the tropics) where clouds and weather phenomena 

occur.

topkill

death of aboveground tree stem and branches.

uncertainty 

an expression of the degree to which a value (such as 

the future state of the climate system) is unknown. 

Uncertainty can result from lack of information or 

from disagreement about what is known or even 

knowable. It may have many types of sources, 

from quantifiable errors in the data to ambiguously 

defined concepts or terminology, or uncertain 

projections of human behavior. Uncertainty can 

be described by using quantitative measures or by 

qualitative statements.

veneer

a roundwood product from which veneer is sliced 

or sawn and that usually meets certain standards of 

minimum diameter, length, and maximum defect. 

vulnerability 

the degree to which a system is susceptible to, and 

unable to cope with, adverse effects of climate 

change, including climate variability and extremes. 

Vulnerability is a function of the impacts and 

adaptive capacity of a system. For this assessment, 

a system may be considered to be vulnerable if it 

is at risk of a composition change leading to a new 

identity, or if the system is anticipated to suffer 

substantial declines in health or productivity.

weather 

the state of the atmosphere at a given time and 

place, with respect to variables such as temperature, 

moisture, wind velocity, and barometric pressure. 

windthrow 

trees uprooted or broken by wind.

woodland

highly variable natural communities with a canopy 

of trees ranging from 30- to 100-percent openness, 

a sparse understory, and a dense ground flora rich in 

grasses, sedges, and forbs.

xeric

pertaining to sites or habitats characterized by 

decidedly dry conditions. 
169

Literature cited

Aber, J.D.; Ollinger, S.V.; Driscoll, C.T. 1997. 

Modeling nitrogen saturation in forest 

ecosystems in response to land use and 

atmospheric deposition. Ecological Modelling. 

101(1): 61-78.

Aber, J.R.; Neilson, P.; McNulty, S.; Lenihan, 

J.M.; Bachelet, D.; Drapek, R.J. 2001. Forest 

processes and global environmental change: 

predicting the effects of individual and 

multiple stressors. BioScience. 51(9): 735-751.

Ainsworth, E.A.; Long, S.P. 2005. What have 

we learned from 15 years of free-air CO2

enrichment (FACE)? A meta-analytic review 

of the responses of photosynthesis, canopy 

properties and plant production to rising CO2

. 

New Phytologist. 165(2): 351-372.

Ainsworth, E.A.; Rogers, A. 2007. The response of 

photosynthesis and stomatal conductance to 

rising [CO2

]: mechanisms and environmental 

interactions. Plant, Cell & Environment. 30(3): 

258-270.

Albert, D.A. 1995. Regional landscape ecosystems 

of Michigan, Minnesota, and Wisconsin: a 

working map and classification. Gen. Tech. 

Rep. NC-178. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Forest 

Experiment Station. 250 p.

Albert, D.A.; Cohen, J.G.; Kost, M.A.; Slaughter, 

B.S.; Enander, H.D. 2008. Distribution maps of 

Michigan’s Natural Communities. Lansing, MI: 

Michigan Natural Features Inventory. 174 p.

Anderegg, W.R.L.; Berry, J.A.; Smith, D.D.; 

Sperry, J.S.; Anderegg, L.D.L.; Field, C.B. 2012. 

The roles of hydraulic and carbon stress in 

a widespread climate-induced forest dieoff. Proceedings of the National Academy of 

Sciences. 109(1): 233-237.

Andreadis, K.M.; Lettenmaier, D.P. 2006. Trends 

in 20th century drought over the continental 

United States. Geophysical Research Letters. 

33(10): L10403.

Andresen, J.; Hilberg, S.; Kunkel, K. 2012. 

Historical climate and climate trends in the 

Midwestern USA. In:. Winkler, J.; Andresen, J.; 

Hatfield, J.; Bidwell, D.; Brown, D., coordinators. 

U.S. National Climate Assessment Midwest 

technical input report. Available at http://glisa.

umich.edu/docs/NCA/MTIT_Historical.pdf. 

(Accessed November 21, 2013).

Apple, B.A.; Reeves, H.W. 2007. Summary of 

hydrogeologic conditions by county for the 

State of Michigan—U.S. Geological Survey 

open-file report 2007-1236. 87 p. Available at 

http://pubs.usgs.gov/of/2007/1236/. (Accessed 

November 22, 2013).

Arbogast, A.F.; Wintle, A.G.; Packman, S.C. 2002. 

Widespread middle Holocene dune formation 

in the eastern Upper Peninsula of Michigan 

and the relationship to climate and outletcontrolled lake level. Geology. 30(1): 55-58.

Auclair, A.N.D.; Heilman, W.E.; Brinkman, B. 2010. 

Predicting forest dieback in Maine, USA: a 

simple model based on soil frost and drought.

Canadian Journal of Forest Research. 40: 

687-702.
Literature Cited

170

Babin-Fenske, J.; Anand, M. 2011. Agent-based 

simulation of effects of stress on forest tent 

caterpillar (Malacosoma disstria Hubner) 

population dynamics. Ecological Modelling. 

222(14): 2561-2569.

Bailey, R.G. 1995. Description of the ecoregions 

of the United States. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 108 p.

Baker, F.; Hansen, M.; Shaw, J.D.; Mielke, M.; 

Shelstad, D. 2012. The incidence of dwarf 

mistletoe in Minnesota black spruce stands 

detected by operational inventories. Northern 

Journal of Applied Forestry. 29(3): 109-112.

Bal, T. 2013. Evaluation of sugar maple 

dieback in the Upper Great Lakes region 

and development of a forest health youth 

education program. Houghton, MI: Michigan 

Technological University. 174 p. Ph.D. 

dissertation.

Benedict, M.A.; Frelich, L.E. 2008. Site factors 

affecting black ash ring growth in northern 

Minnesota. Forest Ecology and Management. 

255(8-9): 3489-3493.

Birdsey, R.; Pregitzer, K.; Lucier, A. 2006. Forest 

carbon management in the United States: 

1600-2100. Journal of Environmental Quality. 

35: 1461-1469.

Blann, K.; Nerbonne, J.F.; Vondracek, B. 2002. 

Relationship of riparian buffer type to water 

temperature in the driftless area ecoregion of 

Minnesota. North American Journal of Fisheries 

Management. 22(2): 441-451.

Boer, G. 2009. Changes in interannual variability 

and decadal potential predictability under 

global warming. Journal of Climate. 22(11): 

3098-3109.

Bonan, G.B. 2008. Forests and climate change: 

forcings, feedbacks, and the climate benefits of 

forests. Science. 320(5882): 1444-1449.

Borken, W.; Matzner, E. 2009. Introduction: 

impact of extreme meteorological events on 

soils and plants. Global Change Biology. 

15(4): 781-781.

Bourque, C.P.A.; Cox, R.M.; Allen, D.J.; Arp, P.A.; 

Meng, F.R. 2005. Spatial extent of winter thaw 

events in eastern North America: historical 

weather records in relation to yellow birch 

decline. Global Change Biology. 11(9): 

1477-1492.

Box, G.E.; Draper, N.R. 1987. Empirical modelbuilding and response surfaces. New York: 

John Wiley & Sons. 669 p.

Bridgham, S.D.; Pastor, J.; Dewey, B.; Weltzin, 

J.F.; Updegraff, K. 2008. Rapid response of 

peatlands to climate change. Ecology. 89(11): 

3041-3048.

Brzostek, E.R.; Blair, J.M.; Dukes, J.S.; Frey, S.D.; 

Hobbie, S.E.; Melillo, J.M.; Mitchell, R.J.; 

Pendall, E.; Reich, P.B.; Shaver, G.R.; Stefanski, 

A.; Tjoelker, M.G.; Finizi, A.C. 2012. The effect 

of experimental warming and precipitation 

change on proteolytic enzyme activity: positive 

feedbacks to nitrogen availability are not 

universal. Global Change Biology 18(8): 

2617-2625.

Burnett, A.W.; Kirby, M.E.; Mullins, H.T.; Patterson, 

W.P. 2003. Increasing Great Lake-effect 

snowfall during the twentieth century: A 

regional response to global warming? Journal 

of Climate. 16(21): 3535-3542.

Burns, R.M.; Honkala, B.H. 1990. Silvics of 

North America: 1. Conifers; 2. Hardwoods.

Washington, DC: U.S. Department of Agriculture, 

Forest Service: 877 p.

Burton, A.J.; Pregitzer, K.S.; Zogg, G.P.; Zak, D.R. 

1996. Latitudinal variation in sugar maple 

fine root respiration. Canadian Journal of Forest 

Research. 26(10): 1761-1768.
Literature Cited

171

Campbell, J.L.; Ollinger, S.V.; Flerchinger, G.N.; 

Wicklein, H.; Hayhoe, K.; Bailey, A.S. 2010. Past 

and projected future changes in snowpack and 

soil frost at the Hubbard Brook Experimental 

Forest, New Hampshire, USA. Hydrological 

Processes. 24(17): 2465-2480.

Campbell, J.L.; Rustad, L.E.; Boyer, E.W.; 

Christopher, S.F.; Driscoll, C.T.; Fernandez, 

I.J.; Groffman, P.M.; Houle, D.; Kiekcusch, 

J.; Magill, A.H.; Mitchell, M.J.; Ollinger, 

S.V. 2009. Consequences of climate change 

for biogeochemical cycling in forests of 

northeastern North America. Canadian Journal 

of Forest Research. 39: 264-284.

Cartwright, L. 2005. An examination of flood 

damage data trends in the United States.

Journal of Contemporary Water Research & 

Education. 130(1): 20-25.

CCSP [Climate Change Science Program]. 2008. 

Weather and climate extremes in a changing 

climate. Regions of focus: North America, 

Hawaii, Caribbean, and U.S. Pacific Islands. 

A report by the U.S. Climate Change Science 

Program and the Subcommittee on Global 

Change Research. [Karl, T.R.; Meehl, G.A.; 

Miller, C.D.; Hassol, S.J.; Waple, A.M.; Murray, 

W.L., eds.] Washington, DC: Department of 

Commerce, NOAA National Climatic Data 

Center: 164 p.

Changnon, D.; Changnon, S.A. 2007. Snowstorm 

dimensions across the central and eastern 

United States. Physical Geography. 28(3): 

218-232.

Changnon, S. 2011a. Temporal distribution of 

weather catastrophes in the USA. Climatic 

Change. 106(2): 129-140.

Changnon, S.A. 2003. Geographical and temporal 

variations in thunderstorms in the contiguous 

United States during the 20th century. Physical 

Geography. 30(2): 138-152.

Changnon, S.A. 2011b. Major damaging 

convective storms in the United States. 

Physical Geography. 32(3): 286-294.

Cherkauer, K.A.; Sinha, T. 2010. Hydrologic 

impacts of projected future climate change in 

the Lake Michigan Region. Journal of Great 

Lakes Research. 36(SP2): 33-50.

Chhin, S. 2010. Influence of climate on the growth 

of hybrid poplar in Michigan. Forests. 1(4): 

209-229.

Chiang, J.M.; Iverson, L.R.; Prasad, A.; Brown, 

K.J. 2008. Effects of climate change and shifts 

in forest composition on forest net primary 

production. Journal of Integrative Plant Biology. 

50(11): 1426-1439.

Choat, B.; Jansen, S.; Brodribb, T.J.; Cochard, H.; 

Delzon, S.; Bhaskar, R.; Bucci, S.J.; Feild, T.S.; 

Gleason, S.M.; Hacke, U.G.; Jacobsen, A.L.; 

Lens, F.; Maherali, H.; Martinez-Vilalta, J.; Mayr, 

S.; Mencuccini, M.; Mitchell, P.J.; Nardini, A.; 

Pittermann, J.; Pratt, R.B.; Sperry, J.S.; Westoby, 

M.; Wright, I.J.; Zanne, A.E. 2012. Global 

convergence in the vulnerability of forests to 

drought. Nature. 491(7426): 752-755.

Clark, J.S. 1999. Michigan’s major watersheds.

Lansing, MI: Michigan Department of 

Environmental Quality. Available at http://www.

dnr.state.mi.us/spatialdatalibrary/PDF_Maps/

Watersheds/Major_Watersheds_24k.pdf. 

(Accessed December 7, 2011). 

Cleland, D.T.; Crow, T.R.; Saunders, S.C.; 

Dickmann, D.I.; Maclean, A.L.; Jordan, J.K.; 

Watson, R.L.; Sloan, A.M.; Brosofske, K.D. 

2004. Characterizing historical and modern 

fire regimes in Michigan (USA): a landscape 

ecosystem approach. Landscape Ecology. 

19: 311-325.
Literature Cited

172

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). 

Cohen, J. 2000a. Natural community abstract for 

mesic northern forest. Lansing, MI: Michigan 

Natural Features Inventory. 9 p. Available at 

http://mnfi.anr.msu.edu/abstracts/ecology/Mesic_

Northern_Forest.pdf. (Accessed November 22, 

2013).

Cohen, J. 2000b. Natural community abstract 

for oak-pine barrens. Lansing, MI: Michigan 

Natural Features Inventory. 7 p. Available at 

http://mnfi.anr.msu.edu/abstracts/ecology/Oakpine_barrens.pdf. (Accessed November 22, 

2013).

Cohen, J. 2001. Natural community abstract for 

oak barrens. Lansing, MI: Michigan Natural 

Features Inventory. 10 p. Available at http://mnfi.

anr.msu.edu/abstracts/ecology/Oak_barrens.pdf. 

(Accessed November 22, 2013).

Cohen, J. 2002a. Natural community abstract 

for dry-mesic northern forest. Lansing, MI: 

Michigan Natural Features Inventory. 13 p. 

Available at http://mnfi.anr.msu.edu/abstracts/

ecology/Dry-mesic_northern_forest.pdf. 

(Accessed November 22, 2013).

Cohen, J. 2002b. Natural community abstract for 

dry northern forest. Lansing, MI: Michigan 

Natural Features Inventory. 15 p. Available at 

http://mnfi.anr.msu.edu/abstracts/ecology/Dry_

northern_forest.pdf. (Accessed November 22, 

2013).

Cohen, J. 2006. Natural community abstract for 

poor conifer swamp. Lansing, MI: Michigan 

Natural Features Inventory. 23 p. Available at 

http://mnfi.anr.msu.edu/abstracts/ecology/Poor_

Conifer_Swamp.pdf. (Accessed November 22, 

2013).

Cohen, J. 2007. Natural community abstract for 

boreal forest. Lansing, MI: Michigan Natural 

Features Inventory. 24 p. Available at http://mnfi.

anr.msu.edu/abstracts/ecology/boreal_forest.pdf. 

(Accessed November 22, 2013).

Cole, C.T.; Anderson, J.; Lindroth, R.L.; Waller, 

D.M. 2010. Rising concentrations of 

atmospheric CO2

 have increased growth in 

natural stands of quaking aspen (Populus 

tremuloides). Global Change Biology. 16(8): 

2186-2197.

Comer, P. 1996. Natural community abstract for 

pine barrens. Lansing, MI: Michigan Natural 

Features Inventory. 4 p. Available at http://mnfi.

anr.msu.edu/abstracts/ecology/Pine_barrens.pdf. 

(Accessed November 22, 2013).

Comer, P.J.; Albert, D.A.; Wells, H.A.; Hart, 

B.L.; Raab, J.B.; Price, D.L.; Kashian, D.M.; 

Corner, R.A.; Schuen, D.W.;. Leibfreid, T.R.; 

Austin, M.B.; DeLain, C.J.; Prange-Gregory, 

L.; Scrimger, L.J.; Korroch, K.M.; Spitzley, J.G. 

1995. Michigan’s presettlement vegetation, 

as interpreted from the General Land Office 

Surveys 1816-1856. Lansing, MI: Michigan 

Natural Features Inventory. 17 p.

Coniglio, M.C.; Stensrud, D.J. 2004. Interpreting 

the climatology of derechos. Weather and 

Forecasting. 19(3): 595-605.

Cornett, M.W.; Frelich, L.E.; Puettmann, K.J.; 

Reich, P.B. 2000. Conservation implications of 

browsing by Odocoileus virginianus in remnant 

upland Thuja occidentalis forests. Biological 

Conservation. 93(3): 359-369.
Literature Cited

173

Cornett, M.W.; Reich, P.B.; Puettmann, K.J.; 

Frelich, L.E. 2000. Seedbed and moisture 

availability determine safe sites for early Thuja 

occidentalis (Cupressaceae) regeneration.

American Journal of Botany. 87(12): 1807-1814.

Côté, S.D.; Rooney, T.P.; Tremblay, J.P.; Dussault, 

C.; Waller, D.M. 2004. Ecological impacts 

of deer overabundance. Annual Review of 

Ecology, Evolution, and Systematics. 35: 

113-147.

Coumou, D.; Rahmstorf, S. 2012. A decade of 

weather extremes. Nature Climate Change. 

2(7): 491-496.

Courteau, J.B.; Cohen, J.G.; Kost, M.A. 2006. 

Abstract on the Oak Association of northern 

Michigan. Lansing, MI: Michigan Natural 

Features Inventory.

D’Amato, A.W.; Bradford, J.B.; Fraver, S.; Palik, 

B.J. 2011. Forest management for mitigation 

and adaptation to climate change: insights 

from long-term silviculture experiments. Forest 

Ecology and Management. 262(5): 803-816.

Dai, A.; Trenberth, K.E.; Qian, T. 2004. A global 

dataset of Palmer Drought Severity Index for 

1870-2002: relationship with soil moisture 

and effects of surface warming. Journal of 

Hydrometeorology. 5: 1117-1129.

Daniels, A.E.; Morrison, J.F.; Joyce, L.A.; 

Crookston, N.L.; Chen, S.C.; McNulty, S.G. 

2012. Climate projections FAQ. Gen. Tech. 

Rep. RMRS-GTR-277WWW. Fort Collins, 

CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 32 p. 

Available at http://www.fs.fed.us/rm/pubs/rmrs_

gtr277.html. (Accessed November 22, 2013).

Davis, M.B. 1983. Quaternary history of 

deciduous forests of eastern North-America 

and Europe. Annals of the Missouri Botanical 

Garden. 70(3): 550-563.

Davis, M.B.; Shaw, R.G. 2001. Range shifts and 

adaptive responses to Quaternary climate 

change. Science. 292(5517): 673-679.

Davis, M.B.; Shaw, R.G.; Etterson, J.R. 2005. 

Evolutionary responses to changing climate.

Ecology. 86(7): 1704-1714.

Dawson, J.; Scott, D. 2010. Climate change and 

tourism in the Great Lakes region: a summary 

of risks and opportunities. Tourism in Marine 

Environments. 6(2-3): 119-132.

Delworth, T.L.; Broccoli, A.J.; Rosati, A.; Stouffer, 

R.J.; Balaji, V.; Beesley, J.A.; Cooke, W.F.; 

Dixon, K.W.; Dunne, J.; Dunne, K.A.; Durachta, 

J.W.; Findell, K.L.; Ginoux, P.; Gnanadesikan, 

A.; Gordon, C.T.; Griffies, S.M.; Gudgel, 

R.; Harrison, M.J.; Held, I.M.; Hemler, R.S.; 

Horowitz, L.W.; Klein, S.A.; Knutson, T.R.; 

Kushner, P.J.; Langenhorst, A.R.; Lee, H.C.; 

Lin, S.J.; Lu, J.; Malyshev, S.L.; Milly, P.C.D.; 

Ramaswamy, V.; Russell, J.; Schwarzkopf, 

M.D.; Shevliakova, E.; Sirutis, J.J.; Spelman, 

M.J.; Stern, W.F.; Winton, M.; Wittenberg, A.T.; 

Wyman, B.; Zeng, F.; Zhang, R. 2006. GFDL’s 

CM2 global coupled climate models. Part I: 

Formulation and simulation characteristics.

Journal of Climate. 19(5): 643-674.

Devall, M.S. 2009. Effect of global climate change 

on rare trees and shrubs. Unasylva (English 

edition). 60 (231/232): 29. 

Dickmann, D.I.; Leefers, L.A. 2003. The forests 

of Michigan. Ann Arbor, MI: University of 

Michigan Press: 297 p.

Dieleman, W.I.J.; Vicca, S.; Dijkstra, F.A.; 

Hagedorn, F.; Hovenden, M.J.; Larsen, K.S.; 

Morgan, J.A.; Volder, A.; Beier, C.; Dukes, 

J.S. 2012. Simple additive effects are rare: a 

quantitative review of plant biomass and soil 

process responses to combined manipulations 

of CO2

 and temperature. Global Change 

Biology. 18(9): 2681-2693.
Literature Cited

174

Dietze, M.C.; Moorcroft, P.R. 2011. Tree mortality 

in the eastern and central United States: 

patterns and drivers. Global Change Biology. 

17(11): 3312-3326.

Diffenbaugh, N.S.; Pal, J.S.; Trapp, R.J.; Giorgi, 

F. 2005. Fine-scale processes regulate the 

response of extreme events to global climate 

change. Proceedings of the National Academy 

of Sciences of the United States of America. 

102(44): 15774-15778.

Diffenbaugh, N.S.; Trapp, R.J.; Brooks, H. 

2008. Does global warming influence 

tornado activity? Eos Transactions American 

Geophysical Union. 89(53): 553-554.

Dragoni, D.; Rahman, A.F. 2012. Trends in fall 

phenology across the deciduous forests of 

the Eastern USA. Agricultural and Forest 

Meteorology. 157: 96-105.

Drever, C.R.; Bergeron, Y.; Drever, M.C.; Flannigan, 

M.; Logan, T.; Messier, C. 2009. Effects of 

climate on occurrence and size of large fires 

in a northern hardwood landscape: historical 

trends, forecasts, and implications for climate 

change in Témiscamingue, Québec. Applied 

Vegetation Science. 12(3): 261-272.

Dukes, J.S.; Pontius, J.; Orwig, D.; Garnas, 

J.R.; Rodgers, V.L.; Brazee, N.; Cooke, B.; 

Theoharides, K.A.; Stange, E.E.; Harrington, R.; 

Ehrenfeld, J.; Gurevitch, J.; Lerdau, M.; Stinson, 

K.; Wick, R.; Ayres, M. 2009. Responses of 

insect pests, pathogens, and invasive plant 

species to climate change in the forests of 

northeastern North America: What can we 

predict? Canadian Journal of Forest Research. 

39(2): 231-248.

Eagle, A.C.; Hay-Chmielewski, E.M.; Cleveland, 

K.T.; Derosier, A.L.; Herbert, M.E.; Rustem, R.A. 

2005. Michigan’s wildlife action plan. Lansing, 

MI: Michigan Department of Natural Resources.

 Eisenhauer, N.; Fisichelli, N.A.; Frelich, L.E.; 

Reich, P.B. 2012. Interactive effects of global 

warming and ‘global worming’ on the initial 

establishment of native and exotic herbaceous 

plant species. Oikos. 121(7): 1121-1133.

Elmqvist, T.; Folke, C.; Nyström, M.; Peterson, 

G.; Bengtsson, J.; Walker, B.; Norberg, J. 

2003. Response diversity, ecosystem change, 

and resilience. Frontiers in Ecology and the 

Environment. 1(9): 488-494.

Emanuel, W.R.; Shugart, H.H.; Stevenson, M.P. 

1985. Climatic change and the broadscale distribution of terrestrial ecosystem 

complexes. Climatic Change. 7(1): 29-43.

Emery, M.R.; Barron, E.S. 2010. Using local 

ecological knowledge to assess morel decline in 

the US Mid-Atlantic Region. Economic Botany. 

64(3): 205-216.

Erdman, J. 2012. Tornadoes...in Michigan...in 

March? Available at http://www.weather.com/

outlook/weather-news/news/articles/tornadomichigan-damage-map-radar_2012-03-16. 

(Accessed March 25, 2012).

Fang, X.; Stefan, H.G.; Eaton, J.G.; McCormick, 

J.H.; Alam, S.R. 2004a. Simulation of thermal/

dissolved oxygen habitat for fishes in lakes 

under different climate scenarios: Part 

1. Cool-water fish in the contiguous US.

Ecological Modelling. 172(1): 13-37.

Fang, X.; Stefan, H.G.; Eaton, J.G.; McCormick, 

J.H.; Alam, S.R. 2004b. Simulation of thermal/

dissolved oxygen habitat for fishes in lakes 

under different climate scenarios: Part 

2. Cold-water fish in the contiguous US.

Ecological Modelling. 172(1): 39-54.

Fang, X.; Stefan, H.G.; Eaton, J.G.; McCormick, 

J.H.; Alam, S.R. 2004c. Simulation of thermal/

dissolved oxygen habitat for fishes in lakes 

under different climate scenarios: Part 3. 

Warm-water fish in the contiguous US.

Ecological Modelling. 172(1): 55-68.
Literature Cited

175

Feng, S.; Hu, Q. 2007. Changes in winter snowfall/

precipitation ratio in the contiguous United 

States. Journal of Geophysical Research. 

112(D15): D15109.

Fine, G.A. 2003. Morel tales: the culture of 

mushrooming. Chicago, IL: University of 

Illinois Press. 336 p.

Fisichelli, N.; Frelich, L.E.; Reich, P.B. 2012. 

Sapling growth responses to warmer 

temperatures ‘cooled’ by browse pressure.

Global Change Biology. 18(11): 3455-3463.

Flannigan, M.; Stocks, B.; Turetsky, M.; Wotton, M. 

2009. Impacts of climate change on fire activity 

and fire management in the circumboreal 

forest. Global Change Biology. 15(3): 549-560.

Foster, J.R.; Burton, J.I.; Forrester, J.A.; Liu, F.; 

Muss, J.D.; Sabatini, F.M.; Scheller, R.M.; 

Mladenoff, D.J. 2010. Evidence for a recent 

increase in forest growth is questionable.

Proceedings of the National Academy of 

Sciences. 107(21): E86-87.

Frelich, L.E.; Hale, C.M.; Scheu, S.; Holdsworth, 

A.R.; Heneghan, L.; Bohlen, P.J.; Reich, P.B. 

2006. Earthworm invasion into previously 

earthworm-free temperate and boreal forests.

Biological Invasions. 8(6): 1235-1245.

Frelich, L.E.; Peterson, R.O.; Dovčiak, M.; Reich, 

P.B.; Vucetich, J.A.; Eisenhauer, N. 2012. 

Trophic cascades, invasive species and bodysize hierarchies interactively modulate climate 

change responses of ecotonal temperate-boreal 

forest. Philosophical Transactions of the Royal 

Society B: Biological Sciences. 367(1605): 

2955-2961.

Frelich, L.E.; Reich, P.B. 1995a. Neighborhood 

effects, disturbance, and succession in 

forests of the western Great-Lakes Region.

Ecoscience. 2(2): 148-158.

Frelich, L.E.; Reich, P.B. 1995b. Spatial patterns 

and succession in a Minnesota southern-boreal 

forest. Ecological Monographs. 65(3): 325-346.

Frelich, L.E.; Reich, P.B. 1996. Old growth in 

the Great Lakes Region. In: Davis, M.B., ed. 

Eastern old growth forests. Washington, DC: 

Island Press: 144-160.

Frelich, L.E.; Reich, P.B. 2009. Wilderness 

conservation in an era of global warming 

and invasive species: a case study from 

Minnesota’s Boundary Waters Canoe Area 

Wilderness. Natural Areas Journal. 29(4): 

385-393.

Frelich, L.E.; Reich, P.B. 2010. Will environmental 

changes reinforce the impact of global 

warming on the prairie-forest border of 

central North America? Frontiers in Ecology 

and the Environment. 8(7): 371-378.

Froese, R.; Hyslop, M.; Miller, C.; Garmon, B.; 

McDiarmid, H., Jr.; Shaw, A.; Leefers, L.; 

Lorenzo, M.; Brown, S.; Shy, M. 2007. Largetract forestland ownership change: land use, 

conservation, and prosperity in Michigan’s 

Upper Peninsula. Ann Arbor, MI: National 

Wildlife Federation. 54 p.

Fry, J.; Xian, G.; Jin, S.; Dewitz, J.; Homer, C.; 

Yang, L.; Barnes, C.; Herold, N.; Wickham, J. 

2011. Completion of the 2006 National Land 

Cover Database for the conterminous United 

States. PE&RS. 77(9): 858-864.

Garroway, C.J.; Bowman, J.; Holloway, G.L.; 

Malcolm, J.R.; Wilson, P.J. 2011. The genetic 

signature of rapid range expansion by flying 

squirrels in response to contemporary climate 

warming. Global Change Biology. 17(5): 

1760-1769.

Gellesch, E.; Hein, R.; Jaeschke, A.; Beierkuhnlein, 

C.; Jentsch, A. 2013. Biotic interactions in the 

face of climate change. Progress in Botany. 

74: 321-349.
Literature Cited

176

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.

Glick, P.; Stein, B.A.; Edelson, N.A. 2011. Scanning 

the conservation horizon: a guide to climate 

change vulnerability assessment. Washington, 

DC: National Wildlife Federation. 168 p.

Gobster, P.H.; Rickenbach, M.G. 2004. Private 

forestland parcelization and development 

in Wisconsin’s Northwoods: perceptions of 

resource-oriented stakeholders. Landscape and 

Urban Planning. 69(2): 165-182.

Gorham, E. 1991. Northern peatlands: role in 

the carbon cycle and probable responses to 

climatic warming. Ecological Applications. 

1(2): 182-195.

Gough, C.M.; Vogel, C.S.; Schmid, H.P.; Curtis, 

P.S. 2008. Controls on annual forest carbon 

storage: lessons from the past and predictions 

for the future. BioScience. 58(7): 609-622.

Groffman, P.M.; Rustad, L.E.; Templer, P.H.; 

Campbell, J.L.; Christenson, L.M.; Lany, N.K.; 

Socci, A.M.; Vadeboncoeur, M.A.; Schaberg, 

P.G.; Wilson, G.F. 2012. Long-term integrated 

studies show complex and surprising effects 

of climate change in the northern hardwood 

forest. BioScience. 62(12): 1056-1066.

Groisman, P.Y.; Knight, R.W.; Karl, T.R. 

2012. Changes in intense precipitation 

over the central United States. Journal of 

Hydrometeorology. 13(1): 47-66.

Gustafson, E.J.; Sturtevant, B.R. 2013. Modeling 

forest mortality caused by drought stress: 

implications for climate change. Ecosystems. 

16: 60-74.

Haines, A.L.; Kennedy, T.T.; McFarlane, D.L. 2011. 

Parcelization: forest change agent in northern 

Wisconsin. Journal of Forestry. 109(2): 101-108.

Hale, C.M.; Frelich, L.E.; Reich, P.B. 2005. Exotic 

European earthworm invasion dynamics in 

northern hardwood forests of Minnesota, USA.

Ecological Applications. 15(3): 848-860.

Hall, K. 2012. Climate change in the Midwest: 

impacts on biodiversity and ecosystems. In: 

Winkler, J.; Andresen, J.; Hatfield, J.; Bidwell, 

D.; Brown, D., coordinators. U.S. National 

Climate Assessment Midwest technical input 

report. Available at http://glisa.msu.edu/docs/

NCA/MTIT_Biodiversity.pdf. (Accessed 

November 22, 2013).

Handler, S.D.; Swanston, C.W.; Butler, P.R.; Brandt, 

L.A.; Janowiak, M.K.; Powers, M.D.; Shannon, 

P.D. 2012. Climate change vulnerabilities 

within the forestry sector for the Midwestern 

United States. In: Winkler, J.; Andresen, J.; 

Hatfield, J.; Bidwell, D.; Brown, D., coordinators. 

U.S. National Climate Assessment Midwest 

technical input. Available at http://glisa.msu.

edu/docs/NCA/MTIT_Forestry.pdf. (Accessed 

November 22, 2013).

Hansen, J.; Sato, M.; Ruedy, R. 2012. Perception 

of climate change. Proceedings of the National 

Academy of Sciences. 109(37): E2415-E2423.

Hanson, P.J.; Weltzin, J.F. 2000. Drought 

disturbance from climate change: response of 

United States forests. The Science of the Total 

Environment. 262(3): 205-220.
Literature Cited

177

Hardy, J.; Groffman, P.; Fitzhugh, R.; Henry, K.; 

Welman, A.; Demers, J.; Fahey, T.; Driscoll, 

C.; Tierney, G.; Nolan, S. 2001. Snow depth 

manipulation and its influence on soil frost 

and water dynamics in a northern hardwood 

forest. Biogeochemistry. 56(2): 151-174.

Hayhoe, K. 2010a. 1/8 degree CONUS daily 

downscaled climate projections 

*PROVISIONAL*. USGS Geodata Portal 

[Online]: Texas Tech University, Center for 

Integrated Data Analytics. Available at http://cida.

usgs.gov/thredds/catalog.html?dataset=cida.usgs.

gov/thredds/dcp/conus. (Accessed July 11, 2011).

Hayhoe, K.A. 2010b. A standardized framework 

for evaluating the skill of regional climate 

downscaling techniques. Urbana-Champaign, 

IL: University of Illinois at Urbana-Champaign. 

153 p. Ph.D. dissertation.

Headwaters Economics. 2011. Economic profile 

system - human dimensions toolkit. Available 

at http://headwaterseconomics.org/tools/eps-hdt. 

(Accessed December 15, 2011).

Heimpel, G.E.; Frelich, L.E.; Landis, D.A.; Hopper, 

K.R.; Hoelmer, K.A.; Sezen, Z.; Asplen, M.K.; 

Wu, K.M. 2010. European buckthorn and 

Asian soybean aphid as components of an 

extensive invasional meltdown in North 

America. Biological Invasions. 12(9): 

2913-2931.

Hellmann, J.J.; Byers, J.E.; Bierwagen, B.G.; 

Dukes, J.S. 2008. Five potential consequences 

of climate change for invasive species.

Conservation Biology. 22(3): 534-543.

Hicke, J.A.; Allen, C.D.; Desai, A.R.; Dietze, M.C.; 

Hall, R.J.; Hogg, E.H.; Kashian, D.M.; Moore, 

D.; Raffa, K.F.; Sturrock, R.N.; Vogelmann, J. 

2011. Effects of biotic disturbances on forest 

carbon cycling in the United States and 

Canada. Global Change Biology. 18(1): 7-34.

Hicke, J.A.; Johnson, M.C.; Hayes, J.L.; Preisler, 

H.K. 2012. Effects of bark beetle-caused tree 

mortality on wildfire. Forest Ecology and 

Management. 271: 81-90.

Hoving, C.L.; Lee, Y.M.; Badra, P.J.; Klatt, B.J. 

2013. Changing climate, changing wildlife: 

a vulnerability assessment of 400 species of 

greatest conservation need and game species 

in Michigan. Lansing, MI: Michigan Department 

of Natural Resources - Wildlife Division. 82. 

Available at www.michigan.gov/documents/

dnr/3564_Climate_Vulnerability_Division_

Report_4.24.13_418644_7.pdf. (Accessed 

November 22, 2013).

Ibáñez, I.; Clark, J.S.; Dietz, M.C. 2008. Evaluating 

the sources of potential migrant species: 

implications under climate change. Ecological 

Applications. 18(7): 1664-1678.

Ibáñez, I.; Clark, J.S.; Dietze, M.C.; Feeley, 

K.; Hersh, M.; LaDeau, S.; McBride, A.; 

Welch, N.E.; Wolosin, M.S. 2006. Predicting 

biodiversity change: outside the climate 

envelope, beyond the species-area curve.

Ecology. 87(8): 1896-1906.

Intergovernmental Panel on Climate Change 

[IPCC]. 2007. Climate change 2007: synthesis 

report. Contribution of Working Groups I, II, 

and III to the fourth assessment report of the 

Intergovernmental Panel on Climate Change 

[Core Writing Team, Pachauri, R.K. and 

Reisinger, A. (eds.)]. Geneva, Switzerland: 

Intergovernmental Panel on Climate Change. 104. 

Available at http://www.ipcc.ch/publications_

and_data/publications_ipcc_fourth_assessment_

report_synthesis_report.htm. (Accessed 

January 11, 2013).

Irland, L.C.; Adams, D.; Alig, R.; Betz, C.J.; Chen, 

C.-C.; Hutchins, M.; McCarl, B.A.; Skog, K.; 

Sohngen, B.L. 2001. Assessing socioeconomic 

impacts of climate change on U.S. forests, 

wood-product markets, and forest recreation.

BioScience. 51(9): 753-764.
Literature Cited

178

Isard, S.; Schaetzl, R.; Andresen, J. 2007. Soils 

cool as climate warms in the Great Lakes 

region: 1951-2000. Annals of the Association of 

American Geographers. 97(3): 467-476.

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.; Schwartz, M.W.; Prasad, A.M. 2004a. 

How fast and far might tree species migrate 

in the eastern United States due to climate 

change? Global Ecology and Biogeography. 

13(3): 209-219.

Iverson, L.R.; Schwartz, M.W.; Prasad, A.M. 2004b. 

Potential colonization of newly available 

tree-species habitat under climate change: an 

analysis for five eastern US species. Landscape 

Ecology. 19(7): 787-799.

Jannett, F.J.; Broschart, M.R.; Grim, L.H.; Schaberl, 

J.P. 2007. Northerly range extensions of 

mammalian species in Minnesota. American 

Midland Naturalist. 158(1): 168-176.

Jerome, D.S. 2006. Landforms of the Upper 

Peninsula, Michigan. Washington, DC: U.S. 

Department of Agriculture, Natural Resources 

Conservation Service. 56 p. Available at ftp://

ftp-fc.sc.egov.usda.gov/MI/technical/soils/UP_

LandformReport11-06.pdf. (Accessed 

November 22, 2013). 

Johnson, S.; Stefan, H. 2006. Indicators of climate 

warming in Minnesota: lake ice covers and 

snowmelt runoff. Climatic Change. 75(4): 

421-453.

Jones, B.; Scott, D. 2006. Implications of climate 

change for visitation to Ontario’s provincial 

parks. Leisure/Loisir. 30(1): 233-261.

Jump, A.S.; Peñuelas, J. 2005. Running to stand 

still: adaptation and the response of plants to 

rapid climate change. Ecology Letters. 8(9): 

1010-1020.

Karl, T.R.; Melillo, J.M.; Peterson, T.C. 2009. 

Global climate change impacts in the United 

States: a state of knowledge report from the 

U.S. Global Change Research Program. New 

York: Cambridge University Press. 189 p.

Keenan, T.F.; Hollinger, D.Y.; Bohrer, G.; Dragoni, 

D.; Munger, J.W.; Schmid, H.P.; Richardson, A.D. 

2013. Increase in forest water-use efficiency 

as atmospheric carbon dioxide concentrations 

rise. Nature. 499(7458): 324-327.

Kerr, R.A. 2013. Forecasting regional climate 

change flunks its first test. Science. 

339(6120): 638.

Kling, G.W.; Hayhoe, K.; Johnson, L.B.; Magnuson, 

J.J.; Polasky, S.; Robinson, S.K.; Shuter, B.J.; 

Wander, M.M.; Wuebbles, D.J.; Zak, D.R.; 

Lindroth, R.L.; Moser, S.C.; Wilson, M.L. 2003. 

Confronting climate change in the Great 

Lakes region: impacts on our communities 

and ecosystems. Union of Concerned Scientists; 

Ecological Society of America. Available at 

http://ucsusa.org/assets/documents/global_

warming/greatlakes_final.pdf. (Accessed 

November 23, 2013).

Knapp, A.K.; Beier, C.; Briske, D.D.; Classen, A.T.; 

Luo, Y.; Reichstein, M.; Smith, M.D.; Smith, 

S.D.; Bell, J.E.; Fay, P.A. 2008. Consequences 

of more extreme precipitation regimes for 

terrestrial ecosystems. BioScience. 58(9): 

811-821.

Kost, M.A. 2002. Natural community abstract 

for rich conifer swamp. Lansing, MI: Michigan 

Natural Features Inventory. 10 p. Available at 

http://mnfi.anr.msu.edu/abstracts/ecology/Rich_

conifer_swamp.pdf. (Accessed November 22, 

2013).
Literature Cited

179

Kost, M.A.; Albert, D.A.; Cohen, J.G.; Slaughter, 

B.S.; Schillo, R.K.; Weber, C.R.; Chapman, K.A. 

2007. Natural Communities of Michigan:

classification and description. Lansing, MI: 

Michigan Natural Features Inventory. 321 p.

Kunkel, K.; Bromirski, P.; Brooks, H.; Cavazos, 

T.; Douglas, A.; Easterling, D.; Emanuel, K.; 

Groisman, P.Y.; Holland, G.; Knutson, T. 2008. 

Observed changes in weather and climate 

extremes. In: [Karl, T.R.; Meehl, G.A.; Miller, 

C.D.; Hassol, S.J.; Waple, A.M.; Murray, 

W.L., eds.]. Weather and climate extremes in 

a changing climate. Regions of focus: North 

America, Hawaii, Caribbean, and U.S. Pacific 

Islands. Washington, DC: U.S. Climate Change 

Science Program and the Subcommittee on 

Global Change Research: 35-80. Available at 

http://downloads.globalchange.gov/sap/sap3-

3/sap3-3-final-all.pdf. (Accessed December 3, 

2013). 

Kunkel, K.E.; Andsager, K.; Easterling, D.R. 1999. 

Long-term trends in extreme precipitation 

events over the conterminous United States 

and Canada. Journal of Climate. 12(8): 

2515-2527.

Kunkel, K.E.; Palecki, M.A.; Ensor, L.; Easterling, 

D.; Hubbard, K.G.; Robinson, D.; Redmond, 

K. 2009. Trends in twentieth-century U.S. 

extreme snowfall seasons. Journal of Climate. 

22(23): 6204-6216.

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 US 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.

Kunkel, K.E.; Westcott, N.E.; Kristovich, D.A.R. 

2002. Assessment of potential effects of climate 

change on heavy lake-effect snowstorms near 

Lake Erie. Journal of Great Lakes Research. 

28(4): 521-536.

Lambert, S.J.; Hansen, B.K. 2011. Simulated 

changes in the freezing rain climatology of 

North America under global warming using 

a coupled climate model. Atmosphere-Ocean. 

49(3): 289-295.

Larson, E.R.; Kipfmueller, K.F.; Hale, C.M.; 

Frelich, L.E.; Reich, P.B. 2010. Tree rings 

detect earthworm invasions and their effects 

in northern hardwood forests. Biological 

Invasions. 12(5): 1053-1066.

Le Goff, H.; Flannigan, M.D.; Bergeron, Y. 2009. 

Potential changes in monthly fire risk in the 

eastern Canadian boreal forest under future 

climate change. Canadian Journal of Forest 

Research. 39(12): 2369-2380.

Le Quéré, C.; Raupach, M.R.; Canadell, J.G.; 

Marland, G. 2009. Trends in the sources and 

sinks of carbon dioxide. Nature Geoscience. 

2: 831-836.

Lee, C.C. 2012. Utilizing synoptic climatological 

methods to assess the impacts of climate 

change on future tornado-favorable 

environments. Natural Hazards. 62(2): 325-343.

Leefers, L.A. 2007. The U.P. economy and the 

role of forest products industries. Land Policy 

Institute Report 2007-07. East Lansing, MI: 

Michigan State University. 49 p.

Lenihan, J.M.; Bachelet, D.; Neilson, R.P.; Drapek, 

R. 2008. Simulated response of conterminous 

United States ecosystems to climate change 

at different levels of fire suppression, CO2

emission rate, and growth response to CO2 .

Global and Planetary Change. 64(1-2): 16-25.
Literature Cited

180

Linderholm, H.W. 2006. Growing season changes 

in the last century. Agricultural and Forest 

Meteorology. 137(1-2): 1-14.

Mackun, P.; Wilson, S.; Fischetti, T.; Coworowska, 

J. 2011. Population distribution and change: 

2000 to 2010. Washington, DC: U.S. Department 

of Commerce, Census Bureau. Available at www.

census.gov/prod/cen2010/briefs/c2010br-01.pdf. 

(Accessed November 24, 2013).

Madsen, T.; Willcox, N. 2012. When it rains, it 

pours: global warming and the increase in 

extreme precipitation from 1948 to 2011.

Boston, MA: Environment America Research 

& Policy Center. 43 p.

Magnuson, J.J.; Robertson, D.M.; Benson, B.J.; 

Wynne, R.H.; Livingstone, D.M.; Arai, T.; Assel, 

R.A.; Barry, R.G.; Card, V.; Kuusisto, E.; Granin, 

N.G.; Prowse, T.D.; Stewart, K.M.; Vuglinski, 

V.S. 2000. Historical trends in lake and river 

ice cover in the Northern Hemisphere. Science. 

289(5485): 1743-1746.

Magruder, M.; Chhin, S.; Monks, A.; O’Brien, 

J. 2012. Effects of initial stand density and 

climate on red pine productivity within Huron 

National Forest, Michigan, USA. Forests. 

3(4): 1086-1103.

Malmsheimer, R.W.; Bowyer, J.L.; Fried, J.S.; Gee, 

E.; Izlar, R.L.; Miner, R.A.; Munn, I.A.; Oneil, 

E.; Stewart, W.C. 2011. Managing forests 

because carbon matters: integrating energy, 

products, and land management policy. Journal 

of Forestry. 109(7): S7-S48.

Maslin, M.; Austin, P. 2012. Climate models at 

their limit? Nature. 486: 2.

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. Intergovernmental 

Panel on Climate Change (IPCC). Available at 

http://www.ipcc.ch/activities/activities.shtml. 

(Accessed November 23, 2013).

Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, 

M.P. 2011a. Changes in potential habitat of 

147 North American breeding bird species in 

response to redistribution of trees and climate 

following predicted climate change. Ecography. 

34(6): 933-945.

Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, 

M.P.; Rodewald, P.G. 2011b. Modifying climate 

change habitat models using tree speciesspecific assessments of model uncertainty 

and life history-factors. Forest Ecology and 

Management. 262(8): 1460-1472.

Mcboyle, G.; Scott, D.; Jones, B. 2007. Climate 

change and the future of snowmobiling in nonmountainous regions of Canada. Managing 

Leisure. 12(4): 237-250.

McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005. 

Molecular indicators of tree migration 

capacity under rapid climate change. Ecology. 

86(8): 2088-2098.

McMahon, S.M.; Parker, G.G.; Miller, D.R. 2010. 

Evidence for a recent increase in forest growth.

Proceedings of the National Academy 

of Sciences. 107(8): 3611-3615.
Literature Cited

181

McNab, W.H.; Cleland, D.T.; Freeouf, J.A.; Keys, 

J.E.; Nowacki, G.J.; Carpenter, C.A. 2007. 

Description of “Ecological subregions: 

sections of the conterminous United States.” 

Gen. Tech. Rep. WO-76B. Washington, DC: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 80 p. Available at 

http://www.edc.uri.edu/atmt-dss/ReportForecast/

SectionDescriptions.pdf. (Accessed 

November 23, 2013).

Medlyn, B.E.; Duursma, R.A.; Zeppel, M.J.B. 2011. 

Forest productivity under climate change: a 

checklist for evaluating model studies. Wiley 

Interdisciplinary Reviews: Climate Change. 

2(3): 332-355.

Michigan Department of Environmental Quality. 

2013. GeoWebFace. http://ww2.deq.state.mi.us/

GeoWebFace/. (Accessed December 3, 2013). 

Michigan Department of Agriculture. 2009. 

Michigan food and agricultural systems 

profiles. 19 p. Available at http://www.michigan.

gov/documents/mda/Regional_Food_System_

Profiles_292928_7.pdf. (Accessed December 3, 

2013). 

Michigan Department of Natural Resources. 2011. 

Michigan forest health highlights 2011.

Lansing, MI. 25 p. Available at http://fhm.fs.fed.

us/fhh/fhh_11/MI_FHH_2011.pdf. (Accessed 

November 23, 2013).

Michigan Department of Natural Resources. 2012. 

Michigan forest health highlights 2012.

Lansing, MI. 42 p. Available at http://fhm.fs.fed.

us/fhh/fhh_12/MI_FHH_2012.pdf. (Accessed 

November 23, 2013). 

Michigan Natural Features Inventory [MNFI]. 

2013a. Michigan’s special animals. Lansing, 

MI. Available at http://mnfi.anr.msu.edu/data/

specialanimals.cfm2013. (Accessed 

November 23, 2013).

Michigan Natural Features Inventory [MNFI]. 

2013b. Michigan’s special plants. Lansing, 

MI. Available at http://mnfi.anr.msu.edu/data/

specialplants.cfm2013. (Accessed November 23, 

2013).

Millar, C.E. 1940. Soils of Michigan. Extension 

Bulletin. 290: 1-32.

Minnesota Department of Natural Resources. 2008. 

Natural wild rice in Minnesota: a wild rice 

study document submitted to the Minnesota 

legislature by the Minnesota Department of 

Resources. 114 p. Available at http://files.dnr.

state.mn.us/fish_wildlife/wildlife/shallowlakes/

natural-wild-rice-in-minnesota.pdf. (Accessed 

November 23, 2013).

Minnesota Sea Grant. 2012. A self-assessment 

to address climate change readiness in your 

community. Duluth, MN. 16 p.

Miranda, B.R.; Sturtevant, B.R.; Stewart, S.I.; 

Hammer, R.B. 2012. Spatial and temporal 

drivers of wildfire occurrence in the context 

of rural development in northern Wisconsin, 

USA. International Journal of Wildland Fire. 

21(2): 141-154.

Mishra, V.; Cherkauer, K.A.; Bowling, L.C.; Huber, 

M. 2011. Lake ice phenology of small lakes: 

impacts of climate variability in the Great 

Lakes region. Global and Planetary Change. 

76(3-4): 166-185.

Mladenoff, D.J. 2004. LANDIS and forest 

landscape models. Ecological Modelling. 

180(1): 7-19.

Moritz, M.A.; Parisien, M.-A.; Batllori, E.; 

Krawchuk, M.A.; Dorn, J.V.; Ganz, D.J.; Hayhoe, 

K. 2012. Climate change and disruptions to 

global fire activity. Ecosphere. 6(6): 22.
Literature Cited

182

Munck, I.A.; Smith, D.R.; Sickley, T.; Stanosz, G.R. 

2009. Site-related influences on cone-borne 

inoculum and asymptomatic persistence of 

Diplodia shoot blight fungi on or in mature red 

pines. Forest Ecology and Management. 257(3): 

812-819.

Murphy, H.T.; VanDerWal, J.; Lovett-Doust, J. 2010. 

Signatures of range expansion and erosion in 

eastern North American trees. Ecology Letters. 

13(10): 1233-1244.

Myers, P.; Lundrigan, B.L.; Hoffman, S.M.G.; 

Haraminac, A.P.; Seto, S.H. 2009. Climateinduced changes in the small mammal 

communities of the Northern Great Lakes 

Region. Global Change Biology. 15(6): 

1434-1454.

National Drought Mitigation Center. 2013. U.S. 

drought monitor: University of NebraskaLincoln, U.S. Department of Agriculture, 

and the National Oceanic and Atmospheric 

Administration. Available at http://

droughtmonitor.unl.edu/. (Accessed August 28, 

2013).

National Weather Service. 2012. Storm Prediction 

Center. Available at http://www.spc.noaa.gov/

wcm/. (Accessed April 1, 2012).

NatureServe. 2013. NatureServe Explorer: an 

online encyclopedia of life [Web application].

Version 7.1. Arlington, VA: NatureServe. 

Available at http://www.natureserve.org/explorer. 

(Accessed February 11, 2013). 

Nearing, M.; Jetten, V.; Baffaut, C.; Cerdan, O.; 

Couturier, A.; Hernandez, M.; Le Bissonnais, 

Y.; Nichols, M.; Nunes, J.; Renschler, C. 2005. 

Modeling response of soil erosion and runoff 

to changes in precipitation and cover. Catena. 

61(2): 131-154.

Nearing, M.; Pruski, F.; O’Neal, M. 2004. Expected 

climate change impacts on soil erosion rates: a 

review. Journal of Soil and Water Conservation. 

59(1): 43-50.

Nicholls, S. 2012. Outdoor recreation and 

tourism. In: Winkler, J.; Andresen, J.; Hatfield, 

J.; Bidwell, D.; Brown, D., coordinators. U.S. 

National Climate Assessment Midwest technical 

input report. Available at http://glisa.msu.edu/

docs/NCA/MTIT_RecTourism.pdf. (Accessed 

November 23, 2013).

National Oceanic and Atmospheric Administration 

[NOAA] National Climatic Data Center. 2012a. 

State of the climate: global analysis for annual 

2012. Available at http://www.ncdc.noaa.gov/

sotc/global/2012/13. (Accessed December 2012).

National Oceanic and Atmospheric Administration 

[NOAA] National Climatic Data Center. 2012b. 

State of the climate: national overview for 

annual 2012. Available at http://www.ncdc.noaa.

gov/sotc/national/2012/13. (Accessed December 

2012).

National Oceanic and Atmospheric Administration 

[NOAA] National Climatic Data Center. 2013. 

Climate at a glance: time series. Available 

at http://www.ncdc.noaa.gov/cag/time-series. 

(Accessed August 28, 2013).

Norby, R.J.; DeLucia, E.H.; Gielen, B.; Calfapietra, 

C.; Giardina, C.P.; King, J.S.; Ledford, J.; 

McCarthy, H.R.; Moore, D.J.P.; Ceulemans, 

R. 2005. Forest response to elevated CO2

is conserved across a broad range of 

productivity. Proceedings of the National 

Academy of Sciences. 102(50): 18052.

Norby, R.J.; Zak, D.R. 2011. Ecological lessons 

from free-air CO2

 enrichment (FACE) 

experiments. Annual Review of Ecology, 

Evolution, and Systematics. 42: 181-203.

Notaro, M.; Lorenz, D.J.; Vimont, D.; Vavrus, S.; 

Kucharik, C.; Franz, K. 2011. 21st century 

Wisconsin snow projections based on an 

operational snow model driven by statistically 

downscaled climate data. International Journal 

of Climatology. 31(11): 1615-1633.
Literature Cited

183

Nowacki, G.J.; Abrams, M.D. 2008. The demise 

of fire and “mesophication” of forests in the 

eastern United States. Bioscience. 58(2): 

123-138.

Ogden, A.; Innes, J. 2007. Incorporating climate 

change adaptation considerations into forest 

management planning in the boreal forest.

International Forestry Review. 9(3): 713-733.

Ollinger, S.; Goodale, C.; Hayhoe, K.; Jenkins, 

J. 2008. Potential effects of climate change 

and rising CO2

 on ecosystem processes in 

northeastern U.S. forests. Mitigation and 

Adaptation Strategies for Global Change. 

13(5): 467-485.

Ollinger, S.V.; Aber, J.D.; Reich, P.B.; Freuder, R.J. 

2002. Interactive effects of nitrogen deposition, 

tropospheric ozone, elevated CO2

, and land use 

history on the carbon dynamics of northern 

hardwood forests. Global Change Biology. 

8: 545-562.

Opperman, J.J.; Luster, R.; McKenney, B.A.; 

Roberts, M.; Meadows, A.W. 2010. Ecologically 

functional floodplains: connectivity, flow 

regime, and scale. JAWRA Journal of the 

American Water Resources Association. 

46(2): 211-226.

Palik, B.J.; Ostry, M.E.; Venette, R.C.; Abdela, E. 

2011. Fraxinus nigra (black ash) dieback in 

Minnesota: regional variation and potential 

contributing factors. Forest Ecology and 

Management. 261(1): 128-135.

Parmesan, C.; Yohe, G. 2003. A globally coherent 

fingerprint of climate change impacts across 

natural systems. Nature. 421(6918): 37-42.

Passi, P. 2012. Floods wreaked torrent of trouble 

in Duluth trout streams; city starting to 

eye repairs. Northland Outdoors. Available at 

http://www.northlandoutdoors.com/event/article/

id/246934/publisher_ID/36/.

Pastor, J.; Post, W. 1988. Response of northern 

forests to CO2

-induced climate change. Nature. 

334(6177): 55-58.

Pedlar, J.H.; McKenney, D.W.; Aubin, I.; 

Beardmore, T.; Beaulieu, J.; Iverson, L.; O’Neill, 

G.A.; Winder, R.S.; Ste-Marie, C. 2012. Placing 

forestry in the assisted migration debate.

BioScience. 62(9): 835-842.

Perera, E.M.; Sanford, T.; White-Newsome, J.L.; 

Kalkstein, L.S.; Vanos, J.K.; Weir, K. 2012. Heat 

in the Heartland: 60 years of warming in the 

Midwest. Union of Concerned Scientists. 

40 p. Available at http://www.ucsusa.org/global_

warming/science_and_impacts/impacts/globalwarming-and-heat-waves.html. (Accessed 

August 1, 2012).

Peters, E.B.; Wythers, K.R.; Zhang, S.; Bradford, 

J.B.; Reich, P.B. 2013. Potential climate 

change impacts on temperate forest ecosystem 

processes. Canadian Journal of Forest Research. 

43: 939-950.

Peterson, C.J. 2000. Catastrophic wind damage 

to North American forests and the potential 

impact of climate change. The Science of the 

Total Environment. 262: 287-311.

Pingrey, P. 2011. 2011 FSC-US Forest 

Management and Chain of Custody data and 

graphics. Minneapolis, MN: Forest Stewardship 

Council. 17 p.

Pope, V.; Gallani, M.L.; Rowntree, P.R.; Stratton, 

R.A. 2000. The impact of new physical 

parameterizations in the Hadley Centre 

climate model: HadAM3. Climate Dynamics. 

16: 123-146.

Portmann, R.W.; Solomon, S.; Hegerl, G.C. 2009. 

Spatial and seasonal patterns in climate 

change, temperatures, and precipitation across 

the United States. Proceedings of the National 

Academy of Sciences. 106(18): 7324-7329.
Literature Cited

184

Posey, J. 2012. Climate change impacts on 

transportation in the Midwest. In: Winkler, J.; 

Andresen, J.; Hatfield, J.; Bidwell, D.; Brown, D., 

coordinators. U.S. National Climate Assessment 

Midwest technical input report. Available 

from the Great Lakes Integrated Sciences and 

Assessment (GLISA) Center, http://glisa.msu.

edu/docs/NCA/MTIT_Transportation.pdf. 

(Accessed November 23, 2013).

Post, W.M.; Pastor, J. 1996. Linkages—an 

individual-based forest ecosystem model. 

Climatic Change. 34(2): 253-261.

Powers, M.D.; Nagel, L.M. 2009. Pennsylvania 

sedge cover, forest management and deer 

density influence tree regeneration dynamics 

in a northern hardwood forest. Forestry. 

82(3): 241-254.

Prasad, A.M.; Iverson, L.R.; Matthews, S.N.; 

Peters, M. 2007-ongoing. A climate change 

atlas for 134 forest tree species of the Eastern 

United States [database]. Delaware, OH: U.S. 

Department of Agriculture, Northern Research 

Station. Available at http://www.nrs.fs.fed.

us/atlas/tree/tree_atlas.html. (Accessed May 15, 

2013). 

Price, C.G.; Murphy, B.P. 2002. Lightning activity 

during the 1999 Superior derecho. Geophysical 

Research Letters. 29(23).

Price, D.L. 2010. Michigan forest resource 

assessment and strategy. Lansing, MI: 

Michigan Department of Natural Resources and 

Environment. 124 p. Available at http://www.

forestactionplans.org/states/michigan. (Accessed 

December 3, 2013).

Pugh, S.A.; Hansen, M.H.; Pedersen, L.D.; Heym, 

D.C.; Butler, B.J.; Crocker, S.J.; Meneguzzo, 

D.; Perry, C.H.; Haugen, D.E.; Woodall, C.W.; 

Jepsen, E. 2009. Michigan’s forests 2004.

Resour. Bull. NRS-34. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 214 p.

Pugh, S.A.; Pedersen, L.D.; Heym, D.C.; Piva, 

R.J.; Woodall, C.W.; Barnett, C.J.; Kurtz, 

C.M.; Moser, W.K. 2012. Michigan’s forests 

2009. Resour. Bull. NRS-66. Newtown Square, 

PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. Available at 

http://www.fs.fed.us/nrs/pubs/rb/rb_nrs66.pdf. 

(Accessed November 23, 2013).

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I. 2005. 

Rural and suburban sprawl in the US Midwest 

from 1940 to 2000 and its relation to forest 

fragmentation. Conservation Biology. 19(3): 

793-805.

Raupach, M.R.; Marland, G.; Ciais, P.; Le Quéré, 

C.; Canadell, J.G.; Klepper, G.; Field, C.B. 2007. 

Global and regional drivers of accelerating 

CO2

 emissions. Proceedings of the National 

Academy of Sciences. 104(24): 10288-10293.

Ravenscroft, C.; Scheller, R.M.; Mladenoff, D.J.; 

White, M.A. 2010. Forest restoration in a 

mixed-ownership landscape under climate 

change. Ecological Applications. 20(2): 327-346.

Rempel, R.S. 2011. Effects of climate change on 

moose populations: exploring the response 

horizon through biometric and systems 

models. Ecological Modelling. 222(18): 

3355-3365.

Reusch, T.B.H.; Ehlers, A.; Hammerli, A.; Worm, 

B. 2005. Ecosystem recovery after climatic 

extremes enhanced by genotypic diversity.

Proceedings of the National Academy of 

Sciences. 102(8): 2826-2831.

Rich, R.L.; Frelich, L.; Reich, P.B.; Bauer, M.E. 

2010. Detecting wind disturbance severity 

and canopy heterogeneity in boreal forest 

by coupling high-spatial resolution satellite 

imagery and field data. Remote Sensing of 

Environment. 114(2): 299-308.
Literature Cited

185

Rich, R.L.; Frelich, L.E.; Reich, P.B. 2007. Windthrow mortality in the southern boreal forest: 

effects of species, diameter and stand age.

Journal of Ecology. 95: 1261-1273.

Richardson, K.; Steffen, W.; Schellnhuber, H.J.; 

Alcamo, J.; Barker, T.; Kammen, D.; Leemans, 

R.; Liverman, D.; Monasinghe, M.; OsmanElasha, B.; Stern, N.; Waever, O. 2009. Climate 

change: global risks, challenges and decisions.

University of Copenhagen. Available at http://

edepot.wur.nl/10197. (Accessed November 24, 

2013).

Romano, S.P. 2010. Our current understanding of 

the Upper Mississippi River System floodplain 

forest. Hydrobiologia. 640(1): 115-124.

Root, T.L.; Price, J.T.; Hall, K.R.; Schneider, S.H.; 

Rosenzweig, C.; Pounds, J.A. 2003. Fingerprints 

of global warming on wild animals and plants.

Nature. 421(6918): 57-60.

Rustad, L.; Campbell, J.; Marion, G.; Norby, 

R.; Mitchell, M.; Hartley, A.; Cornelissen, J.; 

Gurevitch, J.; Gcte, N. 2001. A meta-analysis of 

the response of soil respiration, net nitrogen 

mineralization, and aboveground plant 

growth to experimental ecosystem warming.

Oecologia. 126(4): 543-562.

Saunders, S.; Findlay, D.; Easley, T.; Spencer, T. 

2011. Great Lakes national parks in peril: the 

threats of climate disruption. Louisville, CO: 

Rocky Mountain Climate Organization; New 

York: Natural Resources Defense Council. 58 p. 

Available at http://www.rockymountainclimate.

org/images/GreatLakesParksInPeril.pdf. 

(Accessed November 24, 2013).

Saunders, S.; Findlay, D.; Easley, T.; Spencer, T. 

2012. Doubled trouble: more Midwestern 

extreme storms. Louisville, CO: Rocky 

Mountain Climate Organization; New York: 

Natural Resources Defense Council. 42 p. 

Available at http://www.rockymountainclimate.

org/images/Doubled Trouble.pdf. (Accessed 

November 24, 2013).

Scheller, R.M.; Domingo, J.B.; Sturtevant, B.R.; 

Williams, J.S.; Rudy, A.; Gustafson, E.J.; 

Mladenoff, D.J. 2007. Design, development, and 

application of LANDIS-II, a spatial landscape 

simulation model with flexible temporal and 

spatial resolution. Ecological Modelling. 

201(3-4): 409-419.

Scheller, R.M.; Mladenoff, D.J. 2005. A spatially 

interactive simulation of climate change, 

harvesting, wind, and tree species migration 

and projected changes to forest composition 

and biomass in northern Wisconsin, USA.

Global Change Biology. 11(2): 307-321.

Scheller, R.M.; Mladenoff, D.J. 2008. Simulated 

effects of climate change, fragmentation, 

and inter-specific competition on tree species 

migration in northern Wisconsin, USA.

Climate Research. 36(3): 191-202.

Schulte, L.A.; Mladenoff, D.J. 2005. Severe wind 

and fire regimes in northern forests: historical 

variability at the regional scale. Ecology. 

86(2): 431-445.

Schwartz, M.D.; R.Ahas; Aasa, A. 2006a. Onset 

of spring starting earlier across the Northern 

Hemisphere. Global Change Biology. 12(2): 

343-351.

Schwartz, M.W.; Iverson, L.R.; Prasad, A.M.; 

Matthews, S.N.; O’Connor, R.J. 2006b. 

Predicting extinctions as a result of climate 

change. Ecology. 87(7): 1611-1615.
Literature Cited

186

Silbernagel, J.; Martin, S.R.; Gale, M.R.; Chen, 

J. 1997. Prehistoric, historic, and present 

settlement patterns related to ecological 

hierarchy in the eastern Upper Peninsula of 

Michigan, U.S.A. Landscape Ecology. 

12: 223-240.

Sinha, T.; Cherkauer, K.A. 2010. Impacts of future 

climate change on soil frost in the midwestern 

United States. Journal of Geophysical ResearchAtmospheres. 115(D8).

Sinha, T.; Cherkauer, K.A.; Mishra, V. 2010. 

Impacts of historic climate variability on 

seasonal soil frost in the midwestern United 

States. Journal of Hydrometeorology. 

11: 229-252.

Slaughter, B.S.; Cohen, J.G.; Kost, M.A. 2007. 

Natural community abstract for hardwoodconifer swamp. Lansing, MI: Michigan Natural 

Features Inventory. 20 p. Available at http://mnfi.

anr.msu.edu/abstracts/ecology/Hardwoodconifer_swamp.pdf. (Accessed November 24, 

2013).

Solomon, A.M. 1986. Transient response of forests 

to CO2

-induced climate change: simulation 

modeling experiments in eastern North 

America. Oecologia. 68(4): 567-579.

Sommers, W.T.; Coloff, S.G.; Conard, S.G. 2011. 

Fire history and climate change. Report 

submitted to the Joint Fire Science Program 

for Project 09-02-1-09. 340 p. Available at 

http://www.firescience.gov/projects/09-2-01-9/

project/09-2-01-9_Final_Report_JFSP_09-2-01-

09_Fire_History_and_Climate_Change_by_

Chapter_111013.pdf. (Accessed December 3, 

2013). 

Sparhawk, W.N.; Brush, W.D. 1929. The economic 

aspects of forest destruction in northern 

Michigan. Tech. Bull. No. 92. Washington, DC: 

U.S. Department of Agriculture. 

St. Claire, S.B.; Sharpe, W.E.; Lynch, J.P. 2008. 

Key interactions between nutrient limitation 

and climatic factors in temperate forests: 

a synthesis of the sugar maple literature.

Canadian Journal of Forest Research. 38(3): 

401-414.

Stanosz, G.R.; Blodgett, J.T.; Smith, D.R.; Kruger, 

E.L. 2001. Water stress and Sphaeropsis 

sapinea as a latent pathogen of red pine 

seedlings. New Phytologist. 149(3): 531-538.

Stearns, F. 1997. History of Lake States forests: 

natural and human impacts. In: Webster, 

H.H.; Vasievich, J.M., eds. Lake States Regional 

Forest Resources Assessment: Technical Papers. 

Gen. Tech. Rep. NC-189. St. Paul, MN: U.S. 

Department of Agriculture, Forest Service, North 

Central Forest Experiment Station: 8-29.

Stefan, H.G.; Fang, X.; Eaton, J.G. 2001. Simulated 

fish habitat changes in North American lakes 

in response to projected climate warming.

Transactions of the American Fisheries Society. 

130(3): 459-477.

Stoner, A.M.K.; Hayhoe, K.; Yang, X.; Wuebbles, 

D.J. 2013. An asynchronous regional 

regression model for statistical downscaling of 

daily climate variables. International Journal of 

Climatology. 33(11): 2473-2494.

Stott, P.A.; Gillett, N.P.; Hegerl, G.C.; Karoly, 

D.J.; Stone, D.A.; Zhang, X.; Zwiers, F. 2010. 

Detection and attribution of climate change: 

a regional perspective. Wiley Interdisciplinary 

Reviews: Climate Change. 1(March/April 2010): 

192-211.

Sturrock, R.N.; Frankel, S.J.; Brown, A.V.; Hennon, 

P.E.; Kliejunas, J.T.; Lewis, K.J.; Worrall, J.J.; 

Woods, A.J. 2011. Climate change and forest 

diseases. Plant Pathology. 60(1): 133-149.

Swanson, D.K.; Grigal, D.F. 1991. Biomass, 

structure, and trophic environment of 

peatland vegetation in Minnesota. Wetlands. 

11(2): 279-302.
Literature Cited

187

Swanston, C.; Janowiak, M.; Iverson, L.; Parker, 

L.; Mladenoff, D.; Brandt, L.; Butler, P.; Pierre, 

M.S.; Prasad, A.; Matthews, S.; Peters, M.; 

Higgins, D.; Dorland, A. 2011. Ecosystem 

vulnerability assessment and synthesis: a 

report from the Climate Change Response 

Framework project in northern Wisconsin.

Gen. Tech. Rep. NRS-82. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 142 p.

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 http://www.nrs.fs.fed.

us/pubs/40543. (Accessed November 24, 2013).

Tans, P.; Keeling, R. 2013. Trends in atmospheric 

carbon dioxide. National Oceanic and 

Atmospheric Administration, Earth System 

Research Laboratory. Available at http://www.

esrl.noaa.gov/gmd/ccgg/trends/. (Accessed 

February 14, 2013).

Tepley, A.J.; Cohen, J.G.; Huberty, L. 2004. 

Natural community abstract for floodplain 

forest. Lansing, MI: Michigan Natural Features 

Inventory. 15 p. Available at http://mnfi.anr.msu.

edu/abstracts/ecology/floodplain_forest.pdf. 

(Accessed November 24, 2013).

Theobald, D. 2005. Landscape patterns of exurban 

growth in the USA from 1980 to 2020. Ecology 

and Society. 10(1): 32.

Tierney, G.; Fahey, T.; Groffman, P.; Hardy, J.; 

Fitzhugh, R.; Driscoll, C. 2001. Soil freezing 

alters fine root dynamics in a northern 

hardwood forest. Biogeochemistry. 

56(2): 175-190.

Tilman, D. 1996. Biodiversity: population versus 

ecosystem stability. Ecology. 77(2): 350-363.

Tilman, D. 1999. The ecological consequences of 

changes in biodiversity: a search for general 

principles. Ecology. 80(5): 1455-1474.

Tomer, M.D.; Schilling, K.E. 2009. A simple 

approach to distinguish land-use and climatechange effects on watershed hydrology. Journal 

of Hydrology. 376(1-2): 24-33.

Trapp, R.J.; Diffenbaugh, N.S.; Brooks, H.E.; 

Baldwin, M.E.; Robinson, E.D.; Pal, J.S. 2007. 

Changes in severe thunderstorm environment 

frequency during the 21st century caused by 

anthropogenically enhanced global radiative 

forcing. Proceedings of the National Academy of 

Sciences. 104(50): 19719.

U.S. Forest Service. 2011. FIA DataMart: FIADB 

Version 4.0. Washington, DC: U.S. Department 

of Agriculture, Forest Service. Available at http://

apps.fs.fed.us/fiadb-downloads/datamart.html. 

(Accessed October 27, 2011). 

U.S. Geological Survey. 2011a. National Land 

Cover Dataset 2006 land cover. Available 

at http://www.mrlc.gov/index.php. (Accessed 

December 8, 2011). 

U.S. Geological Survey. 2011b. Water-data report 

2010. Available at http://waterwatch.usgs.gov/. 

(Accessed December 14, 2011). 

Vanhanen, H.; Veteli, T.O.; Päivinen, S.; Kellomäki, 

S.; Niemelä, P. 2007. Climate change and range 

shifts in two insect defoliators: gypsy moth and 

nun moth–a model study. Silva Fennica. 41(4): 

621-638.

Venette, R.C.; Cohen, S.D. 2006. Potential climatic 

suitability for establishment of Phytophthora 

ramorum within the contiguous United States.

Forest Ecology and Management. 231(1): 18-26.

Villarini, G.; Smith, J.A.; Baeck, M.L.; Krajewski, 

W.F. 2011. Examining flood frequency 

distributions in the Midwest US. JAWRA 

Journal of the American Water Resources 

Association. 47(3): 447-463.
Literature Cited

188

Walther, G.R.; Post, E.; Convey, P.; Menzel, A.; 

Parmesan, C.; Beebee, T.J.C.; Fromentin, 

J.M.; Hoegh-Guldberg, O.; Bairlein, F. 2002. 

Ecological responses to recent climate change.

Nature. 416(6879): 389-395.

Wang, G.G.; Chhin, S.; Bauerle, W.L. 2006. Effect 

of natural atmospheric CO2

 fertilization 

suggested by open-grown white spruce in a dry 

environment. Global Change Biology. 12(3): 

601-610.

Wang, J.; Bai, X.; Hu, H.; Clites, A.; Colton, 

M.; Lofgren, B. 2012. Temporal and spatial 

variability of Great Lakes ice cover, 1973-

2010*. Journal of Climate. 25(4): 1318-1329.

Washington, W.M.; Weatherly, J.W.; Meehl, 

G.A.; Semtner, A.J., Jr.; Bettge, T.W.; Craig, 

A.P.; Strand, W.G., Jr.; Arblaster, J.; Wayland, 

V.B.; James, R.; Zhang, Y. 2000. Parallel 

climate model (PCM) control and transient 

simulations. Climate Dynamics. 16(10): 

755-774.

Weber, C.R.; Cohen, J.G.; Kost, M.A. 2006. 

Abstract on the Aspen Association of northern 

Michigan. Lansing, MI: Michigan Natural 

Features Inventory.

Weber, C.R.; Cohen, J.G.; Kost, M.A. 2007. Natural 

community abstract for northern hardwood 

swamp. Lansing, MI: Michigan Natural Features 

Inventory. 9 p. Available at http://mnfi.anr.msu.

edu/abstracts/ecology/Northern_hardwood_

swamp.pdf. (Accessed November 24, 2013).

Wehrly, K.E.; Wang, L.; Mitro, M. 2007. Fieldbased estimates of thermal tolerance limits 

for trout: incorporating exposure time and 

temperature fluctuation. Transactions of the 

American Fisheries Society. 136(2): 365-374.

White, M.A. 2012. Long-term effects of deer 

browsing: composition, structure and 

productivity in a northeastern Minnesota oldgrowth forest. Forest Ecology and Management. 

269: 222-228.

White, M.A.; Host, G.E. 2008. Forest disturbance 

frequency and patch structure from preEuropean settlement to present in the Mixed 

Forest Province of Minnesota, USA. Canadian 

Journal of Forest Research. 38(8): 2212-2226.

Wiens, J.A.; Stralberg, D.; Jongsomjit, D.; Howell, 

C.A.; Snyder, M.A. 2009. Niches, models, and 

climate change: assessing the assumptions 

and uncertainties. Proceedings of the National 

Academy of Sciences. 106(Supplement 2): 

19729-19736.

Wilson, B.T.; Lister, A.J.; Riemann, R.I. 2012. A 

nearest-neighbor imputation approach to 

mapping tree species over large areas using 

forest inventory plots and moderate resolution 

raster data. Forest Ecology and Management. 

271(0): 182-198.

Winkler, J.A.; Arritt, R.; Pryor, S. 2012. Climate 

projections for the Midwest: availability, 

interpretation and synthesis. In Winkler, J.; 

Andresen, J.; Hatfield, J.; Bidwell, D.; Brown, D., 

coordinators. U.S. National Climate Assessment 

Midwest technical input report. Available at 

http://glisa.msu.edu/docs/NCA/MTIT_Future.pdf. 

(Accessed November 24, 2013).

Wisconsin Initiative on Climate Change Impacts 

[WICCI] Wildlife Working Group. 2011. Wildlife 

Working Group Report. Madison, WI: Nelson 

Institute for Environmental Studies, University 

of Wisconsin-Madison and the Wisconsin 

Department of Natural Resources. Available at 

http://www.wicci.wisc.edu/report/Wildlife.pdf. 

(Accessed October 31, 2013).

Woodall, C.; Zhu, K.; Westfall, J.; Oswalt, C.; 

D’Amato, A.; Walters, B.; Lintz, H. 2013. 

Assessing the stability of tree ranges and 

influence of disturbance in eastern US forests.

Forest Ecology and Management. 291: 172-180.
Literature Cited

189

Woodall, C.W.; Oswalt, C.M.; Westfall, J.A.; 

Perry, C.H.; Nelson, M.D.; Finley, A.O. 2009. 

An indicator of tree migration in forests of 

the eastern United States. Forest Ecology and 

Management. 257(5): 1434-1444.

World Meteorological Organization [WMO]. 2008. 

WMO statement on the status of the global 

climate in 2007. WMO-No. 131. Geneva, 

Switzerland. Available at http://www.wmo.int/

pages/prog/wcp/wcdmp/documents/WMO1031_

EN_web.pdf. (Accessed November 24, 2013).

Worrall, J.J.; Rehfeldt, G.E.; Hamann, A.; Hogg, 

E.H.; Marchetti, S.B.; Michaelian, M.; Gray, L.K. 

2013. Recent declines of Populus tremuloides

in North America linked to climate. Forest 

Ecology and Management. 299: 35-51.

Wright, D.; Posselt, D.; Steiner, A. 2013. Sensitivity 

of lake-enhanced snowfall to lake ice cover 

in the Great Lakes region. Monthly Weather 

Review. 141: 670-689.

Wuebbles, D.J.; Hayhoe, K. 2004. Climate change 

projections for the United States Midwest.

Mitigation and Adaptation Strategies for Global 

Change. 9: 335-363.

Wyckoff, P.H.; Bowers, R. 2010. Response of 

the prairie-forest border to climate change: 

impacts of increasing drought may be 

mitigated by increasing CO2

. Journal of 

Ecology. 98(1): 197-208.

Xu, C.; Gertner, G.Z.; Scheller, R.M. 2009. 

Uncertainties in the response of a forest 

landscape to global climatic change. Global 

Change Biology. 15(1): 116-131.

Zhu, K.; Woodall, C.W.; Clark, J.S. 2012. Failure 

to migrate: lack of tree range expansion in 

response to climate change. Global Change 

Biology. 18(3) 1042-1052.

Zorn, T.G.; Seelbach, P.W.; Rutherford, E.S.; Wills, 

T.C.; Cheng, S.-T.; Wiley, M.J. 2008. A regionalscale habitat suitability model to assess the 

effects of flow reduction on fish assemblages 

in Michigan streams. Lansing, MI: Michigan 

Department of Natural Resources, Fisheries 

Division. Fisheries Research Report 2089. 

50 p. Available at http://www.michigan.gov/

dnr/0,4570,7-153-10364_52259_19056-46193--

,00.html. (Accessed December 5, 2013).
190

Appendix 1: Species Lists

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

tag alder Alnus rugosa

pawpaw Asimina triloba

Japanese barberry Berberis thunbergii

yellow birch Betula alleghaniensis

sweet birch Betula lenta

river birch Betula nigra

paper birch Betula papyrifera

Pennsylvania sedge Carex pensylvanica

American hornbeam Carpinus caroliniana

bitternut hickory Carya cordiformis

pignut hickory Carya glabra

shellbark hickory Carya laciniosa

shagbark hickory Carya ovata

black hickory Carya texana

mockernut hickory Carya tomentosa

sugarberry Celtis laevigata

hackberry Celtis occidentalis

spotted knapweed Centaurea maculosa

eastern redbud Cercis canadensis

flowering dogwood Cornus florida

common persimmon Diospyros virginiana

autumn olive Elaeagnus umbellata

leafy spurge Euphorbia esula

American beech Fagus grandifolia

white ash Fraxinus americana

Common Name Scientific Name

black ash Fraxinus nigra

green ash Fraxinus pennsylvanica

honeylocust Gleditsia triacanthos

St. John’s wort Hypericum perforatum

butternut Juglans cinerea

black walnut Juglans nigra

eastern redcedar Juniperus virginiana

tamarack Larix laricina

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

sycamore Platanus occidentalis

balsam poplar Populus balsamifera

eastern cottonwood Populus deltoides

bigtooth aspen Populus grandidentata

quaking aspen Populus tremuloides

wild plum Prunus americana

pin cherry Prunus pensylvanica

black cherry Prunus serotina

chokecherry Prunus virginiana

white oak Quercus alba

swamp white oak Quercus bicolor

scarlet oak Quercus coccinea

northern pin oak Quercus ellipsoidalis

shingle oak Quercus imbricaria

Table 20.—Common and scientific names of plant species mentioned in this assessment

(Appendix 1 continued on next page)
Appendix 1

191

Common Name Scientific Name

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

black locust Robinia pseudoacacia

multiflora rose Rosa multiflora

Common Name Scientific Name

peachleaf willow Salix amygdaloides

black willow Salix nigra

sassafras Sassafras albidum

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

Table 20 (continued).

Table 21.—Common and scientific names of fauna species mentioned in this assessment

Common Name Scientific Name

northern goshawk Accipiter gentilis

hemlock wooly adelgid Adelges tsugae

bronze birch borer Agrilus anxius

emerald ash borer Agrilus planipennis

moose Alces alces

Asian long-horned beetle Anoplophora glabripennis

ruffed grouse Bonasa umbellus

red-shouldered hawk Buteo lineatus

beaver Castor canadensis

spruce budworm Choristoneura fumiferana

jack pine budworm Choristoneura pinus pinus

larch casebearer Coleophora laricella

earthworms (nonnative) Dendrobaena octaedra, 

Lumbricus rubellus, 

and L. terrestris

eastern larch beetle Dendroctonus simplex

spruce grouse Falcipennis canadensis

birch leaf miner Fenusa pusilla

common opossum Glaucomys sabrinus

northern flying squirrel Glaucomys sabrinus

southern flying squirrel Glaucomys volans

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

Common Name Scientific Name

American marten Martes americana

red-headed woodpecker Melanerpes 

erythrocephalus

wild turkey Meleagris gallopavo

southern red-backed vole Myodes gapperi

Indiana bat Myotis sodalis

woodland jumping mouse Napaeozapus insignis

red-headed pine sawfly Neodiprion lecontei

white-tailed deer Odocoileus virginianus

gray jay Perisoreus canadensis

white-footed mouse Peromyscus leucopus

woodland deer mouse Peromyscus maniculatus 

gracilis

black-backed woodpecker Picoides arcticus

white pine tip weevil Pissodes strobi

balsam fir bark beetle Pityokteines sparsus

Karner blue butterfly Plebejus melissa samuelis

tamarack sawfly Pristiophora erichsonii

brook trout Salvelinus fontinalis

lake trout Salvelinus namaycush

American woodcock Scolopax minor

Kirtland’s warbler Setophaga kirtlandii

eastern massasauga Sistrurus catenatus

least chipmunk Tamias minimus

eastern chipmunk Tamias striatus

sharp-tailed grouse Tympanuchus phasianellus

golden-winged warbler Vermivora chrysoptera
Appendix 1

192

Table 22.—Common and scientific names of other species mentioned in this assessment

Common Name Scientific Name

Armillaria Armillaria mellea

dwarf mistletoe Arceuthobium pusillum

Lyme disease Borrelia burgdorferi

oak wilt Ceratocystis fagacearum

white pine blister rust Cronartium ribicola

Diplodia Diplodia pinea 

and D. scrobiculata

West Nile virus Flavivirus spp.

Common Name Scientific Name

scleroderris canker Gremmeniella abietina

annosum root disease Heterobasidion irregulare

hypoxylon canker Hypoxylon mammatum

morel mushroom Morchella spp.

Dutch elm disease Ophiostoma ulmi

sudden oak death Phytophthora ramorum

sirococcus shoot blight Sirococcus conigenus

sphaeropsis shoot blight Sphaeropsis sapinea
193

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). 

The PRISM model interpolates historical data 

from the National Weather Service cooperative 

stations, the Midwest Climate Data Center, and the 

Historical Climate Network, among others. Data 

undergo strict quality control procedures to check for 

errors in station measurements. PRISM 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 gridded historical 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- 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 23). 

Developers of the ClimateWizard Tool advise users 

to interpret the linear trend maps in relation to the 

respective map of statistical confidence (Figs. 48 and 

49). 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

194

*P-values represent the probability of observing that trend by chance alone. P-values in boldface indicate a 10-percent probability (or less) that the 

trend was due to chance alone. TMean = mean temperature, TMin = minimum temperature, TMax = maximum 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 1.81 0.35 0.15 18.19 0.94 0.51 26.44 0.41 0.76 9.94 1.46 0.34

February 1.45 -0.17 0.33 18.56 5.14 <0.01 28.19 4.68 <0.01 8.93 5.6 0.01

March 1.87 0.09 0.74 27.76 2.66 0.03 37.72 3.33 0.01 17.79 1.99 0.11

April 2.43 0.82 0.01 40.71 2.8 <0.01 51.54 3.31 <0.01 29.89 2.28 0.01

May 2.92 -0.02 0.95 52.18 2.32 0.01 64.35 2.57 0.02 40.01 2.06 0.02

June 3.06 0.59 0.13 61.98 1.11 0.25 74.21 0.94 0.4 49.77 1.27 0.16

July 2.87 0.21 0.53 67.06 0.46 0.47 79.28 -0.11 0.88 54.84 1.03 0.08

August 3.07 0.88 0.02 65.33 1.99 0.01 77.07 1.39 0.09 53.59 2.58 <0.01

September 3.43 0.56 0.15 57.97 0.16 0.86 68.94 0.4 0.67 47.01 -0.08 0.92

October 2.83 0.83 0.04 47.2 -0.21 0.83 57.03 -0.23 0.85 37.38 -0.19 0.83

November 2.59 0.27 0.36 35.09 1.35 0.14 42.53 1.44 0.19 27.66 1.26 0.13

December 1.96 0.48 0.05 23.62 1.98 0.08 30.79 1.46 0.16 16.44 2.5 0.05

Annual 30.3 4.89 <0.01 42.97 1.72 <0.01 53.18 1.64 <0.01 32.77 1.81 <0.01

Winter 5.22 0.65 0.15 20.13 2.7 0.01 28.48 2.2 0.02 11.77 3.2 0.01

Spring 7.21 0.85 0.08 40.21 2.58 <0.01 51.2 3.06 <0.01 29.24 2.11 <0.01

Summer 9.01 1.69 <0.01 64.78 1.2 0.03 76.85 0.76 0.2 52.73 1.63 <0.01

Fall 8.85 1.68 0.02 46.75 0.44 0.48 56.16 0.55 0.45 37.34 0.34 0.57

Table 23.—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 (http://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

195

Figure 48.—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

196

Figure 49.—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.

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.
197

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 24 and 25) and 

maps of projected change for early- and mid-century 

(Figs. 50 through 57).

Baseline temperature (°F) Temperature departure from baseline (°F)

 (1971-2000) Scenario 2010-2039 2040-2069 2070-2099

Mean

Annual 43.2 PCM B1 1.0 1.7 2.2

GFDL A1FI 2.2 6.0 8.1

Winter (Dec.-Feb.) 20.5 PCM B1 1.1 2.2 2.5

GFDL A1FI 2.2 6.1 7.3

Spring (Mar.-May) 41.1 PCM B1 -0.2 0.9 1.7

GFDL A1FI 0.7 4.1 6.0

Summer (June-Aug.) 64.9 PCM B1 1.3 1.8 2.2

GFDL A1FI 3.6 8.6 11.2

Fall (Sept.-Nov.) 46.2 PCM B1 2.3 2.1 2.7

GFDL A1FI 2.6 5.7 8.2

Mean maximum

Annual 53.6 PCM B1 0.8 1.4 1.9

GFDL A1FI 1.9 5.6 7.3

Winter (Dec.-Feb.) 28.9 PCM B1 0.8 1.5 1.6

GFDL A1FI 1.7 4.8 5.7

Spring (Mar.-May) 52.4 PCM B1 -0.9 0.4 1.2

GFDL A1FI -0.3 2.8 4.3

Summer (June-Aug.) 77 PCM B1 1.7 2.0 2.6

GFDL A1FI 3.8 9.0 11.4

Fall (Sept.-Nov.) 55.8 PCM B1 2.1 2.0 2.6

GFDL A1FI 2.6 6.1 8.2

Mean minimum

Annual 32.9 PCM B1 1.3 1.9 2.5

GFDL A1FI 2.5 6.5 8.9

Winter (Dec.-Feb.) 12 PCM B1 1.4 2.9 3.5

GFDL A1FI 2.7 7.4 9.0

Spring (Mar.-May) 29.8 PCM B1 0.4 1.4 2.1

GFDL A1FI 1.6 5.4 7.7

Summer (June-Aug.) 52.8 PCM B1 1.0 1.6 1.8

GFDL A1FI 3.4 8.1 11.0

Fall (Sept.-Nov.) 36.7 PCM B1 2.5 2.1 2.8

GFDL A1FI 2.5 5.3 8.1

Table 24.—Projected changes in mean average, maximum, and minimum temperatures under two future climate 

scenarios for the assessment area over the next century
Appendix 3

198

Baseline precipitation Departure from baseline (inches)

(inches) (1971-2000) Scenario 2010-2039 2040-2069 2070-2099

Annual 31.7 PCM B1 1.1 3.1 2.6

GFDL A1FI 1.8 -1.5 1.0

Winter (Dec.-Feb.) 5.5 PCM B1 0.5 0.8 1.1

GFDL A1FI 0.6 0.4 0.8

Spring (Mar.-May) 7.3 PCM B1 0.1 1.0 0.8

GFDL A1FI 1.0 1.6 2.6

Summer (June-Aug.) 9.8 PCM B1 0.5 1.5 1.0

GFDL A1FI -0.1 -3.0 -3.8

Fall (Sept.-Nov.) 9.2 PCM B1 0.1 -0.3 -0.3

GFDL A1FI 0.2 -0.6 1.4

Table 25.—Projected changes in precipitation under two future climate scenarios for the assessment area over the 

next century

Lupine in the northern Lower Peninsula. Photo by U.S. Forest Service, Huron-Manistee National Forest.
Appendix 3

199

Figure 50.—Projected difference in mean daily temperature (°F) at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000), under two climate scenarios.
Appendix 3

200

Figure 51.—Projected difference in mean maximum temperature (°F) at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000), under two climate scenarios.
Appendix 3

201

Figure 52.—Projected difference in mean minimum temperature (°F) at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000), under two climate scenarios.
Appendix 3

202

Figure 53.—Projected difference in precipitation (inches) at the beginning of the century (2010 through 2039) compared to 

baseline (1971 through 2000), under two climate scenarios.
Appendix 3

203

Figure 54.—Projected difference in mean daily temperature (°F) for the middle of the century (2040 through 2069) compared 

to baseline (1971 through 2000), under two climate scenarios.
Appendix 3

204

Figure 55.—Projected difference in mean maximum temperature (°F) for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000), under two climate scenarios.
Appendix 3

205

Figure 56.—Projected difference in mean minimum temperature (°F) for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000), under two climate scenarios.
Appendix 3

206

Figure 57.—Projected difference in precipitation (inches) for the middle of the century (2040 through 2069) compared to 

baseline (1971 through 2000), under two climate scenarios.
207

Appendix 4: Supplementary Model Results

TREE ATLAS

This section provides additional model outputs 

for the 75 species considered for this assessment. 

Even more information is available online at the 

Climate Change Tree Atlas Web site (http://www.

nrs.fs.fed.us/atlas/tree/tree_atlas.html), including 

detailed methods, importance value change maps, 

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 2070 through 2099, which was used to 

create tables in Chapter 5. For most species, the 

classification rules are listed in Table 26, 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 27). A species was considered rare if it had a 

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 26.—General classification rules used to 

determine change categories for suitable habitat for 

common tree species using the Tree Atlas DISTRIB 

model output (Current IV > 16) 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 27.—Special classification rules used to determine 

change categories for suitable habitat for rare tree 

species using the Tree Atlas DISTRIB model output 

(Current IV < 16)
Appendix 4

208

current modeled area-weighted IV that equaled less 

than 10 percent of the number of 12.5- by 12.5-mile 

pixels in the assessment area. 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 

159 pixels in the Michigan assessment area, so the 

cutoff IV for determining a rare species is 16. 

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 results are displayed in 

Table 28.

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 29 and 30 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, 

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. 58). 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.
Appendix 4 209

American basswood 263 332 Medium 303 361 299 461 299 477 0.91 1.09 0.9 1.39 0.9 1.44 No Change Increase

American beech 270 273 High 306 286 347 279 330 236 1.12 1.05 1.27 1.02 1.21 0.86 Increase No Change

American elm 202 356 Medium 376 653 475 1187 494 1176 1.06 1.83 1.33 3.33 1.39 3.3 Increase Large Increase

American hornbeam 74 83 Medium 111 137 147 221 146 204 1.34 1.65 1.77 2.66 1.76 2.46 Increase Large Increase

Balsam fir 876 872 High 657 237 505 98 433 95 0.75 0.27 0.58 0.11 0.5 0.11 Decrease Large Decrease

Balsam poplar 290 272 High 197 90 131 203 138 158 0.72 0.33 0.48 0.75 0.51 0.58 Decrease Decrease

Bigtooth aspen 790 738 High 714 459 629 269 632 151 0.97 0.62 0.85 0.36 0.86 0.21 No Change Large Decrease

Bitternut hickory 13 11 Low 19 86 31 162 34 195 1.73 7.82 2.82 14.73 3.09 17.73 No Change Large Increase

Black ash 412 463 High 413 392 380 267 371 229 0.89 0.85 0.82 0.58 0.8 0.5 No Change DecreaseBlack cherry 408 522 High 641 809 825 646 799 503 1.23 1.55 1.58 1.24 1.53 0.96 Increase No Change

Black hickory 0 0 High 0 0 0 79 0 138 NA NA NA Mig. NA Mig. NA New Habitat

Black locust 1 17 Low 31 89 74 307 76 415 1.82 5.24 4.35 18.06 4.47 24.41 Large Increase Large Increase

Black oak 182 243 High 287 596 432 700 434 832 1.18 2.45 1.78 2.88 1.79 3.42 Increase Large Increase

Black spruce 419 402 High 288 38 173 5 128 4 0.72 0.1 0.43 0.01 0.32 0.01 Large Decrease Large Decrease

Black walnut 5 4 Medium 23 137 43 338 62 438 5.75 34.25 10.75 84.5 15.5 109.5 Large Increase Large IncreaseBlack willow 37 59 Low 65 125 76 244 85 279 1.1 2.12 1.29 4.14 1.44 4.73 Increase Large Increase

Blackgum 3 4 High 5 11 21 82 20 90 1.25 2.75 5.25 20.5 5 22.5 Increase Large Increase

Blackjack oak 0 0 Medium 0 0 0 82 0 169 NA NA NA Mig. NA Mig. NA New Habitat

Boxelder 5 32 Medium 17 93 25 438 22 545 0.53 2.91 0.78 13.69 0.69 17.03 Decrease Large Increase

Bur oak 22 55 Medium 20 129 49 419 29 476 0.36 2.35 0.89 7.62 0.53 8.66 Decrease Large Increase

Chestnut oak 2 2 High 3 9 9 68 7 53 1.5 4.5 4.5 34 3.5 26.5 No Change Increase

Chinquapin oak 0 0 Medium 0 4 1 107 0 171 NA Mig. Mig. Mig. NA Mig. NA New Habitat

Chokecherry 71 67 Low 64 49 59 61 60 21 0.96 0.73 0.88 0.91 0.9 0.31 No Change Large Decrease

Common persimmon 0 0 Medium 0 0 0 10 0 68 NA NA NA Mig. NA Mig. NA New Habitat

Eastern cottonwood 21 80 Low 59 154 69 679 80 613 0.74 1.93 0.86 8.49 1 7.66 No Change Large Increase

Eastern hemlock 209 275 High 262 222 293 117 277 89 0.95 0.81 1.07 0.43 1.01 0.32 No Change Large Decrease

Eastern hophornbeam 168 229 Medium 211 228 212 322 215 328 0.92 1 0.93 1.41 0.94 1.43 No Change Increase

Eastern redbud 0 0 Medium 0 6 0 196 0 342 NA Mig. NA Mig. NA Mig. NA New Habitat

Eastern redcedar 168 2 Medium 5 209 12 1249 12 1405 2.5 104.5 6 624.5 6 702.5 Increase Large Increase

Eastern white pine 460 514 High 498 526 535 347 522 210 0.97 1.02 1.04 0.68 1.02 0.41 No Change Large Decrease

Flowering dogwood 4 8 High 23 69 61 233 62 253 2.88 8.63 7.63 29.13 7.75 31.63 Increase Large Increase

Green ash 240 318 Medium 256 295 231 635 241 563 0.81 0.93 0.73 2 0.76 1.77 Decrease Increase

Hackberry 0 2 Medium 4 107 20 440 19 529 2 53.5 10 220 9.5 264.5 New Habitat New HabitatHoneylocust 1 0 Low 1 35 6 210 6 279 1 35 6 210 6 279 Increase Increase

Jack pine 903 844 High 708 463 578 280 540 268 0.84 0.55 0.69 0.33 0.64 0.32 Decrease Large DecreaseMockernut hickory 0 7 High 12 35 30 167 32 217 1.71 5 4.29 23.86 4.57 31 New Habitat New Habitat

Mountain maple 38 25 High 12 0 6 0 6 0 0.48 0 0.24 0 0.24 0 Large Decrease Large Decrease

Northern pin oak 59 57 Medium 48 73 60 72 52 173 0.84 1.28 1.05 1.26 0.91 3.04 No Change Large Increase

Northern red oak 792 810 High 887 996 974 895 1008 716 1.1 1.23 1.2 1.11 1.24 0.88 Increase No Change

Northern white-cedar 1246 1147 High 990 508 820 345 779 316 0.86 0.44 0.72 0.3 0.68 0.28 Decrease Large Decrease

Ohio buckeye 1 0 Low 0 4 1 13 2 18 0 4 1 13 2 18 No Change Increase

Osage-orange 0 0 Medium 2 12 12 95 6 133 Mig. Mig. Mig. Mig. Mig. Mig. New Habitat New Habitat

DISTRIB results*

Modeled IV Future : Current Suitable Habitat

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 Change Class

Actual 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 28.—Complete DISTRIB results for the 75 tree species in the assessment area

(Table 28 continued on next page)
Appendix 4 210

Paper birch 462 512 High 422 337 390 91 366 43 0.82 0.66 0.76 0.18 0.72 0.08 Decrease Large Decrease

Pawpaw 0 0 Low 0 0 0 3 0 22 NA NA NA Mig. NA Mig. NA New HabitatPeachleaf willow 9 0 Low 0 0 0 21 0 13 0 0 0 2.33 0 1.44 Large Decrease No Change

Pignut hickory 0 10 High 19 70 56 200 58 245 1.9 7 5.6 20 5.8 24.5 New Habitat New HabitatPin cherry 71 74 Medium 61 50 55 49 61 7 0.82 0.68 0.74 0.66 0.82 0.1 No Change Large Decrease

Pin oak 7 6 Medium 7 51 17 198 18 264 1.17 8.5 2.83 33 3 44 No Change Large Increase

Post oak 0 0 High 0 0 0 281 0 466 NA NA NA Mig. NA Mig. NA New Habitat

Quaking aspen 1562 1563 High 1329 933 1058 356 1042 264 0.85 0.6 0.68 0.23 0.67 0.17 Decrease Large Decrease

Red maple 1932 1907 High 1963 1673 1979 1402 1949 1198 1.03 0.88 1.04 0.74 1.02 0.63 No Change DecreaseRed mulberry 0 1 Low 1 49 2 271 2 376 1 49 2 271 2 376 New Habitat New HabitatRed pine 975 865 Medium 889 509 786 261 747 193 1.03 0.59 0.91 0.3 0.86 0.22 No Change Large Decrease

River birch 2 0 Low 0 2 1 6 0 35 0 1 0.5 3 0 17.5 Decrease IncreaseRock elm 7 3 Low 2 2 1 19 1 26 0.67 0.67 0.33 6.33 0.33 8.67 Decrease Increase

Sassafras 37 35 High 67 109 152 251 155 264 1.91 3.11 4.34 7.17 4.43 7.54 Large Increase Large IncreaseScarlet oak 3 1 High 2 17 12 100 14 138 2 17 12 100 14 138 Increase Increase

Shagbark hickory 3 31 Medium 39 186 74 330 84 384 1.26 6 2.39 10.65 2.71 12.39 Large Increase Large IncreaseShellbark hickory 0 0 Low 0 0 0 16 0 23 NA NA NA Mig. NA Mig. NA New HabitatShingle oak 0 0 Medium 0 0 0 81 0 112 NA NA NA Mig. NA Mig. NA New Habitat

Silver maple 77 106 Medium 120 323 152 674 167 748 1.13 3.05 1.43 6.36 1.58 7.06 Increase Large IncreaseSlippery elm 19 13 Medium 30 162 62 380 81 422 2.31 12.46 4.77 29.23 6.23 32.46 Increase Large IncreaseStriped maple 20 15 High 8 4 12 6 9 3 0.53 0.27 0.8 0.4 0.6 0.2 No Change Decrease

Sugar maple 1613 1680 High 1574 1043 1429 843 1385 698 0.94 0.62 0.85 0.5 0.82 0.42 No Change Large Decrease

Sugarberry 0 0 Medium 0 0 0 1 0 36 NA NA NA Mig. NA Mig. NA New HabitatSwamp white oak 12 17 Low 15 28 17 106 19 115 0.88 1.65 1 6.24 1.12 6.77 No Change Large Increase

Sweet birch 1 4 High 5 10 14 26 12 17 1.25 2.5 3.5 6.5 3 4.25 No Change Increase

Sycamore 1 0 Medium 3 12 12 27 13 101 3 12 12 27 13 101 Increase Increase

Tamarack (native) 204 240 High 181 131 151 97 148 90 0.75 0.55 0.63 0.4 0.62 0.38 Decrease Large Decrease

White ash 255 391 High 418 459 473 724 484 629 1.07 1.17 1.21 1.85 1.24 1.61 Increase Increase

White oak 389 369 High 518 744 604 943 628 937 1.4 2.02 1.64 2.56 1.7 2.54 Increase Large Increase

White spruce 205 213 Medium 159 91 116 72 100 57 0.75 0.43 0.55 0.34 0.47 0.27 Large Decrease Large Decrease

Wild plum 0 0 Low 0 0 0 14 0 114 NA NA NA Mig. NA Mig. NA New Habitat

Yellow birch 176 227 High 195 134 208 43 184 38 0.86 0.59 0.92 0.19 0.81 0.17 No Change Large Decrease

Yellow-poplar 0 3 High 11 24 33 76 30 98 3.67 8 11 25.33 10 32.67 New Habitat New Habitat

DISTRIB results*

Modeled IV Future : Current Suitable Habitat

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 Change Class

Actual 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 28 (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 26-27). NA = not applicable, no suitable habitat projected.Mig. = new migrant, new suitable habitat projected. 
Appendix 4

211

DISTRIB Modifying Factors* Adaptability Scores

Common Name Model 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 +

Chestnut oak High SES VRE ESP FTK INS DISE 1.4 1.3 6.1 +

Chinquapin oak Medium SES 1.2 -0.7 4.8 ○

Chokecherry Low COL 0.2 -0.9 3.8 ○

Common persimmon Medium COL ESP 1.2 1 5.8 +

Eastern cottonwood Low SES INS COL DISE FTK 0.2 -0.8 3.9 ○

Eastern hemlock High COL INS DRO -1.3 -0.9 2.7 ―

Eastern hophornbeam Medium COL ESP SES 1.7 1.3 6.4 +

Eastern redbud Medium 0.9 0 4.9 ○

Eastern redcedar Medium DRO FTK COL INS 0.6 -1.5 3.9 ○

Eastern white pine High DISP DRO FTK INS -2 0.1 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 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 ○

Ohio buckeye Low COL SES FTK 0.4 -1.9 3.5 ○

Table 29.—Modifying factors for the 75 tree species in the assessment area

(Table 29 continued on next page)
Appendix 4

212

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 ○

Pawpaw Low COL DRO -0.48 -0.32 3.7

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 ○

Shellbark hickory Low COL FTK ESP -0.5 -0.3 3.7 ○

Shingle oak Medium ESP COL 1.3 -0.7 4.9 ○

Silver maple Medium DISP SES COL DRO FTK 0.1 1.6 5.6 +

Slippery elm Medium COL FTK DISE 0 0.7 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 ―

Sycamore Medium 1.3 -0.9 4.8 ○

Tamarack (native) High FTK COL INS -0.5 -1.2 3.1 ―

White ash High INS FTK COL -2 -0.5 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 30. Adaptation 

factor scores below 3.2 are considered low (-), and scores above 5.3 are considered high (+).

DISTRIB Modifying Factors* Adaptability Scores

Common Name Model Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

Table 29 (continued).
Appendix 4

213

*These codes are used to describe positive or negative modifying factors in Table 29. 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.

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 and/or severity of known 

pathogens that attack the species

DISP High ability to effectively produce and distribute 

seeds

DRO Drought-tolerant Susceptible to drought

ESP Wide range of soil requirements Narrow range of soil requirements 

FRG Regenerates well after fire

FTK Resistant to fire topkill Susceptible to fire topkill

INS Has a high number and/or severity of insects that 

may attack the species

INP Strong negative effects of invasive plants on the 

species, either through competition for nutrients or 

as a pathogen

SES High ability to regenerate with seeds to maintain 

future populations

Low ability to regenerate with seeds to maintain 

future populations

VRE Capable of vegetative reproduction through stump 

sprouts or cloning

Table 30.—Description of Tree Atlas modifying factor codes

Figure 58.—Schematic showing how biological and disturbance modifying factors are translated into an overall adaptability 

score in the Tree Atlas model.
Appendix 4

214

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 (http://www.

landis-ii.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 31). 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. 40) 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 32). 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 implemented 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 33). 

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

215

American basswood 122 40 791 648 1067 35 733 -7

70 1352 1108 1733 28 925 -32

100 1861 1525 2352 26 926 -50

American beech 211 40 490 232 621 27 488 0

70 650 308 829 28 577 -11

100 840 399 1133 35 806 -4

American elm 43 40 290 681 402 39 295 2

70 459 1078 624 36 459 0

100 533 1251 730 37 589 10

Balsam fir 242 40 858 354 580 -32 406 -53

70 1573 649 1093 -31 275 -83

100 1877 775 1096 -42 61 -97

Balsam poplar 100 40 204 204 185 -9 162 -21

70 194 194 171 -12 120 -38

100 153 153 129 -16 72 -53

Bigtooth aspen 568 40 632 111 652 3 570 -10

70 462 81 494 7 364 -21

100 295 52 334 13 183 -38

Black ash 67 40 102 153 76 -25 93 -9

70 76 113 54 -28 41 -46

100 60 89 36 -39 16 -73

Black cherry 261 40 897 343 1102 23 806 -10

70 1183 452 1406 19 926 -22

100 1252 479 1557 24 1043 -17

Black oak 248 40 361 145 408 13 321 -11

70 327 132 381 17 246 -25

100 250 101 326 30 199 -21

Black spruce 359 40 455 127 339 -26 329 -28

70 403 112 237 -41 204 -50

100 290 81 120 -59 97 -67

Eastern hemlock 89 40 138 155 137 -1 132 -5

70 176 197 173 -2 156 -12

100 216 242 212 -2 172 -21

Eastern white pine 683 40 2571 376 2898 13 2280 -11

70 3,897 570 4,222 8 2,747 -30

100 4,981 729 5,428 9 3,026 -39

Green ash 65 40 186 286 234 26 167 -10

70 169 260 190 12 116 -31

100 115 178 136 18 90 -22

Jack pine 233 40 281 121 253 -10 250 -11

70 266 114 222 -17 171 -35

100 227 97 188 -17 105 -54

Table 31.—Projected aboveground biomass for 26 species assessed with the LANDIS-II model

Current Climate PCM B1 Climate GFDL A1FI Climate

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 31 continued on next page)
Appendix 4

216

Northern pin oak 295 40 531 180 508 -4 442 -17

70 572 194 517 -10 338 -41

100 496 168 425 -14 227 -54

Northern red oak 997 40 1,573 158 1,627 3 1,350 -14

70 1,564 157 1,596 2 1,105 -29

100 1,303 131 1,381 6 853 -35

Northern white cedar 528 40 800 152 657 -18 694 -13

70 914 173 702 -23 655 -28

100 919 174 673 -27 579 -37

Paper birch 187 40 421 224 209 -50 260 -38

70 425 227 134 -68 136 -68

100 292 156 66 -77 22 -92

Quaking aspen 693 40 733 106 729 -1 632 -14

70 532 77 536 1 367 -31

100 265 38 266 0 96 -64

Red maple 787 40 1,226 156 1,445 18 1,170 -5

70 1,166 148 1,423 22 1,114 -4

100 1,173 149 1,504 28 1,151 -2

Red pine 599 40 1,192 199 991 -17 993 -17

70 1,462 244 1,118 -24 904 -38

100 1,391 232 991 -29 727 -48

Sugar maple 719 40 975 136 1,101 13 981 1

70 925 129 1,126 22 1,015 10

100 832 116 1,165 40 1,027 23

White ash 51 40 246 479 356 45 258 5

70 354 689 474 34 336 -5

100 427 829 581 36 462 8

White oak 412 40 845 205 962 14 755 -11

70 1071 260 1,191 11 819 -24

100 1,169 284 1,367 17 941 -19

White spruce 237 40 312 131 172 -45 171 -45

70 305 129 77 -75 57 -81

100 253 107 46 -82 16 -94

Yellow birch 76 40 405 531 368 -9 275 -32

70 753 986 650 -14 258 -66

100 1,080 1,415 855 -21 236 -78

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 31 (continued).

Current Climate PCM B1 Climate GFDL A1FI Climate

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 (%)
Appendix 4

217

Table 32.—Classification rules for creating the maps based on LANDIS-II outputs (Fig. 40)

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, black oak, and northern pin oak

Subtract black spruce, balsam fir, and white spruce

Jack pine Include jack pine and red pine

Aspen-birch Include quaking aspen, bigtooth aspen, balsam poplar, 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 33.—Business-as-usual (BAU) forest management scenario used in the LANDIS-II model for this assessmenta

BAU Management Prescriptions USFS DNR PIF PNIF

Aspen-birch clearcut 1.7206 2.3902 3.4882 0.0641

Jack pine clearcut 0.8174 0.5291 0.0481 0.1198

Northern hardwood shelterwood 0.0808 0.0873 0.0635 0.1101

Northern hardwood patchcut 3.39 3.67 2.67 4.62

Oak clearcut 0.7933 0.5938 0.2915 0.1583

Oak patchcut 1.27 0.95 0.466 1.038

Oak shelterwood 0.508 0.38 0.186 0.415

Oak thinning 1.75 1.31 0.64 1.43

Birch shelterwood 0.0085 0.0129 0.0238 0.0138

Red pine clearcut 0.2313 0.1466 0.0202 0.0515

Red pine patchcut 0.3558 0.2255 0.0311 0.1611

Red pine shelterwood 0.089 0.0564 0.0078 0.0403

Red pine thinning 1.156 0.733 0.101 0.524

Upland spruce-fir clearcut 0.5361 0.9542 1.6218 0.1013

Swamp hardwoods clearcut 0.1126 0.1636 0.4962 0.0154

Swamp hardwoods patchcut 0.0576 0.0839 0.2545 0.1027

Swamp hardwoods shelterwood 0.0259 0.0378 0.1145 0.0462

Swamp hardwoods thinning 0.0115 0.0168 0.0509 0.0205

White pine clearcut 0.1467 0.1114 0.0623 0.0684

White pine patchcut 0.467 0.354 0.198 0.299

White pine shelterwood 0.1867 0.1417 0.0792 0.1198

White pine thinning 0.48 0.364 0.204 0.308

a For each harvest prescription, values represent the percentage of each management area treated per 5-year time step. 

Landowner categories are as follows: USFS = U.S. Forest Service, DNR = Michigan Department of Natural Resources, 

PIF = private industrial forestland, PNIF = private nonindustrial forestland. 
218

Appendix 5: Vulnerability and 

Confidence Determination

METHODS

To assess vulnerabilities to climate change for 

each forest type, we elicited input from a panel 

of 27 experts from a variety of land management 

and research organizations across the assessment 

area (Table 34). 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 on forests 

Name Organization

Tim Baker Hiawatha National Forest

Patricia Butler* Michigan Technological University 

& Northern Institute of 

Applied Climate Science

Sophan Chhin Michigan State University

Eric Clark Sault Ste. Marie Tribe 

Josh Cohen Michigan Natural Features Inventory

Rich Corner Huron-Manistee National Forests

Matthew Duveneck Portland State University

Amy Clark Eagle Michigan Department 

of Natural Resources

Dave Fehringer The Forestland Group

Jon Fosgitt Compass Land Consultants

Jim Gries Hiawatha National Forest

Kim Hall The Nature Conservancy

Tina Hall The Nature Conservancy

Stephen Handler* Forest Service Northern Research 

Station & Northern Institute of 

Applied Climate Science

Table 34.—Participants in the September 2012 expert panel workshop

Name Organization

Bob Heyd Michigan Department 

of Natural Resources

Chris Hoving Michigan Department 

of Natural Resources

Ines Ibáñez University of Michigan

Louis Iverson Forest Service, 

Northern Research Station 

Don Kuhr Michigan Department 

of Natural Resources

Jennifer Muladore Huron Pines 

Knute Nadelhoffer University of Michigan

Dave Neumann Michigan Department 

of Natural Resources

Matt Sands Huron-Manistee National Forests

Rob Scheller Portland State University

Randy Swaty The Nature Conservancy

Leiloni Wonch Sault Ste. Marie Tribe 

Kirk Wythers University of Minnesota 

& Boreal Forest and 

Community Resilience Project

*Workshop facilitators

in northern Michigan. This panel was assembled 

at an in-person workshop in Pellston, Michigan, in 

September 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 used U.S. Forest 

Service, Forest Inventory and Analysis data and 

Michigan Natural Features Inventory abstracts 

(available at http://mnfi.anr.msu.edu/communities/
Appendix 5

219

index.cfm) to describe nine major forest types 

occurring in the assessment area (Chapter 1). These 

forest types were the focus of the expert panel 

workshop. 

For each forest type, we extracted information 

related to the major system drivers, dominant 

species, and stressors that characterize that 

community from Michigan Natural Features 

Inventory abstracts and other sources. The panel was 

asked to comment on and suggest modifications to 

the community descriptions, and those suggestions 

were incorporated into the descriptions. 

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 

9). Panelists were then led through a structured 

discussion process to consider this information for 

each forest community in the assessment. 

Potential impacts on drivers and stressors 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 forest 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 

forest type. Panelists were also asked to consider 

the potential for climate change to facilitate new 

stressors in the assessment area over the next 

century. 

To assess potential impacts on dominant tree species, 

the panelists examined results from Tree Atlas, 

LANDIS-II, and PnET-CN, and were asked to 

consider those results in addition to their knowledge 

of life history traits and ecology of those species. 

The panel evaluated how much the models agreed 

with each other, between climate scenarios, and 

across space and time. 

Box 9: 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. We chose to 

replace the high-end scenario because the Hadley 

A1FI scenario produced extremely high results for 

northern Michigan that seemed to be an unnatural 

artifact of the downscaling process. The authors of 

the vulnerability assessment were uncomfortable 

publishing an assessment with unreliable data. 

The Northern Institute of Applied Climate Science 

coordinated a discussion among all research teams 

conducting ecosystem 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

220

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 using an individual 

worksheet (see example at the end of this appendix). 

Panelists 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 the potential climate change impacts and the 

adaptive capacity of the system. Following 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. 

Panelists were directed to mark their rating in twodimensional space on the individual worksheet and 

on a large group poster (Fig. 59a). 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). Among the group, individual ratings were 

compared and discussed, 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 didn’t completely fall into one or the 

other category, it would receive a low-moderate 

determination.
Appendix 5

221

Confidence 

Panelists were also directed to give a confidence 

rating to each of their individual vulnerability 

determinations (Fig. 59b). 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. 

Forest Community Determinations

Determinations of vulnerability and confidence were 

made for nine 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 

(a) (b)

Figure 59.—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).

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 nine forest types considered 

in this assessment are displayed below (Figs. 60 

through 68). In each figure, individual panelist 

votes are indicated with a small circle and the group 

determination is indicated with a large square. 

We do not intend for direct comparison between 

these figures because the axes represent subjective, 

qualitative scales. 
Appendix 5

222

Figure 60.—Upland spruce-fir vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.

Figure 61.—Jack pine (including pine-oak) 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 62.—Red pine-white pine vulnerability and confidence determinations. Circles indicate individual determinations by 

each panelist and squares indicate the group determination after consensus was reached.

Figure 63.—Lowland conifers 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 64.—Aspen-birch vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.

Figure 65.—Northern hardwoods 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 66.—Lowland and riparian hardwoods vulnerability and confidence determinations. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 67.—Oak associations vulnerability and confidence determinations. Circles indicate individual determinations by each 

panelist and squares indicate the group determination after consensus was reached.
Appendix 5

226

Figure 68.—Barrens vulnerability and confidence determinations. Circles indicate individual determinations by each panelist 

and squares indicate the group determination after consensus was reached.

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 http://www.ipcc.

ch/activities/activities.shtml. (Accessed 

November 5, 2013).

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 http://www.nrs.fs.fed.

us/pubs/40543. (Accessed November 5, 2013).
Appendix 5

227

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

228

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).
229

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 35).

Table 35.—Contributors to Chapter 7

Name Organization Subject Area

Jad Daley Trust for Public Land Land acquisition

Mae Davenport University of Minnesota Forest-associated towns and cities

Marla Emery U.S. Forest Service Northern Research Station Nontimber forest products

David Fehringer The Forestland Group, LLC Forest management operations & infrastructure

Christopher 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; Eagle, Amy Clark; Cohen, Joshua G.; 

Corner, Rich; Reich, Peter B.; Baker, Tim; Chhin, Sophan; Clark, Eric; Fehringer, 

David; Fosgitt, Jon; Gries, James; Hall, Christine; Hall, Kimberly R.; Heyd, 

Robert; Hoving, Christopher L.; Ibáñez, Ines; Kuhr, Don; Matthews, Stephen; 

Muladore, Jennifer; Nadelhoffer, Knute; Neumann, David; Peters, Matthew; 

Prasad, Anantha; Sands, Matt; Swaty, Randy; Wonch, Leiloni; Daley, Jad; 

Davenport, Mae; Emery, Marla R.; Johnson, Gary; Johnson, Lucinda; Neitzel, 

David; Rissman, Adena; Rittenhouse, Chadwick; Ziel, Robert. 2014. Michigan 

forest ecosystem vulnerability assessment and synthesis: a report from 

the Northwoods Climate Change Response Framework project. Gen. Tech. 

Rep. NRS-129. Newtown Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 229 p. 

Forests in northern Michigan will be affected directly and indirectly by a changing 

climate during the next 100 years. This assessment evaluates the vulnerability 

of forest ecosystems in Michigan’s eastern Upper Peninsula and northern 

Lower Peninsula 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. Upland spruce-fir forests were determined to be the most 

vulnerable, whereas oak associations and barrens were determined to be less 

vulnerable to projected changes in climate. Projected changes in climate and the 

associated ecosystem impacts and vulnerabilities will have important implications 

for economically valuable timber species, forest-dependent wildlife and plants, 

recreation, and long-range planning.

Key words: change, vulnerability, adaptive capacity, forests, 

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

 expert elicitation, climate projections, impacts

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