United States Department of Agriculture

Subcontinental-Scale Patterns of 

Large-Ungulate Herbivory and Synoptic Review 

of Restoration Management Implications for 

Midwestern and Northeastern Forests

Forest Service

Northern Research Station

General Technical Report NRS-182

October 2018
Manuscript received for publication June 2018

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ABSTRACT

Browse of forest understory vegetation by deer and other large ungulates alters ecosystem 

processes, making it difficult to regenerate forest land in herbivory-stressed areas. Seventy years 

ago, Aldo Leopold identified problem areas in the United States where overpopulation of whitetailed deer (Odocoileus virginianus) was likely to lead to overbrowsing of nutritive plants. Species 

of plants with little or no nutritive value would thereby gain a competitive advantage. Recent 

measurements of browse impacts on regionwide forest inventory plots in the midwestern and 

northeastern United States provide the opportunity to review the work of Leopold and others. 

A visualization of the probability of browse impact levels that warrant consideration during 

regeneration planning is presented for comparison to historical maps.

Currently, 59 percent of the 182.4 million acres of forest land inventoried in the Midwest and 

Northeast was estimated to have moderate or high browse impacts. The Mid-Atlantic region had 

the highest proportion of forest land with moderate or high browse impacts (79 percent). The 

oak/hickory (Quercus/Carya) and maple/beech/birch (Acer/Fagus/Betula) forest-type groups 

each had percentages of forest land with moderate or high impacts above the regional average, 69 

percent and 65 percent, respectively. The problem areas described by Leopold and others persist 

and new areas have emerged in the Central/Plains, Mid-Atlantic, and New England States.

The study findings confirm three realities of forest regeneration management for forests under 

herbivory stress in the Midwest and Northeast: 1) The scope and persistence of large-ungulate 

herbivory has long-term wide-ranging implications for regeneration management; 2) less 

palatable tree species will continue to have a competitive advantage during the regeneration 

phase and are likely to be different species from the current canopy dominants; and 3) successful 

regeneration management of these forests requires more emphasis on ungulate-compatible 

prescriptions, novel approaches, and adaptive science.

Cover Photo: White-tailed deer herd. Photo by Garen Meguerian, via Flickr.com, used with permission. 

Quality Assurance: This publication conforms to the Northern Research Station’s Quality Assurance Implementation Plan which 

requires technical and policy review for all scientific publications produced or funded by the Station. The process included a blind 

technical review by at least two reviewers, who were selected by the Assistant Director for Research and unknown to the author. 

This review policy promotes the Forest Service guiding principles of using the best scientific knowledge, striving for quality and 

excellence, maintaining high ethical and professional standards, and being responsible and accountable for what we do.

Caution: Pesticides can be injurious to humans, domestic animals, desirable plants, and fish or other wildlife—if they are not 

handled or applied properly. Use all pesticides selectively and carefully. Follow recommended practices for the disposal of surplus 

pesticides and pesticide containers.
William H. McWilliams, James A. Westfall, Patrick H. Brose, 

Daniel C. Dey, Anthony W. D’Amato, Yvette L. Dickinson, 

Mary Ann Fajvan, Laura S. Kenefic, Christel C. Kern, Kenneth M. Laustsen, 

Shawn L. Lehman, Randall S. Morin, Todd E. Ristau, 

Alejandro A. Royo, Andrew M. Stoltman, and Susan L. Stout

Subcontinental-Scale Patterns of 

Large-Ungulate Herbivory and Synoptic Review 

of Restoration Management Implications for 

Midwestern and Northeastern Forests
ABOUT THE AUTHORS

William H. McWilliams is a research 

forester with the Forest Inventory and 

Analysis program at the Northern Research 

Station, USDA Forest Service, Newtown 

Square, PA.

James A. Westfall is a research forester 

with the Forest Inventory and Analysis 

program at the Northern Research Station, 

USDA Forest Service, Newtown Square, PA.

Patrick H. Brose is a research forester 

with the Sustaining Forests in a Changing 

Environment project at the Northern 

Research Station, USDA Forest Service, 

Irvine, PA.

Daniel C. Dey is project leader of the 

Sustainable Management of Central 

Hardwood Ecosystems and Landscapes 

project at the Northern Research Station, 

USDA Forest Service, Columbia, MO.

Anthony W. D’Amato is a professor at the 

Rubenstein School of Environment and 

Natural Resources, University of Vermont, 

Burlington, VT.

Yvette L. Dickinson is an assistant 

professor at the School of Forest Resources 

and Environmental Science, Michigan 

Technological University, Houghton, MI.

Mary Ann Fajvan is a research forester 

with the Ecology and Management of 

Invasive Species and Forest Ecosystems 

program at the Northern Research Station, 

USDA Forest Service, Morgantown, WV.

Laura S. Kenefic is a research forester 

with the Center for Research on Ecological 

Change at the Northern Research Station, 

USDA Forest Service, Bradley, ME.

Christel C. Kern is a research forester 

with the Center for Research on Ecological 

Change at the Northern Research Station, 

USDA Forest Service, Bradley, ME.

Kenneth M. Laustsen is a forest 

biometrician with the Maine Forest Service, 

Augusta, ME.

Shawn L. Lehman is section chief, 

Inventory & Monitoring Section for 

the Bureau of Forestry, Pennsylvania 

Department of Conservation and Natural 

Resources, Harrisburg, PA.

Randall S. Morin is a research forester with 

the Forest Inventory and Analysis program 

at the Northern Research Station, USDA 

Forest Service, Newtown Square, PA.

Todd E. Ristau is a research forester with 

the Sustaining Forests in a Changing 

Environment project at the Northern 

Research Station, USDA Forest Service, 

Irvine, PA.

Alejandro A. Royo is a research forester 

with the Sustaining Forests in a Changing 

Environment project at the Northern 

Research Station, USDA Forest Service, 

Irvine, PA.

Andrew M. Stoltman is a rural and urban 

forest inventory analyst with the Wisconsin 

Division of Forestry, Madison, WI.

Susan L. Stout is project leader with 

the Sustaining Forests in a Changing 

Environment project at the Northern 

Research Station, USDA Forest Service, 

Irvine, PA.

ENGLISH EQUIVALENTS

When you know 

metric:

Multiply by 

conversion factor

To convert to

English:

Kilometers 0.621 Miles

Meters 3.281 Feet

Hectares 2.471 Acres
CONTENTS

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Mapping Deer Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Study Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Forest Monitoring in the Midwest and Northeast . . . . . . . . . . . . . . . . 10

Visualizing Browse Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Oak/Hickory Forest-type Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Maple/Beech/Birch Forest-type Group . . . . . . . . . . . . . . . . . . . . . . . . 18

Other Forest-type Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

LITERATURE CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
ACKNOWLEDGMENTS

The authors would like to thank all of the field staff 

members who conducted the browse impact evaluations 

across such a wide range of conditions. The evaluations 

are completed along with measurements of other 

ecological indicator variables, making for an arduous 

and challenging field season. Without the hard work 

of these experts, the difficult job of evaluating browse 

impact could not have been completed within quality 

assurance tolerances. We are also grateful to those who 

provided valuable feedback on an earlier version of this 

document: Mark Nelson, Forest Service; Henry McNab, 

Forest Service; and three anonymous peers. All their 

comments were incorporated and the improvements are 

very apparent to the authors.
1

INTRODUCTION

In the forests of the 24-State midwestern and 

northeastern United States, herbivory (hereafter, 

“browse”) by large ungulates directly influences 

ecological function, values, and services over the 

lifespan of forest stands by setting compositional and 

structural trajectories during the stand-initiation 

phase of forest development (Côté et al. 2004, Hobbs 

et al. 2013, Waller and Alverson 1997). Historically, 

white-tailed deer (Odocoileus virginianus) have been 

at the center of research to understand and forestall 

large-ungulate impacts on forest regeneration 

in the Midwest and Northeast. Deer browse was 

recognized as an important factor affecting understory 

plant communities in the region as early as 1915 

(Frothingham 1915). Over the past decade or so, the 

role of moose (Alces alces) has become an issue in forest 

regeneration (Wattles and DeStefano 2011).

Browse alters growth rates, successional pathways, 

plant architecture, nutrient and litter cycling, and 

soil microenvironments (Witt and Webster 2010). 

Browse reduces the number of understory taxa by 

removing edible native plants, thereby increasing biotic 

homogenization of the forest understory (Augustine 

and McNaughton 1998, McKinney and Lockwood 1999, 

Olden et al. 2004, Rooney et al. 2004). In the overstory, 

oaks (Quercus spp.), red and sugar maple (Acer rubrum

and A. saccharum), hickories (Carya spp.), northern 

white-cedar (Thuja occidentalis), eastern hemlock 

(Tsuga canadensis), eastern white pine (Pinus strobus), 

and aspen (Populus tremuloides and P. grandidentata) 

have been reported as having difficulty sustaining 

presence and abundance due to high browse, which 

limits regeneration and recruitment into the overstory 

in portions of their range (Dey 2014, McWilliams et 

al. 1995, Rooney and Waller 2003, White 2012). Largeungulate browse also influences floral diversity and 

food webs (biomass, and seed and mast production) 

by modifying the quality and productivity of faunal 

habitat at multiple trophic levels, as indicated by insect 

and bird density (Bressette et al. 2012, Horsley et al. 

2003, Nuttle et al. 2011).

Large-ungulate browse is an agent of change that 

combines with other controlling factors to alter forest 

regeneration potential in the Midwest and Northeast. 

White-tailed deer herd. Photo by USDA Forest Service.
2

Key ecological factors with negative consequences for 

forest regeneration are mesophication (Nowacki and 

Abrams 2008), wildfire exclusion (Abrams 1992, Brose 

2014), soil nutrient degradation (Bailey et. al. 2005, 

Driscoll et al. 2001, Kardol et al. 2014), and forest land 

conversion (Drummond and Loveland 2010, Nowacki 

and Abrams 2014). Other controlling factors are forest 

fragmentation (Allen et al. 2013), native and alien plant 

invasion (Knight et al. 2009, Royo and Carson 2006), 

native and alien pests and diseases (Lovett et al. 2016, 

Potter and Conkling 2016), and climate change (Dukes 

et al. 2009, Iverson et al. 2008). Gaps in knowledge 

about multifactor interactions heighten the need for 

novel forest management approaches based on local 

conditions.

Seventy years ago, Aldo Leopold and others (1947) made 

projections about which areas in the continental United 

States would be susceptible to overbrowsing of nutritive 

plants because of deer overpopulation. He and his 

colleagues mapped areas of deer presence and absence, 

and identified areas of expected deer overpopulation, 

where browse pressure would leave plants that have 

little or no nutritive value. Recent measurements of 

browse impacts on forest inventory plots in the Midwest 

and Northeast provide the opportunity to review this 

pioneering work.

The goals of this report are twofold. The first goal is 

to review existing historical mapping efforts in light 

of the new findings on browse impacts presented 

here. Beguin et al. (2016) have developed a holistic, 

hierarchical framework of guidelines and information 

gaps for ungulate-forest systems globally. This report 

focuses on the extent and severity of browse impacts 

for the Midwest and Northeast. The second goal is to 

present a synoptic review of expected implications for 

regeneration management to restore forests under browse 

stress based on the assumption that forests with at least 

moderate impacts require consideration of ameliorative 

management prescriptions. Specific objectives are to 

review maps of deer presence and density, evaluate the 

extent of browse, identify browse problem areas and 

forest-type groups under pressure, and summarize 

implications for forest regeneration management. The 

results are a visual depiction of the role of browse in 

forest regeneration management that indicates where 

managers will most likely need to consider these 

implications in forest and habitat restoration efforts to 

overcome severe and perennial difficulties.

MAPPING DEER DENSITY

In the past, State wildlife agencies have collected and 

maintained geographic information on deer browse 

impacts. Most mapping efforts have used deer density 

expressed as the number of deer per square mile of 

forest land. Such maps typically use deer density 

estimates from different models, hunting results, 

deer-vehicle collision reports, and opinion surveys. 

Application of deer density information assumes 

that thresholds for delineating problem areas are 

known. For example, Tilghman (1989) found that it 

is necessary to keep densities at less than 18 deer per 

acre to allow development of healthy forest understory 

in northwestern Pennsylvania. The diversity of forest 

ecosystems, disparate modeling approaches, and lack of 

biome-specific thresholds limit the usefulness of deer 

density for large-scale maps covering multiple States. 

Maps covering the eastern United States are described 

next as an introduction to a new visualization based on 

empirical data collected consistently across the Midwest 

and Northeast.

Mapping Efforts in 1947

Leopold and his colleagues published a foundational 

survey in 1947 to assess deer populations and condition 

of their range across the United States (Fig. 1). The 

objective was to delineate areas where high-density deer 

populations would lead to reductions of carrying 

capacity, causing an imbalance between herd size and 

available food. The concern was that overbrowsed 

nutritive plants would be replaced by plants with little to 

no nutritive value (Leopold et al. 1943). Though this work 

was innovative at the time, it was largely based on the 

personal experience of observers, which precludes 

statistical comparison with the new visualization 

presented here.
3

Mapping Efforts in 1950, 1970, 1980, 

1982, and 1988

In the 1980s, population occurrence and density maps 

were developed as part of a series of investigations into 

the geographic distribution of cloven-hoofed animals 

susceptible to hand-foot-and-mouth disease (Coxsackie

viral infection) for the continental United States. The 

project assisted efforts by the University of Georgia 

(n.d.) to stem prospective outbreaks in humans. The 

project created county-level thematic maps for the 

southern United States for 1950, 1970, and 1980 (Fig. 2), 

and for the eastern United States for 1982 and 1988 

(Fig. 3). Trends cannot be determined reliably for a 

variety of reasons. Details of the specific methodologies 

used may differ from year to year because descriptions 

of earlier survey methods were not always available 

and agencies often used different population models 

from survey to survey. Estimation techniques were 

also improved over time. Although the study ended 

in 1988 (John Fischer, University of Georgia, College 

of Veterinary Medicine, pers. comm., 2017), these 

visualizations offer monumental insight into how deer 

browse levels and associated impacts have changed 

across the Midwest, Northeast, and beyond since 

Leopold and his colleagues made their original map.

The visualization for 1950 shows deer population 

occurrences in the southern United States that appear 

new since Leopold and others’ work though early data 

limitations explain some of the expansion depicted. 

Specifically, Leopold and colleagues did not obtain data 

from the mountains of West Virginia and Virginia; the 

Piedmont of Virginia; the Atlantic coast of Georgia, 

North Carolina, and South Carolina; and riverine systems 

in Alabama, Georgia, and Mississippi. The results for 

1970 and 1980 reveal a much larger estimate of density 

across the South. The findings for 1982 and 1988 indicate 

that extensive well-established populations throughout 

the eastern United States exceed the “environmental 

capacity,” defined as 15 deer per square mile of forest.

Figure 1.—Areas in which overpopulations of deer now exist or have existed in the recent past (as of 1947), contiguous United States (Leopold 

et al. 1947). Numbers refer to case histories. Map used with permission of Wiley Inc., with copyright retained by The Wildlife Society.
4

Figure 2.—White-tailed deer population density, southern United States, 1950, 1970, and 1980 (Southeastern Cooperative 

Wildlife Disease Study [University of Georgia, n.d.], used with permission).
5

Figure 3.—White-tailed deer population density, eastern United States, 1982 and 1988 (Southeastern Cooperative Wildlife 

Disease Study [University of Georgia, n.d.], used with permission).
6

Mapping Efforts from 2001 through 2005

A more recent county thematic map was published for 

the eastern United States for 2001 through 2005 

(Russell et al. 2017, Walters et al. 2016) (Fig. 4). The map 

was produced by the Quality Deer Management 

Association (QDMA) as an informative aid for deer 

hunters. The limitations of the map are that it is based 

on information by State agencies that use different 

population estimation procedures and that these 

methods are not repeatable because some agencies now 

use improved approaches or no longer attempt to 

estimate population levels (Kip Adams, Director of 

Education and Outreach, QDMA, pers. comm., 2014). 

Despite these caveats, the map clearly depicts deer 

density information for this region ca. 2005.

Figure 4.—White-tailed deer density per square mile of land area, 

eastern United States, ca. 2005 (Walters et al. 2016). 

American beech (Fagus grandifolia ). Photo by William H. McWilliams, USDA Forest Service. 
7

METHODS

STUDY REGION

The Midwest (Illinois, Indiana, Iowa, Kansas, 

Michigan, Minnesota, Missouri, Nebraska, North 

Dakota, Ohio, South Dakota, and Wisconsin) and 

Northeast (Connecticut, Delaware, Maine, Maryland, 

Massachusetts, New Hampshire, New Jersey, New 

York, Pennsylvania, Rhode Island, Vermont, and West 

Virginia) contain 182.4 million acres of forest land 

covering 29 percent of the land surface of the study 

region (Miles 2018). Forest land is an integral provider 

of ecological services, such as clean water, clear air, 

biodiversity, wildlife habitat, and carbon storage; and 

economic benefits, such as housing, infrastructure, 

fine-wood products, and paper. The following statistics 

illustrate the importance of the region: It is home to half 

of the American population, represents more than onethird of the forest carbon stored, and accounts for just 

over half of the hardwood roundwood production of the 

continental United States (Oswalt et al. 2014).

Located between 35 and 50o N latitude and 66 and 

105o W longitude, the region ranges in elevation 

from sea level to more than 6,900 feet. Climate 

varies widely across the region, with average annual 

temperature ranging from 32 to 61 oF, increasing from 

north to south. Average precipitation varies from 10 

to 50 inches per year, increasing from west to east. 

The region’s forests occur in portions of the Prairie, 

Warm Continental, Hot Continental, and Subtropical 

ecological divisions, and encompass all or part of 16 

ecological provinces (Cleland et al. 2007). For this 

study, provinces were grouped into four ecological 

subdivisions: Central/Plains, Lake, Mid-Atlantic, 

and New England. The delineations do not follow 

State boundaries, but were based on broad ecological, 

physiographic, and phenologic characteristics to 

facilitate discussion of regeneration management 

issues for the major forest-type groups under browse 

pressure (Figs. 5 and 6, Table 1).

American woodcock (Scolopax minor ). Photo by Ricky Layson Photography, via bugwood.com, used with permission.
8

Figure 6.—Distribution of forest land by dominant forest-type group, Midwest and Northeast (Ruefenacht et al. 2008).

Figure 5.—Location of ecological subdivisions used for analysis (Cleland et al. 2007), Midwest and Northeast.
9

Table 1.—Percentage of forest land area and total aboveground biomass for the five 

most prevalent forest-type groups and taxa, by ecological subdivision, Midwest and 

Northeast, 2017. 

Forest-type group

Percentage of

forest land Taxa

Percentage of

aboveground biomass

 All subdivisions

Oak/hickory 36 Maple 23

Maple/beech/birch 25 Red oak 13

Aspen/birch 9 White oak 10

Spruce/fir 8 Ash 5

Elm/ash/cottonwood 8 Hickory/walnut 5

Other 14 Other 44

Total acres 182,359,696 Total short tons 9,135,017,184

 Central/Plains

Oak/hickory 60 White oak 19

Elm/ash/cottonwood 16 Red oak 17

Maple/beech/birch 6 Maple 13

Oak/pine 4 Hickory/walnut 11

Ponderosa pine 3 Ash 6

Other 11 Other 34

Total acres 50,666,979 Total short tons 2,267,355,719

 Lake

Aspen/birch 26 Maple 28

Maple/beech/birch 23 Red oak 14

Spruce/fir 19 Eastern white/red pine 9

Oak/hickory 10 Red oak 8

White/red/jack pine 9 Spruce/fir 8

Other 13 Other 33

Total acres 42,822,294 Total short tons 1,521,565,869

 Mid-Atlantic

Oak/hickory 61 Maple 20

Maple/beech/birch 18 Cottonwood/aspen 18

Elm/ash/cottonwood 5 White oak 14

Oak/pine 5 Yellow-poplar 7

Other coniferous 3 Hickory/walnut 6

Other 8 Other 35

Total acres 42,800,614 Total short tons 2,839,611,768

 New England

Maple/beech/birch 54 Maple 32

Spruce/fir 16 Spruce/fir 10

Oak/hickory 10 Red oak 7

White/red/jack pine 7 Yellow birch 7

Aspen/birch 6 Beech 6

Other 7 Other 38

Total acres 44,833,504 Total short tons 2,455,026,692
10

The study region has 96 percent of the Nation’s maple/

beech/birch (Acer/Fagus/Betula) and 42 percent of the 

oak/hickory (Quercus/Carya) forest land, and species 

associated with these groups have comparable amounts 

of total aboveground biomas. These forest-type groups 

make up 25 percent and 36 percent, respectively, of the 

total forest land in the region. Aspen/birch (Populus/

Betula) (9 percent), spruce/fir (Picea/Abies) (8 percent), 

elm/ash/cottonwood (Ulmus/Fraxinus/Populus) (8 

percent), white/red/jack pine (P. strobus/P. resinosa/P. 

banksiana) (5 percent), and other miscellaneous 

coniferous and deciduous forests make up the rest 

(Miles 2018).

FOREST MONITORING IN THE 

MIDWEST AND NORTHEAST

Monitoring results for large-ungulate effects on forest 

regeneration were obtained using new measurement 

protocols covering regeneration and browse as part of 

the three-phase regionwide forest inventory conducted 

by the USDA Forest Service, Northern Research Station, 

Forest Inventory and Analysis (NRS-FIA) program 

(Bechtold and Patterson 2005). Phase 1 uses classified 

remote sensing imagery to stratify sample plots and 

reduce uncertainty (variance) for estimates of population 

totals (McRoberts et al. 2006).

In Phase 2, sample plots are visited and field 

measurements taken on forested conditions throughout 

the year at an intensity of one plot per roughly 5,500 

acres. Each sample plot is made up of a cluster of four 

24-foot fixed-radius subplots. Each subplot contains a 

6.8-foot-radius microplot offset from the subplot center 

(USDA Forest Service 2016).

In Phase 3, data for a suite of enhanced ecological 

indicators are collected on a subset of NRS-FIA Phase 

2 plots sampled during the leaf-on summer season at 

an intensity of one plot per 95,998 acres (USDA Forest 

Service 2017). A regeneration indicator (RI) was added 

as a Phase 3 indicator starting in 2012 (McWilliams et al. 

2015). (The subset of Phase 2 plots is sometimes referred 

to as “Phase 2-plus.”) This study used 4,162 Phase 3 RI 

sample plots completed from 2012 to 2017 (Table 2). 

All of the data used in this study are publicly available 

from the NRS-FIA data portal (https://apps.fs.usda.gov/

fia/datamart/CSV/datamart_csv.html).

The RI protocols include browse-impact severity 

evaluation and seedling measurements not included on 

Phase 2 plots. That is, all established seedlings from 2.5 

inches in height and less than 5.0 inches in diameter 

at breast height (minimum diameter for saplings) are 

counted by seedling height class (2.0 to 5.9 inches, 6.0 

to 11.9 inches, 1.0 to 2.9 feet, 3.0 to 4.9 feet, 5.0 to 9.9 

feet, and 10 feet and higher). The browse-impact severity 

evaluation indicates the amount of stress that herbivores 

are exerting on tree seedlings and other understory flora.

As used here, browse is defined as animals’ consumption 

of tender shoots, twigs, and leaves of trees or shrubs for 

food (USDA Forest Service 2017). The browse evaluation 

does not target specific edible taxa because forests under 

browse stress may lack palatable vegetation. Latham 

et al. (2005) summarize the difficulty of evaluating 

browse in heavily used regions by noting that it depends 

on what is available, which further depends on local 

population density, recent trends in density, alternative 

food sources, adjacent land uses, disturbance history, 

snow cover, and other factors. This means that even 

though food preference lists are available (see Atwood 

[1941]), herbivores’ preferences are known to differ by 

season and region for the same forest type (Latham et 

al. 2005, Stiteler and Shaw 1966). The coding system was 

developed for forest ecosystems under browse pressure 

from white-tailed deer in the Northeast (Brose et al. 

2008, Marquis et al. 1992). Guidelines for assessing 

impacts of moose and other browsers, such as elk (Cervus 

canadensis), or snowshoe hare (Lepus americanus), are 

not available in the literature, but their impacts were 

evaluated if they met the following deer browse-impact 

definitions:

Low: Plot is inside a well-maintained fence or minimal 

browsing is observed, or vigorous seedlings are present 

and of varied height if no well-maintained fence is 

present. Herbaceous plants are present and are able to 

complete their life cycles.

Moderate: Evidence of browsing is observed but not 

common. Seedlings are common but with limited 

variability in height. Stump sprouts are heavily browsed 

or not present. Herbaceous plants show a lack of or 

inhibited flowering and fruiting. There is little or no 

evidence of browsing on nonpreferred plants.
11

High: Evidence of browsing is common on preferred 

vegetation. Preferred seedlings and herbaceous plants are 

rare or absent. Nonpreferred plants show some evidence 

of browsing. Browse-resistant vegetation is limited in 

height growth. Evidence of browsing is everywhere. 

Nonpreferred, browse-resistant plants show signs of 

heavy repeated browsing, and a browse line is present.

Field crews evaluate browse impact for the forest 

land portion of the four subplots. The evaluation 

can be challenging due to variability in understory 

communities, variability in site quality, and other 

factors. For example, though the coding system was 

designed with the flexibility to cover a wide range of 

forest types across a very large geographic area, there is 

some variability among types that is not fully accounted 

for during the assessment. The aspen type within 

the aspen/birch forest-type group is a good example 

because it regenerates primarily by coppice. A collective 

90 percent of the aspen forest type in the study region 

is in Minnesota (40 percent), Michigan (19 percent), 

Maine (19 percent), and Wisconsin (12 percent); aspen 

in these States is considered a medium-quality food 

that is consumed after preferred taxa such as maple, 

white pine, and northern white-cedar (Michigan State 

University Cooperative Extension Service 1967). Aspen 

is recognized for prolific root sprouting that has been 

estimated as 10 times the amount produced by associated 

conifers, and young “sucker” stands can withstand 

consumption of up to 50 percent and still be considered 

to have an acceptable number of stems (Sampson 

1919). This means that analysis of results for aspen may 

show lower probabilities of occurrence, as is evident in 

northern Minnesota.

Ungulate population density and factors outside the 

evaluation area (e.g., alternative food sources) further 

complicate the assessment. To overcome these issues, 

Table 2.—Number of sample locations on forest land, by ecological subdivision and 

province (Cleland et al. 2007), Midwest and Northeast.

Subdivision Number of samples

 Central/Plains

Central Interior Broadleaf Forest (223) 403

Lower Mississippi Riverine Forest (234) 1

Midwest Broadleaf Forest (222) 403

Prairie Parkland (Temperate) (251) 256

Prairie Parkland (Subtropical) (255) 7

Southeastern Mixed Forest (231) 4

Great Plains-Palouse Dry Step (331) 24

Great Plains Steppe (332) 66

Black Hills Coniferous Forest (M334) 22

Southwest Plateau and Plains Dry Steppe and Shrub (315) 1

Subtotal 1,187

 Lake

Laurentian Mixed Forest (212) 897

 Mid-Atlantic

Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow (M221) 354

Eastern Broadleaf Forest (221) 582

Outer Coastal Plain Mixed Forest (232) 70

Subtotal 1,006

 New England

Adirondack-New England Mixed Forest-Coniferous Forest-Alpine Meadow (M211) 447

Northeastern Mixed Forest (211) 625

Subtotal 1,072

All 4,162
12

field staff members are trained by expert crews, certified 

by trainers, and subject to annual quality assurance 

checks. Another favorable element is that the field crews 

visit plots on an interpenetrating annualized sample 

grid. This sampling approach means that they visit the 

entire work region each year and are familiar with local 

forest conditions.

Note that NRS-FIA does not take measurements in some 

urbanized areas and nonforest conditions where very 

high browse pressure is common. Forest conditions where 

ungulates are typically concentrated, but where few FIA 

samples are collected, are fragmented forests consisting of 

smaller tracts and strips, and riparian forests. 

VISUALIZING BROWSE IMPACTS

New empirical-based monitoring results for largeungulate browse impacts presented here provide an 

opportunity to assess this issue in a spatial context. 

The underlying assumption is that the severity of 

these impacts indicates where adaptive or corrective 

management will most likely be needed for redirecting 

forest succession after a stand-initiating disturbance.

The browse-impact code was used to generate a 

geospatial visualization for the Midwest and Northeast. 

Geographic analysis is challenging because of diverse 

physiography, diverse soils, and fluctuating seasonal and 

annual ungulate populations, as well as the wide range 

of floral and faunal phenologies among taxa that are 

endemic to the study region. Indicator kriging offers a 

means of addressing these issues. The indicator kriging 

function in Esri’s ArcMap 10.2.2 Geostatistical Analyst 

package was used to create interpolated surfaces from 

sample location coordinates (Isaaks and Srivastava 

1989). Predictions using indicator kriging are presented 

as the probability of occurrence for moderate or high 

browse impacts. It was not feasible to produce a separate 

visualization for high impacts because of the limited 

number of samples relative to the large region modeled. 

Indicator kriging models use autocorrelation between 

observations as a function of distance assuming the 

following model:

I(s) = μ + ε(s),

where μ is an unknown constant, ε(s) is the random 

autocorrelation error, and I(s) is a binary variable. The 

two browse-code classes were combined and converted 

to binary data, and the modeled semivariogram 

parameters (nugget and partial sill) were optimized by 

using cross-validation with a focus on estimating the 

range parameter. The semivariogram optimization was 

based on minimizing the mean squared error (see the 

semivariogram and model parameters in Figure 7A). 

Indicator kriging was then used to predict the probability 

of the moderate and high browse-impact classes. In the 

cross-validation of observed versus predicted values 

(Fig. 7B), the range and medians of the predicted 

probabilities increase with the observed browse classes as 

expected. A nonforest mask was then applied to display 

modeled browse impact only on forested areas based on 

the work of Wilson et al. (2012). The final spatial product 

depicts the probability of a binary value that represents 

moderate or high ungulate browse impacts occurring at 

a given pixel. A kriged surface of the distribution of 

standard errors for the probability of occurrence 

estimates was created to portray spatial variation across 

the study region (Fig. 8).
13

Figure 7.—Predicted probabilities by browse impact code (A). The lower and upper edges of the boxes represent the 25th and 75th percentiles 

and the line connects the median values (50th percentile). Vertical lines are the extreme points for the 1.5 interquartile ranges, and values 

outside this range are represented by circles. Semivariogram for the indicator kriging model where output pixel size is 10 km, lag size is 8.6 

km, number of lags is 12, range is 68.9 km, nugget is 0.0977, and sill is 0.0889 (B).

Figure 8.—Distribution of standard errors of estimates of probability of occurrence for browse impact visualization (Fig. 9), 

Midwest and Northeast, 2017.
14

RESULTS

The visualization of browse impacts depicts broad 

regions where forest managers and others tasked with 

reforestation should consider browse impacts when 

planning for regeneration (Fig. 9). A probability of more 

than 70 percent is a somewhat arbitrary but useful and 

well-described minimum for identifying areas where 

forest managers need to consider local browse pressure 

(Brose et al. 2008, Rosenberry et al. 2009). This range is 

based broadly on the percentage of silvicultural samples 

required to prescribe certain management actions based 

on regeneration survey samples (Marquis et al. 1992). 

Areas with probabilities greater than 70 percent for 

moderate or high impacts were found in the northern 

highlands of Wisconsin; the western Upper Peninsula 

and northern Lower Peninsula of Michigan; the Ozark 

Highlands of southwestern Missouri; the southern 

portions of Illinois, Indiana, and Ohio; the Appalachian 

Mountains, which cover much of the Mid-Atlantic States, 

New York, and southern New England; New Hampshire; 

and Vermont.

Probabilities from 31 percent to 70 percent provide a 

practical range for identifying areas where consideration 

of local browse conditions is advisable. Areas in this 

range are scattered throughout the eastern Mid-Atlantic 

States; and the southern Adirondack Mountains, 

Mohawk River Valley, and Appalachian Plateau of New 

Figure 9.—Probability of occurrence for moderate or high ungulate browse impacts on forest land, Midwest and Northeast, 2017.
15

York. Areas with probabilities of 30 percent or less 

probably do not warrant measures to control ungulate 

pressure in management prescriptions. These areas are 

most common in the Ozark Highlands of southeastern 

Missouri, and the Aroostook uplands and eastern 

interior of Maine.

Review of Leopold and others’ 1947 map of 

overpopulated deer ranges in the context of the 

visualization of browse effects presented here offers 

insight into the longevity of existing problem areas 

and new areas of browse stress that have emerged over 

the past 70 years. Leopold and others’ deer survey 

identified seven major problem areas in the Midwest 

and Northeast: northern Wisconsin, the Upper 

Peninsula and northern Lower Peninsula of Michigan, 

most of Pennsylvania, the Adirondack region of New 

York, southwestern New York, and Vermont. The 1947 

map showed that deer were absent or scarce in the 

rest of the region. The findings presented here show 

that the problem areas Leopold and others described 

have persisted, and areas of concern have expanded 

to Illinois, Indiana, Ohio, West Virginia, Maryland, 

Delaware, New Jersey, and the southern New England 

States. The review did not indicate any areas where deer 

problem areas were reduced.

As a general guideline, management prescriptions to 

minimize species loss and encourage regeneration of 

desirable tree species should be considered in areas 

when impacts are moderate or high and where tree 

reproduction is absent or healthy seedling development 

is not possible without additional management inputs 

(Brose et al. 2008). The original Phase 3 RI samples 

used to generate the visualization provide estimates 

of the percentage of forest land with moderate or high 

browse impacts for subdivisions and forest-type groups. 

Overall, 59 percent of the forest land in the Midwest and 

Northeast had evidence of moderate or high impacts 

(Fig. 10). The Mid-Atlantic subdivision had the highest 

proportion (79 percent) of forest land with moderate or 

high impacts, followed by the Central/Plains (61 percent). 

The levels of moderate or high impacts were below the 

regional average for the Lake (43 percent) and New 

England (45 percent) subdivisions.

The oak/hickory and maple/beech/birch forest-type 

groups had the highest percentage of forest land with 

moderate or high browse impacts, 69 percent and 65 

percent, respectively (Fig. 11). Elm/ash/cottonwood and 

other deciduous forest-type groups had percentages near 

the regional average. For spruce/fir, aspen/birch, white/

red/jack pine, and other coniferous forest-type groups, 

the proportion was less than the regional average.

Pine Creek Gorge, Pennsylvania. Photo by William H. McWilliams, USDA Forest Service.
16

Maple/beech/birch 

Oak/hickory 

32 

41 44 

56 56 

0 

10 

20 

30 

40 

50 

60 

70 

80 

Spruce/fir 

Aspen/birch 

White/red/jack pine 

Elm/ash/cottonwood 

Other deciduous 

Other coniferous 

Percent 

Forest-type Group 

57 65 69 

Figure 11.—Percentage of forest land with moderate or high ungulate 

browse impacts by forest-type group, Midwest and Northeast, 2017. 

Error bars represent 68 percent confidence intervals.

Central/Plains

60

50

40

30

20

10

0

Low Moderate High

39 50

11

Percent

Lake

60

50

40

30

20

10

0

Low Moderate High

57

36

7

Percent

Mid-Atlantic

60

50

40

30

20

10

0

Low Moderate High

21

58

21

Percent

New England 60

50

40

30

20

10

0

Low Moderate High

55

43

2

Percent

All Subdivisions

60

50

40

30

20

10

0

Low Moderate High

41 47

12

Percent

Figure 10.—Percentage of forest land by ecological subdivision and browse impact code, 

Midwest and Northeast, 2017. Error bars represent 68 percent confidence intervals.
17

White-tailed deer herd. Photo by Sin H. Ling, Winter Wildlife NJ, used with permission.

DISCUSSION

Although it was not feasible to make direct quantitative 

comparison with Leopold and others’ map due to 

differences in methodology, it is clear that regions 

identified by Leopold and his colleagues remain as 

challenges for regeneration management. Oak/hickory 

and maple/beech/birch are under more browse pressure 

than the other forest-type groups. The proportion of 

forest land with moderate or high browse impacts 

for the other forest-type groups ranged from 32 to 57 

percent, further supporting the notion that ungulatecompatible silviculture, such as prescribed fire, control of 

competing vegetation, and fencing, has become a major 

thrust for managers in the Midwest and Northeast. A 

synoptic review of existing silvicultural systems in the 

context of browse pressure clarifies the spectrum of 

options available to overcome challenges to successful 

regeneration management. Management decisions for 

specific forest stands depend on the mixture of stress 

factors and taxa desired for the regeneration component. 

The two most prevalent forest-type groups in the study 

region (oak/hickory and maple/beech/birch), as well as 

aspen/birch, spruce/fir, and white/red/jack pine foresttype groups, are discussed next to illustrate management 

options for systems under browse stress.

OAK/HICKORY FOREST-TYPE GROUP

Successful regeneration of the oak/hickory foresttype group is a prime example of the role of forest 

management in overcoming regeneration obstacles. 

Most oak/hickory forest land in Missouri has relatively 

low moderate or high browse impacts, though impacts 

were higher in Ohio, Indiana, and Illinois, and the MidAtlantic and New England subdivisions. Oaks, hickories, 

and some associates share traits that work against 

stand replacement efforts, such as high palatability and 

intermediate shade tolerance or shade intolerance. They 

tend to grow where large-gap disturbances (fire) are 
18

uncommon. These characteristics create advantages 

for maples and other taxa that are less palatable, are 

more shade tolerant, and can regenerate in small gaps. 

Ungulate-compatible measures to overcome these 

conditions are large-gap disturbance, prescribed fire, 

control of competing vegetation, and deer impact 

mitigation.

The shelterwood system has been a successful technique 

for establishment and development of advance 

regeneration in oak/hickory forests when used along 

with related prescriptions: fencing, herbicide, or 

prescribed fire (Brose et al. 2008, 2014). Shelterwood 

harvests create large gaps, and burning controls 

competing vegetation by eliminating more fire-sensitive 

species, such as red maple (Fei et al. 2011, Raeker et al. 

2011). Chemical or mechanical control of competing 

vegetation may also be required to foster microsites 

favorable to germination and provide increased growing 

space for seedlings. Some advantages of shelterwood 

systems are the flexibility to provide available light by 

managing stand density through one or more harvests, 

control of seed tree composition, development of 

conditions to promote the establishment of advance 

regeneration, and development of desirable regeneration 

from advance reproduction, stump sprouts, and seed.

In areas that have a history of browsing, recalcitrant 

understory vegetation can inhibit seedling establishment 

and development (Royo and Carson 2006). This 

condition requires control by using herbicides, brush 

cutting, and release cuts. These prescriptions can be 

enhanced by fencing or caging crop trees. Oak/hickory 

stands in the woodlands and savannas of the Plains 

States with abundant reproduction are characterized by 

unique conditions of diverse flora and fauna, and greater 

vegetative biomass in the understory. These natural 

features provide alternative food for herbivores, which 

helps decrease pressure on young seedlings (Dey and 

Kabrick 2015).

MAPLE/BEECH/BIRCH FOREST-TYPE GROUP

Maple/beech/birch forest land is most common in the 

northern ecological subdivisions, where the native 

ranges of deer and moose overlap. Woody vegetation 

food choices of deer and moose are similar, suggesting 

the possibility of competition between these herbivore 

species (Hunter 1990). However, their distribution is 

more influenced by other habitat factors; deer prefer 

shelter, such as forest edges, and moose are comfortable 

in a wide range of habitats with a range extending 

farther northward (Kearney and Gilbert 1976). Both 

deer and moose alter their seasonal browse patterns as 

they seek protection during extreme climatic events. 

For example, deer favor dense forest patches often 

dominated by conifers during snow storms or extreme 

cold weather. Heavy concentrations of ungulates can 

cause localized suppression of conifers and other 

species that serve as winter food sources, regardless 

of overall ungulate abundance on the landscape 

(Witt and Webster 2010). Increasing available light 

for development of shade-intolerant species by forest 

thinning or harvesting is a practical goal, but it is crucial 

to use opening sizes and manage light levels so that 

they meet the physiological needs of desired species 

and promote growth without encouraging competing 

vegetation (see Kern et al. [2016] for details).

Although the northern subdivisions generally have lower 

probabilities of browse impacts, there are areas with 

probabilities of 31 percent to 60 percent for moderate 

Forest ground flora. Photo by William H. McWilliams, USDA Forest 

Service.
19

or high impacts where forest managers will need to 

be cognizant of local conditions. Increases in moose 

populations in the northern part of the New England 

subdivision beginning in the late 1970s have resulted 

in concentrated areas of high pressure in Maine, New 

Hampshire, and Vermont, particularly for deciduous 

species (Andreozzi et al. 2014). Moose populations have 

also expanded into portions of southern New England, 

leading to localized impacts on the regeneration of 

eastern hemlock and maple (Faison et al. 2010). Declines 

in moose have been reported in the news for the Great 

Lakes region, but peer-reviewed findings have not 

confirmed these reports. Current anecdotal evidence 

suggests that a warmer climate in conjunction with an 

increase in occurrence of the moose tick (Dermacentor 

albipictus) is a related concern due to moose mortality 

caused by anemia, particularly along the southern 

edge of the ungulate’s range. Future trends in moose 

population density will ultimately determine the role of 

this herbivore in the development of forest understories.

Maple/beech/birch regeneration management activities 

are made complicated by differences in tree-seedling 

palatability and shade tolerance (Nyland 2002, Oliver 

and Larson 1996, Smith et al. 1997). Gap-based 

silviculture allows tailoring of harvest gaps to address 

these complications, but requires minor or major 

adjustments to business-as-usual management practices 

due to browsing. The selection of mitigation practices 

depends on forest type, condition, and browse levels. 

For instance, managers interested in regenerating a 

particular species within closed-canopy forests typically 

control available light by manipulating canopy openness 

to favor the shade tolerance of regenerating seedlings 

and improve the probability of gaining a dominant 

or codominant canopy position in the future forest 

(Kern et al. 2016). A business-as-usual practice may 

be to create canopy gaps of 33 to 66 feet in diameter to 

establish pockets of shade-intolerant yellow birch (Betula 

alleghaniensis) regeneration within closed-canopy maple 

stands. Current conditions of moderate or high browse 

impacts in the region may thwart implementation of this 

management strategy because yellow birch seedlings are 

a browse-preferred species and small-statured seedlings 

are within an ungulate’s reach. Under these conditions 

future canopy dominants are more likely to be species 

that are browse resistant rather than shade intolerant 

(Kern et al. 2012).

Ungulate mitigation efforts could include gap-level 

protection until the pocket of regeneration outgrows the 

reach of ungulates, such as use of temporary fencing, 

repellents, or bud caps (MacGowan et al. 2004, Ward and 

Mervosh 2008). In most cases, these gaps are dispersed 

and numerous across stands, which means gap-level 

mitigation measures may not be cost effective.

As on oak/hickory forest land, large harvest areas 

are used to foster tree-seedling densities that exceed 

the feeding capacity of the local ungulate population. 

Maintaining high-density mixtures of preferred and 

nonpreferred species has been suggested as a way to 

confer associational resistance to browse-preferred 

species (Herfindal et al. 2015). Leaving unmerchantable 

tops and limbs minimizes nutrient loss and creates 

shelters for seedling development and impediments to 

browsers if slash is piled (Leak et al. 2014, Van Ginkel 

et al. 2013). Managers can also consider landscapescale ungulate mitigation by focusing regeneration 

management in stands where nonforest sources of food 

are available to divert browsers away from preferred 

browse species or, conversely, focus regeneration efforts 

far from known ungulate concentrations, such as in deer 

wintering areas (Millington et al. 2010).

OTHER FOREST-TYPE GROUPS

The northern subdivisions have a large proportion of 

the study region’s aspen/birch, spruce/fir, and white/red/

jack pine forest land. Unique regeneration management 

options are available for these forest-type groups when 

under deer or moose browse pressure. Managers need 

to consider not only palatability, shade tolerance, 

and fire resistance, but also the role of target taxa in 

succession (pioneer or climax). The choice of silvicultural 

prescriptions may include natural regeneration, planting, 

or a combination of both to ensure enough seedlings for 

adequate regeneration. In areas of high browse impact, 

techniques such as fencing, tree cages, and others 

mentioned earlier should be used according to browse 

level and species palatability.
20

CONCLUSIONS

Results of this study underscore three challenges 

for regeneration management of midwestern and 

northeastern forests under browse stress. First, browse 

impacts have extensive and long-term implications. 

Large ungulates still impede regeneration management 

in problem areas identified by Leopold and colleagues 

70 years ago. Leopold did not predict that control or 

reduction of large ungulates would become a perennial 

challenge, but he did correctly anticipate that problem 

areas would expand as now apparent in the Central/

Plains, Mid-Atlantic, and New England subdivisions. 

Since then, moose have repopulated native ranges and 

reached irruptive levels in some areas of New England 

(Wattles and DeStefano 2011). As noted by DeGraff and 

Yamasaki (2000), populations of such predators as gray 

wolf (Canis lupus), coyote (Canis latrans), black bear 

(Ursus americanus), and bobcat (Lynx rufus) have also 

increased since Leopold and colleagues’ study.

Second, less palatable tree species will continue to have 

a competitive advantage during the regeneration stage, 

potentially resulting in a future canopy composition that 

is different from the existing canopy-dominant species. 

Composition of new plant assemblages of browseresistant plants will need to be identified, studied, and 

included in management planning as appropriate (Lugo 

2015). Managers may need to modify desirable species 

lists and accept new taxa and assemblages as surrogates 

for native taxa. Where possible, it is important to 

identify and promote existing native species to ensure 

sustainability of forest values that the public has come 

to expect, such as high-canopy taxa, aesthetics, wildlife, 

nontimber forest products, and marketable wood.

In general, regeneration of deciduous and coniferous 

forests dominated by taxa of intermediate shade tolerance 

or shade intolerance require competitive advance 

regeneration, adequate light, control of competing 

Young red spruce (Abies rubens ) and fir (Abies balsamifera ). Photo by William H. McWilliams, USDA Forest Service.
21

vegetation, and protection of young seedlings from browse 

damage. Study results identified oak/hickory and maple/

beech/birch forest-type groups as having the most forest 

land under browse stress. Of particular concern is the 

restoration of large-seeded taxa with intermediate to 

low shade tolerance that rely on abundant light during 

the establishment stage of regeneration, such as oaks, 

hickories, and walnuts (Juglans spp.). Pine-dominated 

forests in stressed areas will require examination of local 

browse levels and selection of appropriate prescriptions for 

natural and plantation management.

Third, even though the principle that early stand 

management determines future forest condition is 

axiomatic, it takes on a new dimension for forests under 

browse stress given the empirical results presented here. 

Monitoring composition, structure, and browse will be 

critically important for success. Planning for regeneration 

management is made difficult by the interactions of 

multiple factors, such as the size and condition of the 

forest tract, dominant tree species, degree of alien plant 

or pest intrusion, and the population dynamics of the 

browsers, as influenced by birth rate and losses from 

predation, disease, and starvation. As large-ungulate 

browsing delays or curtails regenerative potential, 

recovery of forest understories can take from 20 to 50 

years depending on conditions, varies from full to partial 

restoration, and may never be achieved (Hobbs 1996, 

Latham et al. 2005, Nuttle et al. 2014, Rooney et al. 2004).

Some practical concepts from Pennsylvania’s long history 

of browse stress that have been successful in reducing 

landscape-scale impacts are: managing browsing; 

monitoring and controlling local browser populations; 

coordinating among owners, agencies, and other 

authorities; and implementing silvicultural prescriptions 

compatible with browse impacts (McWilliams et al. 

2017). Cooperation between wildlife and forestry 

agencies has a proven track record in the Mid-Atlantic 

region; for example, landscape-level herd management 

has improved the balance between deer and forest 

habitat in Pennsylvania (Rosenberry et al. 2009). As new 

information becomes available, science-based adaptive 

management will best ensure success in achieving 

management goals.

This study has demonstrated that mitigation of the 

effects of large-ungulate browsing will require forest 

policymakers and managers to be diligent over the long 

term in focusing on management of local conditions 

using the suite of available silvicultural tools and yet 

undiscovered approaches. Results of this study begin to 

fill geospatial knowledge gaps and address policy and 

management realities for the study region. The browseimpacts visualization thus generated illustrates the need 

for consistent empirical-based monitoring of ungulate 

effects and vegetation over time and on broad spatial 

scales. Global climate variability has recently become 

a primary impetus for long-term planning exercises in 

natural resource planning and management, but the 

results of this paper demonstrate that the pervasive 

impacts of historical and current browsing will also 

be a primary driver of future forest conditions in the 

Midwest and Northeast.
22

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Browse of forest understory vegetation by deer and other large ungulates alters 

ecosystem processes, making it difficult to regenerate forest land in herbivory-stressed 

areas. Seventy years ago, Aldo Leopold identified problem areas in the United States 

where overpopulation of white-tailed deer (Odocoileus virginianus) was likely to lead 

to overbrowsing of nutritive plants. Species of plants with little or no nutritive value 

would thereby gain a competitive advantage. Recent measurements of browse impacts 

on regionwide forest inventory plots in the midwestern and northeastern United States 

provide the opportunity to review the work of Leopold and others. A visualization of 

the probability of browse impact levels that warrant consideration during regeneration 

planning is presented for comparison to historical maps.

Currently, 59 percent of the 182.4 million acres of forest land inventoried in the Midwest 

and Northeast was estimated to have moderate or high browse impacts. The Mid-Atlantic 

region had the highest proportion of forest land with moderate or high browse impacts (79 

percent). The oak/hickory (Quercus/Carya) and maple/beech/birch (Acer/Fagus/Betula) 

forest-type groups each had percentages of forest land with moderate or high impacts above 

the regional average, 69 percent and 65 percent, respectively. The problem areas described 

by Leopold and others persist and new areas have emerged in the Central/Plains, MidAtlantic, and New England States.

The study findings confirm three realities of forest regeneration management for forests 

under herbivory stress in the Midwest and Northeast: 1) The scope and persistence of 

large-ungulate herbivory has long-term wide-ranging implications for regeneration 

management; 2) less palatable tree species will continue to have a competitive advantage 

during the regeneration phase and are likely to be different species from the current canopy 

dominants; and 3) successful regeneration management of these forests requires more 

emphasis on ungulate-compatible prescriptions, novel approaches, and adaptive science.

KEYWORDS: browse map, tree regeneration, wildlife habitat, white-tailed deer, forest 

understory, forest monitoring, forest sustainability

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