United States Department of Agriculture
Central Hardwoods Ecosystem
Vulnerability Assessment and Synthesis:
A Report from the Central Hardwoods
Climate Change Response Framework Project
Forest
Service
Northern
Research Station
General Technical
Report NRS-124
February 2014
ABSTRACT
The forests in the Central Hardwoods Region will be affected directly and indirectly by a changing
climate over the next 100 years. This assessment evaluates the vulnerability of terrestrial
ecosystems in the Central Hardwoods Region of Illinois, Indiana, and Missouri to a range of
future climates. We synthesized and summarized information on the contemporary landscape,
provided information on past climate trends, and illustrated a range of projected future climates.
This information was used to parameterize and contextualize 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 to assess ecosystems
through a formal consensus-based expert elicitation process. The summary of the contemporary
landscape identifies major stressors currently threatening forests and other terrestrial ecosystems
in the region. Major current threats to forests in the area include invasive species, habitat
fragmentation, oak decline, and a decrease in fire in fire-adapted systems.
Observed trends in climate over the historical record reveal that precipitation increased in
the area, and that daily maximum temperatures decreased while minimum temperatures
increased. Climate trends projected for the next 100 years by using downscaled global climate
model data indicate a potential increase in mean annual temperature of 2 to 7 °F for this region.
Projections for precipitation show an increase in winter and spring precipitation; summer and
fall precipitation projections differ by model. We identified potential impacts on forests by
incorporating these climate projections into three forest impact models (Tree Atlas, LINKAGES,
and LANDIS PRO). Model projections suggest that northern mesic species such as sugar maple,
American beech, and white ash may fare worse under future compared to current climate
conditions, but other species such as post oak and shortleaf and loblolly pine may benefit from
projected changes in climate. Changes in northern red, scarlet, and black oak differ by climate
model.
We assessed ecosystem vulnerability for nine natural community types in the region by using
these model results along with projected changes in other factors such as wildfire, invasive
species, and diseases. The basic assessment was conducted through a formal elicitation process
of 20 science and management experts from across the region, who considered vulnerability in
terms of potential impacts on a system and the adaptive capacity of the system. Mesic upland
forests were determined to be the most vulnerable, whereas many systems adapted to fire and
drought, such as open woodlands, savannas, and glades, 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 important timber species, forestdependent wildlife and plants, recreation, and long-range planning.
Cover Photo
Closed woodland. Photo by Paul Nelson, Mark Twain National Forest.
Manuscript received for publication July 2013
Published by:
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February 2014
Visit our homepage at: http://www.nrs.fs.fed.us/
Central Hardwoods Ecosystem
Vulnerability Assessment and Synthesis:
A Report from the Central Hardwoods
Climate Change Response Framework Project
Leslie Brandt, Hong He, Louis Iverson, Frank R. Thompson III, Patricia Butler,
Stephen Handler, Maria Janowiak, P. Danielle Shannon, Chris Swanston,
Matthew Albrecht, Richard Blume-Weaver, Paul Deizman, John DePuy,
William D. Dijak, Gary Dinkel, Songlin Fei, D. Todd Jones-Farrand, Michael Leahy,
Stephen N. Matthews, Paul Nelson, Brad Oberle, Judi Perez, Matthew Peters,
Anantha Prasad, Jeffrey E. Schneiderman, John Shuey, Adam B. Smith,
Charles Studyvin, John M. Tirpak, Jeffery W. Walk, Wen J. Wang, Laura Watts,
Dale Weigel, and Steve Westin
AuThoRS
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.
JOHN DEPUY is a soil scientist with the Shawnee National
Forest, 50 Highway 145 South, Harrisburg, IL 62946,
jdepuy@fs.fed.us.
HONG HE is a professor at the University of Missouri-Columbia,
School of Natural Resources, 203 Anheuser-Busch Natural
Resources Building, Columbia, MO 65211, heh@missouri.edu.
WILLIAM D. DIJAK is a wildlife biologist and geographic
information systems specialist with the U.S. Forest Service,
Northern Research Station, 202 Anheuser-Busch Natural
Resources Building, University of Missouri-Columbia,
Columbia, MO 65211, wdijak@fs.fed.us.
LOUIS IVERSON is a landscape ecologist with the U.S. Forest
Service, Northern Research Station, 359 Main Road, Delaware,
OH 43015, liverson@fs.fed.us.
FRANK R. THOMPSON III is a research wildlife biologist with the
U.S. Forest Service, Northern Research Station, 202 AnheuserBusch Natural Resources Building, University of MissouriColumbia, Columbia, MO 65211, frthompson@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.
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.
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 specialist with
the Northern Institute of Applied Climate Science, U.S. Forest
Service, 410 MacInnes 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.
MATTHEW ALBRECHT is an assistant curator of conservation
biology with the Missouri Botanical Garden, Center for
Conservation and Sustainable Development, P.O. Box 299,
St. Louis, MO 63166, matthew.albrecht@mobot.org.
RICHARD BLUME-WEAVER is a planning and resources staff
officer with the Shawnee National Forest, 50 Highway 145
South, Harrisburg, IL 62946, rblume-weaver@fs.fed.us.
PAUL DEIZMAN is the coordinator for forest management and
utilization, and forest stewardship and forest legacy, programs
with the Illinois Department of Natural Resources, Division
of Forest Resources, 1 Natural Resources Way, Springfield, IL
62702, paul.deizman@illinois.gov.
GARY DINKEL is an ecosystem program manager with the
Hoosier National Forest, 248 15th Street, Tell City, IN 47586,
gdinkel@fs.fed.us.
SONGLIN FEI is an assistant professor at Purdue University,
Department of Forestry and Natural Resources, FORS 111,
195 Marsteller Street, West Lafayette, IN 47907,
sfei@purdue.edu.
D. TODD JONES-FARRAND is science coordinator for the
University of Missouri-Columbia, Central Hardwoods Joint
Venture, 302 Anheuser-Busch Natural Resources Building,
Columbia, MO 65211, david_jones-farrand@fws.gov.
MICHAEL LEAHY is the natural areas coordinator for the
Missouri Department of Conservation, P.O. Box 180,
Jefferson City, MO 65102, mike.leahy@mdc.mo.gov.
STEPHEN N. MATTHEWS is a research assistant professor at
Ohio State University, School of Environment and Natural
Resources, and an ecologist with the U.S. Forest Service,
Northern Research Station, 359 Main Road, Delaware, OH
43015, snmatthews@fs.fed.us.
PAUL NELSON (retired) was a forest ecologist with the Mark
Twain National Forest, 401 Fairgrounds Road, Rolla, MO 65401.
BRAD OBERLE is a postdoctoral research associate at George
Washington University, Department of Biological Sciences,
2023 G Street NW., Washington, DC 20053,
brad.oberle@gmail.com.
JUDI PEREZ is a planning and public affairs staff officer with the
Hoosier National Forest, 811 Constitution Avenue, Bedford, IN
47421, japerez@fs.fed.us.
MATTHEW PETERS is a geographic information systems
technician with the U.S. Forest Service, Northern
Research Station, 359 Main Road, Delaware, OH 43015,
matthewpeters@fs.fed.us.
ANANTHA PRASAD is an ecologist with the U.S. Forest Service,
Northern Research Station, 359 Main Road, Delaware, OH
43015, aprasad@fs.fed.us.
JEFFREY E. SCHNEIDERMAN is a Ph.D. candidate at the
University of Missouri-Columbia, School of Natural Resources,
Anheuser-Busch Natural Resources Building, Columbia, MO
65211, jesg37@mail.missouri.edu.
JOHN SHUEY is director of conservation science with The
Nature Conservancy Indiana Chapter, 620 East Ohio Street,
Indianapolis, IN 46202, jshuey@tnc.org.
ADAM B. SMITH is an ecologist with the Missouri Botanical
Garden, Center for Conservation and Sustainable Development,
P.O. Box 299, St. Louis, MO 63166, adam.smith@mobot.org.
CHARLES STUDYVIN (retired) was a forest silviculturist with the
Mark Twain National Forest, 401 Fairgrounds Road, Rolla, MO
65401.
JOHN M. TIRPAK is science coordinator for the Gulf Coastal
Plains and Ozarks Landscape Conservation Cooperative,
700 Cajundome Boulevard, Lafayette, LA 70506,
john_tirpak@fws.gov.
JEFFERY W. WALK is director of science with The Nature
Conservancy Illinois Chapter, 301 SW Adams Street, Suite 1007,
Peoria, IL 61602, jwalk@tnc.org.
WEN J. WANG is a postdoctoral fellow at the University of
Missouri-Columbia, School of Natural Resources, AnheuserBusch Natural Resources Building, Columbia, MO 65211,
wwfy6@mail.missouri.edu.
LAURA WATTS is a forest planner with the Mark Twain
National Forest, 401 Fairgrounds Road, Rolla, MO 65401,
ljwatts@fs.fed.us.
DALE WEIGEL is a forester with the Hoosier National Forest,
811 Constitution Avenue, Bedford, IN 47421,
dweigel@fs.fed.us.
STEVE WESTIN is the forestry program supervisor with the
Missouri Department of Conservation, Forestry Division,
Planning & Emerging Issues, P.O. Box 180, Jefferson City,
MO 65102, Steve.Westin@mdc.mo.gov.
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PRefACe
This assessment is a fundamental component
of the Central Hardwoods 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 132 million acres
in the northeastern quarter of the 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 those who work, study, recreate, and
care about the ecosystems in the Central Hardwoods
Region. As new scientific information arises, we
will develop future versions to reflect that acquired
knowledge and understanding. Most important, this
assessment does not make recommendations about
how this information should be used.
The scope of the assessment is terrestrial
ecosystems, with a particular focus on tree species.
Model projections in the region to date have focused
primarily on the direct impacts of temperature and
precipitation on tree species. We anticipate future
modeling will incorporate the interactions between
these direct impacts and disturbances such as insect
outbreaks, invasive species, and wildfire. 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. Leslie
Brandt served as the primary writer and editor
of the assessment. Hong He, Louis Iverson, and
Frank Thompson led the forest impact modeling
and contributed writing and expertise to much of
the assessment. Patricia Butler, Maria Janowiak,
Stephen Handler, P. Danielle Shannon, and Chris
Swanston provided significant investment into the
generation and coordination of content, data analysis
and interpretation, and coordination among other
Climate Change Response Framework assessments.
Matthew Albrecht, Richard Blume-Weaver, Paul
Deizman, John DePuy, William D. Dijak, Gary
Dinkel, Songlin Fei, D. Todd Jones-Farrand, Michael
Leahy, Stephen N. Matthews, Paul Nelson, Brad
Oberle, Judi Perez, Matthew Peters, Anantha Prasad,
Jeffrey E. Schneiderman, John Shuey, Adam B.
Smith, Charles Studyvin, John M. Tirpak, Jeffery W.
Walk, Wen J. Wang, Laura Watts, Dale Weigel, and
Steve Westin provided significant input to specific
chapters.
In addition to the authors listed, a number of people
made valuable contributions to the assessment.
John Taft (Illinois Natural History Survey)
provided a crosswalk to Illinois and Indiana natural
communities for Appendix 1. Beth Middleton (U.S.
Geological Survey) and Susan Romano (Western
Illinois University) provided input to sections
on baldcypress swamps and bottomland forests
for Chapters 1 and 5. Jenny Juzwik (U.S. Forest
Service, Northern Research Station) provided
valuable insights to the sections on insects and
disease in Chapters 1 and 5 and in the appendixes.
Keith Cherkauer (Purdue University) provided
hydrologic data for Chapter 4. Theresa Davidson,
Nancy Feakes, Keri Hicks, and Bennie Terrell (Mark
Twain National Forest); Charles Sams (U.S. Forest
Service, Eastern and Southern Regions); Jan Schultz
and Linda Schmidt (U.S. Forest Service, Eastern
Region); and Nick Kuhn (Missouri Department
of Conservation) provided input to sections in
Chapter 7.
We would especially like to thank David Diamond
(University of Missouri), Steve Shifley (U.S.
Forest Service, Northern Research Station), and
Mike Jenkins (Purdue University), who provided
formal technical reviews of the assessment. Their
thorough review greatly improved the quality of this
assessment.
ConTenTS
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
Chapter 1: The Contemporary Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter 2: Climate Change Science and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Chapter 3: Past Climate Changes and Current Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Chapter 4: Projected Changes in Climate and Other Physical Processes . . . . . . . . . . . . . . . . . . . . . 79
Chapter 5: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Chapter 6: Ecosystem Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Chapter 7: Management Implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Appendix 1: Common and Scientific Names of Flora and Fauna . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Appendix 2: Crosswalk of Natural Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Appendix 3: Forest Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
Appendix 4: Common Nonnative Invasive Species in the Central Hardwoods Region . . . . . . . . . 212
Appendix 5: Common Diseases in the Central Hardwoods Region. . . . . . . . . . . . . . . . . . . . . . . . . 215
Appendix 6: Common Insect Pests in the Central Hardwoods Region . . . . . . . . . . . . . . . . . . . . . . 216
Appendix 7: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Appendix 8: Additional Climate Projection Data and Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Appendix 9: Additional Impact Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Appendix 10: Vulnerability and Confidence Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
Appendix 11: Expert Panelists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
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exeCuTive SummARy
This assessment evaluates key ecosystem
vulnerabilities to a range of future climate scenarios
across the Central Hardwoods Region of Missouri,
Illinois, and Indiana (Fig. 1). This assessment is part
of the Central Hardwoods 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 local knowledge and
expertise, are used to identify what factors contribute
to the vulnerability of forests across the Central
Hardwoods Region and what forest community
types may be more vulnerable than others over
the next 100 years. A final chapter summarizes the
implications of these impacts and vulnerabilities for
forest management across the region.
ChAPTeR 1: The ConTemPoRARy
LAnDSCAPe
Summary
This chapter describes the forests and related
ecosystems across the Central Hardwoods landscape
and summarizes current threats and management
trends. This information lays the foundation for
understanding how shifts in climate may contribute
to changes in Central Hardwoods ecosystems, and
how climate may interact with other stressors on the
landscape.
main Points
●
Forty percent of the area is forested, of which
about 80 percent is privately owned.
●
Current major stressors and threats to forest
ecosystems in the region include:
▪ Fragmentation and loss of forest cover
▪ Loss of historical fire regime in fire-adapted
systems
▪ Nonnative species invasion
▪ Insects and disease
▪ Loss of soil
▪ Overgrazing and overbrowsing
▪ Extreme weather events
▪ Reduced diversity of species and age classes
▪ Lack of management on private lands
●
Management practices over the past several
decades have increasingly emphasized restoring
fire-adapted ecosystems while providing
sustainable forest products.
Figure 1.—Assessment area (in color).
1
exeCuTive SummARy
ChAPTeR 2: CLimATe ChAnGe
SCienCe AnD moDeLinG
Summary
This chapter provides a brief background on climate
change science, models that simulate future climate,
and models that project the effects of changes in
climate on species and ecosystems.
main Points
●
Temperatures have been increasing at a global
scale and across the United States over the past
century.
●
More than 95 percent of climate scientists
attribute this increase in temperature to human
activities.
●
Major contributors to warming are greenhouse
gases from fossil fuel burning, agriculture, and
changes in land use.
Bloodroot in bloom on the Hoosier National Forest in spring.
Photo by Teena Ligman, Hoosier National Forest.
ChAPTeR 3: PAST CLimATe
ChAnGeS AnD CuRRenT TRenDS
●
A decrease in snow cover has led to an increase in
soil frost across the area since the 1970s.
Summary
●
There are no clear trends in severe weather such
as tornadoes, derechos, and thunderstorms.
This chapter summarizes our current understanding
of past changes in climate in the Central Hardwoods
Region, with a focus on the last century. It also
highlights emerging climate trends.
main Points
●
Minimum temperatures increased by 1 to 2 °F,
and maximum temperatures decreased by a
similar amount since the turn of the last century.
●
The region is receiving 12 to 17 percent more
precipitation, particularly in the spring and fall
since the turn of the last century.
●
More rain has been falling as heavy precipitation
events of 3 inches or greater over the past 30
years.
2
ChAPTeR 4: PRoJeCTeD ChAnGeS
in CLimATe AnD oTheR PhySiCAL
PRoCeSSeS
Summary
This chapter examines how climate may change
over the next century using two models representing
a range of possible futures that are downscaled to
be relevant to land management decisions. In some
cases, these downscaled data are then incorporated
into hydrologic models to better understand
impacts on such variables as soil moisture,
evapotranspiration, and streamflow.
exeCuTive SummARy
main Points
●
●
●
Model projections suggest an increase in
temperature over the next century across all
seasons by 2 to 7 °F.
Precipitation is projected to increase in winter
and spring by 2 to 5 inches for the two seasons
combined.
The climate models examined disagree about
how precipitation may change in summer, with
one projecting an increase of up to 3 inches
in summer and the other a decrease of up to
8 inches.
●
Little information is currently available regarding
how extreme weather events such as tornadoes
and thunderstorms may change.
●
Hydrologic model projections indicate that soil
moisture, runoff, and streamflow may increase
during the spring as precipitation increases.
●
Model projections suggest that snow cover and
duration will continue to decrease over the next
century.
●
Models also project that habitat suitability for
shortleaf pine will increase, along with post and
blackjack oak.
●
Model projections for northern red, scarlet, and
black oak vary by impact model and climate
scenario across much of the region.
●
Changes in climate are not projected to have a
dramatic effect on many common species in the
region, including eastern redcedar and white oak.
●
The modeled projections of tree species do not
account for many other physical and biological
factors that may change under a changing
climate. Other factors include:
▪
▪
▪
▪
▪
▪
▪
Drought stress
Changes in hydrology and flood regime
Soil erosion
Wildfire frequency and severity
Increased carbon dioxide
Altered nutrient cycling
Changes in invasive species, pests, and
pathogens
▪ Changes in herbivory
ChAPTeR 5: fuTuRe CLimATe
ChAnGe imPACTS on foReSTS
ChAPTeR 6: eCoSySTem
vuLneRABiLiTieS
Summary
Summary
This chapter summarizes the potential impacts of
climate change on forests in the Central Hardwoods
Region over the next century, with an emphasis on
changes in tree species distribution and abundance
using three different impact models.
This chapter focuses on the collective vulnerability
of natural communities in the Central Hardwoods
Region to climate change over the next 100 years,
focusing on shifts in dominant species, system
drivers, and stressors. The adaptive capacity of
systems within the Central Hardwoods Region
was also examined as a key component to overall
vulnerability to climate change. Finally, relative
vulnerability of nine major forest community types
in the region was assessed (Table 1).
main Points
●
All three models project habitat suitability for
sugar maple will decline over the next century
across the region.
3
exeCuTive SummARy
Table 1.—Vulnerability determinations by natural community type.
Community Type
Dry-mesic upland forest
Mesic upland forest
Mesic bottomland forest
Wet bottomland forest
Flatwoods
Closed woodland
Open woodland
Barrens and savannas
Glade
Vulnerability
Evidence
Agreement
Low-Moderate
High
Moderate
Moderate- High
Low-Moderate
Low
Low
Low
Low-Moderate
Medium
Medium
Limited -Medium
Limited-Medium
Limited-Medium
Limited
Limited-Medium
Medium
Medium
Medium-High
Medium-High
Medium
Medium
Medium
Medium
Medium
Medium-High
Medium-High
vulnerability of the Region
●
Snow will decrease, with subsequent decreases
in soil frost (high evidence, high agreement).
Evidence suggests that winter temperatures will
increase in the area, even under low emissions,
leading to changes in snow and soil frost.
●
Soil moisture patterns will change (medium
evidence, high agreement), with drier soil
conditions later in the growing season
(medium evidence, low agreement).
Some studies show that climate change will
have impacts on soil moisture, but there is
disagreement among climate and impact models
on how soil moisture will change during the
growing season.
●
Droughts will increase in duration and area
(medium evidence, low agreement). A study
using multiple climate models suggests that
drought may increase in extent and area, but
another suggests a decrease in drought.
●
Climate conditions will increase fire risks by
the end of the century (medium evidence, high
agreement). National and global studies agree
that wildfire risk will increase in the area, but few
studies have specifically looked at the Central
Hardwoods Region.
●
Many invasive species, insect pests, and
pathogens will increase or become more severe
(medium evidence, high agreement). Evidence
suggests that an increase in temperature and
greater ecosystem stress will lead to increases in
these threats, but research to date has examined
few species.
Potential Impacts on Drivers and Stressors:
●
Temperatures will increase (robust evidence,
high agreement). All global climate models
project that temperatures will increase due to a
rise in greenhouse gas concentrations both locally
and globally.
●
Growing seasons will lengthen (medium
evidence, high agreement). There is a strong
agreement among information that an increase
in temperature will lead to longer growing
seasons, but few studies have specifically
examined projected growing season length
in the assessment area.
●
The nature and timing of precipitation will
change (robust evidence, high agreement). A
large number of global climate models agree that
precipitation patterns will change at both local
and global scales.
●
An increase in heavy precipitation events
(medium evidence, medium agreement) may
result in flood risks (limited evidence, medium
agreement) and soil erosion (limited evidence,
medium agreement). There is disagreement
among models about whether heavy precipitation
events will continue to increase in the assessment
area. If they do increase, it is expected that
flooding and soil erosion will increase as well,
but these effects have not been modeled for this
region.
4
exeCuTive SummARy
Potential Impacts on Ecosystems
Adaptive Capacity Factors
●
Suitable habitat for northern species will
decline (medium evidence, high agreement).
All three impact models project a decrease in
suitability for northern species such as sugar
maple, American beech, and white ash.
●
●
Habitat will become more suitable for
southern species (medium evidence, high
agreement). All three forest impact models
project an increase in suitability for southern
species such as shortleaf pine.
Low diversity systems are at greater risk
(medium evidence. high agreement). Studies in
other areas have consistently shown that diverse
systems are more resilient to disturbance, but
studies examining this relationship have not been
conducted in the assessment area.
●
Species in fragmented systems will have a
reduced ability to expand into new areas
(limited evidence, high agreement). Evidence
suggests that species may not be able to disperse
the distances required to keep up with climate
change, but little research has been done in the
region on this topic.
●
Increased fire frequency and harvesting may
accelerate shifts in forest composition across
the landscape (medium evidence, medium
agreement). Studies from other regions (e.g.,
northern hardwoods and boreal forests) show
that increased fire frequency can accelerate the
decline of species negatively affected by climate
warming and accelerate the northward migration
of southern tree species.
Fire-adapted systems will be more resilient
to climate change (high evidence, medium
agreement). Studies have shown that fireadapted systems are better able to recover after
disturbances and can promote many of the species
that are projected to do well under a changing
climate.
●
A major transition in forest composition is
not expected to occur in the coming decades
(medium evidence, medium agreement).
Although some models indicate major changes in
habitat suitability, results from spatially dynamic
forest landscape models indicate that a major
shift in forest composition across the landscape
may take 100 years or more in the absence of
major disturbances.
Systems that are highly limited by hydrologic
regime or geologic features may be constrained
(limited evidence, medium agreement).
Our current understanding of the ecology of
Central Hardwoods systems suggests that some
rare communities will be too topographically
constrained to migrate to new areas.
ChAPTeR 7: mAnAGemenT
imPLiCATionS
●
●
●
●
Communities will shift across the landscape
(low evidence, high agreement). Although
few models have examined community shifts
specifically, model results from individual species
and ecological principles suggest communities
may also shift.
Little net change in forest productivity is
expected (medium evidence, low agreement).
Although a number of studies have examined the
impact of climate change on forest productivity,
they disagree on how multiple factors may
interact to influence it.
Summary
This chapter summarizes climate change impacts
on decisionmaking and management for public and
private lands across the Central Hardwoods Region.
These impacts will vary by ecosystem, ownership,
and management objective. This chapter does not
make recommendations as to how management
should be adjusted to deal with these impacts.
5
main Points
●
Plants, animals, and people that depend on forests
may face additional challenges as temperatures
increase and precipitation patterns shift.
●
Greater financial investments may be required
to maintain healthy forests and resilient
infrastructure and to prepare for severe weather
events.
Open woodland. Photo by Paul Nelson, Mark Twain National Forest.
●
The seasonal timing of management activities
such as prescribed burns or recreation activities
such as waterfowl hunting may need to be altered
as temperatures and precipitation patterns change.
●
Confronting the challenge of climate change
presents opportunities for managers and other
decisionmakers to plan ahead, foster resilient
landscapes, and ensure that the benefits that
forests provide are sustained into the future.
inTRoDuCTion
ConTexT
This assessment is part of a regional effort across
the Central Hardwoods Region of Illinois, Indiana,
and Missouri called the Central Hardwoods Climate
Change Response Framework (Framework; www.
forestadaptation.org). The Framework project was
initiated in 2011, and is one of three ecoregional
projects in the Midwest, Mid-Atlantic, and
Northeast. These projects build off the lessons
learned from a pilot project in northern Wisconsin,
initiated in 2009, which has since expanded into the
Northwoods project. The overarching goal of all
three Framework projects is to incorporate climate
change considerations into forest management.
To meet the challenges brought about by climate
change, a team of federal and state land management
agencies, universities, conservation organizations,
and others have come together to accomplish three
objectives:
●
●
●
Provide a forum to share the experiences and
lessons learned of managers and scientists
regarding forest management and climate change
in the Central Hardwoods Region of Missouri,
Illinois, and Indiana.
Develop new user-friendly tools that can help
public and private land managers include climate
change considerations in decisionmaking,
including a forest ecosystem vulnerability
assessment and a forest adaptation resources
document.
Support efforts by public land managers, private
landowners, and conservation organizations to
put these new tools to work on the ground across
the Central Hardwoods Region.
The Framework is designed to work at multiple
scales. The Central Hardwoods Framework is
coordinated across the region, but activities are
generally conducted at the state level to allow
for greater specificity. The assessment is written
to encompass three states within the Central
Hardwoods Region, but information is provided at
the level of individual states whenever possible.
The Central Hardwoods Climate Change Response
Framework has been supported in large part by the
U.S. Department of Agriculture (USDA), Forest
Service, but is guided by the greater community of
the Central Hardwoods Region to serve the needs
of multiple end-users. Current partners in the effort
include:
●
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)
●
Illinois Department of Natural Resources
●
Missouri Department of Conservation
●
The Nature Conservancy
●
The Central Hardwoods Joint Venture
●
The Gulf Coastal Plains and Ozarks Landscape
Conservation Cooperative
●
Missouri Botanical Garden
●
Purdue University
●
University of Missouri
The assessment bears some similarity to other
synthesis documents about climate change science,
such as the National Climate Assessment (draft
7
inTRoDuCTion
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-scale changes.
However, this assessment differs from these reports
in a number of ways. This assessment was neither
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 communities may
be the most vulnerable in the Central Hardwoods
Region. 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 terrestrial ecosystems in
the Central Hardwoods Region under a range of
future climates, and determine the vulnerability of
terrestrial natural communities to those changes over
the next 100 years. The assessment also includes a
synthesis of information about the current landscape
as well as projections of climate and vegetation
changes used to assess these vulnerabilities.
Uncertainties and gaps in understanding are
discussed throughout the document. This assessment
covers 42 million acres throughout the Missouri
Ozarks and the southern portions of Illinois and
Indiana (Fig. 2). The assessment area boundaries are
defined by a combination of state boundaries and the
boundaries of the Central Interior Broadleaf Forest
Province, with a small portion of one section in the
Coastal Plains-Loess Section (McNab et al. 2007).
In addition to these ecological boundaries, we used
state-level and county-level data when ecoregional
information was not available.
8
Figure 2.—Assessment area and counties used to approximate
the ecoregional boundaries when county-level data were
required.
This assessment area covers more than 8 percent
of the forested area within Illinois, Indiana, and
Missouri (U.S. Forest Service 2011a). Within this
landscape, about 80 percent of the forested land
is privately owned (U.S. Forest Service 2011a).
The remainder is divided among the U.S. Forest
Service (12 percent), state agencies (5 percent), and
other federal agencies (3 percent). Supplementary
information specific to these landowners was
used when available and relevant to the broader
landscape. This assessment synthesizes information
covering all of the Central Hardwoods Region,
recognizing the broad diversity of ownerships and
forest communities that encompass the area.
ASSeSSmenT ChAPTeRS
This assessment comprises the following chapters:
Chapter 1: The Contemporary Landscape
describes existing conditions, providing background
on the physical environment, ecological character,
and broad socioeconomic dimensions of the Central
Hardwoods Region.
inTRoDuCTion
Chapter 2: Climate Change Science and
Modeling contains background information 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: Past Climate Changes and Current
Trends provides information on the past and
current climate of the Central Hardwoods Region,
summarized from The Nature Conservancy’s
interactive ClimateWizard database and published
literature. This chapter also summarizes some
relevant ecological indicators of observed climate
change.
Chapter 4: Projected Changes in Climate and
other 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 Illinois, Indiana,
and Missouri, and the Midwest.
Chapter 5: Future Climate Change Impacts on
Forests summarizes model projections of forest
change that were prepared for this assessment.
Different modeling approaches were used to
model climate change impacts on forests: a species
distribution model (Climate Change Tree Atlas), a
forest simulation model (LANDIS PRO), and an
ecosystem model (LINKAGES). This chapter also
includes a review of literature about other climaterelated impacts on forests.
Chapter 6: Ecosystem Vulnerabilities synthesizes
the potential effects of climate change on forested
and other terrestrial communities in the Central
Hardwoods Region and provides detailed
vulnerability determinations for nine terrestrial
natural communities common to the region.
Chapter 7: Management Implications addresses
some of the implications of a changing climate
for major components of the forest sector within
the Central Hardwoods Region, including forest
products, recreation, cultural resources, and forestdependent wildlife.
Missouri Ozarks in fall. Photo by Steve Shifley, U.S. Forest Service.
9
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
The Central Hardwoods Region represents a
mosaic of forests, woodlands, savannas, and other
ecosystems dominated by oak, hickory, and other
hardwood species (for common and scientific names
of species, see Appendix 1). This landscape sustains
the people of the region by providing economically
important forest products, outdoor recreation
opportunities, and other benefits. Here we describe
the forests and related ecosystems across the Central
Hardwoods landscape and summarize current threats
and management trends. This information lays the
foundation for understanding how shifts in climate
may contribute to changes in Central Hardwoods
ecosystems, and how climate may interact with other
stressors on the landscape.
LAnDSCAPe SeTTinG
This assessment covers the part of Ecological
Province 223 (Central Interior Broadleaf Forest;
McNab et al. 2007) that falls within five sections
in Missouri, Illinois, and Indiana (Fig. 3). The
assessment also covers one section (Coastal PlainsLoess) in Ecological Province 231 (Southeastern
Mixed Forest). Sections are based on differences
in geologic parent material, elevation, plant
distribution, and regional climate within the U.S.
Forest Service National Hierarchical Framework of
Ecological Units (McNab and Avers 1994, McNab et
al. 2007). The area covers three national forests and
many other federal, state, and private lands. Below,
we summarize the major physical and biological
features of the assessment area. Additional
descriptions of the landscape setting can be found
in the resources listed in Box 1.
10
Physical Environment
Climate
The current climate of the Central Hardwoods
Region of Illinois, Indiana, and Missouri is generally
characterized as a humid continental climate, with
cool winters and long, hot summers. Due to a
general lack of influence by topography or large
bodies of water, the region is influenced by large air
masses from the Arctic in the winter and the Gulf of
Mexico in the summer. Average annual temperatures
follow an east-west gradient, and range from 54.4 °F
(12.3 °C) in Indiana to 55. °F (13.1 °C) in Missouri
(see Chapter 3). Annual average precipitation ranges
from 44.9 inches in Indiana to 42.9 inches in Illinois,
with Missouri being in between the two (43.9 inches;
see Chapter 3).
Conditions are distinct between winter and summer,
and extreme weather events occur throughout the
year. Precipitation often falls as snow between
December and February. Summers are hot,
averaging 75. ºF (24.2 °C) in the Missouri and
Illinois portions of the assessment area, and 73.8 ºF
(23.2 °C) in the Indiana portion of the assessment
area (see Chapter 3). Extreme weather events in
the area include high-intensity rains, long drought
periods, heat waves and cold waves, ice storms,
windstorms, and tornadoes. Missouri is ranked
9th, Illinois is ranked 8th, and Indiana is ranked
21st among states for the number of tornadoes
experienced annually from 1981 to 2010 (National
Weather Service, Storm Prediction Center 2012). A
more detailed description of past and contemporary
climate of the region can be found in Chapter 3.
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Figure 3.—Assessment area. The assessment area covers portions of five sections of the Central Interior Broadleaf Forest Province
(223) and one section of the Southeastern Mixed Forest Province (231) within Missouri, Illinois, and Indiana. Dashed areas represent
purchase area boundaries of national forests within the assessment area.
Geology and Landform
missouri
The Ozark Highlands of southern Missouri are a low
structural dome, with the dome center consisting
of the oldest (1.5 billion years) igneous rock in the
St. Francois Mountains (Nigh and Schroeder 2002).
Precambrian volcanic rocks are exposed across
700-foot-high igneous dome mountains within the
St. Francois Mountains. Cambrian sandstone and
dolomite, and Ordovician dolomite, sandstone, and
limestone stretch out several hundred miles from the
dome center (Fig. 4). Farther out from the structural
center of the Ozark Highlands are Mississippian
limestone formations, which almost completely
encircle the dome. This outer formation forms the
boundary of the Ozark Highlands.
A quarter billion years of geologic erosion, wind
transport, and subterranean karst (see Box 2)
dissolution has created a diversity of landforms that
vary in degree of relief, dissection, and geologic
parent materials. None of the four major continental
glaciation events of the past 2 million years extended
into the Ozarks.
illinois
Southern Illinois encompasses parts of the Ozark
Highlands, Central Till Plains—Oak Hickory,
Shawnee Hills, and Coastal Plains Sections. The
Illinois portion of the Ozark Highlands Section is
primarily composed of rolling hills with Devonian
and Silurian limestone bedrock. One exception is the
Mississippi River Floodplain, which is characterized
11
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Box 1: more about the Assessment Area
This chapter summarizes information from a few key
resources that describe the assessment area in much
greater depth. Please consult these resources if you
are interested in learning more about the forest
resources, natural communities, or major threats
present in the area.
The Hoosier-Shawnee Ecological Assessment
(Thompson 2004)
This assessment covers most of the Illinois and
Indiana portions of the assessment area. It includes
descriptions of ecological sections and soils; water
resources; forests, plants, and communities; aquatic
animals; terrestrial animals; forest diseases and
pests; and nonnative animals. The chapter on forest
conditions and disturbance regimes (Parker and
Ruffner 2004) provides substantial information
on land-use history and prehistoric vegetation
conditions. The chapter on plants and communities
(Olson et al. 2004) was used in conjunction with
Nelson (2010) (see below) to provide the basis
for the description of natural communities in this
chapter.
The Ozark-Ouachita Highlands Assessment
(U.S. Forest Service 1999a,b,c,d)
This assessment covers the Missouri Ozark Highlands
Section of the assessment area. It includes a
summary report and four detailed reports on trends
and conditions in social and economic factors,
aquatic ecosystems, terrestrial vegetation and
wildlife, and air quality.
The Terrestrial Natural Communities of Missouri
(Nelson 2010)
This book gives detailed descriptions of the natural
communities found in the Missouri Ozark Highlands,
and served as the foundation for the natural
community descriptions in this chapter. It discusses
vegetation history, current threats to Missouri
ecosystems, and other information about the
landscape.
Presettlement, Present, and Projected Forest
Communities of the Shawnee National Forest,
Illinois: an Ecological Classification System
(Fralish 2010)1
This report describes the presettlement forest
community for seven subregions of southern Illinois
and compares data on past composition with those
of the present overstory and understory forest
composition.
Unpublished report (134 p.) on file at the Shawnee
National Forest Supervisor’s office, Harrisburg, IL.
1
Figure 4.—Predominant bedrock types found across the assessment area (Gray et al. 1987, Missouri Department of Natural
Resources 2005, U.S. Geological Survey 2013).
12
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Box 2: Karst Topography
The assessment area is known for its karst
topography. Karst landscapes occur where the
topography and its distinctive features are formed by
the dissolution of soluble rock, especially dolomite
and limestone (Fig. 5). The resulting surface features
include subterranean drainages, caves, sinkholes,
springs, disappearing streams, dry valleys and
hollows, natural bridges, arches, and other related
features (Rea 1992). Sinkholes are karst features that
develop as a result of a collapse of surface material
into nearby cavities (usually caves). Coldwater
springs are characterized by a continuous flow of
mineralized groundwater when surface precipitation
percolates through fractures in bedrock including
sinkholes, losing streams, caves, and bedrock
aquifers.
Figure 5.—Karst topography. Diagram by Mark Raithel,
Missouri Department of Conservation.
The Missouri Ozark Highlands contain the
assessment area’s largest karst regions. Five distinct
karst regions occur in the Ozarks, each physically
distinct and harboring its own endemic subterranean
aquatic and terrestrial species (Culver et al. 2003).
Karst features are also found in southern Illinois and
Indiana, primarily in the Mitchell Plateau, Crawford
Escarpment, and Crawford Uplands Subsections,
where the Hoosier National Forest is located
(McCreedy et al. 2004).
Caves provide habitat to rare and endangered
species in the assessment area. More than 600
caves are recorded on the Mark Twain National
Forest (about 10 percent of 6,400 known Missouri
caves). More than 190 caves have been identified
on the Hoosier National Forest, with 50 designated
as nationally significant by the Eastern Regional
Forester. The Shawnee National Forest has identified
15 caves (McCreedy et al. 2004). Forty-six aquatic
and 31 terrestrial species that are dependent on
caves are recorded in Missouri’s caves and springs
(Culver et al. 2003). Most species of state or global
viability concern in the Indiana and Illinois portions
of the assessment area live in cave and karst
habitats (McCreedy et al. 2004). The Indiana bat is
probably the most well-known of these threatened
or endangered cave-dwelling organisms. Little is
known about the ecology and life history of many
of the cave-dwelling species in the assessment area,
making it difficult to determine whether they may be
affected by a changing climate.
by low-lying areas of unconsolidated Tertiary and
Quaternary alluvium (gravel, sand, silt, and clay)
overlying bedrock (McNab and Avers 1994).
also contain exposed Mississippian limestone and
sandstone as well Pennsylvanian sandstone and
shale.
The Central Till Plains—Oak Hickory Section is
largely covered by glacial till from the Illinoian
glacier, which ended 130,000 years ago (McNab and
Avers 1994). The area was not covered by the most
recent Wisconsin glaciation, but loess and slackwater
lake deposits from this glacier can be found in the
area (McNab and Avers 1994). Parts of the area
Sandstone bluffs, steep-sided ridges and hills,
gentler hills and broader valleys, karst terrain, gently
rolling lowland plains, and bottomlands characterize
the Shawnee Hills Section (McNab and Avers
1994). Elevation ranges from 325 to 1,00 feet.
About 50 percent of the underlying bedrock is
Pennsylvanian sandstone, with minor amounts of
13
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
siltstone, shale, and coal. Mississippian limestone
forms the bedrock along the southern border of the
Section in Illinois.
The Coastal Plain is composed primarily of marine
sediments from the Cenozoic era, with smaller
amounts of Mesozoic marine sediments (McNab and
Avers 1994). The area is flat, rarely exceeding relief
of greater than 100 feet.
Indiana
Much of the assessment area in southern Indiana
incorporates the Shawnee Hills and the Transition
Hills Sections with a small amount of the Bluegrass
Section. This area is derived primarily from
Pennsylvanian and Mississippian bedrock units.
Bedrock is exposed in the south-central part of
the state. The limestone plateau developed on
Mississippian limestone extends south to the Ohio
River. Layers of rock (limestone, sandstone, and
shale) more than 400 feet thick were built up by
ancient seas that once covered this area.
Two well-developed areas of karst topography occur
in the southern part of Indiana, the Mitchell Plateau
and the Muscatatuck Plateau (Hasenmueller et al.
2011). Erosion has worn away the upper layers in
the Mitchell Plateau, making karst features such
as sinkholes and disappearing streams common
elements across the landscape. West of the Mitchell
Plateau is the Crawford Upland. The Crawford
Upland retains the upper strata of shale and
sandstone over limestone. The area’s drainage is still
subterranean, and exhibits dry-beds, rises, sinking
streams, swallow holes, and other karst features.
The part of Indiana within the assessment area was
largely unglaciated by the most recent (Wisconsin)
glaciation. A substantial portion of the assessment
area was covered by older ice sheets, but the
boundaries of these glaciations are unclear due to
subsequent weathering (Gray 2009).
14
Soils
missouri
Soils of the Ozark Highlands are moderately well
drained to well drained and have slow to moderate
permeability. Soils are generally old, shallow, stony,
highly weathered, and acidic, except on some broad
ridges and bottomlands (McNab and Avers 1994).
Some soils, particularly those on steeper ground,
have very gravelly or stony surfaces and more than
35 percent rock fragments by volume throughout
the profile.
Soils that have formed from local sandstone and
dolomite bedrock are very deep, well-drained
mineral soils. Alluvial soils, consisting mainly of
stratified silt, sand, and gravel, are usually found
on valley floor floodplains. These soils are usually
well drained, although valley bottoms and areas
with perched water tables can have areas of poor
drainage.
illinois
Soils vary across southern Illinois, depending on
section and topography. The Ozark Highlands
soils are similar to those found in Missouri (old,
shallow, and highly weathered). In the Central
Till Plains Section, soils are developed from thin
loess and till. Upland soils are light colored and
strongly developed, with poor internal drainage
because of fragipan and claypan layers (McNab
and Avers 1994). Soils in the Shawnee Hills vary
from poorly drained on a few soils to well drained
on the majority of soils. Soils in the Coastal Plain
are generally deep and medium textured, and have
adequate moisture supply throughout the year
(McNab and Avers 1994).
Indiana
Weathered siltstone, fine-grained sandstone, shale,
and limestone bedrock, as well as alluvium along
streams, provide the parent materials for soils in the
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
assessment area in southern Indiana. In the Shawnee
Hills Section of southern Indiana, loess covers some
of the material weathered from bedrock. Soils are
generally well drained to moderately well drained,
and many have silt loam or loam textures. On steep
slopes, soils are typically thin with gravelly or
channery (containing thin, flat fragments of rock)
textures. Subsoil permeability for upland soils is
generally slow to very slow, and floodplain soils
typically have slow to moderately slow permeability.
The soils occur on gently sloping to very steep
topography, often on narrow ridges bordered by
steep slopes and bedrock outcrops. Permeability on
ridge tops is generally slow to very slow.
The Transition Hills Section occurs as two main
bodies in southern Indiana. The eastern portion
is separated by deep stream valleys and is mostly
wooded hillside land with little suitable cropland.
The western portion of the Section has stony
hillside lands with rock outcrops, but more area
of productive land (Ponder 2004).
Bluegrass Section soils are fine textured and most
are deep (McNab and Avers 1994). The area features
wide alluvial and lacustrine plains bordering major
streams. Since glacial drift partially filled the
northern portion of the section, lowlands are not well
defined. Conversely, lowlands become more defined
in the southern portion. Topography in this Section is
relatively homogenous. Several prominent moraines
can be found, especially in the west-central part of
the state.
Hydrology
missouri
The Missouri Ozark Highlands are deeply dissected
by thousands of miles of spring-fed streams and
rivers. For example, more than 350 miles of
floatable streams are found within the boundary of
the Mark Twain National Forest. Streams within
the Missouri Ozark Highlands tend to be in better
condition than those in the United States as a whole,
due to relatively high forest cover (U.S. Forest
Service 1999a).
The characteristics of spring flows and the quality
of their water chemistry in the Ozark Highlands are
primarily a function of the ability of the land surface
to capture rainwater. Prior to European settlement,
deep soils covered by deep-rooted, long-lived
perennial grasses and forbs beneath open oak and
pine woodlands captured precipitation. This waterabsorbing soil process moved water into the water
table, which likely buffered coldwater spring flows
and fed streams for longer time periods. Changes
in vegetation cover and soil erosion from past land
management practices have led to a reduction in
this important process, leading to effects on local
hydrology.
illinois
Southern Illinois is flanked by the Wabash, Ohio,
and Mississippi Rivers and is populated with
many rivers and smaller perennial and ephemeral
streams. Riparian areas in the assessment area
include forested, agricultural, and other developed
lands (Whiles and Garvey 2004). A survey on the
Shawnee National Forest showed that streams that
drained primarily forested uplands were of higher
water quality and biological integrity than those
that drained primarily agricultural areas (Hite et al.
1990). Efforts have been made to increase water
quality in agricultural zones in the area through
the use of conservation easements, but benefits
thus far have been marginal due to insufficient
recovery time and lack of placement in the most
effective areas (Davie and Lant 1994, Lant 1991).
A 1999 assessment of water quality of watersheds
in southern Illinois using the U.S. Environmental
Protection Agency (EPA)’s Index of Watershed
Indicators found that most were considered of
poor quality due to high levels of nutrients and
contaminants (Whiles and Garvey 2004).
15
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Although there are no natural lakes in the Illinois
portion of the assessment area, thousands of lakes
and reservoirs have been created for water supply,
recreational, and flood control purposes (Whiles
and Garvey 2004). Despite the many benefits, these
reservoirs can upset natural stream flow and lead
to water loss from evaporation (Whiles and Garvey
2004).
The area has had a dramatic decline in wetlands,
which once were common. Illinois has lost more
than 70 percent of its natural wetlands, which have
been primarily drained for agricultural use (Whiles
and Garvey 2004). Wetland area has declined in
other states in the assessment area for the same
reason. Other estimates suggest Illinois, Indiana, and
Missouri lost more than 80 percent of their original
wetlands between 1780 and 1980 (Mitsch and
Gosselink 2007). This loss of wetlands can change
local hydrology by increasing susceptibility to floods
and loss of base flows.
Indiana
Similar to patterns in Illinois, past land management
practices and development have affected watersheds
across southern Indiana. The Ohio River makes up
Indiana’s southern boundary, and the Wabash River
marks the western boundary of the state within the
assessment area. Many larger watercourses traverse
southern Indiana. Tributaries of the White River, the
Little Blue River, and the Lost River flow through
the Hoosier National Forest. No natural lakes occur
in the Indiana portion of the assessment area, but
two large reservoirs, Monroe and Patoka, provide
water for surrounding homes and communities.
Unnatural stream channels also occur throughout the
Indiana portion of the assessment area. These are
often composed of drainage ditches and channels
to connect other water bodies. Many of these
constructed features follow historical channels, but
the channelized ditches have replaced the natural
features (Whiles and Garvey 2004).
1
Prior to settlement, extensive wetlands and rich
riparian areas were found in abundance. European
settlers cleared and drained floodplains for
farmland. Road placement and channelization of
streams have changed water flow patterns over
time. Riparian habitat structure and function have
been altered as streams lost their floodplains and
riparian vegetation was removed. Contaminants,
discharges, nutrient pollution, and wastewater have
been identified as the main factors affecting water
quality in the assessment area within Indiana. Most
of the watersheds are considered of poor water
quality according to the EPA’s Index of Watershed
Indicators (Whiles and Garvey 2004).
Land Use and Vegetation Cover
Land Cover and Composition
The assessment area covers more than 42 million
acres of land, of which 40 percent is classified
as forest land by the U.S. Forest Service’s Forest
Inventory and Analysis (FIA) Program (U.S. Forest
Service 2011a) (Fig. , Table 2). About two-thirds
of the assessment area that is classified as forest
land is in Missouri, and the remaining third is
divided roughly equally between Indiana and Illinois
(Table 2). About 98 percent of the forest land in the
assessment area is classified as timberland (U.S.
Forest Service 2011a). Timberland is forest land that
is currently producing or capable of producing more
than 20 cubic feet of wood per acre per year. This
pattern is similar across the three states.
Satellite imagery from the National Land Cover
Dataset (NLCD) (Fry et al. 2011) estimates
forest cover at a slightly higher percentage
(44.9 percent). According to the NLCD, the
remaining land cover is classified as agricultural
land (43.1 percent), developed (7.5 percent), water
(1. percent), herbaceous (1.5 percent), and wetlands
(1 percent). Shrublands and barren land (containing
no vegetation) make up less than 1 percent of the
assessment area. The relative breakdown of these
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Figure 6.—Forest cover across the assessment area, by forest-type group (Ruefenacht et al. 2008).
Table 2.—Total area, forest land, and timberland within the assessment area (divided by state) as determined by FIA
(U.S. Forest Service 2011a).
Analysis area
Illinois
Indiana
Missouri
Area (acres)
42,038,347
10,988,502
9,411,371
21,638,473
Forest land (acres)
Proportion of forest land in assessment area
16,999,521
2,364,798
14%
3,239,959
19%
11,394,761
67%
Timberland (acres)
Proportion of timberland in assessment area
16,618,582
2,329,862
14%
3,186,467
19%
11,102,251
67%
cover types varies by state (Fig. 7). Agricultural
lands are the most common land cover type in
Illinois and Indiana, whereas forest is the most
common land cover type in Missouri. Illinois has
the highest percentage of developed land among the
three states within the assessment area.
Based on FIA data, the oak/hickory forest-type
group is the most common in the assessment area,
covering 79.3 percent of the total forest land
(Fig. , Table 3). Forest-type groups are a
combination of forest types that share closely
associated species or site requirements. Other
common forest-type groups across the assessment
area include elm/ash/cottonwood and oak/pine. The
maple/beech/birch group makes up 7 percent of the
total forest land in Indiana but is a much smaller
component in the other two states. Differences
17
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Illinois
Indiana
Missouri
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Figure 7.—Percent cover within the assessment area, divided by state boundaries (Fry et al. 2011).
Table 3.—Forest land (in acres and as a percentage of total forest land) by FIA forest-type group (U.S. Forest Service
2011a).
Forest-type group
Assessment area
Proportion
Area
of total
Illinois
Proportion
Area
of total
Indiana
Proportion
Area
of total
Missouri
Proportion
Area
of total
Oak/hickory
Elm/ash/cottonwood
Oak/pine
Other eastern softwoods
Maple/beech/birch
Loblolly/shortleaf pine
Oak/gum/cypress
Other hardwoods
White/red/jack pine
Aspen/birch
Exotic hardwoods
Exotic softwoods
13,484,660
1,376,266
1,010,816
351,161
307,763
276,840
123,382
34,203
22,527
4,207
4,171
3,525
79.3
8.1
5.9
2.1
1.8
1.6
0.7
0.2
0.1
0.0
0.0
0.0
1,500,096
711,126
49,233
2,273
31,981
26,061
34,819
7,436
1,773
—
—
—
63.4
30.1
2.1
0.1
1.4
1.1
1.5
0.3
0.1
—
—
—
2,444,838
306,676
109,267
16658
229,898
35,400
62,123
8,419
20,754
4,207
800
919
75.5
9.5
3.4
0.5
7.1
1.1
1.9
0.3
0.6
0.1
0.0
0.0
9,539,726
358,465
852,316
332229
45,883
215,378
26,439
18,348
—
—
3,372
2,605
83.7
3.1
7.5
2.9
0.4
1.9
0.2
0.2
—
—
0.0
0.0
Total forest land (acres)
16,999,521
100
2,364,798
100
3,239,959
100
11,394,761
100
18
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
among forest types can influence the amount of
carbon stored aboveground and belowground (see
Box 3). These forest-type groups are broader than
the natural communities described later in this
chapter, and may include areas dominated by trees
that would be classified as woodlands, savannas, or
swamps based on their structure (see Box 4).
Box 3: forest Carbon
Each year, the United States releases about 1.5
billion metric tons of carbon into the atmosphere,
largely due to combustion of fossil fuels (U.S.
EPA 2013). One ton of carbon is equivalent to
3.7 metric tons of carbon dioxide. Forests in the
Central Hardwoods Region play an important role
in storing carbon and thus reducing the amount of
greenhouse gases in the atmosphere. Across the
assessment area, an average of 53 metric tons per
acre is stored aboveground and belowground (U.S.
Forest Service 2011a). Carbon storage density (the
mass of carbon per unit area) in this region is lower
than in some parts of the United States, such as the
Pacific Northwest, the northern Great Lakes, and the
Appalachians (Heath et al. 2011). However, carbon
density is still greater than many forests in the Rocky
Mountain region, and much greater than that of
most nonforested lands.
Within the assessment area, carbon density varies by
forest type and ownership. The maple/beech/birch
forest-type group has the highest carbon density,
followed by the elm/ash/cottonwood group (Fig. 8).
These forest types are typically found in more mesic,
nutrient-rich sites that can support higher levels of
Total carbon (metric tons/acre)
S o il o rg a n ic
L ive b e lo w g ro u n d
aboveground productivity. The most common
forest-type group (oak/hickory) has a slightly lower
carbon density. Across all forest types, public lands
store a slightly higher density of carbon (55 versus
52 metric tons per acre), but private lands store a
higher amount of carbon in total due to a higher
total area of forest in private ownership.
Several other factors also influence carbon storage.
Younger forests accumulate more carbon per year
than older forests because they are adding mass as
trees mature (Shifley et al. 2012). Forest types can
also vary in how much carbon is stored aboveground
versus belowground. Bottomland forests, like
elm/ash/cottonwood and oak/gum/cypress, typically
have more carbon stored in soil than do upland
forest types. This difference occurs because low-lying
areas tend to accumulate carbon from areas upslope
and because decomposition (and thus the release
of soil carbon into the atmosphere) is suppressed
when soils are flooded. Forest management and
disturbances such as insects, fire, and windstorms
can also influence carbon storage (Hicke et al. 2012,
Ryan et al. 2010).
L itte r
Dead wood
L ive a b o ve g ro u n d
80
70
60
50
40
30
20
10
0
Figure 8.—Forest carbon density by forest-type group. Forest-type groups are arranged from
left to right by area (U.S. Forest Service 2011a).
19
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Box 4: Forest Types and Natural Communities
In this assessment, we describe two different ways
of classifying forests: FIA forest-type groups and
natural communities. These classification systems
are used for different reasons and convey different
types of information. Although there are some
general relationships between the 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.
Forest Inventory and Analysis 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.
Forest types are a classification of forest land based
upon and named for the dominant tree species.
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.
However, it does not make any inferences about
what vegetation was historically on the landscape
and does not distinguish between naturally occurring
and human-influenced conditions. Something that
Land Ownership and Use
About 20 percent of forest land within the
assessment area is publicly owned and managed
(U.S. Forest Service 2011a) (Table 4). National
forests make up the largest percentage of public
forest land within the area. Other major public
entities include state agencies, federal agencies
such as the U.S. Department of Defense and the
Department of the Interior, U.S. Fish and Wildlife
Service; and county and municipal governments.
20
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. Finally, the coarse scale of FIA
measurements may miss small, but ecologically
important, types.
By contrast, natural community classifications
describe an assemblage of native plants and animals
and their physical environment that reflects the
composition, structure, and function that would
have occurred under the historical range of natural
variability (Nelson 2010). Forests are just one type
of natural community. Natural communities also
include other terrestrial and aquatic assemblages
not dominated by trees. The advantage of the
natural community system is that it is based on
ecological relationships between native organisms
and their physical environment. Therefore, natural
communities describe what would have been
present at a particular location if the landscape
had been left unaltered by European settlement.
The disadvantage of using natural community
classifications is that they have not yet been
quantified spatially and described in a consistent
manner across the country.
The majority of forests in the assessment area,
however, are privately owned. Most of the privately
owned forest lands are held by hundreds of
thousands of individual nonindustrial family forest
owners (Butler 2008). According to the National
Woodland Owners Survey, primary reasons for
forest ownership are for enjoyment of scenery,
protection of nature, long-term investment, or
recreational purposes (Butler 2008). Making forest
products was a much less common reason for
ownership in the assessment area. In addition, most
privately owned forests in the assessment area lack a
management plan.
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 4.—Forest land (in acres and as a percentage of total forest land) owned by different entities within the
assessment area and by state within the assessment area (U.S. Forest Service 2011a).
Ownership
Assessment area
Proportion
Area
of total
Illinois
Proportion
Area
of total
Indiana
Proportion
Area
of total
Missouri
Proportion
Area
of total
Private
13,551,052
National forest
1,970,093
State
895,059
County and municipal
91,066
Federal
492,246
National Park Service
65,352
Fish and Wildlife Service
87,491
Department of Defense
269,718
Other federal
69,685
79.7
11.6
5.3
0.5
2.9
0.4
0.5
1.6
0.4
1,886,251
294,360
102,077
42,089
40,022
—
23,888
7,227
8,907
79.8
12.4
4.3
1.8
1.7
0.0
1.0
0.3
0.4
2,617,377
194,641
272,339
7,022
148,580
—
44,419
85,916
18,245
80.8
6.0
8.4
0.2
4.6
0.0
1.4
2.7
0.6
9,047,425
1,481,094
520,643
41,956
303,644
65,352
19,184
176,575
42,533
79.4
13.0
4.6
0.4
2.7
0.6
0.2
1.5
0.4
Total forest land
100
2,364,799
100
3,239,959
100
11,394,762
100
16,999,516
SoCiAL AnD eConomiC
ConDiTionS
About 7.1 million people reside within the
assessment area (Headwaters Economics 2012).
Fifty-three percent of the population is located
in Missouri, 27 percent is in Indiana, and the
remaining 20 percent is in Illinois. The Missouri
portion of the assessment area has experienced the
largest population growth over the past 40 years
(50 percent). Indiana has experienced modest growth
during that time (27 percent), and the population in
Illinois has had only a minor increase of 4 percent.
These trends for larger population and growth in
Missouri are primarily due to the presence of the
St. Louis metropolitan area within the assessment
area boundary. By contrast, the largest metropolitan
areas in Illinois and Indiana are located north of
the assessment area boundaries in those states. In
addition, several areas in Missouri have grown
because they serve as retirement destinations
(U.S. Forest Service 1999b). Despite this growth,
population density in the Missouri portion is
relatively low, at 110 people per square mile.
Population density is highest in the Indiana portion
(129 people per square mile), and lowest in the
Illinois portion (83 people per square mile).
The economic well-being of the people of the
assessment area varies across the three states.
Unemployment has been highest in the Illinois
portion of the assessment area over the past
20 years, and lower in Missouri and Indiana
(Headwaters Economics 2012). In Missouri,
growth in employment and personal income over
the last 40 years has been greater in the Ozark
Highlands section than in the state as a whole,
and similar trends have occurred in southern
Indiana (Headwaters Economics 2012). The
entire assessment area has had an increase in
unemployment since 2007, similar to trends across
the United States (Headwaters Economics 2012).
Forest Products Industry
The forest products industry represents a significant
proportion of the total economy of each state, as
measured by percentage of gross domestic product
(GDP) (Table 5). However, it is a much larger
percentage of GDP in Indiana and Missouri than in
Illinois. The timber industry represents 1.1 percent
of total employment for Indiana, 0.7 percent for
Missouri, and 0.5 percent for Illinois (Headwaters
Economics 2012). Major timber-related businesses
in the three states include sawmills, paper mills, and
paper products manufacturing. Wood office furniture
21
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 5.—Gross domestic product (GDP) (billions of
dollars for all industries and for the forest products
industry. Note: Data are for the entire state. Sources:
Bureau of Economic Analysis (2012), ILDNR (2010),
INDNR (2010), MDC (2010).
GDP
Illinois
Indiana
Missouri
All industries
Forest products industry
Percentage of GDP
651.5
2.5
0.4
275.7
7.5
2.7
244
5.7
2.3
manufacturing is a major industry in Indiana,
ranking first in the nation (Bratkovich et al. 2007).
Between 1998 and 2009, timber-related employment
decreased about 33 percent for the three-state area
(Headwaters Economics 2012), which is similar to
trends for the United States as a whole.
Hardwood species (primarily oak, hickory, and
walnut) make up the majority of timber harvested
in the area (Treiman and Piva 2005, U.S. Forest
Service 2011a). In addition, shortleaf pine
constitutes a substantial portion of timber harvested
in Missouri. In the eastern part of the assessment
area, maple species, black cherry, and yellow-poplar
are also important timber species.
Agriculture
Most of the assessment area in Illinois and
Indiana, and a large portion in Missouri, is used for
agriculture (Fry et al. 2011), making agriculturerelated industry a large part of the economy in
the assessment area. Crop and animal production
accounts for about 1 percent of GDP in all three
states (Bureau of Economic Analysis 2012). Food
manufacturing accounts for an additional 1.5 to
2 percent of GDP in the three states (Bureau of
Economic Analysis 2012). About 143,000 people
are employed in the farming industry within the
assessment area (Headwaters Economics 2012).
Farming accounts for about 4 percent of total
employment in the Illinois portion of the assessment
area, and 3 percent in Indiana and Missouri.
The primary crops in all three states are corn and
soybeans. Other important crops in the assessment
area include winter wheat, sorghum, oats, and hay.
Illinois ranks second in the country for corn and
soybean production and fourth in hog production
(National Agricultural Statistics Service [NASS]
2012). Indiana is known for its production of
peppermint and spearmint, which are primarily used
in chewing gum (NASS 2012). Missouri is also a
major producer of rice, cotton, and potatoes (NASS
2012).
Recreation
The forested lands within the assessment area are
a primary destination for recreation, which is also
economically important to the region. Travel and
tourism-related employment in the three-state
area makes up 13.9 percent of total employment
(Headwaters Economics 2012). Total spending on
local and non-local visits to the three national forests
within the assessment area is approximately
$39 million per year (National Visitor Use
Monitoring Program [NVUM] 2011). About half
of the spending occurs on the Mark Twain National
Forest ($19 million) and the other half is divided
roughly equally between the Shawnee and Hoosier
National Forests. The majority (55 percent) of visits
are for local day use by people living 50 or fewer
miles from the national forests. Primary activities
people undertake while visiting national forests are
viewing natural features, hiking, hunting, fishing,
camping, and horseback riding (NVUM 2011).
Total expenditures on fishing, hunting, and wildlife
viewing for the three-state area on all public and
private lands are about $.5 billion (Table ).
Table 6.—Total expenditures (millions of dollars) on
wildlife-related recreation activities by state. Note:
Estimates are for entire state (U.S. Fish and Wildlife
Service and U.S. Census Bureau 2006).
Fishing
Illinois
Indiana
Missouri
Total
22
Hunting Wildlife viewing
Total
722
627
955
334
223
892
1,030
934
739
2,086
1,784
2,586
2,304
1,449
2,703
6,456
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
eCoSySTemS
The assessment area is part of the Central Interior
Broadleaf Forest Province (223; McNab et al. 2007).
The Province comprises six ecological sections
spanning from far eastern Oklahoma to southwestern
Ohio and includes large portions of Kentucky and
Tennessee. The Central Hardwoods assessment
area includes the five sections that encompass the
Missouri Ozark Highlands and the unglaciated
sections of southern Illinois and Indiana (Fig. 2).
In addition, one section (Coastal Plains-Loess)
from Province 231 (Southeastern Mixed Forest) is
included in the assessment area because it overlaps
with the Shawnee National Forest. A mosaic of
natural communities can be found in this area, which
is dominated by mixed oak, shortleaf pine, and
various hickory species.
Natural Communities
A natural community is an assemblage of native
plants and animals that tend to recur over space and
time. These assemblages interact with each other
and their physical environment in ways minimally
modified by nonnative species and adverse human
disturbances. A natural community is a grouping of
plants and animals and their physical environment
that still contains a semblance of the composition,
structure, and function that would have occurred
under the historical range of natural variability
(Nelson 2010). Natural communities serve as a
means to describe and analyze departures between
historical reference and current forest conditions
(as described above using FIA data). Natural
communities are representative of what occurred
on a site prior to European immigration, and what
presumably could be restored there. Except on the
relatively scarce sites that have remained largely
undisturbed, they do not represent the current
condition.
The natural communities for the assessment area are
grouped into broad categories based on similarities
in vegetation appearance, structure, and composition
(Table 7). Descriptions are based on Nelson (2010)
and Olson (2004). These natural communities can
be compared to NatureServe’s plant associations
and the FIA forest types (see Appendix 2). FIA
forest types in the area, which are more specific than
forest-type groups described above, are listed by
area in Appendix 3.
forests
Mature forests are multistoried with a tree canopy,
and a subcanopy of small trees, shrubs, saplings,
vines, and ground flora adapted to shade. Forests
essentially have a permanent layer of leaf litter.
Forests have high canopy cover (80 percent or
greater). Little light penetrates the forest canopy
except in gaps created by wind, tornadoes, ice
and snowstorms, drought, fire, or other natural or
human-caused disturbances. Forests can further be
divided into upland and bottomland (floodplain)
forests and flatwoods based on their landscape
position and soil moisture.
The low percentage of forest (as opposed to
woodland) cover in the Missouri portion of the
assessment area reflects the historical importance
of the fire regime that occurred across the Ozark
Highlands as well as the drier climatic and edaphic
conditions in the area. Closed-canopy forests
developed where the topography and presence
of Ozark streams and rivers created more mesic,
nutrient-rich conditions and protected them from
fire, predominantly in deep coves and river break
valleys. Because most forests generally occurred on
north- and east-facing slopes or under mesic to wet
soil conditions, fires were infrequent and generally
of low intensity.
23
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 7.—Natural community types and dominant tree species found within each type. Modified from Olson et al.
(2004). *Assessed for climate change vulnerability in Chapter 6.
Community Type
Community Sub-type
Community
Dominant Tree Species
Forest
Upland forest
Dry-Mesic*
black, white, northern red, and scarlet oak; shagbark, pignut,
bitternut, and mockernut hickory; sugar and red maple,
yellow-poplar, shortleaf pine (MO)
Mesic*
IL and IN: sugar maple, American beech, northern red and white oak,
yellow-poplar, bitternut hickory, white ash, black cherry
MO: white and northern red oak, sugar maple, American basswood
Bottomland
(floodplain) forest
Woodland
Savanna
Mesic*
white and bur oak, sycamore, eastern cottonwood, sugar maple,
American and slippery elm, American beech, hackberry, black walnut
Wet-Mesic
American and slippery elm, sweetgum, honeylocust, black walnut
Wet*
river birch; green ash; silver and red maple; shellbark and water
hickory; boxelder; eastern cottonwood; black willow; pin, willow, and
overcup oak
Flatwoods*
pin, post, and blackjack oak; shortleaf pine, mockernut and shagbark
hickory; blackgum
Open woodland*
white, post, black, blackjack, scarlet, and chinquapin oak; shortleaf
pine; mockernut, shagbark, and black hickory; eastern redcedar
Closed woodland*
shortleaf pine; white, black, and scarlet oak; mockernut and
shagbark hickory
Savanna*
post and chinquapin oak
Barrens*
black, blackjack, post, scarlet, white, bur, and chestnut oak;
blackgum; shagbark and black hickory; eastern redcedar
Prairie
not applicable
Glade*
post oak; eastern redcedar
Wetlands
24
Fen
not applicable
Seep
not applicable
Spring
not applicable
Swamp
baldcypress, water tupelo; water hickory, pumpkin ash, water locust,
red maple
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Closed-canopy forests are more common than
more open woodlands, barrens, and savannas in the
eastern portion of the assessment area. Hardwood
forests compose 78 percent of the forested land on
the Hoosier National Forest (U.S. Forest Service
200a). Most stands on the Hoosier National
Forest are even-aged and consist of one or two
canopy layers. Mature stands where no cutting or
reintroduction of fire has taken place in recent years
are transitioning from oak and hickory into more
shade-tolerant species such as maple and beech.
Upland Forests
Upland forests typically range from dry-mesic to
mesic. Dry-mesic forests occur most frequently
on deeper, well-drained soils where the climate is
drier and less humid. Infrequent, low-intensity fires
are also an important system driver. As rainfall
and humidity increase from west to east across
the assessment area, soils become moderately
well drained, typically resulting in optimal growth
that develops a maximum canopy height. To the
west (particularly in western Illinois and most of
Missouri), dry-mesic forest is of greater importance
than in the eastern part of the assessment area.
Mesic forests typically occupy steep, north-facing
hills, coves, and the base of bluffs. In the western
part of the assessment area, dry-mesic forests are
most prevalent along the steep hills and breaks
of the larger Ozark streams, where fire occurred
less frequently because of the proximity of deeply
dissected hills and numerous streams and rivers.
Upland forest types occupy less than 10 percent of
the Ozark landscape, and are much more common in
the eastern part of the assessment area.
Bottomland Forests
Bottomland (or floodplain) forests can range from
dry-mesic to wet, although dry-mesic bottomland
systems are found only in the Missouri Ozarks
portion of the assessment area. As the name implies,
bottomland forests are found in low-lying areas and
floodplains. Mesic bottomland forests are similar
in tree species composition to mesic upland forests,
dominated by sugar maple, beech, and white oak.
Wet and wet-mesic bottomland forests occur along
major streams and rivers. Both wet and wet-mesic
forests are frequently flooded, but flooding is
sufficient to limit productivity and diversity only
in wet forests.
Flatwoods
Flatwoods are a unique community type
characterized by a layer of clay in the subsoil that
leads to poor drainage. Flatwoods are waterlogged
in the spring and very dry in summer, leading to a
low diversity of species. In Illinois, flatwoods are
classified as a type of woodland (described below).
Frequent fires of low-moderate intensity were
common historically in this community type.
Woodlands
Woodlands are highly variable natural communities
with a canopy of trees ranging from 30 to 90
percent cover, a sparse woody understory, and a
dense ground flora dominated by grasses, sedges,
and forbs (Nelson 2010). Woodlands can be further
divided into open (30 to 50 percent canopy cover)
and closed (50 to 90 percent cover) types. These
systems are often the product of fire dynamics.
Historically, periodic fires promoted patches of oak
shrubs, saplings, and mature trees in irregular but
widespread patterns, which were determined by fire
behavior characteristics and fire effects across a
varied, dissected landscape.
Woodlands are the most common land cover type on
the Mark Twain National Forest and the surrounding
Ozark Highlands Section. Open woodlands make
up approximately 80 percent of woodlands in
Missouri, with the remainder classified as closed
types. Ladd (1991) and Schroeder (1981) provide
many historical accounts and references offering
evidence of the widespread occurrence of woodlands
(and savannas; see below) throughout the Ozark
Highlands prior to European settlement. Since
25
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
European settlement, the composition of woodlands
has changed from systems dominated by oak,
shortleaf pine, and post oak toward denser stands
of red, black, and scarlet oak.
The Illinois Natural History Survey has recently
updated its classification system to include
woodlands as a distinct natural community type.
In the past, these systems were classified as forests
or savannas. In Illinois, systems with between
50 and 95 percent cover are classified as woodlands,
whereas those with less than 50 percent cover are
classified as savannas. Flatwoods (described above)
and some barrens communities (see below) are also
included under the woodland community type in
Illinois (B. Anderson and J. Taft, Illinois Natural
History Survey, personal comm.).
that grow on poor, thin, or excessively drained soils
and have a stunted, open-growth appearance (Olson
et al. 2004). Barrens communities are more common
than savannas in Indiana and Illinois, and occur
throughout the Shawnee Hills and Transition Hills
Sections of the assessment area. Less than 1 percent
of the National Forest System lands in southern
Indiana are barrens (U.S. Forest Service 200a).
Sandstone barrens communities differ from
limestone barrens, and both are found within the
assessment area. Sandstone barrens in the Shawnee
Hills Section tend to be dominated by white, post,
and blackjack oaks. Limestone barrens are more
open, with as little as 20 percent canopy dominated
by post and chinquapin oaks and eastern redcedar.
The openings in these habitats consist of grasses
and shrubs.
In Indiana, land is typically not classified as
woodland, and is instead classified as forests with
a more open understory or as barrens communities
(M. Homoya, Indiana Department of Natural
Resources, personal comm.).
Savannas and Barrens
Savannas are fire-maintained grasslands with opengrown, scattered, orchard-like trees or groupings of
trees and shrubs. Warm-season grasses and a great
variety of forbs dominate the groundcover. Savannas
are distinguished from woodlands in that they
are strongly associated with prairies. Historically
savannas were maintained by frequent fires and
grazing by elk and bison. The tree canopy cover
is generally less than 30 percent. Eight percent
(119,700 acres) of Mark Twain National Forest
lands were once fire-mediated savanna. Currently
only local isolated remnants occur in portions of
the Missouri Ozark Highlands and the Mark Twain
National Forest.
Barrens communities are a subtype of savanna (or
in the case of some barrens in Illinois, woodland)
characterized by trees tolerant of xeric conditions
2
Barrens on the Hoosier National Forest. Photo by Teena Ligman,
Hoosier National Forest.
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Prairies
Prairies are natural communities dominated by
perennial grasses and forbs with scattered shrubs and
very few trees (less than 10 percent canopy cover).
Historically prairies were maintained by frequent
fires and grazing by elk and bison. In Missouri,
most prairies are degraded or destroyed except for
a few patches on deep loess-glacial till soils of the
Cedar Creek unit of the Mark Twain National Forest.
Prairies are not currently a notable component of
the Illinois or Indiana portions of the assessment
area, although evidence suggests they were present
historically (Samson and Knopf 1994).
Glades
Glades are open areas of exposed bedrock or shallow
soil over rock dominated by drought-adapted
herbaceous vegetation. Tree growth is absent or
stunted, but shrubs are present. Glades often contain
seeps and are associated with bordering open
woodlands. Their size ranges from those creating
canopy gaps in woodlands to complexes of up to
1,000 acres.
The largest glades occur mostly on dolomite in
the White River Hills Subsection and on igneous
substrates in the St. Francois Knobs and Basins
Subsection of the Ozark Highlands of Missouri.
Small glades, generally less than 10 acres, occur
on limestone and sandstone rock. Glades cover
approximately 8,000 acres on and adjacent to the
Mark Twain National Forest. Historically, fire and
native ungulate grazing played an important role in
maintaining their character. Missouri glades contain
several endemic species, many of which are listed
as species of concern. Most glades are threatened
by eastern redcedar invasion and nonnative invasive
species.
Glades are also present in the Illinois and Indiana
portions of the assessment area, and vegetation
structure is similar to that found in Missouri (Baskin
and Baskin 2000). However, parent material is
typically limestone in these areas. Dominant
vegetation is perennial warm-season grasses,
and thus some have suggested that these areas
could be classified instead as prairies (Baskin and
Baskin 2000). Areas that would be classified as
glades elsewhere are often referred to as barrens
communities in Indiana (see Appendix 2).
Wetlands
Wetlands include seeps, springs, fens, and swamps.
Seeps, springs, and fens are associated with a
constant supply of groundwater seepage, creating
conditions that form peaty, mucky shallow to
deep marly soils. Swamps are tree-dominated
communities with surface freshwater throughout all
or most of the year.
The Missouri Natural Heritage Database identifies
42 significant fens and seeps, totaling 3,905 acres,
occurring on the Mark Twain National Forest
(Missouri Department of Conservation [MDC]
2013b). These include Ozark fens, forested fens, and
acid seeps. A host of distinctive and often restricted
plant and animal species characterize this bog-like
natural feature.
Swamps are found in far southern parts of the
Illinois and southwestern Indiana portions of the
assessment area and in Missouri directly southeast
of the assessment area. Swamps are located on areas
of flat topography or with small depressions, and
are often covered by floodwaters 10 feet deep or
greater. In Indiana, they mostly occur along major
watercourses, such as the Ohio and Wabash Rivers.
Other Communities
In addition to the natural communities described
above, other communities not natural to the
assessment area are present in significant amounts.
27
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Pine Plantations
Nonnative pine plantations are common throughout
the Illinois and Indiana portions of the assessment
area. Sixteen percent of the forested area on the
Hoosier National Forest is now planted to nonnative
pines. In Illinois, about 45,000 acres of the Shawnee
National Forest is occupied by nonnative pine
plantations (U.S. Forest Service 200b). These
species were planted mainly from the 1940s to the
1980s when previously farmed lands were put into
the National Forest System and reforested. These
plantations were created with the intent of keeping
fragile, over-farmed soils in place and controlling
erosion. A variety of pines were planted, including
red pine, shortleaf pine, Virginia pine, and white
pine. Of the four varieties, shortleaf pine and white
pine were the most frequently planted species on
National Forest System lands. The mortality rate in
pines is dependent on the species. Red pine shows
the greatest adverse effect of being planted off site
and is experiencing a high mortality rate and little
regeneration. Both white and shortleaf pine seem
more adapted to the area, grow well, and are able to
regenerate, although shortleaf is north of its normal
range and white pine is south of its normal range. In
the past several years, the Hoosier National Forest
has been removing these pine stands to replace them
with native hardwood species.
other vertebrate species can be found throughout the
area and can serve as indicators of overall ecosystem
health.
Birds
The assessment area falls within the Central
Hardwoods Bird Conservation Region and is home
to more than 100 species of birds of conservation
concern, many of them Neotropical migrants
(McCreedy et al. 2004). These species rely on the
many unique natural communities of the assessment
area, and a major threat to these species is the
destruction and fragmentation of habitat (Thompson
et al. 1992).
A variety of bird species rely on habitats of different
successional stages within the assessment area.
Many reports indicate that the number of species that
use early successional habitat is declining (Oliver
and Larson 199, Thompson and Dessecker 1997).
For example, habitat loss and maturation of forests
in Indiana are contributing to population declines of
American woodcock (McAuley and Clugston 1998).
Species including the black-and-white and wormeating warblers prefer the high stem densities and
closed canopy characteristics of mid-successional
habitats (Thompson et al. 1995). Juvenile migratory
birds have been documented using early and midsuccessional habitats (Marshall et al. 2003, Pagen
Almost all of the pine plantations on the Mark Twain
National Forest were planted with native shortleaf
pine, with a small amount (less than 0.5 percent of
total acreage) of white pine planted in the 1930s and
1940s. Most of the nonnative pine has now been
harvested, blown down, or died out, although some
remnants remain.
Associated Species
Wildlife
Wildlife species depend on and, to some extent,
shape the many natural communities within the
assessment area. Hundreds of mammal, bird, and
28
Ruffed grouse. Photo by Darren Noorington, Hoosier National
Forest.
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
et al. 2000, Rappole and Ballard 1987). Late
successional forest stands benefit interior songbirds,
in addition to many other vertebrate species that
depend on large snags and downed woody material
(Shifley et al. 1997).
The Mississippi River floodplain bottomlands of the
Shawnee National Forest are dotted with remnant
wetlands. Restoration of the bottomland hardwood
ecosystems with a strong wetland component
provides needed habitat for a host of migratory
birds. Hundreds of thousands of shorebirds, marsh
birds, ducks, and geese use these wetlands as critical
resting and feeding habitat.
Game Species
White-tailed deer are common throughout the
assessment area, relying on the many edges created
through fragmentation of forest land. The whitetailed deer population is fairly stable to increasing
in Missouri. The goal of Missouri’s deer regulations
over the past decade has been to decrease deer
numbers in many parts of the state. Visible browselines in some areas in Missouri and on the Mark
Twain National Forest indicate that the deer
population may be too high, although definitive
data are lacking.
Deer populations in southern Indiana and Illinois
are in a stable pattern. Deer browsing can influence
plant composition and regeneration, particularly in
the understory. There is little sign in the Shawnee
or Hoosier National Forest of deer overpopulation,
generally evidenced by herbivory browse-lines.
Overpopulation may be affecting more-protected
areas where there is little hunting pressure, and
heavily fragmented forests in Illinois and Indiana
(Hurley et al. 2012, Ruzicka et al. 2010). However,
hunting is permitted in a large percentage of public
land in southern Indiana, which helps control these
impacts. Providing more habitat across the southern
part of the state also encourages more movement,
thus reducing potential for pressure on any one area
(C. Stewart, personal comm.).
The eastern wild turkey is another important game
species in the assessment area. The oak/hickory
forest type provides an ideal habitat to the species.
The population is fairly stable in Missouri, with the
exception of a few counties in the southwestern part
of the state where there has been a decrease. The
Missouri Department of Conservation determined
that the 2011 turkey population was around 308,000
birds. Hunters harvested 4,000 turkeys in spring
2010 in Missouri (MDC 2012). The eastern wild
turkey population in Illinois is about 150,000, with
residents in every county. Illinois hunters harvested
about 1,400 turkeys in 2011 (Illinois Department
of Natural Resources [ILDNR] 2013). In Indiana,
eastern wild turkey roadside counts show a
4-percent increase in turkeys compared to 2010. The
2011 harvest data showed that 11,9 turkeys were
harvested in 2011, with more than 7,200 coming
from within the assessment area (Backs 2012).
Large mammals
Other large mammal species can be found
throughout the assessment area. The Shawnee
National Forest has bobcats in residence. The black
bear population in Missouri appears to be increasing;
the MDC is conducting studies to determine
population size, habitat preferences, and movements.
Mountain lion sightings have been increasing in
Missouri in recent years. The MDC reintroduced
elk to Carter County, Missouri in 2011, and another
small group of elk has also been reported in Taney
County. It is likely that these animals will use Mark
Twain National Forest lands as they expand their
range.
Management Indicator Species
The national forests in the assessment area monitor
ecosystem health by using a few key management
indicator species. Management indicator species
are identified in the land and resource management
plans of each national forest. They are selected
because they represent habitat types typical of their
forest or because they are thought to be sensitive to
management activities. The Mark Twain National
29
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Forest has selected the northern bobwhite, summer
tanager, Bachman’s sparrow, worm-eating warbler,
and red bat as their management indicator species.
Northern bobwhite, summer tanager, Bachman’s
sparrow, and the red bat were chosen specifically
due to the loss of open-canopy oak and pine
woodlands in the area. On the Shawnee National
Forest, the northern bobwhite and worm-eating
warbler were also selected, in addition to the wood
thrush, scarlet tanager, and yellow-breasted chat.
The Hoosier National Forest has selected the yellowbreasted chat, American woodcock, Louisiana
waterthrush, wood thrush, and Acadian flycatcher.
23 percent of the recharge area for the aquatic
ecosystem of the cave is on Mark Twain National
Forest lands. Critical habitat does not occur on the
Forest, but activities in the recharge area may impact
critical habitat.
vertebrates
The endangered Ozark hellbender occurs in the
Eleven Point River, Current River, and North Fork
of the White River on the Mark Twain National
Forest. The Forest has partnered with the MDC to
monitor the population status of the species on the
Forest and in Missouri. The species continues to
decline across its range.
Rare and Endangered Species
The natural communities within the Central
Hardwoods Region also support a variety of rare and
endangered plant and animal species. Although these
species are uncommon, they can serve as indicators
of overall ecosystem health. Many of these species
rely on unique habitats within the assessment area,
such as seeps, prairies, glades, rock outcrops, and
caves (U.S. Forest Service 1999d). In addition,
management decisions are often made to conserve
or restore habitat of these species.
Invertebrates
The endangered Hine’s emerald dragonfly occurs on
the Mark Twain National Forest. Critical habitat was
designated on 13 units on the Forest in 2010. As a
result of recent genetic research, it was discovered
that some of the sites may not be occupied by Hine’s
emerald dragonfly, but rather another closely related
species. There are now six fens that have confirmed
occupancy by Hine’s emerald dragonfly and seven
unconfirmed sites.
Tumbling Creek cavesnail is an endangered species
with designated critical habitat. The species has
been documented from only one cave in the world
(Tumbling Creek Cave). It is on private land
adjacent to Mark Twain National Forest lands in the
Ozark Highlands of Missouri, and approximately
30
The bald eagle has continued a remarkable recovery
from the near devastation of the populations during
the 190s and 1970s. During this time, populations
plummeted to critical levels due to a loss of habitat,
illegal shooting, and the widespread use of certain
persistent pesticides. Both Illinois and Missouri are
important winter areas for bald eagles. Missouri has
one of the highest wintering populations of bald
eagle in the lower 48 states, with about 2,200 birds
recorded each winter. Between 75 and 100 nests are
recorded, and that number is increasing annually.
Both bald eagle nesting and winter use have
continued to rise. In Indiana, a small population of
bald eagles winter along major rivers and large water
bodies such as the Monroe and Patoka reservoirs.
Midwinter eagle surveys conducted since 1979 show
an increase in the number of eagles wintering in
Indiana (INDNR 2001).
Both the Indiana bat and the gray bat are protected
under the Endangered Species Act and are found
throughout the assessment area. Nationwide, the
winter population level of Indiana bats has declined
about 17 percent, but this decline is not as large in
the assessment area.
White-nosed syndrome is a fungal disease infecting
bats across much of the Midwestern and northeastern
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
regions of the United States. This disease has led
to the death of millions of bats, leading to almost
100-percent mortality at many sites. It was first
observed in the United States during the winter of
200-2007 in caves and mines in upstate New York.
As of April 2011, white-nosed syndrome had been
either suspected or confirmed present in 18 states,
affecting more than 17 hibernacula, and resulting
in the first sustained epizootic affecting bats.
White-nosed syndrome has now been confirmed
present in Missouri and Indiana, with an outbreak
of the disease in a hibernaculum in western-central
Kentucky less than 200 miles from the Illinois
border. Bat researchers have projected that the
disease is likely to occur in Illinois by 2013.
within the proclamation boundary. The running
buffalo clover is endangered, and also occurs on
the southern portion of the Hoosier National Forest.
Blue monkshead, another state endangered species,
is found in a few specific locations in the Indiana
portion of the assessment area.
Plants
The Wisconsin glaciation was the most recent
ice sheet that covered much of North America.
Although it did not stretch as far south as the
assessment area, the ice age greatly influenced
the ecosystems of the region. Boreal and northern
hardwood species were the dominant vegetation
cover during this time (28,000 to 12,000 years
before present) (Delcourt and Delcourt 1981)
(Table 8). After the glacier retreated about 12,000
years ago, oak, hickory, and elm species migrated
into the region, and oak-dominated savannas and
woodlands became common as the climate warmed
during the Hypsithermal period from about 8,500
to 4,500 years before present, which resulted in the
expansion of prairie species to many of the drier
upland sites (Parker and Ruffner 2004). A cooler
period followed, allowing the return of tree species
to the area. However, much of the area maintained
a degree of openness through natural and humancaused fire (Abrams and Nowacki 2008, Delcourt
and Delcourt 1998).
In addition to animals, a number of rare and
endangered plant species can be found throughout
the assessment area. In the Illinois and Indiana
portions of the assessment area, 53 plant species are
listed as being of global concern, and 21 of those
live on the Hoosier and Shawnee National Forests
(Olson et al. 2004). Mead’s milkweed is a federally
listed species found on the Shawnee and Mark
Twain National Forests. Running buffalo clover
and Virginia sneezeweed are listed plant species
that occur on the Mark Twain National Forest or
Trout lily on the Hoosier National Forest. Photo by Kirk Larson,
Hoosier National Forest.
Past Ecosystem Change
The ecosystems of the assessment area have
undergone substantial changes over the past several
thousand years. Large-scale changes in climate along
with the settlement of humans in the area shaped the
landscape into what it is today.
Changes Prior to European Settlement
Native Americans played a role in shaping changes
across the assessment area for as long as 12,500
years (Nelson 2010, Parker and Ruffner 2004).
Abrams and Nowacki (2008) suggest that the subhumid climate of the area would not have supported
31
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 8.—Changes in vegetation in the Central Hardwoods Region over the last 12,000 years. Adapted from Nelson
(2010).
Time
(years before present)
Vegetation
Human activities
Pre-12,000
Boreal forest
Initial settlement-big game hunters
12,000 to 8,500
Mesic oak savanna in north, oak-ironwood
woodland southeast
Mobile hunter-gatherers, numerous short-term
settlements, widespread use of fire
8,500 to 4,500
Large expanses of grassland intrude into region,
oak-hickory savanna
Same as above
4,500 to 1,000
Shortleaf pine moves in, variable canopy
woodland and forest in southeast, prairie
savanna in northwest
Semi-sedentary to sedentary hunter-gatherers,
appearance of domesticated plants, increasing
impacts from settlements, increased fire use,
clearance of bottomlands for fields
1,000 to 200
Pre-European appearance
Same as above, plus agricultural settlements along
river bottoms
200-present
Remaining forested lands more dense than
what was historically present, increase in maple
component, nonnative pines planted
Land cleared for agriculture, urban development;
some areas reforested, fire suppression during
mid-20th century
prairie, savanna, and open woodland without the
influence of Native Americans through the use
of fire. In addition to hunter-gatherer societies,
agricultural communities were established along
river bottoms from about 1,000 to 500 years ago,
cultivating corn, beans, and squash. However,
these communities disintegrated prior to European
settlement, returning some formerly developed
lands back to forest. Smaller tribal groups of native
people continued to manipulate some areas with fire
into the 19th century (Parker and Ruffner 2004).
By the time of European contact (circa 150), the
landscape resembled a mosaic pattern of croplands
near settlements, abandoned clearings with early
successional species, and open forest stands
dominated by fire-adapted species of oak, hickory,
and walnut (Abrams and Nowacki 2008, Delcourt
1987, Delcourt and Delcourt 1998).
Presettlement Vegetation
Presettlement conditions are used as a reference
condition for evaluating ecological integrity
and determining restoration goals. The Missouri
32
Historic Vegetation Survey data indicate that the
Ozark Highlands contained more than 25 different
tree associations, many of them attributed to
the influences of fire, topography, and geology.
According to General Land Office witness tree
survey records, oak species, shortleaf pine, and a
variety of hickory species were dominant species
in the early 19th century (Hanberry et al. 2012).
According to models of witness tree structure and
openness (a measure of diameter and distance from
section corners and section lines), much of the
Ozarks was open in character, thus confirming the
historical presence of savanna and open woodlands
(Batek et al. 1999, Hanberry et al. 2012, Nelson
2010). Forest was confined to dissected river breaks.
Pre-European-settlement forests of southern
Illinois can be categorized into four basic types:
(1) oak-hickory, (2) mixed hardwoods, (3) lowlanddepression forests, and (4) floodplain forests (Parker
and Ruffner 2004). Fragments of prairie and savanna
were present in the upland, north-central portions of
the area, and hills and bluffs along the Mississippi
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
River (Fralish 2010, Fralish et al. 1991). Small
native populations of shortleaf pine occurred on
extremely xeric uplands of the Ozark Hills (Davis
and Ruffner 2002). Mesophytic species, such as
American beech and sugar maple, were restricted
to the low and alluvial sites mainly in the Illinois
Ozark Hills and, to a lesser extent, in the Lesser and
Greater Shawnee Hills (Fralish and McArdle 2009).
Prior to European settlement, common vegetation
in southern Indiana consisted of mainly deciduous
forests similar to those found in southern Illinois.
American beech, hickories, oaks, yellow-poplar, and
sugar maple were generally found on well-drained
upland sites (Parker and Ruffner 2004). On the more
shallow upland sites, scrub oaks including blackjack
and scarlet oak were common. The Transition Hills
Section of southern Indiana is believed to have
been primarily forested with maple and beech due
to a low influence of Native Americans in the area
(Parker and Ruffner 2004). Although the area was
primarily forested, prairies and savannas could also
be found.
Post-settlement Changes
Forest harvesting over the past 200 years has greatly
shaped the landscape into what it is today, which is
markedly different from its presettlement condition
(reviewed in Parker and Ruffner 2004). Although
past management across this region is quite variable,
a few trends generally occurred. Settlers harvested
timber across the area throughout the 19th century,
cutting most of the old-growth forests (Fralish
1988). As sawmills were introduced into the area
with the rapid increase in towns and villages, the
harvest of timber for high-value products greatly
accelerated. The practice of cutting only desirable
high-value species left residual stands of trees that
were generally of little economic value (Den Uyl
192, Westveld 1949).
By 1900, most of the forests in the assessment area
had been cut, subjected to grazing, or burned (Parker
and Ruffner 2004). In addition, wetlands had been
drained and prairies had been converted to farmland.
Generally, more land was cleared in the flat
Hickory nuts. Photo by Teena Ligman, Hoosier National Forest.
33
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
bottomland areas than in the more hilly topography.
Stands clearcut in the late 1800s regenerated to a
mixture of tree species that are essentially of the
same age, but varied in size due to differences in
growth rate among species (Marquis and Johnson
1989, Roach and Gingrich 198). Burning and
grazing left open understories in woodlands
throughout the early 20th century. As these practices
became more uncommon by the mid-20th century,
substantial regrowth occurred in understories in the
area.
Harvest of forest lands in the assessment area
increased until the turn of the 20th century and then
began a steady decline. During the 1930s, much of
the land was transferred to public management under
the National Forest System and was reforested.
In Illinois and Indiana, some of the uplands
were planted to nonnative pine, and some of the
floodplains were planted to yellow-poplar (U.S.
Forest Service 200a). Between 192 and 1985, the
upland oak-hickory forests decreased by 12 percent
and maple-beech forests increased more than tenfold
(Hahn 1987).
European settlement also dramatically altered fire
regimes in the area, shifting fire-return intervals and
reducing fire in many areas that previously depended
on it. The following section contains further
discussion on the changes in fire regime.
Primary Stressors and Threats
Forests and other natural communities within the
assessment area currently face a number of stressors
and threats (Table 9). Alteration of the landscape
by human activities continues to be arguably the
greatest threat to the ecological integrity of the
area. Past forest harvesting and land conversion
has led to an altered, fragmented landscape.
Other major threats include shifts in fire regime,
nonnative invasive species, insect pests, and disease.
Additional threats may be important to particular
geographic areas or community types. This section
describes many substantial threats to the forest
ecosystems within the assessment area.
Table 9.—Current major stressors to natural communities, by type
Community
Major current stressors and impacts
Reference
Dry-mesic upland forest
Reduced fire frequency has led to an increase in mesic species
such as red and sugar maple in the east and a reduction in
shortleaf pine in the west.
Batek et al. (1999),
Fralish and McArdle (2009),
Shang et al. (2007)
White-tailed deer browsing limits oak seedling establishment in McEwan et al. (2011)
some areas.
Oak decline causes mortality of red, black, and scarlet oak in
the Missouri Ozarks.
Fan et al. (2011), Shifley et al.
(2006), Woodall et al. (2005)
Invasive plants such as garlic mustard, Japanese honeysuckle,
bush honeysuckle, autumn olive, Japanese stiltgrass, and
multiflora rose outcompete native vegetation.
Emery et al. (2011),
Gibson et al. (2002),
Olson et al. (2004)
Oak wilt causes damage and mortality to red and white oak
species.
Rexrode and Brown (1983)
There is a potential for gypsy moth to spread to this community
type, leading to a reduction in oak species.
(Table 9 continued on next page)
34
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 9 (continued)
Community
Major current stressors and impacts
Reference
Mesic upland forest
Reduced fire frequency has led to a decrease in oak species and Fralish and McArdle (2009)
an increase in fire-sensitive species from historic levels.
White-tailed deer browsing reduces height and reproduction
ability in herbaceous species.
Webster et al. (2001)
Invasive plant species such as princesstree, silktree, garlic
mustard, creeping charlie, Japanese stiltgrass, honeysuckles,
and tree-of-heaven outcompete native vegetation.
Olson et al. (2004)
Fungal diseases lead to defoliation and mortality of many
dominant tree species.
Burns and Honkala (1990)
Forest tent caterpillar defoliation leads to reduced growth of
many hardwood species.
Scarbrough and Juzwik (2004)
There is a potential for emerald ash borer and gypsy moth to
spread to this community type, leading to a reduction in ash
and oak species.
Mesic bottomland forest
Drainage for agricultural use has led to losses of this
community type
Anfinson (2003),
Nelson et al. (2009)
White-tailed deer browsing reduces stature of oak and
hackberry, potentially leading to a reduction in these species.
Ruzicka et al. (2010)
Changes in flood regime and a rising water table can lead to
shifts in species composition and loss of diversity.
Romano (2006)
Dutch elm disease has led to a reduction in the elm component
from historical levels.
Phillippe and Ebinger (1973)
Sedimentation from upland soil erosion and channelization
leads to shifts in vegetation composition.
Oswalt et al. (2005)
Invasive plants such as wintercreeper, Chinese yam/cinnamon
Lavergne and Molofsky (2207),
vine, Japanese knotweed, Japanese stiltgrass, creeping jenny,
Nelson et al. (2009), Romano (2010)
creeping charlie, Japanese hop, garlic mustard, and reed canary
grass outcompete native species.
Feral hogs’ rooting and feeding behavior can cause severe
damage to native wildlife and plant communities.
Wet-mesic bttomland
Wet bottomland
Pierce and Martensen (2009)
(see mesic bottomland forest)
(see mesic bottomland forest)
There is a potential for emerald ash borer to spread to this
community type, leading to a reduction in ash species.
Flatwoods
Reduction in fire frequency has led to a reduced groundcover
diversity and woody species encroachment in some areas.
Taft (2005), Taft et al. (1995)
Past overgrazing has led to a reduction in native understory
diversity.
Faber-Langendoen (2001a)
Conversion to invasive cool-season grasses and fescue has led
to a reduction in native understory species.
Burns and Honkala (1990)
(Table 9 continued on next page)
35
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 9 (continued)
Community
Major current stressors and impacts
Reference
Open woodland
Past harvesting of shortleaf pine has led to a reduction in the
shortleaf pine component in Missouri compared to the early
20th century.
Tremain et al. (2007)
Reduced fire frequency has led to a reduction in shortleaf
pine (in Missouri) and an increase in woody species in the
understory compared to presettlement conditions.
Batek et al. (1999)
Oak decline causes mortality of red, black, and scarlet oak in
the Missouri Ozarks.
Fan et al. (2011), Shifley et al.
(2006), Woodall et al. (2005)
Invasive species such as sericea lespideza, yellow sweetclover,
crown vetch, Oriental bittersweet, garlic mustard, common
periwinkle, multiflora rose, Japanese honeysuckle, bush
honeysuckle, and autumn olive outcompete native vegetation.
Olson et al. (2004)
Eastern redcedar encroachment crowds out native understory
vegetation.
Hanberry et al. (2012)
Insect attack by Nantucket pine tip moth, redheaded sawfly,
and reproduction weevils causes mortality in young shortleaf
pine.
Burns and Honkala (1990)
Oak wilt causes damage and mortality to red and white oak
species.
Rexrode and Brown (1983)
Past harvesting of shortleaf pine has led to a reduction in that
species in Missouri compared to the early 20th century.
Tremain et al. (2007)
Reduced fire frequency has led to a reduction in shortleaf pine
(in Missouri) compared to presettlement conditions and an
increase this community type.
Batek et al. (1999)
Oak decline causes mortality of red, black, and scarlet oak in
the Missouri Ozarks.
Fan et al. (2011), Shifley et al.
(2006), Woodall et al. (2005)
Invasive species such as garlic mustard, Japanese honeysuckle,
bush honeysuckle, autumn olive, Japanese stiltgrass, and
multiflora rose outcompete native vegetation.
Olson et al. (2004)
Insect attack by Nantucket pine tip moth, redheaded sawfly,
and weevils causes mortality in young shortleaf pine.
Burns and Honkala (1990)
Oak wilt causes damage and mortality to red and white oak
species.
Rexrode and Brown (1983)
Conversion to agriculture has led to a dramatic loss of this
community type on the landscape, making remnants highly
fragmented.
Nuzzo (1986)
Reduced fire frequency has led to encroachment of woody
and shade-tolerant species that out-compete shade-intolerant
understory vegetation.
Bowles and McBride (1998)
Closed woodland
Savanna
Olson et al. (2004)
Invasive species such as autumn olive, multiflora rose, teasel,
garlic mustard, white and yellow sweetclover, sericea lespideza,
and spotted knapweed outcompete native vegetation.
(Table 9 continued on next page)
3
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 9 (continued)
Community
Major current stressors and impacts
Reference
Barrens
Reduction in fire frequency has led to conversion to forest and
lower understory species diversity.
Anderson et al. (2000), Heikens and
Robertson (1995), Taft (2003)
Conversion to fescue reduces understory diversity.
MDC 2013(d)
Invasive species such as autumn olive, multiflora rose, teasel,
Olson et al. (2004)
garlic mustard, white and yellow sweetclover, sericea lespideza,
and spotted knapweed outcompete native vegetation.
Prairie
Glade
Conversion to agriculture has led to a loss of more than 99
percent of former area, leaving highly fragmented remnants.
Samson and Knopf (1994)
Loss of fire has led to a reduction in herbaceous species
diversity and an increase in woody species in many prairie
remnants.
Leach and Givnish (1996)
Invasive species, including sericea lespedeza, yellow sweet
clover, spotted knapweed, common teasel, crown vetch, cheat
and brome grasses, plume grass, meadow fescue, and tall
fescue outcompete native vegetation.
Smith and Knapp (2001)
Soil erosion is leading to reduced soil depth and susceptibility
to drought.
Ware (2002)
Loss of fire has led to Eastern redcedar invasion and a
reduction in glade species.
Guyette and McGinnes (1982),
Ware (2002)
Overgrazing has led to soil erosion, loss of species diversity, and Guyette and McGinnes (1982)
Eastern redcedar invasion.
Fen
Feral hog digging and rooting leads to soil erosion and loss of
biodiversity.
Nelson (2010)
Invasive species, including sericea lespedeza, yellow sweet
clover, spotted knapweed, common teasel, crown vetch, cheat
and brome grasses, plume grass, meadow fescue, and tall
fescue outcompete native vegetation.
Nelson and Fitzgerald (2013)
Fragmentation from road building and development.
Nelson and Fitzgerald (2013)
Previous grazing and fire suppression has led to woody species Bowles et al. (1996)
encroachment which has reduced herbaceous species diversity.
Drainage and conversion to agriculture and pasture has led to a
reduction in native species diversity and altered hydrology.
Mills (2010)
Invasive species such as purple loosestrife, narrow-leaved
cattail, common reed, and reed canarygrass outcompete native
vegetation.
Lavergne and Molofsky (2007)
Groundwater contamination from development leads to loss of
biodiversity.
Panno et al. (1999)
(Table 9 continued on next page)
37
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Table 9 (continued)
Community
Major current stressors and impacts
Reference
Seeps and springs
Pollution from agricultural runoff and livestock waste may
eliminate some aquatic species that are very sensitive to water
quality.
Faber-Langendoen (2001b)
Grazing or ditching can reduce site quality.
Swamp
Agricultural development, which has led to altered hydrology
and habitat fragmentation, alters seedbank composition and
distribution.
Middleton (2003),
Middleton and Wu (2008)
Fungal attack causes a brown pocket rot known as “pecky
cypress” that damages the heartwood of living baldcypress
trees.
Burns and Honkala (1990)
Various insect species can cause defoliation of baldcypress.
Burns and Honkala (1990)
Nutria clip or uproot newly planted cypress seedlings, leading
to seedling death.
Conner et al. (1987)
Fragmentation and Land-use Change
European settlement led to development and
fragmentation of the landscape across the assessment
area, resulting in a patchwork of public and private
parcels of natural, agricultural, and developed lands.
As mentioned earlier, 43 percent of the assessment
area is now agricultural land and about 8 percent is
now developed land (Fry et al. 2011). In addition,
remaining forest land is often heavily dissected
by roads, private property, trails, and utility lines.
Forests in the assessment area are much more
heavily fragmented than forests in the northern Great
Lakes and Appalachians, but are less fragmented
than the northern portions of each state, as measured
by the percentage of interior forest in each county
(U.S. Forest Service 2011b). Fragmentation of
natural landscapes creates isolated populations that
are unable to migrate easily and exchange genetic
information, leading to a reduction in biological
and genetic diversity (Fahrig 2003, Harrison and
Bruna 1999, Robinson et al. 1995). It also leads to
increased incidence of edges along forest boundaries
(Sisk et al. 1997).
38
Fragmentation and land-use change were cited as the
number one issue facing forests in Indiana, based on
a survey conducted as part of the Indiana Statewide
Forest Assessment (INDNR 2010). It was also listed
as a major issue of concern in Missouri’s Forest
Resource Assessment and Strategy (MDC 2010).
Housing growth, particularly in rural areas, can
lead to forest fragmentation and nonnative species
invasions (Radeloff et al. 2005, 2010). Ecoregions
in southern Missouri have had particularly high
growth in rural sprawl compared with much of
the Midwest (Radeloff et al. 2005). In addition,
Indiana forests have the highest housing density
surrounding them in the entire Midwest (Radeloff et
al. 2005). By contrast, the central Ozarks in Missouri
represent some of the least fragmented forests in
the Midwest and are therefore of high conservation
value (Radeloff et al. 2005). Housing growth from
1940 to 2000 within 30 miles of national forests in
the Central Hardwoods Region varied by forest. The
Mark Twain National Forest underwent the highest
growth (greater than 400,000 new units), followed
by the Hoosier (200,000 to 300,000 new units), and
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
the Shawnee National Forests (100,000 to 200,000
new units; Radeloff et al. 2010). Housing growth
rates for all forests were substantially lower than
for forests in the western United States.
Fragmentation and edge effects from wildlife
openings on the Shawnee National Forest have
declined since 1992, due to the general reduction
in wildlife-opening management across the forest.
There also have been small amounts of reduction in
edges, especially agricultural edges on private lands
within the forest boundary, linked to the forest’s
acquisition and land-consolidation programs and to
conservation reserve programs administered by the
Natural Resources Conservation Service on private
lands. All of these factors have resulted in improved
habitat quantity and quality for species associated
with mature hardwood forests. These habitat
improvements appear to have had some beneficial
effects locally on species such as the wood thrush,
but do not yet appear to have had a similar and
associated effect on populations of these species
at state and regional levels.
The Indiana Statewide Forest Assessment (INDNR
2010) lists fragmentation or conversion of forests
to another use as the most important threat to
sustaining the forests of Indiana. The assessment
area contains more contiguous forest land than the
northern portion of the state. The Hoosier National
Forest is working to reduce fragmentation from
permanent wildfire openings by organizing these
habitat features into complexes and reducing the
number of them across the landscape. A primary
objective of the land acquisition program on the
Hoosier is to acquire properties that consolidate
the forest’s ownership.
Shifts in Fire Regime
The assessment area has undergone dramatic shifts
in fire regime over the past several hundred years,
and these shifts threaten the character of the natural
communities in the area. The historical role of fire
in the development and maintenance of oak forests
has been well established across much of the eastern
deciduous biome (Abrams 1992, Brose et al. 1999,
Lorimer 1985). Both natural and human-caused fire
has been a component of southern Illinois, Indiana,
and Missouri for thousands of years (Abrams 1992,
Heikens and Robertson 1995, Ruffner and Abrams
2003).
It is generally accepted that European settlement
during the 19th century shortened fire-return
intervals throughout the assessment area compared
to previous levels. Fire history studies for the
Missouri Ozarks indicate that fire-return intervals
during the period of Native American habitation
(1701 to 1820) averaged about 12 years, compared
to an average of 4 years during Euro-American
settlement (Guyette and Cutter 1991). Similar shifts
from longer to shorter return intervals have been
noted for the Central Hardwoods forests of southern
Illinois and Indiana as well (Olson 199, Parker
and Ruffner 2004). Regional studies reporting fire
histories from the 19th century indicate that fireignitions were high at that time due to farmers
clearing underbrush from the forest (Miller 1920,
Robertson and Heikens 1994).
During the 20th century, numerous laws and local
bans on fire marked the beginning of major efforts
to control wildfires. After wildfire controls were
enacted, the effects of periodic fire in maintaining
healthy forests were removed from the ecosystem.
Numerous authors suggest that a growing shift in
species composition occurred during this time across
much of southern Illinois and Indiana when fireintolerant species, such as sugar maple, began to
replace fire-adapted oak and hickory species (Fralish
et al. 1991, Lorimer 1985, Nowacki and Abrams
2008). The exclusion of fire or other disturbances
from mature oak-hickory forests has altered the
ecology of these ecosystems, to the detriment of
39
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Sunbeams filtering through smoke on a prescribed burn on the Hoosier National Forest. Photo by Teena Ligman, Hoosier National
Forest.
established oak regeneration (Van Lear and Johnson
1983). The negative effects of the lack of fire on
grassland communities, barrens, and populations of
shortleaf pine have also been documented (Anderson
et al. 2000, Stambaugh et al. 2002).
Invasive Species
Invasive species—organisms that are not native
to the ecosystems under consideration and whose
introduction causes or is likely to cause economic or
environmental harm or harm to human health—are
of concern not only in the Central Hardwoods
Region but also nationwide because they compete
with native species and can lead to other detrimental
ecological and economic effects (Mack et al.
40
2000). The Indiana Statewide Forest Assessment
listed nonnative species invasions as the third most
important issue facing forests in the state (INDNR
2010). Some of the most common and problematic
invasive plant species in the assessment area are
listed in Appendix 4. Although invasive plants and
larger animals are what often come to mind when
considering invasive species, invasive insect species
and diseases can be among the most disruptive to
forest communities (see next section).
Invasive plant species can have a serious adverse
effect on biological, economic, social, and aesthetic
values in the region. Invasive plant species can
be introduced into native ecosystems by the
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
transportation of seed on vehicles, equipment, or
the soles of shoes; in manure from domestic or
wild animals; or via wind and water. Across the
assessment area, invasive vines, shrubs, and herbs
and grasses can all be found, and are generally
more common in fragmented areas and near roads
than in large areas of intact forest cover (Fan et
al. 2013, Flory and Clay 200, Yates et al. 2004).
Invasive vines, such as Japanese honeysuckle, are
more common in the assessment area than in the
Midwest as a whole (Fan et al. 2013). Some species
of particular concern in the area include Japanese
stiltgrass and sericea lespedeza (Brandon et al. 2004,
Gibson et al. 2002). These species are somewhat
fire tolerant, so they pose problems for areas where
historical fire regimes are being restored (Emery
et al. 2011, Flory and Lewis 2009). In addition,
disturbance by herbivores such as white-tailed deer
has been shown to increase success of invasive
plant species in the area, such as Japanese stiltgrass
(Knight et al. 2009, Webster et al. 2011).
Invasive vertebrate species can also have strong
environmental and economic effects across the
United States, including the assessment area
(Pimentel et al. 2001). Feral hogs are a particular
problem in the Missouri portion of the assessment
area, causing severe damage to glades, bottomland
forests, and wetlands through their rooting,
wallowing, and feeding behavior (MDC 2013a). In
rivers throughout the assessment area, nonnative fish
species such as several species of carp can reduce
water quality and outcompete natives (Garvey et al.
2010).
biological and physical factors, has had a major
negative influence on the health of species in the
red oak group (Dwyer et al. 1995, Fan et al. 200,
Jenkins and Pallardy 1995, Wang et al. 2008).
Factors such as stand age, site conditions, and
drought, can predispose these species to secondary
attack by insects and pathogens (see Box 15 in
Chapter 5).
A current emerging threat in the Central Hardwoods
Region is emerald ash borer, which has the potential
to completely wipe out populations of all ash species
in the region (MacFarlane and Meyer 2005). The
emerald ash borer has killed tens of millions of ash
trees across the Midwest and Northeast (Emerald
Ash Borer Info 2013). This devastation has cost
municipalities, property owners, nursery operators,
and forest products companies tens of millions of
dollars. The Hoosier National Forest is working with
the State of Indiana to slow ash mortality and reduce
the population of the nonnative insect that occurs in
south-central Indiana. The insect is also present in
parts of southern Missouri.
In addition to the threats listed, numerous threats
outside the assessment area are emerging that could
affect Central Hardwoods forests in the near future
(Scarbrough and Juzwik 2004). These pests and
diseases include gypsy moth, thousand cankers
disease, sudden oak death, and southern pine beetle.
In 2009, gypsy moth treatments were conducted
on the Hoosier National Forest and seem to have
sharply reduced the local population. Monitoring of
this species will continue.
Insects and Disease
Loss of Soil
Trees in the Central Hardwoods Region are currently
vulnerable to numerous diseases and insects, many
of which are also nonnative invasive species (see
Appendixes 5 and ). Chestnut blight and Dutch
elm disease have had devastating effects on their
hosts across the area (Scarbrough and Juzwik 2004).
In Missouri, oak decline, caused by a complex of
Soil loss and erosion has occurred over the entire
assessment area, and is one of the major stressors
to ecosystems in the region. Soil and water
conservation was listed as the second most important
issue facing Indiana’s forest resources in the recent
Indiana Statewide Forest Assessment (INDNR
2010). According to the report, much of southern
41
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Indiana is considered at “severe” risk for soil erosion
(INDNR 2010). Throughout the Indiana and Illinois
portions of the assessment area, it is estimated that
25 to 75 percent of the surface horizon has been lost
in some areas, primarily from timber harvests and
agriculture (Ponder 2004). This loss of important
topsoil has led to loss of nutrients and organic
matter, leading to decreased soil water-holding
capacity and ultimately a decrease in productivity.
The use of best management practices in the
assessment area can reduce the risk of soil erosion
on forest soils, and use of these practices has led to
a reduction in potential erosion in portions of the
assessment area (U.S. Forest Service 1999d).
Overgrazing and Overbrowsing
Overgrazing and overbrowsing can be a stressor in
some portions of the assessment area. Overgrazing
by domestic livestock was pervasive throughout the
Missouri Ozarks until the mid-190s, when much
of the landscape was subject to open-range grazing.
Overgrazing has led to reductions in grass and forb
groundcover and resulted in soil loss and erosion of
gravel into Ozark streams (Nelson 2010). Watershed
hydrology has consequently changed, with
increased runoff (even under dense, overstocked
canopies) and subsequent moisture loss from the
landscape. Overgrazing and overbrowsing have also
dramatically reduced or eliminated flower and seed
production, thereby decreasing the abundance of
insect populations important for foraging by birds
and bats.
White-tailed deer overbrowsing is becoming evident
in the assessment area as well. In Missouri, the
deer population increased dramatically over the
20th century, reaching a statewide population of
1.4 million (Sumners et al. 2012). Deer
overabundance has necessitated special hunts to
reduce population size in Missouri state parks,
urban areas, the Ozark National Scenic Riverways,
and other lands. The effects of overabundant
deer populations have been major problems in
fragmented forests in Illinois and Indiana as well
42
(Hurley et al. 2012, Ruzicka et al. 2010). Hunts have
been used statewide in Indiana for nearly 20 years to
reduce deer populations in state parks.
Extreme Weather Events
Current climate- and weather-related events include
wind-disturbance, winter storms, droughts, and
floods (see Chapter 3). Tornadoes and downbursts
are frequent features on the landscape. These events
can be seen as threats in some cases, but also as
important disturbance mechanisms for removing
overstory trees and creating early successional
habitat (reviewed in Parker and Ruffner 2004).
Snow and ice damage occurs occasionally in the
assessment area, and can cause damage to species
such as eastern redcedar, yellow-poplar, American
basswood, American elm, and sweetgum; white
oak and shagbark hickory appear less susceptible
(Parker and Ruffner 2004, Rebertus et al. 1997).
Drought in the area can lead to reduced growth rates
and death of mesic species on drier sites, as well as
secondary effects of fire and pest infestations (Parker
and Ruffner 2004). Current and future projected
impacts of extreme weather events on forests in the
assessment area are reviewed in Chapter 5.
CuRRenT LAnD mAnAGemenT
TRenDS
Public Lands
Public lands in the assessment area are managed
for a variety of goals and objectives, including
recreation opportunities, wildlife habitat, timber
production, and conservation of rare and endangered
species. Although timber production continues to
be important in many areas, there has also been an
increased interest in the restoration of historical
vegetation and natural communities. An increased
awareness of the role of fire in maintaining natural
communities has led to a shift from a goal of fire
suppression during the mid-20th century toward the
use of prescribed fire (Parker and Ruffner 2004).
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
Prescribed Fire
Prescribed fire is a primary tool used to maintain
or restore the dominance of oak and other fireadapted species on the Central Hardwoods landscape
(Abrams 2005, Van Lear et al. 2000). Oaks have
several adaptive features that enable them to survive
periodic fire, including thick bark and the ability
to re-sprout vigorously from dormant buds at the
base of the tree when the bole has been topkilled
(Lorimer 1985). Fire can also reduce acorn predation
by insects and rodents (Galford et al. 1988, Lorimer
1985). Maples, by contrast, are susceptible to
fire because these trees are thin barked and have
seedlings that suffer high mortality due to both
rootkill and topkill. Studies have documented the
beneficial effects of prescribed fire to foster oak
regeneration and reduce competing mesophytic
species in forest lands, but effects can vary with
burn regime, season, and stem diameter of trees in
a particular stand (Brose et al. 2012, Ruffner and
Groninger 200).
Prescribed fire is also a primary tool to restore other
historical vegetation and fire-dependent natural
communities. Fire has been shown to help perpetuate
barrens communities, where many threatened,
endangered, and sensitive species occur (Anderson
and Schwegman 1971). It is also used to restore the
understory and improve wildlife habitat in shortleaf
pine systems (U.S. Forest Service 1999d). The use
of prescribed fire to maintain unique vegetation and
habitats in glades and oak savannas has also been
noted (Parker and Ruffner 2004).
Use of prescribed fire has increased over the past
several decades across the assessment area. Publicly
managed forests in southern Illinois have been
using fire since the mid-1980s (Parker and Ruffner
2004). The Shawnee National Forest 200 Land
Management Plan anticipates 124,389 acres of
prescribed burning forest-wide over a 10-year period
(U.S. Forest Service 200). To date, about 5,000 to
,000 acres of prescribed burning occur in a given
year. The Mark Twain National Forest’s use of
prescribed fire has increased from 8,000 to 10,000
acres per year in the mid-1990s and early 2000s to
an average of 30,000 acres per year currently. Since
implementing the 200 Land Management Plan,
prescribed burns on the Hoosier National Forest
have averaged about 1,4 acres per year, with
a high of 2,583 acres in 2009 (J. Perez, Hoosier
National Forest, personal comm.).
Timber Harvest
Timber harvests are still a component of forest
management on public lands in the assessment
area. In the past several decades, clearcut harvests
in Missouri have been virtually eliminated in
favor of other silvicultural techniques such as
the shelterwood and seed-tree methods (U.S.
Forest Service 1999d). Likewise, the shelterwood
technique is a primary timber management technique
anticipated on the Shawnee National Forest (U.S.
Forest Service 200a). Clearcutting is still one of the
primary techniques to remove nonnative pine species
on the Hoosier National Forest, along with group
selection, shelterwood, and single tree selection
techniques (U.S. Forest Service 200b). Once trees
are harvested, most hardwood species are allowed to
regenerate naturally, whereas artificial regeneration
is generally used for shortleaf pine in Missouri
(U.S. Forest Service 1999d).
In Missouri, statewide harvest removals were
estimated at 1,70 million board feet (mmbf) in
2010, a 13-percent increase from 2005 (Moser et
al. 2011). The amount of timber sold by the Mark
Twain National Forest has averaged about 4 mmbf
per year over the last 5 years (Periodic Timber Sale
Accomplishment Reports [PTSAR] 2012). This
amount is an increase from an average of 34 mmbf
between 199 and 2005, but below the 198 to 1995
average of 2 mmbf. The sale quantity has ranged
from a high of 79 mmbf in 1987 to a low of 13 in
2001. Oak, predominantly black and scarlet, makes
up on average 78 percent of the total, with shortleaf
pine composing the rest.
43
ChAPTeR 1: The ConTemPoRARy LAnDSCAPe
In Illinois, timber harvests showed a dramatic
increase from 190 to the early 1980s, but have
since declined (ILDNR 2010). Of the harvests that
occur currently, the majority take place on private
lands. Statewide, annual harvest removals were
estimated at 505 mmbf in 2011 (Crocker 2012).
Timber removals are low in public lands in Illinois,
and the Shawnee National Forest in particular.
According to the 200 Shawnee National Forest
Land Management Plan, maximum probable timber
harvests from combined management activities
would be about mmbf per year for the next 10
years, but actual harvests have not yet occurred
(U.S. Forest Service 200b). There have been no
timber sales on the Shawnee National Forest in
nearly 20 years because of a number of factors.
Across Indiana, harvest removals were estimated at
907 mmbf in 2011, a 13-percent increase from 2007
(Woodall and Gallion 2012). Timber harvests on
the Hoosier National Forest have averaged 7 mmbf
per year from 2007 to 2011 under the current land
management plan (PTSAR 2012).
Private Lands
As mentioned earlier in the chapter, about 80
percent of forested land in the Central Hardwoods
Region is privately owned, and these private lands
account for the majority of timber harvested on
the landscape. The majority of these lands lack a
specific management plan (Butler 2008). However,
several programs exist to provide incentives to
private landowners in the region for the development
of management plans in order to ensure long-term
sustainability of forest resources.
National programs for forest certification on these
private lands include the Sustainable Forestry
Initiative (SFI), Forest Stewardship Council
(FSC), and American Tree Farm System. These
programs help landowners develop sustainable
forestry practices. Products from these sustainably
44
managed forests are tracked over time from harvest
to purchase, allowing consumers to purchase forest
products that they know are produced in the most
sustainable way. It is projected that consumer
demand for these products will grow, providing a
market incentive for certified wood products, and
thus sustainable forest management. Currently, there
are 14,235 acres of FSC land in Missouri, 1,794
acres in Illinois, and 7,370 acres in Indiana. In
Indiana, 148,019 acres are dual certified as SFI, and
528,351 are dual certified as Tree Farm (Pingrey
2011).
ChAPTeR SummARy
The climate, geology, and soils of the Central
Hardwoods Region of Missouri, Illinois, and
Indiana support a mosaic of natural communities
dominated by oak and hickory species. These
communities supply important benefits to the people
of the area, including forest products and recreation
opportunities. Past changes in climate, fire regime,
and land use have shaped the landscape into its
current condition. About half of the land in the area
has been converted to agriculture or developed for
industrial or residential use. Many of the remaining
forests on the landscape are less open and contain
more mesophytic species such as sugar maple than
before European settlement. Shifts in fire regime,
habitat fragmentation, species invasions, insect pests
and diseases, and other alterations to the landscape
threaten the integrity and diversity of the ecosystems
and the benefits they provide these ecosystems.
Management on public lands in recent decades has
focused on reducing these stressors and improving
ecosystem function. About 80 percent of the forested
land in area is privately owned, however, and the
majority of these lands lack a management plan.
New opportunities and incentives have arisen in
recent years to help private and public land managers
to restore and conserve the ecosystems of the Central
Hardwoods systems for future generations.
ChAPTeR 2: CLimATe ChAnGe SCienCe
AnD moDeLinG
This chapter provides a brief background on climate
change science, models that simulate future climate,
and models that project the effects of changes in
climate on species and ecosystems. Throughout
the chapter, boxes point to recent nontechnical
reports based on the best available science. A more
detailed 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 terrestrial, marine, and biological systems. The
IPCC’s Fifth Assessment Report is underway and
scheduled to be released in 2014. The United States
Global Change 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 5
for more information).
The Warming Trend
CLimATe ChAnGe
Climate is not the same thing as weather. Weather
is a set of the meteorological conditions for a given
point in time in one particular place (such as the
temperature at 3:00 p.m. on June 22 in St. Louis).
Climate, in contrast, is the average, long-term
(30 years or more) meteorological conditions and
patterns for a geographic area. The IPCC (2007:
30) defines climate change as “a change in the state
of the climate that can be identified (e.g., by using
statistical tests) by changes in the mean and/or the
variability of its properties, and that persists for an
extended period, typically decades or longer.” A
key finding of the IPCC in its Fourth Assessment
Report (IPCC 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.
Current observations of higher global surface, air,
and ocean temperatures and thousands of long-term
(more than 20 years) data sets from all continents
and oceans contributed to this conclusion. These
data sets showed significant changes in snow, ice,
and frozen ground; hydrology; coastal processes;
The Earth is warming, and the rate of warming
is increasing (IPCC 2007, Raupach et al. 2007).
Measurements from weather stations across the
globe indicate that the global mean temperature
has risen by 1.4 °F (0.8 °C) over the past 50 years,
nearly twice the rate of the last 100 years (IPCC
2007) (Fig. 9). Including 2012, all 12 years to date in
the 21st century rank among the warmest 14 years in
the 133-year period of record of global temperature
(National Oceanic and Atmospheric Administration
[NOAA], National Climatic Data Center [NCDC]
2012). Temperatures in the United States have risen
by 2 °F (1.1 °C) in the last 50 years (Karl et al.
2009). The 2012 continental U.S. average annual
temperature of 55.3 °F was 3.3 °F above the 20thcentury average, and was the warmest year in the
1895 through 2012 period of record for the nation
(NOAA NCDC 2013).
Average temperature increases are just one aspect
of a more complex and wide-ranging set of climate
changes. For example, the frequency of cold days,
cold nights, and frosts has decreased over many
regions of the world while the frequency of hot
45
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
Box 5: Global and National Assessments
Intergovernmental Panel on Climate Change
The Intergovernmental Panel on Climate Change
(IPCC; http://www.ipcc.ch/) is the leading
international body for the assessment of climate
change. It was established by the United Nations
Environment Programme (UNEP) and the World
Meteorological Organization (WMO) in 1988 to
provide the world with a clear scientific view on the
current state of knowledge in climate change and its
potential environmental and socioeconomic impacts.
The most recent report is available for download at
the Web address below.
Climate Change 2007: Synthesis Report
http://www.ipcc.ch/publications_and_data/ar4/syr/
en/contents.html
U.S. Global Change Research Program
The U.S. Global Change Research Program (USGCRP;
globalchange.gov) is a federal program that
coordinates and integrates global change research
across 13 government agencies to ensure that it
most effectively and efficiently serves the nation
and the world. Mandated by Congress in the Global
Change Research Act of 1990, the USGCRP has since
made the world’s largest scientific investment in the
areas of climate science and global change research.
It has released several national synthesis reports
on climate change in the United States, which are
available for download at the Web addresses below.
Global Change Impacts on the United States
http://library.globalchange.gov/2009-global-changeimpacts-in-the-united-states
Synthesis and Assessment Products
http://library.globalchange.gov/products/
assessments/
National Climate Assessment
http://ncadac.globalchange.gov/
Effects of Climatic Variability and Change on Forest
Ecosystems: a Comprehensive Science Synthesis for
the U.S.
http://www.treesearch.fs.fed.us/pubs/42610
Figure 9.—Trends in global temperature compared to the 1951 to 1980 mean. Data source: NASA Goddard Institute for Space
Studies. Image courtesy of NASA Earth Observatory, Robert Simmon; www.giss.nasa.gov/research/news/20120119.
4
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
days and nights has increased (IPCC 2007). The
frequency of heat waves and heavy precipitation
events has increased over this period, with new
records for both heat and precipitation in portions
of the United States in July 2011 and March 2012
(NOAA NCDC 2012). 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 temperature increases of a few degrees may
seem small, but even small increases can result in
substantial changes to the severity of storms, the
nature and timing of precipitation, droughts and heat
waves, ocean temperature and volume, and snow
and ice—all of which affect humans and ecosystems.
Increases of more than 3. °F (2 °C) above the
average temperature are expected to cause major
societal and environmental disruptions through
the rest of the century and beyond (Richardson et
al. 2009). The synthesis report of the International
Scientific Congress on Climate Change concluded
that “recent observations show that societies and
ecosystems are highly vulnerable to even modest
levels of climate change, with poor nations and
communities, ecosystem services and biodiversity
particularly at risk” (Richardson et al. 2009: 12).
Based on available evidence, 97 percent of the
climate science community attributes this increase in
temperature and associated changes in precipitation
and other weather events to human activities
(Anderegg et al. 2010, Doran and Zimmerman
2009, Stott et al. 2010). 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 recent
climate trends for the assessment area.
The Greenhouse Effect
The greenhouse effect is the process by which
certain gases in the atmosphere absorb and re-emit
energy that would otherwise be lost into space
(Fig. 10). This greenhouse effect is necessary for
human survival: without it, Earth would have an
average temperature of about 0 °F (-18 °C) and
be covered in ice, rather than a comfortable 59 °F
(15 °C). Several naturally occurring greenhouse
gases in the atmosphere, including carbon dioxide
(CO2), methane (CH4), nitrous oxide (N2O), and
water vapor, contribute to the greenhouse effect.
Water vapor is the most abundant greenhouse gas;
its residence time in the atmosphere, however, 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. Thus, these
other long-lived gases are of primary concern with
respect to long-term warming.
Human Influences on Greenhouse Gases
Humans have increased the concentrations of CO2,
CH4, and N2O in the atmosphere since the beginning
of the industrial era (Fig. 11). More CO2 has been
released by humans into the atmosphere than any
other greenhouse gas. In the United States, the
average person releases about 17.3 metric tons of
CO2 per year, more than twice as much per person
as in China or European countries (Olivier et al.
2012). Carbon dioxide levels increased at a rate of
1.4 parts per million (ppm) per year from 190 to
2005 (IPCC 2007), and reached an average of 395
ppm in January 2013 (Tans and Keeling 2013). In
recent decades, fossil fuel burning has accounted for
an estimated 83 to 94 percent of the human-induced
increase in CO2. The remaining to 17 percent of
human-induced emissions comes primarily from
deforestation and degradation of land for conversion
to agriculture, which releases CO2 when forests burn
47
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
Figure 10.—An idealized model of the natural greenhouse effect. Source: IPCC (2007).
Figure 11.—Concentrations of greenhouse gases
showing increases in concentrations since 1750
attributable to human activities in the industrial era;
concentration units are parts per million (ppm) or parts
per billion (ppb), indicating the number of molecules of
the greenhouse gas per million or billion molecules of
air. Source: IPCC (2007).
48
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
or decompose (van der Werf et al. 2009). 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).
powerful greenhouse gases. Currently HFCs account
for about 1 percent of greenhouse gas emissions
(IPCC 2007).
Methane is responsible for approximately 14 percent
of greenhouse gas emissions (IPCC 2007). Methane
concentrations in the atmosphere have also increased
over the past century as a result of human activities,
such as raising livestock and growing rice. Livestock
production is responsible for 35 to 40 percent of
global CH4 emissions, primarily from fermentation
in the guts of cattle and other ruminants (Steinfeld
et al. 200). Rice production, the second largest
source of CH4 emissions, requires wet conditions
that are also ideal for microbial CH4 production.
Other human-caused sources of CH4 include biomass
burning, microbial emissions from landfills, fossil
fuel combustion, and leakage of natural gas during
mining and distribution.
Scientists use models, which are simplified
representations of reality, to simulate future climates.
Models can be theoretical, mathematical, conceptual,
or physical. General circulation models (GCMs),
which combine complex mathematical formulas
representing physical processes in the ocean,
atmosphere, and land surface within large computer
simulations, are important in climate science. They
are used in short-term weather forecasting as well as
long-term climate projections.
Nitrous oxide accounts for about 8 percent of global
greenhouse gas emissions (IPCC 2007). The primary
human source of N2O is agriculture. Increased
fertilizer use (both synthetic and animal-based)
increases emissions from soil as microbes break
down nitrogen-containing products. In addition,
converting tropical forests to agricultural lands
increases microbial N2O production. Another main
source of N2O is the combustion of fossil fuels.
Humans have reduced stratospheric ozone in the
atmosphere 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
CLimATe moDeLS
General Circulation Models
General circulation models simulate physical
processes (such as the exchange of energy and the
movement of matter) in the Earth’s surface, oceans,
and atmosphere through time using mathematical
equations in three-dimensional space. They work
in time steps as small as minutes or hours and in
simulations covering decades to centuries. Because
of their complexity, GCMs require the intensive
computing power of supercomputers.
Although GCMs use highly sophisticated computers,
limits on computing power mean that projections of
future climate 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. 12). Each cell within the grid is able to interact
with adjacent cells (making it “spatially dynamic”).
Although there is variation, grid cells are usually
between 2 and 3º latitude and longitude. For the
middle latitudes, this is about one-quarter of the
size of Missouri. Cells are stacked in interconnected
vertical layers that simulate ocean depth or
atmospheric thickness at increments usually ranging
from 5 to 3,280 feet.
49
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
Figure 12.—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. Source: NOAA (2008).
Several GCMs have been used in climate
projections for the IPCC reports and elsewhere
(see Box ). These models have been developed by
internationally renowned climate research centers
such as NOAA’s Geophysical Fluid Dynamics
Laboratory (GFDL CM2; Delworth et al. 200), the
United Kingdom’s Hadley Centre (HadCM3; Pope et
al. 2000), and the National Center for Atmospheric
50
Research (PCM; Washington et al. 2000). These
models use slightly different grid sizes and differ in
the way they represent 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 similar increases in greenhouse gas
concentrations.
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
Box 6: More Resources on Climate Models and Emissions Scenarios
U.S. Forest Service
Intergovernmental Panel on Climate Change
Climate Projections FAQ
www.treesearch.fs.fed.us/pubs/40614
Chapter 8: Climate Models and Their Evaluation
www.ipcc.ch/publications_and_data/ar4/wg1/en/
ch8.html
U.S. Global Change Research Program
Climate Models: an Assessment of Strengths and
Limitations
library.globalchange.gov/sap-3-1-climate-models-anassessment-of-strengths-and-limitations
Like all models, GCMs have strengths and
weaknesses (see Box 7). In general, they are
useful because they are based on well-understood
physical processes. Simulations of past climates by
GCMs generally correspond well with measured
and proxy-based reconstructions of past climate.
However, GCM projections are not perfect. Climate
scientists’ understanding of some climate processes
is incomplete, and some influential climate
processes occur at spatial scales that are too small
to be modeled given current computing power.
Technological advances in computing along with
scientific advances in our understanding of Earth’s
physical processes will allow future improvements
in GCM projections.
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 populations, economies, and
technological developments will certainly affect
future greenhouse gas concentrations, they cannot
be completely foreseen. One common approach for
dealing with uncertainty about future greenhouse gas
concentrations is to develop storylines about how
the future may unfold and calculate the potential
greenhouse gas concentrations for each storyline.
The IPCC’s set of standard emissions scenarios is a
Special Report on Emissions Scenarios:
Summary for Policymakers
http://www.ipcc.ch/ipccreports/sres/emission/index.
php?idp=0
widely accepted set of such storylines (IPCC 2007).
In GCMs, the use of different emissions scenarios
results in different climate projections.
Emissions scenarios quantify the effects of
alternative demographic, technological, or
environmental developments on atmospheric
greenhouse gas concentrations. None of the current
scenarios include any changes in national or
international policies directed specifically at climate
change such as the Kyoto Protocol. However,
some of the scenarios that include a reduction in
greenhouse gases through other means suggest what
we could expect if these policies were implemented.
Six different emissions scenarios are commonly used
in model projections for reports such as the IPCC
Fourth Assessment Report (Fig. 13).
The A1FI scenario is the most fossil-fuel intensive,
and thus projects the highest future greenhouse gas
concentrations; GCM simulations using the A1FI
scenario project the highest future warming. On the
other end of the spectrum, the B1 scenario represents
a future where alternative energies decrease
our reliance on fossil fuels and greenhouse gas
concentrations increase the least. GCM simulations
using the B1 scenario project the lowest increase in
global temperature. Although these scenarios were
designed to describe a range of future emissions
over the coming decades, it is important to note that
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Box 7: 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 in simulating 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 stand-alone
components of this vulnerability assessment because
there are a number of assumptions made about the
processes simulated by GCMs and impact models,
uncertainty in future greenhouse gas concentrations,
and limitations on the numbers of inputs that a
model can reliably handle. Precipitation projections
usually have much more variability among models
than do temperature projections. Regions with
complex topography contain much more diversity
in microclimates than many models can capture.
Many non-climate 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 interpreted by local
experts to identify regional caveats and limitations
of each model, and are 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 LINKAGES model projects species
establishment probability. The LANDIS PRO model
projects changes in basal area and abundance.
There are similarities between some inputs into
these models—downscaled climate models and
scenarios, simulation time 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 Central Hardwoods forest
ecosystems are discussed in more detail in Chapter 5.
Figure 13.—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.
Source: IPCC (2007).
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the future will conceivably be different from any
of the developed scenarios. It is highly improbable
that future greenhouse gas emissions will be less
than described by the B1 scenario even if national
or international policies were implemented
immediately. In fact, current emissions more closely
track the greenhouse gas emissions of the A1FI
scenario, and global emissions since the year 2000
have even exceeded the A1FI scenario values in
some years (Raupach et al. 2007).
Another approach, dynamical downscaling, uses a
regional climate model (RCM) embedded within
a GCM. Like GCMs, RCMs simulate physical
processes through mathematical representations on
a grid. However, RCMs operate on a finer resolution
than GCMs, typically ranging from 15.5 to 31.0
miles, but can be as fine as .2 miles or less. Thus,
they can simulate the effects of topography, land
cover, lakes, and regional circulation patterns that
operate on smaller scales.
Downscaling
As with statistical downscaling, dynamical
downscaling has pros and cons (Daniels et al. 2012).
It is advantageous for simulating the effects of
climate change on regional phenomena such as lakeeffect snow and extreme weather events. However,
like GCMs, RCMs require a lot of computational
power. Therefore, dynamically downscaled data
are usually available only for one or two GCMs
or emissions scenarios and for limited geographic
areas. Because dynamically downscaled data are
currently limited for the assessment area, we use
statistically downscaled data in this report.
As mentioned previously, GCMs simulate climate
conditions only for relatively large areas. To
examine the future climate of areas within the
Central Hardwoods Region, a smaller grid scale is
needed. One method of projecting climate on smaller
spatial scales is 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.
Resolution for downscaled climate projections is
typically around .2 miles.
Statistical downscaling has advantages and
disadvantages (Daniels et al. 2012). It is a relatively
simple and inexpensive way to produce smallerscale projections from GCMs. However, statistical
downscaling assumes that past relationships between
modeled and observed temperature and precipitation
will hold true under future change, which may
or may not be true. Statistical downscaling also
depends on local climatological data. If there are
no weather stations in the area of interest, it may be
difficult to obtain a good downscaled estimate of
future climate for that area. Finally, local influences
on climate that occur at finer scales (such as
land cover type, lake-effect snow, topography, or
particulate matter) also add to uncertainty when
climate projections are downscaled.
Downscaled GCMs Used in this Report
In this assessment, we report statistically
downscaled climate projections for two modelemissions scenario combinations: GFDL A1FI and
PCM B1 (unless otherwise noted). Both models
and both scenarios were included in the IPCC
Fourth Assessment Report (IPCC 2007). The latest
version of the NCA (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
several other models, which are represented as a
multi-model average in its reports. The NCA takes
a similar approach in using a multi-model average.
For this assessment, we instead selected two models
that simulated climate in the eastern United States
fairly accurately and that bracketed a range of
53
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
temperature and precipitation futures. This approach
gives readers a better understanding of the level of
agreement among models and provides a range of
alternative scenarios that can be used by managers
in planning and decisionmaking. Working with a
range of plausible futures helps managers avoid
placing false confidence in a single scenario given
uncertainty in projecting future climate.
The Geophysical Fluid Dynamics Laboratory’s
Climate Model (GFDL CM2; Delworth et al. 200)
is considered moderately sensitive to changes in
radiative forcing. In other words, any change in
greenhouse gas concentration included in the model
would lead to a change in temperature that is higher
than some models and lower than others. The
National Center for Atmospheric Research’s Parallel
Climate Model (PCM; Washington et al. 2000), by
contrast, is considered to have low sensitivity to
radiative forcing. 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 of greenhouse gas emissions over the
past decade. Therefore, the two model-scenario
combinations span a large range of possible futures,
with the GFDL A1FI model-scenario combination
leading to a high-end projection of possible future
temperature increases, and the PCM B1 projecting
a low end of the range. 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 it is possible that
actual emissions and temperature increases could be
lower or higher than these projections.
This assessment uses a statistically downscaled
data set for the continental United States (Hayhoe
2010a). Daily mean, maximum, and minimum
54
temperature and total daily precipitation were
downscaled to an approximately 7.5-mile resolution
grid across the United States. This data set uses a
sophisticated statistical approach (asynchronous
quantile regression) to downscale daily GCM output
and historical climate data (Stoner et al. 2012).
This approach is advantageous because GCM
and historical data do not need to be temporally
correlated, and it is much better at capturing extreme
temperatures and precipitation events than a linear
regression approach. This statistically downscaled
data set is different from that used in the NCA,
which uses a simpler “delta” approach (Kunkel
et al. 2013). This data set was chosen for several
reasons. First, the data set covered the entire United
States, and thus allowed a consistent data set to
be used in this and other regional vulnerability
assessments being conducted simultaneously.
Second, it included downscaled projections for the
A1FI emissions scenario, which is the scenario
that most closely matches current trends in global
greenhouse gas emissions (Raupach et al. 2007).
Third, the availability of data at daily time steps
was advantageous because it was needed for some
impact models used in this report and provides
the opportunity to examine questions related to
growing season length, heavy precipitation events,
and droughts. Fourth, the statistical technique used
is more accurate at reproducing extreme values at
daily time steps than simpler statistical downscaling
methods (Hayhoe 2010b). Finally, the resolution
was fine enough to be useful for informing land
management decisions.
To show projected changes in temperature and
precipitation in Chapter 4, we calculated the average
daily mean, maximum, and minimum temperature
for each season and the entire year for three 30-year
time periods (2010 to 2039, 2040 to 209, 2070
to 2099). Mean cumulative precipitation was also
calculated for each season and annually for the
same time periods. We then subtracted these values
from the corresponding 1971 to 2000 averages
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
to determine the departure from current climate
conditions. Historical climate data used for the
departure analysis were taken from ClimateWizard
based on the PRISM data set (see Chapter 3 and
Appendix 7).
This data set was also used in the forest impact
models described below. Some of these models
require monthly precipitation and temperature values
as inputs, and thus daily data were summed or
averaged for each month when necessary. They also
operate on grid scales that may be larger or smaller
than the grid scale of the downscaled data set, and
grid scales of the downscaled data were adjusted
accordingly.
imPACT moDeLS
Downscaled climate projections from GCMs provide
important information about future climate, but they
tell us nothing about how climate change might
affect soil moisture, hydrology, forest composition,
or productivity. Other models, commonly called
impact models, are needed to project impacts on
physical and biological processes (Fig. 14). Impact
1. emission
scenarios
developed
and used as
inputs
B1 or A1FI
models use downscaled GCM projections as inputs,
as well as information such as soil types, landform,
tree species distribution, and life history traits.
Hydrology plays a key role in forest ecosystem
functioning and processing. The ways in which
hydrology drives individual trees and ecosystems
depend on precipitation, soil moisture, soil waterholding capacity, and rate of evapotranspiration.
Precipitation itself may vary in physical form,
amount, timing, and regularity. To project future
change in hydrologic cycling, one model that is
commonly used is the Variable Infiltration Capacity
model (VIC) (Liang et al. 1994). This large-scale
hydrologic process model is similar to many
land surface models that are commonly coupled
to GCMs. The land surface is modeled on a
0.-mile or greater grid scale based on drivers such
as precipitation, air temperature, and wind speed
and using daily time steps. Flow of water and
energy between the land and atmosphere are also
simulated at daily time steps. Model outputs can
include evapotranspiration, frozen soil formation,
snow, runoff, and hydrologic dynamics in lakes and
wetlands. Each grid cell is simulated independently
2. Model
projections
are run by
using GCms
3. GCM
projections
are
downscaled
to a smaller
grid scale
4.Downscaled
GCM data and
other
information
are used as
inputs into
impact
models
PCM or GFDL
~3 o 1/8 o
long./lat
LANDIS PRO,
LINKAGES,
Tree Atlas
Figure 14.—Steps in the development of climate impact models using projections from general circulation models (GCMs) and
specific steps taken in this assessment.
55
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
without horizontal water flow, and stream flow is
simulated using a separate model. More information
about this model can be found on the model Web
site: www.hydro.washington.edu/Lettenmaier/.
In Chapters 3 and 4, we discuss the results of several
published studies that used the VIC model to project
changes in hydrology. Future climate models and
scenarios used as inputs into the VIC model are
slightly different from those used for much of the
rest of the assessment. Two models (GFDL and
Hadley CM3) for each of three future emissions
scenarios (A2, A1B, and B1) were used to address
questions about past and future changes in climate
across the Midwest related to streamflow and runoff
(Cherkauer and Sinha 2010), drought (Mishra
et al. 2010), and soil frost (Sinha and Cherkauer
2010, Sinha et al. 2010). The highest emissions
scenario analyzed (A2) does not project as high of
greenhouse gas emissions as the A1FI scenario, and
A1B is a mid-range scenario. Therefore, projections
presented for the highest emissions scenarios from
these studies indicate more modest temperature
increases than the GFDL A1FI scenario. Hadley and
GFDL are also more sensitive to changes in radiative
forcing than PCM, so the low-emissions scenario
used in the VIC model (GFDL B1) simulates slightly
greater warming than PCM B1.
Models for Assessing Forest Change
Forest impact models generally fall in one of two
main categories: species distribution models and
process models. This assessment uses one SDM,
the Climate Change Tree Atlas (Prasad et al. 2007ongoing), and two process models, LANDIS PRO
(Wang et al. 2013, in press) and LINKAGES
(v2.2; Wullschleger et al. 2003). For an overview
of differences between these three models, see
Table 11 in Chapter 5. These models operate at
different spatial scales and provide different kinds of
information about potential future forest composition
and productivity. They provide useful information on
potential climate change impacts on ecosystems in
5
our geographic area of interest, and have stood up to
rigorous scientific review.
Species distribution models establish a statistical
relationship between the current distribution of
a species or ecosystem and key attributes of its
habitat. This relationship is used to make projections
about how the range of the species will shift as
climate change affects those attributes. Much less
computationally expensive than process models,
SDMs 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
fundamental niche of a species, in contrast, is the
habitat it could potentially occupy in the absence
of competition, disease, or herbivory. Given that
a species’ fundamental niche may be greater
than its realized niche, SDMs may underestimate
current niche size and future suitable habitat. In
addition, species distributions in the future might be
constrained by competition, disease, and predation in
ways that do not currently occur. If so, SDMs could
overestimate the amount of suitable habitat in the
future. If some constraints are removed due to future
change, the opposite could also occur. Furthermore,
fragmentation or other physical barriers to migration
may create obstacles for species otherwise poised to
occupy new habitat.
In contrast to SDMs, process models simulate
ecosystem 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 or temporal dynamics, or both, of a variety
of complex processes and at a finer scale, they
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
typically require more computational power than
an SDM. Therefore, fewer species 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. For example, they assume
that mathematical representations of a species’ life
history traits are accurate, whereas in many cases
they may be based on rather limited data. They
also assume that all individuals of a species can be
modeled using the same parameters, yet there is
often a wide range of variability among genotypes.
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
inaccurate result.
Although useful for projecting future changes, both
process models and SDMs share some important
limitations. They assume that species will not
adapt evolutionarily to changes in climate. This
assumption may be true for species with long
generation times (such as trees), but some shortlived species may be able to adapt even while
climate is rapidly changing. Both types of models
may also magnify the uncertainty inherent in their
input data. Data on the current distribution of
trees, site characteristics, and downscaled GCM
projections are estimates that add to uncertainty.
No single model can include all possible variables,
so there are important inputs that may be excluded
from individual models, such as competition from
understory vegetation, herbivory, and pest outbreaks.
Given these limitations, it is important for all model
results to pass through a filter of local expertise to
ensure that results match with reality on the ground.
Chapter explains the expert elicitation process for
determining the vulnerability of forests based on
local expertise and model synthesis.
Western Star Flatwoods, Mark Twain National Forest. Photo by Paul Nelson, Mark Twain National Forest.
57
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
Climate Change Tree Atlas
LAnDiS PRo
The Climate Change Tree Atlas incorporates a
diverse set of information about potential shifts
in the distribution of tree species’ habitat in the
eastern United States over the next century
(www.nrs.fs.fed.us/atlas; Iverson et al. 2008, Prasad
et al. 2007-ongoing). The species distribution
model DISTRIB measures relative abundance,
referred to as importance values, for 134 eastern
tree species. Inputs are tree species distribution
data from the U.S. Forest Service, Forest Inventory
and Analysis (FIA) program and environmental
variables (pertaining to climate, soil properties,
elevation, land use, and fragmentation), which are
used to statistically model current species abundance
with respect to current habitat distributions. Then
DISTRIB projects future importance values and
suitable habitat for individual tree species by using
projections of future climate conditions on a 12-mile
grid (Prasad et al. 2007-ongoing).
LANDIS PRO (Fraser et al. 2013; Wang et al. 2013,
in press) is a spatially dynamic process model that
simulates tree dispersal, establishment, and growth,
along with disturbances and management. It is
derived from the LANDIS model (Mladenoff 2004),
but has been modified extensively from its original
version. The LANDIS PRO model can simulate very
large landscapes (millions of acres) at relatively
fine spatial and temporal resolutions (typically 200
to 300 feet and 1- to 10-year time steps). One new
feature of the model compared to previous versions
is that inputs and outputs of tree species data in
LANDIS PRO include tree density and volume
and are compatible with FIA data. Thus, the model
can be directly initialized, calibrated, and validated
by using FIA data. This compatibility ensures the
starting simulation conditions reflect conditions on
the ground and allows the modelers to quantify the
uncertainties embedded in the initial data.
Additionally, projected future distributions for each
tree species are further evaluated for factors not
accounted for in the statistical models (Matthews
et al. 2011b). These modifying factors (Appendix 9)
are supplementary information on life history
characteristics such as dispersal ability or fire
tolerance as well as information on current 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.
The LANDIS PRO model can simulate landscapelevel processes such as fire, wind, insect outbreaks,
disease spread, nonnative species invasions,
forest harvesting, fuel treatments, and silvicultural
treatments. Basic inputs to LANDIS PRO are
maps of species composition, land types, stands,
management areas, and disturbance patterns. Species
characteristics such as longevity, maturity, shade
tolerance, average seed production per mature
tree, and maximum diameter at breast height are
given as inputs into the model. Basic outputs are
the number of trees, basal area, biomass, age, and
carbon, by species or by species age cohort as well
as disturbance and harvest history across space and
time. The spatially dynamic nature of the model and
its fine spatial resolution are unique advantages of
LANDIS PRO compared to LINKAGES (described
below) and statistically based models. Disadvantages
of LANDIS PRO are that it is too computationally
For this assessment, DISTRIB uses the GFDL A1FI
and PCM B1 model-scenario combinations. The
results provided in Chapter 5 differ from the online
Climate Change Tree Atlas because they are specific
to the assessment area and use the new statistically
downscaled data set described above.
58
ChAPTeR 2: CLimATe ChAnGe SCienCe AnD moDeLinG
intensive to be run for a large number of species
(in contrast to Tree Atlas) and does not account for
ecosystem processes such as nitrogen cycling or
decomposition (in contrast to LINKAGES).
the model is run for a specified number of plots
in an area of interest, and results are averaged to
determine relative species biomass and composition
across the landscape over time.
For this assessment, LANDIS PRO simulates
changes in basal area and trees per acre at a
295-foot resolution over the next century for six
dominant tree species and species groups across
the Missouri Ozarks part of the assessment area.
The model projects changes in forest composition
using downscaled daily mean temperature and
precipitation from GFDL A1FI and PCM B1,
and compares these projections with those under
a current climate scenario.
For this assessment, LINKAGES simulates changes
in tree species establishment probability over the
next century for seven dominant tree species and
species groups for landforms and subsections across
the Missouri Ozarks portion of the assessment area.
The model projects changes in forest composition
by using downscaled daily mean temperature and
precipitation from GFDL A1FI and PCM B1, and
compares these projections with those under a
current climate scenario. Species establishment
probabilities from LINKAGES under each climate
scenario are used as inputs into LANDIS PRO.
LinKAGeS
LINKAGES (v2.2; Wullschleger et al. 2003)
is a forest succession and ecosystem dynamics
process model modified from an earlier version
of LINKAGES (Pastor and Post 1985). The
LINKAGES model integrates establishment
and growth of individual trees with ecosystem
functions such as soil-water balance, litter
decomposition, nitrogen cycling, soil hydrology,
and evapotranspiration. Inputs to the model include
daily temperature, precipitation, wind speed, and
solar radiation. Model inputs also include soil
moisture capacity for multiple soil layers, wilting
point, percentage of rock, percentage of clay,
percentage of sand, initial organic matter, and
nitrogen contents. Outputs from the model include
tree species composition, number of stems, biomass,
leaf litter, available nitrogen, humus, and organic
matter, as well as hydrologic dynamics such as
runoff. Simulations are done at yearly time steps on
multiple 0.2-acre circular plots, which correspond to
the average gap size when a tree dies and falls over.
Unlike LANDIS PRO, LINKAGES is not spatially
dynamic, and does not simulate tree dispersal or any
other spatial interaction among grid cells. Typically,
ChAPTeR 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 and incorporated into other types of
models that project changes in forest composition
and ecosystem processes to inform local decisions.
Although there are inherent uncertainties in what the
future holds, 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 forest ecosystems.
59
ChAPTeR 3: PAST CLimATe ChAnGeS
AnD CuRRenT TRenDS
Climate is the average weather conditions for
a region over a period of decades. Year-toyear variation in local weather patterns can be
influenced by ocean circulation patterns such as
the El Niño Southern Oscillation and the Pacific
Decadal Oscillation. Changes in particles in the
atmosphere from volcanic eruptions or slight
variations in solar activity can also lead to hotter
or cooler conditions from the long-term average.
Over longer time periods (thousands to millions
of years), climate has changed considerably on a
global scale, ranging from ice ages to warm periods,
all of which are influenced by many factors. This
chapter summarizes our current understanding of
past changes in climate in the Central Hardwoods
Region, with a focus on the last century.
hoLoCene PALeoCLimATe
To understand climate prior to the historical record,
scientists rely on proxies such as ice cores, lake
sediments, tree cores, changes in isotopic ratios, and
fossil pollen. Although proxy data specific to the
Central Hardwoods Region are limited, the available
data indicate that the area has experienced large
shifts in climate over the past 12,000 years that
have led to subsequent shifts in vegetation (see
Chapter 1). Early Holocene (12,000 to 9,000 years
ago) climate appears to have been moderately
cool and moist enough to support oak savannas
in the region (Denniston et al. 2000). Between
approximately 9,000 and 5,000 years ago, the
climate became considerably warmer and drier,
supporting steppe vegetation dominated by warmseason short grasses (Denniston et al. 1999). Some
evidence suggests that extended arid periods
0
occurred in the region between 3,500 and
2,500 years ago and again between 1,200 and
900 years ago, but these dry periods did not include
a corresponding shift in temperature (Denniston et
al. 2007).
Proxy data indicate that long, severe droughts have
occurred in the region over the past 2,000 years,
some of which were longer or more severe than
the “Dust Bowl” era of the 1930s (Woodhouse
and Overpeck 1998). Tree-ring data from Missouri
and Iowa show that several multi-decadal drought
periods have occurred in the region over the past
millennium (Stambaugh et al. 2011). The Stambaugh
et al. (2011) study suggests that the longest drought
occurred over a 1-year period at the end of the 12th
century, corresponding to the middle of the Medieval
Warm Period. Long-term reconstructions of climate
by using tree rings also reveal a 20-year drought
cycle (in other words, peak droughts occurred
about every 20 years) in the region over the past
millennium, although the causes for this pattern
are still unknown (Stambaugh et al. 2011).
hiSToRiCAL CLimATe
Measurements of temperature and precipitation at
weather stations in the area have been recorded for
a little over 100 years. We used the ClimateWizard
custom analysis tool to present the changes in
temperature and precipitation across the assessment
area (ClimateWizard 2012, Girvetz et al. 2009).
Data for the tool are derived from PRISM
(Parameter-elevation Regressions on Independent
Slopes Model; Gibson et al. 2002), which models
historical, measured point data onto a continuous
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
2.5-mile grid over the entire United States. We
examined long-term (1901 through 2011) trends
for annual, seasonal, and monthly temperature
(mean, mean minimum, and mean maximum)
and total precipitation within the assessment area.
Accompanying tables and figures present the
change over the 111-year period estimated from the
slope of the linear trend. In the following text, we
highlight increasing or decreasing trends for which
we have high confidence that they did not occur by
chance. For more precise information regarding how
these trends were calculated, levels of confidence,
and caveats related to the data presented, refer
to Appendix 7. Please note that the information
presented here is meant to give the reader a general
overview of regional trends in climate and is not
intended for interpretation at a particular location.
More information on historical trends in past climate
for specific weather stations can be found online
(see Box 8).
Current Climate
The current climate in the Central Hardwoods
Region can be characterized by examining 30-year
averages in temperature and precipitation (also
called “normals”), which are computed every
10 years at the beginning of each decade. Annual
temperature and precipitation patterns for the 1971
through 2000 period (which is used as a baseline to
compare to future projected climates in Chapter 4)
are similar in the Illinois, Indiana, and Missouri
portions of the assessment area (Table 10, Fig. 15).
Mean annual temperature follows a north-south and
east-west gradient (Fig. 1). Temperatures tend to be
lower in the north and east than the south and west.
Temperatures are highest in Missouri throughout
the year, and mean temperatures fluctuate by about
40 °F (22 °C) between winter and summer.
Box 8: More Historical Climate Information
State-level Information
Regional Information
State climatologists provide information about
current and historical trends in climate throughout
their states. Visit your state climatologist’s Web
site for more information about trends and climate
patterns in your particular state:
The Midwestern Regional Climate Center (MRCC) is a
cooperative program between the National Climatic
Data Center (below) and the Illinois State Water
Survey. The MRCC serves the nine-state Midwest
region (Illinois, Indiana, Iowa, Kentucky, Michigan,
Minnesota, Missouri, Ohio, and Wisconsin).
It provides high-quality climate data, derived
information, and data summaries for the Midwest.
State Climatologist Office for Illinois
www.isws.illinois.edu/atmos/statecli/index.htm
Indiana State Climate Office
http://climate.agry.purdue.edu/climate/narrative.asp
Missouri Climate Center
www.climate.missouri.edu/climate.php
mrcc.isws.illinois.edu/
National Information
The National Climatic Data Center (NCDC) is the
world’s largest active archive of weather data.
The NCDC’s Climate Data Online provides free,
downloadable data from the Global Historical
Climatology Network.
www.ncdc.noaa.gov/oa/ncdc.html
1
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Table 10.—Average mean temperature and total precipitation (1971 through 2000) for the assessment area, by state.
Missouri Ozarks
Temperature
Precipitation
(mean, °F)
(inches)
Annual
Winter
Spring
Summer
Autumn
55.6
34.2
55.4
75.6
57
Total Precipitation (in)
6
43.92
8
12.91
11.25
11.76
Southern Illinois
Temperature
Precipitation
(mean, °F)
(inches)
55
32.7
54.8
75.5
56.7
IL
42.9
8.6
12.72
11.29
10.26
IN
Southern Indiana
Temperature
Precipitation
(mean, °F)
(inches)
54.2
32.6
53.7
74.2
56
44.94
9.24
13.32
12.22
10.13
MO
5
4
3
2
1
0
90
(°F)
mean Temperature (Σf)
80
70
60
50
40
30
20
10
0
Figure 15.—Average (1971 through 2000) total precipitation and mean temperature, by month, for the assessment area divided by
state boundaries.
2
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Figure 16.—Thirty-year averages of mean annual and seasonal daily mean, daily minimum, and daily maximum temperature.
3
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Precipitation is distributed relatively evenly
throughout the year, but spring is the wettest season
and winter the driest (Table 10, Fig. 15). During the
winter, there is a strong precipitation gradient, where
areas in the north experience lower precipitation
than in the south (Fig. 17). Precipitation tends to be
higher during the spring and summer in southern
Indiana than in the rest of the assessment area. In
the fall, this pattern is reversed, with the Missouri
Ozarks experiencing the greatest precipitation.
Figure 17.—Thirty-year averages of mean annual and seasonal precipitation.
4
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Observed Trends in Precipitation
and Temperature (1901 through 2011)
Spatially interpolated trends in temperature and
precipitation are available through 2011 and are
presented below. For a discussion of recent trends in
temperature and precipitation over the past decade,
see Box 9. Between 1901 and 2011, mean annual
temperatures fluctuated from year to year by several
degrees across the assessment area (Fig. 18). The
warmest year on record for the assessment area as
a whole was 1921. Temperatures were warmer than
the long-term average during the “Dust Bowl” era
of the 1930s. That period had many of the warmest
and driest years on record, and summers were
particularly hot and dry. By contrast, temperatures
were cooler during the 1970s and early 1980s.
Box 9: Early 21st-Century Climate Changes
In this chapter, we present changes in climate
over the entire historical record for which spatially
interpolated data trends are available for the
assessment area. Looking across the entire record
is helpful in detecting long-term changes, but it can
also obscure short-term trends.
The decade from 2001 to 2010 was the warmest
on record both globally and averaged across North
America (World Meteorological Organization [WMO]
2012). Across the assessment area, temperatures
were also generally warmer than average between
2000 and 2012. However, with the exception of
2012, temperatures were not as warm as the 1930s
or 1950s. The year 2012 was the warmest year
on record for Missouri and Illinois and one of the
warmest in Indiana (NOAA NCDC 2013). Until 2012,
1921 had been the warmest year on record for most
of the region, with the exception of the western
Missouri Ozarks, which had its highest temperature
in 1954.
Trends in precipitation from 2000 to 2011 across
the assessment area indicate a continuing pattern
toward wetter conditions. Several locations in
southern Illinois, southern Indiana, and southeastern
Missouri had their wettest years on record in 2011
(NOAA 2012), and 2011 was in the top five wettest
years across most of the assessment area excepting
the Missouri Ozarks (Southern Climate Impacts
Planning Partnership [SCIPP] 2012). The year 2012
was an exception to the trend of wetter conditions,
with the area experiencing drought conditions that
had not been experienced in the region for many
decades (NOAA NCDC 2013).
And what about the “warming hole” patterns of
low summer temperatures and high spring and
summer precipitation? Across the assessment
area, summer temperatures during 2010, 2011,
and 2012 were much higher than the long-term
average for the area (SCIPP 2012). Although it is
too early to determine whether this is a trend,
those years were the warmest summers most of
the region had experienced since 1954 (with the
exception of southwestern Missouri, which had its
previous warmest summer in 1980). Although the
recent warming temperatures suggest a possible
reversal of the “warming hole,” precipitation
trends have not changed markedly in recent years.
Spring and summer precipitation continued to
increase across southern Indiana, with 2008 and
2011 among the wettest years on record. In Illinois,
spring precipitation also continued to be high, and
summer precipitation was about average. Spring
precipitation in Missouri was also high during the
early 21st century, and summer precipitation showed
a slight decrease. The 2012 drought was an obvious
exception to this overall trend.
Overall, the climate information from the past
decade seems to be consistent with the trends over
the past century in some ways but not others. The
area is still getting generally wetter, and the 1930s
continues to be the warmest decade on record. The
past decade was warmer than the late 20th century,
but there is currently insufficient information to tell
whether the higher temperatures represent a trend
toward increasing temperature in the region.
5
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Change rate = 0 °F/yr; p-value = 0.90828; r-squared = NA.
Annual Mean Temperature (°F)
58
57
56
55
54
53
1900
1920
1940
1960
1980
2000
year
Change rate = 0.052 inches/yr; p-value = 0.005; r-squared = NA.
Annual Mean Precipitation (inches)
55
50
45
40
35
30
1900
1920
1940
1960
1980
2000
year
Figure 18.—Time series showing annual mean temperature and total precipitation across the assessment area, 1901 through 2011.
Open circles represent mean for each year. The blue line shows the 5-year moving average, and the red line is the slope of the linear
regression. Note high temperatures and low precipitation values between 1930 and 1940 (ClimateWizard 2012).
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Temperature
Even though temperatures increased both globally
and across the United States over the same time
period, the mean annual temperature in the Central
Hardwoods Region actually decreased slightly in
some areas; the change was small enough, however,
that it could have occurred by chance (Fig. 19). We
also evaluated trends beginning in the years 1951
and 1971, but did not find dramatic changes in the
direction of these trends (data not shown). Although
mean seasonal temperatures did not change overall,
there were a few trends when changes by month
were examined (Fig. 20). January temperatures
appear to have decreased and February temperatures
to have increased. However, the year-to-year and
spatial variation during these months was high,
and these trends could be due to chance. Mean
temperatures increased in April, particularly in
Illinois and Indiana, and decreased in September
and October slightly, especially in Illinois.
Figure 19.—Change in annual and seasonal mean daily mean, daily minimum, and daily maximum temperature, 1901 through 2011.
Stippling indicates there is less than 10-percent probability that the trend could have occurred by chance alone.
7
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
(°F)
Change in mean (Σf)
3
**
2
Illinois
Indiana
Missouri
*
1
0
-1
*
-2
*
-3
-4
(°F)
Change in mean minimum (Σf)
month
4
*
3
*
**
2
1
**
*
*
**
*
0
-1
-2
-3
(°F)
Change in mean maximum (Σf)
month
4
3
*
2
*
1
0
-1
**
-2
-3
-4
**
**
**
*
*
month
Figure 20.—Change in mean daily mean, daily minimum, and daily maximum monthly temperature for the assessment area by state,
1901 through 2011. Asterisks indicate there is less than 10-percent probability that the trend could have occurred by chance alone.
8
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Compared to mean temperatures, there were more
noticeable trends in average seasonal maximum
and minimum temperatures across the region: mean
maximum temperatures generally decreased and
mean minimum temperatures generally increased
across all seasons, leading to less daily variation
in temperature (Figs. 19 and 20). This pattern was
especially apparent in summer and fall. In the
Illinois portion of the assessment area, mean summer
maximum temperatures decreased by 1.9 °F
(1.1 °C) on average, and summer minimum
temperatures increased by 2.0 °F (1.1 °C). In
addition, mean autumn maximum temperatures
decreased by 1.9 °F (1.1 °C) in southern Illinois.
In southern Indiana, mean summer maximum
temperatures decreased by 2.1 °F (1.2 °C), and
summer minimum temperatures increased by 1.1 °F
(0. °C). In the Missouri Ozarks, autumn maximum
temperatures decreased by 1.8 °F (1.0 °C) and
summer lows increased by 1. °F (0.8 °C).
Precipitation
Precipitation trends over the past century differed
across the assessment area, but there was a general
increasing trend in annual precipitation (Figs. 21
and 22). In southern Illinois, annual precipitation
increased by 5.7 inches (14-percent increase from
the long-term average) over the 111-year period.
This change was mainly driven by increases in the
southeast during spring (March, April, May). Mean
annual precipitation increased in southern Indiana
by 7.0 inches (an increase of 1 percent), and
increases occurred during the entire growing season.
In Missouri, precipitation increased in the fall by an
average of 2.7 inches (25 percent), contributing to
an increase in annual precipitation of 5.3 inches
(12.5 percent). There appears to have been a
decrease in precipitation in that area in the summer,
but there is relatively low statistical confidence in
that trend. The north-central Missouri Ozarks also
had increases in winter and spring precipitation of
up to 9.0 inches over the 111-year period.
2.5
Change in Precipitation (in)
Illinois
2
1.5
*
*
*
Indiana
Missouri
*
**
*
*
*
1
0.5
0
-0.5
*
-1
month
Figure 21.—Change in monthly precipitation, 1901 through 2011. Asterisks indicate there is less than 10-percent probability that the
trend could have occurred by chance alone.
9
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Figure 22.—Change in annual and seasonal precipitation, 1901 through 2011. Stippling indicates there is less than 10-percent
probability that this trend could have occurred by chance alone.
70
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
The “Warming hole”
Several studies have observed a decrease in
temperatures, especially summer highs, in the
southeastern and central United States over the
past century, in what has been referred to as a
“warming hole” (Kunkel et al. 200, Pan et al.
2004, Portmann et al. 2009). These decreases in
summer high temperature appear to be related to
increases in precipitation (Pan et al. 2004, Portmann
et al. 2009). A recent study suggests the higher
precipitation and lower temperature may be due to
an increase in aerosols (particulate matter in the air),
which increase cloud formation and light scattering
(Leibensperger et al. 2012). Others suggest it may
be due to feedbacks from increased soil moisture
availability (Pan et al. 2004). Still other studies
suggest that the local temperature decrease may
be driven by sea-surface temperatures in the North
Atlantic and central Pacific (Kunkel et al. 200).
Further research is needed to understand the
“warming hole” and its implications for the region
as global temperatures continue to rise. If the
decreasing temperature trends were indeed caused
by increased aerosols, it is possible that these trends
will be reversed because of current regulations and
improvements in air quality (Leibensperger et al.
2012). In fact, an analysis of recent climate trends
in the region suggests that the warming hole may
have already disappeared, but that study examined
trends in only mean annual temperature and not
summer highs (Tebaldi et al. 2012). However, if the
temperature trends were instead due to other climatic
processes, it is possible that these trends could
continue into the future (see Chapter 4).
TRenDS in exTReme WeATheR
EVENTS
Extreme weather events, such as tornadoes,
thunderstorms, and winter storms are important
disturbance agents in forested systems. Some
evidence suggests that extreme events have been
increasing across the United States and globally
over recent decades, and this increase is consistent
with global climate change (Coumou and Rahmstorf
2012, Kunkel et al. 2008). Below, we summarize
changes in extreme events that have been observed
in the Central Hardwoods Region.
Tornadoes and Wind Storms
Tornadoes are a common phenomenon in the
Central Hardwoods Region. The central United
States has the highest frequency of tornadoes in the
world (Bates 192). Among the 50 states, Missouri,
Illinois, and Indiana are ranked 9th, 8th, and 21st,
respectively, for the annual number of tornadoes that
occurred from 1981 through 2010 (National Weather
Service, Storm Prediction Center 2012). Peak
tornado season in the Central Hardwoods Region
is from March through June, when interactions
between warm, moist air and the jet stream make
conditions favorable (Wilson and Changnon 1971).
The largest tornado on record in the United States
occurred in the assessment area in 1925, crossing
all three states at 70 miles per hour and killing
25 people. Although the total number of tornadoes
detected in the region increased over the 20th
century, this increase was probably due to greater
detection of low-severity tornadoes (Kunkel et al.
2008) (see Box 10).
On May 8, 2009 the majority of the assessment
area was struck by a new class of storm named a
“super derecho” by the National Weather Service.
Derechos are widespread, long-lived wind storms
that are associated with a band of rapidly moving
showers or thunderstorms. Although a derecho can
produce destruction similar to that of tornadoes, the
damage typically is directed in one direction along
a relatively straight swath. Because of its unusual
shape on radar, displaying an eye-like center, and
extremely high winds gusting beyond 100 miles per
hour, the storm was called an “inland hurricane.”
Tens of thousands of trees were uprooted, snapped
off, or knocked down across the affected area by
71
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Box 10: Tornadoes and Climate Change
The recent devastating tornado that struck Joplin,
Missouri, on May 22, 2011, spurred questions about
the link between climate change and the frequency
and severity of tornadoes in the Midwest (Fig. 23).
This tornado was one of the deadliest and most
economically distressing tornadoes in U.S. history,
costing 116 lives and $2.6 billion in damages (NOAA
2012). It occurred following a week-long tornado
outbreak sequence that caused severe damage
across the central United States, leading to recordbreaking losses to property and crops (NOAA 2012).
Were the Joplin tornado and the other tornadoes
in the sequence a sign of a changing climate? The
answer is not a simple yes or no.
At first glance, the historical record seems to indicate
an increase in the total number of tornadoes in the
United States over the past century (Diffenbaugh
et al. 2008). However, this trend is largely the
result of an increase in the detection of tornadoes
through technological enhancements and improved
monitoring networks (Kunkel et al. 2008). It also
appears that the number of severe tornadoes in the
United States has decreased over the past century
(Diffenbaugh et al. 2008). However, the severity of
a tornado is determined not by its wind speed but
by the level of damage done to structures. Since
building construction has also changed over the
past century, it is difficult to tell whether we are
observing weaker storms or simply less damage
from changes in construction practices.
Some recent analysis suggests that the number
of tornadoes has probably not changed over the
past century, but there has been a trend toward
tornadoes occurring in clustered events such as the
May 2011 outbreak sequence (H. Brooks, National
Weather Center, National Severe Storms Laboratory,
personal comm.). This leads to further speculation
about a possible link between tornadoes and a
changing climate.
Modelers are also uncertain about what the
future trends in tornadoes will be (see Chapter 4).
Tornadoes are a result of both convective available
potential energy and wind shear. In general, current
global climate models suggest that convective
available potential energy may increase, while wind
shear may decrease (Diffenbaugh et al. 2008). The
balance of these two forces, as well as potential
seasonal and geographic shifts in that balance,
remains relatively unknown. In addition, the small
spatial scale of tornadoes makes them impossible
to simulate at large grid scales in general circulation
models. However, some evidence suggests that there
may be a shift toward fewer summer tornadoes and
more winter tornadoes as temperatures increase
(H. Brooks, National Weather Center, National
Severe Storms Laboratory, personal comm.).
Figure 23.—Joplin, MO, spring 2011. (Photo by Jill Johnson,
U.S. Forest Service)
72
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
the intense, straight-line winds. Because this storm
is an isolated event, it is impossible to attribute it to
local changes in climate. However, current model
projections suggest that the convective conditions
necessary to create these types of storms may
become more frequent (see Chapter 4).
Thunderstorms and
Heavy Precipitation Events
Thunderstorms are frequent during summer months
throughout the assessment area. Thunderstorms
account for 50 to 0 percent of annual
precipitation in Illinois, and are most prevalent
in the southwestern corner of the state (Angel
2012a). Since recordkeeping began in the 1800s,
thunderstorms have occurred an average of 40 to
55 days per year across the assessment area
(Changnon 2003). The highest incidence has
occurred in western Missouri, representing a
regional maximum in storm frequency (Changnon
2003). About half of these storms occur during the
summer (June, July, August), with the remainder
distributed across spring and fall (Changnon 2003).
There is no evidence of a change in the severity
or frequency of thunderstorms across the United
States over the past 100 years (Kunkel et al.
2008). Thunderstorms are reported as days when
thunder audibly occurs and, therefore, there is a
propensity toward human error and inconsistency in
recordkeeping for these measurements (Changnon
2003).
However, studies suggest that heavy precipitation
has become more frequent and intense in the United
States over the past several decades (Groisman
et al. 2012, Kunkel et al. 2008). Across the entire
central United States (including the assessment
area), moderately heavy precipitation events (0.5
to 1.0 inches) became less frequent, but very heavy
precipitation events (greater than 3 inches) increased
between 1979 and 2009 compared to the 1948 to
1978 period (Groisman et al. 2012). In addition, the
number of extreme precipitation events (greater than
inches) has increased up to 40 percent (Groisman
et al. 2012). A recent report examined trends in
heavy precipitation events in the Midwest from 191
to 2011 (Saunders et al. 2012). The authors found
that the number of precipitation events of 3 inches
or more nearly doubled in Illinois and Missouri, and
increases were even greater in Indiana (Saunders
et al. 2012).
Winter Storms
The assessment area in Illinois, Indiana, and
Missouri can experience both ice storms and
snowstorms, although the incidence is relatively
rare. Snowstorms occur about once per year on
average in the area, and decreased over the last
century in Missouri and southern Illinois (Changnon
200). The frequency of snowstorms was similar at
the beginning and end of the last century across most
of southern Indiana (Changnon 200). In a study
examining winter storms from 1949 to 2003, there
appeared to be neither a negative nor positive trend
in the number of winter storms in the central United
States (including the assessment area). However,
there was a trend toward an increasing amount of
damage from those storms due to both an increase
in infrastructure and an increase in storm intensity,
which was interpreted as a trend consistent with
increased warming (Changnon 2007).
Although rare, ice storms can be particularly
damaging to forests in the region, leading to stem
and branch breakage and crown loss (Brommit et al.
2004, Rebertus et al. 1997). Ice storms are a severe
form of freezing-rain event. The Central Hardwoods
Region has on average 3 to 4 days of freezing-rain
events per year, which can occur between November
and April, with a peak in January (Changnon
and Karl 2003). A study examining changes in
freezing rain over the United States from 1949 to
1999 showed no positive or negative trend in the
number of freezing-rain events for much of the
Central Hardwoods Region, with the exception
of far southeastern Indiana, which had a decrease
(Changnon and Bigley 2005).
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ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
ChAnGeS in SoiLS
AnD hyDRoLoGy
Increases in global temperature are resulting in an
intensification of the global water cycle, leading to
changes in soil moisture, groundwater availability,
and streamflow (Huntington 200). These variables
can have important influences on terrestrial and
aquatic ecosystems.
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. Drought can be characterized in
several ways, notably as meteorological, hydrologic,
or agricultural drought. Meteorological drought
is a function of precipitation frequency, and
hydrologic drought is a measure of how much water
is available in a watershed. Agricultural drought
takes into account changes in the amount of water
that evaporates from the soil and is transpired by
plants, as well as information about soil moisture
and groundwater supply. All three indicators can
be important in understanding the effects on forest
water supply. However, examining agricultural
drought can give a more holistic picture of the
effects on vegetation in the soil.
Over the past century (191 to 2007), the
frequency of extreme and exceptional droughts
(meteorological, hydrologic, and agricultural) in
Illinois and Indiana decreased (Mishra et al. 2010).
(Data were not analyzed for Missouri.) Exceptional
droughts are the most severe form of drought
experienced in the region, and extreme droughts are
the second most severe. Until the recent drought of
2012, all of the exceptional droughts were before
1970, and most of them occurred during the “Dust
Bowl” era of the 1930s. In general, more recent
drought events have been less intense in their
severity, duration, and spatial extent compared
74
to earlier in the 20th century. However, the 1988
drought was the fifth-driest year on record in llinois,
which led to severe water shortages throughout the
assessment area (Lamb 1992). In addition, the 2012
drought was the most extensive drought on record
across the United States since 195 (NCDC 2012).
One study examined the drought trends during
specific points in the growing season in Illinois and
Indiana from 191 to 2007 (Mishra and Cherkauer
2010). They found an overall decrease in drought
severity and frequency in southern Indiana and no
change in southern Illinois in spring (March through
May), summer (June through August), and the entire
growing season (May through October).
Snow
Although snow does not play as large a role in the
Central Hardwoods Region as it does in states farther
north, it is still an important aspect of hydrology for
the region. The amount of snow influences annual
runoff, recharge, and water supplies and can have
local effects on temperature through its reflectivity
(albedo). In addition, rapid melting following a large
snowfall event can lead to flooding. Between 1981
and 2010, the region received on average roughly
to 12 inches of snow per year (Kunkel et al. 2013).
Long-term records reveal a general decrease in
snowfall in Missouri since the 1930s (Kunkel et al.
2009). Trends in snowfall in southern Illinois and
Indiana are less clear, with some stations reporting
increases and others decreases, over the past
80 years (Kunkel et al. 2009). The ratio of snow to
total precipitation during the winter decreased in the
area between 1949 and 2005 due to both a decrease
in snowfall and an increase in rain during that time
(Feng and Hu 2007). According to the Illinois state
climatology office, statewide snowfall has decreased
in the most recent 20 years and is below the longterm average (Angel 2012b). There is also a trend
toward earlier snowmelt and decreasing snow depth
in the area (Dyer and Mote 200).
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Soil Frost
The duration and depth of soil frost can affect winter
and spring hydrologic cycles in the Midwest. An
increase in frozen soil can lead to increases in spring
peak flows due to a reduction in soil infiltration.
Soil frost can also increase water storage in the
soil over the winter. Soil temperatures during the
winter months, and thus soil frost, can be influenced
by changes in air temperature and the amount and
duration of snowpack. The number of days with
frozen soil has increased slightly over the past
century in southern Illinois and Indiana (Sinha et al.
2010). Soil freeze and thaw dates have also shifted
later on average in the area. It would appear that this
trend is partially driven by a decrease in snow cover
over this period, as winter temperatures have not
shown any strong trends. A decrease in snow cover
reduces soil insulation, leading to increased frost
susceptibility during snow-free periods in the winter.
Streamflow and Flooding
It can be difficult to attribute any trends in
streamflow specifically to climate change, as
there have been large-scale land-use changes in
the area (primarily agricultural development) that
can obscure any climate-related signal (Tomer and
Schilling 2009, Zhang and Schilling 200). A study
examining trends in streamflow in the Mississippi
River Basin from 1940 to 2003 showed a trend
toward increasing streamflow across the region,
mostly due to an increase in baseflow attributed
to agricultural land-use changes (Zhang and
Schilling 200). One study in Iowa, Missouri, and
Illinois showed that when changes in land use are
accounted for, an increase in discharge consistent
with local climate changes could be observed
(Tomer and Schilling 2009). These changes were
largely observed since the 1970s, and are due to
an increase in the ratio of precipitation to potential
evapotranspiration (i.e., evaporative demand).
Floods in the assessment area typically peak in the
spring, ranging from an average peak in mid-March
in far southern Illinois to early June in the northern
Missouri Ozarks (Villarini et al. 2011). Across
the Midwest, economic losses from flooding have
been increasing at a greater rate than elsewhere in
the nation. Over a 45-year period (1955 to 1999),
Illinois had more than $5 billion in flood losses, and
74 percent of these losses have occurred since 1985
(Angel 2012a). During spring 2011, record-breaking
floods occurred across the Mississippi, Missouri, and
Ohio River valleys, but it is hard to link these flood
events with climate change (see Box 11).
GRoWinG SeASon LenGTh
A large body of research indicates that the growing
season has been getting longer on a global scale,
largely from an earlier onset of spring (Christidis
et al. 2007, Parmesan and Yohe 2003, Root et
al. 2003, Schwartz et al. 200a). Growing season
length is often determined biologically, through the
study of phenology (see Box 12), but can also be
estimated climatologically. Growing season length
can be defined as the period between the dates of
the last spring freeze and first autumn freeze, as
determined by minimum temperatures of 32 °F
(0 °C). Using this definition, one study determined
the climatological growing season lengthened by
about 1 week on average between 190 and 1997
across Illinois, mostly due to an earlier date of the
last spring freeze (Robeson 2002). However, this
trend was stronger in the more northern portions of
the state, with many areas in the south experiencing
later spring frosts and an overall reduction in
growing season length. Another study examined
changes in growing season length from 1911 to 2000
across the Corn Belt, including Illinois and Indiana
(Miller et al. 2005). Although qualitative increases in
growing season length were found across the region,
there was no discernible trend in the data, which
75
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Box 11: Focus on Floods
In spring 2011, major storms, combined with a heavy
spring snowmelt, led to record-breaking flooding
along the Mississippi and Missouri Rivers. To save
the town of Cairo, Illinois, and the rest of the levee
system along the Mississippi River, the U.S. Army
Corps of Engineers blasted a 2-mile hole in a levee,
flooding 130,000 acres of farmland and displacing
200 residents in Mississippi County, Missouri
(Fig. 24). Flood events such as this pose a threat to
human lives and infrastructure as well as to natural
communities. Is there a link between this flood and
changes in climate?
Although there are signs that flooding has increased
in recent years, the link to changes in climate is
less clear. Flooding in the region is partially linked
to climate factors such as snowmelt and heavy
precipitation events, but is more strongly influenced
by non-climate factors such as land-use change
and the construction of dams and other water
infrastructure (Changnon and Demissie 1996). In a
study examining rain and stream gauge records over
the past 75 years in the Midwest (including Missouri
and Illinois, but not Indiana), there was no strong
evidence of a link between flood frequency and
were largely driven by a cool period in the 1920s
and a warm period in the 1990s. Since these studies
were conducted, a number of years have had last
freezes that occurred very early in the spring, such
as the spring of 2012, which may be indicative of
things to come.
Alternatively, growing season length can be defined
by other threshold temperatures exceeded by 1 or
more days. One study examined several different
temperature thresholds (24, 28, and 42 °F; -4.4, -2.2,
and 5. °C) for Illinois and found that the threshold
selected affected the overall trend in growing season
7
Figure 24.—Flooded region south of the confluence of
the Ohio and Mississippi Rivers (Birds Point) prior to the
levee breach, spring 2011. (Photo by U.S. Army Corps of
Engineers)
anthropogenic climate change (Villarini et al. 2011).
Other studies have found trends toward increased
flooding in the area, but have also not attributed the
cause to climate change (Olsen et al. 1999, Pinter
et al. 2008).
length (Robeson 2002). Thresholds of 24 and 42 °F
tended to show trends toward shorter growing season
length in southern Illinois, while growing season
length trended longer on average when a threshold
of 28 °F was used. A recent study examined trends
in the last spring day that was less than or equal to
28 °F (hard freeze) between 1901 and 2007 for areas
including the Missouri Ozarks, southern Illinois,
and southern Indiana (Marino et al. 2011). They
found trends toward an earlier last hard freeze by
0.5 to 1.5 days per decade for some portions of the
assessment area, most notably in Missouri.
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
Box 12: Phenological Indicators of Change
Changes in growing season length can be observed
through changes in phenology. Phenology is the
study of recurring plant and animal life-cycle
stages, such as leaf-out and senescence, flowering,
maturation of agricultural plants, emergence
of insects, and migration of birds. A few studies
examining changes in phenology in the Central
Hardwoods Region indicate recent changes:
• In a survey of 270 flowering plants in
southwestern Ohio, 60 percent showed earlier
spring flowering over the period from 1976 to
2003 of about 10 to 32 days (McEwan et al.
2010). The variation among species may be
attributed to differences in sensitivity to climate
as a clue to begin flowering as opposed to other
indicators such as day-length.
• A study examining the migratory patterns of
eight species of North American wood warblers
between southern Illinois and northern
Minnesota from 1903 to 2002 showed that their
migration season was being compressed by up to
20 days due to later springs in Illinois and earlier
springs in Minnesota (Strode 2003). Spring onset
was determined by the date when 300 degreedays over 41 °F (5 °C) were reached, which is
the beginning of the peak in spruce budworm
caterpillar activity, a primary food consumed by
many warbler species. This study shows that,
although average temperatures have not changed
significantly in the spring in southern Illinois,
certain climate indicators important to biological
functioning have changed.
ChAPTeR SummARy
The climate of the Central Hardwoods Region has
changed considerably over thousands of years, but
recent changes over the past 100 years have been
more subtle. Temperatures have increased both
globally and across the United States over the same
time period, yet the mean annual temperature in
• Despite global and national trends, there do not
appear to be trends toward an earlier start of the
growing season as determined by leaf emergence
in Missouri, southern Illinois, or southern
Indiana between 1901 and 2007 (Marino et al.
2011). In fact, there is a general trend (though
not significant) toward later leaf emergence in
much of the area by 5 to 10 days over the period
examined. By contrast, the date of the last hard
freeze in the area does appear to be about 5 to
16 days earlier (Marino et al. 2011). This trend
indicates an overall decrease of risk of “false
springs,” where leaf emergence occurs before the
last hard frost (Marino et al. 2011).
• A study using satellite data of forest leaf
emergence found a trend toward a later end
of the growing season between 1989 and 2008
across much of the eastern United States,
including the Missouri Ozarks and southern
Indiana (Dragoni and Rahman 2012).
• Measurements of tree leaf-out and
photosynthesis taken in a forest in southcentral Indiana indicate that the growing season
lengthened by 30 days from 1998 to 2008
(Dragoni et al. 2011). This study measured
carbon uptake as well as leaf emergence and
senescence, and determined that the end of the
season trended later over the course of the 10
years. The authors attributed this increase to a
warming trend in air and soil temperatures and
a decrease in cold degree-days (the sum of the
deviation of daily mean air temperature from the
10-year average) during the summer. A caveat to
this study is that it was for one isolated site over a
short period.
the Central Hardwoods Region actually decreased
slightly in some areas—a change small enough that
it could have occurred by chance. The difference
between daily high and low temperatures also
appears to be decreasing. High temperatures during
summer months have decreased by about 2 °F over
the last century while summer lows have increased
by about the same amount. Data indicate that
77
ChAPTeR 3: PAST CLimATe ChAnGeS AnD CuRRenT TRenDS
much of the area is receiving between 12 and
17 percent more precipitation annually, with
increases in heavy precipitation events and decreases
in severe droughts. There is insufficient information
to determine whether tornadoes and thunderstorms
are more frequent now than they have been since
measurements began, but there is some evidence that
winter storms, though less frequent now than in the
past, are more intense when they do occur. Flooding
in the area has increased, but this increase has been
attributed to changes in human land use and not
climate. Although there are no strong indications
of changes in winter temperature, soil frost has
increased in the area, which has been attributed
to a decrease in snow. In addition, some evidence
suggests that stream discharge has increased in some
areas, which has important implications for local
hydrology. These changes in climate in the region
may already be leading to forest response
(see Box 13).
Box 13: Species Range Shifts
Given that there are some indications of warming
temperatures across the Northern Hemisphere, one
might expect that species may be starting to move
northward along with their climatological niches.
Evidence across the globe is beginning to support
this hypothesis (Chen et al. 2011, Parmesan and Yohe
2003). Is there any evidence of northward migration
in the Central Hardwoods Region?
In order to determine species range shifts, longterm data over large spatial scales must be
available. The Forest Service’s Forest Inventory and
Analysis (FIA) Program is one source of this type of
information. A recent study using FIA data (Woodall
et al. 2009) examined range shifts in tree species
across the eastern United States by comparing
the mean latitude of seedlings to that of mature
trees. The researchers found a strong northward
shift in northern species such as sugar maple and
basswood. However, trends in southern species were
more mixed, with some species shifting northward
(shortleaf pine, yellow-poplar) and others shifting
southward (southern red oak, blackjack oak).
78
In a subsequent study using FIA data, Zhu et al.
(2011) examined changes in the 5th and 95th
percentile of latitudinal bands for seedlings, saplings,
and mature trees in order to examine latitudinal
shifts in range limits (as opposed to averages) in the
eastern United States. In contrast to the Woodall et
al. (2009) study, this study found that the majority
of trees did not undergo northward migration, but
rather showed range contraction, where seedlings
had smaller northern and southern range limits than
mature trees. One caveat to that study was that few
plots fall into the 5th and 95th percentiles, meaning
sample size was low.
These studies indicate that biological responses to
climate change are not always clear or predictable.
In addition, they suggest that there may be barriers
to northward migration for some tree species, such
as habitat fragmentation or inherent biological
differences in seed dispersal ability. Finally, the
methods used for determining northward migration
(mean latitude versus range limit changes) can lead
to different conclusions.
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe
AnD oTheR PhySiCAL PRoCeSSeS
In Chapter 3, we examined how climate has changed
in the assessment area over the past based on
measurements and proxy data. In this chapter, we
examine how climate may change over the next
century. General circulation models (GCMs) are
used to project future change at coarse spatial scales
and then downscaled to be relevant at scales where
land management decisions are made. In some cases,
these downscaled data are then incorporated into
hydrologic models to better understand impacts on
such variables as soil moisture, evapotranspiration,
and streamflow. Downscaled data are also
incorporated into forest species distribution models
and process models (see Chapters 2 and 5). If you
are unfamiliar with GCMs, downscaling, and impact
models, an overview and suggestions for further
reading are provided in Chapter 2.
TemPeRATuRe AnD
PReCiPiTATion PRoJeCTionS
In this chapter, we report downscaled climate
projections for two model-emissions scenario
combinations: GFDL A1FI and PCM B1 (unless
otherwise noted). The GFDL A1FI model-scenario
combination represents a higher-end projection
for future temperature increases, and the PCM B1
represents a lower end (see Chapter 2). It is possible
that actual emissions and temperature increases
could be lower or higher than either of these
projections. However, the GFDL A1FI scenario
represents a more realistic projection of future
greenhouse gas emissions and temperature increases
based on current trends. 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.
Daily mean, maximum, and minimum temperature
and total daily precipitation were downscaled to an
approximately 7.5-mile grid across the United States
(see Chapter 2). To visualize changes, we calculated
the modeled average daily mean, maximum, and
minimum temperature for each season and the entire
year for three 30-year time periods (2010 to 2039,
2040 to 209, 2070 to 2099). Daily precipitation
values were summed by year and season, and
30-year means were calculated. We subtracted
temperature and precipitation values from the 1971
to 2000 mean values as a baseline to determine the
departure from current climate conditions. Historical
climate data used for the departure analysis were
taken from ClimateWizard based on the PRISM
data set (Girvetz et al. 2009; see Chapter 3 and
Appendix 7).
Temperature
Both models project increases in mean, minimum,
and maximum temperatures across all time periods
and for all seasons. Mean annual temperature across
the assessment area is projected to increase by 7.3 °F
(4.0 °C) under the GFDL A1FI scenario and 1. °F
(0.9 °C) under PCM B1 for the final 30 years of
the 21st century (Fig. 25; see also Table 20 in
Appendix 8) compared to the 1971 to 2000
baseline. The most dramatic increase in temperature
is projected for winter for the PCM B1 scenario and
summer for the GFDL A1FI scenario. Temperature
increases are projected to be greatest in Missouri and
least in Indiana, especially for the PCM B1 scenario
(Fig. 2).
79
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
Annual
100
90
▲
▄
Temperature (°f)
80
70
▲
▄
60
50
▲
▄
40
30
G FDL A1FI mea n
P C M B 1 mea n
G FDL A1FI minimum
P C M B 1 minimum
G FDL A1FI ma ximum
P C M B 1 ma ximum
20
1971 - 2000
2040 - 2069
2070 - 2099
Winter
100
90
90
80
80
70
60
50
70
60
50
40
40
30
30
20
20
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
1971 - 2000
Summer
100
90
90
80
80
70
60
50
2040 - 2069
2070 - 2099
70
60
50
40
40
30
30
20
2010 - 2039
fall
100
Temperature (°f)
Temperature (°f)
Spring
100
Temperature (°f)
Temperature (°f)
2010 - 2039
20
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
Figure 25.—Daily mean, minimum, and maximum temperature averaged over 30-year time periods. Annual, winter (December
through February), spring (March through May), summer (June through August), and fall (September through November) values are
shown. The 1971 through 2000 value is based on observational data from weather stations. The 21st-century data are averages of
downscaled daily projections under two climate model-emissions scenario combinations.
80
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
Figure 26.—Projected difference in mean daily temperature at the end of the century (2070 through 2099) compared to baseline
(1971 through 2000) for two climate model-emissions scenario combinations.
81
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
The average daily minimum temperature is projected
to increase 7.0 °F (3.9 °C) under the GFDL A1FI
scenario and 1.0 °F (0. °C) under PCM B1for
the final 30 years of the 21st century compared to
the 1971 to 2000 baseline. Similar to daily means,
increases are greatest in the summer for GFDL and
greatest in the winter for PCM. Southern Illinois is
projected to have the greatest increase in minimum
temperatures, and Indiana the least, across all
seasons (Fig. 27). These patterns are generally true
for the 2010 to 2039 and 2040 to 209 periods as
well (see Appendix 8 for these time periods).
Figure 27.—Projected difference in mean daily minimum temperature at the end of the century (2070 through 2099) compared to
baseline (1971 through 2000) for two climate model-emissions scenario combinations.
82
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The average daily maximum temperature is
projected to increase 7. °F (4.2 °C) under the GFDL
A1FI scenario and 1. °F (0.9 °C) under PCM B1
for the final 30 years of the 21st century, a slightly
greater increase than for daily mean and minimum
temperatures. As with daily means and minimums,
the most dramatic increase in daily maximum
temperatures appears to be during the winter for
PCM and summer for GFDL (see Box 14). Increases
in daily maximum temperatures are projected to be
greatest in Missouri, especially in winter (Fig. 28).
These patterns are also true for the earlier 30-year
periods (see Appendix 8).
Differences between the two model-scenario
combinations are projected to be more distinct by
the end of the century (Fig. 2). In general, changes
in temperature are projected to be similar between
the two scenarios for the 2010 to 2039 period. By
the end of the century, however, temperatures are
projected to be much higher under the GFDL A1FI
scenario than PCM B1.
Box 14: Revisiting the “Warming Hole”
In Chapter 3, we discussed the “warming hole”
that has been observed across the central United
States, characterized by a reduction in summer high
temperatures over the past several decades. Will this
pattern continue into the future? If we examine just
the statistically downscaled GCM data presented in
this chapter, we might conclude that the warming
hole will be gone in the next century.
However, at least one study suggests that the large
grid-scale of GCMs fails to account for regional-scale
processes that are important contributors to the
warming hole (Liang et al. 2006). Using a dynamical
downscaling approach with the PCM model as an
input, this study found a large discrepancy between
the downscaled projections and the original coarsescale PCM projections in summer temperatures
in the central United States, particularly Missouri
and southern Illinois. Although both projected an
increase in summer temperature, the dynamically
downscaled model projected an increase of less than
0.5 °F (1 °C), while the coarse-scale PCM projected
an increase of 5.4 °F (3 °C) or more at mid-century.
The statistically downscaled projections for PCM
presented in this chapter also suggest a more
modest increase in summer temperatures.
So what do these projections mean for the “warming
hole”? The results suggest that, as with past
observations, there may continue to be regional
climate processes that reduce the amount of
warming experienced during the summer in the
central United States, at least over the short term.
However, dynamical downscaling studies such as this
one remain limited, justifying the consideration of
a range of potential future climate scenarios when
preparing for future climate change.
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Figure 28.—Projected difference in mean daily maximum temperature at the end of the century (2070 through 2099) compared to
baseline (1971 through 2000) for two climate model-emissions scenario combinations.
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Precipitation
The magnitude and seasonal direction of projected
changes in precipitation are not consistent between
the two models used in this assessment. Mean annual
precipitation is projected to decrease by 3.1 inches
under the GFDL A1FI scenario for the final 30 years
of the 21st century (Fig. 29; see also Table 21 in
Winter
18
16
16
14
14
12
10
8
6
4
12
10
8
6
4
2
2
0
0
1971 - 2000
2010 - 2039
2040 - 2069
1971 - 2000
2070 - 2099
Summer
18
16
16
14
14
12
10
8
6
2010 - 2039
2040 - 2069
2070 - 2099
fall
18
Precipitation (inches)
Precipitation (inches)
Spring
18
Precipitation (inches)
Precipitation (inches)
Appendix 8) compared to the 1971 to 2000 baseline.
Annual decreases are projected to be greatest in
Missouri under that scenario (Fig. 30). By contrast,
annual precipitation is projected to increase under
the PCM B1 scenario by an average of 2.9 inches for
the final 30 years of the century.
12
10
8
6
4
4
2
2
0
0
1971 - 2000
48
2010 - 2039
2040 - 2069
Annual
Annual
2070 - 2099
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
Precipitation (inches)
47
46
P CM B 1
45
44
G F D L A1F I
43
42
41
40
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
Figure 29.—Annual and seasonal precipitation for the assessment area over 30-year time periods. The 1971 through 2000 value is
based on observational data from weather stations. The 21st-century data are averages of downscaled daily projections under two
climate model-emissions scenario combinations.
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Figure 30.—Projected difference in mean annual and seasonal precipitation at the end of the century (2070 through 2099) compared
to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
8
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
Changes in precipitation are projected to vary
greatly by season. Under the GFDL A1FI scenario,
precipitation is projected to be higher in the winter
and spring and much lower in the summer across
all parts of the assessment area. The increases
are projected to be slightly more modest and the
decreases greater in Missouri compared to the
eastern portion of the assessment area. Under the
PCM B1 scenario, winter precipitation increases
are projected to be much more modest than under
GFDL, and projections for spring projections are
similar to GFDL. Summer precipitation under the
PCM B1 scenario is projected to increase—the
opposite of what is projected under the GFDL
model. Fall projections for both scenarios show
decreases in precipitation, with more consistent
decreases under PCM B1.
Unlike changes in temperature, projected changes in
precipitation do not consistently follow a linear path
over time in all seasons (Fig. 29). Projected changes
in summer precipitation are relatively linear for both
models, but in opposite directions. During winter,
both models project an increase in precipitation over
the next century, but PCM B1 projects the greatest
increase will be between 2040 and 209, whereas
GFDL A1FI projects the greatest increase at the
end of the century. Spring precipitation increases
initially and then decreases slightly under PCM,
while it remains steady under GFDL. The GFDL
A1FI scenario projects fall precipitation amounts
just slightly below historical averages for the 2040
to 209 and 2070 to 2099 periods, but shows a dip
in precipitation during the 2010 to 2039 period.
By contrast, PCM, shows a linear decrease in
precipitation during the fall.
EXTREME WEATHER EVENTS
As mentioned in Chapter 3, extreme weather
events such as tornadoes and thunderstorms can
be devastating to natural and human systems. In
general, there is less confidence in model projections
of the magnitude and direction of change in extreme
events over the next century compared with general
temperature and precipitation changes, but recent
research is beginning to provide more evidence for
projected increases in many extreme weather events
across the Midwest (Kunkel et al. 2013).
Heavy Precipitation Events
Climate models project an overall increase in the
number of heavy precipitation events globally by
the end of the century (Intergovernmental Panel
on Climate Change [IPCC] 2007, 2012). There is
greater agreement among models at high latitudes
and in the tropics, but model projections for the
central United States suggest a potential increase in
these events, especially during winter months (IPCC
2012). Other future climate projections indicate
that the Midwest may experience 2 to 4 more days
of extreme precipitation by the end of the century
(Diffenbaugh et al. 2005). However, downscaled
projections for the Midwest indicate less projected
change in heavy precipitation events (greater than
1 inch) in the Central Hardwoods Region than the
Midwest as a whole (Kunkel et al. 2013). With
the exception of south-central Indiana, fewer than
50 percent of climate models project an increase
in the number of heavy precipitation events in the
region (Kunkel et al. 2013).
Thunderstorms
Although GCMs do not operate at a scale small
enough to model thunderstorms explicitly, evidence
suggests that temperature increases will lead to
conditions more favorable to convective storms
such as thunderstorms (Kunkel et al. 2008; Trapp
et al. 2007, 2009). One study examined changes in
thunderstorm potential over the 21st century using
a mid-range emissions scenario (A1B; Trapp et al.
2009). A slight increase was found in the frequency
of conditions favorable for intense thunderstorms
in the Midwest. A similar study found an increase
in thunderstorm potential in the region at the end of
the century under a higher emissions scenario (A2;
Trapp et al. 2007).
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Tornadoes and Hail
Very little is known about how the frequency,
severity, and seasonal patterns of tornadoes and
hail may change over the next century. A recent
synthesis report on extreme weather events stated
that “there is low confidence in projections of small
spatial-scale phenomena such as tornadoes and hail
because competing physical processes may affect
future trends and because current climate models do
not simulate such phenomena” (IPCC 2012). As the
sophistication of global and regional climate models
increases, so will our understanding of how patterns
in hail and tornadoes may change in the future.
Winter Storms
Although winter storms such as snowstorms and
ice storms are relatively rare in the area, they can
nonetheless be devastating when they do occur.
Warming temperatures may lead to a decrease in
the overall frequency of ice storms and snowstorms
due to a reduction in the number of days that are
cold enough for those events to occur. However,
there is also some evidence to suggest that these
events could be more intense when they do happen.
Wang and Zhang (2008) examined changes in risk
of extreme precipitation during the winter months
under the A2 emissions scenario using statistically
downscaled climate projections. They found an
increased risk for extreme winter events at the end
of the century for the central United States, which
includes the western part of the assessment area.
Whether these events occur as rain, snow, or ice will
depend on the exact timing of these events and their
interaction with projected changes in temperature.
In general, more research is needed before we can
determine the most likely effects of future climate
change on winter storms.
Temperature extremes
In addition to changes in means, temperature
extremes are also projected to shift across the region.
Studies from across the Midwest indicate that there
will be more days per year that are warmer than
88
95 °F (35 °C) and a greater frequency of multi-day
heat waves over the 21st century (Diffenbaugh et
al. 2005, Kunkel et al. 2013, Winkler et al. 2012).
Within the Midwest, the Central Hardwoods
Region is projected to see the greatest increase in
such events, and could experience 20 to 30 more
extremely hot days by mid-century (Kunkel et al.
2013). The number of consecutive days above 95 °F
(35 °C) could increase by 8 to 1 days in the Central
Hardwoods Region by mid-century (Kunkel et al.
2013). Downscaled climate scenarios also project
that the Midwest will experience between 25 and
38 fewer days below freezing by the end of the
21st century (Sinha and Cherkauer 2010). However,
less of a decrease is projected in the western
Midwest (Kunkel et al. 2013). A decrease in extreme
cold days (less than 10 °F [-12 °C]) is projected to
be more moderate in the Central Hardwoods Region,
where there are not as many extremely cold days to
begin with, than in the rest of the Midwest (Kunkel
et al. 2013).
hyDRoLoGiC imPLiCATionS
Information regarding how temperature and
precipitation patterns may change across the
assessment area can further be used to examine how
these changes may affect the cycling of water in
terrestrial and aquatic ecosystems. Across the globe,
increases in temperature are projected to intensify
the hydrologic cycle, leading to greater evaporative
losses and more heavy precipitation events (IPCC
2007).
By examining soil moisture, evapotranspiration, and
various drought indices, we can gain an important
understanding of how these changes may affect
water availability for trees, understory plants,
wetlands, and rivers. In addition, examining changes
in runoff and streamflow can help us assess potential
flood risks and changes in watershed dynamics. The
dynamics of snow and frozen soil can affect soil
water availability, soil temperatures, streamflow
dynamics, and soil erosion processes.
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
As more water becomes available, more can be
evaporated or transpired. Temperature can also
increase evapotranspiration, but is limited by the
amount of water that is available in the soil, water
bodies, and atmosphere.
Waterfall on the Hoosier National Forest. Photo by Gerald Scott,
Hoosier National Forest.
Many of the results presented below use the Variable
Infiltration Capacity (VIC) hydrologic model,
which is described in more detail in Chapter 2.
Model results are currently available only for the
Illinois and Indiana portions of the assessment
area, so implications for the Missouri Ozarks are
not yet known. However, because the soil types
and projected changes in climate for the Missouri
Ozarks are similar to southwestern Illinois, it can be
assumed that the general patterns observed there will
be similar, especially in the east.
Evapotranspiration
Evapotranspiration, the combination of evaporation
from the soil and transpiration from plants, is an
important indicator of moisture availability in an
ecosystem and the amount of water available to
be lost as runoff. According to one study using
statistically downscaled GCM projections under two
emissions scenarios, evapotranspiration is projected
to increase during the winter and spring and decrease
during the summer in southern Illinois and Indiana
by the end of the 21st century (2070 to 2099)
compared to the 1977 to 200 average (Cherkauer
and Sinha 2010). These trends are strongly tied to
projected increases in winter and spring precipitation
and decreases in summer precipitation in the region.
Projected changes in evapotranspiration vary
considerably by hydrologic model and climate
models used, and whether changes in vegetation
are also considered. Another recent study,
using the same hydrologic model as above but
different climate projections, found an increase in
evapotranspiration across the assessment area in
spring, summer, and fall and no change in winter
from 2071 through 2100 (Ashfaq et al. 2010). As
we will discuss in Chapters 5 and , climate change
is also projected to affect the distribution of trees
and other plant species, which could also affect
evapotranspiration on the landscape. Increases in
carbon dioxide are expected to lead to changes in
water use efficiency of vegetation (Drake et al.
1997), but these changes are not currently accounted
for in model projections of evapotranspiration across
the region.
Examining changes in seasonal ratios of
evapotranspiration to precipitation can give a
general sense of how much water is available in
the soil and watershed. Ratios greater than 1.0
signify evapotranspiration exceeds precipitation, an
indication of drier conditions. Ratios less than 1.0
signify precipitation exceeds evapotranspiration, an
indication of wetter conditions. Changes in this ratio
were calculated by using data from Cherkauer and
Sinha (2010), and mapped (Fig. 31). An increase
in the ratio over time signifies a decrease in water
availability compared to historical levels, and a
decrease indicates an increase in water availability.
The data indicate a slight increase in water
availability on an annual basis. Spring precipitation
was projected to show the biggest increase in
water availability, and a decrease was projected
for summer.
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CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND OTHER PHYSICAL PROCESSES
Figure 31.—Difference in the ratio of evapotranspiration (ET) to precipitation (P) between the 1977 to 2006 average and the
projected average for 2070 through 2099 under a low (B1) and high (A2) emissions scenario using a GFDL/HadCM3 ensemble as
input. Blue areas indicate a decrease in the ratio, meaning more water is projected to be available in the soil and watershed than in
the past. Brown areas indicate less water is projected to be available. Figure shows the Illinois and Indiana portions of the assessment
and is based on data from Cherkauer and Sinha (2010) and used with permission of the authors.
90
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Soil Moisture and Drought
Changes in soil moisture are largely driven by the
balance of precipitation and evapotranspiration, and
thus there is some uncertainty about future changes.
Based on projected decreases in precipitation during
summer and fall and increases in temperature
throughout the year, one study found that surface
soil moisture was projected to decrease in the area
over the next century (2009 to 2099) by a small
amount (1.2 to 1. percent, depending on scenario;
Mishra et al. 2010). Total soil moisture was also
projected to decrease in the late summer and fall
and increase in the winter and spring. Another study
in the region suggests a decrease in soil moisture
during winter and early spring and increases in
soil moisture during the growing season (Winter
and Eltahir 2012). The difference between the two
studies suggests that model assumptions made
and scenarios chosen can have a large impact on
projections of future soil moisture in the Midwest.
Currently, most climate models project a decrease
or no change in precipitation during summer months
over the assessment area, leading to an overall
decrease in summer soil moisture when coupled with
increased temperature (Wang 2005). However, there
is a lot of variation among models, and an increase
in precipitation (and also soil moisture) is not
outside the realm of possibility.
Changes in precipitation are also expected to lead to
changes in drought characteristics, such as intensity,
duration, frequency, and spatial extent. According
to one study, the projected changes in duration of
drought periods in Illinois and Indiana over the
next century differ among models, scenario, and
time period, with most projecting an increase in
drought duration (Mishra et al. 2010). That study
also suggested the spatial extent of droughts may
increase, indicating that future droughts may shift
from more local to more regional phenomena
(Mishra et al. 2010). However, the number of
exceptional droughts (the most severe type of
drought) and the number of multi-year droughts
was not projected to change much from the number
experienced in the 20th century (Mishra et al. 2010).
Another study projected an increase in drought
frequency and severity when climate models that
projected a decrease in precipitation were used as
inputs, but no change in drought for those projecting
a precipitation increase (Wang et al. 2011). This
study was conducted for a primarily agricultural
area north of the assessment area, so it is unclear if
these results can be translated directly to the soils
and vegetation types in the Central Hardwoods
Region. No current information is available related
to drought characteristics for the Missouri Ozarks.
Runoff, Streamflow, and Flooding
Runoff in southern Illinois and Indiana is projected
to increase slightly over the next century compared
to the 30-year average from 1977 to 200,
particularly in the winter and spring (Cherkauer and
Sinha 2010). This increase reflects in part projected
increases in precipitation during these seasons.
Future changes in summer and fall runoff are less
certain, with some scenarios and locations projecting
a decrease in runoff and others projecting no change
or an increase (Cherkauer and Sinha 2010).
Streamflow is also projected to change in the area,
with changes varying by season. In recent decades,
winter and spring have had the highest number of
high-flow days, and, in general, the number of highflow days is projected to increase further during
these seasons (Cherkauer and Sinha 2010). Projected
changes in high-flow days in the summer and fall are
more mixed and vary based on location. Changes in
low-flow days also will vary by season: the number
of low-flow days is projected to increase in summer
and fall and decrease in the winter and spring.
Simulations for streamflow in the Wabash River
watershed showed increases of about 20 percent for
both peak and mean streamflow by the end of the
century (Cherkauer and Sinha 2010). Similarly,
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ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
mid-century projections of the Upper Mississippi
basin showed a 50-percent increase in annual
average streamflow, with the largest increase
occurring in spring and summer as a result of
increases in snowfall, snowmelt, runoff, and
recharge upstream of those areas (Jha et al. 2004).
21st century are less clear, but most models project
a decrease across the assessment area (Brown and
Mote 2009). These broader trends are expected to be
manifested as a reduction in snow across the Central
Hardwoods Region, as it is already a marginal area
for snow.
Change in flood risk under future climate change
is difficult to determine because there are currently
insufficient records to even determine flood risk
at a particular location, irrespective of climate
(Stedinger and Griffis 2011). As discussed in
Chapter 3, flooding is caused by a combination of
climate, infrastructure, and human land-use factors,
so the relative amount of change in these different
factors will determine the overall flood risk. The
studies described above suggest that the magnitude
of flooding could potentially increase in the winter
and spring due to increases in total runoff and peak
streamflow during those times (Cherkauer and Sinha
2010). During the summer and fall, there could be
an increase in “flashiness,” with periods of very low
flow followed by rapid flooding in response to heavy
rain events (Cherkauer and Sinha 2010). Because of
the lack of research specifically addressing future
flood dynamics in the region, we currently have low
certainty about future changes in flooding.
To examine changes in winter processes at a
more regional level, Sinha and Cherkauer (2010)
simulated changes in snow water equivalent, soil
frost, and other winter processes by using two
downscaled GCMs (HadCM3 and GFDL) and B1,
A1B, and A2 emissions scenarios as inputs into
the VIC model for Illinois and Indiana. With the
exception of the high emissions scenario in the 2010
to 2039 period, their study projected an overall
reduction in the amount of snow water equivalent,
which was due to an increase in temperature and a
decrease in snowfall (Sinha and Cherkauer 2010).
Their study also indicated a reduction in the number
of days the soil is frozen in the middle and late
century, and suggested that far southern Illinois and
Indiana may experience years without soil frost
at the end of the century. Although a reduction in
soil frost days could increase water infiltration
into the soil and reduce runoff, it could also lead
to greater soil water losses through increased
evapotranspiration and an increased susceptibility to
pest outbreaks (Sinha and Cherkauer 2010).
Snow and Other Winter Processes
Increases in temperature during winter months are
expected to lead to decreases in snow duration and
extent across the region in the coming decades.
Simulations of changes in snow cover extent in
North America over the 21st century suggest that it
will continue to decrease, and at a faster rate than it
did during the 20th century (Frei and Gong 2005).
Similarly, another study projected declining snow
cover duration in the eastern United States over the
21st century (Brown and Mote 2009). Trends in the
annual maximum snow water equivalent (i.e., the
amount of water contained in snowpack) over the
92
Freeze-thaw cycles can be important determinants
of soil erosion risk. All other things being equal,
fewer freeze-thaw cycles may result in less erosion.
Southern Illinois and Indiana are projected to
experience as many as three fewer freeze-thaw
cycles by the end of the century under both high
and low emissions scenarios (Sinha and Cherkauer
2010). Because much of the area currently
experiences three or fewer cycles in an average year,
this projection indicates that many years at the end
of the century may not have any freeze-thaw cycles.
ChAPTeR 4: PRoJeCTeD ChAnGeS in CLimATe AnD oTheR PhySiCAL PRoCeSSeS
GROWING SEASON LENGTH
ConCLuSionS
As noted in Chapter 3, a variety of metrics describe
trends in growing season length, and trends in the
Central Hardwoods Region over the past century
have depended on the metric used. Information
for future projections of growing season length
is primarily limited to length of time between the
last day below 32 °F (0 °C) in the spring and the
first day below 32 °F in the fall. A study covering
the entire Midwest region examined the changes
in dates for the last spring frost and first fall frost
under a range of climate scenarios (Wuebbles and
Hayhoe 2004). This study projected that the growing
season would be extended by 30 to 70 days by the
end of the century, both from an earlier last spring
frost date and a later first fall frost. A more recent
study suggests a more modest increase in the frostfree season at mid-century of 20 to 28 days across
the Central Hardwoods Region, with the largest
increase in southern Indiana (Kunkel et al. 2013).
How this projection translates into the actual length
of the growing season, as determined by leaf-out
and senescence, has not yet been examined for the
region.
Across a wide spectrum of potential models and
emissions scenarios, it appears that temperatures
will almost certainly increase across all seasons over
the 21st century, reaching annual temperatures that
are 2 to 7 °F (1.1 to 3.9 °C) higher than the last 30
years of the 20th century. However, it is uncertain
which seasons will have the greatest change in
temperature. Precipitation is projected to increase
in winter and spring by 2 to 5 inches for the two
seasons combined, leading to increased runoff
and streamflow. Climate models disagree about
how precipitation may change in summer and fall.
Summer precipitation may increase up to 3 inches
in summer or decrease up to 8 inches. Changes in
temperature and precipitation will subsequently
lead to changes in extreme weather events and
local hydrology. We are fairly certain that heavy
precipitation events will increase, snow cover will
decrease, and eventually soil frost will decrease
as well. However, more uncertainty remains with
respect to changes in tornadoes and thunderstorms,
seasonal soil moisture patterns, and flooding.
Missouri Ozarks in fall. Photo by Steve Shifley, U.S. Forest Service.
93
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS
on foReSTS
Changes in climate have the potential to have
profound effects on forests of the Central Hardwoods
Region. Many tree species that are currently present
may fare worse under warmer temperatures and
altered precipitation patterns. Other species may
do better under these conditions, and some species
not currently present may have the potential to do
well if conditions allow them to disperse to newly
suitable areas. In addition, climate change can have
indirect effects on forests in the region by changing
insect pests, pathogens, invasive species, nutrient
cycling, and the probability, severity, and extent of
wildfire and severe storms. This chapter summarizes
the potential impacts of climate change on forests
in the Central Hardwoods Region over the next
century, with an emphasis on changes in tree species
distribution and abundance.
moDeLeD PRoJeCTionS of
foReST ChAnGe
Climate change has the potential to alter the
distribution of tree species across the Central
Hardwoods Region. Over the past several thousand
years, species ranges in the Central Hardwoods
Region have fluctuated with large-scale changes in
climate (see Chapter 1). The ranges of tree species
in eastern North America have generally shifted
northward as the climate has warmed over the past
several thousand years since the last ice age (Davis
1981, Delcourt and Delcourt 1987, Webb et al.
1987). Evidence is mounting that plant and animal
species are currently undergoing range shifts in
response to a changing climate (Woodall et al. 2005,
2009; see Chapter 3). Such shifts are expected to
continue and even accelerate in the coming decades
as the rate of temperature increase accelerates.
94
Projections of potential tree species distribution and
abundance across the Central Hardwoods Region are
currently available from three modeling efforts:
Tree Atlas, LINKAGES, and LANDIS PRO
(Table 11). These models use two sets of projected
changes in temperature and precipitation (PCM B1
and GFDL A1FI; Chapter 4) to forecast alterations
in tree species distribution and abundance. Tree
Atlas provides projections for dozens of tree species
over large areas, but does not include dynamic
processes such as nutrient cycling or migration. The
LINKAGES model provides projections for fewer
species, but at finer scales, and incorporates changes
in nutrient cycling. The LANDIS PRO model also
focuses on fewer species, but works on a fine-scale,
spatially dynamic grid to simulate succession and
species migrations. Each model projects slightly
different variables that relate to distribution and
abundance. For a more thorough description of the
different models, and their strengths and limitations,
see Chapter 2.
Tree Atlas
Importance values of 134 eastern tree species were
modeled for potential habitat suitability in the
assessment area by using the DISTRIB model, a
component of the Tree Atlas toolset (Iverson and
Prasad 2002, Iverson et al. 2008). Importance value
is an index of the relative abundance of a species
in a given community, and can range from 0 (not
present) to 100 (one species covering the entire
area). Cell-by-cell 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. In Missouri, 79 of the 134 species were of
interest because they currently have or are projected
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
Table 11.—overview of models used in this assessment.
Tree Atlas
LinKAGeS
LAnDiS PRo
Model type
species distribution
model (DISTRIB) plus
supplementary information
(modifying factors)
temporally dynamic process spatially and temporally dynamic
model
process model
Primary output
area-weighted importance
values by species
establishment probability
by species
basal area, trees per acre by
species, biomass, importance values
Number of species evaluated
80
7 species or species groups
6 species or species groups
Areas evaluated
IL, IN, MO
MO
MO
Spatial resolution
12-mile grid
landforms in subsections
295-foot grid
Climate periods evaluated
2010 to 2039
1980 to 2003
1980 to 2003
2040 to 2069
2080 to 2099
2001 to 2100
2070 to 2099
Simulation period
n/a
30 years
100 years
Migration simulated
No
No
Yes
Disturbance simulated
No (but addressed through
modifying factors)
No
simulated current harvest and
suppressed fire
Succession simulated
No
No
Yes
Nutrient and
water dynamics simulated
No
Yes
No
to have suitable habitat in the area. For Illinois there
were 75, and 82 species were of interest in Indiana.
The following tables show the projected change in
potential suitable habitat for these species of interest
for 2070 to 2099 compared to present values.
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 9 for projections
of importance values under each model-scenario
combination for three periods (2010 to 2039, 2040 to
209, and 2070 to 2099).
A plus or minus sign after a species name indicates
that certain modifying factors could lead it to fare
better or worse than model projections. Modifying
factors include life history traits or environmental
factors that make a species more or less likely to
persist on the landscape (Matthews et al. 2011b).
Examples of modifying factors are fire or drought
tolerance, dispersal ability, shade tolerance, site
specificity, and susceptibility to insect pests and
diseases. These factors can then be weighted by their
intensity, level of uncertainty about their impacts,
and relative importance to future changes to arrive
at a numerical score (Matthews et al. 2011) (see
Appendix 9). Modifying factors are highly related to
the adaptive capacity of a species (see Chapter ). A
species with a large number of very strong positive
modifying factors would have a high adaptive
capacity, and a species with a large number of very
95
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
strong negative modifying factors would have a low
adaptive capacity (Table 12). See Appendix 9 for
specific modifying factors for each species and a
description of the numerical scoring system.
more prevalent across the landscape. Other species,
such as white ash, are expected to get a double hit
from negative climate impacts coupled with pest and
disease impacts.
When examining these results, it is important to keep
in mind that model reliability was generally higher
for more common species than for rare species. See
Appendix 9 for specific rankings of model reliability
for each species.
Suitable habitat for 12 species in the Illinois portion
of the assessment area was projected to remain
relatively stable under projected climate change.
Some species may actually increase in importance
given their positive modifying factors. Because of
strong dispersal and seedling establishment ability,
red maple is expected to fare well across much of
the assessment area, as long as individuals are not
exposed to fire. Pin oak, pecan, and black willow
have various factors that could cause them to decline
despite being relatively unaffected by changes in
climate alone.
illinois
Of the 75 species examined for the Illinois portion
of the assessment area, suitable habitat for 12 of
them was projected to decline or be extirpated under
both climate scenarios (Table 13). One species,
butternut, was projected to lose all suitable habitat
in the area. Butternut is expected to experience
additional negative impacts from butternut canker.
Among the major species in the area, more were
projected to experience small decreases than large
decreases in suitable habitat. Some decreasers, such
as sugar maple (Fig. 32), chestnut oak, and white
oak have positive modifying factors that could allow
them to do better than expected. Chestnut oak and
white oak are tolerant of drought and fire, which
may allow them to persist if these factors become
Twenty-two species were projected to have an
increase in suitable habitat in the assessment
area. Species such as bur oak and blackjack oak
have several adaptations such as drought and fire
tolerance that could further benefit the species. Some
species may not be as successful as projections
would suggest, however. Shortleaf pine, for
example, is highly susceptible to southern pine
beetle attack, which may expand into the area as
Table 12.—Species with the five highest and lowest ratings for adaptive capacity, based on adaptability score
determined from modifying factors (see Appendix 9).
Species
Factors that affect rating (modifying factors)
Highest adaptive capacity
1. red maple
high probability of seedling establishment, wide range of habitats, shade-tolerant, high dispersal ability
2. boxelder
high probability of seedling establishment, shade-tolerant, high dispersal ability, wide range of temperature
tolerances, drought-tolerant
3. sourwood
shade-tolerant, wide range of habitats
4. Nuttall oak
wide range of habitats
5. bur oak
drought-tolerant, fire-tolerant
Lowest adaptive capacity
1. pecan
fire-intolerant, susceptibility to insect pests, shade-intolerant
2. butternut
shade-intolerant, drought-intolerant, butternut canker, susceptible to fire topkill
3. white ash
emerald ash borer, drought-intolerant, susceptible to fire topkill
4. blue ash
emerald ash borer, drought-intolerant, susceptible to fire topkill, shade-intolerant, narrow habitat range
5. swamp tupelo drought-intolerant, susceptible to fire topkill, shade-intolerant, narrow habitat range
9
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
temperatures increase. Green ash is at risk for a
dramatic decline from the emerald ash borer despite
its projected tolerance of future climate shifts.
Habitat was projected to become suitable for four
species currently not found in the area (water oak,
water locust, cedar elm, and slash pine), but negative
modifying factors may reduce the ability of many of
these species to colonize new areas.
Table 13.—Classes of suitable habitat for tree species in the Illinois portion of the assessment area, 2070 through
2099, under the PCM B1 and GFDL A1FI scenarios. Species are assigned to change classes based on the ratio of endof-century (2070 through 2099) to current area-weighted importance value. See Appendix 9 for details in assigning
change class. (+) species with a high adaptability score (>5.2); (-) species with a low adaptability score (<3.3).
Decrease under Both Scenarios
Common name
PCM B1
Black cherry (-)
small decrease
Butternut (-)
extirpated
Chestnut oak (+)
small decrease
Eastern white pine (-)
large decrease
Ohio buckeye
small decrease
Shagbark hickory
small decrease
Shingle oak
small decrease
Sugar maple (+)
small decrease
Swamp chestnut oak
small decrease
Swamp white oak
small decrease
White ash (-)
small decrease
White oak (+)
small decrease
No Change
Common name
Baldcypress
Bitternut hickory (+)
Black willow (-)
Eastern redcedar
Flowering dogwood
Kentucky coffeetree
Mockernut hickory (+)
Pecan (-)
Pin oak (-)
Red maple (+)
Shellbark hickory
Willow oak
Mixed Results
Common name
American basswood
American beech
American elm
Black oak
Black walnut
Blackgum (+)
Chinquapin oak
Common persimmon (+)
Eastern hophornbeam (+)
Green ash
Hackberry (+)
Honeylocust (+)
Jack pine
Northern catalpa
GFDL A1FI
large decrease
extirpated
small decrease
large decrease
small decrease
large decrease
small decrease
large decrease
small decrease
large decrease
large decrease
large decrease
PCM B1
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
GFDL A1FI
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
PCM B1
small decrease
no change
no change
no change
no change
large increase
small increase
no change
no change
no change
no change
no change
small decrease
small decrease
GFDL A1FI
small increase
large decrease
small decrease
small decrease
large decrease
small decrease
small decrease
small increase
small increase
small increase
small decrease
small increase
small increase
no change
Mixed Results (continued)
Common name
PCM B1
Northern pin oak (+)
small decrease
Northern red oak (+)
no change
Osage-orange (+)
small increase
Overcup oak (-)
no change
Pawpaw
small increase
Pignut hickory
no change
Sassafras
no change
Scarlet oak
no change
Shumard oak (+)
small decrease
Silver maple (+)
no change
Slippery elm
no change
Swamp tupelo (-)
small increase
Sycamore
no change
Wild plum
no change
Yellow-poplar (+)
small increase
GFDL A1FI
small increase
large decrease
no change
small increase
large decrease
small decrease
large decrease
large decrease
large increase
small increase
small decrease
no change
small decrease
small increase
small decrease
increase under Both Scenarios
Common name
PCM B1
American hornbeam
small increase
Black hickory
large increase
Black locust
small increase
Blackjack oak (+)
large increase
Boxelder (+)
no change
Bur oak (+)
large increase
Cherrybark oak
large increase
Eastern cottonwood
small increase
Eastern redbud
small increase
Loblolly pine
large increase
Post oak (+)
small increase
Red mulberry
small increase
River birch
small increase
Shortleaf pine
small increase
Southern red oak (+)
large increase
Sugarberry
large increase
Sweetgum
small increase
Winged elm
large increase
GFDL A1FI
large increase
large increase
small increase
large increase
large increase
large increase
small increase
small increase
large increase
large increase
large increase
large increase
small increase
large increase
large increase
large increase
small increase
large increase
new habitat
Common name
Cedar elm (-)
Slash pine
Water locust
Water oak
PCM B1
new habitat
new habitat
new habitat
new habitat
GFDL A1FI
new habitat
new habitat
new habitat
new habitat
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ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
Climate scenarios were not consistent on the
classification of potential change for 29 of the
species examined. For the most part, these
differences were small, usually between no projected
change and a small increase or decrease. However,
pawpaw, yellow-poplar, and chinquapin oak were
projected to increase in suitable habitat under PCM
B1, but decrease under GFDL A1FI. Conversely,
jack pine, American basswood, and northern pin oak
were projected to increase in suitable habitat under
GFDL A1FI, but decrease under PCM B1. These
final three species are all more-northern species that
are relatively rare in the assessment area, so their
projected increase under GFDL A1FI may reflect
low model reliability at the edge of their ranges.
Figure 32.—Modeled importance values for sugar maple across
the assessment area using the DISTRIB model for current
climate conditions (top) and projected for 2070 through 2099
under the PCM B1 and GFDL A1FI climate scenarios. Importance
values can range from 0 to 100. An importance value of zero
(light yellow) indicates that the species is not present.
98
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
indiana
Of the 82 species evaluated for Indiana, suitable
habitat for 10 of them was projected to decline by
the end of the century under both climate scenarios
(Table 14). No species was projected to experience
a complete loss of suitable habitat in the area under
both scenarios. More species were projected to
experience small declines than large declines in
Table 14.—Classes of suitable habitat for tree species in the Indiana portion of the assessment area, 2070 through
2099, under the PCM B1 and GFDL A1FI scenarios. Species are assigned to change classes based on the ratio of endof-century (2070 through 2099) to current area-weighted importance value. See Appendix 9 for details in assigning
change class. (+) species with a high adaptability score (>5.2); (-) species with a low adaptability score (<3.3).
Declines under Both Scenarios
Common name
PCM B1
American basswood
small decrease
Bigtooth aspen
large decrease
Black cherry (-)
small decrease
Black maple (+)
small decrease
Blue ash (-)
small decrease
Butternut (-)
small decrease
Kentucky coffeetree
small decrease
Swamp chestnut oak
small decrease
Yellow birch
large decrease
Yellow buckeye (-)
small decrease
No Change
Common name
Baldcypress
Black oak
Northern catalpa
Pignut hickory
Red maple (+)
Wild plum (-)
Mixed Results
Common name
American beech
American elm
American hornbeam
Black locust
Black walnut
Blackgum (+)
Cherrybark oak
Chestnut oak (+)
Chinquapin oak
Eastern hophornbeam (+)
Eastern redcedar
Eastern white pine (-)
Flowering dogwood
Hackberry (+)
Jack pine
Mockernut hickory (+)
Northern pin oak (+)
Northern red oak (+)
Ohio buckeye
Overcup oak (-)
Pawpaw
Pecan (-)
Rock elm (-)
Sassafras
Scarlet oak
GFDL A1FI
large decrease
extirpated
large decrease
extirpated
small decrease
extirpated
large decrease
small decrease
large decrease
small decrease
PCM B1
no change
no change
no change
no change
no change
no change
GFDL A1FI
no change
no change
no change
no change
no change
no change
PCM B1
no change
no change
no change
no change
small increase
small increase
small increase
no change
small increase
no change
no change
no change
no change
small increase
small decrease
no change
small decrease
no change
no change
no change
small increase
small decrease
small increase
no change
small increase
GFDL A1FI
large decrease
small decrease
small increase
small decrease
large decrease
no change
no change
small decrease
small decrease
small increase
small decrease
large decrease
small decrease
no change
no change
small increase
no change
small decrease
large decrease
small increase
large decrease
no change
large decrease
large decrease
large decrease
Mixed Results
Common name
Shagbark hickory
Shellbark hickory
Shingle oak
Shumard oak (+)
Silver maple (+)
Slippery elm
Sourwood (+)
Sugar maple (+)
Swamp tupelo (-)
Swamp white oak
Sycamore
Virginia pine
White ash (-)
White oak (+)
Yellow-poplar (+)
PCM B1
no change
small increase
no change
small decrease
no change
no change
small increase
no change
small increase
small increase
no change
small increase
no change
no change
no change
GFDL A1FI
small decrease
no change
small increase
large increase
small increase
large decrease
no change
large decrease
large decrease
large decrease
small decrease
no change
large decrease
small decrease
large decrease
increase under Both Scenarios:
Common name
PCM B1
Bitternut hickory (+)
small increase
Black hickory
large increase
Black willow (-)
small increase
Blackjack oak (+)
large increase
Boxelder (+)
small increase
Bur oak (+)
large increase
Common persimmon (+) small increase
Eastern cottonwood
small increase
Eastern redbud
small increase
Green ash
small increase
Honeylocust (+)
small increase
Osage-orange (+)
small increase
Pin oak (-)
small increase
Post oak (+)
large increase
Red mulberry
small increase
River birch
small increase
Shortleaf pine
large increase
Southern red oak (+)
large increase
Sugarberry
small increase
Sweetgum
large increase
Winged elm
large increase
GFDL A1FI
small increase
large increase
small increase
large increase
small increase
large increase
large increase
small increase
small increase
large increase
small increase
small increase
small increase
large increase
large increase
smalli
large increase
large increase
large increase
small increase
large increase
new habitat
Common name
Cedar elm (-)
Loblolly pine (-)
Slash pine
Water oak
Willow oak
PCM B1
new habitat
new habitat
new habitat
new habitat
new habitat
GFDL A1FI
new habitat
new habitat
new habitat
new habitat
new habitat
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ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
suitable habitat. Many of these species are relatively
rare on the landscape. Species such as black cherry
and blue ash may decline more than projected
by climate alone due to factors such as their
susceptibility to insect pests and fire topkill.
Suitable habitat for six species in Indiana was not
projected to change much over the next century
based on changes in climate alone. However, red
maple may increase on the landscape due to its high
dispersal and establishment abilities and its wide
habitat tolerances, at least in areas not subjected to
increased wildfire.
Twenty-six species were projected to experience
an increase in suitable habitat. Some species may
do even better than projected by climate alone.
For example, post oak and blackjack oak have
high tolerance for both drought and fire. Habitat
was projected to become suitable for five species
currently not found in the area (water oak, willow
oak, cedar elm, loblolly pine, and slash pine).
Modifying factors may limit the ability of some
of these species to spread to newly suitable areas,
however.
There was an inconsistent classification of change
between scenarios for 40 species. As with Illinois,
many of these differences were small, such as
between no change and a small or large decrease.
Eastern redcedar, for example, is widely distributed
across the assessment area and suitable habitat was
projected to remain stable in the coming century,
despite a small decline projected under GFDL
A1FI (Fig. 33). However, black walnut, pawpaw,
chinquapin oak, swamp white oak, and scarlet oak
were projected to increase under PCM B1, but
decrease under GFDL A1FI. Jack pine, American
basswood, and northern pin oak were projected to
increase under GFDL A1FI, but decrease under PCM
B1, but as mentioned earlier, this could be due to
low model reliability at the edge of their range.
100
Figure 33.—Modeled importance values for eastern redcedar
across the assessment area using the DISTRIB model for current
climate conditions and projected for 2070 through 2099 under
the PCM B1 and GFDL A1FI climate scenarios. 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
missouri
Of the 79 species evaluated in the Missouri Ozarks,
14 were projected to decline in suitable habitat under
both scenarios (Table 15). Butternut was the only
species projected to have a complete loss of suitable
habitat. Although white oak fell into the decrease
category, its tolerance of drought and fire suggest it
may fare better than projected. Other species may
Table 15.—Classes of suitable habitat for tree species in the Missouri Ozarks portion of the assessment area, 2070
through 2099, under the PCM B1 and GFDL A1FI scenarios. Species are assigned to change classes based on the ratio
of end-of-century (2070 through 2099) to current area-weighted importance value. See Appendix 9 for details in
assigning change class. (+) species with a high adaptability score (>5.2); (-) species with a low adaptability score (<3.3).
Decrease under Both Scenarios
Common name
PCM B1
American beech
small decrease
American elm
small decrease
Butternut (-)
extirpated
Cherrybark oak
small decrease
Ohio buckeye
small decrease
Rock elm (-)
small decrease
Sassafras
small decrease
Scarlet oak
large decrease
Shagbark hickory
small decrease
Shellbark hickory
small decrease
Slippery elm
small decrease
Sugar maple (+)
small decrease
Swamp white oak
small decrease
White oak (+)
small decrease
No Change
Common name
Baldcypress
Bitternut hickory (+)
Black cherry (-)
Black hickory
Blue ash (-)
Common persimmon (+)
Eastern redbud
Eastern redcedar
Hackberry (+)
Mockernut hickory (+)
Northern catalpa
Pecan (-)
Sycamore
Virginia pine
Willow oak
Mixed Results
Common name
American basswood
Black oak
Black walnut
Black willow (-)
Blackgum (+)
Boxelder (+)
Bur oak (+)
Chinquapin oak
Chittamwood:
gum bumelia (+)
Eastern cottonwood
GFDL A1FI
large decrease
small decrease
extirpated
large decrease
small decrease
extirpated
small decrease
large decrease
small decrease
small decrease
small decrease
large decrease
extirpated
large decrease
PCM B1
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
GFDL A1FI
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
no change
PCM B1
small decrease
no change
no change
no change
small increase
no change
no change
small increase
GFDL A1FI
large increase
small decrease
large decrease
large increase
no change
large increase
large increase
small decrease
small decrease
no change
no change
large increase
Mixed Results (continued)
Common name
PCM B1
Eastern hophornbeam (+)
no change
Flowering dogwood
no change
Green ash
no change
Honeylocust (+)
no change
Northern red oak (+)
no change
Nuttall oak (+)
no change
Overcup oak (-)
no change
Pawpaw
no change
Pignut hickory
small decrease
Post oak (+)
no change
Red mulberry
no change
River birch
no change
Shingle oak
large decrease
Silver maple (+)
no change
Swamp tupelo (-)
no change
White ash (-)
no change
GFDL A1FI
large increase
small decrease
small increase
large increase
small decrease
small decrease
small increase
large decrease
no change
small increase
small increase
small increase
small increase
large increase
no change
small decrease
increase under Both Scenarios
Common name
PCM B1
American hornbeam
small increase
Black locust
small increase
Blackjack oak (+)
small increase
Chestnut oak (+)
large increase
Osage-orange (+)
small increase
Pin oak (-)
small increase
Red maple (+)
small increase
Shortleaf pine
large increase
Shumard oak (+)
small increase
Southern red oak (+)
large increase
Sugarberry
large increase
Sweetgum
large increase
Wild plum
small increase
Winged elm
large increase
Yellow-poplar (+)
small increase
GFDL A1FI
large increase
small increase
small increase
small increase
small increase
small increase
large increase
large increase
small increase
large increase
large increase
large increase
small increase
large increase
small increase
new habitat
Common name
Cedar elm (-)
Jack pine
Loblolly pine
Longleaf pine
Northern pin oak (+)
Quaking aspen
Slash pine
Sourwood (+)
Water oak
PCM B1
new habitat
NA
new habitat
new habitat
NA
NA
new habitat
new habitat
new habitat
GFDL A1FI
new habitat
new habitat
new habitat
NA
new habitat
new habitat
new habitat
NA
new habitat
101
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
fare even worse than projected. For example, rock
elm has a narrow habitat specificity and has poor
seedling establishment.
Suitable habitat for 15 species was projected to
stay similar to today when modifying factors are
not considered. Some species may do worse than
projected, however. For example, black cherry is
susceptible to fire topkill and insect outbreaks, and
blue ash is susceptible to emerald ash borer. Other
species may fare better than projected. Bitternut
hickory and hackberry can both tolerate drought,
which may allow them to do better than other
species if droughts become more widespread.
Twenty-four species were projected to increase in
suitable habitat, of which nine are not currently
found in the assessment area. Suitable habitat for
shortleaf pine, for example, is projected to expand
beyond its current range (Fig. 34). Many of these
species possess modifying factors that could affect
their ability to expand into newly suitable habitats.
Winged elm may fare worse than projected due
to its susceptibility to Dutch elm disease. The
strong dispersal ability of red maple may allow it
to disperse into new areas. Blackjack oak is both
fire- and drought-tolerant, making it resilient to
many of the stressors that are expected to increase
as the climate changes. All oak species are dispersal
limited to some extent, however, due to their heavy
seeds.
Model projections showed some disagreement
between scenarios in the classification of change
for 25 of the species examined, but most of these
differences were small. Black walnut was projected
to remain stable under PCM B1 but experience a
large decrease under GFDL A1FI. Shingle oak and
American basswood were projected to decrease in
suitable habitat under PCM B1, but increase under
GFDL A1FI.
102
Figure 34.—Modeled importance values for shortleaf pine
across the assessment area using the DISTRIB model for current
climate conditions (top) and projected for 2070 through 2099
under the PCM B1 and GFDL A1FI climate scenarios. 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
calculated for the entire Ozark Highlands Section in
Missouri.
LinKAGeS
missouri
Tree species establishment probability was estimated
based on biomass predictions from the LINKAGES
model for seven species and species groups for two
climate periods in the Missouri Ozarks portion of the
assessment area. Species establishment probability is
the seedling establishment rate for a given landtype,
where zero represents no ability to establish and one
represents optimal conditions for establishment. It
can be interpreted as a measure of habitat suitability,
but does not account for effects of interspecific
competition and disturbance. Species establishment
probability was modeled for 50 sites in the Missouri
Ozark Highlands section (5 landtypes within each
of 10 ecological subsections) for current climate
(1980 through 2003) and projected climate (2080
through 2099) under the PCM B1 and GFDL A1FI
climate scenarios. An area-weighted mean was then
Establishment probability was projected to remain
relatively unchanged for white oak, American elm,
and eastern redcedar (Fig. 35). For all three of these
species, establishment probability was slightly
better under PCM B1 than under either GFDL
A1FI or current climate conditions. Establishment
probability for sugar maple was projected to decline
to zero under both climate scenarios. Establishment
probability for red oak species (northern red and
black oak) was projected to increase under the
PCM B1 scenario, and decrease under GFDL A1FI.
Establishment probability was projected to increase
for shortleaf pine and loblolly pine under both future
climate scenarios. Shortleaf pine was projected to
be most successful under the PCM B1 scenario,
whereas loblolly was projected to be most successful
under GFDL A1FI.
0.7
Current Climate
PCM B1
0.6
Species establishment Probability
GFDL A1FI
0.5
0.4
0.3
0.2
0.1
0
American Elm
Red Oak Group
White Oak
Sugar Maple
Eastern Redcedar
Shortleaf Pine
Loblolly Pine
Figure 35.—Average area-weighted mean species establishment probability for current climate (1980 through 2003 average) and
projected for 2080 through 2099 under the PCM B1 and GFDL A1FI climate scenarios across the Missouri Ozark Highlands. The red
oak group value is the average for northern red and black oak.
103
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
LAnDiS PRo
missouri
Landscape change was modeled over the 21st
century for the Missouri Ozarks with the LANDIS
PRO model for current climate (1980 through 2003)
and projected climate (2001 through 2100) by using
the species establishment probabilities developed
from the LINKAGES model. Changes in basal
area (cross-sectional area of tree boles measured
at a height of 21.5 inches above the ground) and
the number of trees per acre by species or species
group were simulated over a 100-year period. The
LANDIS PRO model uses the two climate change
model-emissions scenarios used by the other impact
models (PCM B1 and GFDL A1FI) and a current
climate scenario. Percentage change in future
compared to current climate was summarized at
years 2040, 2070, 2090, and 2100 (Figs. 3
and 37). In contrast to LINKAGES, LANDIS
PRO is able to simulate stand- and landscape-level
processes such as competition, seed dispersal, and
disturbance. In the scenarios below, however, these
factors were held constant among model simulations
so that differences between the current climate
and future climate scenarios are limited to the
effects of precipitation and temperature on species
establishment probabilities. Model simulations
assumed current harvest levels on public and
private lands, and fire was suppressed. However,
it is expected that both of these factors will also
change in the future as climate changes. The species
establishment probabilities were determined by
using LINKAGES as described in the previous
section. Species establishment probabilities changed
over time during the simulations to match the
expected changes in temperature and precipitation
for each scenario.
No dramatic changes in basal area or trees per
acre were projected for the soft hardwoods group
(American elm, slippery elm, and willow species).
104
The number of trees per acre is projected to increase
by 3 to percent by the end of the century relative
to current climate, depending on future climate
scenario. Changes in basal area are even more
subtle, reaching a 2- to 4-percent increase by 2100.
A slightly higher basal area and trees per acre were
projected under the GFDL A1FI scenario than under
the PCM B1 scenario.
The LANDIS PRO model projected a dramatic
decrease in the number of sugar maple trees per
acre over the next century under the PCB B1 and
GFDL A1FI climate scenarios compared to the
current climate scenario, reaching about 80-percent
decline by 2100. This projection indicates that
maple seedlings may be unable to establish on the
landscape and replace older trees as they die. The
model projects a less dramatic, but still substantial,
decrease in basal area under both climate modelemissions scenarios, suggesting that larger, older
trees may persist on the landscape. Over a longer
timeframe, these projections suggest that sugar
maple species would disappear from the landscape
as mature trees die and new trees fail to establish
because of a lack of regeneration under both future
climate scenarios as projected in LINKAGES.
Projected changes in the red oak group (northern
red, black, southern red, pin, Shumard, scarlet, and
blackjack oak) varied by climate scenario. Red oak
group species had an increase in basal area and
trees per acre under the PCM B1 scenario, possibly
due to increased seedling establishment projected
in LINKAGES. The GFDL A1FI scenario resulted
in decreased trees per acre, attributable to low
seedling establishment projected in LINKAGES.
Basal area was projected to stay relatively constant
under GFDL A1FI. Conditions thus are projected
to continue to be suitable for mature trees, at least
in the short term, but lack of establishment of new
individuals may eventually lead to decline in the
species once the older individuals die.
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
10
5
10
eastern Soft hardwoods
5
0
% Change in Basal Area
% Change in Basal Area
0
-5
-10
-15
-20
-25
-15
-20
-25
-30
-35
-40
2040
5
2070
2090
2040
2100
10
Red oak Group
5
0
-5
-10
-15
-20
-25
2100
2090
2100
eastern Redcedar
-10
-15
-20
-25
-30
-35
-35
-40
-40
2040
5
2090
-5
-30
10
2070
0
% Change in Basal Area
% Change in Basal Area
-10
-35
-40
2070
2090
2100
2040
10
White oak Group
5
0
2070
Shortleaf Pine
0
% Change in Basal Area
% Change in Basal Area
-5
-30
10
Sugar maple
-5
-10
-15
-20
-25
-5
-10
PCM B1
-15
-20
GFDL A1FI
-25
-30
-30
-35
-35
-40
-40
2040
2070
2090
2100
2040
2070
2090
2100
Figure 36.—Projected changes in basal area for six species and species groups across the Missouri Ozark Highlands using the LANDIS
PRO model. Values represent the percentage change in basal area between projected and current climate at simulation year 2040,
2070, 2090, and 2100 under two future climate scenarios: PCM B1 and GFDL A1FI. A positive value indicates an increase relative to
current climate and a negative value a decrease. Eastern soft hardwoods group: American elm, with slippery elm and, to a lesser
extent, willow species. Red oak group: northern red, black, southern red, pin, Shumard, scarlet, and blackjack oak. White oak group:
white, post, swamp white, and bur oak.
Basal area and the number of trees per acre in the
white oak group (white, post, swamp white, and bur
oak) were projected to increase slightly under both
future climate scenarios. A greater increase in both
basal area and trees per acre was projected in GFDL
A1FI than in PCM B1. This result is in contrast to
the projections in LINKAGES, which suggested
white oak group species may be most successful
under the PCM B1 scenario. One explanation for
this difference is that establishment was projected to
be higher for the white oak group under GFDL A1FI
conditions and higher for the red oak group under
105
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
20
20
eastern Soft hardwoods
10
% Change in Trees per Acre
% Change in Trees per Acre
10
0
-10
-20
-30
-40
-50
-60
2040
2090
-50
-60
20
10
-20
-30
-40
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-70
2070
2090
2100
2090
2100
eastern Redcedar
0
-10
-20
-30
-40
-50
-60
-70
-80
-80
2040
2070
2090
2100
2040
20
White oak Group
10
0
% Change in Trees per Acre
% Change in Trees per Acre
-40
2040
Red oak Group
0
10
-30
2100
% Change in Trees per Acre
% Change in Trees per Acre
2070
-10
20
-20
-80
-80
10
0
-10
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-70
20
Sugar maple
-10
-20
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-40
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-70
2070
Shortleaf Pine
0
-10
-20
PCM B1
-30
-40
GFDL A1FI
-50
-60
-70
-80
-80
2040
2070
2090
2100
2040
2070
2090
2100
Figure 37.—Projected changes in trees per acre for six species and species groups across the Missouri Ozark Highlands using the
LANDIS PRO model. Values represent the percentage change in trees per acre between projected and current climate at simulation
year 2040, 2070, 2090, and 2100 under two future climate scenarios: PCM B1 and GFDL A1FI. A positive value indicates an increase
relative to current climate and a negative value a decrease. Eastern soft hardwoods group: American elm, with slippery elm and, to a
lesser extent, willow species. Red oak group: northern red, black, southern red, pin, Shumard, scarlet, and blackjack oak. White oak
group: white, post, swamp white, and bur oak.
PCM B1 and current climate scenarios. Therefore,
the model projections suggest that the white oak
group may have a competitive advantage over the
red oak group under GFDL A1FI.
Simulations in LANDIS PRO suggested that
changes in climate were not projected to have
significant effects on eastern redcedar in the region.
10
Changes in both basal area and trees per acre were
less than 3 percent, even at the end of the century.
By 2100, a small increase of a few percent in both
basal area and trees per acre under both scenarios
could be observed. This positive trend could
continue if simulations were carried out into the next
century.
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
Basal area and number of trees per acre of
shortleaf pine were projected to increase under
both future climate scenarios relative to the current
climate scenario. Increases in both values were
modest, reaching 18 percent more trees per acre
and percent greater basal area under the GFDL
A1FI scenario. The number of trees per acre was
projected to increase more rapidly than basal area,
reflecting the establishment of new individuals
on the landscape rather than enhanced growth of
established trees. In contrast to LINKAGES, model
results for LANDIS PRO suggested establishment
and growth would be greatest under the GFDL A1FI
scenario. This difference in result was probably
driven by shortleaf pine colonizing newly suitable
areas made available by declining species in the
LANDIS PRO simulations.
Comparison of model Results
Despite the differences in approach and variables
modeled, all three models show some remarkable
similarities in projected species distribution over the
next century. For example, all three models suggest
that habitat suitability for sugar maple may decline
over the next century, while suitability for shortleaf
pine may increase. The modeling approach used by
the Tree Atlas (DISTRIB) allows a greater area and
number of species to be modeled, so it is unclear if
the model projections for species and geographic
areas not modeled by LINKAGES and LANDIS
PRO might be similar to DISTRIB projections.
Below is a comparison of the similarities and
differences in projections for those species that
were modeled using all three approaches.
eastern Redcedar
All three models suggest that conditions are
projected to continue to be favorable for eastern
redcedar across the landscape, and changes in
climate are not projected to have a dramatic effect
on the ability of this species to spread to new areas.
Both LINKAGES and DISTRIB project slightly
more favorable conditions for eastern redcedar under
the PCM B1 scenario than under current climate
conditions. By contrast LANDIS PRO projections
suggest that eastern redcedar may have slightly
greater growth (as measured by basal area) under
the GFDL A1FI scenario than under PCM B1.
Nevertheless, the wide distribution of this species
suggests that it will probably continue to do well
under a range of climate conditions.
Eastern Soft Hardwoods
Simulations in LINKAGES and LANDIS PRO
suggest that changes in climate may not have a
strong effect on American elm and associated
species. The DISTRIB model suggests these species
may react differently to projected climate, however,
with slight increases for willow and slight decreases
for the elm species. These results suggest that this
species group as a whole may remain relatively
stable, but conditions may favor a slight increase
in hackberry and a slight decrease in elm species.
White oak Group
Projections for white oak group species among the
three models were mixed, and may be indicative
of the differences in modeling approach. Results
in LANDIS PRO, which combined white, post,
swamp white, and bur oak into one species group,
suggested that conditions may be slightly more
favorable for this group of species under future
climate conditions compared to current climate. By
contrast, LINKAGES did not project a substantial
change in establishment probability for white oak,
which was slightly more favorable under PCM B1.
The DISTRIB model, which modeled individual
species, projected an increase in habitat suitability
for post oak and bur oak under the GFDL scenario,
and a decrease for white and swamp white oak under
both scenarios in Missouri. Neither LINKAGES nor
DISTRIB accounts for competition among species,
which could explain some of the discrepancy
between these two models and LANDIS PRO.
Another possible explanation for the difference
among projections is whether the species were
grouped or modeled separately.
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ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
Red oak Group
Both LINKAGES and LANDIS PRO projected an
increase in establishment for red oak group species
under PCM B1, but decreases under the GFDL
A1FI scenario. This result is probably because
PCM projects an increase in summer precipitation
over the next century, whereas GFDL projects a
decrease. Many red oak group species are sensitive
to these seasonal precipitation changes, and are
projected to expand or contract depending on
whether precipitation increases or decreases during
the summer. The DISTRIB model did not show
a consistent difference between the two climate
models for this group of species as a whole.
Projections for northern red oak are consistent with
this pattern, however, suggesting a slight increase
in habitat suitability under PCM B1 and decrease
under GFDL A1FI. The projected changes using
the DISTRIB model for other red oak group species
suggest that black and scarlet oak may decline in
habitat suitability under one or both scenarios, and
blackjack, pin, Shumard, and southern red oak may
increase in habitat suitability.
Sugar Maple
The LINKAGES model projected a decline in
establishment probability near 100 percent for sugar
maple for both future climate scenarios. Simulations
in LANDIS PRO for sugar maple showed that basal
area was not projected to decline as dramatically as
trees per acres over the next century, suggesting that
older trees may persist on the landscape. By contrast,
the DISTRIB model projected only a slight decline
in suitable habitat for sugar maple under the PCM
B1 scenario but a large decline under the GFDL
A1FI scenario. Despite these subtle differences, it
appears that conditions generally will be unfavorable
for this species.
Shortleaf and Loblolly Pine
All three models projected favorable conditions for
shortleaf pine across the assessment area. However,
there were some slight differences among models
108
regarding which climate scenario would be most
favorable. Both LANDIS PRO and DISTRIB
projected slightly more favorable conditions under
the GFDL A1FI scenario, but LINKAGES projected
the greatest increase under the PCM B1 scenario.
Both the DISTRIB and the LINKAGES models
projected favorable conditions for loblolly pine to
expand into the area, with greater increases under
the GFDL A1FI scenario. Loblolly pine was not
modeled with LANDIS PRO because the model
simulated change only for species already present on
the landscape. In general, conditions are expected to
be favorable for both of these species across a range
of future climates.
SummARy of
CuRRenT SCienTifiC KnoWLeDGe
on foReST imPACTS
The results presented above provide us with
important projections of tree species distributions
across a range of future climates, but these models
do not account for all factors that may influence tree
species and forest communities under a changing
climate. Climate change has the potential to alter the
distribution, abundance, and productivity of forests
and their associated species in a variety of ways
(Climate Change Science Program 2008, Vose et
al. 2012). These effects can broadly be divided into
the direct effects of temperature and precipitation
on forests and the indirect effects on forests through
the alteration of current stressors or the development
of additional stressors. For the most part, models
such as the ones described above consider only
direct effects such as average temperature and
precipitation. Information regarding the current state
of our scientific knowledge on additional direct and
indirect effects of climate change on forests in the
Central Hardwoods Region is described below.
Drought Stress and Mortality
Severe and long-term droughts can have dramatic
impacts on the forests of the Central Hardwoods
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
Region. For example, drought events can lead to
the mortality of species in the red oak group via oak
decline (Starkey et al. 2004, Voelker et al. 2008) (see
Box 15). Research in dry-mesic upland forests in
the Missouri Ozarks suggests a positive relationship
between a species’ vessel (part of the anatomical
structure of the tree that carries water) length and
mortality during drought events (Fig. 38). Species
such as sassafras and scarlet oak have long vessels
and appear to be particularly susceptible to droughtrelated mortality, whereas species such as red maple
and black cherry have shorter vessels and lower
mortality. If drought duration and area increase as
projected (Mishra et al. 2010), drought-susceptible
tree species could be particularly vulnerable to
mortality.
Blowdowns
Blowdowns from large and small windstorms
can have an important influence on the structure
and species composition of forests in the Central
Hardwoods Region. Some model projections
suggest there may be an overall increase in the
average windspeed in the area, but there does not
appear to be a projected increase in the number of
extreme wind events in the central United States
Box 15. Oak Decline
Oak decline is a phenomenon affecting species in
the red oak group, especially northern red, black,
and scarlet oak. It is a disease complex caused by
a combination of physical and biological stressors.
Older trees growing on sites with shallow soils
can become stressed from drought in particular,
but also from pollution, late spring frosts, or other
environmental stressors. These physical stressors
can make them more vulnerable to attack by insects
and pathogens. The result is a decline in species in
the red oak group. Within the assessment area, oak
decline is a chronic problem in the Ozark Highlands,
affecting hundreds of thousands of acres.
Insects involved in oak decline include the red oak
borer, carpenterworm, and two-lined chestnut borer.
Infestation by the red oak borer appears to increase
when trees are drought stressed (Haavik et al. 2008),
and infestation also increases in conjunction with
warmer mean annual and mean annual minimum
temperatures (Muzika and Guyette 2004). Although
these infestations are often associated with oak
decline, they alone are not typically responsible for
mortality (Fan et al. 2008, Haavik et al. 2008).
Armillaria and Hypoxylon fungi are two pathogens
involved in oak decline. Hypoxylon species commonly
cause a canker-like disease on red and black oaks
that have been stressed by drought, and can lead
to tree death. Armillaria species normally act as
decomposers, but can become parasitic when trees
become stressed and, thus, contribute to tree death.
If climate change increases the duration and extent
of drought or increases the amount of defoliation by
insects due to warmer temperatures, trees could be
more susceptible to attack by this pathogen (Dukes
et al. 2009).
Historical and dendrochronological records indicate
a strong relationship between drought years and
oak decline (Dwyer et al. 1995, Jenkins and Pallardy
1995). As droughts are projected to increase in
duration and aerial extent (Mishra et al. 2010), oak
decline could become an even larger problem for
species in the red oak group across the Missouri
Ozarks, especially for older trees on marginal
sites. Oak decline could be exacerbated by other
stressors: insect defoliation may increase with rising
temperatures, and red oak species may already be
stressed due to a decline in habitat suitability as
projected by the tree species models, especially
under the GFDL A1FI scenario. As these species
decline, new opportunities could open up for other
species that are better adapted to projected climate,
such as pine and white oak group species.
109
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
many Central Hardwoods ecosystems, but existing
scientific literature provides no clear indication of
how blowdowns will be affected by the changing
climate.
Winter Storm Damage
Figure 38.—Species vessel length correlates with cumulative
mortality between 1981 and 1988 summer droughts at the
Tyson Research Center, St. Louis County, MO. Species with high
ranks for vessel length are more likely to have high ranks for
mortality than expected by chance. Increasing rank denotes
increasing values on each axis. Figure used with permission of
Brad Oberle and Amy Zanne, George Washington University.
(IPCC 2012). In addition, the amount of evidence
to date of changes in extreme storms in this region
is rather limited (IPCC 2012) (see Chapters 3 and
4). Therefore, it is unclear whether blowdowns
will increase across the region. If blowdowns do
increase, the species that are most susceptible
are expected to vary across the assessment area
because of differences in species composition and
stand characteristics. In the southeast Missouri
Ozarks region, past blowdown events appear to
have disproportionately affected older scarlet oaks,
as well as trees on north- and east-facing slopes
(Rebertus and Meier 2001). These events appear
to have created opportunities for regeneration
of white oak, flowering dogwood, and various
hickory species. In the Shawnee National Forest,
a recent blowdown primarily affected oak species
and provided more opportunity for succession by
immature shade-tolerant species in the understory
(Holzmueller et al. 2012). Blowdowns are expected
to continue to be an important disturbance in
110
Snow and ice damage occurs occasionally across
the area, and is projected to decrease with warmer
temperatures (Chapter 4). This trend could
decrease mortality of trees that are susceptible to
damage from these events. Species such as eastern
redcedar, yellow-poplar, and sweetgum appear
to be particularly susceptible to top breakage and
uprooting from these events (Parker and Ruffner
2004). A study of a 1994 ice storm in Missouri found
that basswood and American elm were the species
most susceptible to ice storm damage, whereas white
oak and shagbark hickory were less susceptible
(Rebertus et al. 1997). Within species, damage
appears to be greater in older, taller individuals and
those on mesic aspects and lower slopes (Rebertus
et al. 1997). These events also create gaps, allowing
growth and expansion of immature trees in the
understory. If these events decrease or are eliminated
from the area, recruitment of shade-intolerant
species in particular may be reduced.
Although snow and ice are projected to decrease
across the area, some evidence suggests that storm
events may actually increase during the winter
(Wang and Zhang 2008). With the projected
increases in temperatures, these events may result
more often in flooding and wind damage than in
snow and ice damage, suggesting winter storms may
function more like summer storms across the region.
Hydrologic Impacts on Forests
Although all forests are expected to be affected
to some extent, bottomland forests are the most
susceptible to the effects of altered hydrologic
regimes as temperatures increase and precipitation
patterns change. Past forest management practices,
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
the development of infrastructure, and drainage of
low-lying areas for agriculture have dramatically
altered the hydrologic regimes in bottomland
forests across the region, leading to shifts in species
composition (Romano 2010). Runoff and high-flow
days are projected to increase in the area during
winter and spring, when precipitation is projected
to be greater than current conditions (Chapter 4).
These changes could have important implications
for bottomland forests, which are often waterlogged
in the spring. Changes in flood frequency, duration,
height, and seasonality could all have important
impacts on bottomland forest species.
Information from past flooding events can help
us understand how species in bottomland forests
may respond to future changes in flood frequency,
severity, or duration. The 1993 flood, one of the
largest recorded flooding events to affect the Upper
Mississippi River Basin (including the northern
boundary of the assessment area), resulted in higher
levels of mortality in maple, elm, and minor species
such as river birch and hackberry compared to
oak, hickory, and ash species (Yin et al. 2009a).
Smaller, younger individuals were also more
susceptible to mortality from the flood than older
individuals. Since this event, however, survival
and recruitment of new seedlings has favored
maple and ash and led to a reduction in the oak
component in the understory, such as swamp white
oak, pin oak, and black oak. Based on these results,
ashes are classified as flood-stimulated species;
Wet bottomland forest. Photo by Paul Nelson, Mark Twain National Forest.
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ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
maple, hickory, and a variety of minor species are
considered flood-tolerant species; and oaks and
elms are considered flood-intolerant species (Yin
et al. 2009a). Research suggests that oak species in
bottomland systems could potentially decline and
hickory species could increase if floods as severe
as the 1993 event occur more than once every 100
years (Yin et al. 2009b). Flood severity and duration
can also affect species composition. Model results
indicate that maple and oak species are favored
under floods that are less severe than the 1993 flood,
and ash is favored when floods are more severe (Yin
et al. 2009b). An observational study of the Upper
Mississippi watershed north of the assessment area
suggests that areas that remain flooded for more
than 40 percent of the growing season are severely
limited in species diversity (De Jager et al. 2012).
Other research in the region suggests that changes
in flood regime can affect species composition.
An analysis of the forest community on the Lower
Kaskaskia River, one of the largest contiguous
floodplain forests remaining in the region, indicated
that a hydrologic modification resulting in high
flood frequency and duration would support
floodplain forest assemblages dominated by
boxelder, silver maple, and green ash. Conversely,
lower flood frequency and duration would support
river birch and American elm assemblages (Romano
200). A recent study in the Ozarks examined the
flood tolerance of six species in a Missouri river
floodplain under three different flood regimes
(Kabrick et al. 2012). Pecan and black walnut were
found to be flood-intolerant. Eastern cottonwood
survival was negatively affected by flooding, but
the growth of surviving individuals appeared to
not be affected. Contrary to the study in the Upper
Mississippi River Basin above, swamp white oak
was found to do well even under the most severe
flooding, suggesting this species has the potential to
do well in the absence of competition from shadetolerant species like maple and ash. Pin and bur
oak, however, appear to suffer negative impacts on
112
growth when inundated by standing water for longer
periods of time, suggesting an increase in flood
duration could negatively impact these species.
Bottomland forests can withstand periodic flooding
but cannot tolerate being waterlogged throughout the
year. Swamps, by contrast, are flooded year-round
and are populated by species adapted to standing
water throughout the year. These systems, dominated
by species such as baldcypress, reach their northern
extent in the assessment area, and have the potential
to respond favorably to altered conditions as long as
natural hydrologic regimes are kept intact (see
Box 1).
Soil erosion
Soil erosion is considered one of the major threats
to the Central Hardwoods Region (Chapter 1). Some
research suggests that an increase in heavy rainfall
events that is projected to occur (and is already
occurring) will lead to an even greater increase in
soil erosion (Nearing et al. 2004, 2005). One study
estimates that for every 1-percent increase in rainfall,
erosion could increase by 2 percent (Nearing et al.
2004). No studies to date have examined the effects
of climate change on soil erosion specifically in the
Central Hardwoods Region. One study examined
changes in erosivity across the United States at a
very large spatial resolution and found that erosion
may increase or decrease in the assessment area
depending on climate model (Nearing 2001).
This study looked only at broad-scale changes in
precipitation, and does not account for other factors
that may affect the vulnerability of soil to erosion
such as vegetation cover, slope, or soil type.
Other climate change factors may also affect soil
erosion in the Central Hardwoods Region. As
mentioned in Chapter 4, soil freeze-thaw cycles
may decrease in the area by the end of the century,
which could reduce the susceptibility of soil to
erosion (Sinha and Cherkauer 2010). Vegetation
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
Box 16. Baldcypress Swamps
Southwestern Indiana, southern Illinois, and
southeastern Missouri represent the northern
extent of the range of baldcypress swamps in the
Mississippi Alluvial Valley. These unique habitats
are among the world’s most prolific producers of
biomass, and thus serve as an important carbon sink.
They also provide important habitat or food for fish
and wildlife, including bald eagles, wild turkeys, and
wood ducks. Baldcypress swamps also help reduce
the severity and damage of flooding during the
growing season by absorbing water and increasing
infiltration into the soil.
The northern extent of the range for baldcypress in
lowlands is determined primarily by an interaction of
freezing water and recruitment limitations because
of lack of upstream seed dispersal—not because of
tree physiological constraints (B. Middleton, U.S.
Geological Survey, National Wetlands Research
Center, personal comm.). In fact, mature trees that
are planted on upland sites can withstand very cold
temperatures (-29 to -34 °C) (Burns and Honkala
1990).
Baldcypress swamps are of course dependent
on appropriate topographic conditions, but also
on precipitation and periodic flooding, which are
expected to change across the Central Hardwoods
Region based on current model projections.
Regeneration and recruitment of baldcypress
and associated species are determined by
specific periodic flooding regimes (Middleton
2000, Middleton and Wu 2008). A reduction in
precipitation, which is projected to occur later in the
growing season, could result in reduced recruitment
of rare species in this community such as American
featherfoil and increased recruitment of other
species such as buttonbush (Middleton 2006).
The northern extent of baldcypress swamps
may serve as a refuge to more southern species
associated with this community type (Middleton
2006). Dispersal of associated southern species
to the north may be limited, however, as seeds
disperse by water, and the prevailing direction
of the watersheds where they are located is
southward (Middleton and McKee 2004). In addition,
baldcypress swamps have become more fragmented
in the north as they have been drained for
agriculture and as local rivers have been dammed,
making dispersal even more difficult (Middleton and
Wu 2008).
Baldcypress productivity may increase at its northern
extent with increasing temperature due to an
extended growing season (Middleton and McKee
2004). More research is needed to assess whether
genetic variation across its current range may limit
this effect in northern genotypes (Kusumi et al.
2010).
Associated species within baldcypress swamps may
vary in seedling recruitment and seedling biomass
in their response to warming (Middleton and McKee
2011). For example, Virginia threeseed mercury is
currently near its northern range limit, and responds
to increasing temperature through increases in root
biomass (Middleton and McKee 2011). Warmer
future spring temperatures to the north of its current
range could allow this annual species to expand
northward, depending on dispersal constraints
(Middleton and McKee 2011). However, other
species in these systems do not show a strong
response to temperature or have a much greater
northern range extent than baldcypress swamps
themselves.
Overall, baldcypress swamps and their associated
species have the potential to adapt positively to
increases in temperature in Illinois, Indiana, and
Missouri, but only if connectivity and hydrologic
function are restored.
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also protects soil from erosion by reducing rainfall
impacts through the canopy and litter layer, and by
stabilizing soil through roots. Reductions in biomass
and vegetative cover, resulting from a variety of
climate impacts such as drought, insects, diseases, or
catastrophic wildfire, could thus lead to an increase
in erosion susceptibility (Nearing 2001).
Wildfire
Fires, both natural and human-caused, have played
an important role in the forests of the Central
Hardwoods Region for thousands of years (Abrams
1992, Nowacki and Abrams 2008). Regardless of
the cause, the risk and severity of fire depend on the
atmospheric conditions present before, during, and
after the time of ignition (Guyette et al. 2012).
At a global scale, the scientific consensus is
that fire risk may increase by 10 to 30 percent
over the next century because of higher summer
temperatures (IPCC 2007). Studies using climate
models suggest that fire potential could increase
across North America from increases in temperature
and decreases in precipitation in some areas, and
fire seasons in the southeastern United States could
nearly double in length (Liu et al. 2010). In addition,
fire severity in the Southeast could increase by up
to 30 percent, depending on the general circulation
model (GCM) used (Flannigan et al. 2000). An
analysis of fire probability across the globe projected
by 1 downscaled climate models found low
agreement among projections of climate change
effects on fire probability in the central United States
in the near term (2010 to 2039), but the majority of
models projected an increase in wildfire probability
by the end of the century (2070 to 2099) (Moritz
et al. 2012).
How a change in fire risk across the region
translates to effects at local scales in Central
Prescribed fire in shortleaf pine woodland. Photo by Steve Shifley, U.S. Forest Service.
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Hardwoods forests depends on land use and
management decisions. Currently, no fine-scale
projections of these interactions are available. The
model projections presented in this chapter using
LANDIS PRO assumed no climate-induced changes
in wildfire or management regime, but future
simulations could explore these interactions.
A study across the entire United States conducted
model simulations of vegetation types under both
suppressed and unsuppressed wildfire by using
two emissions scenarios (A2 and B2) to examine
the relationship among climate change, potential
vegetation cover, and wildfire (Lenihan et al.
2008). When future wildfires were not suppressed,
the Central Hardwoods Region was projected to
convert from a temperate deciduous forest type
to a woodland or savanna type. When fire was
suppressed, on the other hand, the temperate
deciduous forest type was projected to remain
similar in the assessment area while moving
northward across the eastern United States. A
projected shift in potential vegetation type with
unsuppressed fire was driven by climatic conditions
that made the area more susceptible to wildfire,
including increased temperature, drought, and
flammability of coarse and fine fuels (Lenihan et al.
2008). These results underscore the importance of
fire management in determining potential climate
effects on vegetation. However, it is also important
to note that these model simulations were run by
using potential vegetation across the area. They
do not include human-induced alterations to the
landscape such as agriculture and urban areas, nor
do they account for human intervention once a fire
is ignited.
Carbon Dioxide Increases
In addition to effects on climate, carbon dioxide
(CO2) itself can affect plant productivity and
species composition. Elevated CO2 may enhance
growth and water use efficiency of some species
(Ainsworth and Rogers 2007, Norby et al. 2005),
potentially offsetting the negative effects of drier
growing seasons. There is already some evidence for
increased forest growth under elevated CO2 in the
eastern United States (Cole et al. 2010, McMahon
et al. 2010), but it remains unclear if long-term
enhanced growth can be sustained (Bonan 2008,
Foster et al. 2010). Nutrient and water availability,
ozone pollution, and tree age and size all play major
roles in the ability of trees to capitalize on CO2
fertilization (Ainsworth and Long 2005). Ecosystem
community shifts may take place as some species
are genetically better able to take advantage of CO2
fertilization than others (Souza et al. 2010). Some
models are available that account for changes in
CO2, but these models tend to focus on nutrient
cycling and general vegetation types, and not
specific species (Lenihan et al. 2008, Ollinger
et al. 2008).
Changes in Nutrient Cycling
As air temperatures warm and precipitation patterns
change, changes in the way nutrients are cycled
between plants, soils, and the atmosphere may also
occur. These changes have important implications
for the productivity of trees, which are often limited
by nutrients such as phosphorus and nitrogen (N).
To date, research has not been done to specifically
examine the effects of climate change on nutrient
cycling in the Central Hardwoods Region. Studies
in other areas and at broader scales can give some
insight into potential effects, however.
Decomposition of vegetation is carried out primarily
by enzymes released from bacteria and fungi. These
enzymes are sensitive to changes in temperature, and
there is generally a positive effect of temperature
on the rate of enzymatic activity as long as moisture
is also sufficient (Brzostek et al. 2012, Rustad et
al. 2001). In addition to increases in temperature,
changes in drought, flooding, and the interaction
among these factors can affect nutrient cycling and
the availability of N to trees and other vegetation
(Rennenberg et al. 2009, and references therein).
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Many studies have examined the effects of extended
dry periods followed by moisture pulses on nutrient
cycling (Borken and Matzner 2009, and references
therein). Although these moisture pulses 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 wet-dry cycles appears to lead
to a reduction in nutrient availability for trees.
invasive Plant Species
As described in Chapter 1, nonnative invasive
species are a major threat to forests in the Central
Hardwoods Region. Many invasive species that
currently threaten forests in the Central Hardwoods
Region may benefit from projected climate change
as well. Some species, such as sericea lespedeza
(Gucker 2010), are tolerant of drought and fire
and may be at an even greater advantage in the
future. Although Japanese stiltgrass reproduction
is inhibited during drought years, its large, longlived seedbank enables it to recover in wetter years
(Gibson et al. 2002). In addition, deer herbivory
of native vegetation following a drought event
can maintain dominance of stiltgrass (Webster et
al. 2008). Other species, such as garlic mustard,
are not particularly drought-tolerant and may fare
worse if summer drying increases (Byers and Quinn
1998). Currently, however, no modeling efforts have
been undertaken to assess the influence of climate
change on invasive species that have already been
established in the area.
Changes in climate may allow some invasive
plant species to survive farther north than they
had previously (Dukes et al. 2009). Kudzu is an
invasive vine that has devastated forests in the
southeastern United States. Economic damage to
managed forests and agricultural land is estimated
at $100 to $500 million per year (Blaustein 2001).
The current northern distribution of kudzu is limited
by winter temperature. One study found the risk for
kudzu invasion at the end of the century in Missouri,
Illinois, and Indiana could be heightened under
11
future projected warming (Bradley et al. 2010).
Another examined the potential future distribution
of kudzu for the year 2035 using trends in observed
climate data and found habitat suitability may
increase slightly in Indiana but may decrease slightly
in western Missouri (Jarnevich and Stohlgren 2009).
Chinese and European privet are invasive flowering
shrubs that crowd out native species and form dense
thickets. Model projections suggest that the risks for
privet invasion into Missouri, Illinois, and Indiana
may be even greater than that of kudzu by the end
of the century (Bradley et al. 2010). Some areas in
the Ozark Highlands and south-central Indiana were
projected to be most susceptible, with a mediumhigh risk; that is, the majority of GCMs and impact
models project an increase in suitable habitat.
Insect Pests and Pathogens
Warmer temperatures and stressed trees may
increase the abundance of pests and pathogens
that are currently present in the assessment area.
Many insects and their associated pathogens
are exacerbated by drought including forest tent
caterpillar, hickory bark beetle and its associated
canker pathogen, bacterial leaf scorch, and Diplodia
shoot blight (Babin-Fenske and Anand 2011, Park
et al. 2013, Sinclair and Lyon 2005, U.S. Forest
Service 1985). High spring precipitation has been
associated with severe outbreaks of bur oak blight
in Iowa (Harrington et al. 2012). Another important
stressor that could be exacerbated by climate change
is oak decline, which is largely driven by drought
conditions that predispose species to insect pest
and pathogen attack (see Box 15).
Warmer temperatures are also expected to increase
the susceptibility of tree species to pests and diseases
that are not currently a problem in the assessment
area. Projections of gypsy moth population dynamics
under a changing climate suggest substantial
increases in the probability of establishment in the
coming decades (Logan et al. 2003). The spread of
ChAPTeR 5: fuTuRe CLimATe ChAnGe imPACTS on foReSTS
the gypsy moth could put at risk oak species that
would otherwise do well under a changing climate.
However, wetter springs could curtail its spread to
some extent: a fungal pathogen of the larvae has
been shown to reduce populations in years with wet
springs (Andreadis and Weseloh 1990). In addition,
future northward range expansion attributed to
warming temperatures has been projected for
southern pine beetle (Ungerer et al. 1999), which
could become a problem if shortleaf pine expands
in the region and stand density is not kept in check.
Effects of Vertebrate Species
Herbivory, seed predation, and disturbance by
vertebrates can be important stressors in the Central
Hardwoods Region. Deer browsing, seed predation,
or disturbance by feral hogs may reduce the overall
success of species that are otherwise projected to
do well under future climate change (Ibañez et al.
2008). Currently, there is little evidence to indicate
how deer, feral hogs, and other vertebrate species
will respond to climate change in the Central
Hardwoods Region. An analysis of climate change
impacts on white-tailed deer in Wisconsin suggests
that deer in that area are expected to experience a
mixture of positive impacts from milder winters
coupled with negative impacts from increased
disease outbreaks (Wisconsin Initiative on Climate
Change Impacts 2011). How these two factors may
influence deer populations in Missouri, Illinois, and
Indiana remains unknown.
ConCLuSionS
Results from three independent modeling efforts
suggest that habitat suitability for many tree species
may shift across the Central Hardwoods Region,
leading to declines in some species and increases
in others. The Tree Atlas, LANDIS PRO, and
LINKAGES models all project a potential decline
in suitability for sugar maple compared to current
climate conditions. These models also agree that
conditions should become more favorable for
shortleaf pine. Model projections vary for oak and
hickory species and will depend in part on how
precipitation patterns shift in the coming years.
Other factors that are not included in models,
such as changes in invasive species, insects and
diseases, wildfire, and soil conditions, may also
affect species composition and forest productivity.
Increased drought stress could increase susceptibility
to oak decline in red oak group species, and higher
temperatures could facilitate invasion of kudzu,
privet, and southern pine beetle. Climate conditions
are also expected to make conditions more favorable
to wildfire and soil erosion. All of these factors
need to be taken into account when evaluating
the vulnerability of Central Hardwoods forests
to climate change.
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Changes in species distribution and abundance due
to climate change can have important implications
for the habitats in which those species live, leading
to shifts in community composition and changes
in ecosystem processes (Climate Change Science
Program [CCSP] 2008, Vose et al. 2012). In
addition, climate change itself can alter system
drivers and exacerbate or ameliorate current
stressors (CCSP 2008, Vose et al. 2012). This
chapter focuses on the collective vulnerability of
natural communities in the Central Hardwoods
Region to climate change, emphasizing shifts in
dominant species, system drivers, and stressors over
the next century. Vulnerability is the susceptibility
of a system to the adverse effects of climate change
(Intergovernmental Panel on Climate Change
[IPCC] 2007). It is a function of potential climate
change impacts and the adaptive capacity of the
system. We consider a system to be vulnerable if it
is at risk for no longer being recognizable as that
community type, or if the system is anticipated to
suffer substantial declines in health or productivity.
The vulnerability of a system to climate change
is independent of the economic or social values
associated with the system, and the ultimate decision
of whether to conserve vulnerable systems or allow
them to shift to an alternate state will depend on
the individual objectives of land management
organizations.
This chapter is organized into two sections. First,
we present an overall synthesis of vulnerability
of the Central Hardwoods Region, organized
according to drivers and stressors, ecosystem
impacts, and factors that influence adaptive capacity.
This synthesis is based on the current scientific
118
consensus of published literature (Chapters 4
and 5). In the following section, we present
individual vulnerability determinations for the
nine natural community types considered in this
assessment.
vuLneRABiLiTy of The CenTRAL
hARDWooDS ReGion
Potential Impacts on Drivers
and Stressors
Many physical and biological factors contribute to
the current state of Central Hardwoods systems.
Some of these factors serve as drivers, defining
variables that make that system what it is. Other
factors can serve as stressors, reducing forest
productivity or increasing mortality. Many factors,
such as flooding or fire, may be drivers in one
situation and stressors in another.
Potential impacts are the direct and indirect
consequences of climate change on systems.
Impacts are a function of exposure of a system to
climate change and its sensitivity to any resulting
changes. Impacts could be beneficial or harmful to
a particular forest or ecosystem type. The summary
below includes the potential impacts of climate
change on major drivers and stressors in the Central
Hardwoods Region over the next century based
on the current scientific consensus of published
literature, which is described in more detail in the
preceding chapters.
After each statement is a confidence statement,
phrased according to the IPCC’s guidance for
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
authors (Mastrandrea et al. 2010) (Fig. 39).
Confidence was determined by gauging both the
level of evidence and level of agreement among
information. Evidence was considered robust when
multiple observations or models were available as
well as an established theoretical understanding
to support a statement. Agreement referred
to the agreement among the multiple lines of
evidence. Agreement was rated as high if theories,
observations, and models tended to suggest similar
outcomes. Agreement does not refer to the level of
agreement among the authors of this assessment.
Temperatures will increase (robust evidence,
high agreement). All global climate models project
that temperatures will increase due to a rise in
greenhouse gas concentrations both locally and
globally.
A large amount of evidence from across the globe
shows that temperatures have been increasing and
will continue to increase due to human activities
(IPCC 2007) (Chapter 2). Although temperatures in
the Central Hardwoods Region have not changed
much in the past (Chapter 3), all models suggest an
increase in temperatures across all seasons in the
coming century (Chapter 4).
Growing seasons will lengthen (medium evidence,
high agreement). There is a strong agreement
among information that an increase in temperature
will lead to longer growing seasons, but few studies
have specifically examined projected growing season
length in the assessment area.
Evidence at both global and local scales indicates
that growing seasons have been getting longer,
and this trend is expected to become even more
pronounced over the next century (IPCC 2007)
(see 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, Dragoni et al.
2011). Earlier springs and longer growing seasons
are expected to translate into shifts in the phenology
Figure 39.—Confidence determination used in the assessment.
Adapted from Mastrandrea et al. (2010).
of plant species that rely on temperature as a cue
for the timing of leaf-out, reproductive maturation,
and other developmental processes (Schwartz et al.
200a, Walther et al. 2002). Longer growing seasons
could also result in greater growth and productivity
of trees and other vegetation (Dragoni et al. 2011),
but only if sufficient water is available throughout
the growing season.
The nature and timing of precipitation will
change (robust evidence, high agreement). A
large number of global climate models agree that
precipitation patterns will change at both local and
global scales.
There is large variation in projected changes in
precipitation from global to local scales (IPCC
2007, Karl et al. 2009). Model projections for the
Central Hardwoods Region are in agreement for
an increase in precipitation in winter and spring
(Chapter 4). There is less model agreement later in
the growing season, but evidence seems to indicate
there may be a decrease in precipitation in either
summer or fall, depending on scenario (Chapter 4).
Even if the total annual amount of precipitation does
not change substantially, evidence suggests it may
occur as heavier rain events interspersed among
relatively drier periods (IPCC 2012), a trend that is
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
already occurring in the area (Saunders et al. 2012).
In addition, more winter precipitation is expected to
shift from snow or ice to rain as winter temperatures
rise (Brown and Mote 2009, Frei and Gong 2005).
An increase in heavy precipitation events
(medium evidence, medium agreement) will
increase flood risks (limited evidence, medium
agreement) and soil erosion (limited evidence,
medium agreement). There is disagreement
among models about whether the number of heavy
precipitation events will continue to increase in the
assessment area. If the number does increase, it is
expected that flooding and soil erosion will increase
as well, but these effects have not been modeled for
this region.
Heavy precipitation events have already been
increasing in number and severity in the area
(Groisman et al. 2012, Saunders et al. 2012), and
some models suggest an increase over the next
century (IPCC 2007, 2012). The magnitude or
frequency of flooding could potentially increase
in the winter and spring due to increases in total
runoff and peak streamflow during those time
periods (Cherkauer and Sinha 2010). Flood risks
will ultimately depend on local geology and soils as
well as human infrastructure and land use, however.
Increases in runoff following heavy precipitation
events, especially following periods of drought,
could also lead to an increase in soil erosion, which
may be exacerbated by a reduction in vegetation
cover from climate stress and fire (Nearing et al.
2004). However, a reduction in soil freeze-thaw
cycles across the region may help reduce soil erosion
to some extent (Sinha and Cherkauer 2010) because
freezing and thawing can break up soil aggregates,
making soil more susceptible to erosion.
Snow will decrease, with subsequent decreases
in soil frost (high evidence, high agreement).
Evidence suggests that winter temperatures will
increase in the area, even under low emissions,
leading to changes in snow and soil frost.
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The Central Hardwoods Region is already
experiencing a decline in snowfall, depth, and cover
(Chapter 3). Decreased snowfall and increased
snowmelt from higher temperatures are projected to
decrease the amount of snow on the ground in the
region, and may make some locations snow-free in
some years (Sinha and Cherkauer 2010). In recent
years, this reduction in snow cover has led to an
increase in soil frost from decreased snow insulation
(Sinha et al. 2010). However, as temperatures
increase in the coming decades, this pattern is
projected to reverse, and far southern Illinois and
Indiana may no longer experience freezing soil
conditions by the end of the century (Sinha and
Cherkauer 2010). Although these conditions could
increase water infiltration into the soil and reduce
runoff, they could also lead to greater soil water
losses through increased evapotranspiration. This
decrease in snow cover and frozen soil is projected
to be coupled with more heavy precipitation events
during winter, which are expected to occur as rain
instead of snow (Wang and Zhang 2008).
Soil moisture patterns will change (medium
evidence, high agreement), with drier soil
conditions later in the growing season (medium
evidence, low agreement). Some studies show that
climate change will have impacts on soil moisture,
but there is disagreement among impact model
projections on how soil moisture will change during
the growing season.
Due to projected decreases in precipitation during
summer or fall and increases in temperature
throughout the year, some evidence suggests a
slight decrease in surface soil moisture in the
Central Hardwoods Region over the next century
(Mishra et al. 2010). In addition, total soil moisture
is projected to increase during winter and spring
and decrease in the late summer and autumn
(Diffenbaugh and Ashfaq 2010, Mishra et al. 2010).
Even if there are increases in precipitation in the
summer, as a few models suggest, increases in
evapotranspiration are projected to lead to lower
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
soil water availability (Mishra et al. 2010, Ollinger
et al. 2008). Even a slight decrease in soil moisture
could lead to dramatic declines in tree species,
especially broadleaf species (Choat et al. 2012).
However, model projections vary, and at least one
study in Illinois suggests that increases in summer
precipitation may be sufficient to offset increases in
evapotranspiration (Winter and Eltahir 2012).
Droughts will increase in duration and area
(medium evidence, low agreement). A study using
multiple climate models suggests that drought may
increase in extent and area, but another suggests a
decrease in drought.
The 2012 drought dramatically affected the
assessment area, with many areas reaching
“exceptional” drought status. However, droughts
have generally been decreasing in frequency
across the area, and overall there is relatively
low confidence in the projected future trajectory
of agricultural, meteorological, and hydrologic
droughts across the central United States (IPCC
2012) (see Chapters 3 and 4). The projected changes
in duration of droughts in Illinois and Indiana over
the next century vary among model, scenario, and
time period, with most projecting an increase in
drought duration (Mishra et al. 2010). In addition,
the spatial extent of droughts is projected to increase,
indicating that future droughts may shift from local
to more regional phenomena (Mishra et al. 2010).
Since many species are already functioning at their
hydraulic limits, even a small increase in drought
could lead to widespread decline and mortality
(Choat et al. 2012). However, there is still the
possibility that conditions will become wetter in
the area during summer months, decreasing the
possibility of drought (Winter and Eltahir 2012).
The intensity of precipitation events and associated
infiltration or runoff will strongly affect how
ecosystems experience drought.
Climate conditions will increase fire risks by
the end of the century (medium evidence, high
agreement). National and global studies agree that
wildfire risk will increase in the area, but few studies
have specifically looked at the Central Hardwoods
Region.
At a global scale, the scientific consensus is that fire
risk will increase by 10 to 30 percent due to higher
summer temperatures and occasional increased
periods of droughts (IPCC 2007). Projections for the
central United States show low agreement among
climate models on changes in fire probability in
the near term, but the majority of models project
an increase in wildfire probability by the end of
the century (Moritz et al. 2012). Fire seasons in
the southeastern United States could nearly double
in length and increase in severity (Flannigan et
al. 2000, Liu et al. 2010). In addition to the direct
effects of temperature and precipitation, increases
in fuel loads from pest-induced mortality could
also increase fire risk, but the precise relationship
between these two factors can be complex (Hicke
et al. 2012). The extensive fragmentation of forests
by roads, agriculture, and other land uses in much
of the Central Hardwoods may limit the scale of
individual fires even as fire risk increases.
Many invasive plants, insect pests, and pathogens
will increase or become more severe (medium
evidence, high agreement). Evidence suggests that
an increase in temperature and greater ecosystem
stress will lead to increases in these threats, but
research to date has examined few species.
A warming climate is allowing some invasive plant
species, insect pests, and pathogens to survive
farther north than they had previously (CCSP 2008,
Dukes et al. 2009). One particular emerging threat to
the region is the southern pine beetle, which attacks
shortleaf and other pines (Ungerer et al. 1999). Oak
decline, a disease complex brought about by drought
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
and other stressors, is expected to become a larger
problem in the red oak group as droughts become
longer and more widespread (Haavik et al. 2011).
Some drought- and fire-tolerant invasive plants, such
as sericea lespedeza, may also benefit from projected
climate changes. In addition, a warming climate may
make conditions more favorable for invasive species
that are currently invading from south of the area,
such as kudzu (Bradley et al. 2010).
Potential Impacts on Ecosystems
Shifts in drivers and stressors mentioned above are
expected to lead to shifts in suitable habitat for some
dominant species and changes in composition and
function of the natural communities in the Central
Hardwoods Region.
Suitable habitat for northern species will decline
(medium evidence, high agreement). All three
impact models project a decrease in suitability for
northern species such as sugar maple, American
beech, and white ash compared to current climate
conditions.
Across northern latitudes, warmer temperatures will
be more favorable to species that are located at the
northern extent of their range and less favorable to
those in the southern extent (Parmesan and Yohe
2003). Results from climate impact models suggest
a decline in suitable habitat for northern species
such as sugar maple, white ash, and American beech
when compared with habitat suitability under current
climates (Chapter 5). These northern species may
be able to persist in some southern portions of their
range if potential new competitors from farther south
are unable to colonize these areas (Iverson et al.
2008), although they are expected to have reduced
vigor and be under greater stress.
Habitat is projected to become more suitable
for southern species (medium evidence, high
agreement). All three forest impact models project
an increase in suitability for southern species such
as shortleaf pine.
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Model results suggest an increase in suitable
habitat for many species at or near the northern
extent of their current range, including shortleaf
pine, post oak, and blackjack oak (Chapter 5). In
addition, habitat may become favorable to species
not currently found in the assessment area, such as
loblolly pine. However, habitat fragmentation and
the limited dispersal ability of seeds are expected
to hinder the northward movement of the more
southerly species despite the increase in habitat
suitability (Ibañez et al. 2008). Most species can be
expected to migrate more slowly than their habitats
will shift (Davis and Shaw 2001). Indeed, in a
simulation for five species, only a maximum of
15 percent of newly suitable habitat would have
much of a chance of getting colonized over 100
years (Iverson et al. 2004a,b).
Communities will shift across the landscape
(low evidence, high agreement). Few models have
examined community shifts specifically, but model
results from individual species and ecological
principles suggest a potential shift in communities.
Decoupling of drivers, stressors, and dominant
species that defined communities is expected
to lead to a rearrangement across the landscape
of suitable conditions for natural communities
within the assessment area. As a result, traditional
community relationships may dissolve, as has
occurred in the past according to paleoecological
evidence (Davis et al. 2005, Root et al. 2003, Webb
and Bartlein 1992). Shifts in overstory structure
may follow more predictable pathways based on
shifts in soil moisture, fire frequency, and flooding.
However, future species composition, especially
in the understory, may not be representative of
what currently composes these systems (Root et
al. 2003). If associated species such as pollinators
and mycorrhizae do not migrate into newly suitable
areas, further constraints could be placed on native
species colonization (Clark 1998). Thus, nonnative
invasive plants may be better able to fill newly
created niches (Hellmann et al. 2008).
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Increased fire frequency and harvesting may
accelerate shifts in forest composition across
the landscape (medium evidence, medium
agreement). Studies from other regions (e.g.,
northern hardwoods and boreal forests) show that
increased fire frequency can accelerate the decline
of species negatively affected by climate warming
and accelerate the northward migration of southern
tree species.
Frequent, low-intensity fires can reduce or inhibit
the seedling establishment of tree species negatively
impacted by climate warming such as sugar maple.
In addition, infrequent, high-intensity fires can
remove mature trees and release growing space for
tree species that may be better adapted to future
conditions. Sites exposed to fire (including lowintensity prescribed fire) are expected to undergo
an accelerated transition in forest composition
compared to those under fire suppression (He
et al. 2002, Shang et al. 2004). In addition, forest
harvesting in the Central Hardwoods Region is often
targeted at species that are dominant under current
climate conditions. Evidence from other regions
suggests harvesting of declining species may help
promote the growth of other species that are better
adapted to projected changes, thereby accelerating
a shift in forest composition (He et al. 2002).
A major transition in forest composition is not
expected to occur in the coming decades (medium
evidence, medium agreement). Although some
models indicate major changes in habitat suitability,
results from spatially dynamic forest landscape
models indicate that a major shift in forest
composition across the landscape may take 100
years or more in the absence of major disturbances.
Model results from Tree Atlas and LINKAGES
indicate substantial changes in habitat suitability
or establishment probability for many species on
the landscape, but do not account for migration
constraints or differences among age classes.
Forest landscape models such as LANDIS PRO
can incorporate spatial configurations of current
forest ecosystems, seed dispersal, and potential
interactions between native species and the invasion
and establishment of nonnative plant species (He
et al. 1999, 2005). In addition, forest landscape
models can account for differences among age
classes, and have generally found mature trees to
be more tolerant of warming (He and Mladenoff
1999). Because mature trees are expected to remain
on the landscape, and recruitment of new species
is expected to be limited, it is not expected that
major shifts in species composition will be observed
in the near future, except in areas that undergo
harvests or major stand-replacing disturbance
events (CCSP 2008). Climate change is projected
to increase the intensity, scope, or frequency of
some stand-replacing events such as wildfire and
insect outbreaks, making major shifts in species
composition possible where these events occur
(CCSP 2008).
Little net change in forest productivity is
expected (medium evidence, low agreement). A
few studies have examined the impact of climate
change on forest productivity, but they disagree on
how multiple factors may interact to influence it.
Increases in drought, invasive plants, insects,
disease, and wildfire are expected to negatively
affect forest productivity in some parts of the region
(Hanson and Weltzin 2000). Lags in migration of
species to newly suitable habitat may also result
in reduced productivity, at least in the short term.
However, some of these declines may be offset by
the positive effects increased carbon dioxide (CO2)
has on photosynthetic rates and water use efficiency,
and by a longer growing season (Drake et al.
1997). Changes in productivity may be mixed and
localized, with warming and CO2-induced increases
in some areas and decreases from pests, diseases,
and other stressors in others (Medlyn et al. 2011).
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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.
It is strongly related to the concept of resilience
(CCSP 2009). Summarized below are factors that
could affect the adaptive capacity of systems within
the Central Hardwoods Region, influencing overall
vulnerability to climate change.
Low-diversity systems are at greater risk
(medium evidence. high agreement). Studies in
other areas have consistently shown that diverse
systems are more resilient to disturbance, but studies
examining this relationship have not been conducted
in the assessment area.
Species-rich communities have exhibited greater
resilience to extreme environmental conditions
and greater potential to recover from disturbance
(Tilman 199, 1999). Conversely, ecosystems
that have low species diversity or low functional
diversity (where multiple species occupy the same
niche) may be less resilient to climate change, its
associated stressors, or both (Peterson et al. 1998;
Walker 1992, 1999). For example, the mountain pine
beetle has devastated the conifer-dominated forests
in the Rocky Mountains; stands with low diversity
of species, age classes, and genotypes have been
more vulnerable to outbreaks than diverse stands
(Raffa et al. 2008). Genetic diversity within species
is also critical for the ability of populations to adapt
to climate change, because species with high genetic
variation tend to have more individuals that can
withstand a wide range of environmental stressors
(Reusch et al. 2005).
Species in fragmented systems will have a
reduced ability to expand into new areas (limited
evidence, high agreement). Evidence suggests that
species may not be able to disperse the distances
required to keep up with climate change, but little
research has been done in the region on this topic.
124
Habitat fragmentation can hinder the ability of
species to migrate to more suitable habitat on the
landscape, especially if the surrounding area is
nonforested (Iverson et al. 2004a,b; Noss 2001).
Modeling results in this assessment and elsewhere
indicate that trees would need to migrate at rates of
hundreds of feet to several miles per year to keep
pace with the changes in climate that are projected
to occur over the next century (Iverson and Prasad
2002, Petit et al. 2008). Species in community types
that tend to be more rare and fragmented may be at
a particular disadvantage (CCSP 2009). This rate
of migration may be unattainable through natural
means, even in the absence of fragmentation (Davis
and Shaw 2001, McLachlan et al. 2005). Humans
may be able to assist in the migration of species to
newly suitable areas, but this kind of intervention
remains a contentious issue for many species,
especially those of conservation concern (Pedlar
et al. 2012, Schwartz et al. 2012).
Fire-adapted systems will be more resilient
to climate change (high evidence, medium
agreement). Studies have shown that fire-adapted
systems are better able to recover after disturbances
and can promote many of the species that are
expected to do well under a changing climate.
In general, fire-adapted systems that have a more
open structure and composition are less prone to
high-severity wildfire (Shang et al. 2004). Frequent
low-severity fire has also been shown to promote
many species projected to do well under future
climate projections, such as shortleaf pine and many
oak species (Brose et al. 2012, Dey and Hartman
2005, Stambaugh et al. 2002). Fire-suppressed
systems, on the other hand, tend to have heavy
encroachment of woody species in the understory
that reduce regeneration potential for these fireadapted trees (Fralish et al. 1991, Lorimer 1985,
Nowacki and Abrams 2008). In addition, firesuppressed systems can be more vulnerable to insect
attack (McCullough et al. 1998). Since the mid1900s, lack of fire has led to at least a temporary
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
increase in sugar maple in the eastern portion of
the assessment area (Ozier et al. 200), and this
species is not projected to fare well under projected
climate change (Chapter 5). However, it is important
to note that effects of fire on species regeneration
and disturbances can vary by site, species, and burn
regime (Brose et al. 2012, McCullough et al. 1998).
Systems that are highly limited by hydrologic
regime or geologic features may be
topographically constrained (limited evidence,
medium agreement). Our current understanding of
the ecology of Central Hardwoods systems suggests
that some communities will be too topographically
constrained to migrate to new areas.
Communities that require specific hydrologic
regimes, unique soils or geology, or narrow
elevation ranges may not be able to migrate to new
areas, even if conditions are favorable. For example,
flatwoods ecosystems have soils that are seasonally
saturated in the spring and dry in the summer or
fall. Even though future climate conditions should
favor species adapted to this soil moisture pattern,
these systems are constrained to areas with a lowpermeability soil layer (Taft et al. 1995), making
it doubtful they will spread to new areas. Glade
ecosystems are also strongly tied to specific geologic
features (Kucera and Martin 1957), and thus are
not expected to expand to new areas even though
climate conditions may be favorable. If conditions
worsen for these systems, it is doubtful that alternate
sites will be hospitable for these communities.
ASSeSSinG vuLneRABiLiTy
of CenTRAL hARDWooDS
CommuniTieS
Shifts in drivers, stressors, and dominant tree species
are expected to affect each natural community within
the assessment area in a unique way, and some
communities may have a greater capacity to adapt to
these changes than others. These considerations can
lead to relative differences in vulnerability among
natural communities to projected changes in climate
over the next century. Vulnerability was assessed for
nine community types selected from those described
in Chapter 1 (Table 1). A panel of 20 experts from
across the assessment area evaluated the evidence on
the potential impacts and adaptive capacity of each
community type and assigned a level of confidence
in that evidence by using the same confidence scale
described above. For a description of the methods
used to determine vulnerability, see Appendix 10.
Vulnerability of the nine communities assessed
ranged from low to high (Table 17). In general, there
was more consistency in the experts’ assessment
of potential impacts than in their assessment of
adaptive capacity (see Appendix 10). The ratings
of agreement among information and the amount
of evidence tended to be in the medium range. In
general, ratings were slightly higher for agreement
than for evidence. Evidence appears not to be as
robust as the experts would like, but what evidence
is available leads to a similar conclusion.
As an input to determining vulnerability, projected
changes in distributions (summarized in Chapter 5)
of tree species that are dominant in each community
type were synthesized across models and organized
into four categories (Table 18). “Winners” were
species that were projected to increase under both
climate scenarios and both forest impact models
(if available). “Losers” were species projected to
decrease. Those labeled as “little change” had only
slight projected increases or decreases, or modifying
factors cancelled out any projected changes. Species
labeled as “conflicting evidence” showed some
discrepancy among climate scenarios or impact
models on whether they will increase or decrease. In
cases where there is geographic variation in potential
outcomes, state abbreviations indicate the area that
would be affected.
The specific impacts on drivers, stressors, and
dominant tree species that contribute to the potential
impacts on each community type are summarized
on the following pages. Factors contributing to the
adaptive capacity of each community type are also
summarized.
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Table 16.—Natural communities assessed for vulnerability. For a more complete description of these communities
and their major drivers and stressors, see Chapter 1.
Community Type
Current Major Drivers
Current Major Stressors
Dry-mesic upland forest
dry-mesic moisture regime; low fire frequency
decrease in fire frequency; oak decline; reduction
in shortleaf pine; nonnative species invasion
Mesic upland forest
cooler temperatures; mesic moisture regime;
absence of fire
deer overbrowsing; emerald ash borer; nonnative
species invasion
Mesic bottomland forest
short, infrequent floods; mesic moisture regime changes to flood regime; nonnative species
invasion; sedimentation from erosion
Wet bottomland forest
prolonged, frequent flooding; wet, poorly
drained soils
changes to flood regime; nonnative species
invasion; sedimentation from erosion; emerald
ash borer
Flatwoods
soils wet in cool season, dry in summer;
claypan or fragipan layer; frequent, lowmoderate intensity fires
woody plant invasion; overgrazing; conversion to
nonnative cool-season grasses and fescue
Closed woodland
well-drained soils; steeper slopes than open
woodland; frequent, low-intensity fires
fire exclusion; woody species encroachment
in understory; oak decline; nonnative species
invasion
Open woodland
well-drained soils; frequent, low-intensity fires
fire exclusion; woody species encroachment
in understory; oak decline; nonnative species
invasion; overgrazing
Barrens and savanna
frequent low-intensity fires; shallow,
excessively well drained soils (barrens); deeper,
more nutrient-rich soils (savannas)
fire exclusion; nonnative species invasion;
overgrazing; conversion to fescue; fragmentation
Glade
shallow soils with exposed bedrock; frequent,
low-intensity fires
soil erosion; feral hogs; overgrazing; fire exclusion;
eastern redcedar invasion
Table 17.—Vulnerability determinations by natural community type. See Appendix 10 for a description of the relative
ratings.
Community Type
Potential Impacts
Adaptive Capacity
Vulnerability
Evidence
Agreement
Dry-mesic upland forest
Moderate
High
Low-Moderate
Medium
Medium-High
Mesic upland forest
Negative
Low
High
Medium
Medium-High
Mesic bottomland forest
Moderate
Moderate
Moderate
Limited -Medium
Medium
Wet bottomland forest
Moderate-Negative
Moderate
Moderate-High
Limited-Medium
Medium
Flatwoods
Moderate- Positive
Moderate
Low-Moderate
Limited-Medium
Medium
Closed woodland
Positive
High
Low
Limited
Medium
Open woodland
Positive
High
Low
Limited-Medium
Medium
Barrens and savanna
Positive
Moderate
Low
Medium
Medium-High
Moderate- Positive
Moderate
Low-Moderate
Medium
Medium-High
Glade
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Table 18.—Projected changes in dominant species by end of century for each community type. Projections are based
on a synthesis of Tree Atlas, LINKAGES, and LANDIS PRO results under both high and low emissions scenarios, taking
modifying factors into account. Note that these projections are for the entire assessment area, and species impacts
will vary geographically due to site-specific conditions. Climate scenario disagreement indicates that the climate
scenarios disagreed strongly on the direction of change for that species; refer to Chapter 5 and Appendix 9 for more
details.
Community Type
Winners
Little Change
Losers
Climate Scenario Disagreement
Dry-mesic upland forest
shortleaf pine,
yellow-poplar (MO),
red maple (MO)
white, black (IN)
oak; pignut (IL, IN),
bitternut, mockernut
hickory; red maple
(IL, IN)
scarlet oak (MO),
shagbark and
pignut (MO) hickory,
sugar maple
black (IL, MO), northern red,
and scarlet (IL, IN) oak;
yellow-poplar (IL, IN)
Mesic upland forest
yellow-poplar (MO),
red maple (MO)
white oak,
bitternut hickory,
red maple (IL, IN),
black cherry (MO)
sugar maple,
American beech,
American basswood
(IN), white ash,
black cherry (IL, IN)
northern red oak,
yellow-poplar (IL, IN),
American basswood (MO, IL)
Mesic bottomland forest
bur oak (IL, IN),
sweetgum,
eastern cottonwood
bur oak (MO);
white, bitternut
hickory; sycamore,
hackberry, American
and slippery elm
sugar maple,
American beech,
black walnut
Wet bottomland forest
overcup, willow,
pin (MO, IN) oak;
boxelder; silver,
red (MO) maple;
eastern cottonwood
pin oak (IL),
shellbark hickory
(IL, IN),
red maple (IL,IN),
black willow (IL, IN)
shellbark hickory
(MO), green ash*
Flatwoods
shortleaf pine;
blackjack, post,
pin (MO,IN) oak;
blackgum
pin oak (IL),
mockernut hickory
shagbark hickory
Closed woodland
shortleaf pine
white and
black (IN) oak,
mockernut hickory
scarlet oak (MO),
shagbark hickory
black (IL, MO)
and scarlet (IL,IN) oak
Open woodland
shortleaf pine;
white and black (IN)
blackjack, post oak; oak, mockernut and
black hickory (IL, IN); black (MO) hickory
eastern redcedar±
scarlet oak (MO),
shagbark hickory
black (IL, MO), scarlet (IL,IN),
and chinquapin oak
Barrens and savanna
shortleaf pine;
blackjack, post,
bur (IL, IN) oak;
black hickory (IL, IN),
eastern redcedar±
shagbark hickory
black (IL, MO)
and chinquapin oak
Glade
post oak,
eastern redcedar±
white, black (IN),
bur (MO), and
chestnut oak;
black hickory (MO)
black willow (MO)
* Green ash is projected to remain stable due to climate alone, but the threat of emerald ash borer makes this species vulnerable.
± Eastern redcedar is projected to remain stable due to climate alone, but other factors will allow it to expand to new areas.
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Dry-Mesic Upland Forest
Low-Moderate Vulnerability (medium evidence, medium-high agreement)
Increases in temperature, coupled with potential decreases in soil moisture and increases in wildfire,
could be favorable for some species and detrimental to others. However, a wide distribution and high
species diversity may enhance the adaptive capacity of dry-mesic systems and allow them to persist on
the landscape.
Moderate Potential Impacts
Drivers—There is currently little evidence
regarding the potential effects of climate change
on several important factors for this type, including
the potential severity of fire and frequency of
intermittent droughts during the growing season.
An increase in fire frequency is expected to have
positive effects on overstory tree species, but may
have negative impacts on the understory. If fires
become too severe or frequent, this type could shift
toward a woodland or savanna. If soil moisture
decreases in the summer, it could have a negative
impact on the system.
Dominant Species—The forest impact models tend
to agree about how certain species are projected to
decline or increase. Climate change is not projected
to have a large influence on many of the dominant
tree species in this community type (Table 18).
Habitat suitability for shortleaf pine is projected to
increase, while habitat suitability for sugar maple
is projected to decline. Although white oak is
projected to decline slightly based on temperature
and precipitation alone, its tolerance to drought and
fire should allow it to persist. Changes in the red oak
group (northern red, scarlet, and black oak) tend to
vary with climate scenario and are expected to be
driven by the extent to which oak decline affects the
area in the future (see stressors).
Stressors—A major current stressor has been a
decrease in fire frequency, leading to an increase in
sugar maple in the eastern part of the assessment
128
area and a decrease in shortleaf pine in Missouri.
If conditions improve for fire, and soil moisture
decreases, these factors could lead to a reduction
in this current stressor. Oak decline is expected
to remain a threat to the red oak group, and may
become a larger threat to trees that become stressed
by increased drought frequency. Many nonnative
invasive plant species are expected to continue to
be a problem. However, one of the many invasive
plants, garlic mustard, is relatively droughtintolerant and could decrease if conditions become
significantly drier during the growing season.
Southern pine beetle could become a new threat to
the area as the area warms, especially if the shortleaf
pine component increases.
High Adaptive Capacity
This community type is widely distributed on a
variety of soils and topographies, making it probable
that at least some of these areas will remain suitable
in the future. This type also tends to have high tree
species diversity relative to other community types
in the assessment area, allowing for some species
to increase in abundance as others decrease. This
community type tends to develop on more welldrained soils in the east than in the west. Therefore,
eastern communities may be less buffered against
drought conditions than western communities, but
more evidence is needed to support this claim. Any
declines in this community type on drier sites may
be offset by transition from more mesic forests to
this type.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Dry-mesic upland forest. Photo by Paul Nelson, Mark Twain
National Forest.
Missouri Ozark forests in autumn. Photo by Steve Shifley, U.S.
Forest Service.
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Mesic Upland Forest
High Vulnerability (medium evidence, medium-high agreement)
Changes in climate are expected to reduce habitat suitability for mesic upland forests and the species that
currently dominate them in the Central Hardwoods Region. Increases in fire and drought are key factors
that may reduce the adaptive capacity of this system, making it expected that this community type will be
one of the most negatively affected by projected climate changes.
Negative Potential Impacts
Drivers—This community type is adapted to
cooler, wetter conditions that are typical of northfacing slopes and ravines. A projected increase in
temperature and decrease in precipitation during the
growing season is expected to have negative impacts
on the community. The increased risk of wildfire
projected by the end of the century could have
negative impacts on this fire-intolerant community.
Dominant Species—Few of the current dominant
species are projected to increase under any of the
model projections (Table 18). Many of the species
in this system are at the southern extent of their
range, which makes this community type, and the
species within it, susceptible to extensive changes
in the area under warmer conditions. In particular,
sugar maple, the most dominant species in this
type, is projected to decline significantly under both
scenarios. Bitternut hickory, red maple, and white
oak are among the few species for which conditions
may continue to be favorable in some areas. There is
also disagreement between the two climate models
about whether northern red oak, yellow-poplar, and
American basswood would increase or decline.
130
Stressors—Current stressors such as overbrowsing
by deer in some areas and nonnative species
invasion such as emerald ash borer, are expected
to continue to be problems. Some invasive plant
species, such as bush honeysuckle and kudzu, may
benefit from the extended growing season length and
warmer winters. It is hypothesized that nonnative
plant species such as these will fill in the gaps
created as dominant species decline.
Low Adaptive Capacity
Several factors reduce the adaptive capacity of this
system. Mesic uplands are generally intolerant of
fire and drought, which are expected to increase
in the area. Because this type currently occupies
the coolest, wettest (but not flooded) sites, newly
suitable sites are not expected to arise within the
assessment area. However, this community type
may continue to persist in some places, especially
at the eastern end of the assessment area. Areas
slightly downslope from current areas (but above
the floodplain) and north-facing coves may act as
refugia throughout the landscape. In addition, a high
soil water-holding capacity in many locations might
buffer this community from drought and wildfire and
allow it to persist on the landscape.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Mesic upland forest. Photo by Paul Nelson, Mark Twain National
Forest.
Mesic upland forest. Photo by Paul Nelson, Mark Twain National
Forest.
Mesic upland forest. Photo by Paul Nelson, Mark Twain National
Forest.
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Mesic Bottomland Forest
Moderate Vulnerability (limited-medium evidence, medium-high agreement)
Changes in climate are projected to be favorable or neutral to many of the dominant species in mesic
bottomland forests, but an increase in flooding could have negative impacts. However, the connectivity of
this type along rivers may allow the species in this system to migrate to newly suitable areas.
Moderate Potential Impacts
Drivers—This system is characterized by short,
infrequent floods. A projected increase in heavy
precipitation in the winter and spring could
potentially increase the duration and frequency
of flooding, having a negative impact on this
community.
Dominant Species—The models used in this
assessment are not equipped to capture the complex
hydrologic processes that occur in these systems, so
actual habitat suitability might differ from what is
projected. With that caveat in mind, the models tend
to agree about the general trajectory of the dominant
species in these systems. Climate conditions are
projected to be more favorable for sweetgum and
eastern cottonwood, and remain relatively stable
for species such as bitternut hickory, sycamore, and
white oak (Table 18). Several species are projected
to decline in abundance, such as American beech and
black walnut. Boxelder is not currently a dominant
species in this community type, but may increase
in abundance because of its positive relationship to
projected climate conditions.
132
Stressors—Alteration to the landscape by human
activity has led to changes in flood regimes for
this community type, which may be exacerbated
by changes in precipitation or increased human
demands on watersheds during drought periods. In
addition, heavy precipitation events could intensify
soil erosion in these areas. Scouring floods could
also increase the spread of many of the invasive
plants that threaten these areas.
Moderate Adaptive Capacity
Bottomland systems are not as well understood
as upland systems, and are largely unmanaged.
However, seeds from species like sycamore, elm,
sweetgum, cottonwood, and hackberry can readily
disperse downstream to newly suitable locations.
This type’s association with floodplains along
riverways increases its connectivity, facilitating
migration. A number of species, such as bur oak and
cottonwood, tolerate a wide range of conditions,
including drought. This system is largely constrained
by topography, and there may be an even smaller
range of suitable sites under future conditions. This
community type could be at risk for both droughts
and floods, and species in this type have the
opportunity to migrate farther downslope or
upslope to escape drought or flood risks.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Mesic bottomland forest. Photo by Paul Nelson, Mark Twain
National Forest.
Riparian forest along the Cache River, Illinois. Photo by Susan
Crocker, U.S. Forest Service.
Wet mesic bottomland. Photo by Paul Nelson, Mark Twain National Forest.
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Wet Bottomland Forest
Moderate-High Vulnerability (limited-medium evidence, medium agreement)
The future of wet bottomland forests largely depends on how flood dynamics will change, which remains
largely unknown. Any change in flood dynamics is expected to have a negative impact. This system
already occupies the lowest-lying areas on the landscape, so it has a limited capacity to occupy new areas
if conditions change.
Moderate-Negative Potential Impacts
Drivers—This community type is characterized by
prolonged, frequent flooding and wet, poorly drained
soils. Although flooding is expected to increase
during some parts of the year, this community type
may also become drier in the summer or fall.
Dominant species—Many of the species that
dominate this type, such as willow oak, overcup
oak, and shellbark hickory, are relatively rare
across the landscape as a whole, reducing overall
model reliability. Although green ash is projected
to remain stable due to climate projections alone,
emerald ash borer will almost certainly lead to
reductions in this species. Other species may be able
to persist, including boxelder, red maple, and eastern
cottonwood. There are many unknowns regarding
shifts in flood regime and their potential impacts on
the dominant species in this community type.
134
Stressors—Stressors for this type are similar to
mesic bottomland communities, including alteration
of flood regime and erosion leading to sediment
buildup. If conditions become drier, this system may
be threatened by encroachment of mesic bottomland
species. If conditions lead to semi-permanent
flooding in some areas, this type could convert to a
more swamp-like system.
Moderate Adaptive Capacity
Although this community type is highly tolerant of
flooding and species have high dispersal ability, it
has several factors that reduce its adaptive capacity.
Low species diversity reduces its potential to persist
as a community. This community type is highly
constrained by topography, and cannot migrate
any farther downslope to avoid dry conditions if
they occur. However, an increase in flooding could
potentially create opportunities for restoration of this
community type in some bottomland areas if other
land uses, such as farmland, are abandoned.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Wet bottomland forest. Photo by Paul Nelson, Mark Twain National Forest.
Wet bottomland understory. Photo by Paul
Nelson. Mark Twain National Forest.
Wet bottomland forest. Photo by Paul Nelson, Mark Twain National Forest.
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Flatwoods
Low-Moderate Vulnerability (limited-medium evidence, medium agreement)
Climate change projections suggest that many of the conditions that are favorable to flatwoods
communities and their dominant species will be intensified, such as spring flooding, late season drying,
and frequent fire. However, this system’s geological limitations and low overstory diversity limit its
adaptive capacity.
Moderate-Positive Potential Impacts
Drivers—This system is characterized by soils
that are saturated during the cool season and dry
during the summer. This soil moisture pattern is
expected to be intensified in the future as winter
and spring precipitation increases and summer
or fall precipitation decreases. This change could
have positive or negative impacts on the system
depending on the relative magnitude of these
changes. In addition, this system is adapted to
frequent low- to moderate-intensity fire. The
projected increase in fire frequency could have
a positive impact on this system as long as fire
severity is not too high.
Dominant species—The model projections
presented for the species that dominate this system
are for the entire assessment area, and may not
reflect the trajectories of the individuals in this
uncommon community type. With that caveat in
mind, the projected trajectories are similar across the
range of climate models presented. Most dominant
species in this community type are projected to
increase or remain relatively stable under both
climate scenarios. The only dominant species not
projected to do well under future change is shagbark
hickory. Although blackgum is projected to do well
overall, it may be negatively impacted if droughts
become too severe.
13
Stressors—Current stressors to this system include
invasion of the understory by woody plants, reed
canarygrass, and fescue. In the short term, increases
in CO2 could make conditions more favorable
for cool-season grasses like reed canarygrass and
fescue. However, increases in temperature coupled
with decreases in water availability during summer
could have negative impacts on these species toward
the end of the century (Yu et al. 2012). Woody
plant encroachment is largely the result of fire
suppression. As conditions become more favorable
for fire by the end of the century, a reduction in
woody plant encroachment could occur, depending
on management actions and the fragmented nature of
the landscape.
Moderate Adaptive Capacity
This community type is unique in its ability to
handle a wide range of disturbances, including
drought, flooding, and fire. However, it is strongly
tied to geologic and soil conditions and therefore
is not usually able to expand to new areas. This
community type has low overstory species
diversity, which could result in canopy loss if one
or two species disappear or decline severely. This
community type is also rare across the landscape,
reducing the probability that it will be able to persist
in some locations.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Flatwoods. Photo by Paul Nelson, Mark Twain National Forest.
Saunders Woods, Indiana. Photo used with permission of John
Shuey, The Nature Conservancy, Indiana.
Upland flatwoods. Photo by Paul Nelson, Mark Twain National Forest.
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Closed Woodland
Low Vulnerability (limited evidence, medium agreement)
The vulnerability of closed woodlands largely depends on how both natural and human-caused fire
dynamics may change over the next century. However, most of the overstory species are expected to
persist under projected climate change over a range of fire conditions. In addition, the wide distribution
and high tolerance to disturbance of this system should be beneficial.
Positive Potential Impacts
Drivers—This community type is characterized by
frequent, low-intensity fires, which are expected
to become more common as conditions become
warmer and drier. However, if fire severity increases
too greatly, fire could have a negative impact. This
type is common on excessively well-drained soils
with steeper slopes than open woodlands. Because
this type is adapted to low soil moisture conditions,
decreases in soil moisture during the growing season
should not have a strong negative impact.
Dominant Species—The modeled trajectory of the
species that dominate these systems is mixed, but
the model projections tend to agree with one another.
Species in this system should do well in general.
Only one of the dominant species in this community
type, shagbark hickory, is projected to decline (Table
18). Shortleaf pine is projected to increase, while
white oak and mockernut hickory are projected
to remain relatively stable. Changes in black
and scarlet oak may depend on whether summer
precipitation increases or decreases. In addition, this
system is also defined by its herbaceous layer, and
no information is available on how these species
may respond to future climatic conditions.
Stressors—Past fire exclusion has led to an
increase in woody species in the understory. This
change in composition has suppressed regeneration
of overstory species in the eastern part of this
138
community type’s range, and suppressed herbaceous
species establishment in the western part. An
increase in fire frequency could help reduce this
stressor. Oak decline is expected to remain a threat
to black and scarlet oak, and may become a larger
threat to trees that become stressed by an increased
duration and extent of drought conditions, which
appear to be more likely under the GFDL A1FI
scenario. Nonnative invasive plants are expected to
continue to be a problem in the future, but increased
drought could decrease garlic mustard invasion.
Southern pine beetle could become a new threat to
the area in communities dominated by shortleaf pine.
High Adaptive Capacity
This community type is widely distributed across the
western half of the assessment area and is tolerant
of fire and drought. This type has the potential
to expand if sites currently characterized as drymesic communities become drier and subject to
more frequent fire. The extent to which fire is a
component of the system may ultimately determine
the success of this community type. If the system
experiences frequent fire, this system could benefit
or undergo transition to an open woodland. If fire
is suppressed, it could shift to a dry-mesic forest.
The long-term fate of this system may also vary
dramatically from east to west, especially if black
and scarlet oak decline in the west because of
increased drought.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Closed woodland. Photo by Mike Leahy, Missouri Department of Conservation.
Closed woodland. Photo by Paul Nelson, Mark Twain National
Forest.
Shortleaf pine woodland. Photo used with permission of L-A-D
Foundation.
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Open Woodland
Low Vulnerability (limited-medium evidence, medium agreement)
Future conditions should be favorable for open woodlands and many of the species that dominate them,
but some current and potential stressors could be exacerbated by future climate conditions. In general,
this community type is expected to persist due to its drought tolerance and wide distribution.
Positive Potential Impacts
Drivers— This system is similar to closed woodland
systems but receives more frequent fire and tends to
be on flatter ridge-tops. Dry soils during the summer
coupled with conditions suitable for fire should be
beneficial for this type unless conditions become so
severe that dominant species can no longer tolerate
them. Early-season increases in precipitation that
result in vegetation growth, followed by summer
drought, may increase fire probability.
Dominant Species— Many tree species in this
community type are projected to do better under
future climate conditions, such as shortleaf pine,
blackjack and post oak, and black hickory (Table
18). Changes in black, chinquapin, and scarlet
oak may depend on whether summer precipitation
increases or decreases. Eastern redcedar, which
outcompetes herbaceous vegetation, is projected to
remain relatively stable under future climate changes
and could expand for other reasons. This outcome
could have a negative impact on the community.
Importantly, this community type is largely defined
by its species in the herbaceous layer, and no
information is available on how these species may
respond to future climatic conditions.
Stressors— An increase in fire frequency could
help reduce the stress of woody species invasion,
but eastern redcedar could continue to be a problem
in this type. Nonnative invasive plants are expected
140
to continue to be a problem in the future. Sericea
lespedeza invasion is a particular problem in this
community type and responds positively to both
drought and fire, making it potentially an even
greater problem in the future. Increased sericea
lespideza abundance could reduce regeneration of
tree species and change community structure. Other
herbaceous invasive species are less tolerant of fire
and may be reduced if fire frequency, severity, or
both increase. Southern pine beetle could become a
new threat to the area if shortleaf pine increases.
High Adaptive Capacity
This community type is widely distributed across
the western half of the assessment area and is fireand drought-tolerant. Across the assessment area,
the open woodland community type will probably
not decrease substantially and may even increase.
In general, this type may be most successful in the
western part of the assessment area, where soils
tend to be drier. Decreases in this community type
in areas that become too dry or fire-prone could be
offset by transition from closed woodlands to this
type. In these new areas, overstory woody species
may do better than understory herbaceous species
because many of the endemic herbaceous species are
not present in the seedbank and have a limited ability
to disperse. As with closed woodland systems, the
success of this type depends largely on fire regime
and long-term soil moisture.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Open woodland. Photo by Paul Nelson, Mark Twain National
Forest.
Open woodland. Photo by Mike Leahy, Missouri Department of
Conservation.
Open woodland. Photo by Paul Nelson, Mark Twain National
Forest.
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Barrens and Savanna
Low Vulnerability (medium evidence, medium-high agreement)
Conditions should generally be favorable to the species that dominate barrens and savannas, which are
generally adapted to fire and drought. However, a high level of fragmentation and a fragile understory
community could reduce the ability of this community type to take advantage of these favorable
conditions.
Positive Potential Impacts
Drivers—Barrens communities develop on
extremely shallow, well-drained soils, making them
well adapted to drought conditions. However, the
low water-holding capacity typical of soils in barrens
communities could increase stress under extreme
drought conditions. Savanna communities, which
are similar in structure to barrens, are characterized
by deeper, more nutrient-rich soils and may be more
buffered against drought. Barrens and savannas are
also fire-adapted and may benefit from increased fire
frequency as long as fires do not become frequent
enough to shift the system to a grassland community.
Dominant Species—Many species in this
community type are projected to do better under
future climate conditions, such as shortleaf pine,
blackjack oak, post oak, and black hickory (Table
18). Changes in black and chinquapin oak may
depend on whether summer precipitation increases
or decreases. Eastern redcedar, which can encroach
on glades, is projected to remain relatively stable
under future climate changes and could expand
for other reasons. Expansion of this species could
have a negative impact on the community. As with
woodlands, this system is largely defined by its
herbaceous species, and no information is available
on how these species may respond to future climatic
conditions.
142
Stressors—An increase in fire frequency could help
reduce the stress of woody species invasion, but
eastern redcedar could continue to be a problem in
this type. Nonnative invasive species are expected
to continue to be a problem in the future, as many
are drought-tolerant. These include autumn olive,
multiflora rose, teasel, white and yellow sweetclover,
sericea lespideza, and spotted knapweed. Garlic
mustard is among the few invasive plant species that
is not drought-tolerant. Southern pine beetle could
become a new threat to the area if shortleaf pine
increases.
Moderate Adaptive Capacity
Barrens and savanna systems are tolerant of both
drought and fire. As conditions become hotter,
and potentially drier, open woodlands could shift
into more open barrens or savanna systems. This
transition could lead to an increase in area occupied
by this type, which is currently extremely low (about
1 percent of the assessment area). As with open
woodlands, increased fire frequency and drought
duration could allow other communities to convert
structurally to barrens or savannas. However,
herbaceous and graminoid species that are typically
found in the understory in these communities may be
dispersal limited. Barrens and savanna communities
are currently rare and highly fragmented; many
lack a healthy herbaceous community. The adaptive
capacity of these systems is also largely dependent
on fire regime and whether they are on thin or more
well-developed soils.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Savanna. Photo used with permission of Paul Deizman, Illinois
Department of Natural Resources.
Barrens. Photo by Teena Ligman, Hoosier National Forest.
Burning savanna. Photo used with permission of Paul Deizman,
Illinois Department of Natural Resources.
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Glade
Low-Moderate Vulnerability (medium evidence, medium-high agreement)
Glade species tend to be tolerant of hot, dry conditions, which should allow them to persist. However,
there is a potential for increased soil erosion with increased heavy precipitation events, which could
have a negative impact on glades in the western part of the assessment area in particular. Areas that are
invaded by eastern redcedar may have a reduced capacity to adapt to projected changes.
Moderate-Positive Potential Impacts
Drivers—This community type develops on areas
of exposed bedrock with very thin soils. Thus it is
adapted to hot, xeric conditions during the growing
season, which are projected to become more
common in the future. This system is also adapted to
frequent, low-intensity fires, which could possibly
increase by the end of the century.
Dominant Species—Unlike the other communities
assessed, glades are dominated by herbaceous
species, with a few sparse trees distributed
throughout. Although future distribution of these
herbaceous species has not been modeled, many are
adapted to the hot, dry conditions that are expected
to become more common. Despite this advantage,
several modeling studies in other areas have shown
that species with narrow geographic distributions,
like many glade species, are more at-risk to climate
change (Broennimann et al. 200, Damschen et
al. 2010, Loarie et al. 2008). Post oak is typically
found in glade systems, and is projected to increase.
Eastern redcedar invasion has led to dominance of
this species in glades, and it is projected to expand
in the future because of other factors besides climate
change.
144
Stressors—Soil erosion due to past overgrazing and
feral hog invasion may be exacerbated by heavy
precipitation events, especially in the western part
of the assessment area, where glades are located on
steeper slopes. Soil erosion may be less of an issue
in glades east of the Mississippi, which are on more
level terrain. Eastern redcedar invasion may continue
to be a problem in this community type, as climate
change is not projected to dramatically impact that
species. Increases in winter and spring precipitation
could benefit eastern redcedar thickets where spring
water is channeled.
Moderate Adaptive Capacity
This community type and the species that live within
it are adapted to extreme drought and heat. Other
community types, such as barrens, could potentially
shift to glades if conditions become sufficiently hot
and dry. However, this is a rare, highly fragmented
system that is limited to specific geologic features,
limiting opportunity for expansion to new areas. In
addition, past invasion of eastern redcedar decreases
the ability of this system to positively respond to
potential increases in fire frequency. Intact glades
that have not been heavily invaded by redcedar will
probably fare better.
ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
Glade. Photo by Paul Nelson, Mark Twain National Forest.
Glade. Used with permission of Matthew Albrecht, Missouri
Botanical Garden.
Dolomite glade. Photo used with permission of Matthew
Albrecht, Missouri Botanical Garden.
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ChAPTeR 6: eCoSySTem vuLneRABiLiTieS
ConCLuSionS
Numerous factors contribute to the overall
vulnerability of the Central Hardwoods Region
to climate change; some communities are more
vulnerable than others. Impacts such as increased
temperature, changes in precipitation, and shifts
in wildfire regime are expected to combine to
influence the distribution and productivity of
tree species. In general, species that are adapted
to wetter, cooler conditions are expected to fare
worse, such as sugar maple or American beech.
14
Species adapted to warmer, drier climates may fare
better, such as shortleaf pine and post oak. These
changes can lead to shifts in community structure
and composition. Communities that lack the ability
to withstand disturbances, such as mesic upland
forests, or are constrained by topographic barriers,
such as bottomland forests, may be particularly
vulnerable. Communities that are adapted to a wide
range of disturbances and can persist on a wide
range of topographies, such as dry-mesic forests and
woodlands, are expected to be less vulnerable to a
changing climate.
ChAPTeR 7: mAnAGemenT imPLiCATionS
Changes in climate, impacts on forest ecosystems,
and ecosystem vulnerability will combine to
create both challenges and opportunities in forest
management. This chapter briefly addresses some
of the implications of a changing climate on
major components of the forest sector within the
Central Hardwoods Region. Climate impacts and
implications will vary by ecosystem, ownership,
and management objective. This chapter does not
make recommendations as to how management
should be adjusted to respond to climate impacts.
Other documents and resources are available to
assist land managers in integrating climate change
considerations into natural resource planning and
activities (e.g., Swanston and Janowiak 2012).
The management implications in this chapter are
summarized for a variety of themes, which were
selected to encompass major resource areas of
interest to public and private land managers. These
themes and their descriptions are by no means
comprehensive, but provide a springboard for
thinking about management implications of climate
change. When available, the “more information”
sections provide links to key resources for managers
about the impacts of climate change on that resource
area.
fiSh AnD WiLDLife mAnAGemenT
The subject of climate change effects on fish
and wildlife species and their management is an
area of active research, and is summarized only
briefly here. Climate and weather influence fish
and wildlife species in many ways, both directly
and indirectly. Climate can have a direct influence
on breeding behavior of fish and wildlife. Egg
deposition of Ozark bass, for example, begins when
stream temperatures reach 3 °F (17 °C) (Walters
et al. 2000). Fish survival and recruitment are also
affected to a certain degree by climatic factors.
Flooding can lead to brood mortality in sunfish
in Illinois streams (Jennings and Philipp 1994).
Many migratory species, such as mallards and
other dabbling ducks, rely on temperature cues to
signal northward and southward migration each
year (Nichols et al. 1983, Schummer et al. 2010).
As temperatures warm and precipitation patterns
change, some wildlife species may experience a shift
in breeding and migration dates, as has already been
observed for North American wood warblers
(Strode 2003).
Besides direct climate effects on the behavior
and reproduction of species, temperature and
precipitation also influence the distribution of
habitats upon which wildlife depend, which
may be altered as climate shifts (Matthews et al.
2011a). As discussed in Chapter , some terrestrial
community types are projected to fare better than
others. Certain wildlife species may benefit if their
habitats expand in the future, but species that rely
on highly vulnerable habitats could be negatively
affected. Wetland habitat may decline or disappear
with rising temperatures and altered precipitation,
limiting or shifting already scarce habitat for
waterfowl (Johnson et al. 2010). Remaining wetland
habitat in the area may become more important for
overwintering as temperatures warm.
Negative impacts on tree species could have positive
impacts on some wildlife, at least in the short term.
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ChAPTeR 7: mAnAGemenT imPLiCATionS
If changes in flood conditions lead to increased
mortality in bottomland forests, for example, there
could be an increase in snag habitat for terrestrial
species that require such habitat, such as the Indiana
bat (Carter and Feldhamer 2005). However, more
frequent, severe, or longer flooding could destroy
habitat for other terrestrial species.
More Information
Fish and other aquatic organisms are also expected
to be affected by a combination of both direct and
indirect climate change effects. Many fish species
in the region are sensitive to even slight changes in
water temperatures and experience negative effects
on growth at extremely high water temperatures
(Jennings and Philipp 1994, Jones et al. 2011,
Michaletz et al. 2012, Smale and Rabeni 1995,
Whitledge et al. 200). Degradation of aquatic
habitats could occur in streams and riparian areas,
which are important in maintaining habitat structure
and temperature control (Whitledge et al. 200).
Water levels in lakes could also change, although
the magnitude and direction of those changes remain
uncertain (Angel and Kunkel 2010). In drought
periods, lake levels could drop and temperatures
could increase, causing fish kills and reducing food
availability for other species.
• 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
Many potential impacts on wildlife and their habitats
remain unknown. Animal species that are already
rare, threatened, or endangered, or that live in a very
narrow habitat range, may be particularly vulnerable
to shifts in temperature and precipitation (Walk
et al. 2011). However, the limited range of these
species also makes it difficult to model the effects of
climate and climate change on their distribution and
abundance (Schwartz et al. 200b). Many diseases
that threaten wildlife species may be able to expand,
increasing stress and mortality in species already
threatened by direct climate effects (Harvell et al.
2002). No research on climate change effects on
wildlife diseases in the Central Hardwoods Region is
currently available, however. The effects of climate
change on cave-dwelling species are also unknown.
148
• The Climate Change Bird Atlas is a companion
to the Climate Change Tree Atlas and projects
changes in bird species distributions by using
information about direct climate change effects
and changes in habitat.
www.nrs.fs.fed.us/atlas/bird
• In Illinois, an update to the Illinois Wildlife
Action plan was created by The Nature
Conservancy to assess wildlife vulnerabilities to
climate change. The report can be downloaded
at: https://adapt.nd.edu/resources/223/download/
IWAP_Climate_Change_Update_11May2011.pdf
PLAnT SPeCieS of ConCeRn
Changes in climate may impose increased
challenges for the conservation of rare, threatened,
or endangered plant species. The characteristics
that make these species rare, such as narrow
niche specificity, low dispersal ability, and highly
fragmented populations, reduce the adaptive
capacity of these species and make them more
vulnerable to climate change than more common
species (Broennimann et al. 200). In addition, many
of these species rely on specific pollinator species
to reproduce. Pollinator species, such as butterflies
and bees, are expected to be affected by changes
in climate, asynchrony in phenology, and colony
collapse (Potts et al. 2010).
Research that specifically examines the effects of
climate change on plant species of concern in the
Central Hardwoods Region is underway. Researchers
at the Missouri Botanical Garden are examining
ChAPTeR 7: mAnAGemenT imPLiCATionS
Bumblebee pollinating goldenrod on the Hoosier National Forest. Photo by Gerald Scott, Hoosier National Forest.
potential changes in suitable habitat under multiple
climate change scenarios for 23 species that are
primarily endemic to glade ecosystems. The
federally listed running buffalo clover and Virginia
sneezeweed are among the species being evaluated.
This research will help managers identify areas
where these species may be able to persist under
future climate changes and identify species that are
most at-risk for losing future suitable habitat.
More Information
Botanical gardens are among the leading
organizations working to understand the impacts of
climate change on rare and endangered plant species
and conserve them into the future. A few local
examples are below:
• Researchers at the Chicago Botanic Garden are
engaging with the public on the issue of climate
change through citizen science programs to
monitor changes in phenology and population
dynamics of plant species of concern.
www.bgci.org/resources/article/058/
• Researchers at the Missouri Botanical Garden
are assessing the vulnerability of rare and
endangered plant species in the Missouri Ozarks
region to climate change, and developing
strategies for their conservation.
www.mobot.org/MOBOT/Research/
climateChange/climateChangeResearchMO.shtml
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ChAPTeR 7: mAnAGemenT imPLiCATionS
invASive SPeCieS mAnAGemenT
fiRe AnD fueLS mAnAGemenT
As summarized in Chapters 5 and , climate
change is expected to expand the distribution and
abundance of many nonnative invasive plant species
across the region. Many plant species that currently
threaten the region, such as Japanese stiltgrass
and sericea lespedeza, are expected to withstand
or even benefit from projected changes in climate.
Reducing or preventing the spread of those species
will thus remain a challenge in the coming decades.
Resources are already insufficient to control these
species under current conditions, so this problem
may be exacerbated. A few invasive species adapted
to more mesic conditions, such as garlic mustard,
may potentially be negatively affected by a reduction
in late-season soil moisture on drier sites. Although
it is possible that changes in climate could reduce the
need for management of a few species, this benefit
may be offset by increases in management needs for
other species.
Weather and climate are major drivers of fire
behavior. Unlike most parts of the country, the
prescribed fire season and wildfire season tend
to occur during the same times in the Central
Hardwoods Region: fall and spring. However, if
higher-than-average temperatures or dry conditions
occur, wildfires can occur at any time of year,
causing damage to natural resources and other
resources, as well as endangering the public. For
example, the summer of 2012 was abnormally hot
and dry, and fires in the Midwest behaved more like
what is typically experienced in the western United
States.
Changes in climate will also create additional
management challenges as conditions become more
favorable for invasive plant species not currently
prevalent in the assessment area. Available modeling
research suggests that conditions are projected to be
more favorable for kudzu and Chinese and European
privet as temperatures increase across the area
(Bradley et al. 2010, Jarnevich and Stohlgren 2009).
Additional resources may be required to prevent the
spread of these species into new areas and control
them if they do invade.
Projected changes in climate could affect fire and
fuels management in the Central Hardwoods Region.
The data presented in Chapter 4 suggest that the
summer or fall could be drier. Drier conditions later
in the growing season following wet springs could
cause some tree mortality, increasing forest fuel
loads and the potential for more intense fires. Highintensity wildfire can result in species mortality,
increases in invasive species, changes in soil
dynamics (e.g., compaction, altered nutrient cycling,
sterilization), or altered hydrology (e.g., increased
runoff or erosion). Under intense fire weather
conditions, large-scale fires could also become a
hazard and safety risk to the public, firefighters,
and infrastructure. More resources may be needed
to reduce fuel loads to prevent these catastrophic
wildfires, fight them when they do occur, and restore
ecosystems after a catastrophic event.
More Information
• The Midwest Invasive Plant Network’s mission is
to reduce the impact of invasive plant species in
the Midwest.
mipn.org
150
Changes in climate may also affect the ability to
use fire as a restoration tool. For example, wetter
springs could make it difficult to conduct prescribed
burns during that season, leaving opportunities for
dormant-season burning for the fall. On the other
hand, if fall becomes too dry, prescribed burning
opportunities could also be reduced.
ChAPTeR 7: mAnAGemenT imPLiCATionS
Although some ecosystems may be negatively
affected by wildfire, the projected increases in
wildfire could also be beneficial in some areas.
Increased fire potential may increase opportunities
for restoring open woodlands, barrens, and savannas,
for example.
and references therein). Heavy precipitation and
increased runoff could also reduce river and lake
quality through increased sedimentation, pollution,
and nutrient deposition. More resources may be
needed to lower stream temperatures and reduce
runoff.
More Information
The direct and indirect effects of a changing climate
have important implications for air quality and its
management. A number of studies have shown that
tropospheric ozone is projected to increase with
increasing temperature, especially in urban areas
(Jacob and Winner 2009). Particulate matter may
also be affected by changes in climate, although
changes are less predictable than for ozone (Jacob
and Winner 2009). An increase in wildfire frequency,
as projected to occur by the end of the century, could
lead to increases in particulate matter and other
pollutants in the area.
• The Oak Woodlands and Forests Fire Consortium
provides fire science information to resource
managers, landowners, and the public about the
use, application, and effects of fire across the
Central Hardwoods Region. Climate change is
one of the consortium’s “hot topics.”
www.oakfirescience.com
SoiL, WATeR, AnD AiR QuALiTy
Changes in climate may have implications for the
management of soil, water, and air resources. Soils
in the region are projected to experience an increase
in waterlogging in the spring, followed by a potential
decrease in moisture later in the growing season
(Chapter 4). These stressors could be exacerbated
in soils with a fragipan or claypan layer, which
are common in flatwoods communities. Increased
efforts to control soil erosion may be needed to cope
with the effects of increased heavy precipitation
events across the region, especially on steeper slopes
(Nearing 2001, Nearing et al. 2004). In addition,
soil nutrient availability is expected to be affected
by changes in temperature, moisture, and species
composition, but the magnitude and direction of
these changes remain uncertain (Rennenberg et al.
2009).
Water quality may be affected by warming
temperatures and shifts in precipitation and
hydrology. Increased temperatures can lead to
decreases in dissolved oxygen, increased toxicity
of pollutants, and increases in harmful algae
and bacteria (Lofgren and Gronewold 2012,
More Information
• A recent report submitted for the National
Climate Assessment summarizes the impacts of
climate change on water resources across the
Midwest, including the assessment area. The
report can be downloaded at
glisa.msu.edu/docs/NCA/MTIT_WaterResources.
pdf
CARBon mAnAGemenT
As the climate changes in the Central Hardwoods
Region, changes in carbon dynamics are also
expected to occur. Many of these changes remain
uncertain. As mentioned in Chapters 5 and , the
benefits of a longer growing season and carbon
dioxide fertilization may be offset by an increase
in physical and biological disturbances, leading
to increases in carbon storage and sequestration
in some areas and decreases in others (Hicke et
al. 2012). In this region, mesic hardwood forests
dominated by species like sugar maple and American
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ChAPTeR 7: mAnAGemenT imPLiCATionS
beech tend to be the most carbon-dense (i.e.,
have greater amounts of carbon per acre) (see
Chapter 1), so declines in these species may also
lead to decreased carbon storage in these forests.
However, the majority of forest land in the area is
dominated by oak and hickory species, which are
projected to persist on the landscape.
Changes in climate may present both challenges and
opportunities for carbon management in the Central
Hardwoods Region. Future conditions are projected
to be more favorable for more open systems, such as
barrens, glades, and open woodlands that are driven
by fire. These systems tend to be less carbon-dense
than more mesic systems. Systems that are adapted
to disturbance and are less carbon-dense may also
have a lower risk of major carbon losses from largescale disturbances that are expected to become more
prevalent in the future (Hurteau and Brooks 2011).
The relative carbon losses from managing for more
open systems versus the benefit of avoided losses
from events such as severe wildfire have not been
explored specifically for the Central Hardwoods
Region (Bowman et al. 2013).
More information
• The Forest Service’s Climate Change Resource
Center provides several synthesis products
on ecosystem carbon. Included are a written
summary of how climate change may affect the
ability of forests to store carbon, video series on
forest and grassland carbon, and a compilation of
tools for measuring carbon.
www.fs.fed.us/ccrc/topics/forests-carbon/
• A recent article, “A Synthesis of the Science on
Forests and Carbon for U.S. Forests,” summarizes
the key issues related to forest management and
carbon.
www.fs.fed.us/rm/pubs_other/rmrs_2010_ryan_
m002.pdf
152
foReST PRoDuCTS
Information presented in Chapters 5 and indicates
that species composition in the Central Hardwoods
Region is expected to change over the long term,
which could have important implications for the
forest products industry. Some important timber
species may experience negative effects. In Illinois
and Indiana, hardwood species like black cherry,
American beech, and white ash are projected to
decline in habitat suitability under both climate
scenarios. Although these species are highly valued
for their timber elsewhere, they do not constitute
a large portion of the timber industry in southern
Illinois and Indiana and are not as valuable locally
as in areas north and east of the assessment area.
One potential exception may be sugar maple, which
is projected to decline across the assessment area
and is economically important in Indiana (but still
less so than areas north and east). Black walnut is
also projected to experience declines, and is very
valuable for timber across the assessment area.
The Central Hardwoods Region is an important
producer of oak and hickory for wood products.
Some oak species are projected to decline, but others
are projected to remain stable or even increase.
There is more uncertainty about the fate of red oak
group species, such as scarlet, northern red, and
black oak, than the fate of white oak group species
like white and post oak. Even if some oak species
become less common, standing oaks that remain
may become more valuable. Some economically
important species of hickory are expected to decline
as well, such as shagbark hickory. Other species
of hickory are expected to remain similar to the
current distribution, such as mockernut and bitternut
hickory, and may continue to be part of the local
economy.
Some higher-valued species are projected to benefit
from the changing climate. Habitat suitability for
ChAPTeR 7: mAnAGemenT imPLiCATionS
Timber sale on the Hoosier National Forest. Photo by Chris Zimmer, Hoosier National Forest.
sweetgum is anticipated to increase as a result of
changing climate. Sweetgum wood is used for
flooring, furniture, veneers, and other lumber
applications. Shortleaf pine, an economically
important species in Missouri, is another species
where suitable habitat is projected to increase across
the assessment area. The economic importance of
shortleaf pine has decreased since the turn of the
last century, but it is still important for general
construction, pulpwood, and exterior and interior
finishing. The economic importance of shortleaf pine
could potentially expand in the future if the forest
products industry takes advantage of its tolerance to
projected future climate.
Projected increases in severe weather events could
increase the amount of salvage harvests versus
green harvests that are undertaken. Harvesting green
timber allows resource managers to strategically
achieve desired objectives and outcomes. Salvage
harvesting following a severe weather event, by
contrast, generally arises from a more immediate
need to decrease fuel loading or open impacted
forest areas. Salvage sales also do not garner the
same amount of financial return as does a green
timber sale opportunity.
153
ChAPTeR 7: mAnAGemenT imPLiCATionS
More Information
• The 2010 Resources Planning Act Assessment
includes future projections for forest products
and other resources through the year 200 and
examines social, economic, land-use, and climate
change influences. The report can be downloaded
here:
www.fs.fed.us/research/rpa
nonTimBeR foReST PRoDuCTS
Changes in climate could also have important
implications for nontimber forest products. Black
walnut and pecan are both grown for their nuts in
the Central Hardwoods Region, primarily in the
Missouri Ozarks. Habitat suitability for pecan is
projected to increase across the assessment area,
which could open up opportunities for pecan nut
production. Black walnut, however, is projected to
undergo declines in habitat suitability, which could
make it more difficult to cultivate for nuts in the
coming decades.
Christmas tree production is an important industry
throughout the Midwest that could be affected
by a changing climate. In Indiana, for example,
Christmas tree sales are a $12.5 million industry
(Bratkovich et al. 2007). Many species of Christmas
trees, especially young seedlings, do not tolerate
drought or extremely wet conditions, and are
susceptible to diseases from being planted close
together in monoculture. Scotch and white pines
are the predominant Christmas trees grown in the
Central Hardwoods Region. We have not modeled
potential changes in habitat suitability for nonnative
Scotch pine, but our projections suggest that
habitat suitability will be dramatically reduced
for white pine. Irrigation and other management
techniques employed by Christmas tree farmers
may allow white pine to persist, however. The short
rotation length of Christmas trees also presents an
opportunity for tree growers to plant new species
154
and varieties that may be better suited to a changing
climate.
Maple syrup is another nontimber product that is
made in small amounts in Indiana. A survey from the
Indiana Department of Natural Resources reported
that about 200 families produce a little over 5,000
gallons annually in the state (Bratkovich et al.
2007). Maple sap flow is driven by temperatures
that fluctuate around the freezing point in the late
winter or early spring. As spring temperatures
increase, the prime season for syrup production may
shift to earlier in the season, and the number of sap
flow days could eventually decrease in areas at the
southern extent of the species’ range (Skinner et al.
2010).
Climate change may have implications for
economically and culturally valuable forest
understory species. For example, black cohosh is
a herbaceous species native to the eastern United
States that is important for a variety of medicinal
uses. Native Americans and alternative medicine
practitioners have used it for centuries to treat
conditions such as rheumatism, menopause
symptoms, and menstrual problems. Economic
demand for this species has been increasing in
the past few decades. Black cohosh ranked as the
eighth top-selling herb in the United States in 2005,
with a reported value of $9.7 million (Blumenthal
et al. 200). The species is considered critically
imperiled in Illinois and vulnerable in Indiana, due
to a combination of habitat loss and overharvesting
of wild populations (NatureServe 2013). Within
the Central Hardwoods Region, black cohosh is
primarily found in mesic upland forests dominated
by ash, beech, and sugar maple, a community type
that was found to be highly vulnerable to climate
change in the region. Therefore, conservation
efforts to maintain this species within the Central
Hardwoods Region may face additional challenges
from a changing climate in the coming decades.
ChAPTeR 7: mAnAGemenT imPLiCATionS
foReST mAnAGemenT
oPeRATionS
Changes in climate and weather patterns could
influence forest management operations on public
and private lands. Erosion control is a serious
concern during logging operations in the area.
In Indiana, for example, most statewide best
management practices (BMPs) recommend that soils
be dry or frozen when heavy equipment is used for
forest operations. It is unclear how climate change
may affect the number of days where harvesting is
possible; the number of days when soil is frozen
is projected to decrease over the next century, but
soil conditions are also expected to become drier
during the summer and fall. Heavier, more frequent
precipitation may require greater use of erosion
control measures when forest products are harvested.
Recent increases in heavy precipitation events have
already caused increased costs to reduce erosion
potential in areas with soil exposed by logging and
construction.
Changes in weather patterns could also change
restrictions imposed on forest management
operations in order to protect threatened and
endangered species. For example, the U.S. Fish and
Wildlife Service recently expanded its tree removal
restrictions in Indiana bat habitat by 2 weeks in
the spring and fall in response to a longer breeding
season. Longer periods of warmer, drier weather
could cause these timeframes to lengthen further.
These restrictions can affect managers’ ability to
efficiently complete projects involving tree removal,
such as hazard tree reductions and certain vegetation
management practices.
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,
may overload existing infrastructure that has not
been built to that capacity. For example, improper
location or outdated building standards make older
road systems particularly susceptible to increased
rainfall events. Engineers are already adapting to
these changes: as current infrastructure is replaced,
it is being constructed with heavier precipitation
events in mind. This level of preparedness often
comes at an increased cost to upgrade to higher
standards and capacity.
As described in Chapter 4, changes in precipitation
may also lead to changes in streamflow, which may
affect roads, bridges, and culverts around streams
and rivers. Spring flooding has increased in recent
years across the assessment area, and many areas are
backlogged with repairs because of reduced funding.
The projected increase in spring precipitation and
high flow days could exacerbate the problem.
An increase in the intensity of wind storms, which
could potentially occur over the next century, could
also increase operating and repair expenses related to
infRASTRuCTuRe
on foReST LAnD
Changes in climate and extreme weather events
may have impacts on infrastructure on forest lands
throughout the region, such as roads, bridges, and
Road on the Shawnee National Forest. Photo by Leslie Brandt,
U.S. Forest Service.
155
ChAPTeR 7: mAnAGemenT imPLiCATionS
infrastructure. For example, frequent high-intensity
windstorms across the assessment area in 2012 led
to major damage to infrastructure. As a result, roads
and trails had to be cleared and facilities repaired.
More Information
• A technical report summarizing climate change
impacts on the transportation sector (including
infrastructure) was recently released as input for
the Midwest Region for the National Climate
Assessment:
glisa.msu.edu/docs/NCA/MTIT_Transportation.
pdf
CuLTuRAL ReSouRCeS
Climate change may present challenges for
managers of cultural resources on public lands in the
Central Hardwoods Region. Extreme wind events
such as tornadoes and derechos present challenges
for the management of cultural resources for several
reasons. These events can directly damage cultural
resources such as buildings and other structures.
Cultural resources damaged by storms may be
further damaged by subsequent salvage harvest
operations because unsafe walking conditions
and low ground surface visibility often make it
impossible to take a cultural resources inventory
before the salvage sale.
A change in the frequency, severity, or duration of
heavy precipitation and flooding could affect cultural
resources as well. Historic and prehistoric habitation
sites are often located near waterways. Flood
events result in increased erosion or obliteration of
significant archaeological sites located along stream
and river banks. Similarly, torrential rains can trigger
or exacerbate erosion of cultural resources. When
built reservoir levels drop (because of drought or
intentional levee breaks), prehistoric human remains
and other cultural resources are at risk of being
exposed by wave action.
15
Projected changes in wildfire could also affect
cultural resources in the region. Wildfire and
wildfire suppression activities have the potential to
destroy or damage cultural resources. Aboveground
combustible features are the most at-risk, although
extreme heat can damage noncombustible features
or artifacts such as rock art, ceramics, and lithic
artifacts (e.g., projectile points).
Optimal fieldwork conditions for cultural resource
managers are largely determined by weather and
climate. Identification of cultural resources is
hindered by leaf-on conditions. The highest quality
cultural resource inventories and monitoring of
known sites occur during fall, winter, and early
spring in the Central Hardwoods Region. Similarly,
field opportunities for volunteers, such as the Mark
Twain National Forest’s Passport in Time projects,
are scheduled during leaf-off conditions when the
weather and climate are mild and biting insects such
as ticks and chiggers are less abundant, typically fall
or late spring. A lengthening growing season could
reduce periods of optimal conditions for fieldwork
related to cultural resource management.
Historic properties with no standing structures
are sometimes identified in the field by legacy
vegetation or “cultural resources indicator species.”
If any of these indicator species (typically nonnative
ornamentals planted by Euro-American inhabitants)
are vulnerable to climate change and vanish from
the landscape, historic properties (such as unmarked
graves) may escape field identification and suffer
unintentional damage from ground-disturbing
activities.
More Information
• The report Wildland Fire in Ecosystems: Effects
of Fire on Cultural Resources and Archeology
summarizes the impacts of fire and fire
management activities on cultural resources.
www.fs.fed.us/rm/pubs/rmrs_gtr042_3.html
ChAPTeR 7: mAnAGemenT imPLiCATionS
• The document 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
Outdoor recreation across the Central Hardwoods
Region is typically highest in spring and fall.
Warmer springs and falls may improve conditions
for outdoor recreation activities such as camping,
boating, and kayaking (Nicholls 2012). Lengthening
of the spring and fall recreation seasons may have
implications for staffing, especially for recreationrelated businesses that rely on student labor that
will be unavailable during the school year (Nicholls
2012). However, shifts in precipitation could also
have negative impacts on spring and fall recreation
activities. Increased spring precipitation could
increase risks for flash flooding or simply lead to
unpleasant conditions for recreation. Severe storms
and flooding might threaten resources such as visitor
centers, campsites, and trails. Fall, on the other
hand, will potentially be drier, which could cause
lower water levels and reduce boating and kayaking
opportunities. Warmer, drier conditions in the fall
may also increase the risk of wildfire, increasing
visitor safety risk and restrictions on open flames.
Winter recreation in the Central Hardwoods Region
is typically an extension of spring and fall activities,
as snow and ice are often insufficient for activities
such as skiing or ice skating. Currently, a moderate
amount of hiking; camping; picnicking; horseback
riding; and off-road vehicle, motorcycle, and
mountain bike riding occurs throughout the winter.
Visitors find it easier to enjoy the views of the rock
formations and other scenery from a distance during
leaf-off. On the rivers, boating and catch-and-release
fishing are typical winter activities. As winter
temperatures increase in the coming decades, more
people could potentially take advantage of milder
conditions for recreation activities.
A recent study suggests that climate conditions
during the summer will become unfavorable for
tourism in the region by mid-century under a high
emissions scenario (Nicholls 2012). Under that
scenario, the number of extremely hot days is
projected to increase significantly, which could
reduce demand for camping facilities and make
outdoor physical activity unpleasant or potentially
dangerous to sensitive individuals. One exception
may be recreation on rivers. Evidence from previous
summers across the area suggests that local residents
increase their visits to rivers to cool off during
extremely hot periods. The increase in temperature
could lead to fewer visits to public lands in the area
overall during the summer, and potential declines
in summer tourism revenue. These changes could
be particularly important for recreation-dependent
communities, such as the Lake of the Ozarks.
Climate can also have important influences on
hunting and fishing. The timing of certain hunts or
fishing seasons correspond to seasonal events, which
are partially 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 could shift later in the year
as temperatures warm. A recent study in Illinois
showed that the number of mallards and other
dabbling ducks taken during the hunting season
improved as low temperatures during the hunting
season became colder (Stafford et al. 2010). These
results suggest that rising fall temperatures could
potentially reduce success rates. If reductions in
precipitation and increases in evaporation decrease
waterfowl habitat, waterfowl could also shift their
migration patterns to new areas, further reducing
hunting opportunities.
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ChAPTeR 7: mAnAGemenT imPLiCATionS
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 numerous
hunting and conservation organizations, includes
many resources on how climate change may
affect hunting and fishing.
www.seasonsend.org
WiLDeRneSS
The Central Hardwoods Region is home to
17 wilderness areas administered by federal
agencies. The primary effect of weather and climate
on wilderness management is through recreation use,
but other resource management decisions can also
be affected. For example, managers try to remove
trees that are posing immediate safety threats to
visitors on a regular basis. Weather-related mortality
from storm events, drought, or insect and disease
attack can increase the need for this activity. Weather
conditions also affect the need for maintenance of
the trail tread, particularly when heavy rains cause
excessive erosion, or when wind events uproot trees
and leave craters that include part of the trail.
Projected change in climate and extreme weather
events may affect wilderness management to some
extent. Wilderness managers may need to provide
additional information to the public and increase
wilderness education, so potential visitors will be
better prepared for the changing challenges and
hazards that they may encounter in the wilderness.
These changes may also create a need for increased
monitoring of the trail conditions and the locations
of invasive species, so that appropriate management
actions may be taken. Changes in forest community
composition may not affect vegetation management
158
in wilderness areas because of the requirement
that wildernesses be natural and untrammeled by
humans. However, if these changes significantly
alter fuels, insects and disease, or other resource
functions, inclusion of wilderness areas in largescale management proposals may need to be
evaluated. This evaluation may also need to take
place if changes to species of concern result
in management proposals that would include
wilderness.
More Information
• The Wilderness.net Climate Change Toolbox
provides information about climate change and
wilderness, including management guidelines and
strategies.
www.wilderness.net/climate
• The wilderness and climate change topic page on
the Climate Change Resource Center provides a
summary of the considerations for management
of wilderness within the context of climate
change.
www.fs.fed.us/ccrc/topics/wilderness
LAnD ACQuiSiTion
Changes in climate may affect decisions related
to land acquisition across the Central Hardwoods
Region. For example, projections of suitable habitat
under a changing climate can be used to identify
lands that have the best potential to serve as refugia
for a species or community that is projected to
decline (Keppel et al. 2012). Lands that may be most
suitable for species or communities to migrate to
new areas can also be identified (Anderson
et al. 2012).
Current land acquisition projects across the region
may help forests withstand the effects of climate
change. In Missouri, The Nature Conservancy, The
Conservation Fund, and the Mark Twain National
ChAPTeR 7: mAnAGemenT imPLiCATionS
Forest are working to consolidate federal and state
ownership in the Current River watershed, one of
the last extensive areas in the region with intact
forest cover. The planned acquisitions are part of a
long-term goal to lower forest fragmentation and
help to create continuous riparian zones along the
Current River and its tributaries. These efforts may
potentially reduce the impacts of soil erosion and
increased stream temperatures, and may also help
facilitate the migration of species to newly suitable
habitats.
In Indiana, The Nature Conservancy and the Hoosier
National Forest are working to acquire key parcels
in the Lost River area, increasing blocks of forest
land to allow species movement. Acquisition of a
parcel south of the Lost River may aid in conserving
the Lost River Cave System, which is expected to
improve opportunities for bats to find optimum
microclimates during winter months. Consolidation
could help reduce the possible movement of invasive
species into the core areas of existing lands on the
national forest. All of the proposed parcels have
been identified through The Nature Conservancy’s
ecoregional planning process as parcels of high
ecological value. Additionally, each ecoregional
priority site plan has been analyzed for climate
viability and adaptation, taking into account site
specifics such as buffers, corridors, and refugia.
In Illinois, the American Land Conservancy assisted
the Shawnee National Forest with the acquisition of
792 acres along the Middle Mississippi River, in the
heart of the internationally significant Mississippi
Flyway. The mid-continental region of the flyway
has become increasingly important in recent years
as the wintering populations of many migratory
bird species have moved northward in part because
of climate change. Continued warming will further
increase the importance of habitat and foraging
conditions in the Middle Mississippi River area,
as species that in the past utilized the area only
temporarily during the fall and spring migrations
now linger longer and in greater numbers. In
addition, this project is part of a larger conservation
strategy aimed at providing forested north-to-south
running corridors to enhance habitat connectivity
along the Middle Mississippi River, similar to
ongoing efforts by multiple agencies that are
occurring in the Upper and Lower Mississippi River.
PLAnninG
Until recently, climate change has not played a
large role in natural resource planning on public
lands. However, many federal and state-level land
management agencies are 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 (U.S. Forest Service 2012). Climate
change is named as one of several “system drivers”
that must be considered in assessing the existing
conditions of planning areas, in developing plan
components that maintain or restore ecological
integrity of ecosystems, and in developing plan
components for multiple uses of National Forest
System lands. The 2012 Planning Rule also
specifically requires the monitoring of “measurable
changes on the plan area related to climate change...”
Land Management Plans on national forests are
written to guide management for a 10- to 15-year
period, and within this short planning horizon
itmay be more difficult to foresee given projected
shifts in climate. Major storm events that result
in downed trees cannot be planned for, and often
force managers on national forests to deviate from
planned analysis or treatment cycles to quickly deal
with the salvage of the downed materials. If climate
change results in more of these storm events, it may
alter planned management on national forests more
significantly than in the past. Likewise, an increase
in invasive plant species could lead to a change
in the goals, objectives, and priorities in order to
attempt to deal with the spread of these plants.
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ChAPTeR 7: mAnAGemenT imPLiCATionS
Future plan revision efforts on national forests
within the assessment area (and nationally) will
have to consider, analyze, and disclose the impacts
of climate change on the natural resources of the
plan area, as required by the 2012 Planning Rule.
New information about potential impacts and
vulnerabilities of communities within the area could
lead to different plan objectives. For example,
the Mark Twain National Forest Plan is centered
on maintaining and restoring distinct natural
communities that have historically occurred in the
Ozarks. This approach may help make this forest
more resilient to disturbances brought about by a
changing climate in the coming decades. However,
if long-term shifts in climate make conditions
unfavorable to communities that historically
occupied the area, future Plans may need to account
for community-level shifts.
Other state and federal agencies are also beginning
to address climate change in their planning. State
agencies are beginning to address climate change
in their state forest assessments and strategies
and their state wildlife action plans. Missouri,
for example, has highlighted climate change as a
major issue facing forests in the state. Agencies
in the Department of the Interior, such as the Fish
and Wildlife Service and Park Service, are also
developing strategies for incorporating climate
change considerations into their planning.
More Information
• Missouri’s Forest Resource and Assessment
Strategy includes climate change as one of
11 Issue Themes used to discuss the conditions,
trends, threats, and opportunities facing Missouri
forests.
mdc.mo.gov/sites/default/files/
resources/2010/08/9437_407.pdf
• More information on the Forest Service’s 2012
Planning Rule can be found here:
www.fs.usda.gov/planningrule
10
uRBAn foReSTRy
Climate change is expected to affect urban forests in
the assessment area as well. Urban environments can
pose additional stresses to trees not experienced in
natural environments, such as pollution from vehicle
exhaust, road salts, and fertilizer runoff. Urban
environments also cause a “heat island effect,” and
thus warming in cities will be even greater than that
experienced in natural communities. Impervious
surfaces can make urban environments more
susceptible to flash floods, placing flood-intolerant
species at risk. Tall buildings can create wind tunnels
that make street trees more susceptible to wind
damage. All of these abiotic stressors can make
urban forests more susceptible to exotic species
invasion and insect and pathogen attack, especially
because a limited range of species and genotypes is
often planted in urban areas.
Projected changes in climate can pose both
challenges and opportunities for the management
of urban forests. Shifts in temperature and changes
in extreme events may have effects on selection of
species for planting. Deciding what species would be
appropriate to plant given future climate change may
pose a new challenge, but the practice of planting
species novel to an area is not a new concept for city
foresters. Because of urban effects on climate, many
city foresters already select species for planting that
are from one planting zone south of the area or select
nonnative species or cultivars that tolerate a wide
range of climate conditions. Once trees are planted,
changes in climate may require more maintenance,
such as watering, irrigation, mulch application,
pruning, and staking, to allow them to survive more
severe weather events.
Severe weather events, which may become more
frequent or intense in the future, will also require
response after they occur. Cities will need to remove
trees and branches causing traffic obstructions,
downed power lines, or damage to property. More
ChAPTeR 7: mAnAGemenT imPLiCATionS
yards. Extension specialists may need additional
resources and training to help inform the public of
the most suitable species to plant to withstand higher
temperatures and more severe weather events.
More Information
• 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
Teena Ligman and Tom Thake plant trees at the Moffit Wetland
on the Hoosier National Forest. Photo by Pat Merchant, Hoosier
National Forest.
people and larger budgets may be required to handle
an increase in the frequency or intensity of these
events, which may become more difficult as many
cities have reduced their budgets and staffing in
recent years. In addition, some events may be too
large to budget for on a city level, such as the recent
Harrisburg and Joplin tornadoes. These events may
require state or federal assistance if they do occur.
Public outreach and education will be another major
consideration for the urban forestry community with
respect to climate change. Because the effects of
decisions related to the planting and maintenance
of urban trees are highly visible, any changes made
to prepare for shifts in climate will need to be
explained to the public in a way that is accessible
and apolitical. Some members of the public will also
be seeking advice on the best trees to plant in their
ChAPTeR SummARy
Changes in climate and impacts on trees and forest
ecosystems can have important implications for
management in the Central Hardwoods Region.
Some key timber species may experience negative
effects, such as sugar maple, black cherry, and white
ash. Improved conditions for shortleaf pine could
make it a potentially more important timber species
in the future if markets develop to take advantage of
its success. Improved climate conditions for invasive
species such as kudzu and privet could mean more
resources will be required to control their spread.
The seasonal timing of management activities
such as prescribed burns or recreation activities
such as waterfowl hunting may need to be altered
as temperatures and precipitation patterns change.
However, confronting the challenge of climate
change also presents opportunities for managers and
other decisionmakers to plan ahead, build resilient
landscapes, and ensure that the benefits that forests
provide are sustained into the future.
11
GLoSSARy
aerosol
alluvium
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 either
by scattering and absorbing radiation or by acting
as condensation nuclei for cloud formation or
modifying the properties and lifetime of clouds
(IPCC 2007).
a deposit of clay, silt, sand, and gravel left by
flowing streams in a river valley or delta, typically
producing fertile soil.
agricultural drought
a phenomenon that occurs when there is not enough
moisture to support average crop production on
farms or average grass production on range land.
Although agricultural drought often occurs during
dry, hot periods of low precipitation, it can also
occur during periods of average precipitation when
soil conditions or agricultural techniques require
extra water.
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).
allelopathic
a plant species that has the ability to suppress
the growth of another due to the release of toxic
substances.
12
asynchronous quantile regression
a type of regression used in statistical downscaling.
Quantile regression models the relation between a
set of predictor variables and specific percentiles
(or quantiles) of the response variable.
barrens
a subtype of savanna characterized by trees tolerant
of xeric conditions which have a stunted, opengrowth appearance and which grow on poor, thin,
or excessively drained soils.
basal area
the cross-sectional area of all stems of a species or
all stems in a stand measured at 4.5 feet above the
ground and expressed per unit of land area.
biomass
the mass of living organic matter (plant and animal)
in an ecosystem; biomass also refers to organic
matter (living and dead) available on a renewable
basis for use as a fuel; biomass includes trees and
plants (both terrestrial and aquatic), agricultural
crops and wastes, wood and wood wastes, forest and
mill residues, animal wastes, livestock operation
residues, and some municipal and industrial wastes.
carbon dioxide (Co2) fertilization
increased plant uptake of CO2 through
photosynthesis in response to higher concentrations
of atmospheric CO2.
GLoSSARy
claypan
confidence
a dense, compact, slowly permeable layer in the
subsoil having a much higher clay content than the
overlying material, from which it is separated by a
sharply defined boundary. Claypans are usually hard
when dry, and plastic and sticky when wet. They
limit or slow the downward movement of water
through the soil.
a qualitative assessment of uncertainty as determined
through evaluation of evidence and agreement (see
also evidence, agreement).
clearcut
the cutting of essentially all trees, producing a fully
exposed microclimate for the development of a
new age class. Note 1: Regeneration can be from
natural seeding, direct seeding, planted seedlings, or
advance reproduction. Note 2: Cutting may be done
in groups or patches (group or patch clearcutting),
or in strips (strip clearcutting). Note 3: The
management unit or stand in which regeneration,
growth, and yield are regulated consists of the
individual clearcut stand.
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.
convective available potential energy
a measure of the amount of energy available for
convection. It is directly related to the maximum
potential vertical speed within an updraft; thus,
higher values indicate greater potential for severe
weather.
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.
degree-days
a measure of accumulated heat used in the study
of phenology. Degree-days are calculated by
subtracting a baseline temperature (e.g. 5 °F)
from the average of the maximum and minimum
temperature for each day and summing.
derecho
see general circulation model.
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.
climate normal
disturbance
the arithmetic mean of a climatological element
computed over three consecutive decades.
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.
climate model
community
an assemblage of plants and animals living together
and occupying a given area.
13
GLoSSARy
downscaling
ecosystem
a method for obtaining high-resolution climate or
climate change information from relatively coarseresolution general circulation models (GCMs);
involves examining the statistical relationship
between past climate data and on-the-ground
measurements.
a system of living organisms interacting with
each other and their physical environment. The
boundaries of what could be called an ecosystem
are somewhat arbitrary, depending on the focus of
interest or study. Thus, the extent of an ecosystem
may range from very small spatial scales to,
ultimately, the entire Earth.
driver
any natural or human-induced factor that directly or
indirectly causes a change in an ecosystem.
edaphic
drought
El Niño-Southern Oscillation (ENSO)
see agricultural, hydrologic, and meteorological
drought.
The term El Niño was initially used to describe a
warm-water current that periodically flows along
the coast of Ecuador and Peru, disrupting the local
fishery. It has since become identified with a basinwide warming of the tropical Pacific Ocean east of
the dateline. This oceanic event is associated with a
fluctuation of a global-scale tropical and subtropical
surface pressure pattern called the Southern
Oscillation. This coupled atmosphere-ocean
phenomenon, with preferred time scales of 2 to
about 7 years, is collectively known as the El NiñoSouthern Oscillation (ENSO). It is often measured
by the surface pressure anomaly difference between
Darwin and Tahiti and the sea surface temperatures
in the central and eastern equatorial Pacific. During
an ENSO event, the prevailing trade winds weaken,
reducing upwelling and altering ocean currents
such that the sea surface temperatures warm, further
weakening the trade winds. This event has a great
impact on the wind, sea surface temperature, and
precipitation patterns in the tropical Pacific. It has
climatic effects throughout the Pacific region and
in many other parts of the world, through global
teleconnections. The cold phase of ENSO is called
La Niña.
dynamical downscaling
a method for obtaining high-resolution climate or
climate change information from relatively coarseresolution general circulation models (GCMs) using
a limited-area, high-resolution model (a regional
climate model, or RCM) driven by boundary
conditions from a GCM to derive smaller-scale
information.
ecoregion
a region characterized by a repetitive pattern of
ecosystems associated with commonalities in soil
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.
14
of or pertaining to soil characteristics.
GLoSSARy
emissions scenario
forest type
a plausible representation of the future development
of emissions of greenhouse gases and aerosols that
are potentially radiatively active, based on certain
demographic, technological, or environmental
developments.
a classification of forest land based on the dominant
species present, as well as associate species
commonly occurring with the dominant species.
ensemble average
the average value of a large number of output values
from a climate model; a way to address some of the
uncertainties in the system.
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.
fragipan
evapotranspiration
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).
a natural subsurface horizon which has very low
organic matter and high bulk density; is slowly or
very slowly permeable to water; is considered root
restricting; and usually has few to many bleached,
roughly vertical planes that are faces of coarse or
very coarse polyhedrons or prisms. A fragipan has
hard or very hard consistency (seemingly cemented)
when dry but shows a moderate to weak brittleness
when moist.
fen
fragmentation
a wetland fed by surface water or groundwater, or
both; characterized by the chemistry of the water,
which is neutral or alkaline.
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.
the sum of evaporation from the soil and
transpiration from plants.
evidence
fire-return interval
the number of years between two successive fire
events at a specific location.
functional diversity
forest
multistoried communities with a canopy, subcanopy
of small trees, shrubs, saplings, and vines; and
ground flora adapted to shade and essentially
permanent leaf litter. Forests have high canopy cover
(80 percent or greater).
the value, range, and relative abundance of
functional traits in a given ecosystem.
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.
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.
15
GLoSSARy
general circulation model (GCM)
Hypsithermal
numerical representation of the climate system based
on the physical, chemical, and biological properties
of its components, and their interactions and
feedback processes, and accounting for all or some
of its known properties (also called climate model).
a period from 7,500 to 5,000 years ago when global
temperatures were higher than modern temperatures.
glade
an open area of exposed bedrock or shallow soil
over rock dominated by drought-adapted herbaceous
vegetation.
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.
impact
direct and indirect consequences of climate change
on systems, particularly those that would occur
without adaptation.
impact model
simulations of impacts on trees, animals, and
ecosystems; these models use GCM projections
as inputs, and include additional inputs such as
tree species, soil types, and life history traits of
individual species.
importance value
an index of the relative abundance of a species in
a given community (0 = least abundant, 50 = most
abundant).
intensity
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).
holocene
a geologic period that started approximately
12,000 years ago following the last glacial period
and continues to the present.
hydrologic drought
a phenomenon that occurs when water reserves
in aquifers, lakes, and reservoirs fall below an
established statistical average. Hydrologic drought
can happen even during times of average or aboveaverage precipitation, if human demand for water
is high and increased usage has lowered the water
reserves.
1
amount of precipitation falling per unit of time.
invasive species
any species that is nonnative (or alien) to the
ecosystem under consideration and whose
introduction causes or is likely to cause damage,
injury, or disruption to ecosystem processes or other
species within that ecosystem.
karst
geologic formation shaped by the dissolution of a
layer or layers of soluble bedrock, usually carbonate
rock such as limestone or dolomite.
Kyoto Protocol
adopted at the 1997 Third Session of the Conference
of Parties to the UN Framework Convention on
Climate Change in Kyoto, Japan, it contains legally
binding commitments to reduce anthropogenic
greenhouse gas emissions by at least 5 percent below
1990 levels in the period 2008-2012.
GLoSSARy
lacustrine
parcelization
pertaining to or formed in a lake.
the subdivision of a single forest ownership into
two or more ownerships. Parcelization may result
in fragmentation if habitat is altered under new
ownership.
marly
having a loose or crumbling deposit of sand, silt, or
clay that contains a substantial amount of calcium
carbonate.
Medieval Warm Period
a period from approximately 950 to 1250 AD that
was warmer than average in the North Atlantic and
northeastern North America.
mesic
pertaining to sites or habitats characterized by
intermediate (moist, but not wet nor dry) soil
moisture conditions.
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.
process model
meteorological drought
occurs when there is a prolonged period of belowaverage precipitation, which creates a natural
shortage of available water.
a model that relies on computer simulations based
on mathematical representations of physical and
biological processes that interact over space and
time.
model reliability score
projection
for the Tree Atlas: a “tri-model” approach to assess
reliability of model predictions for each species,
classified as high, medium, or low.
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.
modifying factor
environmental variables (e.g., site conditions,
interspecies competition, disturbance, dispersal
ability) that influence the way a tree may respond
to climate change.
prairie
natural community
an assemblage of native plants and animals that tend
to recur over space and time, which interact with
each other and their physical environment in ways
minimally modified by exotic species and negative
human disturbances.
a natural community dominated by perennial grasses
and forbs with scattered shrubs and very few trees
(less than 10 percent canopy cover).
productivity
the rate at which biomass is produced per unit area
by any class of organisms, or the rate of energy
utilization by organisms.
17
GLoSSARy
proxy
runoff
a local record that is interpreted, using physical
and biophysical principles, to represent some
combination of climate-related variations back
in time. Climate-related data derived in this
way are referred to as proxy data. Examples of
proxies include pollen analysis, tree ring records,
characteristics of corals, and various data derived
from ice cores.
that part of the precipitation that appears in surface
streams. It is the same as streamflow unaffected by
artificial diversions or storage.
pulpwood
scenario
roundwood, whole-tree chips, or wood residues used
for the production of wood pulp for making paper
and paperboard products.
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.
radiative forcing
the change in net irradiance between different
layers of the atmosphere. A positive forcing
(more incoming energy) tends to warm the system.
A negative forcing (more outgoing energy) tends to
cool it. Causes include changes in solar radiation
or concentrations of radiatively active gases and
aerosols.
realized niche
the portion of potential habitat a species occupies;
usually it is less than what is available because
of predation, disease, and competition with other
species.
refugia
locations and habitats that support populations of
organisms that are limited to small fragments of
their previous geographic range.
resilience
capacity of a system to absorb a disturbance and
continue to develop with similar fundamental
function, structure, identity, and feedbacks.
18
savanna
fire-maintained grasslands with open-grown,
scattered, orchard-like trees or groupings of trees
and shrubs.
seed tree method
the cutting of all trees except for a small number of
widely dispersed trees retained for seed production
and to produce a new age class in fully exposed
microenvironment. Note: Seed trees are usually
removed after regeneration is established.
seep
a small area of groundwater discharge, either
nonforested or shaded by trees rooted in adjacent,
upland habitats.
severity
the proportion of aboveground vegetation killed and
the degree of forest floor and soil disruption.
GLoSSARy
shelterwood
species distribution model
the cutting of most trees, leaving those needed
to produce sufficient shade to produce a new age
class in a moderated microenvironment. Note: The
sequence of treatments can include three types of
cuttings: (1) an optional preparatory cut to enhance
conditions for seed production, (2) an establishment
cut to prepare the seed bed and to create a new age
class, and (3) a removal cut to release established
regeneration from competition with the overwood;
cutting may be done uniformly throughout the
stand (uniform shelterwood), in groups or patches
(group shelterwood), or in strips (strip shelterwood);
in a strip shelterwood, regeneration cuttings may
progress against the prevailing wind.
a model that uses statistical relationships to project
future change.
significant trends
least-squares regression p-values of observed climate
trends. In this report, significant trends (p<0.10) are
shown by stippling on maps of observed climate
trends. Where no stippling appears (p>0.10),
observed trends have a higher probability of being
due to chance alone.
spring
a continual or intermittent natural flow of water from
the ground following a rather well-defined channel.
statistical downscaling
a method for obtaining high-resolution climate or
climate change information from relatively coarseresolution general circulation models (GCMs) by
deriving statistical relationships between observed
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 and 30 miles above the Earth.
silvicultural
streamflow
pertaining to the art and science of controlling the
establishment, growth, composition, health, and
quality of forests and woodlands to meet the diverse
needs and values of landowners and society on a
sustainable basis.
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.
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.
suitable habitat
in the context of the Climate Change Tree Atlas
(a species distribution model), the area-weighted
importance value, or the product of tree species
abundance and the number of cells with projected
occupancy.
swamp
softwood
a coniferous tree, usually evergreen, having needles
or scale-like leaves.
freshwater, woody communities with surface water
throughout most of the year.
19
GLoSSARy
timberland
veneer
forest land that is producing or capable of producing
>20 cubic feet per acre per year of wood.
a roundwood product from which veneer is sliced
or sawn and that usually meets certain standards of
minimum diameter and length, and maximum defect.
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 miles in altitude in mid-latitudes (ranging
from 5.5 miles in high latitudes to 10 miles in the
tropics on average), where clouds and weather
phenomena occur.
topkill
vulnerability
the degree to which a system is susceptible to, and
unable to cope with, adverse effects of climate
change, including climate variability and extremes.
Vulnerability is a function of the impacts and
adaptive capacity of a system.
weather
the state of the atmosphere at a given time and
place, with respect to variables such as temperature,
moisture, wind velocity, and barometric pressure.
death of aboveground tree stem and branches.
wind shear
uncertainty
an expression of the degree to which a value (such as
the future state of the climate system) is unknown.
Uncertainty can result from lack of information or
from disagreement about what is known or even
knowable. It may have many types of sources,
from quantifiable errors in the data to ambiguously
defined concepts or terminology, or uncertain
projections of human behavior. Uncertainty can
be described using quantitative measures or by
qualitative statements.
170
the rate at which wind velocity changes from point
to point in a given direction.
woodland
highly variable natural communities with a canopy
of trees ranging from 30 to 100 percent openness, a
sparse understory, and a dense ground flora rich in
grasses, sedges, and forbs.
xeric
pertaining to sites or habitats characterized by
decidedly dry conditions.
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APPenDix 1: Common AnD SCienTifiC nAmeS
of fLoRA AnD fAunA
fLoRA
Common name
Scientific Name
Common name
Scientific Name
American basswood
Tilia americana
common persimmon
Diospyros virginiana
American beech
Fagus grandifolia
common teasel
Dipsacus fullonum
American elm
Ulmus americana
creeping charlie
Glechoma hederacea
American featherfoil
Hottonia inflata
creeping jenny
Lysimachia nummularia
American hornbeam
Carpinus caroliniana
crown vetch
Coronilla varia
autumn olive
Elaeagnus umbellata
curly leaf pondweed
Potamogeton crispus
baldcypress
Taxodium distichum
cut-leaved teasel
Dipsacus laciniatus
bigtooth aspen
Populus grandidentata
eastern cottonwood
Populus deltoides
bitternut hickory
Carya cordiformis
eastern hophornbeam
Ostrya virginiana
black cherry
Prunus serotina
eastern redbud
Cercis canadensis
black cohosh
Actaea racemosa
eastern redcedar
Juniperus virginiana
black hickory
Carya texana
eastern whitepine
Pinus strobus
black locust
Robinia pseudoacacia
Eurasian water-milfoil
Myriophyllum spicatum
black oak
Quercus velutina
European privet
Ligustrum vulgare
black walnut
Juglans nigra
flowering dogwood
Cornus florida
black willow
Salix nigra
garlic mustard
Alliaria petiolata
blackgum
Nyssa sylvatica
green ash
Fraxinus pennsylvanica
blackjack oak
Quercus marilandica
hackberry
Celtis occidentalis
blue ash
Fraxinus quadrangulata
honeylocust
Gleditsia triacanthos
blue monkshead
Aconitum uncinatum
Japanese honeysuckle
Lonicera japonica
boxelder
Acer negundo
Japanese hop
Humulus japonicus
bur oak
Quercus macrocarpa
Japanese knotweed
Fallopia japonica
bush honeysuckles
Lonicera maackii, L. tatarica,
L. morrowii
Japanese stiltgrass
Microstegium vimineum
buttonbush
Cephalanthus occidentalis
johnsongrass
Sorghum halepense
Kentucky coffeetree
Gymnocladus dioicus
cedar elm
Ulmus crassifolia
Bromus tectorum
kudzu
Pueraria lobata
cheatgrass
loblolly pine
cherrybark oak
Quercus falcata var.
pagodifolia
Pinus taeda
longleaf pine
Pinus palustris
chestnut oak
Quercus prinus
mahaleb cherry
Prunus mahaleb
Chinese privet
Ligustrum sinense
Mead’s milkweed
Asclepias meadii
Chinese yam/cinnamon vine
Dioscorea oppositifolia
mockernut hickory
Carya tomentosa
chinquapin oak
Quercus muehlenbergii
multiflora rose
Rosa multiflora
common periwinkle
Vinca minor
musk thistle
Carduus nutans
(Appendix 1 continued on next page)
199
APPenDix 1
Common name
Scientific Name
Common name
Scientific Name
northern catalpa
Catalpa speciosa
tree-of-heaven
Ailanthus altissima
northern pinoak
Quercus ellipsoidalis
Virginia pine
Pinus virginiana
northern red oak
Quercus rubra
Virginia sneezeweed
Helenium verginicum
Ohio buckeye
Aesculus glabra
Virginia threeseed mercury
Acalypha virginica
Oriental bittersweet
Celastrus orbiculatus
water oak
Quercus nigra
osage-orange
Maclura pomifera
white ash
Fraxinus americana
overcup oak
Quercus lyrata
white oak
Quercus alba
pawpaw
Asimina triloba
white sweetclover
Melilotus albus
pecan
Carya illinoensis
wild plum
Prunus americana
pignut hickory
Carya glabra
willow oak
Quercus phellos
pin oak
Quercus palustris
winged elm
Ulmus alata
post oak
Quercus stellata
wintercreeper
Euonymus fortunei
princess-tree
Paulownia tomentosa
yellow birch
Betula alleghaniensis
purple loosestrife
Lythrum salicaria
yellow sweetclover
Melilotus officinale
red maple
Acer rubrum
yellow-poplar
Liriodendron tulipifera
red mulberry
Morus rubra
reed canarygrass
Phalaris arundinacea
river birch
Betula nigra
rock elm
Ulmus thomasii
running buffalo clover
Trifolium stoloniferum
sassafras
Sassafras albidum
sawtooth oak
scarlet oak
fAunA
Common name
Scientific Name
Quercus acutissima
Acadian flycatcher
Empidomax virescens
Quercus coccinea
American woodcock
Philohela minor
Scotch pine
Pinus sylvestris
Bachman’s sparrow
Peucaea aestivalis
sericea lespedeza
Lespedeza cuneata
bald eagle
Haliaeetus leucocephalus
shagbark hickory
Carya ovata
black-and-white warbler
Mniotilta varia
shellbark hickory
Carya laciniosa
black bear
Ursus americanus
shingle oak
Quercus imbricaria
bobcat
Lynx rufus
shortleaf pine
Pinus echinata
Curtis pearlymussel
Epioblasma florentina curtisi
Shumard oak
Quercus shumardii
eastern wild turkey
Melagris gallapavo
silktree
Albizia julibrissin
elk
Cervus elaphus
silver maple
Acer saccharinum
emerald ash borer
Agrilus planipennis
slash pine
Pinus elliottii
feral hog
Sus scrofa
slippery elm
Ulmus rubra
forest tent caterpillar
Malacosoma disstria
southern red oak
Quercus falcata var. falcata
gray bat
Myotis grisescens
sugar maple
Acer saccharum
gypsy moth
Lymantria dispar
sugarberry
Celtis laevigata
Hine’s emerald dragonfly
Somatochlora hineana
swamp tupelo
Nyssa sylvatica var. biflora
Indiana bat
Myotis sodalis
swamp white oak
Quercus bicolor
Japanese beetle
Popillia japonica
sweetgum
Liquidambar styraciflua
Louisiana waterthrush
Seiurus motacilla
sycamore
Platanus occidentallis
mallard
Anas platyrhynchos
tall fescue
Lolium arundinaceum
mountain lion
Puma concolor
(Appendix 1 continued on next page)
200
APPenDix 1
Common name
Scientific Name
Common name
Scientific Name
northern bobwhite
Colinus virginianus
southern pine beetle
Dendroctonus frontalis
Ozark bass
Ambloplites constellatus
summer tanager
Piranga rubra
Ozark hellbender
Cryptobranchus alleganiensis
bishopi
Tumbling Creek cavesnail
Antrobia culveri
white-tailed deer
Odocoileus virginianus
red bat
Lasiurus borealis
wood thrush
ruffed grouse
Hylocichla mustelina
Bonasa umbellus
Leptodea leptodon
worm-eating warbler
Helmitheris vermivora
scaleshell
yellow-breasted chat
Icteria virens
scarlet tanager
Piranga olivacea
201
APPenDix 2: CRoSSWALK of nATuRAL CommuniTieS
natural
community
(this
assessment)
illinois natural
community
classification*
indiana natural
community
classification*
Terrestrial
natural
communities
of missouri
(nelson 2010)
fiA forest type
American beech,
maple unglaciated
forest
sugar maple/
beech/yellow
birch
red oak-sugar
maple forest
central maple,
American basswood
forest
hard maple/
basswood
red oak-sugar
maple forest
white oak-northern
red oak-sugar maple
mesic forest
white oak/red
oak/hickory
white oaksugar maple
white oak-red oaksugar maple mesic
forest
white oak/red
oak/hickory
mixed oak-red
cedar
central maple,
American basswood
forest
hard maple/
basswood
sugar maple,
elm, boxelder
mesic
sandstone
forest
white oak-red oaksugar maple mesic
forest
white oak/red
oak/hickory
departures
in overstory
composition
mesic sand
forest
beech-maple
unglaciated forest
sugar maple/
beech/yellow
birch
none known
dry-mesic
limestone/
dolomite
forest
white oak-mixed
oak/dry-mesic
alkaline forest
white oak
mixed oak-red
cedar
white oak-dogwood
dry-mesic forest
white oak
red/black oak,
some red cedar
white oak-red oak
dry-mesic acid forest
white oak/red
oak/hickory
red/black oak,
some red cedar
interior highlands
shortleaf pine-oak
dry-mesic forest
shortleaf pine/
oak
white oak/red
oak
interior highlands
shortleaf pine-oak
dry-mesic forest
shortleaf pine/
oak
white oak/red
oak
white oak-red oak
dry-mesic acid forest
white oak/red
oak/hickory
red/black oak
decline
mesic loess/
glacial till
forest
mesic upland
forest
dry-mesic
upland forest
mesic upland
forest
dry-mesic
upland forest
mesic upland
forest
dry-mesic upland
forest
Artifact (out
of character)
association
natureServe
associations
mesic
limestone/
dolomite
forest
dry-mesic
chert forest
dry-mesic
sandstone
forest
(Appendix 2 continued on next page)
202
APPenDix 2
natural
community
(this
assessment)
dry-mesic
upland forest
dry-mesic
bottomland
forest
mesic
bottomland
forest
wet-mesic
bottomland
forest
illinois natural
community
classification*
indiana natural
community
classification*
dry-mesic
upland forest
dry-mesic upland
forest
dry-mesic
sandstone
forest
dry-mesic
sand forest
(northern IL
only)
not recognized
dry-mesic
sand forest
dry woodland
shortleaf pine is
absent
dry-mesic
upland forest
dry-mesic upland
forest
dry woodland
shortleaf pine is
absent
not recognized
mesic
floodplain
forest
wet-mesic
floodplain
forest
wet floodplain
forest
wet bottomland
forest
Terrestrial
natural
communities
of missouri
(nelson 2010)
wet-mesic
floodplain
forest
not recognized
mesic floodplain
forest
wet-mesic
floodplain forest
dry-mesic
igneous forest
dry-mesic
bottomland
forest
mesic
bottomland
forest
wet-mesic
bottomland
forest
wet floodplain
forest
wet-mesic
floodplain forest
wet
bottomland
forest
natureServe
associations
fiA forest type
Artifact (out
of character)
association
shortleaf pineblueberry forest
shortleaf pine
red/black oak
decline
white oak-dogwood
dry-mesic forest
white oak
red/black oak
decline
increases in
red-black oak
group
none listed
interior highlands
shortleaf pine-oak
dry-mesic forest
shortleaf pine/
oak
white oak/red
oak
white oak-dogwood
dry-mesic forest
white oak
red/black oak
decline
white oak-red oak
dry-mesic forest
white oak/red
oak/hickory
red/black oak
decline
shortleaf pineblueberry forest
shortleaf pine
red/black oak
decline
white oak-red
oak dry-mesic
bottomland acid
forest
white oak/red
oak/hickory
sycamore,
box elder,
multiflora rose
sugar maple-oakbitternut hickory
bottomland forest
sugarberry/
hackberry/elm/
green ash
mostly
destroyed
ash-oak-sycamore
mesic bottomland
forest
sycamore/
pecan/
American elm
mostly
destroyed
swamp chestnut
oak, sweetgum
mesic floodplain
forest
sweetgum/
nuttall oak/
willow oak
mostly
destroyed
bur oak-swamp
white oak mixed
bottomland forest
bur oak
bottomland
woodland
species
overcup oak-nuttall
oak bottomland
forest
overcup oak/
water hickory
pin oak
increases
red maple/
lowland
many variants
mix in
pin oak-mixed
hardwood forest
red maple-water
locust mixed
bottomland forest
mixed oakhardwood sand
pond forest
(Appendix 2 continued on next page)
203
APPenDix 2
natural
community
(this
assessment)
illinois natural
community
classification*
indiana natural
community
classification*
Terrestrial
natural
communities
of missouri
(nelson 2010)
wet-mesic
floodplain
forest
wet-mesic
floodplain forest
wet
bottomland
forest
wet bottomland wet floodplain
forest
forest
wet floodplain
forest
wet-mesic
floodplain
forest
wet-mesic
floodplain forest
not present
not present
dry woodland
(if warm
season grasses
present, would
be classed as
barrens)
dry (and
possibly drymesic) barrens
riverfront
forest
overcup oaksweetgum
bottomland forest.
fiA forest type
river birch-sycamore
forest
river birch/
sycamore
slippery elm-green
ash-hackberry forest
sugarberry/
hackberry/elm/
green ash
Ozark ashe’s juniper
glade woodland
dry limestone/
dolomite
woodland
limestone bedrock
barren
red cedar alkaline
bluff woodland
Artifact (out
of character)
association
mostly
destroyed
red cedar
invasion
eastern
redcedar/
hardwood
chinquapin oakash/little bluestem
woodland
dry-mesic
limestone/
dolomite
woodland
red cedar
increases
red cedar codominant
chinquapin oak-red
cedar dry alkaline
forest
eastern
redcedar/
hardwood
red cedar/red
oak
not present
shortleaf pine/little
bluestem woodland
shortleaf pine
red/black oak
decline
chert barren
post oak-black jack
oak/little bluestem
woodland
post oak/
blackjack oak
variable red
cedar/black
oak
shortleaf pine-black
oak forest
shortleaf pine
red/black oak
decline
Midwest post oakblack jack oak forest
post oak/
blackjack oak
red/black oak
decline
Ozark black oakscarlet oak forest
chestnut
oak/black oak/
scarlet oak
red/black oak
decline
shortleaf pine-oak
dry forest
shortleaf pine/
oak
red/black oak
decline
dry (or
dry-mesic)
woodland
open woodland
dry barrens
not present
dry woodland
(if warm
season grasses
present, would
be classed as
barrens)
natureServe
associations
chert barren
not present
dry chert
woodland
(Appendix 2 continued on next page)
204
APPenDix 2
natural
community
(this
assessment)
illinois natural
community
classification*
indiana natural
community
classification*
Terrestrial
natural
communities
of missouri
(nelson 2010)
not present
dry barrens
sandstone barren
dry sandstone
woodland
dry woodland
not present
open woodland
dry barrens
barren or dry
upland forest; no
igneous substrate
in in
dry igneous
woodland
dry-mesic
barrens (red
oak would not
be expected)
not recognized
sand barren
shortleaf pine
red/black oak
decline
shortleaf pine-black
oak forest
shortleaf pine/
oak
red/black oak
decline
post oak-black jack
oak/little bluestem
woodland
post oak/
blackjack oak
red/black oak
decline
Midwest post oakblack jack oak forest
post oak/
blackjack oak
red/black oak
decline
Ozark black oakscarlet oak forest
chestnut
oak/black oak/
scarlet oak
red/black oak
decline
shortleaf pine-oak
dry woodland
shortleaf pine/
oak
red/black oak
decline
shortleaf pine-black
oak forest
shortleaf pine/
oak
red/black oak
decline
post oak-black jack
oak/little bluestem
woodland
post oak/
blackjack oak
red/black oak
decline
shortleaf pine/little
bluestem woodland
red/black oak
decline
red/black oak
decline
Ozark black oakscarlet oak forest
chestnut
oak/black oak/
scarlet oak
red/black oak
decline
shortleaf pine-oak
dry woodland
shortleaf pine/
oak
red/black oak
decline
shortleaf pine-oak
dry-mesic woodland
shortleaf pine/
oak
red/black oak
decline
white oak-post oak/
bluestem woodland
white oak
red/black oak
decline
dry-mesic
chert
woodland
white oak-post oak/
bluestem woodland
white oak
red/black oak
decline
dry sand
woodland
post oak-black jack
oak/little bluestem
woodland
post oak/
blackjack oak
red/black oak
decline
dry-mesic
igneous
woodland
dry barrens
shortleaf pine/little
bluestem woodland
post oak/
blackjack oak
not present
barren
(undifferentiated)
or dry upland
forest; no igneous
substrate in in
fiA forest type
Artifact (out
of character)
association
Midwest post oakblack jack oak forest
dry woodland
dry-mesic
woodland
natureServe
associations
post oak-mixed oak
sand woodland
red/black oak
decline
(Appendix 2 continued on next page)
205
APPenDix 2
natural
community
(this
assessment)
illinois natural
community
classification*
indiana natural
community
classification*
dry-mesic
barrens
sandstone barren
not recognized
not recognized
open woodland
closed
woodland
flatwoods
natureServe
associations
fiA forest type
Artifact (out
of character)
association
dry-mesic
sandstone
woodland
white oak-post oak/
bluestem woodland
white oak
red/black oak
decline
dry-mesic
bottomland
woodland
none listed
dry-mesic
chert
woodland
shortleaf pine-oak
dry-mesic woodland
shortleaf pine/
oak
red/black oak
decline
dry-mesic
sandstone
woodland
shortleaf pine-oak
dry-mesic woodland
shortleaf pine/
oak
red/black oak
decline
variable elm,
locust, red
cedar, other
dry-mesic
woodland
not present
dry-mesic
sand
woodland
(northern IL
only)
sand barren
dry-mesic
sand
woodland
none listed
mesic
floodplain
forest
mesic floodplain
forest
mesic
bottomland
woodland
bur oak bottomland
woodland
bur oak
mostly
destroyed
wet floodplain
forest
wet floodplain
forest
cottonwood
floodplain woodland
cottonwood
mostly
destroyed
wet-mesic
floodplain
forest
wet-mesic
floodplain forest
bur oak bottomland
woodland
bur oak
mostly
destroyed
dry-mesic
woodland
dry-mesic upland
forest
loess/glacial
till woodland
central Midwest
white oak-mixed oak
woodland
mixed upland
hardwoods
dry flatwoods
upland
flatwoods
post oak flatwoods
post oak/
blackjack oak
bottomland
flatwoods
pin oak-post oak
lowland flatwoods
pin oak
increases
sinkhole
flatwoods
pin oak-swamp
white oak sinkhole
flatwoods
pin oak
increases
dry-mesic
loess/glacial
till savanna
central bur oak
openings
mesic loess/
glacial till
savanna
central bur oak
openings
variable elm,
locust, red
cedar, other
limestone/
dolomite
savanna
chinquapin oak
limestone-dolomite
savanna
red cedar
southern
flatwoods
southwestern
lowland mesic
flatwoods
dry-mesic
savanna
mesic savanna
savanna
Terrestrial
natural
communities
of missouri
(nelson 2010)
mesic savanna
dry-mesic
savanna
limestone bedrock
barren
wet-mesic
bottomland
woodland
red/black oak
decline
bur oak
black oak/red
cedar
variable elm,
locust, red
cedar, other
(Appendix 2 continued on next page)
20
APPenDix 2
natural
community
(this
assessment)
savanna
illinois natural
community
classification*
indiana natural
community
classification*
southern
flatwoods
(if claypan
present;
otherwise,
absent from
s IL)
dry-mesic sand
savanna
dry-mesic
barrens
Terrestrial
natural
communities
of missouri
(nelson 2010)
dry-mesic
barrens
red/black oak,
red cedar
post oak-white oak
dry-mesic barrens
red/black oak,
red cedar
post oak central dry
barrens
red/black oak,
red cedar
post oak-white oak
dry-mesic barrens
red/black oak,
red cedar
post oak central dry
barrens
red/black oak,
red cedar
loess hills little
bluestem dry prairie
red cedar, oak,
sumac, invasive
herbs
Midwest dry-mesic
prairie
red cedar, elm,
sumac, locust,
invasives
central tallgrass
big bluestem loess
prairie
red cedar, elm,
sumac, locust,
invasives
mesic loess/
glacial till
prairie
central mesic
tallgrass prairie
red cedar, elm,
sumac, locust,
invasives
dry limestone/
dolomite
prairie
none listed
red cedar, elm,
sumac, invasive
herbs
dry-mesic
limestone/
dolomite
prairie
central dry-mesic
limestone-dolomite
prairie
red cedar, elm,
sumac, invasive
herbs
dry-mesic
chert prairie
Midwest chert
prairie
red cedar,
sumac, invasive
herbs
chert barren
chert savanna
sandstone barren
sandstone/
shale savanna
dry barrens
dry loess/
glacial till
prairie
loess hill
prairie
prairie
dry-mesic
prairie (unless
on loess hill);
or loess hill
prairie
not present
mesic prairie
mesic prairie
limestone
glade (dry
dolomite
prairie in n IL)
limestone
glade
(dry-mesic
dolomite
prairie in
northern IL)
dolomite
hill prairie
(prairies on
chert mostly
gone)
limestone barren
not present
fiA forest type
Artifact (out
of character)
association
post oak-mixed oak
sand woodland
sand savanna
dry barrens
barrens
natureServe
associations
dry-mesic
loess/glacial
till prairie
(Appendix 2 continued on next page)
207
APPenDix 2
natural
community
(this
assessment)
illinois natural
community
classification*
indiana natural
community
classification*
dry-mesic
barrens; shale
barrens (in
southern IL,
these always
occur in a
woodland
context)
dry-mesic prairie
dry-mesic
sand prairie (n
and central IL
only)
dry-mesic sand
prairie
dry sand
prairie
(northern and
central IL only)
dry sand prairie
mesic prairie
mesic prairie
dry-mesic
prairie
(typically, a
very local
inclusion in
southern
flatwoods)
dry-mesic prairie
Terrestrial
natural
communities
of missouri
(nelson 2010)
dry-mesic
sandstone/
shale prairie
natureServe
associations
fiA forest type
Midwest sandstone/
shale prairie
red cedar,
sumac, invasive
herbs
Midwest dry-mesic
sand prairie
oak, sumac,
invasive herbs
Midwest dry sand
prairie
oak, sumac,
invasive herbs
prairie swale
unglaciated mesic
tallgrass prairie
elm, sumac,
red cedar,
invasive herbs
hardpan
prairie
little bluestem
hardpan prairie
sumac, red
cedar, invasive
herbs
wet-mesic
bottomland
prairie
central wet-mesic
tallgrass prairie
woody
encroachment,
invasives
wet
bottomland
prairie
central cordgrass
wet prairie
woody
encroachment,
invasives
central shale glade
red cedar
sand prairie
prairie
wet-mesic
prairie (n and
central IL only)
wet prairie
wet prairie
glade
Artifact (out
of character)
association
shale glade
siltstone barren
limestone
glade
limestone barren
dry dolomite
prairie; limited
to glade-like
margins to
loess hill
prairie
not present
not recognized
limestone
glade
Ozark limestone
glade
eastern
redcedar
red cedar
dolomite
glade
Ozark dolomite
glade
eastern
redcedar
red cedar
chert barren
chert glade
Ozark chert glade
red cedar
sandstone
glade
sandstone barren
sandstone
glade
Ozark sandstone
glade
red cedar
not recognized
not recognized
igneous glade
Ozark igneous glade
eastern
redcedar
red cedar,
sumac
(Appendix 2 continued on next page)
208
APPenDix 2
natural
community
(this
assessment)
illinois natural
community
classification*
shrub swamp
indiana natural
community
classification*
shrub swamp
Terrestrial
natural
communities
of missouri
(nelson 2010)
swamp
forested fen (n
and central IL
only)
forest swamp
forested fen
fen
graminoid fen
(depending on
structure)
fen
seep
circumneutral seep
seep
spring
Artifact (out
of character)
association
northern
buttonbush swamp
drained,
dehydrated,
farmed, exotics
southern
buttonbush swamp
intact if not
overgrazed or
drained
water tupelo swamp
forest
mostly
destroyed
bald cypress-(water
tupelo) swamp
baldcypress/
water tupelo
altered flood
regimes
pond shrub
swamp
buttonbush sinkhole
pond swamp
pond swamp
water tupelo
sinkhole pond
swamp
baldcypress/
water tupelo
some drained
and overgrazed
overcup oak pond
forest
overcup oak/
water hickory
some drained
and overgrazed
some drained
and overgrazed
Ozark fen
Ozark fen
many drained
and overgrazed
Ozark prairie
fen
Ozark prairie fen
many drained
and overgrazed
forested fen
red maple forested
seep
acid seep
acid gravel
seep
red maple/
lowland
many drained
and overgrazed
central tallgrass fen
many drained
and overgrazed
great plains neutral
seep
many drained
and overgrazed
great plains acid
seep
many drained
and overgrazed
Midwest sand seep
many drained
and overgrazed
Midwest acid seep
many drained
and overgrazed
glacial fen
acid gravel
seep
sand seep
fiA forest type
shrub swamp
swamp
swamp
natureServe
associations
brackish
marsh
not present
saline seep
eastern great plains
saline marsh
many drained
and overgrazed
calcareous
seep
calcareous seep
limestone/
dolomite
spring
none listed
invasive plants;
culturally
altered sites
*Crosswalk for Illinois and Indiana provided by John Taft, Illinois Natural History Survey. The crosswalk classification for natural communities in Illinois
and Indiana focuses primarily on the FIA and NatureServe designations because in some cases the Missouri classification is too broad and other times
too specific for direct crosswalk comparison on a 1:1 basis.
209
APPenDix 3: foReST TyPeS
Forest type*
White oak / red oak / hickory
White oak
Post oak / blackjack oak
Mixed upland hardwoods
Eastern redcedar / hardwood
Sugarberry / hackberry / elm / green ash
Cherry / white ash / yellow-poplar
Shortleaf pine / oak
Chestnut oak / black oak / scarlet oak
Eastern redcedar
Shortleaf pine
Elm / ash / black locust
River birch / sycamore
Sassafras / persimmon
Silver maple / American elm
Sugar maple / beech / yellow birch
Sycamore / pecan / American elm
Scarlet oak
Yellow-poplar
Yellow-poplar / white oak / northern red oak
Sweetgum / yellow-poplar
Northern red oak
Black walnut
Hard maple / basswood
Cottonwood
Chestnut oak
Willow
Black locust
Sweetbay / swamp tupelo / red maple
Cottonwood / willow
Black ash / American elm / red maple
Red maple / oak
Red maple / lowland
Other hardwoods
Virginia pine / southern red oak
Eastern white pine / northern red oak / white ash
Swamp chestnut oak / cherrybark oak
Virginia pine
Sweetgum / Nuttall oak / willow oak
Overcup oak / water hickory
Assessment area
(acres)
illinois
(acres)
indiana
(acres)
missouri
(acres)
7,335,073
1,651,005
1,413,711
879,481
584,346
509,837
388,612
375,485
358,860
351,161
254,980
240,723
222,072
210,723
207,866
207,390
199,103
165,322
159,104
147,150
141,197
131,818
116,043
100,227
62,451
50,199
49,492
48,699
45,973
45,549
43,847
42,772
36,049
34,203
28,072
22,833
21,571
20,531
20,200
19,631
819,004
128,258
44,731
223,341
30,709
290,304
55,891
13,130
2,269
2,273
24,731
68,338
66,845
27,558
143,132
25,154
84,864
15,168
21,680
52,612
11,084
5,238
6,681
29,766
1,773
25,887
15,435
3,885
30,426
18,864
7,714
21,039
7,436
5,394
1,797
13,130
1,028,712
136,787
252,869
60,541
76,776
304,287
4,618
57,695
16,658
14,870
74,513
68,644
84,717
36,819
149,594
42,874
6,031
142,771
125,470
80,140
19,863
25,695
80,304
25,960
48,426
11,341
24,975
42,088
15,124
16,213
31,887
12,925
8,419
28,072
15,955
6,616
20,531
11,576
1,844
5,487,357
1,385,959
1,368,980
403,271
493,095
142,756
28,434
357,737
298,896
332,229
215,378
97,871
86,582
98,448
27,915
32,642
71,366
159,291
1,164
8,444
100,870
85,110
13,241
6,725
12,264
8,289
8,770
3,170
2,085
18,348
1,485
13,158
8,624
4,657
(Appendix 3 continued on next page)
210
APPenDix 3
Forest type*
Baldcypress / water tupelo
Eastern white pine
Red pine
Aspen
Other exotic hardwoods
Bur oak
Scotch pine
Loblolly pine
Black cherry
Other pine / hardwood
Assessment area
(acres)
16,008
11,285
11,242
4,207
4,171
4,171
3,525
1,330
146
80
Illinois
(acres)
16,008
1,773
1,330
146
-
Indiana
(acres)
Missouri
(acres)
9,512
11,242
4,207
800
919
80
3,372
4,171
2,605
-
*Forest types in the assessment area are based on U.S. Forest Service, Forest Inventory and Analysis. Source: U.S. Forest Service (2011a).
211
APPenDix 4. Common nonnATive invASive SPeCieS
in The CenTRAL hARDWooDS ReGion
Communities Affected
Threat
States Affected
Autumn olive
prairie, savanna, open woodland
nitrogen fixer that outcompetes
native species
IL, IN, MO
Bush honeysuckles
woodlands
shades out native wildflowers and
young native trees on the forest
floor, allelopathic
IL, IN, MO
Japanese honeysuckle
openings and borders of forests,
woodlands
climbs over and shades out native
vegetation
IL, IN, MO
Mahaleb cherry
forest, streambanks
displaces native vegetation
IL, MO
Multiflora rose
prairies, savannas, open woodlands
and forest edges
forms impenetrable thickets,
smothers other vegetation
IL, IN, MO
Princess-tree
forests, streambanks, and steep rocky
slopes
grows rapidly, crowds out native
vegetation
IL
Sawtooth oak
forests
displaces native vegetation
IL, MO
Silktree
forest
displaces native vegetation
IL, MO
Tree-of-heaven
rock cliffs, streams, disturbed forests
crowds out natives, allelopathic
IL, IN, MO
Cheatgrass
roadsides, openlands
displaces native vegetation
MO
Japanese stiltgrass
stream banks, river bluffs, floodplains,
forest wetlands, moist woodlands,
early successional fields, uplands
unpalatable to wildlife, outcompetes
native species, increases intensity of
prescribed fires
IL, IN, MO
Johnsongrass
riverbank communities, fallow fields,
glades, prairies, savannas and forest
edges
crowds out native species
IL, IN, MO
Reed canarygrass
marshes, wet prairies, wet meadows,
fens, stream banks and swales
crowds out native plants, constricts
waterways and irrigation canals
IL, IN, MO
Tall fescue
roadsides, openlands
displaces native vegetation
MO
Species
Woody Plants
Grasses
(Appendix 4 continued on next page)
212
APPenDix 4
Communities Affected
Threat
States Affected
woodlands
forms thick mats
IL, IN, MO
Common and cut-leaved teasel prairie, savanna
outcompete natives
IL, IN, MO
Creeping jenny
floodplain forests
outcompetes natives
IL, IN, MO
Crown vetch
roadsides, riparian
spreads vegetatively, outcompetes
natives
IL, IN, MO
Garlic mustard
upland and floodplain forests,
savannas, open woodlands
allelopathic, crowds out native plants IL, IN, MO
Ground ivy/creeping charlie
floodplain and mesic upland forests
forms thick mats
Japanese knotweed
riparian and floodplain forests
forms dense thickets that exclude
native vegetation
Musk thistle
prairies
crowds out native plant and
grassland species through
competition for resources
IL, IN, MO
Purple loosestrife
marshes, fens, sedge meadows, and
wet prairies
destroys marshes and wet prairies
and chokes waterways
IL, IN, MO
Sericea lespedeza
open woodlands, prairies, borders of
ponds and swamps, meadows
unpalatable to grazers, which in turn
overgraze the surrounding native
plants
IL, IN, MO
White and yellow sweetclover
prairies, savannas, open woodlands
and forest edges
outcompete natives
IL, IN, MO
Chinese yam/cinnamon vine
bottomland forests, riparian areas
shades out understory plants and
trees, eventually killing them
IL, IN, MO
Japanese hop
riparian and floodplain forests
displaces native vegetation, prevents
the emergence of new plants, kills
newly planted trees
IL, IN, MO
Kudzu
potentially all, currently limited by cold forms dense mats over the ground,
IL, IN, MO
winters
shrubs, mature trees; kills understory
plants and trees
Oriental bittersweet
disturbed forest edges, woodlands
climbs over and smothers vegetation, IL, IN, MO
which may die from excessive
shading or breakage
Wintercreeper
floodplain forest, moist and dry-moist
forest, and banks of streams and rivers
forms a dense groundcover that
reduces or eliminates native plant
species
IL, IN, MO
Curly leaf pondweed
ponds, lakes, slow-moving streams
prevents light penetration for native
aquatic plants
IL, IN, MO
Eurasian watermilfoil
ponds, lakes, slow-moving streams
prevents light penetration for native
aquatic plants
IL, IN, MO
Species
herbaceous Plants
Common periwinkle
IL, IN, MO
vines
Aquatic Plants
(Appendix 4 continued on next page)
213
APPenDix 4
Communities Affected
Threat
States Affected
Emerald ash borer
forests, any area with ash trees
present
kills healthy ash trees
IL, IN, MO
Gypsy moth
oak species, basswood, poplar species,
hawthorn species
defoliates trees; repeated defoliation
leads to death
IL, IN, MO (but
not prevalent
in assessment
area)
Japanese beetle
all
adults defoliate broad-leaved
species; larvae consume grass roots
IL, IN, MO
bottomland forests, seeps, glades,
fens, springs, and streams
behavior causes soil erosion, reduces
water quality; acorn are consumed
IL, IN, MO
Species
Terrestrial invertebrates
Terrestrial vertebrates
Feral hog
Sources: Missouri Department of Conservation (2013c), Olson et al. (2004), Plant Conservation Alliance (2013).
214
APPenDix 5: Common DiSeASeS in The CenTRAL
hARDWooDS ReGion
Disease name
Species affected
Climate factors
States affected
Annosus root rot
conifers (esp. red and white pine)
drought, stress
IL, IN, MO
Anthracnose diseases
ash, basswood, birch, catalpa, elm,
hickory, horse chestnut, maple, oak,
sycamore, yellow-poplar, and walnut
thrives in cool, wet environments
IL, IN, MO
Armillaria root disease
hardwoods and conifers
warming would allow decay to occur
for longer periods in the year; droughtstressed trees more susceptible
IL, IN, MO
Bacterial leaf scorch
elm, oak species (esp. pin, northern,
and southern red oak), sycamore
drought stress exacerbates the disease
IL, IN, MO
Branch flagging
and tip dieback
oak species (esp. white)
N/A
IL, IN, MO
Butternut canker
butternut
N/A
IL, IN, MO
Bur oak blight
bur oak (only on variety oliviformis)
spring precipitation favors disease
(Harrington et al. 2012)
IL, MO
Chestnut blight
American chestnut
N/A
IN
Diplodia blight of pines
pines and other conifers
can be deadly when combined with
drought
IL, IN, MO
Dogwood anthracnose
flowering dogwood
more problematic in cool, wet climates
MO, IN
Dothistroma needle blight
pine species
conidia dispersed in wet weather
IL, IN, MO
Dutch elm disease
elm species
N/A
IL, IN, MO
Fomes annosus
pine species
drought, stress
Hypoxylon canker
oaks and other hardwoods
drought
throughout the
South
Littleleaf disease
shortleaf pine
N/A
(currently none,
possible in MO)
Oak decline
northern red, southern red, scarlet,
and black oak
drought-stressed trees more
susceptible
IL, IN, MO
Oak wilt
oak species (esp. black, blackjack, bur,
northern red, pin and shingle)
earlier warming in spring changes
susceptibility period for insect
transmission
IL, IN, MO
Verticillium wilt
boxwood, Kentucky coffee tree, horse- N/A
chestnut, Ohio buckeye, magnolia,
maple, privet, redbud, serviceberry,
sumac, tulip tree, viburnum, and
many others
IL, IN, MO
Sources: Marshall (2010), Miller (2011), Missouri Forest Health Highlights (2010), Scarbrough and Juzwik (2004).
215
APPenDix 6: Common inSeCT PeSTS
in The CenTRAL hARDWooDS ReGion
insect pest
Species affected
Assorted leaf and stem gall wasps oak species
Climate factors
States affected
N/A
IL
Bagworm
honeylocust, hackberry, bald
cypress
N/A
IL
Bark beetles
conifers and hardwoods
attacks drought-stressed trees
IL
cottony maple scale
maple species
N/A
IL
Elm flea weevil
elm species
N/A
IL
Emerald ash borer
ash species
N/A
IL, IN, MO
Fall webworm
various
second generation per year and longer
feeding in warmer southern areas
IL
Forest tent caterpillar
oak and other hardwood
species (aspen, birch, cherry,
basswood, ash)
forests affected by stressors such as climate
change and pollution likely to have more
severe and frequent defoliation (Babinfenske and Anand 2011)
IN
Gypsy moth
oak species, basswood, poplar
species, hawthorn species
larvae susceptible to fungal attack during
wet springs (Andreadis and Weseloh 1990)
IL, IN, MO
Hickory bark beetle
hickory species
drought stress predisposes hickory to attack IL, IN, MO
(Park et al. 2013)
Honeylocust plant bug
honeylocust
N/A
IL
Japanese beetle
>300 species affected
N/A
IL
Jumping oak gall wasp
oak species
N/A
IN, MO
Looper complex
red oak, basswood, maples,
hickories
N/A
IL, IN, MO
Red oak borer
red, black, and scarlet oak
attacks trees stressed by drought
MO
Shingle oak skeletonizers
shingle oak
N/A
MO
Southern pine beetle
shortleaf and other southern
pine species
currently not found in MO, but they may
move here if climate becomes warmer; are
attracted to stressed trees (Ungerer et al.
1999)
throughout the
South
Two-lined chestnut borer
oak species
trees weakened by drought more
susceptible to attack (Scarbrough and
Juzwik 2004)
IN
Sources: Marshall (2010), Miller (2011), Missouri Forest Health Highlights (2010), Scarbrough and Juzwik (2004).
21
APPenDix 7: 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, Girvetz et al. 2009). Data
for ClimateWizard are derived from PRISM
(Parameter-elevation Regressions on Independent
Slopes Model, Gibson et al. 2002). The PRISM
model interpolates historical data from the National
Weather Service cooperative stations, the Midwest
Climate Data Center, and the Historical Climate
Network, among others. Data undergo strict quality
control procedures to check for errors in station
measurements. The PRISM model finds linear
relationships between these station measurements
and local elevation by using a digital elevation
model (digital gridded version of a topographic
map). Temperature and precipitation are then
derived for each pixel on a 2.5-mile grid across the
conterminous United States. The closer a station is
in distance and elevation to a grid cell of interest,
and the more similar it is in its proximity to coasts
or topographic features, the higher the weight the
station will have on the final, predicted value for that
cell. More information on PRISM can be found at:
http://www.prism.oregonstate.edu/.
This historical gridded data set is different from that
used in the National Climate Assessment, which
uses a new gridded historical data set (CDDv2)
from the National Climatic Data Center (NCDC)
(Kunkel et al. 2013). The new gridded data set had
not been peer reviewed and published at the time
this assessment was completed, and therefore we
cannot fully compare this new version with the
one available through PRISM. However, both are
based on cooperative weather station data, cover the
period from 1895 through 2011, and have similar
resolutions (3.1-mile vs. 2.5-mile grid). In addition,
the overall trends reported as input into the National
Climate Assessment are generally consistent with
those reported in this assessment (Kunkel et al.
2013).
Linear trend analysis for the period from 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 (Trenbarth et al. 2007). A first-order
autoregression was assumed for the residuals,
meaning that values one time step away from each
other are assumed to be correlated. This method was
used to examine trends for every 2.5-mile grid cell.
The slope and p-values for the linear trend over time
were calculated annually, seasonally, for each month,
and for each climate variable, and then mapped.
An overall trend for an area is based on the trend
analysis of the average value for all grid cells within
the area over time (Table 19).
Developers of the ClimateWizard Tool advise users
to interpret the linear trend maps in relation to the
respective map of statistical confidence (Figs. 40
and 41). 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 <0.1 were considered to have sufficient statistical
217
APPenDix 7
Table 19.—Average annual, seasonal, and monthly values and linear trend analysis over the 111-year period for
the assessment area, divided by state. P-values represent the probability of observing that trend by chance alone.
P-values in boldface indicate <10-percent probability that the trend was due to chance alone.
month/
season
mean Change
precip in precip Precip
(inches) (inches)
p
mean
Tmean
(°f)
January
February
March
April
May
June
July
August
September
October
November
December
Annual
Fall
Spring
Summer
Winter
2.86
2.5
3.83
4.09
4.39
4
3.53
3.36
3.28
3.04
3.42
3.08
41.4
9.74
12.31
10.89
8.45
-0.65
0.51
0.32
0.82
1.42
0.61
1.16
-0.84
-0.51
0.49
1.71
0.68
5.66
1.76
2.48
0.93
0.52
0.39
0.3
0.63
0.21
0.03
0.27
0.02
0.11
0.31
0.37
0.01
0.18
0.01
0.15
0.03
0.29
0.61
31.03
34.41
44.41
55.31
64.89
73.76
77.6
75.9
68.94
57.49
44.99
34.42
55.26
57.13
54.87
75.75
33.29
January
February
March
April
May
June
July
August
September
October
November
December
Annual
Fall
Spring
Summer
Winter
3.4
2.77
4.15
4.15
4.53
4.08
3.85
3.44
3.26
2.97
3.43
3.39
43.42
9.66
12.82
11.37
9.56
-0.84
0.37
-0.2
1.27
1.99
0.76
1.48
-0.2
0.01
0.42
1.64
0.34
20.92
2.13
3.01
2.03
-0.14
0.33
0.53
0.79
0.03
0
0.15
0.01
0.68
0.98
0.44
0
0.5
0
0.06
0.01
0.01
0.91
January
February
March
April
May
June
July
August
September
October
November
December
Annual
Fall
Spring
Summer
Winter
2.41
2.33
3.59
4.28
4.82
4.34
3.5
3.58
3.93
3.32
3.33
2.72
42.17
10.58
12.7
11.42
7.46
-0.39
0.54
0.66
0.49
0.8
-0.33
0.62
-0.84
0.34
0.46
1.9
0.99
5.25
2.71
1.88
-0.55
1.13
0.48
0.25
0.28
0.48
0.2
0.57
0.23
0.11
0.61
0.45
0
0.02
0.03
0.04
0.1
0.59
0.13
218
Change
in Tmean
(°f)
Tmean
p
mean
Tmin
(°f)
Change
in Tmin
(°f)
Tmin
p
mean
Tmax
(°f)
Change
in Tmax
(°f)
Tmax
p
-2.91
1.36
-0.03
2.13
0.25
0.74
-0.57
-0.08
-1.76
-1.08
1.11
-0.27
-0.06
-0.57
0.78
0.03
-0.54
illinois
0.14
0.5
0.98
0.03
0.75
0.42
0.46
0.92
0.08
0.25
0.27
0.83
0.9
0.32
0.21
0.96
0.67
22.13
24.93
33.96
44.04
53.57
62.24
65.94
64
56.5
44.92
34.58
25.82
44.39
45.33
43.85
64.05
24.3
-2.23
1.49
0.27
2.04
1.03
2.42
2
1.53
-0.71
-0.03
2.91
0.03
0.93
0.75
1.11
1.98
-0.17
0.27
0.46
0.84
0.02
0.21
0
0.01
0.03
0.5
0.98
0
0.98
0.05
0.24
0.06
0
0.9
39.93
43.9
54.87
66.59
76.23
85.28
89.27
87.81
81.39
70.07
55.4
43.03
66.15
68.95
65.89
87.45
42.28
-3.59
1.22
-0.33
2.22
-0.53
-0.94
-3.15
-1.68
-2.83
-2.13
-0.69
-0.57
-1.06
-1.87
0.45
-1.92
-0.92
0.07
0.56
0.84
0.06
0.55
0.42
0
0.07
0.02
0.05
0.57
0.66
0.07
0.01
0.54
0.02
0.46
30.83
33.71
43.52
54.12
63.75
72.57
76.3
74.73
68.02
56.44
44.44
34.04
54.37
56.29
53.79
74.53
32.86
-2.46
1.69
-0.03
2.27
0.21
0.08
-1.27
-0.36
-1.43
-0.9
1.65
0.69
0.04
-0.21
0.82
-0.52
0.03
indiana
0.23
0.4
0.98
0.01
0.79
0.93
0.08
0.6
0.13
0.37
0.08
0.6
0.93
0.72
0.19
0.36
0.98
21.91
24.05
32.9
42.54
52.02
60.95
64.7
62.88
55.66
43.92
34.15
25.34
43.42
44.57
42.48
62.84
23.77
-1.74
1.52
-0.23
1.82
1.01
1.49
0.9
0.93
-0.82
-0.42
2.37
0.98
0.69
0.41
0.87
1.1
0.32
0.41
0.47
0.86
0.02
0.22
0.06
0.19
0.19
0.45
0.72
0.01
0.49
0.18
0.55
0.16
0.02
0.82
39.75
43.37
54.15
65.7
75.48
84.2
87.91
86.58
80.38
68.98
54.74
42.73
65.33
68.02
65.1
86.22
41.94
-3.19
1.85
0.16
2.73
-0.58
-1.34
-3.44
-1.65
-2.02
-1.37
0.95
0.4
-0.6
-0.8
0.77
-2.14
-0.27
0.11
0.35
0.92
0.01
0.53
0.21
0
0.05
0.07
0.23
0.4
0.76
0.25
0.27
0.27
0.01
0.83
32.39
36.13
45.62
56.18
64.68
73.2
77.7
76.54
68.89
57.85
45.74
35.49
55.87
57.49
55.49
75.81
34.67
-2.23
1.48
-0.42
1.34
0.31
0.28
0.27
0.38
-1.6
-0.92
0.23
-0.32
-0.08
-0.76
0.4
0.31
-0.32
missouri
0.2
0.42
0.78
0.18
0.68
0.77
0.77
0.65
0.12
0.32
0.83
0.79
0.87
0.19
0.49
0.66
0.78
21.71
24.89
33.64
43.81
52.76
61.6
65.69
64.18
56.37
44.73
34.13
25.21
44.06
45.07
43.4
63.82
23.95
-1.73
1.48
-0.32
1.01
0.97
1.36
1.93
1.35
-0.86
-0.15
1.79
-0.17
0.59
0.27
0.55
1.56
-0.08
0.34
0.4
0.81
0.26
0.24
0.1
0.01
0.05
0.41
0.88
0.09
0.89
0.22
0.67
0.33
0.01
0.94
43.08
47.38
57.61
68.56
76.62
84.8
89.72
88.91
81.42
70.97
57.36
45.78
67.68
69.91
67.59
87.8
45.41
-2.75
1.48
-0.51
1.66
-0.34
-0.81
-1.4
-0.58
-2.4
-1.7
-1.35
-0.45
-0.76
-1.8
0.26
-0.93
-0.55
0.13
0.46
0.76
0.16
0.68
0.5
0.25
0.58
0.08
0.14
0.29
0.73
0.22
0.03
0.7
0.32
0.64
APPenDix 7
Figure 40.—Map of statistical confidence (p-values for the linear regression) of the 111-year time series for temperature. Gray values
represent areas of low statistical confidence.
219
APPenDix 7
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.
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 (NCDC 2012).
Figure 41.—Map of statistical confidence (p-values for the linear
regression) of the 111-year time series for precipitation. Gray
values represent areas of low statistical confidence.
confidence. Areas with low statistical confidence in
the rate of change (gray areas on the map) should be
interpreted with caution.
220
We selected the time period 1901 through 2011 as it
was long enough 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.
To test the sensitivity of our trends to the selection
of beginning and end dates, we also analyzed the
data for the years since 1951 and since 1971 (data
not shown). In general, selecting this period resulted
in trends that were similar in direction and spatial
pattern to the 1901 through 2011 trends, but different
in slope and sometimes different in their statistical
significance. Therefore, trends should be interpreted
based on their relative magnitude and direction, and
the slope of the particular trend should be interpreted
with caution.
APPenDix 8: ADDiTionAL CLimATe PRoJeCTion
DATA AnD mAPS
Table 20.—Projected difference in 30-year average mean, minimum, and maximum temperature during the 21st
century compared to baseline (1971 through 2000) under two climate model-emissions scenario combinations.
Baseline
model
2010-2039
Departure from baseline (°f)
2040-2069
2070-2099
mean
Annual
55.09
GFDL A1FI
PCM B1
1.53
0.30
5.01
1.01
7.33
1.56
Winter
33.41
GFDL A1FI
PCM B1
1.08
0.30
3.64
1.82
4.61
2.31
Spring
54.84
GFDL A1FI
PCM B1
0.14
-0.03
4.45
0.59
6.77
1.48
Summer
75.24
GFDL A1FI
PCM B1
2.85
0.51
7.42
0.74
10.09
1.03
Fall
56.64
GFDL A1FI
PCM B1
2.04
1.07
4.56
1.13
8.1
1.22
minimum
Annual
43.91
GFDL A1FI
PCM B1
1.33
0.32
4.74
1.85
7.04
0.98
Winter
23.58
GFDL A1FI
PCM B1
2.55
1.83
5.31
3.53
6.26
3.60
Spring
43.18
GFDL A1FI
PCM B1
0.38
0.02
4.41
0.55
6.65
1.53
Summer
63.76
GFDL A1FI
PCM B1
2.59
0.58
6.80
0.97
9.27
1.21
Fall
44.93
GFDL A1FI
PCM B1
1.73
0.99
4.31
0.72
7.97
0.98
maximum
Annual
66.27
GFDL A1FI
PCM B1
1.58
0.37
5.17
1.04
7.66
1.59
Winter
43.25
GFDL A1FI
PCM B1
1.25
0.22
3.74
1.81
4.65
2.88
Spring
66.52
GFDL A1FI
PCM B1
-0.06
-0.14
4.44
0.63
6.80
1.36
Summer
86.74
GFDL A1FI
PCM B1
3.07
0.50
8.04
0.51
10.90
0.78
Fall
68.36
GFDL A1FI
PCM B1
2.35
1.08
4.72
1.47
8.22
1.39
Season
221
APPenDix 8
Table 21.—Projected difference in 30-year average annual and seasonal precipitation during the 21st century
compared to baseline (1971 through 2000) under two climate model-emissions scenario combinations.
Season
Baseline
model
2010-2039
Departure from baseline (inches)
2040-2069
Annual
43.84
GFDL A1FI
PCM B1
-3.17
2.04
-3.65
2.28
-3.12
2.85
Winter
8.38
GFDL A1FI
PCM B1
0.55
0.23
1.16
0.76
2.34
0.37
Spring
12.94
GFDL A1FI
PCM B1
1.10
1.92
1.80
1.42
2.31
1.86
Summer
11.5
GFDL A1FI
PCM B1
-3.25
1.87
-6.04
2.43
-7.57
3.17
Fall
11.01
GFDL A1FI
PCM B1
-1.61
-2.02
-0.61
-2.37
-0.24
-2.68
222
2070-2099
APPenDix 8
Figure 42.—Projected difference in mean daily temperature at the beginning of the century (2010 through 2039) compared to
baseline (1971 through 2000), under two climate model-emissions scenario combinations.
223
APPenDix 8
Figure 43.—Projected difference in mean daily minimum temperature at the beginning of the century (2010 through 2039) compared
to baseline (1971 through 2000), under two climate model-emissions scenario combinations.
224
APPenDix 8
Figure 44.—Projected difference in mean daily maximum temperature at the beginning of the century (2010 through 2039)
compared to baseline (1971 through 2000), under two climate model-emissions scenario combinations.
225
APPenDix 8
Figure 45.—Projected difference in mean daily temperature at mid-century (2040 through 2069) compared to baseline (1971
through 2000), under two climate model-emissions scenario combinations.
22
APPenDix 8
Figure 46.—Projected difference in mean daily minimum temperature at mid-century (2040 through 2069) compared to baseline
(1971 through 2000), under two climate model-emissions scenario combinations.
227
APPenDix 8
Figure 47.—Projected difference in mean daily maximum temperature at mid-century (2040 through 2069) compared to baseline
(1971 through 2000), under two climate model-emissions scenario combinations.
228
APPenDix 8
Figure 48.—Projected difference in mean annual and seasonal precipitation at the beginning of the century (2010 through 2039)
compared to baseline (1971 through 2000), under two climate model-emissions scenario combinations.
229
APPenDix 8
Figure 49.—Projected difference in mean annual and seasonal precipitation at mid-century (2040 through 2069) compared to
baseline (1971 through 2000), under two climate model-emissions scenario combinations.
230
APPenDix 9: ADDiTionAL imPACT moDeL ReSuLTS
The following pages contain additional model results
and modifying factors from the Climate Change Tree
Atlas (Tables 22-2) and LANDIS PRO (Table 27).
Tables 22-24 show results of the DISTRIB model
used in the Tree Atlas for the Illinois, Indiana, and
Missouri portions of the assessment area. Measured
area-weighted importance values (IVs) from Forest
Inventory and Analysis (FIA) as well as modeled
current (191-1990) and future (2010-2039, 2040209, 2070-2099) IVs from the DISTRIB models
were calculated for each time period. One hundred
thirty-four tree species were initially 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 2070-2099, which was used to create
tables in Chapter 5. For most species, the following
rules applied, based on the ratio of future IVs to
current modeled IVs:
Future:Current modeled IV
Class
<0.5
0.5 to 0.8
>0.8 to <1.2
1.2 to 2.0
>2
large decrease
small decrease
no change
small increase
large increase
A few exceptions applied to these general rules.
When there was a zero in the numerator or
denominator, a ratio could not be calculated. Instead,
a species was classified as gaining new habitat if
its FIA value was 0 and the future IV was greater
than 3. A species’ habitat was considered to be
extirpated if the future IV was 0 and FIA values
were greater than 3.
Special rules were created for rare species. A species
was considered rare if it had a current modeled
area-weighted IV that equaled <10 percent of the
number of 12.5-mile by 12.5-mile pixels in the
assessment area. The change classes are calculated
differently for these species because their current
infrequency tends to inflate the percentage change
that is projected. The cutoffs for each portion of the
assessment area were as follows:
Assessment area
Pixels
Cutoff IV
for rare species
IL
IN
MO
138
125
255
14
12
25
When a species was below the cutoff above, the
following rules applied:
Future:Current modeled IV
Class
<0.2
large decrease
0.2 to <0.
small decrease
0. to <4
no change
4 to 8
small increase
>8
large increase (not used
when current modeled
IV ≤3)
“Extirpated” was not used in this case because of
low confidence.
231
APPenDix 9
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.
Tables 25 and 2 describe the modifying factors and
adaptability scores used in the Tree Atlas. These
factors were developed using a literature-based
scoring system to capture the potential adaptability
of species to changes in climate that cannot be
adequately captured by the DISTRIB model
(Matthews et al. 2011b). This approach was used
to assess the capacity for each species to adapt and
considered nine biological traits reflecting innate
characteristics like competition 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 draws distinction 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
232
confidence. 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-) 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. 50).
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.
Only the traits that elicited a combination of a strong
positive or negative response, high certainty, and
high future relevance for a combined score of 4.5 or
greater are listed in the tables for each species.
Table 22.—Complete DISTRIB model results for tree species in the Illinois portion of the assessment area. FIA importance values (FIA IV) are current
importance values based on forest inventory and Analysis data, and current modeled importance values (Current iv) are based on results from the DiSTRiB
model. Early-, mid-, and late-century projected importance values and ratios of future to current modeled values are shown. Change classes are described in
the appendix text. *When Current IVs were <4 and FIA IVs were >0, the ratio was instead calculated by using the FIA IV.
Common name
24
34
934
23
7
191
382
36
106
605
289
177
54
34
347
31
13
0
25
4
59
189
204
112
107
129
43
205
473
616
184
9
5
6
322
21
51
929
27
8
202
404
40
100
596
326
169
69
33
336
25
4
0
36
18
58
202
196
117
134
184
25
258
481
587
238
9
1*
23
337
Medium
High
Medium
Medium
Medium
Low
High
High
Low
High
Medium
Low
High
Medium
Medium
Medium
Low
Low
Medium
High
Medium
Medium
Low
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
Low
High
High
2010-2039
PCm GfDL
B1
A1fi
10
42
1106
31
11
220
327
87
110
575
357
241
121
98
327
68
1
10
81
11
73
237
233
99
138
265
7
249
510
640
235
3
3
73
305
10
19
654
65
6
182
184
193
198
578
215
200
52
209
380
55
0
158
37
13
70
276
316
116
284
330
4
225
824
661
287
8
2
91
397
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
8
43
961
30
11
202
303
121
128
564
351
202
162
120
326
60
2
11
104
7
98
239
238
102
132
292
6
258
518
628
234
1
5
115
312
4
44
921
35
11
189
300
152
136
562
336
184
163
150
326
65
0
18
98
12
107
240
236
96
134
317
5
257
534
639
224
1
4
171
314
0.48
0.82
1.19
1.15
1.38
1.09
0.81
2.18
1.10
0.97
1.10
1.43
1.75
2.97
0.97
2.72
0.25
Inf
2.25
0.61
1.26
1.17
1.19
0.85
1.03
1.44
0.28
0.97
1.06
1.09
0.99
0.33
0.60
3.17
0.91
3
16
385
146
7
174
140
198
140
424
65
188
47
252
559
192
0
179
47
11
32
249
344
150
122
425
2
224
861
416
284
8
3
219
343
97
16
404
144
6
198
128
226
124
344
38
201
53
266
807
363
0
213
48
12
32
323
356
180
115
516
3
229
918
395
321
49
4
264
320
0.48
0.37
0.70
2.41
0.75
0.90
0.46
4.83
1.98
0.97
0.66
1.18
0.75
6.33
1.13
2.20
0.00
Inf
1.03
0.72
1.21
1.37
1.61
0.99
2.12
1.79
0.16
0.87
1.71
1.13
1.21
0.89
0.40
3.96
1.18
0.38
0.84
1.03
1.11
1.38
1.00
0.75
3.03
1.28
0.95
1.08
1.20
2.35
3.64
0.97
2.40
0.50
Inf
2.89
0.39
1.69
1.18
1.21
0.87
0.99
1.59
0.24
1.00
1.08
1.07
0.98
0.11
1.00
5.00
0.93
0.14
0.31
0.41
5.41
0.88
0.86
0.35
4.95
1.40
0.71
0.20
1.11
0.68
7.64
1.66
7.68
0.00
Inf
1.31
0.61
0.55
1.23
1.76
1.28
0.91
2.31
0.08
0.87
1.79
0.71
1.19
0.89
0.60
9.52
1.02
0.19 4.62
0.86 0.31
0.99 0.44
1.30 5.33
1.38 0.75
0.94 0.98
0.74 0.32
3.80 5.65
1.36 1.24
0.94 0.58
1.03 0.12
1.09 1.19
2.36 0.77
4.55 8.06
0.97 2.40
2.60 14.52
0.00 0.00
Inf
Inf
2.72 1.33
0.67 0.67
1.85 0.55
1.19 1.60
1.20 1.82
0.82 1.54
1.00 0.86
1.72 2.80
0.20 0.12
1.00 0.89
1.11 1.91
1.09 0.67
0.94 1.35
0.11 5.44
0.80 0.80
7.44 11.48
0.93 0.95
Change class
2070-2099
PCm B1
GfDL A1fi
small decrease
no change
no change
small increase
no change
no change
small decrease
large increase
small increase
no change
no change
no change
large increase
large increase
no change
large increase
large decrease
new habitat
large increase
small decrease
small increase
no change
small increase
no change
no change
small increase
large decrease
no change
no change
no change
no change
large decrease
no change
large increase
no change
small increase
large decrease
small decrease
large increase
no change
no change
large decrease
large increase
small increase
small decrease
large decrease
no change
small decrease
large increase
large increase
large increase
large decrease
new habitat
small increase
small decrease
small decrease
smal increase
small increase
smal increase
no change
large increase
large decrease
no change
small increase
small decrease
small increase
small increase
no change
large increase
no change
(Table 22 continued on next page)
APPenDix 9
233
American basswood
American beech
American elm
American hornbeam
Baldcypress
Bitternut hickory
Black cherry
Black hickory
Black locust
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Boxelder
Bur oak
Butternut
Cedar elm
Cherrybark oak
Chestnut oak
Chinquapin oak
Common persimmon
Eastern cottonwood
Eastern hophornbeam
Eastern red cedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Jack pine
Kentucky coffeetree
Loblolly pine
Mockernut hickory
fiA
iv
Current
DiSTRiB
modeled
model
iv
reliability
Common name
Northern catalpa
Northern pin oak
Northern red oak
Ohio buckeye
Osage-orange
Overcup oak
Pawpaw
Pecan
Pignut hickory
Pin oak
Post oak
Red maple
Red mulberry
River birch
Sassafras
Scarlet oak
Shagbark hickory
Shellbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slash pine
Slippery elm
Southern red oak
Sugar maple
Sugarberry
Swamp chestnut oak
Swamp tupelo
Swamp white oak
Sweetgum
Sycamore
Water locust
Water oak
White ash
White oak
Wild plum
Willow oak
Winged elm
Yellow-poplar
fiA
iv
27
9
284
13
120
17
38
34
461
320
356
493
240
108
572
24
583
56
423
91
10
522
0
294
36
436
80
8
1
58
204
281
0
0
527
880
7
1
70
119
Current
DiSTRiB
modeled
model
iv
reliability
10
2*
322
9
199
17
34
135
430
325
395
474
152
104
536
31
628
31
400
76
1*
544
0
299
40
505
58
1*
1
46
243
296
2
0
597
899
0*
20
117
117
Low
Medium
High
Low
Medium
Medium
Low
Low
High
Medium
High
High
Low
Low
High
High
Medium
Low
Medium
High
Low
Medium
High
Medium
High
High
Medium
Medium
High
Low
High
Medium
Medium
High
High
High
Low
Medium
High
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
10
15
2
0
294 238
3
7
289 200
15
61
54
18
84
56
409 353
391 403
546 1689
532 527
265 322
141
98
526 375
39
6
557 314
45
30
316 327
93 223
4
57
599 713
1
6
317 234
127 223
415 199
129 384
3
2
1
0
44
28
418 290
271 210
14
4
2
57
505 345
722 558
6
0
40
54
308 749
162
90
10
14
1
0
299 132
8
2
298 186
14
80
63
2
101
84
393 329
375 311
593 1722
524 559
234 356
141 105
496 232
39
4
447 212
41
28
236 294
118 280
4
70
500 678
3
11
278 169
194 282
374
63
184 396
4
2
4
0
40
4
516 313
270 172
9
4
15 259
460 245
669 412
13
0
46
74
494 920
184
71
8
21
1
40
288 106
2
2
316 197
15
84
58
2
123 123
351 336
365 300
636 2137
516 564
237 403
151 142
469 239
34
4
396 221
30
28
213 307
135 350
5 102
457 731
5
16
264 168
208 310
326
44
212 451
3
2
5
1
26
7
534 349
261 188
12
3
26 311
410 242
644 359
21
46
55
74
564 1197
183
69
1.00
0.22
0.91
0.33
1.45
0.88
1.59
0.62
0.95
1.20
1.38
1.12
1.74
1.36
0.98
1.26
0.89
1.45
0.79
1.22
0.40
1.10
Inf
1.06
3.18
0.82
2.22
0.38
1.00
0.96
1.72
0.92
7.00
Inf
0.85
0.80
0.86
2.00
2.63
1.39
1.50
0.00
0.74
0.78
1.01
3.59
0.53
0.42
0.82
1.24
4.28
1.11
2.12
0.94
0.70
0.19
0.50
0.97
0.82
2.93
5.70
1.31
Inf
0.78
5.58
0.39
6.62
0.25
0.00
0.61
1.19
0.71
2.00
Inf
0.58
0.62
0.00
2.70
6.40
0.77
1.00
0.11
0.93
0.89
1.50
0.82
1.85
0.75
0.91
1.15
1.50
1.11
1.54
1.36
0.93
1.26
0.71
1.32
0.59
1.55
0.40
0.92
Inf
0.93
4.85
0.74
3.17
0.50
4.00
0.87
2.12
0.91
4.50
Inf
0.77
0.74
1.86
2.30
4.22
1.57
1.40
0.00
0.41
0.22
0.94
4.71
0.06
0.62
0.77
0.96
4.36
1.18
2.34
1.01
0.43
0.13
0.34
0.90
0.74
3.68
7.00
1.25
Inf
0.57
7.05
0.13
6.83
0.25
0.00
0.09
1.29
0.58
2.00
Inf
0.41
0.46
0.00
3.70
7.86
0.61
0.80 2.10
0.11 4.44
0.89 0.33
0.22 0.22
1.59 0.99
0.88 4.94
1.71 0.06
0.91 0.91
0.82 0.78
1.12 0.92
1.61 5.41
1.09 1.19
1.56 2.65
1.45 1.37
0.88 0.45
1.10 0.13
0.63 0.35
0.97 0.90
0.53 0.77
1.78 4.61
0.50 10.20
0.84 1.34
Inf
Inf
0.88 0.56
5.20 7.75
0.65 0.09
3.66 7.78
0.38 0.25
5.00 1.00
0.57 0.15
2.20 1.44
0.88 0.64
6.00 1.50
Inf
Inf
0.69 0.41
0.72 0.40
3.00 6.57
2.75 3.70
4.82 10.23
1.56 0.59
Change class
2070-2099
PCm B1
GfDL A1fi
no change
large decrease
no change
small decrease
small increase
no change
small increase
no change
no change
no change
small increase
no change
small increase
small increase
no change
no change
small decrease
no change
small decrease
small increase
small decrease
no change
new habitat
no change
large increase
small decrease
large increase
small decrease
small increase
small decrease
small increase
no change
new habitat
new habitat
small decrease
small decrease
no change
no change
large increase
small increase
no change
small increase
large decrease
small decrease
no change
small increase
large decrease
no change
small decrease
no change
large increase
no change
large increase
small increase
large decrease
large decrease
large decrease
no change
small decrease
large increase
large increase
small increase
new habitat
small decrease
large increase
large decrease
large increase
small decrease
no change
large decrease
smal increase
small decrease
new habitat
new habitat
large decrease
large decrease
small increase
no change
large increase
small decrease
APPenDix 9
234
Table 22 (continued).
Table 23.—Complete DISTRIB model results for tree species in the Indiana portion of the assessment area. FIA importance values (FIA IV) are current
importance values based on forest inventory and Analysis data, and current modeled importance values (Current iv) are based on results from the DiSTRiB
model. Early-, mid-, and late-century projected importance values and ratios of future to current modeled values are shown. Change classes are described in
the appendix text. *When Current IVs were <4 and FIA IVs were >0, the ratio was instead calculated by using the FIA IV.
Common name
62
325
562
139
19
54
147
267
2
175
12
335
347
86
166
3
19
280
20
9
0
11
97
121
91
162
127
167
284
48
565
279
334
135
11
62
268
654
113
8
29
144
383
11
196
8
416
333
98
162
8
4
253
13
2*
0
15
100
104
118
131
149
150
342
35
458
275
308
168
1*
Medium
High
Medium
Medium
Medium
High
Low
High
High
Low
Low
High
Medium
Low
High
Medium
Low
Medium
Medium
Low
Low
Medium
High
Medium
Medium
Low
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
54
265
803
131
15
20
192
284
68
194
7
462
399
133
227
87
13
295
49
4
0
58
105
170
158
173
143
176
427
30
494
342
474
218
1
49
277
636
130
16
16
178
273
85
208
7
459
440
118
266
139
16
296
33
3
1
67
111
194
163
173
154
183
458
39
488
352
459
210
1
37
258
643
130
16
12
176
260
102
198
6
473
430
123
265
162
13
292
36
2
3
63
105
197
171
178
155
180
458
33
477
359
451
207
1
22
109
569
106
11
0
157
268
206
184
1
551
258
118
156
227
10
276
29
0
61
16
61
117
296
199
129
220
439
15
414
460
375
227
1
0
51
381
175
12
0
159
127
282
173
0
486
112
134
138
331
8
318
100
0
199
21
52
79
277
244
162
154
530
4
288
592
336
272
1
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
19 0.87 0.36 0.79 0.00 0.60 0.31
43 0.99 0.41 1.03 0.19 0.96 0.16
360 1.23 0.87 0.97 0.58 0.98 0.55
174 1.16 0.94 1.15 1.55 1.15 1.54
12 1.88 1.38 2.00 1.50 2.00 1.50
0 0.69 0.00 0.55 0.00 0.41 0.00
177 1.33 1.09 1.24 1.10 1.22 1.23
133 0.74 0.70 0.71 0.33 0.68 0.35
337 6.18 18.73 7.73 25.64 9.27 30.64
103 0.99 0.94 1.06 0.88 1.01 0.53
0 0.88 0.13 0.88 0.00 0.75 0.00
415 1.11 1.33 1.10 1.17 1.14 1.00
71 1.20 0.78 1.32 0.34 1.29 0.21
142 1.36 1.20 1.20 1.37 1.26 1.45
158 1.40 0.96 1.64 0.85 1.64 0.98
368 10.88 28.38 17.38 41.38 20.25 46.00
10 3.25 2.50 4.00 2.00 3.25 2.50
482 1.17 1.09 1.17 1.26 1.15 1.91
227 3.77 2.23 2.54 7.69 2.77 17.46
0 0.44 0.00 0.33 0.00 0.22 0.00
230
NA
Inf
Inf
Inf
Inf
Inf
29 3.87 1.07 4.47 1.40 4.20 1.93
51 1.05 0.61 1.11 0.52 1.05 0.51
59 1.64 1.13 1.87 0.76 1.89 0.57
327 1.34 2.51 1.38 2.35 1.45 2.77
259 1.32 1.52 1.32 1.86 1.36 1.98
199 0.96 0.87 1.03 1.09 1.04 1.34
119 1.17 1.47 1.22 1.03 1.20 0.79
635 1.25 1.28 1.34 1.55 1.34 1.86
4 0.86 0.43 1.11 0.11 0.94 0.11
300 1.08 0.90 1.07 0.63 1.04 0.66
636 1.24 1.67 1.28 2.15 1.31 2.31
300 1.54 1.22 1.49 1.09 1.46 0.97
289 1.30 1.35 1.25 1.62 1.23 1.72
9 0.09 0.09 0.09 0.09 0.09 0.82
Change class
2070-2099
PCm B1
GfDL A1fi
small decrease
no change
no change
no change
no change
large decrease
small increase
small decrease
large increase
no change
no change
no change
small increase
small increase
small increase
large increase
no change
small increase
large increase
small decrease
new habitat
small increase
no change
small increase
small increase
small increase
no change
no change
small increase
no change
no change
small increase
small increase
small increase
large decrease
large decrease
large decrease
small decrease
small increase
no change
extirpated
small increase
large decrease
large increase
small decrease
large decrease
no change
large decrease
small increase
no change
large increase
no change
small increase
large increase
large decrease
new habitat
no change
small decrease
small decrease
large increase
small increase
small increase
small decrease
small increase
large decrease
small decrease
large increase
no change
small increase
no change
(Table 23 continued on next page)
APPenDix 9
235
American basswood
American beech
American elm
American hornbeam
Baldcypress
Bigtooth aspen
Bitternut hickory
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Blue ash
Boxelder
Bur oak
Butternut
Cedar elm
Cherrybark oak
Chestnut oak
Chinquapin oak
Common persimmon
Eastern cottonwood
Eastern hophornbeam
Eastern red cedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Jack pine
fiA
iv
Current
DiSTRiB
modeled
model
iv
reliability
Common name
Kentucky coffeetree
Loblolly pine
Mockernut hickory
Northern catalpa
Northern pin oak
Northern red oak
Ohio buckeye
Osage-orange
Overcup oak
Pawpaw
Pecan
Pignut hickory
Pin oak
Post oak
Red maple
Red mulberry
River birch
Rock elm
Sassafras
Scarlet oak
Shagbark hickory
Shellbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slash pine
Slippery elm
Sourwood
Southern red oak
Sugar maple
Sugarberry
Swamp chestnut oak
Swamp tupelo
Swamp white oak
Sweetgum
Sycamore
Virginia pine
fiA
iv
19
0
96
23
21
260
67
135
5
69
25
290
136
42
416
53
72
37
592
53
326
64
84
42
7
336
0
333
1
7
1186
8
10
18
37
252
317
99
Current
DiSTRiB
modeled
model
iv
reliability
1*
12
212
7
6
306
37
122
7
72
104
333
140
130
485
63
54
11
508
77
333
24
106
43
0*
287
0
300
4
22
1082
20
0*
1*
34
211
282
69
Low
High
High
Low
Medium
High
Low
Medium
Medium
Low
Low
High
Medium
High
High
Low
Low
Low
High
High
Medium
Low
Medium
High
Low
Medium
High
Medium
High
High
High
Medium
Medium
High
Low
High
Medium
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
7
3
50
50
197 278
9
13
4
1
311 253
49
34
226 139
7
24
101
44
76
56
348 350
266 209
337 1439
493 422
105 168
83
68
50
24
571 389
85
79
382 275
46
43
144 139
76 275
2
25
368 446
1
1
316 259
3
1
83 209
1035 389
50 208
4
2
6
2
53
25
353 252
295 260
89
63
8
1
94 140
219 273
9
11
1
0
328 204
46
13
234 161
7
33
109
12
73
97
347 333
242 242
387 1798
505 453
102 191
85
77
68
10
551 251
97
29
340 184
37
31
125 139
95 379
1
98
326 507
11
2
288 157
8
1
132 277
979 112
103 370
4
2
30
1
49
8
435 238
300 198
107
62
8
4
106 233
217 272
7
21
1
8
325 161
36
9
233 169
8
35
108
9
79 113
344 331
249 228
431 2048
489 485
105 235
87 102
58
3
534 238
94
20
340 181
33
28
118 141
105 591
2 124
317 526
8
7
281 148
8
1
136 402
904
56
117 427
4
3
32
3
44
4
431 354
289 191
93
69
0.37 0.16
4.17 4.17
0.93 1.31
1.29 1.86
0.67 0.17
1.02 0.83
1.32 0.92
1.85 1.14
1.00 3.43
1.40 0.61
0.73 0.54
1.05 1.05
1.90 1.49
2.59 11.07
1.02 0.87
1.67 2.67
1.54 1.26
4.55 2.18
1.12 0.77
1.10 1.03
1.15 0.83
1.92 1.79
1.36 1.31
1.77 6.40
0.29 3.57
1.28 1.55
Inf
Inf
1.05 0.86
3.00 1.00
3.77 9.50
0.96 0.36
2.50 10.40
0.40 0.20
0.33 0.11
1.56 0.74
1.67 1.19
1.05 0.92
1.29 0.91
0.42
7.83
1.03
1.29
0.17
1.07
1.24
1.92
1.00
1.51
0.70
1.04
1.73
2.98
1.04
1.62
1.57
6.18
1.09
1.26
1.02
1.54
1.18
2.21
0.14
1.14
Inf
0.96
8.00
6.00
0.91
5.15
0.40
1.67
1.44
2.06
1.06
1.55
0.05
11.67
1.29
1.57
0.00
0.67
0.35
1.32
4.71
0.17
0.93
1.00
1.73
13.83
0.93
3.03
1.43
0.91
0.49
0.38
0.55
1.29
1.31
8.81
14.00
1.77
Inf
0.52
1.00
12.59
0.10
18.50
0.20
0.06
0.24
1.13
0.70
0.90
0.42
8.83
1.02
1.00
0.17
1.06
0.97
1.91
1.14
1.50
0.76
1.03
1.78
3.32
1.01
1.67
1.61
5.27
1.05
1.22
1.02
1.38
1.11
2.44
0.29
1.11
Inf
0.94
8.00
6.18
0.84
5.85
0.40
1.78
1.29
2.04
1.03
1.35
0.21
19.42
1.28
3.00
1.33
0.53
0.24
1.39
5.00
0.13
1.09
0.99
1.63
15.75
1.00
3.73
1.89
0.27
0.47
0.26
0.54
1.17
1.33
13.74
17.71
1.83
Inf
0.49
1.00
18.27
0.05
21.35
0.30
0.17
0.12
1.68
0.68
1.00
Change class
2070-2099
PCm B1
GfDL A1fi
small decrease
new habitat
no change
no change
large decrease
no change
no change
small increase
no change
small increase
small decrease
no change
small increase
large increase
no change
small increase
small increase
small increase
no change
small increase
no change
small increase
no change
large increase
small decrease
no change
new habitat
no change
small increase
large increase
no change
small increase
small decrease
no change
small increase
large increase
no change
small increase
large decrease
new habitat
small increase
no change
no change
small decrease
large decrease
small increase
small increase
large decrease
no change
no change
small increase
large increase
no change
large increase
smalli
small decrease
large decrease
large decrease
small decrease
no change
small increase
large increase
large increase
small increase
new habitat
large decrease
no change
large increase
large decrease
large increase
small decrease
large decrease
large decrease
small increase
small decrease
no change
(Table 23 continued on next page)
APPenDix 9
23
Table 23 (continued).
Table 23 (continued).
Common name
Water oak
White ash
White oak
Wild plum
Willow oak
Winged elm
Yellow birch
Yellow buckeye
Yellow-poplar
fiA
iv
0
765
499
7
0
32
27
4
533
Current
DiSTRiB
modeled
model
iv
reliability
0
751
604
0*
11
51
0*
4
418
High
High
High
Low
Medium
High
High
Medium
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
0
709
591
11
21
186
13
3
432
1 184
649 262
597 423
19
0
26
42
286 1013
12
12
4
2
420 134
4 285
604 242
597 348
23
7
36
46
316 1280
12
11
3
2
402 134
NA
Inf
0.94 0.51
0.98 1.00
1.57 0.00
1.91 2.27
3.65 13.06
0.48 0.44
0.75 0.50
1.03 0.54
23
382
603
0
25
666
12
2
225
Inf
Inf
0.86 0.35
0.99 0.70
2.71 0.00
2.36 3.82
5.61 19.86
0.44 0.44
1.00 0.50
1.01 0.32
Inf
Inf
0.80 0.32
0.99 0.58
3.29 1.00
3.27 4.18
6.20 25.10
0.44 0.41
0.75 0.50
0.96 0.32
Change class
2070-2099
PCm B1
GfDL A1fi
new habitat
no change
no change
no change
new habitat
large increase
large decrease
no change
no change
new habitat
large decrease
small decrease
no change
new habitat
large increase
large decrease
small decrease
large decrease
APPenDix 9
237
Common name
fiA
iv
American basswood
43
American beech
10
American elm
859
American hornbeam
44
Baldcypress
7
Bitternut hickory
290
Black cherry
311
Black hickory
999
Black locust
40
Black oak
3500
Black walnut
561
Black willow
112
Blackgum
242
Blackjack oak
835
Blue ash
21
Boxelder
149
Bur oak
37
Butternut
22
Cedar elm
0
Cherrybark oak
8
Chestnut oak
3
Chinquapin oak
400
Chittamwood
50
Common persimmon
370
Eastern cottonwood
55
Eastern hophornbeam 150
Eastern redbud
204
Eastern red cedar
1344
Flowering dogwood
940
Green ash
244
Hackberry
430
Honeylocust
212
Jack pine
0
Loblolly pine
0
Longleaf pine
0
Current
DiSTRiB
modeled
model
iv
reliability
18
66
917
47
3*
314
358
959
87
3243
570
110
286
859
3*
175
42
3*
0
29
72
375
45
362
98
211
247
1350
976
283
498
246
0
16
0
Medium
High
Medium
Medium
Medium
Low
High
High
Low
High
Medium
Low
High
Medium
Low
Medium
Medium
Low
Low
Medium
High
Medium
Low
Medium
Low
Medium
Medium
Medium
High
Medium
Medium
Low
High
High
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
11
6
3
12
29
11
21
9
1067 589 760 535
71 106
50 246
7
4
4
4
330 305 294 333
361 459 338 398
1077 922 1090 886
95 287
71 135
3422 3088 3299 2435
877 334 585 142
218 125 105 155
377 308 349 314
1073 1408 1116 1427
8
10
8
6
184 291 172 545
120
38
48 271
1
0
0
0
16 265
21 332
21
13
22
15
41
43
21
85
484 321 397 234
28
42
27
51
372 479 358 438
110 115
87 290
249 322 208 516
271 407 246 252
1404 1380 1465 1260
936 627 929 590
362 462 315 604
741 447 528 485
302 331 215 513
0
0
0
0
81 176 133 457
6
0
0
0
2070-2099
PCm GfDL
B1
A1fi
3
35
667
89
5
288
363
1102
142
3148
674
88
407
1198
10
208
34
0
21
23
147
464
25
331
82
264
253
1592
936
362
608
241
0
442
45
183
10
602
250
4
378
388
836
144
2381
130
256
302
1369
7
1212
720
0
310
11
102
241
50
414
346
650
249
1158
608
685
537
595
19
489
0
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
0.61 0.33
0.44 0.17
1.16 0.64
1.51 2.26
1.00 0.57
1.05 0.97
1.01 1.28
1.12 0.96
1.09 3.30
1.06 0.95
1.54 0.59
1.98 1.14
1.32 1.08
1.25 1.64
0.38 0.48
1.05 1.66
2.86 0.91
0.05 0.00
Inf
Inf
0.72 0.45
0.57 0.60
1.29 0.86
0.62 0.93
1.03 1.32
1.12 1.17
1.18 1.53
1.10 1.65
1.04 1.02
0.96 0.64
1.28 1.63
1.49 0.90
1.23 1.35
NA NA
5.06 11.00
Inf
NA
0.17 0.67 0.17 10.17
0.32 0.14 0.53 0.15
0.83 0.58 0.73 0.66
1.06 5.23 1.89 5.32
0.57 0.57 0.71 0.57
0.94 1.06 0.92 1.20
0.94 1.11 1.01 1.08
1.14 0.92 1.15 0.87
0.82 1.55 1.63 1.66
1.02 0.75 0.97 0.73
1.03 0.25 1.18 0.23
0.96 1.41 0.80 2.33
1.22 1.10 1.42 1.06
1.30 1.66 1.40 1.59
0.38 0.29 0.48 0.33
0.98 3.11 1.19 6.93
1.14 6.45 0.81 17.14
0.00 0.00 0.00 0.00
Inf
Inf
Inf
Inf
0.76 0.52 0.79 0.38
0.29 1.18 2.04 1.42
1.06 0.62 1.24 0.64
0.60 1.13 0.56 1.11
0.99 1.21 0.91 1.14
0.89 2.96 0.84 3.53
0.99 2.45 1.25 3.08
1.00 1.02 1.02 1.01
1.09 0.93 1.18 0.86
0.95 0.61 0.96 0.62
1.11 2.13 1.28 2.42
1.06 0.97 1.22 1.08
0.87 2.09 0.98 2.42
NA
NA
NA
Inf
8.31 28.56 27.63 30.56
NA
NA
Inf
NA
Change class
2070-2099
PCm B1
GfDL A1fi
large decrease
small decrease
small decrease
small increase
no change
no change
no change
no change
small increase
no change
no change
extirpated
small increase
small increase
small decrease
no change
no change
large decrease
new habitat
small decrease
large increase
small increase
small decrease
no change
no change
small increase
no change
no change
no change
small increase
small increase
no change
NA
new habitat
new habitat
large increase
large decrease
small decrease
large increase
small decrease
small increase
no change
no change
small increase
small decrease
large decrease
large increase
no change
small increase
small decrease
large increase
large increase
large decrease
new habitat
large decrease
small increase
small decrease
no change
no change
large increase
large increase
no change
no change
small decrease
large increase
no change
large increase
new habitat
new habitat
NA
(Table 24 continued on next page)
APPenDix 9
238
Table 24.—Complete DISTRIB model results for tree species in the Missouri portion of the assessment area. FIA importance values (FIA IV) are current
importance values based on forest inventory and Analysis data, and current modeled importance values (Current iv) are based on results from the DiSTRiB
model. Early-, mid-, and late-century projected importance values and ratios of future to current modeled values are shown. Change classes are described in
the appendix text. *When Current IVs were <4 and FIA IVs were >0, the ratio was instead calculated by using the FIA IV.
Table 24 (continued).
Common name
722
2
0
557
1
40
155
19
78
16
778
62
3292
0
218
252
30
54
520
632
521
54
198
488
22
131
0
427
0
107
421
10
51
28
29
245
2
0
714
2
2
625
2
17
187
8
39
84
744
94
3130
0
329
276
20
19
597
573
539
25
163
530
6
226
0
447
3
95
509
18
13
22
95
283
36
0
High
Low
Medium
High
Low
Low
Medium
Medium
Low
Low
High
Medium
High
High
High
Low
Low
Low
High
High
Medium
Low
Medium
High
Low
Medium
High
Medium
High
High
High
Medium
High
Low
High
Medium
High
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
741 818 760 842 781 808
1
4
1
3
2
4
1
0
0
3
0
78
720 624 666 534 773 406
7
1
4
1
5
1
4
5
2
2
2
2
412 229 270 296 334 333
14
28
13
32
13
33
62
25
42
7
37
8
104
51
73
62
86
68
652 482 538 653 447 674
188 133 124 157 165 159
3468 3819 3243 4051 3410 4035
0
0
0
0
0
15
405 539 353 709 462 721
362 326 293 380 288 493
28
21
24
49
26 106
47
16
24
0
13
0
561 451 510 407 473 407
467 238 361 190 286 194
564 343 432 288 447 307
22
42
12
24
5
22
165 191 108 208
79 234
739 1059 855 1336 1165 1273
30 146
38 162
59 168
360 387 204 535 181 615
3
7
0
17
27
95
412 318 319 253 292 253
0
0
6
0
35
0
286 374 351 405 410 374
516 216 401
34 379
28
121 464 153 457 205 446
11
31
6
32
36
24
16
7
7
0
6
0
205 150 211 211 392 192
281 305 277 286 293 289
22
33
33 130 143 143
3
99
27 358
98 344
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
1.04 1.15
0.50 2.00
0.50 0.00
1.15 1.00
3.50 0.50
0.24 0.29
2.20 1.23
1.75 3.50
1.59 0.64
1.24 0.61
0.88 0.65
2.00 1.42
1.11 1.22
NA
NA
1.23 1.64
1.31 1.18
1.40 1.05
2.47 0.84
0.94 0.76
0.82 0.42
1.05 0.64
0.88 1.68
1.01 1.17
1.39 2.00
5.00 24.33
1.59 1.71
Inf
Inf
0.92 0.71
0.00 0.00
3.01 3.94
1.01 0.42
6.72 25.78
0.85 2.39
0.73 0.32
2.16 1.58
0.99 1.08
0.61 0.92
Inf
Inf
1.06 1.18 1.09 1.13
0.50 1.50 1.00 2.00
0.00 1.50 0.00 39.00
1.07 0.85 1.24 0.65
2.00 0.50 2.50 0.50
0.12 0.12 0.12 0.12
1.44 1.58 1.79 1.78
1.63 4.00 1.63 4.13
1.08 0.18 0.95 0.21
0.87 0.74 1.02 0.81
0.72 0.88 0.60 0.91
1.32 1.67 1.76 1.69
1.04 1.29 1.09 1.29
NA
NA
NA
Inf
1.07 2.16 1.40 2.19
1.06 1.38 1.04 1.79
1.20 2.45 1.30 5.30
1.26 0.00 0.68 0.00
0.85 0.68 0.79 0.68
0.63 0.33 0.50 0.34
0.80 0.53 0.83 0.57
0.48 0.96 0.20 0.88
0.66 1.28 0.49 1.44
1.61 2.52 2.20 2.40
6.33 27.00 9.83 28.00
0.90 2.37 0.80 2.72
NA
Inf
Inf
Inf
0.71 0.57 0.65 0.57
2.00 0.00 11.67 0.00
3.70 4.26 4.32 3.94
0.79 0.07 0.75 0.06
8.50 25.39 11.39 24.78
0.46 2.46 2.77 1.85
0.32 0.00 0.27 0.00
2.22 2.22 4.13 2.02
0.98 1.01 1.04 1.02
0.92 3.61 3.97 3.97
Inf
Inf
Inf
Inf
Change class
2070-2099
PCm B1
GfDL A1fi
no change
no change
NA
small increase
no change
large decrease
small increase
no change
no change
no change
small decrease
small increase
no change
NA
small increase
no change
no change
small decrease
small decrease
large decrease
no change
small decrease
large decrease
large increase
large increase
no change
new habitat
small decrease
new habitat
large increase
small decrease
large increase
no change
small decrease
large increase
no change
no change
new habitat
no change
no change
new habitat
small decrease
small decrease
large decrease
small increase
small increase
large decrease
no change
no change
small increase
small increase
new habitat
large increase
small increase
small increase
large decrease
small decrease
large decrease
small decrease
no change
small increase
large increase
large increase
large increase
new habitat
small decrease
NA
large increase
large decrease
large increase
no change
large decrease
large increase
no change
no change
new habitat
(Table 24 continued on next page)
APPenDix 9
239
Mockernut hickory
Northern catalpa
Northern pin oak
Northern red oak
Nuttall oak
Ohio buckeye
Osage-orange
Overcup oak
Pawpaw
Pecan
Pignut hickory
Pin oak
Post oak
Quaking aspen
Red maple
Red mulberry
River birch
Rock elm
Sassafras
Scarlet oak
Shagbark hickory
Shellbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slash pine
Slippery elm
Sourwood
Southern red oak
Sugar maple
Sugarberry
Swamp tupelo
Swamp white oak
Sweetgum
Sycamore
Virginia pine
Water oak
fiA
iv
Current
DiSTRiB
modeled
model
iv
reliability
Common name
fiA
iv
White ash
White oak
Wild plum
Willow oak
Winged elm
Yellow-poplar
552
3300
70
5
239
28
Current
DiSTRiB
modeled
model
iv
reliability
597
3061
12
19
243
116
High
High
Low
Medium
High
High
2010-2039
PCm GfDL
B1
A1fi
modeled iv
2040-2069
PCm GfDL
B1
A1fi
2070-2099
PCm GfDL
B1
A1fi
527 459 494 405 505 410
2600 2082 2561 1558 2360 1489
117
31
43
13
81
94
16
24
18
30
34
27
586 1316 773 1406 912 1197
101
94 114 142 214 174
future:current suitable habitat
2010-2039
2040-2069
2070-2099
PCm GfDL PCm GfDL PCm GfDL
B1
A1fi
B1
A1fi
B1 A1fi
0.88
0.85
9.75
0.84
2.41
0.87
0.77
0.68
2.58
1.26
5.42
0.81
0.83
0.84
3.58
0.95
3.18
0.98
0.68
0.51
1.08
1.58
5.79
1.22
0.85
0.77
6.75
1.79
3.75
1.85
0.69
0.49
7.83
1.42
4.93
1.50
Change class
2070-2099
PCm B1
GfDL A1fi
no change
small decrease
small increase
no change
large increase
small increase
small decrease
large decrease
small increase
no change
large increase
small increase
APPenDix 9
240
Table 24 (continued).
APPenDix 9
Table 25.—Key to modifying factor codes. These codes are used to describe positive or negative modifying factors
in the following table. A species was given that code if information from the literature suggested that it had these
characteristics. See Matthews et al. (2011b) for a more thorough description of these factors and how they were
assessed.
Code
Title
Type
Description (if positive)
Description (if negative)
COL
Competition-light
Biological
Tolerant of shade or limited light
conditions
Intolerant of shade or limited
light conditions
DISE
Disease
Disturbance
N/A
Has a high number and/or
severity of known pathogens that
attack the species
DISP
Dispersal
Biological
High ability to effectively produce
and distribute seeds
N/A
DRO
Drought
Biological
Drought-tolerant
Susceptible to drought
ESP
Edaphic specificity
Biological
Wide range of soil requirements
Narrow range of soil
requirements
FRG
Fire regeneration
Disturbance
Regenerates well after fire
N/A
FTK
Fire topkill
Disturbance
Resistant to fire topkill
Susceptible to fire topkill
INS
Insect pests
Disturbance
N/A
Has a high number and/or
severity of insects that may attack
the species
INP
Invasive plants
Disturbance
N/A
Strong negative effects of invasive
plants on the species, either
through competition for nutrients
or as a pathogen
SES
Seedling establishment
Biological
High ability to regenerate
with seeds to maintain future
populations
Low ability to regenerate
with seeds to maintain future
populations
VRE
Vegetative reproduction
Biological
Capable of vegetative
reproduction through stump
sprouts or cloning
N/A
241
APPenDix 9
Table 26.—Modifying factor and adaptability information for the 87 tree species in the assessment area modeled
by using the Climate Change Tree Atlas. modifying factor codes are described in Table 25. Adaptability scores are
described in the appendix text.
Common name
American basswood
American beech
American elm
American hornbeam
Baldcypress
Bigtooth aspen
Bitternut hickory
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Blue ash
Boxelder
Bur oak
Butternut
Cedar elm
Cherrybark oak
Chestnut oak
Chinquapin oak
Chittamwood
Common persimmon
Eastern cottonwood
Eastern hophornbeam
Eastern redbud
Eastern redcedar
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Jack pine
Kentucky coffeetree
Loblolly pine
Longleaf pine
Mockernut hickory
Northern catalpa
Northern pin oak
Northern red oak
States
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
MO
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, IN
IL, MO, IN
MO
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
modifying factors
Positive traits
Negative traits
COL
COL
ESP
COL SES
DISP
FRG DISP
DRO
DRO ESP
COL ESP
DRO ESP
SES
COL FTK
DRO SES FRG VRE
SES DISP DRO COL SES
DRO FTK
SES VRE ESP FTK
SES
DRO SES
COL ESP
SES
COL ESP SES
DRO
DISP
COL
DRO
DRO
ESP
FTK
DRO FTK
FTK
INS FTK
DISE INS
FTK DRO
FTK
COL DRO FTK
COL
INS FTK COL
ESP COL
COL INS
FTK
INS DISE
COL DRO
COL FTK DRO
COL FTK
INS DISP FTK COL ESP
FTK
FTK COL DRO DISE
DISE
INS FTK
INS DISE
FTK COL
INS COL DISE FTK
FTK COL INS
DRO FTK INS
INS FTK COL
FTK
COL
COL INS
COL
INS INP DRO COL
COL
FTK
COL ESP
COL
INS
Adaptability score
DistFact BioFact Adapt Adapt class
0.3
-1.1
-0.8
0.6
0.4
1.0
2.2
-1.6
1.0
0.0
0.5
0.5
0.4
-0.3
1.5
1.6
-0.4
2.4
2.8
-1.4
-0.3
-0.5
1.4
1.2
2.0
1.2
0.2
1.7
0.9
0.6
-2.0
0.1
-0.1
1.7
1.9
1.9
0.9
-0.5
1.0
1.7
0.9
2.5
1.4
0.2
0.0
0.3
0.6
-1.0
0.2
-0.8
-0.3
-2.3
-0.6
0.9
0.4
-0.8
-2.1
0.8
0.2
-2.4
2.1
-0.2
-1.3
-1.2
0.1
1.3
-0.7
-0.4
1.0
-0.8
1.3
0.0
-1.5
0.1
1.0
-0.3
0.3
-0.5
-1.2
-1.2
-0.7
-1.7
-0.3
-1.6
-0.6
0.1
4.6
3.6
4.0
5.1
3.9
5.1
5.6
3.0
4.1
3.8
5.2
4.9
4.0
2.8
5.9
5.6
2.7
7.4
6.4
2.3
3.3
3.9
6.1
4.8
5.6
5.8
3.9
6.4
4.9
3.9
3.3
5.0
4.0
5.7
5.5
5.2
4.3
3.4
4.2
5.4
4.2
6.0
5.4
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
High
Low
Moderate
Moderate
High
Moderate
Moderate
Low
High
High
Low
High
High
Low
Low
Moderate
High
Moderate
High
High
Moderate
High
Moderate
Moderate
Low
Moderate
Moderate
High
High
Moderate
Moderate
Moderate
Moderate
High
Moderate
High
High
(Table 26 continued on next page)
242
APPenDix 9
Table 26 (continued).
Common name
Nuttall oak
Ohio buckeye
Osage-orange
Overcup oak
Pawpaw
Pecan
Pignut hickory
Pin oak
Post oak
Quaking aspen
Red maple
Red mulberry
River birch
Rock elm
Sassafras
Scarlet oak
Shagbark hickory
Shellbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slash pine
Slippery elm
Sourwood
Southern red oak
Sugar maple
Sugarberry
Swamp chestnut oak
Swamp tupelo
Swamp white oak
Sweetgum
Sycamore
Virginia pine
Water locust
Water oak
White ash
White oak
Wild plum
Willow oak
Winged elm
Yellow birch
Yellow buckeye
Yellow-poplar
States
MO
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
MO
IL, MO, IN
IL, MO, IN
IL, MO, IN
MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
MO, IN
IL
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO, IN
IL, MO
IL, MO
modifying factors
Positive traits
Negative traits
ESP
COL
ESP ESP
COL
ESP
DRO SES FTK
SES FRG ESP
SES ESP ESP COL DISP
COL DISP
DISP
VRE ESP ESP
COL
ESP
ESP
DRO SES
DISP SES COL
DISP FTK
COL
COL ESP
SES
COL ESP
COL SES
SES
VRE ESP
ESP ESP SES FTK
SES SES
DISP
COL
SES DISP ESP
SES FTK
FTK INS DRO
DRO
FTK INS COL
INS DRO
FTK COL INS DISE
COL INS DISE
COL DRO FTK
FTK
FTK COL DRO
ESP SES
COL FTK
INS DISE FTK
INS FTK
FTK ESP
COL
COL INS DRO
COL
DRO FTK
COL INS
FTK DISE
FTK
COL INS
DRO FTK COL ESP
FTK COL DRO
COL POL
COL
FTK COL
INS FTK COL
INS DISE
COL
COL
INS DISE
FTK INS DISE
DRO SES FTK ESP DISP
INP
Adaptability score
DistFact BioFact Adapt Adapt class
2.8
0.4
2.3
-0.5
-0.5
-1.2
0.2
-0.7
2.2
0.6
3.0
0.1
-0.5
-0.2
0.5
-0.4
-0.2
-0.5
1.3
0.0
2.5
0.1
1.1
0.0
2.6
1.2
0.9
-0.2
1.1
-0.7
1.0
-0.4
1.3
0.1
0.0
-0.2
-2.0
1.7
0.5
0.6
-0.6
-1.4
0.0
0.1
-0.1
-1.9
0.3
-1.0
-0.3
-1.7
0.4
-1.4
-0.6
0.0
3.0
0.6
-0.3
-2.6
-0.6
0.7
0.4
-0.3
-0.7
-1.0
-1.0
1.6
-1.7
0.7
1.0
0.2
1.3
0.6
-0.8
-1.7
-0.3
0.2
-0.9
-0.8
-0.6
-0.6
-0.5
1.0
-1.3
0.0
-0.3
0.0
-2.1
1.3
6.5
3.5
6.3
3.2
3.7
2.2
4.7
2.8
5.7
4.7
8.5
4.7
3.7
2.8
4.2
4.6
4.4
3.7
4.9
3.6
5.8
5.6
4.3
4.8
6.9
5.3
5.8
4.6
4.6
2.7
4.9
4.1
4.8
3.8
3.8
3.7
2.7
6.1
3.9
4.7
3.6
3.4
3.1
5.3
High
Moderate
High
Low
Moderate
Low
Moderate
Low
High
Moderate
High
Moderate
Moderate
Low
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
High
High
Moderate
Moderate
High
High
High
Moderate
Moderate
Low
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Low
High
Moderate
Moderate
Moderate
Moderate
Low
High
243
APPenDix 9
Table 27.—Percentage changes in basal area and number of trees per acre for six species and species groups across
the Missouri Ozark Highlands using the LANDIS PRO model. Values represent the difference between current and
future climate in 2040, 2070, 2090, and 2100 from simulations using two climate change scenarios: PCM B1 and
GfDL A1fi. Species groups indicate several similar species simulated together, using the species establishment
probability (SeP) determined through LinKAGeS.
Percentage change from current climate (on forested acres)
PCm B1
GfDL A1fi
Basal area
Trees/acre
Basal area
Trees/acre
Species or group
year
Eastern soft hardwoodsa
2040
2070
2090
2100
-0.1
0.5
1.2
2.1
0.3
0.8
1.8
2.8
0.9
1.8
1.2
4.3
1.2
2.5
4.7
6.3
Red oak groupb
2040
2070
2090
2100
0.3
1.0
2.1
3.1
1.0
3.2
5.7
7.9
-0.5
-0.4
-0.3
0.0
-1.7
-4.0
-5.3
-5.0
White oak groupc
2040
2070
2090
2100
0.5
1.8
3.8
5.3
2.0
4.9
9.6
12.6
0.4
1.6
3.7
5.3
0.5
1.7
5.3
8.3
Sugar maple
2040
2070
2090
2100
-5.2
-18.0
-31.0
-37.6
-23.6
-55.3
-73.5
-81.2
-4.6
-16.7
-29.2
-35.6
-22.2
-53.7
-72.1
-80.0
Eastern redcedar
2040
2070
2090
2100
-0.3
-0.2
0.4
1.1
-0.3
-0.9
0.0
1.8
-0.5
0.2
1.4
2.4
-1.1
-0.9
-0.1
1.3
Shortleaf pine
2040
2070
2090
2100
0.2
1.0
2.0
2.7
1.8
4.5
7.0
8.8
1.0
2.6
4.8
5.9
3.2
8.1
14.3
18.4
American elm, slippery elm, and, to a lesser extent, willow species.
Mainly northern red, black, southern red, pin, Shumard, scarlet, and blackjack oak.
c
Mainly white, post, swamp white, and bur oak.
a
b
244
APPenDix 9
6
3
biological score
Length
2
High biological, low
disturbance scores
1
Mid-range
adaptability
score: 4.25
0
-1
Lowest
possible
adaptability
score: 0
-2
0
Highest
possible
adaptability
score: 8.5
-3
-3
0
-2
High disturbance,
low biological
scores
-1
0
1
disturbance score
Length
2
3
6
Figure 50.—Illustration of range of possible adaptability scores based on biological and disturbance modifying factors.
245
APPenDix 10: vuLneRABiLiTy AnD ConfiDenCe
DeTeRminATion
meThoDS
To assess vulnerabilities to climate change for
each natural community type, we elicited informed
opinions from a panel of 20 experts (see Appendix
11) from across the assessment area. The primary
criterion for selection on the panel was extensive
knowledge about the ecology, management, or
climate change impacts on forests in the Central
Hardwoods Region. In addition, we strived for a
wide representation across the geographic area and
across institutions.
Natural Communities Assessed
We selected nine natural community types of interest
from across the assessment area to be evaluated for
vulnerability to climate change from the 1 types
described in Chapter 1. We focused on systems for
which tree species were a significant component
of vegetation cover. These communities were
selected based on their relative abundance across
the landscape, the amount of information available
about the climate change impacts on that community
type, and whether panelists felt they had sufficient
knowledge and expertise to evaluate that community
type.
For each community type, the panel was given a
description of the major system drivers, dominant
species, and stressors that characterize that
community based on published sources (summarized
in Chapter 1). The panel was asked to comment
on and suggest modifications to the community
description in a spreadsheet. If there were no
disagreements, those suggestions were incorporated
into the descriptions.
24
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
(summarized in Chapter 5). The panel was directed
to consider impacts on each community type from
2010 through 2099, but more weight was given to
the 2070 through 2099 period. The panel was also
asked to assess impacts under two climate modelemissions scenarios: Hadley A1FI and PCM B1.
The Hadley A1F1 scenario was originally chosen
as the “high-end” scenario instead of GFDL. It
projects slightly higher temperatures and more
modest decreases in summer precipitation than
GFDL, but otherwise is similar. The GFDL A1FI
scenario was later chosen as the high-end scenario
for this assessment to enable comparison with other
assessments in the Upper Great Lakes, for which
Hadley model results were unreliable. All results
summarized in Chapter were vetted with the
panelists to ensure their vulnerability rankings were
still consistent with GFDL projections.
Potential impacts on each community driver and
stressor were summarized in 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
APPenDix 10
alter system drivers in a way that would be more or
less favorable for that community type. Impacts on
stressors were considered negative if they increased
the influence of that stressor, or positive if they
decreased the influence of that stressor, on the
community type.
For each dominant species listed in the community
spreadsheet, the panel considered the Tree Atlas,
LANDIS PRO, and LINKAGES model results, as
well as the life history traits and ecology of those
species. Examining the projected changes in tree
species habitat and distribution, panelists evaluated
the agreement among models, between climate
scenarios, and across space and time. If all of these
factors suggested a decline in habitat suitability for
a species of interest, it was given a negative impact
rating. If all information projected an increase in
habitat suitability, the species was given a positive
impact rating. Species that were not projected to
have a substantial increase or decrease were given
a moderate rating. A mix of projected increases and
decreases among models also led to a moderate
rating for potential impacts, but the species was
given a reduced level of confidence for that rating.
For each community type, each panelist was asked
to identify which impacts he or she felt were most
important to that system by using an individual
worksheet (see example at end of this appendix).
Each panelist 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 examined adaptive capacity for each
community type based on their prior knowledge of
the community types in the assessment area. The
panel was told to focus on community characteristics
that would increase or decrease the adaptive capacity
of that system. Adaptive capacity factors for each
community type were delineated in a spreadsheet.
A system was considered to have high adaptive
capacity if it had: a high ability to spread to new
areas; a wide geographic distribution; a high
ability to tolerate or recover from a wide variety
of disturbances; and high species, functional, and
genetic diversity. A system had lower adaptive
capacity if it lacked some or all of these attributes.
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 these characteristics on the current
condition of the system, given past and current
management regimes, and with no consideration
of potential management changes (adaptation) that
could influence future adaptive capacity. As with
potential impacts, panelists were asked to list the
major factors that would contribute to the adaptive
capacity of that system on an individual worksheet,
and base their ranking on those factors.
vulnerability
Vulnerability is the susceptibility of a system to the
adverse effects of climate change. It is a function
of its potential impacts and its adaptive capacity.
After extensive group discussion and recording of
all impacts and adaptive capacity factors, panelists
individually used their determination of the potential
impacts and adaptive capacity of each community
type (described above) to arrive at a vulnerability
rating. Panelists were directed to mark their rating in
two-dimensional space on an individual worksheet
first and then on a group poster (Fig. 51a). 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 mean of
all individual determinations. However, sometimes
the group determination deviated from the mean
because further discussion caused some group
247
APPenDix 10
members to alter their original response. The group
vulnerability determination was placed into one
of five categories (low, low-moderate, moderate,
moderate-high, and high) based on the discussion
and consensus within the group, as well as the
placement of the group determination on the figure.
For example, if a vulnerability determination was on
the border between low and moderate and the group
agreed that it did not completely fall into one or
the other category, it would receive a low-moderate
determination.
vulnerability determinations described above, along
with information and ideas put forward during the
group discussions, were collected and interpreted to
develop the community-level descriptions presented
in Chapter .
vulnerability Statements
Recurring themes and patterns that transcended
individual community types were identified and
developed into the vulnerability statements (in
boldface) and supporting text in Chapter . The lead
author developed the statements and supporting
text from workshop notes and literature that was
related 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 panel for review.
Panelists were asked to review each statement for
accuracy, whether the confidence determination
should be raised or lowered, if there was additional
literature that was overlooked, and if any additional
statements should be made. Any changes suggested
by a single panelist were brought forth for discussion
and approved by the entire panel.
Confidence
Panelists were also directed to give a confidence
rating to each of their individual vulnerability
determinations (based on Mastrandrea et al. 2010)
(Fig. 51b). Panelists were asked to individually
evaluate the amount of evidence that supported the
impacts and adaptive capacity factors that led to
their vulnerability determination and the level of
agreement among that evidence. Panelists evaluated
confidence individually first and then as a group in a
similar fashion to the vulnerability determination.
Community-level Determinations
Community-level determinations of vulnerability
and confidence were made for nine communities in
the Central Hardwoods Region (Figs. 52-0). The
(a)
(b)
Figure 51.—(a) Figure used for vulnerability determination by expert panelists. Adapted from Swanston and Janowiak (2012).
(b) Figure used for confidence rating among expert panelists. Adapted from Mastrandrea et al. (2010).
248
APPenDix 10
Figure 52.—Dry-mesic upland forest. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
Figure 53.—Mesic upland forest. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
Figure 54.—Mesic bottomland forest. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
249
APPenDix 10
Figure 55.—Wet bottomland forest. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
Figure 56.—Flatwoods. Circles indicate individual determinations by each panelist and squares indicate the group determination after
consensus was reached.
Figure 57.—Closed woodland. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
250
APPenDix 10
Figure 58.—Open woodland. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
Figure 59.—Barrens and savanna. Circles indicate individual determinations by each panelist and squares indicate the group
determination after consensus was reached.
Figure 60.—Glade. Circles indicate individual determinations by each panelist and squares indicate the group determination after
consensus was reached.
251
APPenDix 10
Example Vulnerability Determination Worksheet
name:
ecosystem/forest Type:
how familiar are you with this ecosystem? (circle one)
Low
medium
high
I have some basic
knowledge about this
system and how it
operates
I do some management
or research in this
system, or have read
a lot about it.
I regularly do
management or
research in this system
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?
252
APPenDix 10
Vulnerability Determination
Confidence Rating
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.
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).
253
APPenDix 11: exPeRT PAneLiSTS
Name
Affiliation
Matthew Albrecht
Paul Deizman
John DePuy
Gary Dinkel
Songlin Fei
Hong He
Louis Iverson
D. Todd Jones-Farrand
Michael Leahy
Brad Oberle
Jeffrey E. Schneiderman
John Shuey
Adam B. Smith
Charles Studyvin
Frank Thompson
John M. Tirpak
Jeffery W. Walk
Wen J. Wang
Laura Watts
Steve Westin
Missouri Botanical Garden
Illinois Department of Natural Resources, Division of Forest Resources
Shawnee National Forest
Hoosier National Forest
Purdue University
University of Missouri-Columbia
U.S. Forest Service, Northern Research Station
Central Hardwoods Joint Venture
Missouri Department of Conservation
George Washington University
University of Missouri-Columbia
The Nature Conservancy
Missouri Botanical Garden
Mark Twain National Forest
U.S. Forest Service, Northern Research Station
Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative
The Nature Conservancy
University of Missouri-Columbia
Mark Twain National Forest
Missouri Department of Conservation
254
Brandt, Leslie; He, Hong; Iverson, Louis; Thompson, Frank R., III; Butler, Patricia;
Handler, Stephen; Janowiak, Maria; Shannon, P. Danielle; Swanston, Chris;
Albrecht, Matthew; Blume-Weaver, Richard; Deizman, Paul; DePuy, John; Dijak,
William D.; Dinkel, Gary; Fei, Songlin; Jones-Farrand, D. Todd; Leahy, Michael;
Matthews, Stephen; Nelson, Paul; Oberle, Brad; Perez, Judi; Peters, Matthew;
Prasad, Anantha; Schneiderman, Jeffrey E.; Shuey, John; Smith, Adam B.;
Studyvin, Charles; Tirpak, John M.; Walk, Jeffery W.; Wang, Wen J.; Watts,
Laura; Weigel, Dale; Westin, Steve. 2014. Central Hardwoods ecosystem
vulnerability assessment and synthesis: a report from the Central
Hardwoods Climate Change Response Framework project. Gen. Tech. Rep.
NRS-124. Newtown Square, PA: U.S. Department of Agriculture, Forest Service,
Northern Research Station. 254 p.
The forests in the Central Hardwoods Region will be affected directly and indirectly
by a changing climate over the next 100 years. This assessment evaluates the
vulnerability of terrestrial ecosystems in the Central Hardwoods Region of Illinois,
Indiana, and Missouri 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 assess vulnerability to climate
change. Mesic upland forests were determined to be the most vulnerable to
projected changes in climate, whereas many systems adapted to fire and
drought, such as open woodlands, savannas, and glades, were perceived as less
vulnerable. 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: climate change, vulnerability, adaptive capacity, Missouri, Illinois,
Indiana, Climate Change Atlas, LINKAGES, LANDIS PRO,
expert elicitation
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