Minnesota Forests

2013

United States Department of Agriculture

Forest Service Northern 

Research Station

Resource Bulletin

NRS-104

Publication Date 

August 2016
Abstract

The third full annual inventory of Minnesota forests reports 17.4 million acres of forest land with 

an average live tree volume of 1,096 cubic feet per acre. Forest land is dominated by the aspen 

forest type, which occupies 29 percent of the total forest land area. Twenty-eight percent of forest 

land consists of sawtimber, 35 percent poletimber, 36 percent sapling/seedlings, and 1 percent is 

nonstocked. The average annual net growth of live trees on forest land is approximately 398 million 

cubic feet per year while average annual removals are only 207 million cubic feet per year. Additional 

forest attribute and forest health information is presented, along with information on agents of 

change including changing land use patterns and the introduction of nonnative plants, insects, and 

disease. Information from the Private Woodland Owner and Timber Products Output surveys is 

included along with 50-year projections from the Northern Forest Futures study. Detailed information 

on forest inventory methods, data quality estimates, and important resource statistics are available 

online at http://dx.doi.org/10.2737/NRS-RB-104.

Acknowledgments

The authors would like to thank those who contributed both to the inventory and analysis of 

Minnesota forest resources. Data management personnel included James Blehm, Mark Hatfield, Dan 

Kaisershot, Barb O’Connell, Jay Solomakos, Paul Sowers, and Jeff Wazenegger. Field crew and QA 

staff over the 2009-2013 field inventory cycle included: 

Sue Abrahamsen

Joshua Anderson

Micah Anderson

Steve Aysta

John Benaszeski

Kristen Bergstrand

Thom Bergstrom

James Blehm

Patrick Churack 

Brian Clennon

Dennis Collson

Shawn Conrad

Gary Cummings

Albert Dietz

Michael Downs

Steve Flackey

Joshua Garner

Brian Giersdorf

Bruce Giersdorf

Stan Grossman 

Tim Halberg

Brandon Hanko

Jered Hayes

Larry Hoyt

Dan Huberty

Brent Hummel

Chandler Johnson 

Dan Kaisershot

Dennis Kepler

Peter Koehler

Troy Korpi

Tim Koski

Cassandra Kurtz

Rebecca Landewe

Mark Majewsky

Kelly Meier

Bobby Morris 

Trent Murphy

Sheldon Murphy

Rachael Nicoll

Grant Nielsen

Benjamin Nurre

Cassandra Olson

Melissa Powers

Mike Rosha

Dave Roth

Sjana Schanning

Heidi Schmidt

Jonathan Stelter

Matt Swanson

Leland Swoger

Brad Totten

Josh Trautner

Brian Wall

Brittany Wedgewood

Bryan Wilfong

Derek Wilkerson

Dan Woitalla

Cover: Brown’s Lake near Ely, MN. Photo by Eli Sagor, used with permission (https://creativecommons.org/licenses/

by-nc/2.0/legalcode).

Manuscript received for publication July 2015

For additional copies:

U.S. Forest Service

Publications Distribution

359 Main Road

Delaware, OH 43015-8640

Published by:

U.S. FOREST SERVICE

11 CAMPUS BLVD SUITE 200

NEWTOWN SQUARE PA 19073-3294

August 2016

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

Printed on recycled paper
Minnesota Forests, 2013

Patrick D. Miles, Curtis L. VanderSchaaf, Charles Barnett, Brett J. Butler, Susan J. Crocker, Dale 

Gormanson, Cassandra M. Kurtz, Tonya W. Lister, William H. McWilliams, Randall S. Morin, Mark D. 

Nelson, Charles H. Perry, Rachel I. Riemann, James E. Smith, Brian F. Walters, James A. Westfall, 

Christopher W. Woodall 

Contact Author: 

Patrick Miles

pmiles@fs.fed.us

651-649-5146

About the Authors

Patrick D. Miles, Susan J. Crocker, Mark 

D. Nelson, and Christopher W. Woodall are 

research foresters with the Forest Inventory 

and Analysis (FIA) Program, Northern Research 

Station, St. Paul, MN.

Curtis L. VanderSchaaf, formerly a forest analyst 

for the Forestry Resource Assessment Program, 

Minnesota Department of Natural Resources, 

Grand Rapids, MN, is currently at Louisiana 

Tech University in Ruston, LA.

Charles Barnett is a biological scientist with 

the FIA program, Northern Research Station, 

Newtown Square, PA.

Brett J. Butler is a research forester with 

the FIA program, Northern Research Station, 

Amherst, MA.

Dale Gormanson, Cassandra M. Kurtz, and Brian 

F. Walters are foresters with the FIA program, 

Northern Research Station, St. Paul, MN.

Tonya W. Lister, William H. McWilliams, Randall 

S. Morin, and James A. Westfall are research 

foresters with the FIA program, Northern 

Research Station, Newtown Square, PA.

Charles H. Perry is a research soil scientist with 

the FIA program, Northern Research Station, 

St. Paul, MN.

Rachel I. Riemann is a research forester/

geographer with the FIA program, Northern 

Research Station, Troy, NY.

James E. Smith is a research forester with 

the FIA program, Northern Research Station, 

Durham, NH.
Foreword 

Minnesota is home to three major ecosystems: prairies in the west, sub-boreal forests 

in the northeast, and hardwoods running between the two from the Canadian border 

to the southeastern area of the State. As a result, the forests of Minnesota are many 

and varied. In this report the authors will highlight the current status, ongoing trends, 

and future direction of the State’s forests.

Change in the early years of the 21st century pales in comparison to the dramatic 

changes of the late 1800s and early 1900s. During that period nearly half of 

Minnesota forest land was converted to agriculture and other land uses in the wake 

of widespread lumbering that peaked in 1905 (Waters 1977). Since then, the State’s 

forests have been a remarkable story of resiliency and recovery. However, demands on 

forest resources will continue to increase along with biological threats from nonnative 

plants and insects. Minnesotans face the challenge of managing forests in such a way 

that they are available for use and enjoyment in the future as well as today. 

The ability to report on trends in the condition and status of forest resources is critical 

to knowing whether resources are being used or maintained in a sustainable way. 

The U.S. Department of Agriculture, Forest Service, through its Forest Inventory and 

Analysis program and in partnership with the Minnesota Department of Natural 

Resources, Division of Forestry, inventoried Minnesota forest resources in 1935, 

1953, 1962, 1977, 1990, 2003, 2008, and 2013. Starting in 1999, annual inventories 

have been conducted in which a portion of field plots is inventoried each year and 

a full inventory is completed after 5 years. The first Minnesota annual inventory 

was completed in 2003 and covers 1999 to 2003. The second annual inventory 

was completed in 2008 and covers the period from 2004 to 2008. The third annual 

inventory was completed in 2013 and covers the period from 2009 to 2013. With 

three complete sets of remeasurement data from annual inventory plots, we are able 

to produce better estimates of growth, mortality, and removal trends and to produce 

detailed reports on ground land-use change.

This report provides an overview of the current condition and health of Minnesota 

forests. We hope the information provided will stimulate discussion about the State’s 

forest resources and spur further research and analysis to help improve and maintain 

the productivity, health, and vigor of Minnesota forests. 
Contents

Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  

Forest Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1

Health Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5

Socioeconomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  10

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Appendixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  13

Statistics, Methods, and Quality Assurance and Glossary . . . . . . . . . . . . . .

Online at http://dx.doi.org/10.2737/NRS-RB-

Minnesota Forests Summary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Online at http://dx.doi.org/10.2737/NRS-RB-

Additional Resources......Online at http://dx.doi.org/10.2737/NRS-RB-

 | 1

Highlights

On the Plus Side

• Minnesota ranks 14th among the 50 states in land area, 20th in forest land, 15th in 

area of Federal forest land, and 1st in area of state/county/local government forest 

land.

• Forest land accounts for 17.4 million acres or 34 percent of the land area in 

Minnesota. Most of Minnesota forest land (90 percent or 15.7 million acres) is 

timberland.

• The area of forest land increased from 17.0 million acres in 2008 to 17.4 million 

acres in 2013. Half of the forest gains came from marshes and bogs.

• The total oven-dry biomass of all live trees on forest land increased from 438 

million tons in 2003 to 458 million tons in 2008 to 484 million tons in 2013. 

• Average annual net growth of growing stock on timberland for the 2013 inventory 

was 375 million cubic feet or roughly 2.5 percent of the total growing-stock volume 

in 2013, a slight decrease from the 384 million average annual net growth reported 

in the 2008 inventory.

• Average annual removals of growing stock on timberland for the 2013 inventory 

was 178 million cubic feet, or roughly 1.6 percent of the total growing-stock volume 

in 2013, a significant decrease from the 276 million cubic feet of average annual 

removals reported in the 2008 inventory.

• A growth-to-removals ratio of 1.7 for growing-stock trees on lands that were 

timberland at the time of both the current and previous inventories indicates that 

net growth is 1.7 times greater than removals and that growing-stock volume is 

increasing.

• In Minnesota for every 100 live trees more than 5 inches in diameter on forest land 

there are 13.7 standing dead trees that provide valuable wildlife habitat.

• Over 40 thousand people are directly employed in the forest products 

manufacturing and related sectors of the economy. The forest products industry is 

the fifth largest manufacturing sector in Minnesota.

Opposite: Fall foliage at Kettle River Scientific and Natural Area, Minnesota. Photo by Justin Meissen, 

used with permission (https://creativecommons.org/licenses/by-nc/2.0/legalcode).
2 | 

Issues to watch

• Approximately 44 percent of the forest land in Minnesota is less than fully stocked: 

32 percent of forest land has medium stocking, 11 percent is poorly stocked, and 1 

percent is nonstocked.

• Nearly 73 percent of hardwood sawtimber on timberland is in lower valued trees 

(grades 3 or lower), 21 percent is in tree grade 2, and 6 percent is in tree grade 1.

• The average annual mortality of growing stock on timberland from 2009 to 2013 

was 237 million cubic feet, an amount equal to 1.6 percent of the total growingstock volume on timberland in 2013. This rate is slightly lower than the 1.7 percent 

reported in 2008 and the 1.8 percent reported in 2003.

• High mortality rates have led to a 9 percent decline in the live volume of paper 

birch on forest land, a 7 percent decline in the volume of jack pine, and a 5 percent 

decline in the volume of balsam poplar and eastern cottonwood.

• European gypsy moth egg masses have been discovered in several locations in 

Lake and Cook counties. The Minnesota Department of Agriculture enacted a 

quarantine on July 1, 2014 to restrict movement of potentially infected items from 

these counties.

• The emerald ash borer, which threatens all species of ash, has established itself in 

the Minneapolis-Saint Paul metro area and in the southeastern corner of the State.

• Of the 40 invasive plant species monitored, reed canarygrass was the most 

commonly observed (18 percent of Phase 2 invasive plots). Nonnative bush 

honeysuckles and common buckthorn were the next most commonly observed 

invasive species and occurred on more than 10 percent of forested plots.

• Fragmentation and parcelization of the forest are increasing in Minnesota. Forest 

fragmentation occurs when a contiguous forest area is divided into smaller blocks, 

usually through the construction of roads and housing, clearing for agriculture, or 

other human development. Parcelization occurs when large holdings of one owner 

are broken up into smaller acreages held by multiple owners. 
 | 3

Background

Nerstrand Big Woods State Park, Minnesota. Photo by Eli Sagor, used with permission (https://creativecommons.org/

licenses/by-nc/2.0/legalcode).

 | 3
4 | BACKGROUND

An Overview of Forest Inventory

What is FIA?

The Forest Inventory and Analysis program, commonly referred to as FIA, is the 

nation’s forest census. It was established by the U.S. Congress to “make and keep 

current a comprehensive inventory and analysis of the present and prospective 

conditions of and requirements of the forest and range lands of the United States” 

(Forest and Rangeland Renewable Resources Planning Act of 1974; 16 USC 1601 

note). FIA has been collecting, analyzing, and reporting on the nation’s forest 

resources for over 80 years with the first FIA inventory of Minnesota forests 

completed in 1935. Information is collected on the status and trends of the extent, 

composition, structure, health, and ownership of the forests. This information is used 

by policy makers, resource managers, researchers, and the general public to better 

understand forest resources and to make more informed decisions about the fate of 

the forests.

What is this report?

This report is a summary of the findings from the eighth survey of the forest resources 

of Minnesota conducted by FIA. Data for this survey were collected between 2009 

and 2013, but throughout this report, we refer to 2013 as the inventory year. 

The results of the survey are divided into chapters that focus on forest features, 

health indicators, and socioeconomics. Details about the data collection, estimation 

procedures, and statistical reliability are included in the section “Statistics, Methods, 

and Quality Assurance,” at http://dx.doi.org/10.2737/NRS-RB-104. The Web site also 

includes a glossary of terms and numerous tables summarizing the results reported 

here.

Maps in this report were constructed using (1) categorical coloring of Minnesota 

counties according to forest attributes (such as forest land area), (2) a variation of 

the k-nearest neighbor (kNN) technique to apply information from forest inventory 

plots to remotely sensed MODIS imagery (250-m pixel size) based on the spectral 

characterization of pixels and additional geospatial information, or (3) colored dots to 

represent plot attributes at approximate plot locations.
 | 5

A Guide to Forest Inventory

What is a tree?

We know a tree when we see one and we can agree on some common tree attributes. 

Trees are perennial woody plants with central stems and distinct crowns. In general, the 

Forest Inventory and Analysis (FIA) program of the U.S. Department of Agriculture, 

Forest Service defines a tree as any perennial woody plant species that can attain a 

height of 15 feet at maturity. In Minnesota the problem is in deciding which species 

should be classified as shrubs and which should be classified as trees. A complete list 

of the tree species measured in this inventory can be found in Appendix 1 at the end of 

this printed document.

What is a forest?

We all know what a forest is, but where does the forest stop and the prairie begin? 

It’s an important question. The gross area of forest land or rangeland may determine 

the allocation of funding for certain State and Federal programs. Forest managers 

want more land classified as forest land, range managers want more land classified as 

prairie. Somewhere you have to draw the line.

FIA defines forest land as land that has at least 10 percent canopy cover by live trees 

or had at least 10 percent canopy cover by live trees in the past, based on the presence 

of stumps, snags, or other evidence, and is not currently developed for nonforest 

use. The area with trees must be at least 1 acre in size, and roadside, streamside, and 

shelterbelt strips must be at least 120 feet wide to qualify as forest land.

What is the difference between timberland, reserved forest 

land, and other forest land?

From an FIA perspective, there are three types of forest land: timberland, reserved 

forest land, and other forest land. In Minnesota, 90 percent of the forest land is 

timberland, 7 percent is reserved forest land, and 3 percent is other forest land.

• Timberland is unreserved forest land that meets the minimum productivity 

requirement of 20 cubic feet per acre per year at its peak.

• Reserved forest land is land that is withdrawn from timber utilization (i.e., trees are 

not cut for the purpose of timber utilization) through legislation or administrative 

regulation. In Minnesota, most of the reserved forest land is in the Boundary 

Waters Canoe Area Wilderness and Voyagers National Park.
6 | BACKGROUND

• Other forest land is commonly found on low-lying sites with poor soils where the 

forest is incapable of producing 20 cubic feet per acre per year at its peak.

Prior to 1999 only trees on timberland plots were measured in Minnesota. Therefore, 

volume on timberland can be reported for those inventories, but volume on forest 

land cannot be reported. Under the annual inventory system (in Minnesota the 

annual inventory system was implemented in 1999), trees were measured on all forest 

land, and therefore forest volume estimates can be produced. Annual plots were 

remeasured in 2008 and again in 2013, making it possible to report growth, removals, 

and mortality on all forest land for two points in time using annual inventory plots.

How many trees are in Minnesota?

There are approximately 2.4 billion trees on Minnesota forest land (give or take a few 

million) that have a diameter at breast height (d.b.h.; diameter measured at 4.5 feet above 

the ground) of at least 5 inches. The exact number is not known because only about 1 

out of every 18,000 trees is measured1

. In all, 125,611 trees with a d.b.h. of 5 inches and 

larger were sampled on 6,221 forested plots during the 5 years from 2009 to 2013.

How do we estimate a tree’s volume?

FIA typically expresses volumes in cubic feet, but in Minnesota wood is more 

commonly measured in cords (a stack of logs 8 feet long by 4 feet wide and 4 feet high). 

A cord has approximately 79 cubic feet of solid wood and 49 cubic feet of bark and air.

Volume can be precisely determined by immersing a tree in a pool of water and 

measuring the amount of water displaced. Less precise, but much cheaper, was the 

method used by the North Central Research Station (which later merged with the 

Northeastern Research Station to become the Northern Research Station). Several 

hundred cut trees were measured by taking detailed diameter measurements along 

their lengths to accurately determine their volumes (Hahn 1984). Regression 

lines were then fit to this data by species group. Using these regression equations, 

individual tree volume estimates based on species, diameter, and tree site index can be 

produced. 

The same method was used to determine sawtimber volumes. FIA reports sawtimber 

volumes in International 1⁄4-inch board foot scale. Conversion factors for converting 

to Scribner board foot scale are also available (Smith 1991).

1 During the 2013 inventory of Minnesota one 1/6th-acre plot was measured for approximately every 3,000 acres 

of forest land.
 | 7

How much does a tree weigh?

The U.S. Forest Service Forest Products Laboratory and others developed specific 

gravity estimates for a number of tree species (Miles and Smith 2009). These specific 

gravities were then applied to tree volume estimates (Hahn 1984) to derive estimates 

of merchantable tree biomass (the weight of the bole). To estimate live biomass, 

stump, limb, and bark biomass were added (Heath et al. 2009). The live biomass of 

roots or foliage is currently not reported. 

Forest inventories report biomass as green or oven-dry weight. Green weight is the 

weight of a freshly cut tree; oven-dry weight is the weight of a tree with zero percent 

moisture content. On average, 1 ton of oven-dry biomass is equal to 1.9 tons of green 

biomass.

How do we estimate all the forest carbon pools?

FIA does not directly measure the carbon in standing trees, let alone carbon in 

belowground pools, but assumes that half the biomass in standing live/dead trees 

consists of carbon. The remaining carbon pools (e.g., soil, understory vegetation, 

belowground biomass) are modeled based on stand/site characteristics (e.g., stand age 

and forest type). 

How do we compare data from different inventories?

Data from new inventories are often compared with data from earlier inventories to 

determine trends in forest resources. This is certainly valid when comparing the 2008 

inventory to the 2013 inventory and even the 2003 inventory to the 2008 inventory. 

But comparisons with inventories conducted before 1999 are problematic because 

procedures for assigning stand characteristics like forest type and stand size have 

changed as a result of FIA’s ongoing efforts to improve the efficiency and reliability of 

the inventory. Several changes in procedures and definitions have occurred since the 

1990 Minnesota periodic inventory. Although these changes will have little impact 

on statewide estimates of forest area, timber volume, and tree biomass, they may have 

significant impacts on estimates based on forest type and stand-size class. Some of 

these changes make it inappropriate to directly compare the 2013, 2008, and 2003 

annual inventory tables with periodic inventories published for 1935, 1953, 1962, 

1977, and 1990.

A major difference between periodic and annual inventories is the change in plot 

design. In an effort toward national consistency, a single national plot design was 
8 | BACKGROUND

implemented by all five regional FIA units in 1999. The old North Central plot design 

used in the 1990 Minnesota inventory consisted of variable-radius subplots. The new 

national plot design used in the 2003, 2008, and 2013 inventories uses fixed-radius 

subplots. Both designs have their strong points, but they often produce different 

classifications for individual plot characteristics.

The 1990 inventory also used modeled plots that were measured in 1977 and projected 

forward using the STEMS growth model (Belcher et al. 1982). This was done to save 

money by reducing the number of undisturbed plots that were remeasured, where 

disturbance was determined by examining aerial photographs of the plots. The idea was 

that parameters for the STEMS growth model could be fine-tuned using the measured 

undisturbed plots and then be applied to the remaining unmeasured undisturbed plots. 

Unfortunately, the use of modeled plots appears to have resulted in the overestimate of 

the 1990 live tree volume on timberland by about 6 percent.

Reserve status—improved implementation

FIA defines reserved forest land as forest land withdrawn by law(s) prohibiting the 

management of land for the production of wood products (not merely controlling or 

prohibiting wood-harvesting methods). All private productive forest land, regardless 

of conservation easements that may restrict harvesting, is declared timberland. 

Timberland does not include reserved forest land.

In an effort to increase consistency among states and across inventory years, a refined 

set of procedures determining reserve status have been implemented with version 6.0 

of the FIA field manual which took effect with the 2013 inventory year. Furthermore, 

all previously collected annual inventory data (1999 to present) have been updated 

using the new standardized interpretation. The improved implementation of the 

reserve status definition increases the spatial and temporal precision of timberland 

estimates, allowing for higher quality trend analyses and potentially better forest 

management decisions.

The 2012 inventory is the last inventory in which all data are available under both the 

previous and improved reserve class implementations (Table 1). Small but significant 

changes are associated with estimates of timberland acreage, number of trees, 

volume, and biomass. The large decrease in the estimate of annual other removals 

of growing stock is the result of a more consistent determination of reserved status 

across inventories (i.e., less land was classified as timberland last inventory and then 

classified as reserved forest land this inventory).
 | 9

Table 1.—Impact on timberland estimates resulting from refined procedures for determining reserved status, 

Minnesota, 2012

Timberland

2012 estimate 

previous

2012 estimate 

improved Difference

Difference 

(percent)

Area (thousand acres) 15,990 15,696 294 -2

Number of live trees ≥1 inch diameter 

(million trees)

12,809 12,613 196 -2

Aboveground biomass of live trees 

≥1 inch (thousand oven-dry tons)

447,430 437,366 10,064 -2

Net volume of live trees (million ft3

) 17,501 17,094 407 -2

Net volume of growing-stock trees 

(million ft3

)

15,005 14,656 349 -2

Annual net growth of growing-stock 

trees (thousand ft3

/yr)

370,209 363,274 6,956 -2

Annual mortality of growing-stock trees 

(thousand ft3

/yr)

252,743 245,437 7,306 -3

Annual harvest removals of growingstock trees (thousand ft3

/yr)

186,467 186,023 444 0

Annual other removals of growing-stock 

trees (thousand ft3

/yr)

28,025 8,731 19,294 -69

A word of caution on suitability and availability

FIA does not attempt to identify which lands are suitable or available for timber 

harvesting, particularly since such suitability and availability are subject to changing 

laws, economic/market constraints, physical conditions, adjacency to human 

populations, and ownership objectives. The classification of land as timberland does 

not necessarily mean it is suitable or available for timber production.

FIA endeavors to be precise in definitions and implementation. The program tries 

to minimize changes to these definitions and to collection procedures, but that is 

not always possible or desirable in a world of changing values and objectives. While 

change is inevitable, we hope that through clarity and transparency forest inventory 

data will be of use to analysts for decades to come. 
10 | BACKGROUND

Major Components of the FIA Program

Forest Inventory

Information on the condition and status of forests in Minnesota was obtained from 

the Northern Research Station’s Forest Inventory and Analysis (NRS-FIA) program. 

Previous inventories of the State’s forest resources were completed in 1935, 1953, 

1962, 1977, 1990, 2003, and 2008 (Zon 1935, Cunningham et al. 1958, Stone 1966, 

Jakes 1980, Leatherberry et al. 1995, Miles et al. 2007, Miles et al. 2011, respectively).

Beginning in 1998, several changes in FIA methods have improved the quality of the 

inventory. The most significant change between inventories has been the shift from 

periodic to annual inventory. Historically, FIA inventoried each state on a cycle that 

averaged about 12 years. However, the need for timely and consistent data across 

large geographical regions along with national legislative mandates, resulted in FIA 

implementing an annual inventory program. Annual inventory was initiated in 

Minnesota in 1999.

With the NRS-FIA annual inventory system, approximately one-fifth of all field 

plots are measured in Minnesota each year. The entire inventory is completed every 

5 years. NRS-FIA reports and analyzes results using a moving 5-year average. For 

example, NRS-FIA generates inventory results for 1999 through 2003, 2004 through 

2008, or 2009 through 2013.

Other significant changes between periodic and annual inventories include 

implementing new remote-sensing technology, a new field-plot configuration and 

sample design, and gathering additional remotely sensed field data. The use of new 

remote-sensing technology allows NRS-FIA to use classifications of Multi-Resolution 

Land Characterization (MRLC) data and other remote-sensing products to stratify 

the total area of Minnesota and to improve estimates. 

Identical classification procedures were used to assign forest type and stand-size 

class for the 2003, 2008, and 2013 annual inventories, so comparisons between these 

inventories are relatively simple. Comparisons with earlier inventories (1990, 1977, 

1962, 1953, and 1935) are more problematic due to the changes in plot design and 

data-collection classification methods. Contact NRS-FIA for additional information 

on the algorithms used in various inventories.

Information published in this report and in related tables is based on the Forest 

Inventory and Analysis database (FIADB), accessed in January-June 2015. Data 
 | 11

were collected under field guides 4.0 to 6.02, compiled in National Information 

Management System (NIMS) version 6.0, installed on November 15, 2012. Due to 

occasional changes to NIMS and FIADB, trend analyses should be made using FIA’s 

online estimation tools, not by comparing published reports or tables. FIA estimates, 

tabular data, and maps may be generated at: http://www.fia.fs.fed.us/tools-data/. 

See Bechtold and Patterson (2005) and O’Connell et al. (2014) for definitions and 

technical details.

National Woodland Owner Survey

Information about family forest owners is collected annually through the U.S. Forest 

Service’s National Woodland Owner Survey (NWOS). The NWOS was designed to 

increase our understanding of owner demographics and motivation. Individuals and 

private groups identified as woodland owners by FIA are invited to participate in the 

NWOS. Each year, questionnaires are mailed to 20 percent of private owners; more 

detailed questionnaires are sent out in years that end in 2 or 7 to coincide with national 

census, inventory, and assessment programs. Data presented here are based on survey 

responses from randomly selected families and individuals who own forest land in 

Minnesota. For additional information about the NWOS, visit: www.fia.fs.fed.us/nwos. 

Timber Products Output Inventory 

This study was a cooperative effort of the Division of Forestry of the Minnesota 

Department of Natural Resources (MNDNR) and the Northern Research Station 

(NRS). Using a questionnaire designed to determine the size and composition of the 

Minnesota forest products industry, its use of roundwood (round sections cut from 

trees), and its generation and disposition of wood residues, Minnesota Division of 

Forestry personnel visited all “known” primary wood-using mills within the State. 

Completed questionnaires were sent to NRS for editing and processing. As part 

of data editing and processing, all industrial roundwood volumes reported on the 

questionnaires were converted to standard units of measure using regional conversion 

factors. Timber removals by source of material and harvest residues generated during 

logging were estimated from standard product volumes using factors developed from 

logging utilization studies previously conducted by NRS. 
12 | BACKGROUND
 | 13

Forest Features

Chengwatana State Forest, Minnesota. Photo by Justin Meissen, used with permission (https://creativecommons.org/

licenses/by-nc/2.0/legalcode).

 | 13
14 | FOREST FEATURES

Forest Area

Background

Area estimates are the most basic, most easily understood, and most frequently cited 

of all forest inventory estimates. They are essential in assessing the status and trends of 

Minnesota forest ecosystems. Fluctuations in the forest land base may indicate land use 

trends and changing forest health conditions. Area estimates are reported in acres (640 

acres equal 1 square mile).

What we found

Minnesota forest land area is currently estimated at 17.4 million acres or a little more 

than one-third of the land area of the State (Fig. 1). The presettlement area of forest land 

was estimated to be 31.5 million acres (Marschner 1930). The largest decline in the area 

of forest land occurred before the first forest inventory was conducted in the mid-1930s 

and was due to lumbering followed by homesteading and land clearing (Zon 1935). This 

decline continued through the first four inventories of Minnesota. By 1977, the area of 

forest land was estimated at 16.5 million acres and has remained relatively stable, with 

a slight increase in 1990, followed by a slight decrease in 2003, a 5 percent increase in 

2008, and a further increase of 2 percent in 2013. The slight decrease in reported forest 

area from 1990 to 2003 may be due to the change in plot design.

0

5

10

15

20

25

1936 1953 1962 1977 1990 2003 2008 2013

Forest Land Area (million acres) 

Inventory Year

Figure 1.—Area of forest land by inventory year, Minnesota. 

Error bars represent a 68 percent confidence interval around 

the estimated mean.

In Minnesota, forest land is concentrated in the northeastern and southeastern counties 

(Fig. 2). Changes in the area of forest land vary by region. Eighty-eight percent of 

Minnesota forest land lies above the 46th parallel which runs through the town of 

Hinckley, MN (Fig. 3). Above this line, the area of forest land increased from 14.6 

million acres in 2003 to 15.0 million in 2008 and 15.3 million acres in 2013, a 5 percent 
 | 15

increase over the decade. Below this line the area of forest land increased from 1.6 

million in 2003 to 1.9 million in 2008 and 2.1 million in 2013, a 26 percent decadal 

increase. A more comprehensive examination of land-use change is provided in the 

Land-use Change section of this report.

Overall, Minnesota is 34 percent forest land, which is low for the Lake States but average 

for the United States (Table 2). Minnesota ranks 14th among the 50 states in land area, 

18th in forest land, and 15th in area of timberland. 

Figure 2.—Percentage of forested land by county, 

Minnesota, 2013.

Percent Forest 

Land

0 to 10

11 to 30

31 to 50

51+

Figure 3.—Forest area from National Land Cover 

Dataset (Homer et al. 2007).

46 degrees
16 | FOREST FEATURES

Table 2.—Minnesota in context: Area of forest land

Geographic area Percent forest land

World (FAO 2010) 31 

North America (FAO 2010) 33

United States (Oswalt et al. 2014) 34

Lake States (MN, MI, WI 2013) 45

Minnesota (2013) 34

Approximately 90 percent of Minnesota forest land is classified as timberland. The 

area of timberland was estimated at 14.5 million acres in 2003 and increased to 15.3 

million acres in 2008, and then to 15.7 million acres in 2013. Additional information 

on forest land dynamics is provided in the Land-use Change section.

What this means

The area and extent of Minnesota forests decreased from the first forest inventory 

in 1935 through the fourth inventory in 1977. The slight increase in forest land area 

from 1977 to 1990 was due in part to the Federal Conservation Reserve Program 

(Food Security Act of 1985 [P.L. 99-198, 1985 farm bill]). Under this program, 

erosion-prone cropland was removed from crop production and often reverted 

to forest land. The increase in forest land from 2003 to 2008 was due primarily to 

reversion of marsh and agricultural lands to forest land.

Approximately 16.6 million acres of forest land that was forested in 2008 remained 

forest land in 2013. An estimated 748 thousand acres of land that was not forested 

in 2008 reverted to forest land by 2013 while only 295 thousand acres diverted from 

forest to nonforest. The net result of these diversions and reversions was a 2.3 percent 

increase in the area of forest land from 2008 to 2013.

Increases in the area of timberland between the 1977 and 1990 periodic inventories 

were due in part to changing site productivity estimates. The area of other forest land 

declined from 1.9 million acres in 1977 to 840 thousand acres in 1990, with nearly 

half of this acreage decrease due to conversion to nonforest land and the other half 

due to conversion to timberland. Since the implementation of the annual inventory 

system in 1999, the estimate of other forest land has declined from 501 thousand acres 

in 2003, to 478 thousand acres in 2008, and finally to 444 thousand acres in 2013. 

Using current site productivity estimates, it is likely that three quarters of a million 

acres classified as other forest land in 1977 would be classified as timberland today.
 | 17

Forest Type Distribution

Background

Minnesota is at the confluence of three ecoregion provinces (Bailey 1980), the Laurentian 

Mixed Forest Province in the northeast, the Eastern Broadleaf Forest Province through 

the center and southeastern section of the State, and the Prairie Parkland Province in 

the west (Fig. 4). These provinces, largely determined by geology and climate, are closely 

linked to forest type distributions within Minnesota.

Ecoregion Provinces

Laurentian Mixed Forest

Eastern Broadleaf Forest

Prairie Parkland

Figure 4.—Bailey’s ecoregion provinces of Minnesota.

What we found

Information from forest inventory plots was combined with Modis imagery to produce 

a forest type map. This technique, a variation of the k-nearest neighbor (kNN) approach, 

applied information from forest inventory plots to remotely sensed Modis imagery based 

on the spectral characterization of pixels and additional geospatial information (Wilson 

et al. 2012). The result was a continuous map where aspen/birch, white/red/jack pine, 

and spruce/fir forest-type groups predominate in the north while the oak/hickory and 

elm/ash/cottonwood forest-type groups predominate in the south (Fig. 5).

Aspen is the most abundant cover type in Minnesota (see Appendix 2 for Minnesota 

DNR cover types), accounting for 29 percent of Minnesota forest land (5.0 million acres) 

(Fig. 6) followed by northern hardwoods (9 percent), black spruce (9 percent), oak (9 

percent), and lowland hardwoods (9 percent) forest types.
18 | FOREST FEATURES

Figure 5.—FIA forest-type groups of Minnesota (Wilson et al. 2012).

Forest-type Group

Aspen/birch

Elm/ash/cottonwood

Maple/beech/birch

Oak/hickory

Oak/pine

Spruce/fir

White/red/jack pine

Water

Nonforest
 | 19

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Jack pine

Red pine

Eastern white pine

Balsam fir

White spruce

Black spruce

Tamarack

Northern white-cedar

Oak

Northern hardwoods

Lowland hardwoods

Cottonwood/Willow

Aspen

Birch

Balsam poplar

Nonstocked

Other

Forest Land Area (million acres)

Forest Type

2003

2008

2013

Figure 6.—Forest land area by Minnesota Department of Natural 

Resources (MN-DNR) cover type and inventory year, Minnesota. 

Error bars represent a 68 percent confidence interval around the 

estimated mean.

What this means

Softwood forest types are concentrated in the Laurentian Mixed Forest Province which 

lies in the transition zone between the Canadian boreal forests to the north and the 

broadleaf deciduous forests to the south and west. Aspen/birch is the predominant 

hardwood forest type in the Laurentian Mixed Forest Province. The northern reaches 

of the Eastern Broadleaf Forest Province are dominated by aspen and maple forest 

types giving way to drought-resistant oak/hickory in the south. The Prairie Parkland 

Province is characterized by intermingled prairie, groves, and strips of deciduous trees. 

Trees are commonly found near streams and on north-facing slopes. The upland forest 

in this province is dominated by oak/hickory while floodplains and moist hillsides are 

dominated by the elm/ash/cottonwood forest-type group.

Over the past decade increases in wetland forest types such as tamarack and 

lowland hardwoods have occurred at the expense of nonforested areas. A net gain of 

approximately 96 thousand acres of tamarack forest land and 133 thousand acres of 

lowland hardwoods from nonforest land occurred from 2003 to 2013. Further study is 

needed to determine reasons for these gains in forest land.
20 | FOREST FEATURES

Stocking and Stand-size Class

Background

Stocking provides information on the degree of land occupancy by trees compared 

with a desired level for balanced health and growth. Stocking levels are calculated 

using a combination of number of trees, species, sizes, and spacing. A fully stocked 

stand indicates full utilization of the site. In stands of trees more than 5 inches in 

diameter a fully stocked stand would typically have a basal area of more than 80 

square feet per acre. In a seedling-sapling stand, a fully stocked stand would indicate 

that the present number of trees is sufficient to attain a basal area of 80 square feet per 

acre when the trees are more than 5 inches in diameter.

What we found

Over half (56 percent) of the forest land in Minnesota is fully stocked or overstocked, 

32 percent is medium stocked, and 12 percent is poorly stocked or nonstocked. 

Stocking levels are fairly consistent across the State. The proportion of seedlingsapling stands that are overstocked or fully stocked is 73 percent, followed by large 

diameter stands (54 percent) and medium diameter stands (42 percent). 

Stocking levels vary by cover type (Fig. 7). Aspen forest land is over 68 percent fully 

or overstocked, while tamarack is only 36 percent fully or overstocked. In Minnesota, 

stocking is generally lower on low-lying cover types.

The forests of Minnesota are fairly evenly split between three stand-size classes. Large 

diameter stands, where a plurality of stocking is in hardwoods with a diameter at breast 

height (d.b.h.) of 11 inches and larger and softwoods with a d.b.h. of 9 inches and larger, 

are found on 28 percent of Minnesota forest land. Seedling-sapling stands, where a 

plurality of stocking is in trees less than 5 inches d.b.h., occupy 36 percent of the forest 

land. Medium diameter stands, where a plurality of stocking is in softwood trees with a 

d.b.h. from 5 to 9 inches and hardwood trees from 5 to 11 inches, occupy 35 percent of 

the forest land in Minnesota. The proportion of land area in each of the stand-size classes 

varies considerably by cover type (Fig. 8). Nearly 72 percent of the oak cover type is in 

the large diameter stand-size class. At the other end of the spectrum are tamarack and 

black spruce at less than 10 percent stocking in the large diameter size class. 

Live basal area per acre of forest land for trees with a d.b.h. of 1 inch and larger was 78 

square feet per acre in 2003 and 2008 but increased slightly to 81 square feet per acre 

in 2013. 
 | 21

0

10

20

30

40

50

60

70

80

90

100

Forest Land Area (percent)

Forest Type

Poorly or nonstocked

Medium stocked

Fully or overstocked

Aspen

Oak

Red pine

Black spruce

Balsam poplar

Northern white-cedar

Jack pine

Birch

Lowland hardwoods

Balsam fir

Tamarack

Other

Northern hardwoods

Figure 7.—Proportion of forest land area by stocking class for each 

MN-DNR cover type and stocking class, Minnesota, 2013.

0

10

20

30

40

50

60

70

80

90

100

Forest Land Area (percent)

Forest Type

Nonstocked

Small diameter

Medium diameter

Large diameter

Oak

Red pine

Jack pine

Birch

Aspen

Balsam fir

Balsam poplar

Tamarack

Black spruce

Other

Northern hardwoods

Northern white-cedar

Lowland hardwoods

Figure 8.—Proportion of forest land area by MN-DNR cover type 

and stand-size class, Minnesota, 2013.

What this means

The density and size of stands across Minnesota provide information on the stages of 

stand development and forest stocking levels. Determining stages of stand development 

aids assessment of the future growth and mortality of forest resources. The high 

proportion of large diameter oak stands points to the difficulties in regenerating oak. 

Poor oak regeneration is generally tied to the cumulative impact of human actions and 

interventions. For instance, recurrent fire is important for oak regeneration because it 

eliminates many of oak’s competitors. Fire suppression, therefore, may inadvertently lead 

to a decline in the oak resource. For oaks to remain a large component of Minnesota 

forests, promoting oak regeneration will be necessary. 
22 | FOREST FEATURES

Low stocking levels and a high proportion of small diameter stands for tamarack and 

black spruce are to be expected given the generally low site productivity of areas occupied 

by these lowland types. Of more concern is the small proportion of the northern whitecedar forest type in small diameter stands, as this also points to regeneration problems. 

Regeneration in northern white-cedar is often hindered by deer browsing.

Forest Ownership

Background

Land management objectives vary by owner (see section on Private Woodland Owner 

Survey). State and county lands are managed more intensively for timber production 

than are federal lands. Minnesota has the highest percentage of public ownership of 

any state in the eastern United States and the highest percentage of state and county 

ownership of any state in the Nation. Public ownership of forest land is concentrated in 

northern Minnesota.

What we found

Of 17.4 million acres of Minnesota forest land, 55 percent is in public ownership (Fig. 9), 

including 23 percent administered by the State of Minnesota; 15 percent administered by 

county and local governments; and 17 percent administered by the Federal government. 

Most of the Federal lands are concentrated in the northern part of the State on Voyageurs 

National Park, the Chippewa National Forest, the Superior National Forest, and the 

Superior National Forest’s Boundary Water Canoe Area (BWCA) (Fig. 10).

15.0%

1.6%

23.4%

3.2% 15.1%

3.3%

3.3%

34.9%

National Forest

Other Federal

State

County and 

local government

Forest

industry

Corporate

Native American

Private

Figure 9.—Forest land by ownership or administering governmental 

unit, Minnesota, 2013.
 | 23

The remaining 45 percent of Minnesota forest land is in private ownership and is 

divided between forest industry (3 percent), corporate (3 percent), Native American 

(3 percent), and private (35 percent). For simplicity, forest industry and corporate 

ownership will be lumped together and referred to as corporate throughout the rest 

of this report. The percentage of corporately owned forest land in Minnesota is less 

than that of Wisconsin (10 percent) and Michigan (14 percent). Nearly four-fifths of 

Minnesota corporate forest lands are located in just four counties (Itasca, Koochiching, 

Lake, and St. Louis). Even in these four counties, corporations own only 12 percent of 

the forest land. Additional information on other private forest lands is provided in the 

Private Woodland Owner Survey section.

Figure 10.—Distribution of forest land by public and private ownership, Minnesota, 2013.

Forest Land Ownership

Public

Federal

State

Local

Private

Family

Corporate

Other
24 | FOREST FEATURES

Figure 10 is a modeled representation of public and private forest land by ownership 

type developed by Hewes et al. (2014). Four types of public ownership are depicted: 

federal, state, county, and other public. Private ownerships consist of family (includes 

individuals and families), corporate, and other private (includes conservation and 

natural resource organizations, unincorporated partnerships and associations, and 

Native American tribal lands).

What this means

Management objectives often vary by owner (see Private Woodland Owner Survey 

section). Harvest rates of growing-stock volume on timberland expressed as a percentage 

of current timberland growing-stock volume are currently highest on county lands, 

followed by State, private, and finally Federal. In Figure 11, the estimates for each year are 

based on a rolling average of remeasured plots from the reporting year and the previous 

4 years, so there is a lag in revealing ownership response to market conditions; however, 

there is evidence that some ownerships are more responsive to changes in stumpage 

prices than others. Average stumpage prices sold by public agencies in Minnesota peaked 

in 2005 (Deckard 2014), and while harvest rates for all ownerships declined, the decline 

was greatest on private ownerships.

The majority of public forest land ownership is concentrated in the northern part 

of the State while the population center is in the Twin Cities metro area. Urban and 

suburban forests are where the intense interaction of people and forests presents special 

management challenges that have high potential to affect the quality of life for millions 

of residents (Shifley et al. 2014). The high level of interaction between people and trees 

in and around urban areas makes these areas of particular significance to managers 

(Radeloff et al. 2005). 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Harvest (percent of growingstock volume)

Year

Federal

State

County and local

Private

Figure 11.—Harvest of growing-stock volume on timberland by 

ownership group and year, Minnesota, 2004 through 2013.
 | 25

Number of Trees and Size Distribution

Background

An estimate of the number of trees in a forest is useful when combined with 

information on the diameter class distribution. Young forests have many more trees 

per acre than older forests; older forests usually have fewer trees, but the trees are 

larger and have much more biomass than younger forests. It is the number of trees 

and their diameter distributions that are important.

What we found

In Minnesota there are currently an estimated 14.2 billion trees on forest land. Of 

these trees, 83 percent are saplings (trees from 1 to 4.9 inches at d.b.h), 14 percent 

are poletimber-size trees (5 to 9 inches d.b.h. for softwoods and 5 to 11 inches d.b.h. 

for hardwoods), and 3 percent are sawtimber-size trees. The high number of saplings 

is consistent with natural processes of regeneration and subsequent self-thinning. 

Nearly two-thirds (64 percent) of the trees in Minnesota are hardwoods, and the 

rest are softwoods. Quaking aspen alone accounts for nearly 25 percent of the total 

number of trees in Minnesota.

From 2003 to 2013, the total number of trees on forest land in Minnesota increased, 

with the number of saplings increasing by 20 percent, sawtimber trees increasing by 9 

percent, and poletimber increasing by 8 percent (Fig. 12). Nearly all of the increase in 

the number of poletimber trees occurred between 2008 and 2013.

0

5

10

15

Sawtimber Poletimber Sapling

Number of Trees (billions) 

Size Class

2003

2008

2013

Figure 13 provides a breakdown of the number of trees in Minnesota in each tree-size 

class by species. There have been large increases in the number of white spruce and 

Figure 12.—Number of live trees on forest land by tree size 

class and inventory year, Minnesota.
26 | FOREST FEATURES

-40 -20 0 20 40 60 80

White spruce

Green ash

Eastern white pine

Balsam fir

Red maple

Black ash

Tamarack (native)

Red pine

Northern white-cedar

Bur oak

Sugar maple

American basswood

Black spruce

Paper birch

Quaking aspen

Northern red oak

Jack pine

Other species

Percent Change

Species

Sawtimber

Poletimber

Sapling

Figure 13.—Percent change in the number of live trees on forest 

land by species and size class, Minnesota, 2003 to 2013.

green ash trees in all size classes. Conversely, the number of jack pine trees in each size 

class has decreased. For quaking aspen, there has been a decrease in the number of 

sawtimber-size trees but increases in the number of poletimber and sapling-size trees. 

Basal area is the cross-sectional area of a tree 4.5 feet above the ground. The mean 

basal area per tree for the stand is calculated by measuring the cross-sectional area 

of all the trees in a stand and taking the average. Because it is easier to visualize tree 

diameter than mean basal area, the concept of quadratic mean diameter (QMD) was 

introduced. The QMD of a stand is the diameter of a tree with basal area equal to 

the mean basal area per tree of the stand. QMD is usually calculated for trees over a 

certain minimum diameter, in this case 5 inches d.b.h.

Trees in Minnesota are generally smaller than the average for the conterminous 

United States (Table 3), partly because much of Minnesota lies in the transition zone 

between forest and prairie. Smaller diameters are also due in part to management 

favoring pioneer species, such as aspen and jack pine, that tend to mature quickly 

but rarely attain the size of late successional species like sugar maple and white pine. 

Figure 14 displays the QMD by county for trees over 5 inches d.b.h. A nonforest 

mask, derived from the National Land Cover Dataset (NLCD), was placed over the 

counties so that colored shading would only appear on forested areas.
 | 27

Quadratic Mean 

Diameter (inches)

5 to 8

8 to 10

10 to 12

12 to 14

14 to 34

Table 3.—Minnesota in context: Quadratic mean diameter (QMD) and average number of trees (5 inches d.b.h. or 

larger) per acre, 2013

Geographic area QMD

Number of trees per 

acre of forest land

Number of trees 

per acre of land

Coterminous United States 10.4 135.9 49.4

Lake States (MN, MI, WI) 9.3 154.5 69.4

Minnesota 8.8 139.1 47.5

Figure 14.—Quadratic mean diameter by county for all trees 5 inches d.b.h. and larger on forest land, United States, 2013.

What this means

Increases in the number of poletimber-size white spruce and red pine trees are the 

result of artificial regeneration efforts (planting and seeding) conducted years ago. 

Nearly 80 percent of artificially regenerated timberland in Minnesota is in these two 

species. Jack pine regeneration is facilitated by fire. Fire suppression and the jack 

pine budworm have been significant factors in the decline in the number of jack pine 

trees. Paper birch is susceptible to the bronze birch borer and Armillaria root disease. 

Recent droughts have also had an adverse impact on paper birch. The increase in the 

number of quaking aspen poletimber trees is good news for the timber industry as 

this is the preferred size class for the wood pulp industry.
28 | FOREST FEATURES

Tree Biomass

Background

Biomass estimates are increasing in importance for analyses of carbon sequestration, 

wood fiber availability for fuel, and other issues. Traditionally, timber harvests have 

been measured in board feet or cubic feet. Increasingly they are measured in green tons 

or dry tons. In Minnesota the ratio of green tons to dry tons is approximately 1.9 to 1.0.

The average aboveground dry weight of a tree (including stump, bole, and limbs but 

excluding foliage and roots) increases dramatically with increasing tree diameter 

(Table 4). Trees in the 7.0- to 8.9-inch diameter size class, for example, weigh slightly 

more than twice the weight of trees in the 5.0- to 6.9-inch class. 

Table 4.—Average aboveground tree biomass by diameter class and major species group, Minnesota, 2013

Diameter class Softwoods Hardwoods

--- Inches --- -------------------------------------- Dry pounds ----------------------------------

1.0-2.9 5 7 

3.0-4.9 30 44 

5.0-6.9 90 126 

7.0-8.9 192 269 

9.0-10.9 329 459 

11.0-12.9 507 698 

13.0-14.9 728 993 

15.0-16.9 1,014 1,349 

17.0-18.9 1,408 1,788 

19.0-20.9 1,816 2,255 

21.0-28.9 2,861 3,451 

29.0+ 6,350 7,001 

What we found

Biomass, measured as all live aboveground tree biomass on forest land, was estimated 

at 438 million dry tons in 2003 and had increased to 458 million dry tons by 2008 

and to 484 million dry tons in 2013. In both 2003 and 2008 there was an average of 

27 dry tons of live aboveground tree biomass per acre of forest land. By 2013 this had 

increased to 28 tons per acre. The distribution of forest biomass per acre of land is 

presented in Figure 15 and Table 5.

In 2013, 16 percent of the live tree aboveground biomass on timberland was in 

saplings (trees less than 5 inches d.b.h.) while 84 percent was in trees 5 inches in d.b.h. 

and larger. The boles of trees 5 inches d.b.h. and larger accounted for 64 percent of 

live tree aboveground biomass, while their top and limbs accounted for 16 percent, 
 | 29

and their stumps accounted for 4 percent (Fig. 16). Nearly three-quarters of the total 

biomass was composed of hardwood species. 

The total live tree aboveground dry biomass on timberland in 2013 was 443 million 

tons, a 6 percent increase from the 418 million tons reported in 2008 and a 24 percent 

increase from the 398 million tons reported in 2003.

Figure 15.—Average live tree biomass for all trees on forest land, Minnesota, 2013.

Live Tree Biomass on Forest 

Land (dry tons/acre)

Nonforest/no estimate

<10

10-20

>20

Water

Table 5.—Minnesota in context: Aboveground live tree biomass per acre of forest land

Geographic area Dry tons per acre

Coterminous United States 41

Lake States (MN, MI, WI) 36

Minnesota 28
30 | FOREST FEATURES

4% 64%

16%

16%

Stumps Bole

Saplings

Tops and limbs

Figure 16.—Live tree biomass on timberland by component, 

Minnesota, 2013.

What this means

Minnesota is continuing to gain aboveground live tree biomass due primarily to 

increases in forest land area. However, increasing demand for woody biomass for 

the production of energy will place additional demands on forest planning and 

management to ensure that the resource is managed sustainably.

Volume and Species Composition

Background

Current volumes can be compared to rates of harvest to help determine the 

sustainability of current and projected future harvest levels. Because certain species 

are more economically desirable than other species, it is important to view volume 

information on a species-by-species basis.

What we found

Volume on timberland

The volume of all live trees on timberland increased from 14.3 billion cubic feet in 

1977, to 16.0 billion in 2003, to 16.4 billion in 2008, and 17.3 billion in 2013 (Table 

6). Historically, over 13 percent of live tree volume falls in the rough and rotten cull 

category, and this trend held true in the 1977, 2008, and 2013 inventories. The cull 

proportion reported in 2003, however, was only half this rate. Rough and rotten cull 

volume went from 14 percent of live volume in 1977 to 6 percent in 2003 and back to 

14 percent in 2008 and 2013. If the 2003 cull proportion is adjusted to reflect historic 
 | 31

levels, growing-stock volume would have increased from 12.3 billion cubic feet in 1977 

to 14.1 billion in 2003, 14.2 billion in 2008, and 15.0 billion in 2013 while rough and 

rotten cull would have increased from 1.9 billion in 1977 to 2.2 billion in 2003, 2.3 

billion in 2008, and 2.4 billion in 2013.

Table 6.—Live tree volume on timberland by tree class and inventory year, Minnesota

Tree class 1977 2003 2003 adjusted 2008 2013

-------------------------------------------- Million ft3 --------------------------------------

Growing stock 12,350 14,937 14,100 14,155 14,990

Cull 1,949 1,030 2,200 2,271 2,357

Total 14,299 15,968 16,300 16,425 17,347

Figure 17 shows the change in all live volume on timberland by species for the 12 species 

having the largest volume in 2013 (75 percent of the total). Between 2003 and 2013, there 

were winners and losers. The big winners included red pine, which increased in volume 

by 48 percent, bur oak (27 percent), northern white-cedar (19 percent), black ash (16 

percent), and American basswood (11 percent). The losers included paper birch with a 

19 percent decline and quaking aspen with a 5 percent decline.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Volume (billion ft3

)

Species

Quaking aspen

Red pine

Northern white-cedar

Bur oak

Paper birch

Black ash

American basswood

Northern red oak

Black spruce

Tamarack (native)

Sugar maple

Balsam fir

2003

2008

2013

Figure 17.—Live tree volume on timberland by species and 

inventory year, Minnesota.

Aspen is concentrated in northeastern Minnesota. The decrease in growing-stock 

volume of aspen from 2003 to 2008 followed by a smaller increase from 2008 to 2013 

is reflective of the economic recession of 2008 and the resulting decline in the demand 

for paper and boards. Harvest removals of live aspen declined by 29 percent from the 

2008 inventory to the 2013 inventory. As a result, all live aspen volume on timberland 

increased from 3.2 billion cubic feet in 2008 to 3.3 billion cubic feet in 2013.
32 | FOREST FEATURES

The area of timberland increased by 5 percent from 2003 to 2008. This new 

timberland was relatively sparsely treed, resulting in a decline in the overall volume 

per acre on timberland from 1,101 cubic feet in 2003 to 1,075 cubic feet in 2008. The 

area of timberland increased by 3 percent from 2008 to 2013. Volume per acre on 

timberland also increased over this period, from 1,075 in 2008 to 1,108 in 2013. The 

average volume per acre on lands that reverted from nontimberland to timberland 

over the last 5 years was only 406 cubic feet.

Volume on forest land

Ninety-eight percent of all live tree volume on forest land comes from just 27 of the 

66 species measured during the 2013 inventory (see Appendix 1 for list of species 

measured). Leading the list is quaking aspen at 18 percent, followed by red pine at 

6 percent, northern white-cedar at 6 percent, and paper birch at 6 percent. Maps of 

species volume per acre of forest land by county are presented in Figures 18 and 19.

What this means

Aspen volume on timberland increased slightly from 2008 to 2013 due to a 29 percent 

decline in aspen harvest removals and a 23 percent increase in volume in the 5- to 

7-inch diameter class. In the short term, aspen volumes are expected to increase as 

large areas of regenerated aspen continue to grow into poletimber. Demand for aspen 

and other species may increase as demand for new wood-based products and possibly 

for bioenergy increases in future years

All of the top 30 species by volume on forest land have had increasing or steady 

volumes except for paper birch, jack pine, and balsam poplar, which had high rates of 

mortality (3.2, 2.1, and 5.1 percent, respectively), and northern red oak and eastern 

cottonwood where regeneration may be a problem.
 | 33

Figure 18.—Live tree volume per acre on forest land for selected hardwood species, Minnesota, 2013. (Continued on 

next page)

American Basswood

Volume on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
34 | FOREST FEATURES

Green Ash Volume on 

Forest Land (ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658

Figure 18. (Continued)—Live tree volume per acre on forest land for selected hardwood species, Minnesota, 2013. 

(Continued on next page)
 | 35

Northern Red Oak 

Volume on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658

Figure 18. (Continued)—Live tree volume per acre on forest land for selected hardwood species, Minnesota, 2013. 

(Continued on next page)
36 | FOREST FEATURES

Figure 18. (Continued)—Live tree volume per acre on forest land for selected hardwood species, Minnesota, 2013. 

(Continued on next page)

Paper Birch Volume on 

Forest Land (ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
 | 37

Figure 18. (Continued)—Live tree volume per acre on forest land for selected hardwood species, Minnesota, 2013. 

(Continued on next page)

Quaking Aspen Volume 

on Forest Land (ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
38 | FOREST FEATURES

Figure 18. (Continued)—Live tree volume per acre on forest land for selected hardwood species, Minnesota, 2013. 

Red Maple Volume on 

Forest Land (ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
 | 39

Figure 19.—Live tree volume per acre on forest land for selected softwood species, Minnesota, 2013. (Continued on 

next page)

Balsam Fir Volume 

on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
40 | FOREST FEATURES

Figure 19. (Continued)—Live tree volume per acre on forest land for selected softwood species, Minnesota, 2013. 

(Continued on next page)

Black Spruce Volume 

on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
 | 41

Figure 19. (Continued)—Live tree volume per acre on forest land for selected softwood species, Minnesota, 2013. 

(Continued on next page)

Eastern Redcedar

Volume on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
42 | FOREST FEATURES

Jack Pine Volume 

on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658

Figure 19. (Continued)—Live tree volume per acre on forest land for selected softwood species, Minnesota, 2013. 

(Continued on next page)
 | 43

Figure 19. (Continued)—Live tree volume per acre on forest land for selected softwood species, Minnesota, 2013. 

(Continued on next page)

Red Pine Volume 

on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
44 | FOREST FEATURES

Figure 19. (Continued)—Live tree volume per acre on forest land for selected softwood species, Minnesota, 2013. 

White Pine Volume 

on Forest Land 

(ft3

/acre)

0

0 to 50

50 to 100

100 to 200

200 to 400 

400 to 658
 | 45

Sawtimber Volume and Quality

Background

A board foot is a unit of measure 1 inch by 12 inches by 12 inches. Softwood sawtimber 

is primarily valued for dimensional lumber, while hardwood sawtimber is valued 

for use in flooring and furniture. To qualify as sawtimber-size trees under the FIA 

definition, softwood trees must have a d.b.h. of at least 9 inches and hardwoods must 

have a d.b.h. of at least 11 inches. 

Tree grade, as determined in the field, is based on tree diameter and the presence or 

absence of knots, decay, or curvature of the bole. The value of sawtimber varies greatly 

by species and tree grade. The highest quality trees are graded 1, while the lowest 

quality trees receive a tree grade of 5. Trees not meeting grade 5 standards are classified 

as cull. The grading system has changed a number of times including between the 1990 

and 2003 inventories and again in 2007. Notable changes in 2007 affected softwood 

grading (e.g., added grade 5 for all softwoods) while hardwood grading rules have been 

fairly consistent since 2000 (Pugh et al. 2012).

What we found

Sawtimber volume on timberland totaled 38.6 billion board feet in 2013. This is up 

from the 35.4 billion board feet reported in 2008. This increase was evenly split between 

softwoods and hardwoods (Fig. 20).

0

5

10

15

20

25

Softwoods Hardwoods

Volume (billion board feet)

Major Species Group

2008

2013

Figure 20.—Sawtimber volume on timberland by major species 

group and inventory year, Minnesota. Error bars represent a 68 

percent confidence interval around the estimated mean.

Of the 66 tree species measured on FIA plots during the 2013 inventory, 44 contributed 

to sawtimber volume on timberland. Ninety-one percent of the sawtimber volume on 

timberland was found in just 17 species (Fig. 21). The volume in five species (quaking 

aspen, tamarack, paper birch, bigtooth aspen, and jack pine) decreased from 2008 to 2013.
46 | FOREST FEATURES

0 1 2 3 4 5 6 7

 Black spruce

 Jack pine

 Bigtooth aspen

 Sugar maple

 Green ash

 Balsam fir

 Paper birch

 Tamarack

 White spruce

 Black ash

White pine

 Bur oak

Basswood

Northern red oak

Northern white-cedar

 Red pine

Quaking aspen

Volume (billion board feet)

Species

2008

2013

Figure 21.—Sawtimber volume on timberland by selected 

species and inventory year, Minnesota.

While 49 percent of softwood sawtimber is tree grade 1, only 6 percent of hardwood 

sawtimber volume is in tree grade 1 (Fig. 22). This is the lowest percentage of any 

eastern state with the exception of Oklahoma (Fig. 23). The high concentration of 

short-lived hardwood species (primarily aspen and birch) in the northern part of 

the State combined with the more arid conditions in the prairie west are largely 

responsible for the low percentage of grade 1 hardwood trees.

0

10

20

30

40

50

60

70

80

90

100

Softwoods Hardwoods

Volume (percent)

Major Species Group

Other

Grade 3

Grade 2

Grade 1

Figure 22.—Sawtimber volume on timberland by major species 

group and tree grade, Minnesota, 2013.
 | 47

Figure 23.—Percentage of timberland hardwood growing-stock volume in grade 1, eastern United States, 2013.

Hardwood Volume 

in Grade 1 

(percent)

0

1 to 10

10 to 20

20 to 40

40 to 100

What this means

The volume of sawtimber is decreasing for pioneer species like quaking aspen, paper 

birch, and jack pine. A decrease in the volume of aspen sawtimber is partly due to 

natural succession and partly due to forest management efforts to harvest senescent 

trees to make way for younger more vigorous forests. 

Sawtimber per acre volumes are highest on federally administered timberland (3,408 

board feet), followed by privately owned land (2,711 board feet), and finally state and 

county land (1,884 board feet). This reflects the removals levels of sawtimber trees on 

timberland for each of the ownership groups. Sawtimber removals as a percentage of 

standing sawtimber volume on land that was timberland at both the beginning and end 

of the remeasurement period is lowest for Federal ownership (0.6 percent), followed by 

private ownership (0.9 percent), and finally state and local government ownership (2.0 

percent). 
48 | FOREST FEATURES

Tree Volume Net Growth

Background

Net growth is computed by measuring a tree at two points in time and determining 

the average annual change in volume over the period. If the volume on a tree 

increased from 2008 to 2013, then a net increase in growth would be reported. If 

the volume declined (usually due to mortality), then there would be a net decrease 

in growth. The total volume change divided by the number of years between 

measurements yields the average annual net growth on the tree. 

What we found

The average annual net growth of live trees on forest land for the 2013 inventory was 

398 million cubic feet or roughly 2.1 percent of the total live tree volume in 2013. 

Growth expressed as a percentage of live tree volume is presented for the 17 most 

abundant species (by cubic foot volume) in Minnesota in 2013 (Fig. 24). The growth 

rate for white spruce was the greatest at 4.3 percent; the growth rate for paper birch 

was -0.7 percent due to high mortality rates.

-1 0 1 2 3 4 5

White spruce

Red pine

Green ash

White pine

Balsam fir

Red maple

Quaking aspen

Bur oak

Northern red oak

Sugar maple

Black spruce

Basswood

Jack pine

Northern white-cedar

Black ash

Tamarack

Paper birch

Growth (percent of volume)

Species

Figure 24.—Average annual net growth of live trees on forest 

land as a percentage of live tree volume for the 17 most abundant 

species in Minnesota, 2013.
 | 49

The average annual net growth rate of live trees on forest land as a percentage of volume 

varies by landowner class. The rate is highest for private landowners (2.4 percent) 

followed by state and local governments (2.1 percent), national forests (1.5 percent), 

and finally other Federal (-0.9 percent). This growth rate also varies by county (Fig. 25). 

A nonforest mask, derived from the National Land Cover Dataset (NLCD), was placed 

over the counties so that colored shading would only appear on forested areas where 

growth may have occurred.

Figure 25.—Average annual net growth of live trees on forest land as a percentage of live tree volume, Minnesota, 2013.

Growth Rate 

(percent of volume)

0 to 1

1 to 2

2 to 3

3+
50 | FOREST FEATURES

What this means

Growth rates are useful indicators of sustainability, disturbance trends, forest vitality, 

and direction of succession. But growth provides only one piece of the sustainability 

puzzle. Information on mortality and removals is also needed to identify the changing 

composition of the forest. The three change components (growth, mortality, and 

removals) provide information only on trees 5 inches in diameter and larger. As a result, 

information on the understory component is not reflected in any of these measures.

Tree Volume Mortality

Background

Mortality occurs as a result of adverse weather, disease, insects (native and exotic), 

senescence, competition, succession, fire, and human and animal activity. Trees that 

are killed as a result of harvesting or land clearing are considered removals and are 

not included in mortality.

What we found

The average annual live tree mortality on forest land for Minnesota in 2013 was 362 

million cubic feet or roughly 1.9 percent of the 2013 volume. Mortality expressed as 

a percentage of volume is presented for the 17 most abundant (by cubic foot volume) 

species in Minnesota in 2013 (Fig. 26). The mortality rate for balsam fir was the highest 

at 3.7 percent; the mortality rate for red pine the lowest at 0.2 percent.

The primary cause of mortality could not be determined in 35 percent of the cases. 

This is not surprising considering that the trees are only revisited every 5 years so a 

tree could have been dead for up to 5 years when revisited by the field crews, making 

the call on cause of death problematic. 

Among the identifiable primary causes of tree mortality were weather (30 percent), 

disease (20 percent), insect (10 percent), animal (3 percent), other vegetation (1 

percent), and fire (0.3 percent). Although insects were identified as the primary cause 

of mortality in only a small percentage of cases, they contribute to a much greater 

share of mortality by weakening trees and making them vulnerable to disease and 

other forms of attack.
 | 51

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Balsam fir

Paper birch

Quaking aspen

Tamarack

Jack pine

Black spruce

Northern red oak

Black ash

Red maple

Green ash

Basswood

White spruce

Sugar maple

White pine

Bur oak

Northern white-cedar

Red pine

Mortality (percent of volume)

Species

Figure 26.—Average annual live tree mortality on forest land 

as a percentage of volume for the 17 most abundant species 

in Minnesota, 2013.

The average annual mortality of live trees on forest land reported in 2013 expressed 

as a percentage of the 2013 volume is 1.9 percent. The average annual mortality of 

growing-stock trees on timberland is slightly lower at 1.6 percent of the growing-stock 

volume on timberland. This is higher than the rate reported for the 1977 inventory 

(1.2 percent) or for the 1990 inventory (1.3 percent). The rate of 1.6 percent is also 

higher than the mortality rates for the neighboring states of Iowa (1.5 percent) and 

Wisconsin (1.1 percent). 

The mortality rate of live trees on forest land as a percentage of current live tree 

volume varies by landowner class. The rate is highest for other federal (2.4 percent) 

and national forests (2.3 percent), followed by state, county, and local governments 

(1.9 percent) and private land owners (1.8 percent). The spatial distribution of 

mortality is presented in Figure 27. A nonforest mask was placed over the counties to 

more fairly represent the forest area on which mortality would have occurred.
52 | FOREST FEATURES

Figure 27.—Average annual live tree mortality on forest land as a percentage of live tree volume, Minnesota, 2013. 

Mortality Rate 

(percent of volume)

0 to 1

1 to 2

2 to 3

3+

What this means

Some of the increase in mortality may be due to the increasing age of Minnesota 

forests and natural mortality patterns during stand development and succession. 

Single large weather events also contributed to the increase in mortality. 
 | 53

Tree Volume Removals

Background

There are two types of removals: harvest removals, which include the volume of 

harvested trees and the volume of trees killed during the harvesting process; and 

diversion removals, which include the volume of living trees on land previously 

classified as forest land that has been removed from the forest land base due to landuse change and is now reclassified as nonforest land.

What we found

The average annual live tree removals on forest land for the 2013 inventory of 

Minnesota was 207 million cubic feet or roughly 1.6 percent of the total tree volume 

in 2013. This is significantly lower than the 288 million cubic feet reported in 2008. 

Removals expressed as a percentage of volume is presented for the 17 most abundant 

(by volume) species in Minnesota in 2013 (Fig. 28). The removals rate for jack pine 

was the greatest at 2.9 percent while the removals rate for eastern white pine was the 

lowest at 0.03 percent.

0 0.5 1 1.5 2 2.5 3 3.5

Jack pine

Quaking aspen

Balsam fir

Paper birch

Black spruce

White spruce

Red pine

Sugar maple

Red maple

Tamarack

Northern red oak

Basswood

Green ash

Bur oak

Black ash

Northern white-cedar

White pine

Removals (percent of volume)

Species

Figure 28.—Average annual removals of live trees on forest land 

as a percentage of volume for the 17 most abundant species in 

Minnesota, 2013.
54 | FOREST FEATURES

The removals rate as a percentage of volume varies by landowner class. The rate is 

highest for state and local governments (1.6 percent) followed by private land owners 

(1.0 percent), national forests (0.4 percent), and finally other Federal (0.3 percent). 

The spatial distribution of removals is presented in Figure 29. In this graphic, counties 

were used to plot the rate of removals for Minnesota. A nonforest mask was placed 

over the counties to more fairly represent the forest area on which removals would 

have occurred.

Figure 29.—Average annual live tree removals on forest land as a percentage of live tree volume, Minnesota, 2013.

Removals Rate 

(percent of volume)

0 to 1

1 to 2

2 to 3

3+
 | 55

Most (96 percent) of the removals of live trees from forest land in Minnesota, as 

measured from FIA field plots during the inventory period, were due to harvesting. 

Eighty-nine percent of the removals were cut and utilized; 7 percent were killed as 

a result of the harvesting process and left in the forest (Fig. 30). The remaining 4 

percent of removals were due to land-use change where trees were left standing but 

the land they were on was reclassified by FIA from forest land to nonforest land.

4%

7%

89%

Diversion removals

Harvest removals: 

mortality

Harvest removals: 

utilized

Figure 30.—Average annual growing-stock removals from 

forest land by disposition of timber, Minnesota, 2013.

What this means

Landowner objectives have a large impact on removal rates. In Minnesota, state and 

county lands are more actively managed for timber than other public ownerships. 

Removals rates are highest on state and local government lands and lowest on Federal 

lands, while per acre sawtimber volumes are highest on Federal lands and lowest on 

state and local government lands. Likewise corporate ownerships are more actively 

managed for timber than other private ownerships.

Growth-to-Removals Ratio

Background

One measure of sustainability is the growth-to-removals ratio (G/R), calculated as 

the net volume growth (gross growth minus mortality) divided by volume removals. 

A number greater than 1.0 indicates that the volume of the species is increasing. 
56 | FOREST FEATURES

A number less than 1.0 indicates that the volume is decreasing. This measure is 

typically applied to lands that were forested at the time of the previous measurement 

and continue to be forested at the time of the current measurement. This eliminates 

changes in net growth and removals due to land use-change and focuses on 

management of lands in the current forest land base.

What we found

Overall, the growth-to-removals ratio of live trees on timberland for the 2013 

inventory was 1.7, indicating that overall volume is increasing. By ownership class the 

growth-to-removals rates are 2.9 for the national forests, 2.3 for private ownership, 

1.0 for county and municipal, and 1.3 for state-administered lands. On a speciesby-species basis, the picture is less clear, with G/R varying considerable by species 

(Table 7). Eastern white pine has a G/R of over 65.9; paper birch has a net G/R of -0.3 

because mortality exceeds gross growth, resulting in a negative net growth. 

Table 7.—Ratio of average annual net growth to average annual removals of live trees (on land classified as timberland 

in both 2008 and 2013) for the 17 most abundant species (by volume), Minnesota, 2013

Species

Net growth-toremovals ratio Net growth Removals Live tree volume on 

timberland 

------------------ Million ft3 ------------------

Eastern white pine 65.9 13.1 0.2 428.9

Northern white-cedar 23.0 16.2 0.7 1102.1

Bur oak 6.9 16.6 2.4 1023.6

Black ash 5.1 11.4 2.2 961.3

Green ash 4.3 9.5 2.2 454.3

Northern red oak 4.2 13.7 3.3 868.7

Red pine 3.7 42.1 11.5 1144.6

American basswood 3.5 12.7 3.6 948.1

White spruce 3.3 14.1 4.3 388.5

Red maple 3.1 13.8 4.4 569.4

Sugar maple 2.1 11.0 5.2 665.3

Black spruce 1.5 15.4 10.0 804.5

Balsam fir 1.4 16.3 11.5 660.0

Tamarack (native) 1.1 5.8 5.1 695.2

Quaking aspen 1.1 80.8 76.6 3250.5

Jack pine 0.5 5.3 11.3 305.9

Paper birch -0.3 -4.7 16.5 968.4
 | 57

The average annual removals of live trees on timberland reported for the 2013 

inventory (210 million cubic feet) was lower than the 303 million cubic feet reported 

for the 2008 inventory. Harvest removals of live trees on timberland were estimated at 

197 million cubic feet in 2013, a 29 percent decrease from the 277 million cubic feet 

of harvest removals for the period ending in 2008. Of the three components of change 

(growth, removals, and mortality), removals is the most directly tied to human 

activity and is thus the most responsive to changing economic conditions.

What this means

Insect infestations, disease, and succession can result in low G/R ratios. Paper birch 

had a negative G/R because its mortality exceeded gross growth over the period. Net 

growth is equal to gross growth less mortality.

A G/R of less than 1.0 is sometimes needed to achieve management goals. Sometimes 

it is necessary to manage the forest so that a species will temporarily have a G/R 

ratio of less than 1.0. When short-lived species such as quaking aspen are nearing 

senescence, for example, it may make sense to try to “capture mortality” (harvest a 

tree before it dies of old age).
58 | FOREST FEATURES
 | 59

Health Indicators

Pagami Creek Fire, 2 years later (taken August 2013). Photo by Eli Sagor, used with permission (https://

creativecommons.org/licenses/by-nc/2.0/legalcode).

 | 59
60 | HEALTH INDICATORS

Regeneration Status

Background

The composition and abundance of tree seedlings drives stand development and sets the 

stage for future composition and structure. Forest systems of Minnesota face a number 

of regeneration stressors (e.g., invasive plants, insects, diseases, climate change, and 

herbivory). As stands that make up these systems mature and undergo stand replacement 

disturbances, it is imperative to know the condition of the regeneration component. In 

most situations, establishing desirable reproduction is the key to developing stands with 

high-canopy species that meet manager’s objectives. Tending of young stands to control 

composition and stocking levels is also an important consideration (Johnson et al. 2002, 

Smith et al. 1997). Regeneration data are critically important for understanding and 

projecting future forest character that ultimately determines sustainability of the full suite 

of forest values available from Minnesota forests. 

Early successional young forest habitat provides unique plant biota and landscape 

heterogeneity (Greenberg et al. 2011). Minnesota forests supports many birds and 

mammals which depend on young forest habitat. Some prime examples include 

golden-winged warbler (Vermivora chrysoptera), American Woodcock (Scolopax 

minor), and cottontail rabbit (Sylvilagus floridanus) (Gilbart 2012). The vitality of young 

forest depends directly on the condition of the regeneration component. 

To fill the need for more detailed information on regeneration, the Northern Research 

Station’s Forest Inventory and Analysis program added protocols for data to be 

collected on a subset of sample plots measured during the growing season (McWilliams 

et al. 2015). The results in this report are based on data collected from 151 sample plots 

measured in 2012 and 2013. The procedures measure all established tree seedlings less 

than 1 inch in diameter at breast height (d.b.h.) by length class and include a browse 

impact assessment for the area surrounding the sample location. The regeneration 

indicator data improve NRS-FIA’s ability to evaluate this important aspect of forest 

health and sustainability.

What we found

As Minnesota forest stands continue to age, young forest is becoming rare as older, 

more mature stands are increasing. Since 1977, the area of young forest (0-20 years old) 

decreased from about one-third of Minnesota forest land to 18 percent. The aspen/

birch forest-type group covers 37 percent of the State’s forest land and accounts for 60 

percent of the young forest. All of the other major groups make up less than 10 percent 

of the young forest, except for the white/red/jack pine forest-type group that makes up 

13 percent.
 | 61

Results of the browse impact assessment show that most of the samples had low (54 

percent) or medium (35 percent) levels of browse of understory plants (Fig. 31). Only 

11 percent had high browse levels. Examination of browse impact across the State 

(Fig. 32) reveals that most of the samples with medium or higher levels of browse 

impact are randomly distributed.

0 10 20 30 40 50 60

High

Medium

Low

Percent

Browse Impact

Figure 31.—Browse impact on forested P2+ samples, Minnesota, 

2012-2013. Error bars represent a 68 percent confidence interval 

around the estimated mean.

Figure 32.—Distribution of forested P2+ samples on forest land 

by browse impact, Minnesota, 2012-2013.

(Plot locations are approximate)

Browse Impact

Low

Medium

High
62 | HEALTH INDICATORS

The total number of seedlings is estimated at 106.3 billion, or a state-wide average of 

5,700 seedlings per acre. Forty seven percent of the seedlings are less than 1 foot tall, 

41 percent are 1.0 to 4.9 feet, and 12 percent are 5.0 feet and taller (Fig. 33). Seedling 

abundance most likely reflects the pattern of disturbance. Given the small sample size, 

seedling abundance exhibits no readily apparent geographic pattern across Minnesota 

(Fig. 34).

0 500 1,000 1,500 2,000 2,500 3,000 3,500

2.0-11.9 inches

1.0-4.9 feet

>5.0 feet

Seedlings per Acre

Height Class

Figure 33.—Average number of seedlings per acre on 

forest land by height class, Minnesota, 2012-2013. Error 

bars represent a 68 percent confidence interval around the 

estimated mean.

Figure 34.—Distribution of forested P2+ samples on forest 

land by number of seedlings per acre, Minnesota, 2012-2013.

(Plot locations are approximate)

Seedlings per Acre

0-5,000

5,000-10,000

10,000-25,000
 | 63

In total number, 42 species/species groups have been encountered by the regeneration 

indicator samples so far. Maple and ash are the most abundant genera, accounting for 

22 and 18 percent of the total number of seedlings, respectively. Sugar maple is the 

most prominent species, with 10 percent of the seedlings (Fig. 35). Most of the other 

species with at least 1 percent of the seedling pool were species capable of developing 

as canopy dominants under favorable conditions.

0 100 200 300 400 500 600 700 800 900 1,000

Bur oak

Balsam poplar

Northern red oak

American basswood

Tamarack (native)

Northern white-cedar

Hackberry

Paper birch

Eastern hophornbeam

American elm

Mountain maple

Green ash

Black spruce

Serviceberry spp.

Balsam fir

Quaking aspen

Red maple

Chokecherry

Black ash

Sugar maple

Seedlings per Acre

Species

Figure 35.—Average number of seedlings per acre on forest land for 

all species with at least 1 percent of the total number of seedlings, 

Minnesota, 2012-2013. Error bars represent a 68 percent confidence 

interval around the estimated mean.

Comparing species abundance using the percentage of the total number of trees by 

height and diameter class highlights potential pathways for future canopy dominants. 

Figure 36 depicts results for select species/species groups for seedlings, dominant/

codominant saplings, and adults based on the percentage each contribute to the 

total for each size class. Prospective “gainers” are those species with relatively high 

percentages of stems in the regeneration pool of seedlings and saplings compared 

to larger trees. The aspens, maples, ashes, balsam fir, and the “other” species group 

are the most apparent gainers. Expectations for the ashes should be tempered with 

information on the prospective demise of ash due to impacts of the emerald ash 
64 | HEALTH INDICATORS

borer. Prospective “losers” in the process of developing future canopy dominants are 

species with lower percentages in the regeneration pool than the adult pool. Potential 

losers are the red oaks, white oaks, spruces (mostly black spruce), and red pine. The 

distribution of stem abundance by size class is particularly out of balance for the oaks 

where seedlings, saplings, and young adults are rare compared to older adults.

0

10

20

30

40

50

60

70

80

90

100

Species as a Percentage of Total 

Number of Trees in Class

Seedling Length

Other

Red pine

Balsam fir

Spruces

Ashes

White oaks

Red oaks

Maples

Aspens

2.0-11.9 inches

1.0-4.9 feet

5 feet and larger

1.0-2.9 

3.0-4.9 

5.0-6.9 7.0-8.9 

9.0-10.9 

11.0-12.9 

13.0-14.9 

15.0-16.9 

17.0-18.9 19.0-20.9 

21.0-28.9 

29.0+ 

 ------------------ Diameter Class (inches) --------------

Figure 36.—Species composition for seedlings, dominant/

codominant live saplings, and growing-stock trees on forest land 

for species with the greatest aboveground biomass, Minnesota. 

Seedling estimates are for 2012-2013 and sapling and tree 

estimates are for 2009-2013. 

What this means

Minnesota forests face a variety of forest health risks, and tree reproduction is 

an integral factor for ameliorating most of them during the early phases of forest 

development. Some prominent examples are competition from invasive plants, insects 

and diseases, and forest fragmentation.

Deer browse is a major factor affecting regeneration in the eastern United States 

(Russell et al. 2001, White 2012), but in Minnesota, browse levels are not as high 

as some other states. Still, the finding that nearly half of the regeneration indicator 

samples measured had at least medium browse impact is an indication that browsing 

is having an effect. It would be expected that local areas with high deer populations 

will have limited reproduction of palatable tree species. Impacts of deer browsing are 

especially problematic when occurring in combination with habitat fragmentation 

that occurs more frequently in the more populated areas of the State (Augustine and 

deCalesta 2003). 
 | 65

The most noteworthy regeneration issue found in the results is a general lack of 

young oak seedlings and saplings. Oak/hickory and oak/pine forests compose nearly 

15 percent of Minnesota forest land and are vital for both timber and wildlife. Oak 

regeneration is a common problem in the eastern United States, and management 

challenges, such as lack of fire and herbivory, have been described (Holt and Fischer 

1979). The long-term future of oak-dominated forests will depend on management 

strategies that establish oak seedlings and foster development of saplings and adults 

using stand tending prescriptions that forestall development of shade tolerant species 

such as sugar and red maple (Abrams 1992).

Black spruce and red pine are other species that do not have an abundance of 

reproduction based on measurements to date. The situation for these species is not 

as dire as for the oaks. Although relatively few black spruce seedlings were found, the 

distribution of stems by size class shows an abundance of saplings and young adult 

stems. The red pine distribution lacks both seedlings and saplings but has an abundance 

of young adults and a spike in the number of stems in the 18-inch d.b.h. and larger 

classes. This means that the older red pine stands will eventually need to be replaced. 

Both black spruce and red pine are commonly established by planting, direct seeding, 

and density management to ensure maintenance over the long term. Palik and Johnson 

(2007) provide a thorough discussion of constraints on pine regeneration.

Eventually, most forest stands will experience either anthropogenic or natural stand 

replacement events, such as mortality or harvest, and require regeneration to establish 

new forest. Clearly, forest regeneration will be the key to successful establishment of 

healthy young forests. Management options for establishing regeneration of palatable 

species will also be driven by the amount of browse.

The results presented here for Minnesota reflect only two of the five panels of 

measurements that will eventually compose the first full baseline dataset for the 

regeneration indicator. Barring any extension of the inventory cycle length, the next 

5-year inventory report for Minnesota will have a complete dataset. This will facilitate 

more detailed analyses (e.g., more species-specific details) and improve the level 

of statistical confidence in the estimates. The dataset will also facilitate research to 

evaluate plot-level regeneration adequacy for the major forest-type groups and a more 

complete understanding of future trends in composition, structure, and health of 

Minnesota forests.
66 | HEALTH INDICATORS

Tree Crown Health and Tree Damage

Background

The crown condition of trees is influenced by various biotic and abiotic stressors. 

Abiotic stressors include drought, flooding, cold temperatures or freeze injury, nutrient 

deficiencies, soil physical properties that affect soil moisture and aeration, or toxic 

pollutants. Biotic stressors include native or introduced insects, diseases, invasive plant 

species, and animals.

Exotic diseases and insects are important threats to the productivity and stability 

of forest ecosystems around the world (Liebhold et al. 1995a, Pimentel et al. 2000, 

Vitousek et al. 1996). Over the last century, Minnesota forests have suffered the effects 

of native insect pests such as spruce budworm (Choristoneura fumiferana Clemens) 

and the well-known exotic and invasive agent Dutch elm disease (Ophiostoma ulmi) 

among many others. More recently, invasions by the emerald ash borer (Agrilus 

planipennis) and European gypsy moth (Lymantria dispar) are threatening the health 

of trees. Additionally, although Asian longhorned beetle (Anoplophora glabripennis), 

mountain pine beetle (Dendroctonus ponderosae), and thousand cankers disease 

have not yet been discovered in Minnesota, they are emerging threats that have been 

confirmed in nearby states.

Tree-level crown dieback is collected on P2+ plots. Crown dieback, defined as recent 

mortality of branches with fine twigs, reflects the severity of recent stresses on a tree. 

A crown is labeled as “poor” if crown dieback is greater than 20. This threshold is 

based on findings by Steinman (2000) that associate crown ratings with tree mortality. 

Additionally, crown dieback has been shown to be the best crown variable to use for 

predicting tree survival (Morin et al. 2015).

Tree damage is assessed for all live trees with a d.b.h. of 5.0 inches or greater. Up to 

three of the following types of damage can be recorded: insect damage, cankers, decay, 

fire, animal damage, weather, and logging damage. If more than three types of damage 

are observed, decisions about which three are recorded are based on the relative 

abundance of the damaging agents.

What we found

The incidence of poor crown condition is low across Minnesota with no discernable 

spatial pattern (Fig. 37). Currently the only species with more than 5 percent of live 

basal area containing poor crowns is paper birch (Table 8). Plots with greater than 5 

percent of paper birch basal area containing poor crowns are indicated by the orange 

dots in Figure 38. Mean dieback ranged from less than 1 percent for red pine and black 

spruce to 5.8 percent for northern white-cedar (Table 9).
 | 67

Table 8.—Percent of live basal area with poor crowns by species and inventory year, Minnesota 

Species 2008 2013

---------------------- Percent --------------------

Paper birch 5.7 7.6

Northern white-cedar 7.2 4.6

Quaking aspen 4.1 3.6

Black ash 10.9 3.0

Northern red oak 6.3 2.2

Balsam fir 0.4 1.7

Bur oak 2.1 1.1

Black spruce 2.6 0.3

American basswood 2.3 0.2

Red pine 0.0 0.0

Percent of Live Basal 

Area with Poor Crowns

0

0-5

>5

Forest

Nonforest

Water

Figure 37.—Plot location and percentage of live basal area 

for all species with poor crowns, Minnesota, 2013. Depicted 

plot locations are approximate.
68 | HEALTH INDICATORS

Figure 38.—Plot location and percentage of live basal area for 

paper birch with poor crowns, Minnesota, 2013. Depicted plot 

locations are approximate.

Table 9.—Mean crown dieback and other statistics for live trees (>5 inches d.b.h.) on forest land by species, 

Minnesota, 2013

Species Number of trees Mean SE Minimum Median Maximum

--------------------------- Percent -------------------------

Northern white-cedar 390 5.8 0.78 0 0 99

Northern red oak 170 4.9 0.52 0 5 60

Paper birch 233 4.1 0.68 0 0 99

Black ash 190 4.1 0.71 0 0 65

American basswood 175 3.3 0.58 0 0 95

Bur oak 252 3.2 0.30 0 5 40

Quaking aspen 902 2.2 0.26 0 0 95

Balsam fir 230 1.0 0.20 0 0 25

Black spruce 343 0.8 0.14 0 0 25

Red pine 199 0.3 0.08 0 0 5

Percent of Live Basal 

Area with Poor Crowns

0

0-5

>5

Forest

Nonforest

Water
 | 69

Figure 39 shows the proportion of remeasured trees that survived, died, or were 

cut in each crown dieback class based on the health of the crowns at the previous 

measurement. The proportion of trees that die increases with increasing crown dieback. 

Nearly 40 percent of trees with crown dieback above 20 percent during the 2008 

inventory were dead when visited again during the 2013 inventory.

0

10

20

30

40

50

60

70

80

90

100

0 1-10 11-20 >20

Frequency (percent)

Crown Dieback (percent)

Cut

Mortality

Survivor

Tree damage was recorded on approximately 25 percent of the trees in Minnesota, but 

there is considerable variation between species. The most frequently recorded damage 

was decay, which was present in 17 percent of the trees and ranged from 3 percent on 

red pine and black spruce to 27 percent on northern red oak. Decay was also recorded 

on more than 15 percent of paper birch, American basswood, quaking aspen, and 

northern white-cedar trees. Cankers were present on 8 percent of quaking aspen trees, 

animal damage was recorded on 7 percent of bur oak trees, and weather damage was 

observed on more than 5 percent of northern white-cedar, paper birch, and black ash 

trees. The occurrence of all other injury types was very low (Table 10).

Figure 39.—Crown dieback distribution by tree survivorship for 

remeasured trees, Minnesota, 2008 to 2013.

Damage type

Species None Animal Cankers Decay

Insect 

damage

Logging/

human Other Weather

All 75 3 2 17 2 1 2 4

American basswood 74 6 1 16 2 1 2 5

Balsam fir 85 3 0 7 4 1 2 3

Black ash 78 1 0 12 1 1 3 9

Black spruce 91 1 0 3 1 0 3 2

Bur oak 79 7 0 11 1 2 2 2

Northern red oak 68 3 1 27 1 1 2 5

Northern white-cedar 70 2 0 23 0 0 2 8

Paper birch 72 3 1 15 4 1 3 8

Quaking aspen 70 1 8 22 4 0 1 2

Red pine 93 2 0 3 1 2 0 0

Note that columns do not sum to 100 because multiple damages can be recorded on trees.

Table 10.—Percent of trees with damage by species, Minnesota, 2013
70 | HEALTH INDICATORS

What this means

The trees of most important species in the forests of Minnesota are generally in good 

health, but substantial dieback was observed on paper birch. The cause of poor crown 

health in paper birch is likely due to senescence. Paper birch also has one of the highest 

mortality rates of all species in the State (Fig. 26). The health of tree crowns in ash species, 

oak species, maple species, black walnut, and butternut should be monitored closely due 

to recent and likely future invasions by emerald ash borer, gypsy moth, Asian longhorned 

beetle, and thousand cankers disease (see subsequent sections).

Decay was the most commonly observed type of damage which is not unusual given that 

the majority of Minnesota forests are large and medium diameter stands composed of 

mature trees. The incidence of cankers on quaking aspen is due to Hypoxylon canker, 

caused by the fungus Hypoxylon mammatum (Wahl.), which is one of the most important 

killing diseases of aspen in eastern North America (Anderson et al. 1979).

Exotic Forest Insects and Pathogens

Background

The introduction and establishment of exotic insects and pathogens can have significant 

impacts on the structure and composition of native forest ecosystems due to a lack of 

natural enemies and plant host defenses. Therefore, monitoring the status and impacts 

of these organisms is crucial to assessments of forest health (Jules et al. 2002, Poland and 

Haack 2003, Work et al. 2005). A number of exotic insects and pathogens were active 

during the inventory period. Two of the more significant insects are emerald ash borer 

(Agrilus planipennis; EAB) and the European gypsy moth (Lymantria dispar). EAB was 

first detected in the United States in 2002 and has been present in Minnesota since 2009. 

It is a wood-boring pest of all North American ash. European gypsy moth continues 

to spread west across the United States into eastern Minnesota. Tree species were split 

into preferred and nonpreferred suitability classes based on field and laboratory tests by 

Liebhold et al. (1995b). Species in suitability class 1 were considered preferred and all 

others were considered nonpreferred. 

What we found

Emerald ash borer

There are 1.1 billion ash trees in Minnesota that are at least 1-inch d.b.h. or larger. 

Ash makes up 8 percent of all trees and is well distributed across the State with the 
 | 71

highest concentrations in northern and central Minnesota (Fig. 40). Ash growingstock mortality on timberland was relatively stable between 1977 and 2003; however 

mortality doubled over the past 10 years, reaching 11.0 million cubic feet per year, 

nearly 5 percent of total mortality, in 2013. The rate of ash mortality as a percentage of 

live ash tree volume on forest land is 1.0 percent compared to 2.0 percent for all other 

species. The majority of ash mortality was concentrated in the northcentral part of the 

State, and therefore mortality was not the result of emerald ash borer.

Processing note: This map was produced by linking plot data to MODIS satellite pixels (205 m) using gradient nearest 

neighbor techniques.

Ash Basal Area 

(ft3

/acre)

>25

5-24

<5

Nonforest

EAB positive (as of 12/2013)

EAB positive (as of 02/2015)

Figure 40.—Ash density on forest land, Minnesota, 2013, and counties positive for emerald ash borer.
72 | HEALTH INDICATORS

Gypsy moth

Approximately 48 percent of the live tree volume in Minnesota includes species 

preferred by gypsy moth. The most abundant preferred species in the State are 

quaking aspen, paper birch, and the oaks. The density of preferred gypsy moth host 

species is highest in the western half of northern Minnesota (Fig. 41). In July of 

2014, a quarantine that restricts intrastate movement of material that may spread this 

invasive pest was implemented in Cook and Lake Counties (Minnesota Department 

of Agriculture 2015). 

Figure 41.—Basal area of gypsy moth preferred trees and quarantined counties, Minnesota, 2013.

Percent of Live Basal Area 

Preferred by Gypsy Moth

0-10

10-25

25-50

50-75

>75

Nonforest

Water

Quarantined counties
 | 73

What this means

Emerald ash borer

Rising mortality over the past decade and a relatively high mortality rate are 

indications of declining ash health statewide. Ash decline is likely an important 

contributor to ash mortality, particularly in the north. Mortality due to EAB is 

expected to increase as EAB spreads into forested areas with higher densities of ash. 

Therefore, continued spread of EAB could have a considerable impact on ash health 

and the future composition of Minnesota forests. 

Gypsy moth

The forests of Minnesota have not been impacted by gypsy moth as of yet, but 

moths have been captured as part of the Gypsy Moth Slow the Spread program. 

Quantification of the amount of the forest resource that is preferred by the gypsy 

moth can help land managers prepare for future outbreaks. Minnesota will likely need 

to consider suppression activities to reduce the impacts of defoliation in future years 

as gypsy moth populations continue to spread into the State. 

Invasives

Background

Invasive plant species (IPS) are both native and nonnative species that can cause 

negative ecological effects. These species can quickly invade forests, changing light, 

nutrient, and water availability. IPS can form dense monocultures which not only 

reduce regeneration but also impact wildlife habitat quality through altering forest 

structure and forage availability. Aside from the effects invasive species cause in 

forested environments, they can also impact agricultural systems. An example is 

common barberry (Berberis vulgaris), an alternate host for wheat stem rust (Puccinia 

graminis) which can cause the complete loss of grain fields. Common buckthorn 

(Rhamnus cathartica) is also troublesome as it is one of the alternate hosts for the 

soybean aphid (Aphis glycines). While there are some beneficial uses for these invaders 

(e.g., culinary, medicinal, and soil contaminant extraction (reed canarygrass); Kurtz 

2013), the negative effects are worrisome. Each year inspection, management, and 

mitigation of IPS costs billions of dollars. 
74 | HEALTH INDICATORS

What we found

To aid in monitoring these species, FIA assessed the presence of 39 IPS and one 

undifferentiated genus (nonnative bush honeysuckles)12

 on 888 forested P2 invasive 

plots in Minnesota from 2009-2013. Of the 40 invasives monitored (Table 11), 17 

were observed. Reed canarygrass was the most commonly observed species (164 

plots; 18.5 percent of the forested P2 invasive plots) and was found throughout the 

State. Nonnative bush honeysuckles (110 plots) and common buckthorn (96 plots) 

were the next most commonly observed species and occurred on more than 10.0 

percent of plots. Six of the 17 IPS observed were found on 1.0 percent or more of the 

plots, with 37.8 percent of the plots having one or more of the monitored IPS. 

The number of IPS per forested plot ranged from 0 to 5 (Fig. 42). The distribution 

of the plots where invasive plants were present is fairly homogeneous throughout 

the state (Fig. 43), with the southern plots having a larger number of invasive plant 

species. When reviewing these figures it is important to remember that the inventory 

is of forested areas, so less forested areas have fewer plots. On average, plots with 

invasive species were closer to roads (1,700 feet versus 5,100 feet for plots without 

invasive species), had fewer 5 inches d.b.h. and larger trees per acre (137 versus 155), 

and fewer 1-inch d.b.h and larger trees per acre (650 versus 840).

Figure 44 shows the location of common buckthorn in Minnesota based on forested 

P2 invasive plots. In Minnesota common buckthorn was found primarily in riparian 

areas and the Eastern Broadleaf Ecoregion Province. Buckthorn is widely dispersed by 

birds and mice feeding on its fruit.

In the 2008 report for Minnesota (Miles et al. 2011), the invasive plant data was based 

on the Phase 3 vegetation plots whereas in this report the data is based on the P2 

invasive data. There was a slight increase from the last inventory in the number of 

plots with invasive species present. In addition, there was an increase in the presence 

of common buckthorn and Canada thistle which occurred on 10.8 and 9.2 percent of 

plots, respectively, in 2013. In 2008, common buckthorn was found on approximately 

9.2 percent of plots and Canada thistle was observed on 1.6 percent of plots. In 

addition, multiflora rose was not recorded in the 2008 inventory but was found on 4 

plots in 2013. Over time it will be important to monitor the percentage of plots where 

these species are observed as well as to watch for the presence of new invasive species.

2

 Hereafter the IPS and one undifferentiated genus (nonnative bush honeysuckles) are referred to as invasive 

species, invasive plants, invasives, or IPS.

2

 Hereafter the IPS and one undifferentiated genus (nonnative bush honeysuckles) are referred to as invasive 

species, invasive plants, invasives, or IPS.
 | 75

Table 11.—The 39 invasive plant species and one undifferentiated genus monitored on NRS-FIA P2 invasive plots and 

observances, 2007 to 2013

Tree Species Observances Percentage of plots

Black locust (Robinia pseudoacacia) 

Chinaberry (Melia azedarach) 

Norway maple (Acer platanoides)

Princesstree (Paulownia tomentosa)

Punktree (Melaleuca quinquenervia)

Russian olive (Elaeagnus angustifolia)

Saltcedar (Tamarix ramosissima)

Siberian elm (Ulmus pumila) 5 0.5

Silktree (Albizia julibrissin)

Tallow tree (Triadica sebifera)

Tree-of-heaven (Ailanthus altissima)

Woody Species

Autumn olive (Elaeagnus umbellata)

Common barberry (Berberis vulgaris)

Common buckthorn (Rhamnus cathartica) 96 10.5

European cranberrybush (Viburnum opulus) 1 0.1

European privet (Ligustrum vulgare)

Glossy buckthorn (Frangula alnus) 12 1.3

Japanese barberry (Berberis thunbergii) 6 0.7

Japanese meadowsweet (Spiraea japonica)

Multiflora rose (Rosa multiflora) 4 0.4

Nonnative bush honeysuckles (Lonicera spp.) 110 12.4

Vine Species

English ivy (Hedera helix)

Japanese honeysuckle (Lonicera japonica) 1 0.1

Oriental bittersweet (Celastrus orbiculatus)

Herbaceous Species

Black swallow-wort (Cynanchum louiseae)

Bohemian knotweed (Polygonum xbohemicum)

Bull thistle (Cirsium vulgare) 40 4.4

Canada thistle (Cirsium arvense) 82 9.2

Creeping jenny (Lysimachia nummularia) 2 0.2

Dames rocket (Hesperis matronalis)

European swallow-wort (Cynanchum rossicum)

Garlic mustard (Alliaria petiolata) 6 0.7

Giant knotweed (Polygonum sachalinense)

Japanese knotweed (Polygonum cuspidatum)

Leafy spurge (Euphorbia esula) 1 0.1

Purple loosestrife (Lythrum salicaria) 1 0.1

Spotted knapweed (Centaurea stoebe ssp. micranthos) 5 0.5

Grass Species

Common reed (Phragmites australis) 1 0.1

Nepalese browntop (Microstegium vimineum)

Reed canarygrass (Phalaris arundinacea) 164 18.5
76 | HEALTH INDICATORS

0

100

200

300

400

500

600

700

0 1 2 3 4 5

Number of Inventory Plots

Number of Invasive Plant Species

Figure 42.—Number of invasive plant species per forested 

P2 Invasive plot, Minnesota, 2013.

Projection: NAD83, UTM Zone 15N.

Data Source: USDA Forest Service Forest Inventory and Analysis Program 2009-2013 P2 Invasive 

data; State and county layers source: ESRI Data and Maps 10.1. Forest/nonforest source: NLCD 2006. 

Depicted plot locations are approximate. Cartography: C.M. Kurtz. January 2015

Number of Invasive 

Plant Species per 

P2 Invasive Plot

1

2

3

4

5

Figure 43.—Number of invasive plant species observed on forested FIA 

P2 Invasive plots, Minnesota, 2013.
 | 77

Projection: NAD83, UTM Zone 15N.

Data Source: USDA Forest Service Forest Inventory and Analysis Program 2009-2013 P2 Invasive 

data; State and county layers source: ESRI Data and Maps 10.1. Forest/nonforest source: NLCD 2006. 

Depicted plot locations are approximate. Cartography: C.M. Kurtz. January 2015

Occurrence of 

Common Buckthorn

Present

Absent

Figure 44.—Distribution of common buckthorn on forested FIA P2 Invasive 

plots, Minnesota, 2013.

What this means

Minnesota forests had a lower percentage of forested plots invaded (37.8 percent) 

than neighboring Iowa where 94.4 percent of forested plots had one or more of 

the monitored invasive plant species. Minnesota likely has less invasives within its 

forests due to its settlement history and lower fragmentation than neighboring states. 

However, the presence of IPS within Minnesota forests is still of major and increasing 

concern, and it is important that these species are monitored over time to ensure that 

managers and the general public are aware of their occurrence and spread. Areas of 

higher fragmentation, such as southern Minnesota, are of particular concern due to 

number of invasives currently observed on the plots.

Invasive plants are good competitors and able to alter forested ecosystems by displacing 

native species and impacting the fauna that depend upon them. Several factors 
78 | HEALTH INDICATORS

contribute to their success, such as prolific seed production, ability to propagate 

vegetatively, rapid growth rate, and ability to survive in harsh conditions. Many factors 

contribute to forest invasion, including ungulates, development, fragmentation, and 

timber harvesting. Invasive plants can negatively affect the carbon budget by reducing 

future tree cover. Furthermore, these species can cause negative economic implications 

by reducing timber yield and aesthetic beauty. Further investigation of the inventory 

data may help to reveal influential site and regional trends.

Down Woody Materials

Background

Down woody materials in the various forms, including fallen trees and shed branches, 

fulfill a critical ecological niche in forests of Minnesota. Down woody materials 

provide valuable wildlife habitat, stand structural diversity, a store of carbon/biomass, 

and contribute towards forest fire hazards via surface woody fuels. 

What we found

The total carbon stored in down woody materials (fine and coarse woody debris and 

residue piles) on Minnesota forest land was nearly 53 million short tons (Fig. 45). 

Downed woody debris carbon was unequally distributed by stand-age class with 

moderately aged stands (61 to 80 years) having the highest total carbon (~23 million 

tons). The downed dead wood biomass within Minnesota forests is dominated by 

coarse woody debris (Fig. 46) at approximately 67 million short tons with piles only 

representing 8 percent of statewide totals. The total volume of coarse woody debris 

was highest on private forest land at approximately 3.4 billion cubic feet (Fig. 47). 

State and local forests had the second largest totals of coarse woody debris volume 

(2.4 billion cubic feet). Privately owned lands have the highest volumes of dead wood 

in piles at over 600 million cubic feet. 
 | 79

0

5

10

15

20

25

30

0-20 21-40 41-60 61-80 81-100 101+

Total Carbon (million tons)

Stand-age Class (years)

Figure 45.—Total carbon in down woody materials (fine and coarse 

woody debris and piles) by stand-age class on forest land, Minnesota, 

2006-2010. Error bars represent a 68 percent confidence interval 

around the estimated mean.

28%

64%

8%

Fine Woody 

Debris

Coarse Woody

Debris

Piles

Figure 46.—Percentage of dead wood biomass on forest land 

by component (fine and coarse woody debris and residue piles), 

Minnesota, 2006-2010.

0

0.5

1

1.5

2

2.5

3

3.5

4

Total Volume (billion ft3

)

Coarse Woody Debris

Piles

Fine Woody Debris

National 

Forest

Other 

Federal

State and 

Local

Private

Ownership Group

Figure 47.—Total volume of coarse woody debris and deadwood piles 

on forest land by ownership group, Minnesota, 2006-2010. Error bars 

represent a 68 percent confidence interval around the estimated mean.
80 | HEALTH INDICATORS

What this means

Given the relatively moist temperate forests across Minnesota, the biomass within 

down woody materials would only be considered a fire hazard during times of 

drought. Although the carbon stocks of down woody materials are relatively small 

compared to those of soils and standing live biomass, down woody materials are still 

a critical component of the carbon cycle as a transitory stage between live biomass 

and other detrital pools such as litter. If future temperature and precipitation patterns 

change, there is a potential for a reduction in down woody material stocks due to 

increased rates of decay (Russell et al. 2014a, b). The loss of dead wood carbon stocks 

could indicate the reduction of other pools in the future. Compared to southeastern 

states where there is more pervasive industrial management of forests (Woodall et al. 

2013), there were relatively few residue piles sampled in this first down woody 

materials inventory of Minnesota forests. Given that the vast majority of coarse 

woody debris volume was estimated to be in private ownership, it is the management 

of Minnesota private forests that may most affect the future of down woody material 

contributions to statewide forest carbon stocks and wildlife habitat (i.e., stand 

structure). Overall, because estimates of fuel loadings are not exceedingly high across 

Minnesota, possible fire dangers may be outweighed by the numerous ecosystem 

services provided by down woody materials.

Forest Carbon

Background

Carbon has increasingly become a part of forest resource reporting in recent years. 

This is primarily because forests tend to sequester carbon from the atmospheric 

greenhouse gas carbon dioxide, which is linked to global climate change. Among 

terrestrial ecosystems, forests contain the largest reserves of sequestered carbon. 

Regional and national greenhouse gas reporting forums include forest carbon stocks 

because increases in forest carbon stocks represent quantifiable partial offsets to 

greenhouse gas emissions arising from the burning of fossil fuels. For example, 

carbon sequestration by U.S. forests represented an offset of more than 11 percent 

of total U.S. greenhouse gas emissions in 2013 (U.S. EPA 2015), and the continuing 

increase in Minnesota forest carbon stocks contributes to this effect. Total forest 

ecosystem carbon stocks in Minnesota are estimated to be 1,740 million tons of 

carbon, a 3 percent increase relative to 5 years ago.
 | 81

Carbon accumulates in growing trees via the photosynthetically-driven production 

of structural and energy-containing organic (carbon) compounds that primarily 

accumulate in trees as wood. Over time, this stored carbon also accumulates in dead 

trees, woody debris, litter, and forest soils. For most forests, the understory grasses 

and forbs as well as nonvascular plants and animals represent minor pools of carbon 

stocks. Within soils, the larger woody roots are readily distinguished from the bulk of 

soil organic carbon, so the roots are generally reported as the belowground portion 

of trees and not included in the soils estimates. Carbon loss from a forest stand can 

include mechanisms such as respiration (including live trees and decomposers), 

combustion, runoff or leaching of dissolved or particulate organic particles, or 

direct removal such as the harvest and utilization of wood. From the greenhouse gas 

reporting perspective, it is important to note that not all losses result in release of 

carbon dioxide to the atmosphere; some wood products represent continued longterm carbon sequestration.

The carbon pools discussed here include living plant biomass (live trees ≥1-inch 

d.b.h. and understory vegetation), nonliving plant material (standing dead trees, 

down dead wood, and forest floor litter), and soil organic matter exclusive of coarse 

roots estimated to a depth of 1 meter. Carbon estimates by ecosystem pool are based 

on sampling and modeling; for additional information on current approaches to 

determining forest carbon stocks see U.S. Environmental Protection Agency (2015), 

U.S. Forest Service (2014a, b), and O’Connell et al. (2014). The level of information 

available for making carbon estimates varies among pools; for example the greatest 

confidence is in the estimate of live tree carbon due to the level of sampling and 

availability of allometric relationships applied to the tree data. Limited data and high 

variability result in lower confidence in the soil organic carbon estimates, and for 

this reason interpretation of these estimates is limited. Ongoing research is aimed 

at improving the estimates (U.S. EPA 2015). The carbon estimates provided here 

for Minnesota forests are consistent with the data and methods used to develop the 

forest carbon reported in “U.S. Environmental Protection Agency’s Inventory of U.S. 

Greenhouse Gas Emissions and Sinks: 1990–2013” (U.S. EPA 2015).

What we found

Soil organic carbon accounts for approximately 74 percent of forest carbon stocks; live 

trees account for 17 percent, where 9 percent of that is in the wood and bark of the 

bole of trees with a d.b.h. of at least 5 inches (Fig. 48). Average aboveground carbon 

per acre increases with stand age, and greater net accumulation occurs in living trees 

and the understory (Fig. 49). Estimates for both pools decreased somewhat in the 

101+ age class, but there was also considerably more variability among plots in those 

older stands relative to the younger age classes. Total carbon stocks are the product of 
82 | HEALTH INDICATORS

per acre carbon and total acres of forest within each age class, and these are fairly well 

distributed over all the age classes (Fig. 49) with slightly more than half (54 percent) 

of the stocks in the older age classes. About 50 percent of total aboveground carbon 

stocks are represented by the middle two age classes, while the youngest age class 

accounts for less than 9 percent of forest carbon stocks.

Live trees 

and saplings,

17%

Soil organic carbon, 74%

Forest floor/litter, 5%

Down dead wood, 2%

Understory, 1%

Standing dead trees, 1%

Saplings, 3%

Tops and branches, 3%

Bole, 9%

Stump, 0%

Coarse roots, 2%

Figure 48.—Forest carbon stocks on forest land by carbon 

pool, Minnesota, 2013. 

0

5

10

15

20

25

30

35

0-20 21-40 41-60 61-80 81-100 101+

Carbon (short tons/acre)

Stand-age Class (years)

Aboveground deadwood & litter

Aboveground biomass

Figure 49.—Average carbon per acre by stand-age class 

and carbon pool, Minnesota, 2013.

Species composition can affect carbon stocks, resulting in variations in average carbon 

tons per acre for common forest-type groups identified within Minnesota forests (Fig. 

50). Carbon per acre is classified into four categories: live (live tree and understory), 

dead (standing dead trees and down dead wood), litter, and soil, and variability among 

forest-type groups appears to be most closely associated with variations in the live 

category. Total carbon stocks are the product of per acre carbon and total acres, and 

in Minnesota the largest stocks are in aspen/birch where 578 million tons of carbon, 

or about 33 percent of all Minnesota forest carbon stocks, can be found. More than 90 
 | 83

0 20 40 60 80 100 120 140 160

Other hardwood types

Aspen/birch

Maple/beech/birch

Elm/ash/cottonwood

Oak/hickory

Other softwood types

Spruce/fir

White/red/jack pine

Carbon (short tons/acre)

Forest-type Group

C in biomass

C in dead wood

C in litter

Soil organic C

Figure 50.—Average carbon per acre by forest-type group 

and carbon pool, Minnesota, 2013. 

percent of total carbon stocks are in the five most common forest-type groups: aspen/

birch, spruce/fir, elm/ash/cottonwood, oak/hickory, and maple/beech/birch. 

Actual carbon stocks for a particular stand depend on a combination of influences 

including site history, management, stand age, or component species, so individual 

sites can vary from the averages presented in Figures 48, 49, and 50. As an example, 

the state-wide average for tons of carbon per acre for live trees is 22 for stands that 

are identified as fully stocked, but the site-to-site variability is such that on 50 percent 

of measured plots, carbon stocks fell between 10 and 31 tons of carbon per acre with 

levels being greater than this amount in 25 percent of the plots and less than this 

amount in the remaining 25 percent.

The current carbon estimation methods and data were also applied to the 2008 

Minnesota forest inventory (data not shown) to produce summaries consistent with 

those provided here for the 2013 inventory. Overall forest carbon per acre increased by 

0.7 percent relative to 5 years ago, and live tree carbon values increased by 3.4 percent. 

In addition, total forest area increased over the same period so that total carbon stocks 

in 2013 are 3.0 percent greater than the equivalent values calculated for 2008.

What this means

In general, forest carbon stocks or differences in carbon stocks broadly reflect other 

measures of forest resources such as stand age, volume, or stocking. However, these 

summaries provide a useful reference measure of carbon stocks for the State relative 

to published regional or national forest carbon reports and offer a ready estimate of 

the role of Minnesota forests. In brief, the carbon summaries show: (1) most of the 

carbon is in forest soils, followed by live trees; (2) the majority of carbon is in stands 

of 40 to 80 years; (3) specific stand-level carbon varies; and (4) overall forest carbon in 

Minnesota has increased over the past 5 years.
84 | HEALTH INDICATORS

Soils

Background

Rich soils are the foundation of productive forest land. Inventory and assessment of 

the forest soil resource provides critical baseline information on forest health and 

productivity, especially in the face of continued natural and human disturbance. 

The forest soils of Minnesota were sampled on 326 plots from 2000 through 2005. 

Soils change very slowly, and a remeasurement of the soil was delayed until 2012. 

Additionally, soils data require laboratory analyses to provide the complete suite of 

information (e.g., soil carbon content and nutrient concentrations). Unfortunately, 

these data are not available for analysis in this report. However, several analyses 

completed at the regional level are relevant to discussions of land use and forestry 

interactions with soil resources.

What we found

Peatland soils are relatively common in the northern parts of Minnesota (Fig. 51). 

Minnesota has more peatland area (over 6 million acres) than any other state in the U.S. 

except Alaska (Minnesota Department of Natural Resources 2015b). Their per-unitarea carbon stocks are exceptionally high; they cover 3 percent of the world’s surface 

Figure 51.—Distribution of forested and nonforested 

peatlands in Minnesota, 2013. 

Peatlands

Forested

Nonforested

Source: Soils data (U.S. General Soil 

Map) provied by the USDA Natural 

Resources Conservation Service and 

available online at http://websoilsurvey.

nrcs.usda.gov/.
 | 85

area, but they store 30 percent of the globe’s soil carbon. Peatland soils are also sensitive 

to climate change, and warming temperatures are expected to increase decomposition 

rates and release large amounts of stored carbon into the atmosphere. The Forest Service 

examined several methods of estimating peatland carbon and found that peat thickness 

is the best predictor of total carbon storage (Chimner et al. 2014). 

Several projects are integrating tree measurements with soil chemistry and other factors 

to evaluate the impacts of atmospheric deposition of nutrients on tree growth and 

mortality. One of the trees commonly evaluated is sugar maple, which is also found in 

Minnesota. Tree basal area and geologic factors are powerful predictors of sugar maple 

mortality, along with soil chemical attributes like the ratio of magnesium to manganese. 

Finally, several million dollars are being invested to facilitate Great Lakes restoration 

under the coordination by the U.S. Environmental Protection Agency. To prioritize 

investments, the Forest Service conducted a thorough analysis to identify which 

watersheds were the greatest contributors of sediment and phosphorus runoff 

into Lake Superior. Land use characteristics, such as forest cover, forest harvest, 

agriculture, and watershed storage (the abundance of lakes and wetlands), were 

useful predictors of stream water quality at river mouths. Figure 52 depicts predicted 

nephelometric turbidity unit (NTU) levels for watershed basins along the western 

shores of Lake Superior and Figure 53 depicts phosphorous delivery.

Figure 52.—Predictions of sediment delivery from gauged and 

ungauged watersheds draining into western Lake Superior. Adapted 

from Seilheimer et al. (2013).

Gauged Basins 

(NTU)

Risk in Ungauged 

Basins

>25

11-25

0-10

High

Mod

Low
86 | HEALTH INDICATORS

Figure 53.—Predictions of phosphorus delivery from gauged and 

ungauged watersheds draining into western Lake Superior. Adapted 

from Seilheimer et al. (2013).

Gauged Basins 

(mg/L)

Risk in Ungauged 

Basins

>0.075

0.050-0.075

<0.050

High

Mod

Low

What this means

The Forest Service and its partners are evaluating changes to soil inventory protocols 

which should provide greater precision in estimates of peatland soil carbon. Studies 

like the SPRUCE project in northern Minnesota (http://mnspruce.ornl.gov/), when 

integrated with improved inventories of peatlands, will improve our understanding 

and management of these important soil carbon stocks.

Sugar maple is found in Minnesota, but rates of increased mortality linked to 

atmospheric deposition are more commonly found in the eastern part of its range. 

Minnesota’s location in the mid-continent generally isolates it from wind patterns that 

concentrate and deposit commercial and residential emissions of nitrogen and sulfur.

Forest inventory data, when combined with other observations of land use and its 

characteristics, can help land managers understand patterns of water quality across 

the broader landscape. When predictors offer significant explanatory power, these are 

clues to the types of management and policy actions that can be useful in restoration 

activities. 
 | 87

Land-use Change

Background 

Although the total area of forest land in Minnesota has remained relatively stable 

between 2008 and 2013, some areas of the State have experienced forest loss, while 

other areas have seen increases in forest land. To better understand forest land 

dynamics in Minnesota, it is important to explore the underlying land-use changes 

occurring in the State.

FIA characterizes land area using several land use categories which can be generalized 

to these classes: forest, rangeland, agriculture (including pasture and cropland), 

developed land, water, and other (which, in Minnesota, is primarily composed of 

wetlands, including marshes and bogs). The conversion of forest land to other uses 

is referred to as gross forest loss, and the conversion of nonforest land to forest is 

known as gross forest gain. The magnitude of the difference between gross loss and 

gain is defined as net forest change. By comparing land use on current inventory 

plots with land use recorded for the same plots during the previous inventory, we 

can characterize forest land-use change dynamics. Understanding land-use change 

dynamics is essential for monitoring the sustainability of Minnesota forest resources 

and helps land managers make informed policy decisions.

What we found 

Agricultural land is the dominant land use in Minnesota, covering 47 percent of the State’s 

area (Fig. 54). Other nonforest land uses in the State include marshes and bogs (9 percent), 

developed land including rights-of-way (6 percent), water (6 percent), and rangeland (<1 

percent). Approximately 32 percent of the total area (land and water) of Minnesota was 

forested in 2013. Most of the FIA plots in Minnesota either remained forested or stayed in 

a nonforest use (31 percent and 67 percent, respectively), and only the remaining 2 percent 

of plots experienced either a forest loss or gain from 2008 to 2013 (Fig. 55). 

According to FIA remeasurement data, Minnesota lost 295,000 acres (2 percent) of 

forest land from 2008 to 2013 which was more than offset by a gain of approximately 

748,000 (4 percent) during the same time period (Fig. 56). Forty-nine percent of the 

forest gains come from marshes and bogs. The remaining fifty-one percent of gains in 

forest comes from agricultural uses including cropland (14 percent), pasture (6 percent) 

and other agricultural land (6 percent), developed land (13 percent), water (6 percent), 

rights-of-way (5 percent), and rangeland (1 percent). Forty-five percent of the forest 

losses were diverted to marshes and bogs. The other fifty-five percent of forest loss went 

to development (17 percent), rights-of-way (11 percent), pasture (9 percent), cropland (7 

percent), other agricultural land (5 percent), and water (6 percent) (Fig. 57).
88 | HEALTH INDICATORS

Water

6%

Agricultural 

Land

4%

Pasture

2%

Cropland

41%

Rangeland Developed <1%

5%

Right-of-way

1%

Marsh/bog

9%

Forest

32%

Figure 54.—Land use composition of remeasured plots, 

Minnesota, 2013.

30.7%

1.4%

0.5%

67.4%

Forest in 2008 

and 2013

Nonforest to forest

Forest to nonforest

Nonforest in

2008 and 2013

Figure 55.—Land-use change, Minnesota, 2009 to 2013.

0 200 400 600 800

Forest loss

Forest gain

Area (thousand acres)

Developed

Agriculture

Marsh/bog

Water

Figure 56.—Gross percent forest loss and forest gain by land 

use category, Minnesota, 2009 to 2013. Error bars represent a 68 

percent confidence interval around the estimated mean.
 | 89

Water

6%

Agricultural land

5%

Pasture

9%

Cropland

7%

Developed

17%

Right-of-way

11%

Marsh/bog

45%

Forest Loss

Water

6% Agricultural land

6%

Pasture

6%

Cropland

14%

Rangeland

1%

Developed

13%

Right-of-way

5%

Marsh/bog

49%

Forest Gain

Figure 57.—Forest loss by current land use and forest gain 

by previous land use, Minnesota, 2009 to 2013.

FIA data can be used to characterize the forest land that has been lost and gained 

to see if it differs from the average characteristics of all forest land throughout 

Minnesota. The forests of Minnesota are dominated by stands in the large and 

medium diameter size classes with only 21 percent of forests in small diameter stands. 

However, the forest land that has been lost has a greater proportion of small diameter 

stands (43 percent) than in Minnesota as a whole.

Figure 58 shows the distribution of remeasured plots across Minnesota highlighting 

plots where forest land has been lost and gained. Forest change plots are concentrated 

in the northeastern region of Minnesota where forest cover is most prevalent. In 

the southern and western nonforest portion of the state, forest gains appear to be 

outpacing forest losses.
90 | HEALTH INDICATORS

FIA Remeasured Plots

Forest gain

Forest loss

Remained nonforest

Remained forest

Figure 58.—Distribution of remeasurement inventory plots showing forest 

gains and losses, Minnesota, 2008 to 2013. Plot locations are approximate.

What this means 

Overall, there was a modest (2 percent) net gain in forest land in Minnesota from 2008 

to 2013 which suggests a continued conservation and valuation of the State’s forest 

resources. This trend of increasing forest land is consistent with what was reported 

in the last inventory cycle (Miles et al. 2011), however the magnitude of the net gain 

was greater in the previous inventory. This difference in magnitude can be attributed 

primarily to a drop in the amount of nonforest land that reverted to forest. The amount 

of gross forest loss remained relatively stable between the two inventory cycles. 

Nearly half of the forest losses and gains are going to and coming from wetland areas, 

primarily marshes and bogs. These are likely low-lying areas that are moving between 

forest and nonforest classifications due to weather (drought/flooding) or other 

natural causes such as beaver dams. These conditions are often not permanent and 

fluctuations of this type were also reported in the last inventory cycle. 

Some of the gains and losses of forest land in Minnesota may be from marginal forest 

land moving into and out of the forest land base. This movement between forest 
 | 91

and nonforest classifications may be a result of land meeting or not meeting FIA’s 

definition of forest land due to small changes in understory disturbance, forest extent, 

or forest cover. These changes are also generally not permanent and may be more 

prevalent in forests with smaller sized stands. The fact that much of the forest change 

in Minnesota is occurring in smaller sized stands may support the idea that this type 

of nonpermanent land-use change is occurring in the State.

Some forest losses, including forest conversion to developed land, are likely 

permanent changes. The area of forest land lost to development, however, is relatively 

small in Minnesota. The primary land source for new development in the State is 

agriculture. Eighty percent of the gains in developed land come from converted 

agricultural land, versus 12 percent from forest land.

Some gains in forest land may come from reverting agricultural land, especially land 

in close proximity to Minnesota’s abundant network of streams and rivers. There has 

been a concerted effort in the State’s public and private sectors to prioritize forestation 

of these riparian areas. Agroforestry efforts promote the maintenance of tree cover in 

the form of windbreaks and forest buffers that help sustain a high agricultural output 

while conserving and protecting Minnesota soil and water resources. These forested 

areas are also important to wildlife populations. Riparian forests often connect to 

form wildlife corridors which allow for greater species movement. 

Forest Wildlife Habitat

Background

Forests, woodlands, and savannas provide habitats for many species of Minnesota 

birds (159 species), mammals (57 species), and amphibians and reptiles (31 species) 

(NatureServe, n.d.). Different forest types at different structural stages provide natural 

communities (habitats) at a coarse filter scale of conservation. Rare, imperiled, or 

wide-ranging wildlife species (i.e., species that make long distance movements) may 

not be fully served at this scale, so a “fine filter” approach is used to identify speciesspecific conservation needs. Representing an intermediate or meso-filter scale of 

conservation are specific habitat features (e.g., snags, riparian forest strips) which may 

serve particular habitat requirements for multiple species. This report characterizes 

habitats at the coarse-filter scale (forest age/size) and meso-filter scale (standing dead 

trees). For a description of coarse-, meso-, and fine-filter conservation scales see 

Hunter (2005).
92 | HEALTH INDICATORS

Like all states, Minnesota has developed a State wildlife action plan (SWAP). The plan, 

“Tomorrow’s Habitat for the Wild and Rare–An Action Plan for Minnesota Wildlife” 

(Minnesota Department of Natural Resources 2005), identifies 292 animal species of 

greatest conservation need (SGCN) and their focal habitats. Forest habitat-associated 

mammal, bird, reptile, amphibian, and insect SGCN occur in shrub/woodlandupland habitats such as oak savanna, jack pine woodland, and brush prairie (58 

species); upland conifer (53 species); upland deciduous hardwood (45 species); 

lowland conifer (33 species); lowland deciduous (33 species); and upland deciduous 

aspen (31 species). Note that many of these SGCN are listed for more than one habitat 

type. The condition and trends in forest attributes of forest age and size are reported 

here. One of the fine-scale conservation issues associated with forest habitats is the 

presence and abundance of snags and nest cavities, so the quantity and distribution of 

standing dead trees is reported. 

Wildlife habitat at a coarse-filter scale: stand age and stand size

Some species of wildlife depend upon early successional forests which typically 

have younger and/or smaller trees, while other species require older, interior forests 

containing large trees with complex canopy structure. Still other species inhabit the 

ecotone (edge) between different forest stages, and many require multiple structural 

stages of forests to meet different phases of their life history needs. Abundance and 

trends in structural and successional stages serve as indicators of population carrying 

capacity for wildlife species (Hunter et al. 2001). Historical trends in Minnesota forest 

habitats are reported for timberland, which makes up about 90 percent of all forest land 

in the state. For current habitat conditions, estimates are reported for all forest land. 

Aspen, which is the most abundant forest habitat in Minnesota, provides examples of 

SGCN in both young and old forest. American woodcock favors young aspen and paper 

birch stands with openings, especially on moist soils; boreal owls require much older 

aspens and may be limited by availability of nest cavities in large old aspen trees.

Wildlife habitat at a meso-filter scale: standing dead trees

Specific habitat features like nesting cavities and standing dead trees provide critical 

habitat components for many forest-associated wildlife species. Standing dead trees 

that are large enough to meet habitat requirements for wildlife are referred to as “snags.” 

According to one definition, “for wildlife habitat purposes, a snag is sometimes regarded 

as being at least 10 inches (25.4 cm) in diameter at breast height and at least 6 feet (1.8 

m) tall” (Society of American Foresters, n.d.). Standing dead trees serve as important 

indicators not only of wildlife habitat, but also for past mortality events and carbon 

storage. And, they serve as sources of down woody material (discussed elsewhere in 
 | 93

this report), which also provides habitat features for wildlife. The number and density of 

standing dead trees, together with decay classes, species, and sizes, define an important 

wildlife habitat feature across Minnesota forests. 

What we found 

Stand age and stand size

The area of timberland in the small diameter stand-size class has increased steadily in 

Minnesota during the past several decades, nearly doubling since 1977. Timberland 

area in the large diameter stand-size class increased more gradually between 1977 

and 1990, and area in the medium diameter stand-size class decreased between 1977 

and 1990, but has remained stable during recent decades (Fig. 59). Between 1977 

and 1990, area of timberland under 20 year years of age increased slightly. While 

timberland older than 100 years represents the smallest area of any age class, it has 

seen the largest relative change, nearly tripling in abundance between 1977 and 2013 

(Fig. 60). Similarly, timberland of 81-100 years has more than doubled during that 

same period. In Minnesota, all three stand-size classes contain forests from at least 

five age classes. Medium diameter stand-size class is predominated by forests of 

21-80 years of age, with lower abundance of both young (0-20 years) and old (>80 

years) forest. Large diameter stand-size class has an age distribution skewed slightly 

to the right, predominated by age classes from 41-100 year. Not surprisingly, young 

forest (0-20 years) is the largest age class in the small diameter stand-size class, with 

decreasing area in each successively older age class (Fig. 61).

1977 1990 2003 2008 2013

Timberland Area (million acres)

Inventory Year

Large diameter

Medium diameter

Small diameter

Nonstocked

0

2

4

6

8

10

12

14

16

18

Figure 59.—Area of timberland by inventory year and 

stand-size class, Minnesota.
94 | HEALTH INDICATORS

0

2

4

6

8

10

12

14

16

18

1977 1990 2003 2008 2013

Timberland Area (million acres)

Inventory Year

0-20 years

21-40 years

41-60 years

61-80 years

81-100 years

101+ years

Not measured

Figure 60.—Area of timberland by inventory year and stand-age 

class, Minnesota.

0

 0.50 

 1.00 

 1.50 

 2.00 

 2.50 

 3.00 

Small diameter Medium diameter Large diameter

Forest Land Area (million acres)

Stand-size Class

0-20 years

21-40 years

41-60 years 

61-80 years

81-100 years

101+ years

Figure 61.—Area of forest land by stand-size class and stand-age 

class, Minnesota, 2013. 

Standing dead trees

FIA collects data on standing dead trees (at least 5 inches d.b.h.) of numerous species 

and sizes in varying stages of decay. According to current inventory data (2009-2013), 

more than 330 million standing dead trees are present on Minnesota forest land. This 

equates to an overall density of 19.0 standing dead trees per acre of forest land, with 

slightly higher densities on public (20.5) than on private (17.2) ownership. Five species 

groups each contributed more than 10 million standing dead trees, with the top groups of 

cottonwood and aspen contributing over 100 million trees (Fig. 62), of which 87 million 

are in quaking aspen alone. Relative to the total number of live trees in each species 

group, seven species groups exceeded 10 standing dead trees per 100 live trees (of at least 

5 inches d.b.h.), with the jack pine species group topping the list at 30 standing dead trees 

per 100 live trees (of at least 5 inches d.b.h.) (Fig. 63). The great majority (82 percent) of 

standing dead trees were smaller than 11 inches d.b.h., with 41 percent between 5 and 
 | 95

0 20 40 60 80 100 120

Cottonwood and aspen

Other eastern soft hardwoods

Spruce and balsam fir

Other eastern softwoods

Jack pine

Ash

Soft maple

Eastern white and red pine

Select white oaks

Select red oaks

Hard maple

Basswood

Other red oaks

Eastern noncommercial hardwoods

Yellow birch

Hickory

Other eastern hard hardwoods

Black walnut

Other yellow pines

Standing Dead Trees (millions)

Species

Figure 62.—Number of standing dead trees (for trees 5 inches 

d.b.h and larger) by species group, Minnesota, 2013.

0 5 10 15 20 25 30 35

Black walnut

Other yellow pines

Basswood

Hickory

Eastern white and red pine

Ash

Select white oaks

Hard maple

Eastern noncommercial hardwoods

Soft maple

Select red oaks

Other eastern hard hardwoods

Other eastern softwoods

Other red oaks

Yellow birch

Spruce and balsam fir

Cottonwood and aspen

Other eastern soft hardwoods

Jack pine

Standing Dead Trees per 100 Live Trees 

Species

Figure 63.—Number of standing dead trees per 100 live 

trees (for trees 5 inches d.b.h. and larger) by species group, 

Minnesota, 2013.
96 | HEALTH INDICATORS

6.9 inches d.b.h.; only 2 percent are over 17 inches (Fig. 64). Numbers of standing dead 

trees appear approximately normally distributed among decay classes for most diameter 

classes; the middle decay class (only limb stubs present) contained the most number of 

standing dead trees (32 percent).

0

5

10

15

20

25

30

35

40

45

50

Standing Dead Trees (millions)

Diameter Class (inches)

All limbs and branches present

Few limbs and no fine branches

Only limb stubs present

Few or no limb stubs remain

No evidence of branches remain

5.0-6.97.0-8.9

9.0-10.9

11.0-12.913.0-14.915.0-16.917.0-18.919.0-20.9

21.0-28.9

29.0+

Figure 64.—Number of standing dead trees by diameter 

class and decay class, Minnesota, 2013.

What this means

Stand age and stand size

The area of timberland in large diameter stand-size class increased only modestly 

between 1977 and 2013, while timberland over 80 years is increasing at a more rapid 

rate. Area of timberland in small diameter stands increased markedly between 1977 

and 2013, but during that period timberland area barely increased in the 0-20 yearold class. Both stand-size class and stand-age class are indicators of forest structural/

successional stage. More than 87 percent of 0-20 year old forest is in small diameter 

size class, but only 45 percent of small diameter forest is 0-20 years of age. As 

expected, there is very little small diameter forest in the oldest stand-age classes and 

even less large diameter forest in the youngest stand-age class. The two age classes 

with the most size heterogeneity are 41-60 and 61-80 year classes. Such mixtures of 

different aged or sized trees provide a vertical diversity of vegetation structure that 

can enhance habitat conditions for some species. As illustrated by the aspen wildlife 

example above, there is a need to maintain forest conditions in both younger and 

older age classes (and smaller and larger structural stages) to maintain both early and 

late successional habitats for a diversity of forest-associated species. Managing forest 
 | 97

composition and structure in a variety of conditions may conserve habitat and viable 

populations of many forest-associated wildlife species. 

Standing dead trees

Snags and smaller standing dead trees result from a variety of potential causes, 

including diseases and insects, weather damage, fire, flooding, drought, and 

competition. The cottonwood and aspen species group contained the largest number 

of standing dead trees, but the jack pine species group had the highest density of 

standing dead trees per 100 live trees. About 19 standing dead trees are present for 

every acre of forest land; about 14 are present for every 100 live trees (of at least 

5 inches d.b.h.). Dead trees may contain significantly more cavities per tree than 

occur in live trees (Fan et al. 2003), thereby providing habitat features for foraging, 

nesting, roosting, hunting perches, and cavity excavation for wildlife, from primary 

colonizers such as insects, bacteria, and fungi to birds, mammals, and reptiles. Most 

cavity nesting birds are insectivores which help to control insect populations. The 

availability of very large standing dead trees (snags) may be a limiting meso-scale 

habitat feature for some species of wildlife. Providing a variety of forest structural 

stages and retaining specific features like snags on both private and public lands are 

ways that forest managers maintain the abundance and quality of habitat for forestassociated wildlife species in Minnesota.

Urbanization and Fragmentation of Forest Lands

Background

The expansion of urban lands that accompanies human population growth often 

results in the fragmentation and urbanization of remaining natural habitat (Wilcox 

and Murphy 1985). Forest fragmentation and habitat loss diminish biodiversity and 

are recognized as a major threat to animal populations worldwide (Honnay et al. 

2005, Rosenberg et al. 1999), particularly for species that require interior forest 

conditions for all or part of their life cycle (Donovan and Lamberson 2001), are wideranging, slow-moving, and/or slow reproducing (Charry 2007, Forman et al. 2003). 

Forest fragmentation can also affect forest ecosystem processes through changes in 

microclimate conditions, and it affects the ability of tree species to move in response 

to climate change (Iverson and Prasad 1998). Changes in the size of remaining forest 
98 | HEALTH INDICATORS

patches, in their level of connectivity to other large patches, in the amount of general 

forest cover surrounding each patch, and in the amount of forest-nonforest edge 

all directly affect the amount and quality of interior forest and consequently the 

species and ecosystem functions that depend on these interior conditions. The same 

factors also affect the ease with which exotic, invasive, or generalist species can gain 

a foothold, the ability of wildlife and plant species to move across the landscape, and 

the ability of the forest to protect the quality and quantity of surface and ground 

water supplies.

Spatial landscape pattern metrics help quantify these different characteristics of 

fragmentation. In the last 5-year report (Miles et al. 2011), the amount of forest 

in edge versus core situations was examined with respect to the most widely used 

thresholds for interpreting likely impact. The results highlighted the considerable 

range of conditions between Minnesota’s heavily forested northeast and the prairie 

southwest.

Metric values are sensitive to the resolution of the land cover data source used 

(Moody and Woodcock 1995), similar to the way that animal species see the 

landscape very differently depending on the scale at which they operate (e.g., the same 

patch that supplies interior forest conditions for one species is viewed as an unsuitable 

fragment by another species with higher quality or larger area requirements). Since 

important forest ecosystem processes operate at different scales, in this report current 

levels of fragmentation are examined at two scales. A spatial integrity index (SII) 

developed by Kapos et al.(2000) for the Global Forest Resources Assessment (FRA) 

was adapted that integrates three important facets of fragmentation affecting some 

aspect of forest ecosystem functioning—patch size, local forest density, and patch 

connectivity to core forest areas—to create a single resulting metric for comparison 

where a value of 1 indicates an area that is highly fragmented while a value of 10 

is used for an area of highest forest spatial integrity. Since even acceptably low 

misclassification rates in the source land cover data can be magnified into substantial 

errors in metric values (Langford et al. 2006, Shao and Wu 2008), spatial integrity was 

calculated at the two scales corresponding to two reliable and widely available source 

datasets, the 30 m (98.4 ft) scale of the 2011 National Land Cover Dataset (Jin et al. 

2013), and the 250 m (820 ft) scale of the 2009 FIA forest cover dataset (Wilson et al. 

2012). Both scales fall within the 10-1,000 km2

 scale at which pattern process linkages 

are often of greatest management interest (Forman and Godron 1986).

In the SII calculation, core forest is defined by patch size and local forest density 

within a defined local neighborhood area. An unconnected forest fragment is defined 
 | 99

by its patch size, local forest density, and distance to a core forest area. The spatial 

integrity of all other forest lands are scaled between the core and unconnected 

fragment ends. At the 250 m scale, forest patch size must be greater than 1,544 acres 

(2.41 square miles) in order to be considered core forest, and forest land with a 

patch size of less than 30 acres (0.047 square miles) is considered an unconnected 

fragment. At the 30 m scale, forest patch size must be greater than 22 acres in order 

to be considered core forest, and forest land with a patch size of less than 2.5 acres is 

considered an unconnected fragment. The local forest density component is based on 

a circular area with a radius of 0.78 miles (1,223 acres) for the 250 m scale and on a 

circular area with a radius of 0.09 miles (16 acres) for the 30 m scale. Ninety percent 

of the respective neighborhoods must be classified as forest for a location to qualify as 

core forest (Table 12). These two scales capture a relatively broad range of definitions 

for core forest and spatial integrity that should bracket the scales appropriate for 

understanding impacts on a wide range of wildlife species and ecosystem processes 

affected by forest fragmentation. 

Scale

Definition of core 250 m (820 ft) 30 m (98 ft)

Patch size >1,544 acres >22 acres

Local forest density 90% 90%

Neighborhood radius 0.78 mile 0.09 mile

Definition of unconnected fragment 250 m (820 ft) 30 m (98 ft)

Patch size <30 acres <2.5 acres

Local forest density 10% 10%

Neighborhood radius 0.78 mile 0.09 miles

Distance to core >4.2 miles >0.5 miles

Table 12.—Spatial integrity index (SII) parameters used in calculations at each scale

The population of Minnesota increased by 7.7 percent between 2000 and 2010, to 5.3 

million people. During that same time period, the number of housing units increased 

by 14.4 percent (U.S. Census Bureau 2010). Stated another way, between 2000 and 

2010, housing units increased at a pace 1.9 times the rate of increase in population, a 

trend not unique to Minnesota. In recent decades this housing growth has occurred 

not only in increasing suburban rings around urban areas but also in rural areas. 

Lepczyk et al. (2007), Theobald (2005), and Hammer et al. (2004) observed that 

among the areas facing particularly rapid increases in housing density currently and 

into the future are amenity-rich rural areas around lakes and other forest recreation 

areas. The 24 percent increase in the number of reported second homes from 2000 

to 2010 could be a partial reflection of this trend in Minnesota (U.S. Census Bureau 
100 | HEALTH INDICATORS

2010). This can put additional pressure on forested areas even above the general 

increases in population density and housing density. 

What SII identifies as core does not represent completely intact forest conditions 

because it is calculated from forest canopy and does not consider underlying house 

densities or proximity to roads. Defining the wildland-urban interface (WUI) 

intermix according to Radeloff et al. (2005) as greater than 15.5 houses per square 

mile (6 per square km), the amount of forest, and particularly core or intact forest 

land, which coincided with these areas was identified. The WUI is described as the 

zone where human development meets or intermingles with undeveloped wildland 

vegetation. It is associated with a variety of human-environment conflicts. Radeloff 

et al. (2005) have defined this area in terms of the density of houses (WUI intermix 

areas), the proximity to developed areas (WUI interface areas), and percentage of 

vegetation coverage. WUI intermix areas intersected with forest land in the 2011 

NLCD (Jin et al. 2013) were used to examine changes in the amount of forest land cooccurring with WUI house densities. 

Roads are another important impact of urbanization that affect forest lands but are not 

completely captured by either of the previous two indices. In Minnesota as a whole, 

25 percent of the forest land was within 650 feet of a road of some sort, and 44 percent 

was within 1,310 feet when calculated using NLCD 2006 forest (Fry et al. 2011) and 

U.S. Census Bureau (2000) roads (Fig. 65). Roads have a variety of effects: direct 

hydrological, chemical, and sediment effects; serving as vectors for invasive species; 

facilitating human access and use; increasing habitat fragmentation; and wildlife 

mortality. Actual impacts will vary depending on road width, use, construction, level of 

maintenance, and hydrologic and wildlife accommodations (e.g., Charry 2007, Forman 

et al. 2003); but in general, when greater than 60 percent of the total land area in a 

region is within 1,310 feet of a road, cumulative ecological impacts from roads should 

be an important consideration (Riitters and Wickham 2003). 

What we found

Considering SII classes at the 250 m scale, 61 percent of the forest land in Minnesota 

is core forest, 22 percent has high integrity, 7 percent has medium integrity, 1 percent 

has low spatial integrity, and 9 percent of the forest is in unconnected fragments. At 

the 30 m scale, with 22 acres or greater considered core forest, 62 percent of the forest 

land in Minnesota is core forest, 19 percent has high spatial integrity, 7 percent has 

medium or low integrity, and 13 percent of the forest is in unconnected fragments. 
 | 101

Figure 65.—Forest land by distance from the nearest road, Minnesota, 2006.

Central Hardwood Unit

Aspen-birch Unit

Northern Pine Unit

Distance from Road

Forest<650 feet (200 m)

Forest 650-1310 feet

Forest>1310 feet (400 m)

Prairie Unit

Table 13 contains a breakdown of SII values by FIA unit for both scales. Forest 

connectivity is highest in the Aspen-birch Unit and lowest in the Prairie Unit. The 

spatial distribution of forest land by SII classes at the 250 m scale is depicted in Fig. 

66. Remaining large areas of relatively continuous forest clearly stand out. At the 30 m 

scale, the lower threshold of 22 acres for defining core forest means that more forest 

patches are considered core. Figure 67 compares the SII classes between the two scales 

for an area around Mille Lacs Lake. Note that the forest landscape data being used are 

depicting tree cover only and do not incorporate the presence of any development 

that might be associated with or underlying this tree cover.
102 | HEALTH INDICATORS

Forested Area

Unconnected

Low integrity

Medium integrity

High integrity

Core

Nonforest

Forest by 30 m spatial integrity class Forest by 250 m spatial integrity class

Unit

Forest 

fragment

Low 

SII

Medium 

SII

High 

SII

Core 

forest

Forest 

fragment

Low 

SII

Medium 

SII

High 

SII

Core 

forest

-------------------- Percent ------------------- -------------------- Percent -------------------

Aspen-birch 1 0 3 17 78 0 0 1 14 84

Northern pine 7 1 7 21 64 7 2 10 32 49

Central 

hardwood

37 1 9 2 33 39 2 17 25 17

Prairie 74 1 6 10 8 92 1 5 2 0

Minnesota 13 1 6 19 65 9 1 7 22 61

MN after 

incorporating 

WUI areas

13 1 6 21 59 9 1 7 24 59

Table 13.—Proportion of forest land by spatial integrity index (SII) class at 30 m and 250 m scales and FIA unit, 

Minnesota, 2013

Figure 66.—Forest land by Spatial Integrity Index (SII) at the 250 m scale, Minnesota, 2006.
 | 103

Forested Area

Unconnected

Low integrity

Medium integrity

High integrity

Core

Nonforest

A B

Forest land with sufficient underlying housing density to qualify as WUI areas has 

been steadily increasing. In 1990, 7 percent of the forest land was in low and medium 

density WUI. In 2000 this increased to 8 percent of the forest land, and in 2010 it 

was 10 percent of the forest land in Minnesota. The distribution of forested WUI in 

Minnesota is depicted in Figure 68 and Table 14. Substantial impact to forest land is 

visible along the shores of Lake Superior, around Minneapolis/St. Paul, and around 

the lakes in the center of the State. These underlying house densities are poorly 

captured by the tree canopy cover data used in the calculation of spatial integrity 

above. When SII results at the 250 m scale are integrated with the WUI classes, 2 

percent of Minnesota forest land moves from being core to lower spatial integrity 

classes, decreasing the proportion of forest land in the core class from 61 percent to 

59 percent of forest land. At the 30 m scale, 3 percent of Minnesota forest land moves 

from being core to a lower spatial integrity class—from 62 percent to 59 percent. 

Although this is not a large number state-wide, the effects tend to concentrate in 

amenity-rich areas and the outskirts of major cities, and thus may be locally quite 

noticeable. Figure 69 depicts the changes in SII that occur when WUI status is 

incorporated, in the region between Mille Lacs and Leech lakes (Cass, Crow Wing, 

and Aiken counties). 

Roads remain pervasive in the landscape, existing even in areas that appear to be 

continuous forest land from the air. In 2000, 18 percent of the forest area in the 

Aspen-Birch unit was within 650 feet of a road, and 26, 36, and 39 percent of the 

forest land in the Northern Pine, Central Hardwoods, and Prairie units, respectively, 

was within 650 feet of a road (U.S. Census Bureau 2000) (Table 14). Much of this 

area coincides with WUI areas of housing development. However, it is worth noting 

that the roads included in the U.S. Census Bureau data (TIGER3

 files) do not include 

3

 TIGER (Topologically Integrated Geographic Encoding and Referencing) products are spatial extracts from the 

Census Bureau’s MAF/TIGER database, containing features such as roads, railroads, rivers, as well as legal and 

statistical geographic areas.

Figure 67.—Forest land by Spatial Integrity Index (SII) at the 250 m scale (A) and 30 m scale (B), in the region around 

Mille Lacs Lake, Minnesota, 2006.
104 | HEALTH INDICATORS

Figure 68.—Forest land by wildland urban interface (WUI status), Minnesota, 2006.

Forested Area

Non-WUI

WUI

Nonforest

Unit

Amount of total 

land in foresta

Forest land in WUI 

intermixb

Forest land <650 feet 

from a roadc

-------------------------------------- Percent -------------------------------------

Aspen-birch 82 7 18

Northern pine 60 12 26

Central hardwood 24 12 36

Prairie 4 3 39

State total 34 10 25

a Percent forest estimate based on NLCD 2011. Values are generally higher than estimates from FIA plot data.

b Approximating the forest land potentially affected by underlying or nearby development (U.S. Census Bureau 2010).

c Approximating the forest land potentially affected by roads (U.S. Census 2000).

Table 14.—The distribution of forest land with respect to several urbanization and fragmentation factors, expressed as 

a percentage of the forest land in each FIA unit, Minnesota, 2013 
 | 105

Forested Area

Unconnected

Low integrity

Medium integrity

High integrity

Core

Nonforest

A B

many minor roads not associated with housing development, and that including 

these minor roads actually doubles road densities in areas like northern Wisconsin 

(Hawbaker and Radeloff 2004).

Figure 69.—Forest land by Spatial Integrity Index (SII) at the 30 m scale, with (A) and without (B) incorporating WUI status 

into SII, in the region between Mille Lacs and Leech lakes (Cass, Crow Wing, and Aiken counties), Minnesota, 2006.

What this means

When looking at either the 250 m or the 30 m scales, only about 60 percent of 

the forest land in Minnesota meets the definition of core forest statewide, and 

approximately 9 to 13 percent of the forest land is in unconnected fragments or has 

low spatial integrity. Bringing WUI areas into the calculation does not affect the 

state-wide numbers substantially but has a considerable affect in several local areas. 

Bringing roads into the calculation, even at the levels available in the 2000 Census 

TIGER dataset, reduces the integrity of some areas still further.

Forest fragmentation is recognized as a major threat to wildlife populations, 

particularly for species that require interior forest conditions for all or part of their life 

cycle or are wide-ranging or slow-moving, because it increases edge conditions which 

can change micro-climate conditions and ecosystem processes, and limits the ability 

of plants and animals to move in response to climate change (e.g., Forman et al. 2003, 

Honnay et al. 2005, Iverson and Prasad 1998).

Urbanization increases the proximity of people, development, and other 

anthropogenic pressures to natural habitats. Both urbanization and forest 

fragmentation change the way in which humans use forest land, frequently decreasing 

the likelihood that it will be managed for forest products and potentially increasing its 

use for outdoor recreation, although urbanization has also been observed to increase 

the incidence of no trespassing signs being posted on forested land, which decreases 

outdoor recreation opportunities and alters local cultural use of forest land (Butler 
106 | HEALTH INDICATORS

2008, Kline et al. 2004, Wear et al. 1999). Continuing fragmentation, parcelization, 

and urbanization can be barriers to stewardship if they result in forest tracts that are 

too small or too isolated for effective management (Shifley and Moser 2016).

Invasive species and introduced pests are also a concern, as is the ability of forest 

systems to adapt to changes in season, temperatures, rainfall patterns, and relative 

phenological shifts associated with climate change. An intact functioning forest also 

is critical in protecting both the quantity and quality of surface and groundwater 

resources (McMahon and Cuffney 2000, Riva-Murray et al. 2010). 

Fragmentation and urbanization are changing how Minnesota forests function and 

affect forest sustainability. Fragmentation diminishes the benefits and services forests 

provide and makes forest management more difficult. As Minnesota’s population 

continues to sprawl into rural areas, fragmentation of forest land is a growing 

concern to land managers. Factors that increase fragmentation, such as development 

incursions into core and high integrity forest areas, should become the focus of 

conservation and planning activities. In addition, the characteristics and maintenance 

of roads and development can also play a role in their actual impact on the resilience 

of forest land and its ability to continue to supply the forest products and ecosystem 

services people expect and need.
 | 107

Socioeconomics

A red pine plantation that has been thinned twice, located in southern Carlton County, MN. Photo by Eli Sagor, used 

with permission (https://creativecommons.org/licenses/by-nc/2.0/legalcode).

 | 107
108 | SOCIOECONOMICS

Private Woodland Owner Survey

Background

How land is managed is primarily the owner’s decision. Therefore, to a large extent, 

the availability and quality of forest resources are determined by landowners, 

including recreational opportunities, timber, and wildlife habitat. By understanding 

the priorities of forest land owners, leaders of the forest conservation community can 

better help meet the needs of each owner, and in so doing, help conserve the State’s 

forests for future generations. The National Woodland Owner Survey (NWOS), 

conducted by the U.S. Forest Service Forest Inventory and Analysis program, studies 

attitudes, management objectives, and concerns of private forest landowners. It 

focuses on the diverse and dynamic group of owners that is the least understood—

families, individuals, and other unincorporated groups, collectively referred to as 

“family forest owners.” The NWOS data reported here are based on the responses 

from 351 family forest ownerships from Minnesota that participated between 2011 

and 2013.

What we found

Public agencies control just over half (55 percent) of the forest land in Minnesota 

and private ownerships own the other 45 percent of the forest land in the State (Fig. 

70). Of the private acres, an estimated 6.0 million acres are owned by family forest 

owners, an ownership group discussed in more detail below. Corporations, most of 

which are focused on the commercial production of timber, own 1.1 million acres, 

and the remaining private forest land (nearly 700,000 acres) is owned by conservation 

organizations, unincorporated clubs and partnerships, and Native American tribes. 

Public owners control 9.6 million acres of Minnesota forest land. State forest, park, 

and wildlife agencies are stewards of 4.1 million acres of forest land. Federal and local 

government agencies each control just under 3 million acres.

As was previously mentioned, family forest owners own 6.0 million acres of forest 

land in Minnesota. Most of these owners own 10 or more acres of land. According to 

the NWOS, there are an estimated 114,000 family forest ownerships across Minnesota 

that each own at least 10 acres of forest land, a collective 5.6 million acres. The 

average forest holding size of this group is 49 acres; 74 percent of these family forest 

ownerships own less than 50 acres of forest land, but 64 percent of the family forest 

land is in holdings of at least 50 acres (Fig. 71). The primary reasons for owning forest 

land are related to aesthetics, wildlife, nature protection, and privacy (Fig. 72). The 
 | 109

most common activities on their land are personal recreation, such as hunting and 

hiking, and cutting trees for personal use, such as firewood (Fig. 73). Most family 

forest ownerships have not participated in traditional forestry management and 

assistance programs in the past 5 years (Fig. 74); the most common form of assistance 

is having received management advice, but less than 20 percent of the ownerships 

received advice. Demographic information was collected for the self-proclaimed, 

primary decisionmaker for the forest land. The average age of family forest owners in 

Minnesota is 62 years with 44 percent of the family forest land owned by people who 

are at least 65 years of age (Fig. 75).

0 10 20 30 40 50 60 70 80 90 100

Local

State

Federal

Other

Corporate

Family

Percentage

Ownership Group

Figure 70.—Distribution of forest land by ownership group, 

Minnesota, 2013. Error bars represent a 68 percent confidence 

interval around the estimated mean.

0 10 20 30 40 50 60 70 80 90 100

10-19

20-49

50-99

100-199

200-499

500-999

1000+

Percentage

Size of Forest Holdings (acres)

Acres

Ownerships

Figure 71.—Family forest ownerships (with 10+ acres) and 

acres of forest land by size of forest land holdings, Minnesota, 

2013. Error bars represent a 68 percent confidence interval 

around the estimated mean.
110 | SOCIOECONOMICS

0 10 20 30 40 50 60 70 80 90 100

Timber

Cabin

Firewood

Farm

Investment

Family

Recreation

Hunting

Home

Water

Legacy

Privacy

Nature

Wildlife

Beauty

Percentage

Reasons for Owning

Acres

Ownerships

Nontimber forest products

Figure 72.—Family forest ownerships (with 10+ acres) and acres 

of forest land by reasons for owning, Minnesota, 2013. Numbers 

include ownerships who ranked each objective as very important 

or important on a 5-point Likert scale. Error bars represent a 68 

percent confidence interval around the estimated mean.

0 10 20 30 40 50 60 70 80 90

Controlled burn

Eliminated insects

Cut trees-sale

Roads

Grazing

Reduced fire hazard

Invasives

Wildlife habitat

Trails

Cut trees-personal

Recreation

Percentage

Activities

Acres

Ownerships

Nontimber forest products

Figure 73.—Family forest ownerships (with 10+ acres) and acres 

of forest land by activities in the past 5 years, Minnesota, 2013. 

Categories are not exclusive. Error bars represent a 68 percent 

confidence interval around the estimated mean. 
 | 111

0 10 20 30 40 50 60 70 80 90 100

Easement

Certification

Tax program

Management plan

Cost share

Advice

Percentage

Management Programs

and Activities

Acres

Ownerships

Figure 74.—Family forest ownerships (with 10+ acres) and 

acres of forest land by participation in forest management 

programs during the past 5 years, Minnesota, 2013. 

Categories are not exclusive. Error bars represent a 68 percent 

confidence interval around the estimated mean.

0 10 20 30 40 50 60 70 80 90 100

<45

45-54

55-64

65-74

75+

Percentage

Age of Primary

Owner (years)

Acres

Ownerships

Figure 75.—Family forest ownerships (with 10+ acres) and 

acres of forest land by age of primary owner, Minnesota, 2013. 

Error bars represent a 68 percent confidence interval around the 

estimated mean. 

What this means

The fate of the forests lies primarily in the hands of those who own and control 

the land. It is therefore critical to understand forest owners and what policies and 

programs can help them conserve the forests for current and future generations. 

Looking particularly at family forest ownerships, the group that is the least 

understood and the fate whose land is arguably the most uncertain, they own their 

land primarily for amenity reasons, but many are actively doing things with their land. 

That being said, an estimated 90 percent of them do not report having a management 

plan nor have they participated in most other traditional forest management planning 
112 | SOCIOECONOMICS

or assistance programs. There are significant opportunities to help these owners 

increase their engagement and stewardship of their lands. Programs such as Tools 

for Engaging Landowners Effectively (http://www.engaginglandowners.org) can help 

the conservation community develop and implement programs more effectively and 

efficiently. Another important trend to watch is the aging of the family forest owners. 

With many of them being relatively advanced in age, this portends many acres of land 

passing on to the next generation in the not too distant future. There are programs, 

such as those run through the University of Minnesota Extension program 

(http://z.umn.edu/legacyplanning), that are being implemented to help owners meet 

their bequest goals, but it is uncertain who the future forest owners will be and what 

they will do with their land.

Timber Products Output

Background

In Minnesota, forest products manufacturing and related sectors directly contribute 

$9.7 billion industry output and $3 billion value to the Minnesota economy, 

employing about 40,370 people with a $1.8 billion payroll (Deckard and Skurla 

2011). Timber harvesting and processing produces a stream of economic benefits 

shared by timber owners, managers, marketers, loggers, truckers, and processors. 

Knowledge about this important industry helps decisionmakers better manage 

the State’s forests to insure a steady supply of the necessary raw material. Surveys 

of Minnesota’s primary wood-using industry, its use of roundwood, and its 

generation and disposition of mill residues remaining after processing are conducted 

periodically, with the most recent survey being completed in 2010. This information 

is supplemented with the most recent surveys conducted in surrounding states that 

processed wood from Minnesota.

What we found

There were 417 active primary wood-processing mills in Minnesota in 2010. The 

largest processors of roundwood in Minnesota are pulp mills, of which there are 5 

in the State, sawmills (362 mills), and particleboard mills (4 mills). These three mill 

types processed nearly 95 percent of the total receipts of 239 million cubic feet at 

Minnesota mills (Fig. 76). 
 | 113

64%

17%

14%

5%

<1%

<1%

Pulp mills

Saw mills

Particleboard mills

Other/miscellaneous mills

Veneer mills

Post, pole, piling mills

Figure 76.—Industrial roundwood receipts at primary woodusing mills by type of mill, Minnesota, 2010. 

A total of 226.1 million cubic feet of industrial roundwood was harvested from 

Minnesota forest lands in 2010. Pulpwood accounted for the overwhelming majority 

of the total harvest (Fig. 77). The aspen species group was by far the most harvested 

species group, with most of that volume going to pulp mills and particleboard mills. 

Other species groups that made up a considerable portion of the total harvest were 

spruce, red pine, jack pine, balsam fir, and white birch (Fig. 78). The harvesting of 

industrial roundwood created 110.6 million cubic feet of harvest residues that were 

left on the ground. Nearly 87 percent of harvest residues came from nongrowing stock 

sources, such as tops and limbs, crooked or rotten trees, or dead trees.

The processing of industrial roundwood into products in Minnesota primary woodusing mills generated 1.5 million green tons of wood and bark residues. Nearly 69 

percent of mill residues were used as industrial fuel. Thirteen percent were used as 

mulch and 7 percent were used as animal bedding. Only one-half of one percent of 

mill residues went unused (Fig. 79).

61%

18%

15%

3%

2% 1%

< 1%

Pulpwood

Saw logs

Particleboard wood

Industrial fuelwood

All other products Veneer logs

Posts, poles, pilings

Figure 77.—Industrial roundwood production by type of 

product, Minnesota, 2010.
114 | SOCIOECONOMICS

0

10

20

30

40

50

60

70

80

90

Aspen

Spruce

Red pine

Jack pine

Balsam fir

White birch

All other hardwoods

All other softwoods

Industrial Roundwood Production 

(million ft3

)

Species Group

Saw logs

Pulp mills

Particleboard mills

Industrial fuelwood

All other products

Figure 78.—Industrial roundwood production by species 

group and type of product, Minnesota, 2010.

69%

13%

7%

5%

4%

2% <1%

<1%

Industrial fuel

Mulch

Animal bedding

Fiber products

Residential fuel

Miscellaneous Not used

Pellets

Figure 79.—Disposition of mill residues, Minnesota, 2010.

What this means

The timber products industry plays a vital role in the economy of rural Minnesota. 

This is especially true in the heavily forested northeastern portion of the State, where 

90 percent of industrial roundwood volume harvested in Minnesota originates. 

The numerous mills in that area provide woodland owners with an outlet to sell 

timber and provide jobs to local citizens. Based on current growing-stock volume on 

timberland statewide, the 2010 harvesting of industrial roundwood represents just 2 

percent of that total. This rate of harvest will ensure plenty of raw materials to sustain 

the forest products industry into the future.
 | 115

Future Forests of Minnesota

Background

This section focuses on anticipated changes to the forests of Minnesota between 2010 

and 2060. The analysis is derived entirely from the Northern Forest Futures study 

(Shifley and Moser 2016). A large component of future forest change will be the 

result of normal forest growth, aging, natural regeneration, and species succession. In 

addition, the following external forces will drive forest change: 

• Population increases will cause roughly 280 thousand acres of forest land to be 

converted to urban land (Nowak and Walton, 2005).

• Economic conditions will affect forest products consumption, production, and 

harvest rates.

• Invasive species will spread and affect forest change.

• Changes in population, the economy, energy consumption, and energy production 

will affect future climate change.

• Climate change will affect patterns of forest growth and species succession.

The Northern Forest Futures study utilized several alternative scenarios that cover 

a range of different assumptions about the economy, population, climate, and other 

driving forces. The assumptions were incorporated into analytical models that estimated 

how northern forests are likely to change under each alternative scenario. The seven 

scenarios (A1B-C, A1B-BIO, A2-C, A2-BIO, A2-EAB, B2-C, and B2-BIO) are based on 

a storyline and storyline variation. They are identified by their storyline identifier (A1B, 

A2, or B2) followed by a hyphen and then their storyline variation (C, BIO, or EAB).

The three storylines include:

1) A1B—Rapid economic globalization. International mobility of people, ideas, and 

technology. Strong commitment to market-based solutions. Strong commitment to 

education. High rates of investment and innovation in education, technology, and 

institutions at the national and international levels. A balanced energy portfolio 

including fossil intensive and renewable energy sources. Utilizes the CGCM3.1 

climate model (Canadian Centre for Climate Modelling and Analysis, n.d.b).

2) A2—Consolidation into economic regions. Self-reliance in terms of resources 

and less emphasis on economic, social, and cultural interactions between regions. 

Technology diffuses more slowly than in the other scenarios. International 

disparities in productivity, and hence income per capita, are largely maintained or 

increased in absolute terms. Utilizes the CGCM3.1 climate model.
116 | SOCIOECONOMICS

3) B2—A trend toward local self-reliance and stronger communities. Communitybased solutions to social problems. Energy systems differ from region to region, 

depending on the availability of natural resources. The need to use energy and 

other resources more efficiently spurs the development of less carbon-intensive 

technology in some regions. Utilizes the CGCM2 Coupled Global Climate Model 

(Canadian Centre for Climate Modelling and Analysis, n.d.a.).

The three storyline variations include:

1) C—Standard variation—available for all three storylines (A1B, A2, and B2). 

2) BIO—Increased harvest and utilization of woody biomass for energy variation—

available for all three storylines (A1B, A2, and B2).

3) EAB—Potential impact of continued spread of the emerald ash borer (EAB) with 

associated mortality of all ash trees in the affected areas—available for only one 

scenario (A2).

What we found

The anticipated declines in forest land, which total in the hundreds of thousands 

of acres, reverse the recent trend of increasing forest area in Minnesota (Fig. 80). 

Specifically, over the next 50 years forest land area is projected to decline from an 

estimated 17.0 million acres in 2010 to 16.4 million acres (-4 percent) in 2060 under 

scenario A1B-C; to 16.5 million acres (-3 percent) under scenario A2-C; and to 16.6 

million acres (-2 percent) under scenario B2-C. The anticipated losses of forest land 

are still relatively small compared to the cumulative decrease in forest area since 

the initial inventory of Minnesota forests in the 1930s. Only three scenarios are 

represented in Figure 80 as the climate model and variations on the storylines do 

not impact the area of forest land under this model. Only the storylines (developed 

around differing demographics and levels of economic activity) alter the area of forest 

land in the model. Scenarios with increasing population and economic activity have 

less forest land over the time period.

Emerald ash borer was detected in Minnesota in May 2009. Under scenario A2-EAB 

there is a slight increase in the area of the elm/ash/cottonwood forest-type group but 

not nearly as large as the increase in elm/ash/cottonwood for scenarios A1B-C, A2-C, 

and B2-C (Fig. 81). The loss of the ash component is partially offset by increases in 

other associated species in the elm/ash/cottonwood forest-type group.

The impacts of EAB are more pronounced in Figure 82. Only the three high biomass 

utilization variation scenarios (A1B-BIO, B2-BIO, and A2-BIO) have lower levels 

of live tree volume in 2060 than does the A2-EAB scenario. The ash volume is 
 | 117

16.0

16.5

17.0

17.5

18.0

18.5

19.0

19.5

20.0

1930 1950 1970 1990 2010 2030 2050 2070

Forest Land Area (million acres) 

Year

Historic

A1B-C

A2-C

B2-C

Figure 80.—Projected forest land area by scenario, Minnesota, 

2010-2060. 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

White/red/jack pine

Spruce/fir

Oak/hickory

Elm/ash/cottonwood

Maple/beech/birch

Aspen/birch

Other

Forest Land Area (million acres)

Forest-type Group

Historic in 2010

A2-EAB in 2060

A1B-C in 2060

A2-C in 2060

B2-C in 2060

Figure 81.—Forest land area by forest-type group and 

scenario, Minnesota, 2010 and 2060.

12

14

16

18

20

22

2010 2020 2030 2040 2050 2060

Live Tree Volume (billion ft3

)

Year

A1B-C

A1B-BIO

A2-C

A2-BIO

B2-C

B2-BIO

A2-EAB

Figure 82.—Live tree volume on forest land by scenario, 

Minnesota, 2010-2060.
118 | SOCIOECONOMICS

modeled to increase till 2030 and then fall to zero over the next two decades. Under 

the standard scenarios (A1B-C, A2-C, and B2-C), total live tree volume is expected 

to increase until approximately 2030 (despite losses in forest land area) and then 

decrease slightly for 20 years before increasing again in 2060. The area of forest land 

is expected to decrease, but the volume per acre is expected to increase as forests 

continue to mature.

What this means

The projected losses of forest land are relatively small compared to the cumulative 

decrease in forest area from the 1930s to 2003. In fact the increase in forest area from 

2003 to 2010 is only expected to be partially offset by projected forest land area losses 

from 2010 to 2060.
 | 119

Literature Cited

Abrams, M.D. 1992. Fire and the development of oak forests. Bioscience. 42(5): 346-

353. http://dx.doi.org/10.2307/1311781.

Anderson, R.L.; G.W. Anderson; Schipper, A.L., Jr. 1979. Hypoxylon canker of aspen. 

Forest Insect and Disease Leaflet 6 (revised). Washington, DC: U.S. Department of 

Agriculture, Forest Service, Northeastern Area State & Private Forestry. 7 p. 

Augustine, D.J.; deCalesta, D. 2003. Defining deer overabundance and threats to 

forest communities: from individual plants to landscape structure. Ecoscience. 

10(4): 472-486.

Bailey, R.G. 1980. Description of the ecoregions of the United States. Misc. Pub. 

1391. Washington, DC: U.S. Department of Agriculture, Forest Service. 77 p.

Bechtold, W.A.; Patterson, P.L., eds. 2005. The enhanced Forest Inventory and 

Analysis program—national sampling design and estimation procedures. Gen. 

Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 85 p.

Belcher, D.M.; Holdaway, M.R.; Brand, G.J. 1982. A description of STEMS: the stand 

and tree evaluation and modeling system. Gen. Tech. Rep. NC-79. St. Paul, MN: 

U.S. Department of Agriculture, Forest Service, North Central Forest Experiment 

Station. 18 p.

Brand, G.J.; Nelson, M.D.; Wendt, D.G.; Nimerfro, K.K. 2000. The hexagon/panel 

system for selecting FIA plots under an annual inventory. In: McRoberts, 

R.E.; Reams, G.A.; Van Deusen, P.C., eds. Proceedings of the first annual Forest 

Inventory and Analysis symposium. Gen. Tech. Rep. NC-213. St. Paul, MN: U.S. 

Department of Agriculture, Forest Service, North Central Research Station: 8-13.

Butler, B.J. 2008. Family forest owners of the United States, 2006. Gen. Tech. Rep. 

NRS-27. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 72 p. http://dx.doi.org/10.2737/NRS-GTR-27.

Canadian Centre for Climate Modelling and Analysis. [N.d.a.]. CGCM2-coupled 

global climate model, medium resolution (T47). Ottawa, ON, Canada: 

Environment Canada. http://www.cccma.ec.gc.ca/models/cgcm2.shtml (accessed 

July 27, 2012).
120 | LITERATURE CITED

Canadian Centre for Climate Modelling and Analysis. [N.d.b.]. CGCM3.1-coupled 

global climate model (CGCM3), medium resolution (T47). Ottawa, ON, Canada: 

Environment Canada. http://www.cccma.ec.gc.ca/models/cgcm3.shtml (accessed 

July 27, 2012).

Charry, B. 2007. Conserving wildlife on and around Maine’s roads. Augusta, 

ME: Beginning with Habitat, Maine Audubon, and Maine Department 

of Transportation. 10 p. http://www.beginningwithhabitat.org/pdf/

MARoadsWildlifeFINAL.pdf (accessed March 23, 2015).

Chimner, R.A.; Ott, C.A.; Perry, C.H.; Kolka. R.K. 2014. Developing and evaluating 

rapid field methods to estimate peat carbon. Wetlands. 34: 1241-1246. http://

dx.doi.org/10.1007/s13157-014-0574-6.

Conservation Biology Institute. 2012. Protected areas database of the United States, 

PAD-US (CBI edition). http://consbio.org/products/projects/pad-us-cbi-edition

(accessed March 23, 2015). 

Cunningham, R.N.; Horn, A.G.; Quinney, D.N. 1958. Minnesota’s Forest Resources, 

1958. Forest Resour. Rep. 13. St. Paul, MN: U.S. Department of Agriculture, Forest 

Service, Lake States Forest Experiment Station. 53 p.

Deckard, D.L. 2014. Minnesota 2013 public stumpage price review and price 

indices. St. Paul, MN: Minnesota Department of Natural Resources, Division 

of Forestry. http://files.dnr.state.mn.us/forestry/timber_sales/stumpage/

stumpageReviewReport2013.pdf (accessed March 27, 2015).

Deckard, D.L.; Skurla, J.L. 2011. Economic contribution of Minnesota’s forest 

products industry-2011 edition. St. Paul, MN: Minnesota Department of Natural 

Resources, Division of Forestry. 18 p.

Donovan, T.M., Lamberson, R.H. 2001. Area-sensitive distributions counteract 

negative effects of habitat fragmentation on breeding birds. Ecology. 82(4): 1170-

1179. http://dx.doi.org/10.2307/2679912.

Fan, Z.; Shifley, S.R.; Spetich, M.A.; Thompson, F.R., III.; Larsen, D.R. 2003. Distribution 

of cavity trees in midwestern old-growth and second-growth forests. Canadian 

Journal of Forest Research. 33: 1481-1494. http://dx.doi.org/10.1139/x03-068.

Food and Agricultural Organization of the United Nations [FAO]. 2010. Global forest 

resources assessment 2010: main report. FAO Forestry Paper 163. Rome, Italy: 

Food and Agricultural Organization. 340 p. 
 | 121

Forman, R.T.T.; Godron, M. 1986. Landscape ecology. New York, NY: J. Wiley & 

Sons. 619 p. ISBN: 9780471870371.

Forman, R.T.T.; Sperling, D.; Bissonette, J.A.; Clevenger, A.P.; Cutshall, C.D.; Dale, 

V.H.; Fahrig, L.; France, R.L.; Goldman, C.R.; Heanue, K.; Jones, J.; Swanson, 

F.; Turrentine, T.; Winter, T.C. 2003. Road ecology: science and solutions.

Washington, DC: Island Press. 504 p. ISBN: 9781559639330.

Fry, J.; Xian, G.; Jin, S.; Dewitz, J.; Homer, C.; Yang, L.; Barnes, C.; Herold, N.; 

Wickham, J. 2011. Completion of the 2006 national land cover database for the 

conterminous United States. Photogrammetric Engineering and Remote Sensing. 

77(9): 858-864.

Gilbart, M. 2012. Under cover: wildlife of shrublands and young forest. Cabot, VT: 

Wildlife Management Institute. 87 p.

Greenberg, C.H.; Collins, B.S.; Thompson, F.R., III. 2011. Sustaining young forest 

communities: ecology and management of early successional habitat in the 

Central Hardwood region, USA. Managing Forest Ecosystems, Volume 21. New 

York, NY: Springer. 310 p. http://dx.doi.org/10.1007 /978-94-007-1620-9.

Hahn, J.T. 1984. Tree volume and biomass equations for the Lake States. Res. Pap. 

NC-250. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North 

Central Forest Experiment Station. 10 p.

Hammer, R.B.; Stewart, S.I.; Winkler, R.L.; Radeloff, V.C.; Voss, P.R. 2004. 

Characterizing dynamic spatial and temporal residential density patterns from 

1940-1990 across the north central United States. Landscape and Urban Planning. 

69: 183-199. http://dx.doi.org/10.1016/j.landurbplan.2003.08.011.

Hawbaker, T.J.; Radeloff, V.C. 2004. Roads and landscape pattern in northern 

Wisconsin based on a comparison of four road data sources. Conservation 

Biology. 18(5): 1233-1244. http://dx.doi.org/10.1111/j.1523-1739.2004.00231.x.

Heath, L.S; Hansen, M.H.; Smith, J.E.; Smith, W.B.; Miles, P.D. 2009. Investigation 

into calculating tree biomass and carbon in the FIADB using a biomass 

expansion factor approach. In: McWilliams, W.; Moisen, G.; Czaplewski, R., 

comps. Forest Inventory and Analysis (FIA) symposium 2008. Proceedings RMRSP-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 26 p.
122 | LITERATURE CITED

Hewes, J.H.; Butler, B.J.; Liknes, G.C.; Nelson, M.D.; Snyder, S.A. 2014. Public and 

private forest ownership in the conterminous United States: distribution of six 

ownership types—geospatial dataset. Fort Collins, CO: Forest Service Research 

Data Archive. http://dx.doi.org/10.2737/RDS-2014-0002.

Holt, H.A.; Fischer, B.C. 1979. An overview of oak regeneration problems. In: Holt, 

H.A.; Fischer, B.C., eds. Regenerating oaks in upland hardwood forests: proceedings 

of the 1979 John S. Wright forestry conference. West Lafayette, IN: Purdue 

University, Iowa Cooperative Extension Service. 132 p.

Homer, C.; Dewitz, J.; Fry, J.; Coan, M.; Hossain, N.; Larson, C.; Herold, N.; 

McKerrow, A.; VanDriel, I., Wickham, J. 2007. Completion of the 2001 National 

Land Cover Database for the conterminous United States. Photogrammetric 

Engineering and Remote Sensing. 73(4): 337-341.

Honnay, O.; Jacquemyn, H.; Bossuyt, B.; Hermy, M. 2005. Forest fragmentation 

effects on patch occupancy and population viability of herbaceous plant species. 

New Phytologist. 166: 723-736. http://dx.doi.org/10.1111/j.1469-8137.2005.01352.x.

Huang, C.; Yang, B.; Wylie, B.; Homer, C. 2001. A strategy for estimating tree canopy 

density using Landsat 7 ETM+ and high resolution images over large areas. In: 

Third International Conference on Geospatial Information in Agriculture and 

Forestry; November 5-7, 2001; Denver, Colorado: U.S. Department of Interior, 

Geologic Survey. [CD].

Hunter, M.L., Jr. 2005. A mesofilter conservation strategy to complement fine and 

coarse filters. Conservation Biology. 19(4): 1025-1029. http://dx.doi.org/10.1111/

j.1523-1739.2005.00172.x.

Hunter, W.C.; Buehler, D.A.; Canterbury, R.A.; Confer, J.L.; Hamel, P.B. 2001. 

Conservation of disturbance-dependent birds in eastern North America. Wildlife 

Society Bulletin. 29(2): 440-455.

Iverson, L.R., Prasad, A.N. 1998. Predicting abundance of 80 tree species following 

climate change in the eastern United States. Ecological Monographs. 68(4): 465-

485. http://dx.doi.org/10.2307/2657150.

Jakes, P.J. 1980. The fourth Minnesota forest inventory: area. Resour. Bull. NC-54. 

St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest 

Experiment Station. 37 p.
 | 123

Jin, S.; Yang, L.; Danielson, P.; Homer, C.; Fry, J.; Xian, G. 2013. A comprehensive 

change detection method for updating the National Land Cover Database to circa 

2011. Remote Sensing of Environment. 132: 159-175. http://dx.doi.org/10.1016/j.

rse.2013.01.012.

Johnson, P.S.; Shifley, S.R.; Rogers, R. 2002. The ecology and silviculture of oaks. CABI 

Publishing. 503 p. ISBN: 9780851995700.

Jules, E.S.; Kauffman, M.J.; Ritts, W.D.; Carroll, A.L. 2002. Spread of an invasive 

pathogen over a variable landscape: a nonnative root rot on Port Orford cedar. 

Ecology. 83(11): 3167-3181. http://dx.doi.org/10.2307/3071851.

Kapos, V.; Lysenko, I.; Lesslie, R. 2000. Assessing forest integrity and naturalness in 

relation to biodiversity. Forest Resources Assessment Programme, Working Paper 

54. Rome, Italy: Food and Agriculture Organization of the United Nations, Forestry 

Department. 65 p.

Kline, J.D.; Azuma, D.L.; Alig, R.J. 2004. Population growth, urban expansion, and 

private forestry in western Oregon. Forest Science. 50(1): 33-43.

Kurtz, C.M. 2013. An assessment of invasive plant species monitored by the Northern 

Research Station Forest Inventory and Analysis Program, 2005 through 2010. Gen. 

Tech. Rep. NRS-109. Newtown Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 70 p. http://dx.doi.org/10.2737/NRS-GTR-109.

Langford, W.T.; Gergel, S.E.; Dietterich, T.G.; Cohen, W. 2006. Map misclassification can 

cause large errors in landscape pattern indices: examples from habitat fragmentation. 

Ecosystems. 9(3): 474-488. http://dx.doi.org/10.1007/s10021-005-0119-1.

Leatherberry, E.C.; Spencer, J.S., Jr.; Schmidt, T.L.; Carroll, M.R. 1995. An analysis 

of Minnesota`s fifth forest resources inventory, 1990. Resour. Bull. NC-165. St. 

Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest 

Experiment Station. 102 p.

Lepczyk, C.A.; Hammer, R.B.; Stewart, S.I.; Radeloff, V.C. 2007. Spatiotemporal 

dynamics of housing growth hotspots in the North Central U.S. from 1940 to 2000. 

Landscape Ecology. 22: 939-952. http://dx.doi.org/10.1007/s10980-006-9066-2.

Liebhold, A.M.; Gottschalk, K.W.; Muzika, R-M.; Montgomery, M.E.; Young, R.; O’Day, 

K.; Kelley, B. 1995a. Suitability of North American tree species to the gypsy moth: 

a summary of field and laboratory tests. Gen. Tech. Rep. NE-211. Radnor, PA: U.S. 

Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 

34 p.
124 | LITERATURE CITED

Liebhold, A.M.; MacDonald, W.L.; Bergdahl, D.; Mastro, V.C. 1995b. Invasion by exotic 

forest pests: a threat to forest ecosystems. Forest Science Monograph. 30: 1-49.

Marschner, F.J. 1930. The original vegetation of Minnesota. Map derived from 

General Land Office field notes. Bureau of Agricultural Economics.

McMahon, G.; Cuffney, T.F. 2000. Quantifying urban intensity in drainage basins for 

assessing stream ecological conditions. Journal of the American Water Resources 

Association. 36(6): 1247-1261. http://dx.doi.org/10.1111/j.1752-1688.2000.

tb05724.x.

McRoberts, R.E. 1999. Joint annual forest inventory and monitoring system: the 

North Central perspective. Journal of Forestry. 97: 21-26.

McRoberts, R.E. 2005. The enhanced forest inventory and analysis program. In: 

Bechtold, W.A.; Patterson, P.L., eds. The enhanced Forest Inventory and Analysis 

program—national sampling design and estimation procedures. Gen. Tech. Rep. 

SRS-80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station: 1-10.

McWilliams, W.H.; Westfall, J.A.; Brose, P.H.; Dey, D.C.; Hatfield, M.; Johnson, K.; 

Laustsen, K.M.; Lehman, S.L.; Morin, R.S.; Nelson, M.D.; Ristau, T.E.; Royo, A.A.; 

Stout, S.L.; Willard, T; Woodall, C.W. 2015. A regeneration indicator for Forest 

Inventory and Analysis: history, sampling, estimation, analytics, and potential 

use in the Midwest and Northeast United States. Gen. Tech. Rep. NRS-148. 

Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 74 p. http://dx.doi.org/10.2737/NRS-GTR-148.

Miles, P.D.; Heinzen, D.; Meilke, M.E.; Woodall, C.W.; Butler, B.J.; Piva, R.J.; 

Meneguzzo, D.M.; Perry, C.H.; Gormanson, D.D.; Barnett, C.J. 2011. Minnesota’s 

Forests 2008. Resour. Bull. NRS-50. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 67 p. [DVD included].

Miles, P.D.; Jacobson, K.; Brand, G.J.; Jepsen, E.; Meneguzzo, D.; Mielke, M.E.; Olson, 

C.; Perry, C.H.; Piva, R.; Wilson, B.T.; Woodall, C. 2007. Minnesota’s forests 1999-

2003 (Part A). Resour. Bull. NRS-12A. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 92 p.

Miles, P.D.; Smith, W.B. 2009. Specific gravity and other properties of wood and bark 

for 156 tree species found in North America. Res. Pap. NRS-38. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 35 p. 

http://dx.doi.org/10.2737/NRS-RN-38.
 | 125

Minnesota Department of Agriculture. 2015. Gypsy moth quarantine. http://www.

mda.state.mn.us/gmquarantine (accessed April 2, 2015).

Minnesota Department of Natural Resources, ed. 2005. Tomorrow’s habitat for the 

wild and rare: an action plan for Minnesota wildlife. St. Paul, MN: Minnesota 

Department of Natural Resources. http://archive.leg.state.mn.us/docs/2006/

other/060316/www.dnr.state.mn.us/cwcs/strategy.html (accessed March 16, 2015).

Minnesota Department of Natural Resources. 2015a. The DNR data deli. http://deli.

dnr.state.mn.us/ (accessed March 16, 2015).

Minnesota Department of Natural Resources. 2015b. Peatlands, bogs and fens. http://

www.dnr.state.mn.us/snas/coniferous_peatlands.html (accessed May 5, 2015).

Montreal Process. 1995. Criteria and indicators for the conservation and sustainable 

management of temperate and boreal forests. Hull, Quebec: Canadian Forest 

Service, 27 p.

Moody, A.; Woodcock, C.E. 1995. The influence of scale and the spatial 

characteristics of landscapes on land-cover mapping using remote sensing. 

Landscape Ecology. 10(6): 363-379. http://dx.doi.org/10.1007/bf00130213.

Morin, R.S.; Randolph, K.C.; Steinman, J. 2015. Mortality rates associated with 

crown health for eastern forest tree species. Environmental Monitoring and 

Assessment. 187: 87. http://dx.doi.org/10.1007/s10661-015-4332-x.

NatureServe. [N.d.]. Explorer® online encyclopedia of life. Arlington, VA: 

NatureServe. http://explorer.natureserve.org/servlet/NatureServe?init=Species

(accessed February 17, 2011).

Nowak, D.J.; Walton, J.T. 2005. Projected urban growth and its estimated impact on 

the US Forest resource (2000-2050). Journal of Forestry. 103(8): 383-389.

O’Connell, B.M.; LaPoint, E.B.; Turner, J.A.; Ridley, T.; Pugh, S.A.; Wilson, A.M.; 

Waddell, K.L.; Conkling, B.L. 2014. The Forest Inventory and Analysis database: 

database description and user guide Ver. 6.0.1 for Phase 2. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 748 p. Available at http://www.fia.fs.fed.

us/library/database-documentation/.

Oswalt, S.N.; Smith, W.B.; Miles, P.D.; Pugh, S.A. 2014. Forest Resources of the 

United States, 2012: a technical document supporting the Forest Service 2010 

update of the RPA Assessment. Gen. Tech. Rep. WO-91. Washington, DC: U.S. 

Department of Agriculture, Forest Service, Washington Office. 218 p. 
126 | LITERATURE CITED

Palik, B.; Johnson, J. 2007. Constraints on pine regeneration in northern Minnesota: 

causes and potential solutions. Final report to the north central landscape 

committee. St. Paul, MN: Minnesota Forest Resources Council. 37 p. http://mn.gov/

frc/documents/council/MFRC_Constraints_Red_Pine_Regen_2007-11-07_Report.

pdf (accessed February, 2015).

Pimentel, D.; Lach, L.; Zuniga, R.; Morrison, D. 2000. Environmental and economic 

costs of nonindigenous species in the United States. BioScience. 50(1): 53-65. 

http://dx.doi.org/10.1641/0006-3568(2000)050[0053:eaecon]2.3.co;2.

Poland, T.M.; Haack, R.A. 2003. Exotic forest insect pests and their impact on forest 

management. In: Proceedings of the Society of American Foresters, 2002 national 

convention; 2002 October 5-9; Winston-Salem, NC. SAF Publ. 03-01. Bethesda, 

MD: Society of American Foresters: 132-141.

Pugh, S.A.; Pedersen, L.D.; Heym, D.C.; Piva, R.J.; Woodall, C.W.; Barnett, C.J.; Kurtz, 

C.M.; Moser, W.K. 2012. Michigan’s Forests 2009. Resour. Bull. NRS-66. Newtown 

Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research 

Station. 68 p. [DVD included]. http://dx.doi.org/10.2737/NRS-RB-66.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I.; Fried, J.S.; Holcomb, S.S.; McKeefry, J.F. 

2005. The wildland urban interface in the United States. Ecological Applications. 

15: 799-805. http://dx.doi.org/10.1890/04-1413. http://dx.doi.org/10.1890/04-1413.

Riitters, K.H.; Wickham, J.D. 2003. How far to the nearest road? Frontiers in Ecology 

and the Environment. 1(3): 125-129. http://dx.doi.org/10.2307/3867984.

Riva-Murray, K.; Riemann, R.; Murdoch, P.; Fischer, J.M.; Brightbill, R.A. 2010. 

Landscape characteristics affecting streams in urbanizing regions of the Delaware 

River Basin (New Jersey, New York, and Pennsylvania, US.). Landscape Ecology. 

25(10): 1489-1503. http://dx.doi.org/10.1007/s10980-010-9513-y.

Rosenberg, K.V.; Rohrbaugh, R.W., Jr; Barker, S.E.; Lowe, J.D.; Hames, R.S.; Dhondt, 

A.A. 1999. A land manager’s guide to improving habitat for scarlet tanagers and 

other forest-interior birds. Ithaca, NY: The Cornell Lab of Ornithology. 23 p. 

Russell, F.L.; Zippin, D.B.; Fowler, N.L. 2001. Effects of white-tailed deer (Odocoileus 

virginianus) on plants, plants populations and communities: a review. The 

American Midland Naturalist. 146(1): 1-26. http://dx.doi.org/10.1674/0003-

0031(2001)146[0001:eowtdo]2.0.co;2.
 | 127

Russell, M.B.; Woodall, C.W.; D’Amato, A.W.; Fraver, S.; Bradford, J.B. 2014a. Linking 

climate change and downed woody debris decomposition across forests of the 

eastern United States. Biogeosciences. 11: 6417-6425. http://dx.doi.org/10.5194/bg11-6417-2014.

Russell, M.B.; Woodall, C.W.; Fraver, S.; D’Amato, A.W.; Domke, G.M.; Skog, K.E. 

2014b. Residence time and rate of decay for downed woody debris biomass/

carbon in eastern US forests. Ecosystems. 17: 765-777. http://dx.doi.org/10.1007/

s10021-014-9757-5.

Scott, C.T.; Bechtold, W.A.; Reams, G.A.; Smith, W.D.; Westfall, J.A.; Hansen, M.H.; 

Moisen, G.G. 2005. Sample-based estimators used by the Forest Inventory and 

Analysis national information management system. In: Bechtold, W.A.; Patterson, 

P.L., eds. The enhanced Forest Inventory and Analysis program—national sampling 

design and estimation procedures. Gen. Tech. Rep. SRS-GTR-80. Asheville, NC: 

U.S. Department of Agriculture, Forest Service: 43-67.

Seilheimer, T.S.; Zimmerman, P.L.; Stueve, K.M.; Perry, C.H. 2013. Landscape-scale 

modeling of water quality in the Lake Superior and Lake Michigan watersheds: 

How useful are forest-based indicators? Journal of Great Lakes Research. 39(2): 

211–223. http://dx.doi.org/10.1016/j.jglr.2013.03.012.

Shao, G.; Wu, J. 2008. On the accuracy of landscape pattern analysis using remote 

sensing data. Landscape Ecology. 23(5): 505-511. http://dx.doi.org/10.1007/s10980-

008-9215-x.

Shifley, S.R.; Moser, W.K., eds. 2016. Future forests of the northern United States. 

Gen. Tech. Rep. NRS-151. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 388 p.

Shifley, S.R.; Moser, W.K.; Nowak, D.J.; Miles, P.D.; Butler, B.J.; Aguilar, F.X.; DeSantis, 

R.D.; Greenfield, E.J. 2014. Five anthropogenic factors that will radically alter 

forest conditions and management needs in the Northern United States. Forest 

Science. 60(5): 914-925. http://dx.doi.org/10.5849/forsci.13-153.

Smith, D.M.; Larson, B.C.; Kelty, M.J.; Ashton, P.M.S. 1997. The practice of 

silviculture: applied forest ecology. 9th edition. New York, NY: John Wiley & Sons, 

Inc. 537 p. ISBN: 9780471109419.

Smith, W.B. 1991. Assessing removals for North Central forest inventories. Resour. 

Pap. NC-299. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North 

Central Forest Experiment Station. 48 p.
128 | LITERATURE CITED

Society of American Foresters. [N.d.]. The dictionary of forestry. Bethesda, MD: Society 

of American Foresters. http://dictionaryofforestry.org (accessed February 9, 2012).

Steinman, J. 2000. Tracking the health of trees over time on forest health monitoring 

plots. In: Hansen, M.; Burk, T., eds. Proceedings, integrated tools for natural 

resources inventories in the 21st century; 1998 August 16-20; Boise, ID. Gen. Tech. 

Rep. NC-212. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North 

Central Research Station: 334-339.

Stone, R.N. 1966. A third look at Minnesota’s timber. Resour. Bull. NC-1. 

St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest 

Experiment Station. 70 p.

Theobald, D.M. 2005. Landscape patterns of exurban growth in the USA from 1980 

to 2020. Ecology and Society. 10(1): 34.

U.S. Census Bureau. 2000. United States Census 2000. Washington, DC: U.S. 

Department of Commerce, Bureau of the Census. http://www.census.gov/main/

www/cen2000.html (accessed 2006).

U.S. Census Bureau. 2010. United States Census 2010. Washington, DC: U.S. 

Department of Commerce, Bureau of the Census. www.census.gov/2010census/

(accessed January 2015).

U.S. Environmental Protection Agency [U.S. EPA]. 2015. Inventory of U.S. 

greenhouse gas emissions and sinks: 1990–2013. EPA 430-R-15-004. Washington, 

DC: U.S. Environmental Protection Agency, Office of Atmospheric Programs. 

http://www3.epa.gov/climatechange/Downloads/ghgemissions/US-GHGInventory-2015-Main-Text.pdf (accessed March 10, 2016).

U.S. Forest Service. 2007. Forest inventory and analysis national core field guide: 

field data collection procedures for Phase 3 plots, Ver. 4.0. Washington, DC: U.S. 

Department of Agriculture, Forest Service. Available at http://www.fia.fs.fed.us/

library/field-guides-methods-proc/ (accessed April 9, 2015).

U.S. Forest Service. 2012. Forest Inventory and Analysis national core field guide: 

volume I—field data collection procedures for Phase 2 plots, Ver. 6.0. Washington, 

DC: U.S. Department of Agriculture, Forest Service. Available at http://www.fia.

fs.fed.us/library/field-guides-methods-proc/ (accessed April 9, 2015).
 | 129

U.S. Forest Service. 2014a. The Forest Inventory and Analysis database: database 

description and user guide Ver. 6.0.1 for Phase 3. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 182 p. Available at http://www.fia.fs.fed.

us/library/database-documentation/ (accessed April 9, 2015).

U.S. Forest Service. 2014b. Forest Inventory and Analysis national core field guide: 

Northern Research Station edition, volume I supplement—field data collection 

procedures for P2+ plots, Ver. 6.0. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service. 

Vitousek, P.M.; D’Antonio, C.M.; Loope, L.L.; Westbrooks, R. 1996. Biological 

invasions as global environmental change. American Scientist. 84: 468-478.

Waters, T. 1977. The streams and rivers of Minnesota. Minneapolis, MN: University 

of Minnesota Press. 373 p.

Wear, D.N.; Liu, R.; Foreman, M.J.; Sheffield, R.M. 1999. The effects of population 

growth on timber management and inventories in Virginia. Forest Ecology and 

Management. 118: 107-115. http://dx.doi.org/10.1016/s0378-1127(98)00491-5.

White, M.A. 2012. Long-term effects of deer browsing: composition, structure, and 

productivity in a northeastern Minnesota old-growth forest. Forest Ecology and 

Management. 269: 222-228. http://dx.doi.org/10.1016/j.foreco.2011.12.043.

Wilcox, B.A.; Murphy, D.D. 1985. Conservation strategy: the effects of 

fragmentation on extinction. American Naturalist. 125: 879-887. http://dx.doi.

org/10.1086/284386.

Wilson, B.T.; Lister, A.J.; Riemann, R.I. 2012. A nearest-neighbor imputation 

approach to mapping tree species over large areas using forest inventory plots 

and moderate resolution raster data. Forest Ecology and Management. 271: 182-

198. http://dx.doi.org/10.1016/j.foreco.2012.02.002.

Woodall, C.W.; Conkling, B.L.; Amacher, M.C.; Coulston, J.W.; Jovan, S.; Perry, C.H.; 

Schulz, B.; Smith, G.C.; Will-Wolf, S. 2010. The Forest Inventory and Analysis 

database Ver. 4.0: database description and users manual for Phase 3. Gen. Tech. 

Rep. NRS-61. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 180 p. http://dx.doi.org/10.2737/NRS-GTR-61.

Woodall, C.W.; Monleon, V.J. 2008. Sampling protocol, estimation, and analysis 

procedures for the down woody materials indicator of the FIA program. Gen. 

Tech. Rep. NRS-22. Newtown Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 68 p. http://dx.doi.org/10.2737/NRS-GTR-22.
130 | LITERATURE CITED

Woodall, C.W.; Walters, B.F.; Oswalt, S.N.; Domke, G.M.; Toney, C.; Gray, A.N. 

2013. Biomass and carbon attributes of downed woody materials in forests of 

the United States. Forest Ecology and Management. 305: 48-59. http://dx.doi.

org/10.1016/j.foreco.2013.05.030.

Work, T.T.; McCullough, D.G.; Cavey, J.F.; Komsa, R. 2005. Arrival rate of 

nonindigenous insect species into the United States through foreign trade. 

Biological Invasions. 7: 323-332. http://dx.doi.org/10.1007/s10530-004-1663-x.

Zon, R. 1935. The forests of Minnesota: areas and types. Forest Survey No.1. St. Paul, 

MN: U.S. Department of Agriculture, Forest Service, Lake States Forest Experiment 

Station. 25 p.
 | 131

Common name Genus Species

Balsam fir Abies balsamea

Boxelder Acer negundo

Black maple Acer nigrum

Red maple Acer rubrum

Silver maple Acer saccharinum

Sugar maple Acer saccharum

Mountain maple Acer spicatum

Serviceberry spp. Amelanchier spp.

Yellow birch Betula alleghaniensis

River birch Betula nigra

Paper birch Betula papyrifera

American hornbeam, musclewood Carpinus caroliniana

Bitternut hickory Carya cordiformis

Shagbark hickory Carya ovata

Hackberry Celtis occidentalis

Hackberry spp. Celtis spp.

Hawthorn spp. Crataegus spp.

Russian-olive Elaeagnus angustifolia

White ash Fraxinus americana

Black ash Fraxinus nigra

Green ash Fraxinus pennsylvanica

Butternut Juglans cinerea

Black walnut Juglans nigra

Eastern redcedar Juniperus virginiana

Tamarack (native) Larix laricina

Apple spp. Malus spp.

White mulberry Morus alba

Red mulberry Morus rubra

Mulberry spp. Morus spp.

Eastern hophornbeam Ostrya virginiana

White spruce Picea glauca

Black spruce Picea mariana

Blue spruce Picea pungens

Jack pine Pinus banksiana

Austrian pine Pinus nigra

Red pine Pinus resinosa

Eastern white pine Pinus strobus

Scotch pine Pinus sylvestris

Balsam poplar Populus balsamifera

Eastern cottonwood Populus deltoides

Appendix 1.—List of tree species, greater than or equal to 5 inches in diameter, found on FIA inventory plots, Minnesota, 

2009-2013

(Appendix continued on next page.)
132 | 

Common name Genus Species

Bigtooth aspen Populus grandidentata

Cottonwood and poplar spp. Populus spp.

Quaking aspen Populus tremuloides

American plum Prunus americana

Pin cherry Prunus pensylvanica

Black cherry Prunus serotina

Cherry and plum spp. Prunus spp.

Chokecherry Prunus virginiana

White oak Quercus alba

Swamp white oak Quercus bicolor

Northern pin oak Quercus ellipsoidalis

Bur oak Quercus macrocarpa

Northern red oak Quercus rubra

Black oak Quercus velutina

Black locust Robinia pseudoacacia

Peachleaf willow Salix amygdaloides

Bebb willow Salix bebbiana

Black willow Salix nigra

Willow spp. Salix spp.

Mountain-ash spp. Sorbus spp.

Northern white-cedar Thuja occidentalis

American basswood Tilia americana

American elm Ulmus americana

Siberian elm Ulmus pumila

Slippery elm Ulmus rubra

Rock elm Ulmus thomasii

(Appendix 1. continued) 
 | 133

FIA forest 

type code FIA forest type/forest-type group

Minnesota DNR 

forest type

100 White/red/jack pine group White/red/jack pine group

101 Jack pine Jack pine

102 Red pine Red pine

103 Eastern white pine Eastern white pine

120 Spruce/fir group Spruce/fir group

121 Balsam fir Balsam fir

122 White spruce White spruce

125 Black spruce Black spruce

126 Tamarack Tamarack

127 Northern white-cedar Northern white-cedar

181 Retired (eastern redcedar) Eastern redcedar

380 Exotic softwoods group Other softwoods

381 Scotch pine Other softwoods

400 Oak/pine group Red pine

401 Eastern white pine-northern red oak-white ash Eastern white pine

402 Eastern redcedar-hardwood Eastern redcedar

409 Other pine-hardwood Red pine

500 Oak/hickory group Oak

503 White oak-red oak-hickory Oak

504 White oak Oak

505 Northern red oak Oak

509 Bur oak Oak

519 Red maple-oak Northern hardwoods

520 Mixed upland hardwoods Northern hardwoods

700 Elm/ash/cottonwood group Lowland hardwoods

701 Black ash-American elm-red maple Lowland hardwoods

702 River birch-sycamore Birch

703 Cottonwood Cottonwood/willow

704 Willow Cottonwood/willow

705 Sycamore-pecan-American elm Lowland hardwoods

706 Sugarberry-hackberry-elm-green ash Lowland hardwoods

707 Silver maple-American elm Lowland hardwoods

708 Red maple-lowland Lowland hardwoods

709 Cottonwood-willow Cottonwood/willow

800 Maple/beech/birch group Northern hardwoods

801 Sugar maple-beech-yellow birch Northern hardwoods

805 Hard maple-basswood Northern hardwoods

807 Retired (Elm-ash-locust) Northern hardwoods

809 Red maple-upland Northern hardwoods

Appendix 2.—FIA forest type to Minnesota Department of Natural Resources (DNR) cover type crosswalk

(Appendix continued on next page.)
134 | 

FIA forest 

type code FIA forest type/forest-type group

Minnesota DNR 

forest type

900 Aspen/birch group Aspen

901 Aspen Aspen

902 Paper birch Birch

904 Balsam poplar Balsam poplar

999 Nonstocked Nonstocked

All other types Other

(Appendix 2. continued) 
Miles, Patrick D.; VanderSchaaf, Curtis L.; Barnett, Charles; Butler, Brett J.; Crocker, 

Susan J.; Gormanson, Dale; Kurtz, Cassandra M.; Lister, Tonya W.; McWilliams, 

William H.; Morin, Randall S.; Nelson, Mark D.; Perry, Charles H.; Riemann, Rachel I.; 

Smith, James E.; Walters, Brian F.; Westfall, James A.; Woodall, Christopher W. 2016. 

Minnesota Forests 2013. Resource Bulletin NRS-104. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northern Research Station. 134 p.

The third full annual inventory of Minnesota forests reports 17.4 million acres of forest 

land with an average live tree volume of 1,096 cubic feet per acre. Forest land is 

dominated by the aspen forest type, which occupies 29 percent of the total forest land 

area. Twenty-eight percent of forest land consists of sawtimber, 35 percent poletimber, 

36 percent sapling/seedlings, and 1 percent is nonstocked. The average annual net 

growth of live trees on forest land is approximately 398 million cubic feet per year while 

average annual removals are only 207 million cubic feet per year. Additional forest 

attribute and forest health information is presented, along with information on agents of 

change including changing land use patterns and the introduction of nonnative plants, 

insects, and disease. Information from the Private Woodland Owner and Timber Products 

Output surveys is included along with 50-year projections from the Northern Forest 

Futures study. Detailed information on forest inventory methods, data quality estimates, 

and important resource statistics are available online at http://dx.doi.org/10.2737/NRSRB-104.

KEY WORDS: inventory, forest statistics, forest health, timberland, forest land, biomass, 

volume, growth, mortality, removals

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its 

Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating 

based on race, color, national origin, religion, sex, gender identity (including gender expression), sexual orientation, disability, age, 

marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for 

prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and 

complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, 

American Sign Language, etc.) should contact the responsible Agency or USDA’s TARGET Center at (202) 720-2600 (voice and TTY) 

or contact USDA through the Federal Relay Service at (800) 877-8339. Additionally, program information may be made available in 

languages other than English.

To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online 

at http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA office or write a letter addressed to USDA and provide 

in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your 

completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 

Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov.