United States

Department of

Agriculture

Forest Service

Southern 

Research Station

General Technical 

Report SRS-163

State of Mid-Atlantic 

Region Forests in 2000 

Summary Report 

Kenneth W. Stolte

4.26 – 4.33

4.42 – 4.48

4.18 – 4.25

4.34 – 4.41

4.49 – 4.57

Rain pH

No data

Ecoregion Sections

MAIA boundary

Forest

Forest - Agriculture

Forest – Mixed Use

Agriculture

Mixed Use

Urban - Forest

Urban

Major HUC4 watersheds
September 2012 

Southern Research Station

200 W. T. Weaver Blvd.

Ashville, NC 28804

The Author: 

Kenneth W. Stolte, Research Ecologist, USDA Forest Service’s 

National Forest Health Monitoring Team, under the Southern 

Research Station’s Eastern Environmental Forests Environmental 

Threats Assessment Center, Forestry Sciences Laboratory in Research 

Triangle Park, NC 27709.

www.srs.fs.usda.gov
State of Mid-Atlantic 

Region Forests in 2000

Summary Report

Kenneth W. Stolte

Monongahela watersheds, as well as most of the Kanawha 

River watershed, are entirely within the region’s boundary. 

The MAIA region also includes about two-thirds of Big 

Sandy-Guyandotte, one-fourth of Tennessee River, and onehalf of Upper Ohio River watersheds. 

The report on the condition of forests in the MAIA region 

focused on facets of biological diversity, productivity, 

vitality, soil and water, and carbon; the same factors that 

comprise the five ecological criteria of the Montreal Process 

Criteria and Indicators. The primary issues included in the 

report are: 

• type and extent of forest, land-use, urbanization, and 

fragmentation;

• forest products and economics;

• urban forest condition and street trees;

• native plant species, non-game wildlife, forest birds, 

and habitats; 

• gaseous and particulate air pollution; 

• endemic insects and diseases;

• altered fire regimes;

Evaluation of the health of forests in the MAIA region was 

initiated by the U.S. Environmental Protection Agency’s 

(EPA) Region 3 (Office of Research and Development) and 

the United States Department of Agriculture Forest Service’s 

(FS) Forest Health Monitoring (FHM) program in the late 

1990s. Collection and analyses of ecological, social, and 

economic data to evaluate the health and sustainability of 

forests in the Chesapeake Bay watershed was coordinated 

among the FS FHM Program’s National Office (Research 

Triangle Park, NC), FHM’s Regional Office in the 

Northeastern Research Station (Newtown Square, PA), and 

FHM’s Regional Office in the Southern Research Station 

(Asheville, NC). Two stakeholder meetings were held in 

Annapolis Maryland that were instrumental in deciding the 

content of this report. After two reviews and revisions based 

on stakeholder input and comments, the stakeholders’ list of 

information needs for the report was finalized and ranked 

in order of importance to the stakeholders (table 1). A few 

issues were added by EPA to the report based on postworkshops reviews on the content.

The Mid-Atlantic Integrated Assessment (MAIA) region 

includes all of Virginia, West Virginia, Maryland, Delaware, 

Pennsylvania, and parts of New Jersey, southeastern New 

York, and northeastern North Carolina (fig. 1). The boundary 

for the MAIA region is based primarily on watershed 

landforms, management units, etc. The MAIA region is a 

large area covering about 103,211,100 acres of land. 

Analyses and interpretation of much of the data is based 

on Bailey’s ecoregion sections because the response of 

forest ecosystems to stress is significantly affected by the 

underlying climatic, geological, and topographic features 

of the system, and ecoregions are based on those factors. 

Because many ecoregion sections extend well beyond the 

northern and southern borders of the MAIA region, only 

portions of ecoregion sections in the MAIA region were 

considered in most of the analyses. 

The MAIA region comprises a wide variety of forest 

communities, each of which is home to numerous plant 

and animal species. The Region contains all or portions of 

nineteen ecoregion sections, from the eastern coastal plains 

to the western mountains (fig. 2). 

It also contains all or portions of seventeen watersheds 

(fig. 3). The eastern half of the MAIA region drains into 

the Atlantic Ocean. It is bounded by the Delaware Bay 

watershed to the north and the Pamlico Sound watershed 

to the south. In the western portion, the Allegheny and 

INTRODUCTION

Summary Report 

Figure 1—The Mid-Atlantic Integrated Assessment (MAIA) region 

includes all of Maryland, Virginia, West Virginia, Pennsylvania, 

Delaware, District of Columbia, and whole or partial counties in 

northeast North Carolina (~47), western New Jersey (~12), and 

southeastern New York (~25).

Source: Chapter 2, Figure 1.

 MAIA boundary

Counties in MAIA region

Capitols

Major cities
2

Table 1—Rank of forest health stressors, response, and condition topics 

from two MAIA Stakeholder Workshops in Annapolis, Maryland in 1997 

(Source: Chapter 2, Table 1)

Category Component Assessment issuea Stakeholder priorityb

Stressors Abiotic Air pollution 10.0 

Storms (hurricanes, ice) 6.3 

Fire (lightning strikes) 6.3 

Biotic Insects and diseases 9.5 

Animal damage 

Exotic plants and animals 8.3 

Land use Urban expansion 

Firee

 and fire suppression 6.3 

Mining activities 

Timber harvest 7.6 

Road building 6.3 

Responsec

 Tree vitality Crown dieback 5.9 

Tree damage 6.3 

Tree mortality 8.5 

Tree regeneration 10.0 

Conditiond

Productivity Timber productivity 7.6 

Non-timber productivity 5.0 

Game species productivity 7.5 

Soils systems Erosion 8.3 

 Accumulation of toxins 8.8 

Nutrient pools and cycling 8.3 

Compaction 5.5 

Aquatic systems Sedimentation 10.0 

Chemical Contamination 10.0 

Riparian buffers 

Carbon sequestration Soil carbon 8.3 

Above-ground trees 

Above-ground plants 

Biological diversity Plant species richness 7.5 

Non-game wildlife species richness 9.0 

Forest birds species richness 9.0 

Habitat suitability for T & E species 9.0

Category Component Assessment issuea Stakeholder priorityb

Stressors Abiotic Air pollution 10.0

Storms (hurricanes, ice) 6.3

Fire (lightning strikes) 6.3

Biotic Insects and diseases 9.5

Animal damage

Exotic plants and animals 8.3

Land use Urban expansion

Firee

 and fire suppression 6.3

Mining activities

Timber harvest 7.6

Road building 6.3

Responsec Tree vitality Crown dieback 5.9

Tree damage 6.3

Tree mortality 8.5

Tree regeneration 10.0

Conditiond

Productivity Timber productivity 7.6 

Non-timber productivity 5.0

Game species productivity 7.5

Soils systems Erosion 8.3

Accumulation of toxins 8.8

Nutrient pools and cycling 8.3

Compaction 5.5

Aquatic systems Sedimentation 10.0

Chemical Contamination 10.0

Riparian buffers

Carbon sequestration Soil carbon 8.3

Above-ground trees

Above-ground plants

Biological diversity Plant species richness 7.5

Non-game wildlife species richness 9.0

Forest birds species richness 9.0

Habitat suitability for T & E species 9.0

a

 Issues in italics were addressed in MAIA report.

b

 Based on average of low(3), medium (6), and high (10) ratings.

c

 Addressed Criterion 3 of Montreal Process Criteria and Indicators (Anon. 1995b).

d

 Addressed Criteria 1, 2, 4, and 5 of Montreal Process Criteria and Indicators (Anon. 

1995b).

e

 Human-caused ignitions.
3

• exotic plants, insects, and diseases; 

• tree condition, growth, mortality, and regeneration; 

• aquatic sedimentation, chemical contamination, and 

landuse effects; 

• soil erosion, nutrient pools and cycling, and soil carbon;

• climate change 

This report addresses many of the most relevant issues 

concerning forest health and sustainability. The report 

was generally organized by historical perspectives; 

resource base in 2000; stressors affecting the region 

today; tree-level indicators affected by stressors; and the 

overall condition of the MAIA forest today with respect 

to productivity, soil and aquatic systems, and biological 

diversity.

History of MAIA Forests 

The forests of the MAIA region today are a remnant of 

what existed only 400 years ago, yet this remnant is still an 

impressive organization of plants and animals, magnificent 

vistas, and bountiful aquatic systems. Prior to colonization 

(circa 1650), the entire mid-Atlantic region was a nearcontinuous blanket of forest composed of multiple tree 

species of different ages, sizes, and abundances that covered 

over 95 percent of the land surface, or approximately 100 

million acres. 

The early forests were predominately composed of oaks, 

chestnuts, pines, hemlock, beech, maple, birch, ash, black 

cherry, sweet gum, magnolia, and hickory, with spruce and 

fir at higher elevations, and swamp tupelo and bald cypress 

found on wetter coastal sites. The air, streams, estuaries, 

and bays were clear and unpolluted, abounding in a great 

diversity of aquatic plants, insects, animals, and aquatic life. 

The soils were very deep and rich, derived from millions 

of years of erosion of Appalachian and other mountain 

systems that run from north-to-south. The climate was mild 

and moist, and combined with the topographical diversity 

of the area, nurtured a great diversity of plants, animals, 

and aquatic systems that had coevolved and flourished in a 

balanced, healthy, and sustainable system. 

Early colonization was followed by rapid population growth 

and urban and agricultural expansion in the 17th and 18th 

centuries. The area of forests remained relatively stable 

until the period 1740 to 1930, when 80 percent of the lands 

covered with forests were cleared for agricultural purposes. 

By the early 1900s, tens of millions of acres of forest had 

been cut for farmland, fuel, fences, railroad ties, and other 

products. For the first time in thousands of years much of 

the contiguous forest area of MAIA region were almost 

completely laid barren. Catastrophic fires that started from 

burning residual slash and other activities burned millions 

of acres of forest not cleared, and subsequent excessive soil 

Capitols

Major HUC4 watersheds

Figure 2—Bailey’s ecoregion sections within the MAIA region. The 

U.S. Forest Service’s Forest Health Monitoring, Forest Inventory 

and Analyses, and other programs data were frequently averaged at 

these spatial scales for comparison of forests within the MAIA region. 

Source: Chapter 3, Figure 5. 

Figure 3—Major 4-Digit Hydrologic Unit Code (HUC4) watersheds in 

the MAIA region. Some data from the U.S. Forest Service’s Forest 

Health Monitoring program was averaged at HUC4 spatial scales 

to assess differences with ecoregion section values and provide 

potential for comparisons with water condition.

Source: Chapter 3, Figure 6.
4

erosion erased many millennia of rich soil development. 

Sedimentation of streams and rivers from forest clearing and 

agricultural runoff, a concern in the MAIA region since the 

mid-1750s, became greatly exacerbated. Whole towns were 

buried in mud when heavy rains washed the exposed soil 

from the steep slopes. Many wildlife species—including the 

now common whitetail deer, turkey, and black bear—were 

almost extinct. 

Forests in the MAIA Region Today

Restoration of MAIA region forests started in early 1900s. 

The area of forestland increased as advances in agricultural 

practices required less land to grow more crops, and 

improved timber harvest methods were implemented. 

Forest ecosystems improved: wildlife was protected; soil 

erosion and infertility was mitigated by allowing relatively 

infertile farm land to return to forests; and catastrophic 

wildfires followed by devastating flooding were reduced 

by better forest management practices. Though much 

improved today, the forests are still in recovery from the 

intensive agricultural and removal of tree products of the 

1700s and 1800s. 

Today 63,651,000 acres (about 61 percent) is classified 

as forestland (both federal and non-federal land), and 

39,560,100 acres (about 39 percent) is classified as nonforestland. Federal forestlands cover only 3,626,200 

acres, about 3.5 percent, of the MAIA region. Private land 

owners control 79 percent of the MAIA forests, public land 

managers 14 percent, and forest industry 7 percent. 

Ninety-five percent of forestland today is classified as 

productive timberland (table 2), with more than half (54 

percent) considered harvestable from a timber industry 

perspective (many trees in a stand greater than 10 inches 

in diameter). 

Yet the forests are still very immature from an ecological 

perspective because much of the forest has been recolonizing abandoned, infertile farmlands since the early 

1900s—soils are still redeveloping organic matter and 

nutrients, and most tree species have not reached 50 percent 

of their maximum age and thus are not fully matured. 

There is an obvious absence of old growth trees (large and 

mature) that were common in pre-European times—less 

than 2 percent of the trees today are greater than 20 inches 

in diameter. About 72 percent of trees are saplings (trees less 

than 5 inches in diameter), and 16 percent are in a seedling 

or regenerative phase (less than 1 inch in diameter and 

greater than 10 inches high). 

Most of the forests in the MAIA region today are owned 

by private landowners (79 percent), industry (7 percent), 

Federal (6 percent), State (7 percent), and municipal 

and county governments (1 percent): the future of these 

Table 2—Land cover types in MAIA states circa 2000 (Source: Chapter 2, Table 2)

Total Total 

States Woodland Reserved foresta Non-Forest landb Forest Non-forest

------------------------------------thousand acres------------------------------------------- -----------percent----------- 

Delaware 10.2 2.0 388.2 847.2 1,235.2 31.4 68.6 

Maryland 126.4 152.9 2,703.4 3,592.2 6,295.4 42.9 57.1 

New Jerseyc 5.6 86.2 1,717.6 2,040.5 3,758.6 45.7 54.3 

New Yorkd 47.9 799.9 8,425.0 5,729.1 14,154.9 59.5 40.5 

North Carolinae

 321.0 87.0 8,386.0 6,475.0 14,861.0 56.4 43.6 

Pennsylvania 285.0 834.9 16,992.9 11,735.6 28,728.5 59.2 40.8 

Virginia 47.0 532.0 16,026.0 9,382.0 25,408.0 63.1 36.9 

West Virginia 27.7 181.1 12,126.8 3,309.5 15,436.3 78.6 21.4 

 MAIA total 581.8 2,676.9 66,766.9 43,111.1 109,877.0 60.7 39.3 

a

Total forest = woodland + reserved + timberland. 

b

Total land = total forest + non-forest. 

c

Includes all or part of 12 counties only. 

d

ncludes all or part of 25 counties only.

e

Includes all or part of 47 counties only. 

Source: USDA Forest Service’s Forest Inventory and Analysis Program; (http://www.fia.fs.fed.us).
5

forests lies in the decisions made by these various entities. 

Urban and surrounding metropolitan forests are significant 

component of the MAIA region (table 3). 

Average percentage of total area in all MAIA states was 

10.3 percent in urban areas and 49.7 percent in metropolitan 

areas (MA) (fig. 4), compared to an average 3.5 percent in 

urban area and 24.5 percent in metropolitan area for the U.S. 

(excluding Alaska and Hawaii). With the exception of West 

Virginia, metropolitan areas accounted for more than 34 

percent of all MAIA states. 

The benefits and values of urban forests include esthetics, 

biodiversity, flood control, water preservation during 

droughts, refilling aquifers, water quality maintenance, 

temperature and climate control, energy conservation, and 

recreational activities such as bird watching and hiking. 

Some common stressors affecting urban trees are pollution, 

soil compaction, nutrient deficiencies, drainage problems, 

salt uptake, construction damage, insects, and pathogens.

Forests in the MAIA region provide important recreational 

opportunities, wildlife habitat, aesthetic benefits, timber 

Table 3—Tree cover in metropolitan areas (MA) and urban areas (UA) by State area and tree cover in MAIA region 

and the U.S. circa 2000 (Source: Chapter 5, Table 6)

State 

Total

Areaa

Area

 MA UA

Stateb

 MA UA 

Tree Population 

MA UA 

 Tree Coverc

MA UA 

State 

Tree Coverd

 MA UA 

Trees per 

Capita 

 MA UA 

-------------thousand acres------------ ----percent----- -------million------- -----percent---- -----percent---- 

DE 1,590,192 828 140 52.0 8.8 213 13 50.9 46.3 58.2 9.0 384 27 

MD 7,930,094 4,530 1,118 57.1 14.1 851 89 46.5 40.1 53.2 11.1 192 21 

NJe 5,579,730 5,580 1,708 100.0 30.6 1,597 144 56.6 41.4 100.0 22.3 207 20 

NCe 34,455,837 11,987 1,586 34.8 4.6 4,357 139 52.5 42.9 31.4 3.4 996 36 

NYe 34,804,462 17,992 2,501 51.6 7.2 4,598 133 44.7 26.3 43.9 3.5 278 8 

PA 29,482,538 14,404 2,066 48.9 7.0 3,733 139 48.7 34.4 43.5 4.2 370 16 

VA 27,370,151 10,068 2,191 36.8 8.0 3,648 157 53.3 35.3 34.4 4.9 764 27 

WV 15,643,123 2,497 268 16.1 1.7 891 23 65.6 42.2 13.4 0.9 1,191 33 

U.S.f 991,628,365 488,986 69,407 24.5 3.5 74,426 3,821 33.4 27.1 24.5 2.8 377 17 

a

Includes land and water area combined.

b

Percent of total State area covered by metropolitan areas (MA) and urban areas (UA). c

Percentage of metropolitan and urban areas covered by trees.

d

Percentage of total state tree cover within metropolitan areas and urban areas. e

Includes entire State, not just counties in MAIA region. 

f

Includes District of Columbia, but not Alaska and Hawaii.

Source: Dwyer and others 2000.

Figure 4—Metropolitan and urban areas in the MAIA region.

Source: Chapter 4, Figure 8.
6

products, clean and abundant fresh water, and aquatic habitat 

for a variety of species. The remaining forests contain a rich 

diversity of forest types and species. Some forest stands, 

particularly in the northwestern portion of the region, are 

in the process of returning to the magnificent older-growth 

forests reminiscent of the pre-colonization era.

The significant progress in improving and maintaining forest 

ecosystem health and sustainability over the past century is 

balanced against increasing human demands for good and 

services. The forests face a variety of new and old threats 

from diverse biotic and abiotic stressors, including: 

• continued conversion and/or fragmentation of forests by 

urban expansion 

• introduction of exotic and invasive insects, pathogens, 

plants, and animals

• gaseous, particulate, and acidifying air pollution 

• alteration of historical fire regimes 

• extraction of timber and other resources 

• climate change 

These forest health issues are a great concern to the public, 

and to the land managers and policy makers who are 

responsible for maintaining long-term vitality of the forests 

in the MAIA region. 

STReSSORS AFFeCTINg THe MAIA FOReSTS

Air Pollution

Air pollution is a relatively new force affecting MAIA 

forests. Relatively high exposures of gaseous ozone, and 

deposition of ions such as nitrates, sulfates, and acidic 

precipitation have the potential to decrease growth, increase 

susceptibility to other stressors, increase mortality, and alter 

the structure and function of forest communities. 

Ground-level ozone degrades susceptible plant species 

(ozone bioindicator species) when it enters the foliage 

during normal gas exchange processes and kills the carbonfixing cells that provide food and energy for plants and 

animals. Ozone exposures were sufficiently elevated to 

injure a number of ozone bioindicator species (trees, shrubs, 

and herbs species) throughout the MAIA region from 1993 

through 1996. Ozone effects on bioindicator species was 

often the highest Level 3 injury in the north and western 

parts of the MAIA region. The negative effects of ozone 

air pollution are evident by the potential biomass losses of 

3,250 to 12,000 green pounds for black cherry in the western 

sections of the Region in 1990 (fig. 5). 

Deposition of sulfates, nitrates, and hydrogen ions (pH) 

degrade forest ecosystems by increasing soil acidity and 

removing important plant nutrients, and subsequently 

contaminate aquatic systems. Average wet deposition of 

sulfate for the period from 1979 to 1995 ranged from a low 

of 17.5 to 21.3 kilograms per hectare per year (kg/ha/yr) in 

the southeast regions of MAIA, to the highest levels of 32.8 

to 36.6 kg/ha/yr in the northwest regions. Similarly, average 

wet nitrate deposition for the same period ranged from 10.2 

to 12.4 kg/ha/yr in the southeast region to the highest levels 

of 18.9 to 21.1 kg/ha/yr in the northwest region. The annual 

average pH of the precipitation for this same period ranged 

from 4.49 to 4.57 in the southeast region and 4.18 to 4.26 in 

the northwest part of the MAIA region (fig. 6). 

The northwest portions of the MAIA region were subjected 

to some of the highest ozone exposures and wet deposition 

of air pollutants in the eastern U.S. The effects of these 

pollutants on the forest resources of the MAIA region are 

not known at this time. Additional analysis is needed of 

the relationship between air pollutants and forest growth, 

understory diversity, soil chemistry, crown condition, lichen 

communities, and ozone-susceptible species.

0.1 - 25

26 - 350

351 - 825

826 - 3250

3251 - 12000

Green pounds (millions) 

of black cherry with 

potential ozone damage

Counties with black cherry

MAIA counties

Figure 5—Potential biomass loss in black cherry in 1990 where the 

distribution of this species overlapped areas with phytotoxic ozone 

exposures in the MAIA region.

Source: Chapter 6, Figure 17.
7

Alteration of Fire Regimes

Almost all forests eventually catch fire and burn: fire is 

a natural part of almost all healthy and sustainable forest 

ecosystems. Fires cycles or regimes have historically 

differed according to regional climate, soils, topography, and 

other factors. Fires regimes are basically the average of how 

often fires burn and how severe the fires are—frequent fires 

are typically low intensity fires, and severity of impacts are 

limited because only low amounts of fire fuels accumulated 

in the fire intervals. Frequent, low intensity cycles are 

typically ground fires that burn the understory vegetation 

without much impact on the boles or crowns of the larger 

overstory tree species. 

Fire regimes in the U.S. range from relatively frequently 

(about every 3 decades) with low intensity and severity, to 

long-term, infrequent fires (35 to 100 to 200 years or longer) 

with greater intensity and severity. Forests can only stay 

healthy and in natural conditions if the fire regimes they 

adapted to over thousands of years continue to operate, or 

if some other agent or management activity substitutes for 

the effects of a normal fire regime. Historic fire regimes 

in most of the southern forests in the MAIA region burned 

frequently (0 to 35 years) with low severity. In the northern 

MAIA, fire burned less frequently (35 to 100+ year cycles) 

and with low-to-mixed severity. 

Forest fire-suppression management activities have altered 

the historic fire regimes in the MAIA region and created 

the need for moderate-to-intensive silvicultural activities 

to restore these forests to historic ecological conditions. 

The alteration of fire regimes often facilitates insect and 

pathogen epidemics, and increases the risk of more severe 

crown fires during periods of prolonged drought. The 

greatest deviations from historical fire regimes are in the 

northern and western portions of the MAIA region and 

in scattered fragments within the Piedmont area in the 

southeast (fig. 7). 

Climate Change

Climate is generally the long-term variability of temperature 

and moisture that changes as the earth circles the sun. It 

is unquestionably the most significant factor affecting 

the composition, structure, and functioning of forest and 

other ecosystems. Climate distributed across landscapes of 

varying soil types and topography is the primary basis for 

the categorization of ecoregion units and the forest types and 

species found there. Thus changes in climate regimes can be 

expected to greatly affect forest ecosystems. 

Forests in the MAIA region have changed significantly 

over millennia as the result of changing climate regimes. 

Monitoring, models, evidence from tree rings and sediment 

Figure 6— Average annual precipitation pH in the MAIA region 

from 1979 to 1995. Data from this 16-year period were averaged

and interpolated at 5-km grid scale to estimate average annual wet 

deposition at ecoregion section scales.

Source: Chapter 6, Figure 20.

Figure 7—Condition classes of changes in current fire regimes 

from historic fire regimes in forested areas in the MAIA region. 

Higher numbered condition classes indicate increasing amounts 

of silvicultural treatments would be needed to restore historic fire 

regimes. 

Source: Chapter 7, Figure 22.

4.26 – 4.33

4.42 – 4.48

4.18 – 4.25

4.34 – 4.41

4.49 – 4.57

Rain pH

No data

Ecoregion Sections

MAIA boundary
8

cores, and other factors generally indicate that a period 

of significant climate change is in progress today that is 

expected to significantly affect forest ecosystems. In the 

MAIA region, temperature increases of 4 to 8o F, decreases 

in the amount of precipitation, and increases in the intensity 

of storms are anticipated by the end of the 21st century. A 

few models, however, predict increases rather than decreases 

in precipitation. Generally the expected changes in climate 

are anticipated to increase growth of forests but also change 

the composition of tree species. Major forest types like 

maple-beech-birch and oak-gum-cypress are likely to be 

negatively affected with significant reductions in coverage 

(fig. 8). 

Changes in dominant forest types are likely to increase 

invasions of exotic species and decrease biological diversity. 

It is also likely that negative effects on cold water fish (e.g., 

trout) and some bird species will occur. Forest operations 

are likely to be more negatively affected by increases in 

severe weather (high winds and precipitation events) than by 

increases in temperature. 

Insects and Pathogens

Native insects and pathogens have coevolved with forest 

ecosystems and are an essential part of normal, healthy 

forests, but they are also always a concern because 

significant changes in forest or environmental conditions—

e.g., fire suppression, timber harvest activities, climate 

change, prolonged drought, and other factors—can cause 

native insects and pathogens to become abnormally prolific 

and populations can reach epidemic proportions with 

devastating results. Alternately exotic and highly invasive 

insects and pathogens are almost always a serious threat to 

forest ecosystems because they lack the inherent regulatory 

mechanisms that control populations in their native lands. 

They often cause severe devastation and can even threaten 

the existence of important tree species. For example, 

chestnut blight is an introduced pathogenic fungus that has 

devastated the eastern U.S. forests over the last 100 years, 

virtually eliminating the once abundant and magnificent 

American chestnut by the 1950’s. American chestnut was 

once the most dominant hardwood tree in the vast stands of 

Eastern forests from Maine to Florida. 

Some tree species in the Mid-Atlantic are at extreme risk 

from exotic insects and pathogens, with projections of 

25 percent or more increases in mortality over the next 

15 years. The most serious insects and pathogens in the 

MAIA region include gypsy moth (fig. 9), hemlock woolly 

adelgid, butternut canker, beech bark disease, and dogwood 

anthracnose. Some pests such as gypsy moth are generalists, 

Figure 8—Dominant forest types under circa 2000 climate regimes, 

and potential forest type distributions under Canadian Climate 

Center and Hadley doubled CO2

 equilibrium climate scenarios. 

Source: Chapter 8, Figure 23.

County infested

Water

Figure 9—Gypsy moth infested areas in MAIA counties through 1998 

with gypsy moth caterpillar photograph (inset). 

Source: Chapter 9, Figure 24. 
9

attacking a variety of tree species, while others like hemlock 

woolly adelgid and dogwood anthracnose are host-specific, 

as their common names imply. 

There is a high probability that some tree species such as 

butternut and hemlock may be lost entirely from MAIA 

forests, with serious ramifications for the stability of the 

forest systems they occupy. For example, hemlocks in the 

MAIA region are often found along stream banks where 

they stabilize the soil, provide wildlife habitat, and moderate

water temperatures. The loss of this tree species in large 

numbers will greatly degrade the associated aquatic systems. 

In other areas insects and pathogens will severely degrade, 

but not eliminate, the host species and will consequently 

alter the composition, structure, and function of forests in 

the affected areas. 

Exotic Plant Species

Exotic plant species that are highly invasive are becoming 

a significant part of the flora in the forests of the MAIA 

region. Exotic plant species are also very successful when 

invading native plant communities because of the absence of 

the natural biotic and abiotic agents that keep them in check 

in their native habitats. In the MAIA region, exotic plants—

mostly occurring in the understory—comprised 20.6 to 

34.7 percent of the total flora in some counties, particularly 

around major urban centers or highways. Queen Anne’s lace 

or wild carrot was the most common exotic plant species 

found in 93 percent of the counties evaluated, followed by 

red clover (91 percent of counties), and narrowleaf plaintain 

(90 percent of counties). Seven other exotic species were 

found in 80 percent or more of the MAIA counties (table 4).

The effect of these exotic plants on native plant 

communities in the MAIA region is largely unknown 

at this time, but exotic plant species typically displace 

a disproportionate number of native species and thus 

significantly reduce plant biodiversity, and subsequently 

often affect animal species by eliminating native plant 

species essential for food, nesting, or shelter. 

Population Growth and Urbanization

Rapid expansion of populations and concurrent urban 

development fragments forest ecosystems, negatively 

affects forest health, and causes serious problems for 

natural resource managers, urban planners, and policy 

makers. Urban expansion increases the risk of wildfires, 

disrupts animal habitat and populations, introduces exotic 

invasive species, and often degrades water quality. It creates 

unique situations where the need to protect water quality, 

Table 4—exotic plant species in MAIA region States 

circa 2000 (Source: Chapter 10, Table 10)

wildlife, and forest health must be balanced with the social, 

recreational, health, and safety needs of people. 

Between 1970 and 1990 the population of the MAIA region 

increased by 4.3 million people (14.1 percent) to about 35 

million people in the 171,129 square miles of the MAIA 

region, and the average population density increased from 

179 to 204 people per square mile (psm). Nevertheless, 

population density was lower in 1990 than in 1950 in many 

parts of the region, indicating that most of the population 

increases were in the already urbanized areas and came from 

rural areas. Developed lands increased by 21 percent, while 

rural lands decreased by 2.64 percent from 1982 to 1994, 

indicating that agricultural lands were converted to urban 

development. North Carolina, Delaware, Maryland, New 

Jersey, and Virginia all lost forest land to urbanization, while 

New York and West Virginia showed increases in forest land 

over the same period. Forests still dominated the MAIA 

landscape, making up 61 percent of the land area. 

The average population density by State ranged from 75 

psm in West Virginia to 608 psm in New Jersey. At the 

county level population density ranged from 6 to 45 psm in 

western regions to 2001 to 15000 psm in the eastern regions 

(fig. 10.a). Delaware and Maryland increased by 21 percent, 

and Virginia by 30 percent (fig. 10.b.). 

The highest population densities were concentrated in the 

Erie/Ontario Lake Hills and Plain region, along Interstate 

95 corridor from Philadelphia, PA to Chesapeake, VA. Some 

Note: County-level records for Maryland, and the 

western counties of Pennsylvania and New York, were 

not obtained. Source: The Biota of North America 

Program; (http://www.bonap.org).

Rank Common name 

Occurrence in 

MAIA counties 

-----percent-----

1 Queen Anneʼs lace 93

2 Red clover 91 

3 Narrowleaf plaintain 90 

4 Ox-eye daisy 88 

5 Sheep sorrel 88 

6 Barnyard grass 88 

7 White clover 84 

8 Yellow sweet clover 84 

9 Woolly mullein 81 

10 Asiatic day flower 80 
10

Population density 1990

(persons per sq. mile)

 6 - 45

 46 - 100

 101 - 150

 151 - 500

2001 - 15000

 501 - 2000

Population density

change 1950 -1990

(percent) 

< -15.0

 -15.0 to -2.1

-2.0 to 2.0

2.1 to 15.0

> 15.0

(A) 

(B) 

Figure 10—(a) Population density in MAIA region in 1990. (b) 

Change in population density 1970 to 1990. 

Source: Chapter 11, Figures 31 and 33. Figure 11—(a) Forest cover within 1457 acre landscape units in the 

MAIA region circa 1990. The fine-scale (0.22 acres per pixel) land 

cover map from EPA’s MRLC was generalized to show the proportion of forest cover within the 1457 acre units. Areas with more than 

90 percent forest cover are highly interior forested landscapes; in 

contrast to highly fragmented units with less than 10 percent forest 

cover. A 60 percent cover threshold separates interior forests from 

more fragmented forests. (b) Type and distribution of Landscape 

Pattern Types (LPTs) in the MAIA region circa 1990. LPTs are labeled according to the type and relative amount of forest, agriculture, 

and developed areas within the surrounding 590-hectare units. The 

19 MRLC LPT defined categories were condensed into 7 LPT aggregates to simplify regional patterns.

Source: Chapter 4, Figures 12 and 13.

> 90%

60 - 89%

10 - 59%

< 10%

(A) 

Forest

Forest - Agriculture

Forest – Mixed Use

Agriculture

Mixed Use

Urban - Forest

Urban

(B) 
11

90

80

70

60

50

40

30

20

10

 0 Federal 

land

Water Crop Total 

Ag.

Pasture Forest Urban Total 

rural

Total 

area

Land cover types

Acres 

(millions)

VA

WV

MD

DE

PA

Figure 12— Land cover in five MAIA region states in 1997. Total 

agriculture is the combination of crop lands and pasture lands.

Source: Chapter 11, Figure 37. 

 DE MD PA VA WV 5 states

Change 1982 - 1997

(percent)

States and 5-state total in MAIA

Crop

Total rural

Urban

Forest

Pasture

60

50

40

30

20

10

0

-10

-20

-30

-40

-50

-60

Figure 13— Differences in major land cover types in five MAIA region 

states 1982 to 1997. Total agriculture (AG1) is the combination of 

crop and pasture lands. 

Reference: Chapter 11, Figure 41.

of the most densely populated urban centers in the East are 

found in Erie and Philadelphia, PA; Newark, NJ; Baltimore, 

MD; Washington, DC; and Norfolk-Chesapeake VA. Forest 

cover in these areas is low and highly fragmented. Thus 

interactions of forests and humans can be expected to be 

quite different between the rapidly urbanizing Eastern 

seaboard and the inland rural areas of the western region. 

Increasing human population densities had two important 

effects on the management of private forest lands and on 

timber supply. An obvious impact was the conversion 

of forest land to urban and residential use in areas of 

high population growth. An increasing number of people 

owning smaller forestland holdings leads to increasing 

forest fragmentation in all States of the Region except 

Pennsylvania, New York, and West Virginia (fig. 11 a.) as 

changes in landuse occurred (fig. 11 b). 

Areas of moderate population density increases were also 

affected as landowners changed strategies from long-term 

investments in forest management to expectations of large 

future returns from converting forest lands to urban and 

residential use. The transition from rural to urban uses of 

forest lands occurs between 20 to 70 psm, that is, there is 

a 75 percent probability of managing forest for timber and 

similar uses at 20 psm, but only a 25 percent probability of 

the same at 70 psm. The probability that a forest would be 

managed for timber-related products approaches zero when 

populations exceed 150 psm.

Land Use and Land Use Change 

A relatively small decrease of only 247,100 acres of 

forestlands compared to a much larger decrease in 

agricultural lands (crops and pasture) resulted in an overall 

1.4 percent increase in forest cover of rural lands from 1982 

(62.3 percent) to 1992 (63.7 percent) in the MAIA region. 

In 1992 this total rural acreage used as cropland decreased 

by 8.32 percent and pastureland use dropped 8.55 percent, 

accounting for 20.4 percent and 11.4 percent of the total 

rural lands, respectively. 

Changes in forest, cropland, pasture, urban, Federal 

ownership, and water bodies land use from 1982 through 

1997 were evaluated for Delaware, Maryland, Pennsylvania, 

Virginia, and West Virginia, the five states located entirely 

within the MAIA region (fig. 12). 

In 1997 these five States covered about 80,993,900 acres, 

of which 62,909,900 acres were rural lands (77.7 percent) 

composed of 11,354,000 acres of croplands (14.0 percent), 

6,868,400 acres of pasture (8.5 percent), 42,100,000 acres of 

forests (52.0 percent), and 2,587,500 acres (3.2 percent) of 

other land uses including minor land (farm structures, wind 

breaks, other) and Conservation Reserve Program lands. 

Other land-use types covered 8,943,800 acres of developed 

(urban) lands (11.04 percent), 4,781,600 acres of Federal 

ownership (5.90 percent), and 4,517,700 acres of water 

bodies (5.58 percent). 
12

The percent of urban lands increased substantially in each 

of the five States during this same period, and crops and/or 

pasture lands decreased by about the same extent (fig. 13). 

Urban lands primarily replaced crop and pasture lands in 

West Virginia and Pennsylvania, and primarily replaced crop 

lands in Delaware, Maryland, and Virginia. The percentage 

increase in urban lands was substantial for Delaware (35.0 

percent), Maryland (35.4 percent), Pennsylvania (41.3 

percent), Virginia (42.6 percent), and West Virginia (49.6 

percent), an average percent increase for all five States 

of 41.4 percent. The percent change in all agriculture 

lands (crops and pasture) was highest in Virginia (about 

20 percent). Pasturelands decreased by over 25 percent in 

Delaware and Pennsylvania, and croplands decreased by 

more then 15 percent in Virginia and West Virginia. 

As a result of the increases in urban land use, total rural 

land area decreased in Delaware by 5.6 percent, Maryland 

by 3.1 percent; Pennsylvania by 4.7 percent; Virginia by 

4.1 percent; and West Virginia by 2.9 percent. The average 

decrease in total rural lands for all States was 4.0 percent. 

Delaware also had a decrease in forest lands (5.1 percent), 

as did Maryland (3.5 percent), Pennsylvania (0.2 percent), 

and Virginia (1.0 percent). In West Virginia forest cover 

increased by 1.6 percent. West Virginia had the largest 

percentage land-use change—an increase of 53.3 percent 

due to percent increases in urban lands (49.6), forest cover 

(1.62), water-body acreage (1.96), and Federal lands (0.08), 

and decreases of 21.1 percent in croplands and 19.53 percent 

in pasturelands. 

Change in Forest Land Ownership Patterns 

Parcelization is the breaking-up of single contiguous land 

ownerships into smaller tracts or parcels with increased 

numbers of owners. The number of average acres per 

owner in the MAIA region decreased by 1.18 acres (about 8 

percent) from 1978 to 1994 (fig. 14). 

The largest average decreases by owner were 27.3 acres 

in Maryland (61.1 percent), 13.5 acres in Delaware (40.3 

percent), 7.5 acres in New Jersey (33 percent), and some 

slight average owner increases of 2.9 acres in Pennsylvania 

(0.4 percent) and 3.0 acres in West Virginia (4.6 percent). 

The number of private owners for the entire region increased 

by 11 percent, while the number of acres of forest land in 

private ownership increased by only 2 percent during the 

same period. 

Another way of evaluating parcelization is the number 

of owners and acres by parcel. In 1994 there were still a 

significant number of large forest tracts greater then 1000 

acres in the MAIA region owned by 0.1 percent of all 

owners, representing 7.2 percent of all forestlands, and some 

very large tracts greater then 5000 acres owned by only 0.03 

percent of all owners and accounting for 13.9 percent of 

all forestlands. The smallest forest parcels of from 1 to 19 

acres belonged to 74.2 percent of all owners, and accounted 

for only 12.7 percent of all forest land. Owners of parcels 

20 to 999 acres represented 25.8 percent of all owners and 

represented 66.1 percent of all forest land.

These data suggest that parcelization is continuing in 

most of the region—ultimately it will lead to further 

fragmentation of forestlands. Increasing population densities 

will lead to increasing parcelization that will eventually 

reduce the size of forested land managed for forest 

production. If these trends continue, the quantity and quality 

of wood supplies from the MAIA region will decline.

Fragmentation

The conversion of forests into crop and pasture lands 

to support growing populations in the 18th and 19th 

centuries had the greatest impact on forest fragmentation, 

because many of these lands were later converted to urban 

development. Increased urbanization include loss of forest 

cover and increased impervious surface areas which, in 

turn, lead to a heightened likeliness of flash flooding and 

volume of downstream flows, better access for exotic 

invasive species, and more human disturbance of what little 

forest remains.

Figure 14— Change in number of acres per owner of forest land by 

State and MAIA region 1978 to 1994. 

Source: Chapter 11, Figure 43. 
13

Fragmentation is the breaking-up of larger contiguous 

blocks of forestlands into smaller, unconnected patches. 

Increasing population growth typically results in the 

conversion and fragmentation of forest land to urban 

development (almost always a permanent conversion) or 

agriculture and pasture (which can revert back to forest 

or become urban development). Forestry practices often 

also fragment the forest, but generally the land remains 

forested although forest size, shape, structure, and 

species composition may change. Some of the ecological 

consequences of forest fragmentation included: (1) loss or 

change in quality of habitat; (2) increased sedimentation and 

degradation of streams and aquatic systems; (3) increased 

numbers of edge-dwelling wildlife species; (4) decreased 

forest connectivity; (5) increased opportunities for humanwildlife conflicts; and (6) species extinction or loss of 

species diversity. 

Forest management practices in the MAIA region had much 

lower impact on the degree of fragmentation than urban 

or agricultural uses. Generally, areas with high levels of 

forest fragmentation were found in areas of net growth in 

population, high population density, and net loss of forest 

lands (fig. 11 b.). Increased population density increased 

forest fragmentation as city centers expanded, and rings 

of low-density housing at the edge of cities, often referred 

to as the urban-rural interface, moved into forested areas. 

Agricultural and forest-related uses increased, and forest 

fragmentation decreased appreciably with increasing 

distance from urban centers, particularly away from coastal 

areas. Generally then, much of the MAIA landscape was 

characterized by low (10 to 59 percent) and medium (60 to 

89 percent) forest cover and low (< 10 percent) or medium 

(20 to 59 percent) levels of forest fragmentation, except 

near urban areas where the fragmentation was greater than 

90 percent.

Not only was the quantity of forest land reduced, but the 

quality of forest habitat was also diminished as patterns of 

loss transformed the forest landscape into small, isolated 

patches of trees. As forest loss and fragmentation increase, 

remaining forest patches become smaller and more isolated, 

the amount of high quality interior forest habitat is reduced, 

overall forest connectivity is negatively affected, and the 

safe movement of wildlife between remaining forest patches 

is impeded. 

Urban growth in the Mid-Atlantic States was concentrated 

in three regions: (1) the Allegheny Plateau; (2) along the 

Interstate 95 corridor from Philadelphia, PA, to Raleigh, 

NC, and 3) the Piedmont, Blue Ridge Mountains, and 

Northern Ridge and Valley region (figs. 4, 10 b., and 11 b.). 

Watersheds in the first two regions contained some of the 

most densely populated urban centers in the East, including 

Erie and Philadelphia, PA; Newark, NJ; Baltimore, MD; 

Washington, DC; and Norfolk-Chesapeake, VA. Forest 

cover in all three regions was low and highly fragmented—

the remaining forest patches were small and had little high 

quality interior forest habitat.

Bird community index (BCI) information addresses the 

overall ecological condition of forests based on forest 

composition, structure, and function. Analysis of BCI data 

showed that the species richness of songbirds in the MAIA 

region decreased significantly as forest fragmentation 

increased and the proportion of edge habitat increased 

(fig. 15). 

Forest fragmentation was strongly associated with a 

high proportion of exotic bird species, nest predators and 

parasites, and multi-brood species whose ecological strategy 

of rapid proliferation leads to reduced numbers of native 

bird species. Analyses of behavioral and physiological 

attributes of bird communities at 126 sites in the highlands 

of the MAIA region indicated that 27 percent of this region 

was in good condition, 36 percent in fair condition, and 21 

percent in poor condition. 

highest high medium low 1 low 2

BCI integrity category

Cover (percent)

-20

-10

0

10

20

30

40

50

60

70

80

90

100

110

120 forest

agricultural/herbaceous

residential/commercial

Figure 15—Mean and standard deviation of percent forested, 

agricultural-herbaceous and residential-commercial land cover of 

sites in condition categories determined by the Bird Community 

Index (BCI).

Source: Chapter 12, Figure 49.
14

Forest Vitality

Forest vitality refers to the overall vigor and condition of 

forests. It is generally related to the resilience of a forest to 

withstand or recover from infrequent but historic stressors 

(e.g., violent storms) that have co-evolved with the system, 

and to withstand some level of new forces introduced 

into the system (e.g., air pollution). A forest in a vigorous 

condition will often be less impacted by unusually high or 

severe stressors introduced into the system. Trees are the 

keystone species in forests because they are the primary 

source of new carbon and energy entering the system, and 

the structural foundation of the system. Trees under stress 

have reduced capacity to fix carbon, and consequently 

reduced carbon and energy are available to the forest 

ecosystem. Thus trees are often examined as a leading 

indicator of the overall vigor of the system. 

For example, an epidemic outbreak of native insects will be 

less devastating if the tree species attacked is in generally 

good condition. Conversely, the same insect epidemic would 

be more devastating if the same tree species had already 

been impacted by an unusually severe drought, impaired 

nutrient cycling as a result of soil acidification, and so 

on. Tree condition can be determined by the tree crowns, 

existing levels of damage, growth, mortality, regeneration, 

and the type and amount of down dead wood, etc. For 

example the degradation of tree foliage is a symptom that 

some stressor(s) are affecting the ability of trees to convert 

the energy from sunlight into food and structural materials 

necessary for the health and sustainability of the forest 

ecosystem.

Vitality also includes attributes of key processes that 

underlay the structure and function of forest ecosystems. 

These processes are generally responsive to a variety of 

stressors that negatively impact forests, and the type of 

observed effects can be an indication of the causal agent(s) 

negatively affecting the forest. For example, changes 

in nutrient cycling will negatively affect the vitality of 

many forest ecosystem components, and may be caused 

by increasing soil acidity due to deposition of toxic air 

pollutants. Thus forest vitality can be evaluated based on 

indicators related to the type and magnitude of stressors, 

and whether stressors are increasing, stable, or decreasing. 

Other forest vitality indicators assess the response of 

forest ecosystems to stressors, and whether conditions are 

improving, remaining stable, or are degrading. 

Tree Crown Condition

Trees with dense, full crowns generally have relatively 

high total leaf surface areas and produce dark shade below 

the trees. Substantial loss of leaf surface area reduces light 

interception, the amount of carbon fixed as sugars, and 

subsequently tree growth, the flow of carbon and energy to 

other forest species, and overall forest vitality. Thus trees 

with relatively full, highly-foliated crowns are indicative of 

relatively low levels of stressors and a greater resilience to 

any new stressors because the trees are physiologically more 

active and have more material and energy reserves. Two 

primary indicators used to assess tree crown condition are 

crown dieback and foliar transparency. 

Dieback is the mortality of small twigs (less than 1 inch in 

diameter) in the upper, sunlight growing portion of the tree 

crown. Dieback is often indicative of significant current 

stressors (e.g., lack of nutrients; drought conditions) and 

the lack of resources to maintain new growth on trees, 

or stressors have directly killed these growing tips (e.g., 

late spring freeze; insect predation). Foliar transparency 

is related to the size and amount of foliage throughout the 

tree crown. Tree crowns with high transparency often have 

smaller leaves that are sparsely distributed where they 

occur. Transparency is only evaluated on branches where 

the foliage is present. Dieback and transparency are often 

evaluated for hardwood and softwoods separately because 

of the physiological differences between these two major 

tree groups. The current amount and the amount of change 

over time are two typical ways of evaluating these crown 

indicators. Changes over time for 1991 to 1998 or 1995 to 

1998 varied by State. 

Most of the ecoregion sections in the MAIA region had 

relatively low average hardwood transparency levels of 

12 to 19 percent in 1999 compared to other ecoregion 

sections in the US where transparency levels were as high 

as 42 percent. Only a few scattered plots had relatively 

high transparency levels of 38.1 to 58 percent. However, 

average hardwood transparency was increasing between 

0.1 to 2.0 percent throughout much of the eastern MAIA 

region, but decreasing by 0 to minus 2.0 percent in the 

western MAIA region (fig. 16). 

Average hardwood dieback was very low at 2.8 to 4.7 percent 

throughout most of the MAIA region, and even the highest 

average dieback values of 4.7 to 7.0 percent in the Region 
15

were low compared to hardwoods in other parts of the US. 

Hardwood dieback levels had decreased or were staying 

about the same in most of the MAIA region, with some 

increases of 0.1 to 2 percent per year in the northeastern 

Pennsylvania region, and decreased 0.1 to 2 percent per year 

in much of the eastern MAIA region. 

Average transparency of softwood trees in the Southern 

Unglaciated Allegheny Plateau, Northern Ridge and 

Valley, Allegheny Mountains, and Northern Cumberland 

Mountains ecoregion sections the Appalachian Mountain 

were 21.8 to 35.7 percent, the highest observed in the 

eastern U.S. (fig. 17). Softwood transparency was also 

relatively high at 15.0 to 18.7 percent throughout much 

of the eastern MAIA region. Average softwood foliar 

transparency had increased by 0.1 to 2 percent per year in 

much of the MAIA region, and increased by greater than 2 

percent per year in the Allegheny and Northern Cumberland 

mountain ecoregion sections. 

High softwood crown transparency values were often 

associated with Virginia pine. This and other data indicated 

a real decline in the crown condition of Virginia pine 

throughout the region. Other softwood species with high 

foliar transparency included shortleaf pine, table mountain 

pine, and pitch pine. Natural pests and anthropogenic 

Hardwood transparency 1998

Average annual percent change

Hardwood transparency

1998 plot average

7.7 – 18.0

18.1 – 28.0

28.1 – 38.0

38.1 – 48.0

48.1 – 58.0

Insufficient data

MAIA boundary

Ecoregion boundary

< -2

-2 - 0

0

0 - 2

> 2

Figure 16— Average annual change in percent foliar transparency 

of hardwood tree for the period of record for each State by 

ecoregion section (colored polygons), derived from the average 

foliar transparency of hardwood crowns at each FHM plot (solid 

black dots) in each ecoregion section in 1998. The plot value was 

the actual value if the plot was measured in 1998, and an estimated 

value based on previous plot measurements otherwise. Note legend 

also shows annual percent change in hardwood foliar transparency 

for ecoregion sections outside of the MAIA region for perspective. 

Data collected 1991-1998 in Delaware, Maryland, New Jersey, and 

Virginia; in Pennsylvania in 1995 and 1998; in North Carolina in 

1998.

Source: Chapter 13, Figure 53.

10.9 – 13.0

13.1 – 15.0

15.1 – 18.7

Insufficient data

MAIA boundary

Ecoregion boundary

18.8 – 21.8 

21.9 – 35.7

Softwood transparency

1998 ecoregion average

Softwood transparency

1998 plot average

0.1 – 10.0

10.1 – 20.0

20.1 – 30.0

30.1 – 40.0

40.1 – 50.0

Figure 17—Average percent foliar transparency of softwood trees 

in 1998 by ecoregion section (colored polygons). The black circles 

show the average softwood foliar transparency at each FHM plot 

in 1998; the plot value is the actual value if the plot were measured 

in 1998 and an estimated value based on previous measurements 

otherwise. Data collected 1991-1998 in Delaware, Maryland, New 

Jersey, and Virginia; in Pennsylvania in 1995 and 1998; in North 

Carolina in 1998.

Source: Chapter 13, Figure 56.
16

stressors may be contributing to declining health of the 

pines in this area, but another reason may be the age 

structure of pine stands. Most of the pine stands in the 

region originated between 1880 and 1920 during the period 

of intense farm abandonment and reforestation. For a 

variety of reasons, there have been only a few young pine 

stands replacing these older, senescing stands succeeding 

to hardwoods. If these trends continued, the affected pine 

species would probably become a significantly smaller 

component of forests in the western MAIA region. 

Average crown dieback of softwood trees was highest at 

4.6 to 6.7 percent in the Northern Ridge and Valley, and 

the Southern Unglaciated Allegheny Plateau ecoregions, 

as well as the Upper Atlantic Coastal Plain of New Jersey 

and Delaware. Most of the Coastal Plain and Piedmont 

had low dieback levels of 0.1 to 3 percent relative to 

other parts of the country, where softwood dieback 

average levels as high as 6.7 to 19 percent have been 

observed. Average dieback levels were higher at 4.6 to 6.7 

percent in the Northern Ridge and Valley and Southern 

Unglaciated Allegheny Plateau ecoregion sections, the 

latter the only ecoregion section where softwood crown 

dieback has increased by 0.1 to 2.0 percent per year. 

Softwood crown dieback was unchanging throughout the 

rest of the MAIA region. 

Hardwood and Softwood Crown Condition in 

Watersheds

We also evaluated the condition of hardwood and softwood 

tree crowns by HUC-4 watersheds in the MAIA region, 

because watersheds are based on topographic features that 

cut across different ecoregion section boundaries and are 

sometimes reflective of stressors that are influenced by 

topographic features, such as air pollution. It also produces a

basis for comparisons of forest condition with water quality 

condition in the MAIA region. 

Average hardwood foliar transparency was highest at 

18.1 to 19.4 percent in the lower Susquehanna River, 

Monongahela River, Kanawha River, Big Sandy and 

Guyandotte Rivers, and the Upper Tennessee River 

watersheds. These values were similar to the highest 

average hardwood transparency values by ecoregion 

section, but did spatially locate areas of concern better by 

identifying an area of affected watersheds that ran from the 

southwest to northwest part of the Region.

Average softwood transparency was highest at 25.1 to 31.7 

in the Monongahela River, Kanawha River, Big Sandy 

and Guyandotte Rivers watersheds, and were high at 18.1 

to 25.0 percent throughout the eastern MAIA watersheds. 

Spatial patterns were similar to those in ecoregion section 

analyses. Average softwood dieback by watersheds was low 

throughout the MAIA region, but spatially more pronounced 

in southern watersheds compared to spatial patterns found 

by ecoregion section. 

In general, analyzing crown condition by watershed 

sometimes changed and focused the spatial patterns of 

affected areas, and likely will assist in identifying causal 

agents and relating forest condition to water quality. 

Tree Damage

Damage caused by pathogens, insects, storms, and human 

activities can significantly affect the growth, reproduction, 

and survival of trees. Damages were recorded on each tree if 

it was likely to lead to infection by lethal pathogens, affect 

growth and/or reproduction, or cause premature mortality. 

Generally the type, severity, and location of damages are 

recorded, and this combination is used to calculate a damage 

severity index (DSI) score for each tree. All three factors 

are important. For example, a physical wound near the base 

of the tree is more likely to negatively impact the tree then 

the same wound on the top of the tree would. Individual tree 

damage index scores rarely exceeded 90; trees usually died 

before damage levels got much higher. Generally, a high 

damage index indicates multiple damages of high severity 

often near the bottom of the tree. 

The average number of softwood damaged trees and 

average DSI values were low throughout the MAIA region 

watersheds. No watershed had more than 20 percent average 

softwood trees damaged, many plots had zero damage, 

and plots with damage had DSI scores of 20 or less. The 

average number of hardwood trees damaged was highest 

at 20 to 30 percent in northern watersheds and the Pamlico 

watershed in the southeast MAIA region, but still relatively 

low compared with values observed in other US forests. 

There were scattered individual plots with very high average 

DSI values of 40.1 to 67.5, but many plots with zero or very 

low DSI scores. Scattered plots with high damage scores 

were probably related to localized outbreaks of insects or 

pathogens, storm events, or other causal agents.

Tree Mortality

Loss of trees and wood volume due to mortality is a natural 

part of any healthy and sustainable forest ecosystem. We 

evaluate tree mortality based on the volume of wood lost 

due to death compared to volume of wood gained in growth 

of surviving tress, and compute a mortality ratio (MRATIO) 
17

that quickly summarizes how much wood is being gained in 

forests each year. For example, an MRATIO of 0.6 means 

that for every 1.0 cubic foot of wood gained in annual 

growth, 0.6 cubic feet of wood was lost to mortality, and 

thus the net gain was 0.4 cubic feet. An MRATIO value 

greater than 1.0 indicates that the volume of wood lost 

due to mortality is exceeding the volume of wood gained 

in growth of surviving trees, and thus the live standing 

volume of wood is actually decreasing in that location. The 

MRATIO can be large if an over-mature forest is senescing 

and losing a large cohort of older, bigger trees. If forests are 

not naturally senescing, a high MRATIO (greater than 0.6) 

may indicate high mortality due to some acute cause (insects 

or pathogens), or other generally deteriorating forest health 

conditions. 

Another ecologically-significant component of tree 

mortality is the size of the trees that die relative to the size 

of surviving trees, and thus is related to MRATIO. The dead 

tree diameters (at breast height) (DD) is compared to the live 

tree diameters (LD) as an average ratio (DDLD) whenever 

tree mortality is found on a plot. Low (less than 1) DDLD 

ratios usually indicate competition-induced mortality of 

small trees that typically occurs in young, vigorous stands. 

Higher DDLD ratios (greater than 1) indicate mortality of 

larger trees that can be associated with senescence or some 

external stress factors such as insects or pathogens. 

Combined hardwood and softwood trees had moderate 0.3 

to 0.6 MRATIO values throughout most of the ecoregion 

sections in the MAIA region (fig. 18). The MRATIOs were 

lowest (0.10 to 0.30) in the Allegheny Mountains and the 

Northern Cumberland Mountains. There were many plots 

throughout the Region with little or no mortality, a number of 

scattered plots with high DDLD ratios of 1.3 to 9.8 indicating 

that larger trees had died, and a number of plots with low 

DDLD ratios less than 0.6 where smaller trees had died. 

Evaluation of tree mortality by watershed indicated similar 

spatial patterns with the exception of the Allegheny 

River where MRATIO values were 0.61 to 0.8, indicating 

relatively low gains in wood volume due to tree mortality. 

However, there were few plots in this watershed. DDLD 

ratios in this watershed were also high, but results are 

tempered by the relatively few plots available for mortality 

analysis in this watershed. 

The condition of trees in the MAIA region was generally 

fair, because there were some concerns with the amount 

of foliage (foliage transparency) on both hardwoods and 

softwoods trees. Hardwood transparency was relatively low 

but increasing annually in much of the Region. Softwood 

transparency levels were relatively high and also increasing 

throughout much of the Region. Average tree damage in 

watersheds were generally low for softwoods, with less 

than 20 percent of the trees damaged, and slightly higher for 

hardwoods with 20 to 30 percent of trees showing damage. 

The mortality of trees in the MAIA region was relatively 

low to moderate in many areas, suggesting that whatever 

was causing the poor crown conditions in softwood trees had 

not yet been of a magnitude to cause significant mortality in 

the affected species throughout the Region. 

PRODUCTIVITY

The productivity of a forest is often a very good indicator 

of the general health and sustainability of an ecosystem, 

because it is an overall indication of factors such as tree 

health, soil condition, low levels of insects and pathogens, 

and other factors. It indicates that the amount of carbon 

being fixed is sufficient to meet the demands for basic 

maintenance of systems, with a surplus of carbon and energy 

available to add new wood and support other components 

of the ecosystem dependent on trees. Productive forests are 

usually vigorous and healthy systems—when combined 

with high biological diversity they are good for recreation, 

0.91 – 1.00

0.0 – 0.10

0.11 – 0.30

 0.31 – 0.60

MAIA boundary

Ecoregion boundary

0.61 – 0.90

Insufficient data

1998 MRATIO

Mortality/growth volume

0.00 – 0.45

0.46 – 0.65

0.66 – 0.85

0.86 – 1.25

1.26 – 9.77

No mortality

1998 DDLD ratio

Mortality/growth d.b.h.

0.91 – 1.00

0.0 – 0.10

0.11 – 0.30

 0.31 – 0.60

MAIA boundary

Ecoregion boundary

0.61 – 0.90

Insufficient data

1998 MRATIO

Mortality/growth volume

0.00 – 0.45

0.46 – 0.65

0.66 – 0.85

0.86 – 1.25

1.26 – 9.77

No mortality

1998 DDLD ratio

Mortality/growth d.b.h.

Figure 18—Tree mortality volume by ecoregion section expressed 

as the ratio of annual mortality volume to annual growth volume

(colored polygons). Mortality ratio volumes of 1.0 indicate that there 

was no net gain in tree volume on the plot. Closed black circles 

represent plot-level values of the ratio of the average diameter of 

trees that died to the average diameter of the surviving trees on each 

plot (DDLD ratios). DDLD ratios of 1.0 indicate that on average the 

trees that died were as large as the surviving trees. Data collected 

1991-1998 in Delaware, Maryland, New Jersey, and Virginia; in 

Pennsylvania in 1995 and 1998.

Source: Chapter 13, Figure 66. 
18

wildlife habitat, and timber and non-timber products—as 

well as aesthetically pleasing. 

The productivity of a forest is influenced by many factors: 

the age of the forest stands, soil conditions, temperature, 

precipitation, and the presence of stressors that affect tree 

health. Tree productivity in the MAIA region was evaluated 

by stand age (average age of trees in stand) and stand 

density (basal area per acre), net growth rates (volume 

gained minus volume lost to mortality and harvest), and 

standing volume (volume of wood in cubic feet per acre). 

Stand Age and Density

The average age of forest stands was 60 years, with stand 

age ranging from 0 to 120 years. About 20 percent of the 

stands were in an earlier, regenerative stage (most trees less 

than 5 inches in d.b.h.)—these were primarily loblolly–

shortleaf plantation stands in a regenerative-to-immature age 

stage (most trees 5 to 10 inches d.b.h.) in the southeastern 

MAIA region. The most mature stands were oak–hickory 

types in West Virginia and western Pennsylvania and oak–

gum–cypress forests in the coastal plains of Virginia and 

North Carolina, with over half of these stands containing 

trees that were mostly greater than 10 inches in diameter. 

Stand density averaged 100 square feet per acre. The densest 

stands were oak–gum–cypress with a stand density of 250 

square feet per acre. 

Net growth and Standing Volume

The average net growth rate of forests in the MAIA region 

was 50 cubic feet of wood volume per acre each year, 

with individual stands ranging from 0 to 200 cubic feet 

per acre per year. The loblolly–shortleaf stands had the 

highest annual net growth rate of 90 cubic feet per acre per 

year, expected in relatively younger stands managed for 

timber production. Maple–beech–birch forest types had 

the lowest average net growth of 30 cubic feet per acre per 

year, reflecting the cooler temperatures and shorter growing 

season of these northern forest types. 

The standing volume of wood in forest stands reflects the age 

of the stand, the density of trees in the stand, and the general 

net growth rates (fig. 19). Average standing volume of all 

forests in the MAIA region was 1800 cubic feet per acre. 

Counties with high standing volume of greater than 1900 

cubic feet per acre were found in northern Pennsylvania, 

West Virginia, and parts of the southeast coast. The oak–

gum–cypress forests of the coastal plains had the highest 

average of 2400 cubic feet per acre. Loblolly–shortleaf forest 

types had on average 1800 cubic feet per acre, similar to the 

average for all forest types in the MAIA region. 

Market Benefits and Forest economics

Timber production—The variety of timber products, 

change over time, and spatial patterns of timber produced 

from 1970 to 1990 constituted timber production for 

the MAIA region. It was necessary to use data from 

diverse surveys of wood-product manufacturers that 

were conducted in different years for different products 

in different States, so we compiled information for 1970, 

1980, and 1990 decades as common periods of reference. 

Data from the most recent survey in each decade was 

used to estimate annual timber production in each decade. 

Sawlog production data for 1990 was only available for 

New York, North Carolina, and Virginia, so analysis of 

general timber production trends for the whole MAIA 

region was limited to 1970 and 1980. 

Quantities of sawlogs and pulpwood—Annual timber 

production in the MAIA region was 1,021 million cubic feet 

(mmcf) during the 1970s, and increased to 1,137 mmcf in 

the 1980s; it was 978 mmcf in the 1990s for NY, NC, and 

VA only (table 5). 

Softwood and hardwood sawlogs constituted 60 percent 

of total timber production in the 1970s, and declined by 

6 percent to 54 percent in the 1980s. Total pulpwood 

< 1500 cubic ft / ac 

1500 - 1900 cubic ft / ac 

> 1900 cubic ft / ac 

Figure 19—Forest stand volume in MAIA counties as average tree 

volume in cubic feet per acre prior to 2000. 

Source: Chapter 14, Figure 69.
19

Table 5—Annual hardwood and softwood sawlog and pulpwood production in MAIA region 

States in the 1970s, 80s, and 90s (Source: Chapter 15, Table 16)

a

Surveys were conducted in different years for different products. The decades of 1970, 1980 and 1990 were 

considered a common point of reference. The data from the most recent survey in that decade is used for reporting 

sawlog production. Pulpwood production corresponding to that timeframe, or closest to that timeframe, is reported. b

NA = Not available. 

c

Includes only New York, Virginia, and North Carolina for 1990.

d

mmcf = million cubic feet.

Source: Timber Product Output and pulpwood surveys conducted by the USDA Forest Service; (http://fa.fed.us).

Softwood Hardwood

Region

Yeara Sawlogs Pulpwood Total

 

Sawlogs Pulpwood Total

 Grand 

Total

Share of 

Total MAIA 

production

-------million cubic feet-------- ----- million cubic feet------ -mmcfd

- ---percent--

Delaware 

1970 1.0 3.8 4.8 1.0 0.9 1.9 6.6 0.7 

1980 0.4 1.5 1.9 2. 5 0.2 2.7 4.6 0.4 

1990 NAb

 2.1 NA NA 0.6 0.6 NA NA 

Maryland 

1970 5.3 14.8 20.1 18.3 9.1 27.4 47.5 4.7 

1980 6 17.9 23.9 16.2 10.3 26.5 50.4 4.4 

1990 NA 8.6 NA NA 7.4 7.4 NA NA 

New Jersey 

1970 0. 5 1.8 2.3 2.5 0.09 2.6 4.8 0.5 

1980 0.3 0.8 1.1 2 0.2 2.2 3.3 0.3 

1990 NA 0.1 NA NA 0.01 0.01 NA NA 

New York 

1970 6.1 3.2 9.3 56.8 3.7 60.5 69.8 6.8 

1980 NA 5.8 5.8 NA 4.5 4.5 NA NA 

1990 9.5 5.2 14.7 40 4.7 44.7 59.4 NA

North Carolina 

1970 111.1 73.8 184.9 36.2 48.4 84.6 269.5 26.4 

1980 127.2 118.3 245.5 43.2 76.6 119.8 365.3 32.1 

1990 165.9 104.7 270.6 49.3 92.2 141.5 412.1 NA 

Pennsylvania 

1970 8.7 4.8 13.5 95 72.5 167.5 181 17.7 

1980 7.2 4.7 11.9 109.4 91.8 201.2 213.1 18.7 

1990 NA 4.9 NA NA 55 55 NA NA 

Virginia 

1970 91.4 67.9 159.3 108.3 70.8 179.1 338.4 33.2 

1980 92 94.2 186.2 130.6 83.5 214.1 400.3 35.2 

1990 92.6 105.1 197.7 113 84.4 197.4 395.1 NA 

West Virginia 

1970 2.9 5 7.9 69.3 25.8 95.1 103 10.1 

1980 0.99 5.9 6.89 76 17.2 93.2 100.1 8.8 

1990 NA 7.9 NA NA 24.7 24.7 NA NA 

Mid-Atlantic Region 

1970 226.9 175.1 402.0 387.4 231.3 618.7 1020.7 -----

1980 234.1 249.1 477.4 379.9 284.3 659.7 1137.1 -----

 1990c 268.0 238.6 506.6 202.3 269.0 471.3 977.9 -----
20

Table 6—Average annual sawlog removals of species and species-groups in MAIA region and States in 1980s (Source: Chapter 15, Table 17)

a mmcf = million cubic feet.b percentage of total state production.

Source: USDA Forest Service Eastwide Database. Forest inventories were conducted in different States in different years, hence the table does not have a consistent time period.

SPECIES WV WV MD MD DE DE NJ NJ VA VA PA PA NY NY NC NC

MAIA

Region MAIA

--mmcfa--- --%b-- --mmcf-- --%-- --mmcf-- --%-- --mmcf-- --%-- --mmcf-- --%-- --mmcf-- --%-- --mmcf-- --%-- --mmcf-- --%-- --mmcf-- --%--

 SoftwoodsHemlock 6824 20.9 0 0 0 0 0 0 0 0 21495 56.8 14864.5 32.8 0 0 43294 1.9Yellow pines 11275 34.6 0 0 0 0 0 0 0 0 0 0 852.5 1.9 160500 13.4 172664 7.8White and red pine 159 0.5 0 0 0 0 0 0 91650 10.9 13147 34.8 20009.6 44.1 3900 0.3 128956 5.8Shortleaf/loblolly 0 0 46945 82.8 13843 90.9 0 0 16514 2.0 3172 8.4 0 0 991900 82.9 1072558 48.1Spruce-balsam  fir-cypress 14355 44.0 0 0.0 0 0 0 0 0 0.0 0 0 6991 15.4 33800 2.8 55205 2.5Other softwoods 0 0.0 9734 17.2 1393 9.1 0 0 734348 87.2 0 0 2663.9 5.9 6600 0.6 754858 33.9

Total softwoods 32,613 100 56,679 100 15,236 100 0 0 842,512 100 37,814 100 45,382 100 1,196,700 100 2,227,536 100

 HardwoodsYellow/sweet birch 6254 2 0 0 0 0 0 0 0 0 8693 1 2109 1 0 0 17060 0Hickory 18124 4 5913 2 0 0 0 48264 4 16549 2 5746 2 22700 3 117311 3Beech 25175 6 1407 1 0 0 1063 8 0 0 23802 3 21531 9 8000 1 81005 2Yellow poplar 59219 14 110277 41 0 0 1436 11 282073 24 46246 7 3464 1 173000 21 675814 19Other hardwoods 19956 5 50094 19 4392 34 0 0 126282 11 28623 4 36513 15 23300 3 289248 8Sweet gum 0 0 16175 6 1101 8 1777 14 0 0 0 0 0 0 145500 18 164582 5Ash-walnut-cherry 38533 9 0 0 0 0 290 2 22074 2 117923 17 24936 10 17300 2 221097 6Select white/red oaks 107363 26 49404 19 3293 25 1729 13 299124 26 59737 9 60115 25 126600 16 707508 20Other white/red oaks 92475 22 0 0 3463 27 6738 52 281596 24 267006 38 8160 3 130100 16 789705 22Hard maple 40980 10 33392 13 717 6 0 0 85312 7 126068 18 47342 20 0 0 333884 9Soft maple 0 0 0 0 0 0 0 0 0 0 0 0 28971 12 73400 9 102383 3Tupelo and blackgum 3374 1 0 0 0 0 0 0 20667 2 930 0 0 0 85700 11 110671 3

Total hardwoods 411,453 100 266,662 100 12,966 100 13,033 100 1,165,392 100 695,577 100 238,887 100 805,600 100 3,610,267 100

Softwood percent 7 18 5 0 42 5 16 60 38Hardwood percent 93 82 46 100 58 95 84 40 62

Total 444,066 323,341 28,202 13,033 2,007,904 733,391 284,268 2,002,300 5,837,802
21

production expanded in the region by about 25 percent 

from the 1970s to the 1980s, and pulpwood share of total 

production increased from 40 to 46 percent during this same 

period. Overall the hardwood share of both sawlogs and 

pulpwood total volume produced was higher than softwoods 

in the region. Hardwoods accounted for about 62 to 63 

percent of all sawlogs produced in 1970s and 1980s. 

Virginia accounted for about one third of total timber 

production in the MAIA region in the 1970s and 1980s, 

with North Carolina second, even though only 47 counties 

in northeastern North Carolina were evaluated as part of 

the MAIA region. Sawlogs accounted for 59 percent of 

Virginia’s timber production in the 1970s, 56 percent in the 

1980s, and 52 percent in the 1990s (table 5). North Carolina 

yielded 26 percent of total production in the 1970s, and 

32 percent in the 1980s. By the 1990s, total production of 

softwood and hardwood sawlogs and pulpwood in North 

Carolina exceeded Virginia. 

Timber production in Pennsylvania was lower than in 

Virginia and North Carolina, and was dominated by 

hardwoods, comprising 92 percent (1970s) and 94 percent 

(1980s) of total timber production (table 5). In contrast, 

hardwoods comprised about half of Virginia’s production, 

and about a third of North Carolina’s total timber production 

in the 1970s, 80s, and 90s. Other States’ contribution to 

the manufacture of timber products was relatively small. 

West Virginia produced smaller quantities of sawlogs and 

pulpwood from softwoods (about 7.9 and 6.9 mmcf) then all 

the other larger MAIA states in 1970 and 1980, respectively, 

but production of hardwood sawlogs and pulpwood was 

about 12 times greater (95 mmcf from hardwoods in 1970 

and 93 mmcf in the 1980s). Other MAIA states had a 

more balanced production of sawlogs and pulpwood from 

softwoods and hardwoods. 

Sawlog removals of species and species-groups—The 

market value of sawlogs depends on a number of factors 

such as species, physical and chemical characteristics of the 

wood, availability, and demand. Timber removal records 

from the USDA Forest Service Eastwide Database provided 

the volume (in thousand board feet) of sawtimber harvested 

by species-groups and species, by State and the whole 

MAIA region, during the 1980s using the average annual 

sawlog removals from the most recent forest survey in each 

State (table 6).

The softwood share of total sawlog removals was 38 percent 

and the hardwood share was 62 percent for the entire MAIA 

region. Hardwoods were predominant in New Jersey (100 

percent), Pennsylvania (95 percent), West Virginia (93 

percent), New York (84 percent) and Maryland.

(82 percent (table 6). The softwood share of total removals 

was greatest only in Delaware (54 percent) and North 

Carolina (60 percent), and was high in Virginia (42 percent); 

the proportion of softwood removals in the remaining 

MAIA States ranged from 0 to 18 percent. Shortleaf and 

loblolly pines accounted for 48.1 percent of all softwood 

removals in the region and for softwood sawlog removals in 

North Carolina and Maryland (about 83 percent each) and 

Delaware (91 percent). Hemlock accounted for 57 percent of 

total softwood removals in Pennsylvania and 33 percent in 

New York. 

Red oaks, white oaks, and yellow poplar accounted for 

nearly 61 percent of total hardwood removals for the 

region. Yellow poplar sawlog removals were 41 percent of 

hardwood in Maryland, 24 percent in Virginia, 21 percent 

in North Carolina, 14 percent in West Virginia, and 11 

percent in New Jersey. Sweetgum sawlog removals were 18 

percent and 14 percent of the total hardwood removals in 

North Carolina and New Jersey, respectively, but negligible 

in other MAIA states. Maple sawlogs were the primary 

hardwoods removed in New York and Pennsylvania, 

accounting for 32 percent and 18 percent, respectively, of 

the total hardwoods in those States. Sawlog removals of the 

tupelo and black gum species group were important only 

in North Carolina (11 percent) and Virginia (2 percent). 

The ash-walnut-cherry species group produced about 17 

percent (Pennsylvania) and 10 percent (New York) of 

total hardwood removals, and less than 2 percent in the 

remaining States. 

Timber Inventories—Timber volume is affected by growth, 

mortality, and removals. The latest FIA surveys of the 

MAIA region by State before 2000 were used, but included 

sawlog information in the 1990s for only New York, North 

Carolina, and Virginia. Inventories were estimated at almost 

103 billion cubic feet of growing stock wood of hardwood 

(80 percent) and softwood (20 percent) trees greater than 5 

inches in d.b.h., of which 57.9 billion ft3

 (56.3 percent) was 

in sawlog form (table 7). 

This growing stock covered 63.5 million acres of timberland 

forest, with an average of 1,620 cubic feet per acre. West 

Virginia (94 percent) and Pennsylvania (91 percent) had 

the most hardwood growing stock, while the 47 counties of 

northeastern North Carolina had the most softwood stock 

(43 percent), followed by Delaware (27 percent), New 

Jersey (25 percent), and Virginia (25 percent).
22

About 58 billion cubic feet of wood (56 percent) were in 

a sawlog or sawtimber stage of development (softwoods 

greater than 9 inches d.b.h.; hardwoods greater than 11 

inches d.b.h.); with hardwoods comprising 77 percent and 

softwoods 23 percent (table 7). States had from 51 to 61 

percent of growing stock in sawtimber volume, except New 

Jersey where sawtimber volume accounted for 90 percent. 

Virginia and Pennsylvania had the highest hardwood 

sawtimber volume of over 11 billion cubic feet each, and 

total sawtimber volume representing 26 and 22 percent, 

respectively, of the MAIA region volume. North Carolina 

Table 7—Hardwood and softwood growing stock and sawtimber volume in MAIA region States circa 2000 (Source: 

Chapter 15, Table 18)

a

Growing stock volume is the cubic-foot volume of sound wood in trees at least 5.0-inches dbh from a 1-foot stump to a 4-inch top.. b

Sawtimber volume is the growing-stock volume in the sawlog portion of sawtimber-size trees: 

 Softwoods: volume between 1-foot stump and 7-inch top for sawtimber trees 9.0-inches dbh and larger

 Hardwoods: volume between 1-foot stump and 9-inch top sawtimber trees11.0-inches dbh and larger.

c

Percent of growing stock volume.

Source: USDA Forest Service Eastwide Database; Hansen and others 1992.

had the highest volume of softwood sawtimber at over 4 

billion cubic feet. 

On average, there was an annual increase of 1.4 percent 

of the total growing stock (trees > 5 inches d.b.h.) in 

the MAIA region during the period 1970 through 1990. 

Annual hardwood growing stock increases were 1.8 percent 

and softwoods 0.89 percent. Total growing stock annual 

carryover was highest in Maryland (2.8 percent), New 

Jersey (2.3 percent), and West Virginia (2.3 percent). North 

Carolina had the lowest annual carryover of only 0.4 percent 

Growing stocka

State Softwood Hardwood Total 

Softwood 

Share 

Hardwood 

Share 

State share 

of MAIA 

-------------million cubic feet------------- ------------------------percent----------------------- 

Delaware 176 468 644 27 73 0.6

Maryland 813 3,662 4,475 18 82 4.4

New Jersey 521 1,522 2,042 25 75 2.0

New York 2,365 8,322 10,686 22 78 10.4

North Carolina 6,368 8,360 14,728 43 57 14.3

Pennsylvania 2,332 22,453 24,785 9 91 24.1

Virginia 6,648 19,839 26,487 25 75 25.7

West Virginia 1,219 17,823 19,041 6 94 18.5

Total MAIAc

 20,440 82,448 102,888 20 80 100

Sawtimberb

State Softwood Hardwood Total

Softwood 

share

Hardwood 

share

State share 

of MAIA

---mcf--- -pgsvc- ---mcf--- -pgsv- --- mcf--- -pgsv- --------------------------percent -----------------------

-

Delaware 115 65.3 239 51.1 354 55.0 32 68 0.6

Maryland 506 62.2 2,075 56.7 2,582 57.7 20 80 4.5

New Jersey 483 92.7 1,361 89.4 1,844 90.3 26 74 3.2

New York 1,674 70.8 3,943 47.4 5,616 52.6 30 70 9.7

North Carolina 4,204 66.0 4,810 57.8 9,014 61.2 47 53 15.6

Pennsylvania 1,557 66.8 11,144 49.6 12,701 51.2 12 88 21.9

Virginia 3,801 57.2 11,506 58.0 15,307 57.8 25 75 26.4

West Virginia 805 66.0 9,665 54.2 10,469 55.0 8 92 18.1

 

Total MAIA 13,145 64.3 44,742 54.3 57,887 56.3 23 77 100
23

Table 8—growing stock and sawtimber volume removals, growth, and mortality in MAIA region States 

circa 2000 (Source: Chapter 15, Table 20)

Growing stock 

State 

Total 

volume 

Average net 

annual growth

Average 

annual 

removal

Average 

annual 

mortality 

Total 

volume net 

growth

Total 

volume 

removal 

Total 

volume 

mortality 

-------------------------million cubic feet-------------------------- -------------------percent----------------

Delaware 643.90 13.50 3.20 4.11 2.10 0.50 0.64

Maryland 4474.90 163.29 39.27 27.34 3.65 0.88 0.61

New Jersey 2042.20 53.79 6.09 10.14 2.63 0.30 0.50

New York 10686.10 294.33 81.21 44.90 2.75 0.76 0.42

N. Carolina 14727.80 594.80 536.30 96.10 4.04 3.64 0.65

Pennsylvania 24784.50 631.74 284.05 176.93 2.55 1.15 0.71

Virginia 26487.00 801.61 558.72 161.33 3.03 2.11 0.61

W. Virginia 19041.30 505.17 71.06 46.66 2.65 0.37 0.25

Total MAIA 102887.70 3058.23 1579.90 567.51 2.97 1.54 0.55

Sawtimber 

State 

Total 

volume 

Average 

net annual 

growth

Average 

annual 

removal

Average 

annual 

mortality 

Total 

volume net 

growth

Total 

volume 

removal

Total 

volume 

mortality 

-------------------------million cubic feet------------------------ --------------------percent-----------------

Delaware 354.10 45.59 28.20 11.47 12.88 7.96 3.24

Maryland 2581.60 565.20 338.36 68.08 21.89 13.11 2.64

New Jersey 1843.90 154.36 13.03 22.52 8.37 0.71 1.22

New York 5616.40 1053.38 284.27 97.38 18.76 5.06 1.73

N. Carolina 9014.00 2371.70 2002.30 235.80 26.31 22.21 2.62

Pennsylvania 12701.00 2441.20 942.51 339.67 19.22 7.42 2.67

Virginia 15307.00 3270.42 2019.34 456.64 21.37 13.19 2.98

W. Virginia 10469.10 1978.06 444.03 101.75 18.89 4.24 0.97

Total MAIA 57887.10 11879.90 6072.04 1333.32 20.52 10.49 2.30

Source: USDA Forest Service, Eastwide Database; Hansen and others 1992.

during this period, but led other MAIA states in the amount 

of annual increases in hardwood growing stock at 3.0 

percent, followed by Maryland at 2.9 percent. All States had 

average annual increases in softwood growing stock except 

Virginia (annual decrease of 0.08 percent), with the highest 

annual increases in Pennsylvania at 2.5 percent.

On average total sawtimber annual increase was high in the 

MAIA region at 10.0 percent, with hardwood and softwood 

sawtimber annual increases at 11.0 percent and 6.8 percent, 

respectively, during the period 1970 through 1990. West 

Virginia (14.7 percent), New York (13.7 percent), and 

Pennsylvania (11.8 percent) led the other MAIA states 

in total annual sawtimber carryover. Annual sawtimber 

inventories increased in all States, led by West Virginia at 

14.7 percent and New York at 13.7 percent. Annual softwood 

sawtimber inventories increased and were highest in 

Pennsylvania at 13.3 percent and New York at 12.3 percent, 

and lowest in Delaware where it decreased by 2.6 percent. 

Average net growth of the total growing stock volume was 

3.0 percent per year for the MAIA region, with all States 

above 2 percent and North Carolina highest at 4.0 percent 

(table 8). 
24

Production intensity is the ratio of average annual removals 

to growing stock volume and identifies the proportion of 

growing stock volume removed per year. The average 

production intensity was 1.5 percent for the MAIA region, 

led by the southern MAIA region with 3.64 percent in North 

Carolina and 2.11 percent in Virginia. In the northern MAIA 

region, only Pennsylvania had a removal ratio greater than 

one percent at 1.2 percent. All other northern States had an 

annual average removal rate of less than 1 percent per year. 

Removals of softwoods were highest in North Carolina at 

4.7 percent per year and Virginia at 3.5 percent per year. 

Hardwood removals were less than 1 percent per year for all 

States in the region, except for Pennsylvania at 1.2 percent 

per year and Virginia at 1.6 percent per year. 

The production intensity for sawtimber volume averaged 

10.5 percent for the MAIA region, led by North Carolina 

(22.2 percent), Virginia (13.2 percent), and Maryland (13.1 

percent) (table 8). The percent increase in sawtimber volume 

averaged 20.5 percent for the MAIA region, and average 

annual mortality rates were 2.3 percent. Thus on average 

annual removals of growing stock volume and sawtimber 

volume were about half of the annual net increases for 

the MAIA region, indicating that these forests have been 

annually increasing volume more then losing volume to 

mortality and timber harvesting.

Timber markets—Timber markets were influenced 

by supply and demand within the MAIA region and 

nearby States, Canada, and other markets from 1977 

through 1997. The high sawtimber production of Virginia 

and North Carolina was used primarily for structural 

lumber, and softwood pulpwood used for paper products. 

Hardwood sawtimber from central and northern part of 

the MAIA region was used primarily for the furniture 

industry, housing industry, and pallet industry. Low 

quality hardwoods were used primarily for pallets, but 

were also increasingly used for pulpwood production 

for paper products. Pennsylvania supplied the most high 

quality hardwood for the furniture stock used in secondary 

processing mills in Pennsylvania and other States, 

particularly for the Southeast. Market interchanges between 

States within and without the MAIA region were complex, 

and varied greatly for each State. Details for each State are 

found in the body of the MAIA report.

Timber Prices

Timber prices are determined by the law of supply and 

demand, like many other markets. If demand is up and 

supply is low, prices generally rise; conversely, if supply 

is up and demand is low, prices tend to drop. The prices 

recorded in Pennsylvania were considered to be more 

representative of the northern MAIA region and hardwood 

prices, while prices in Virginia and North Carolina were 

considered more representative of the southern part of 

MAIA region and softwood prices. All prices in the report 

were adjusted to 1982 values for inflation. The stumpage 

price is the price of standing trees in the forest, while the 

mill price is the price of logs delivered to the mills for 

processing. We primarily used mill prices in comparisons 

because they more accurately capture the value in the market 

than stumpage prices. 

The mill prices for sawlogs of white oak, red oak, and black 

cherry by grade (low, medium, high) for Pennsylvania from 

1987 to 1997 represented about 62 percent of all hardwoods 

harvested (fig. 20 a.b.c.). Black cherry—primarily used 

by the furniture and cabinet industries—had the highest 

value, and the greatest increase in price from 1977 to 

1997 of 13.7 to 17.7 percent per year for low to high 

grade wood, respectively. Black cherry logs were also the 

(A)

(B)

(C)

0

100

200

300

400

500

600

700

198719881989199019911992

19931994199519961997

($/mbf, 1982=100)*

. 1987

1988198919901991

1992

1993

1994

1995

1996

1997

($/mbf, 1982=100)* ($/mbf, 1982=100)*

0

100

200

300

400

500

700

600

Low

Medium

High

0

100

200

300

400

500

700

600 1987

1988198919901991

1992

1993

1994

1995

1996

1997

Low

Medium

High

Low

Medium

High

Figure 20—Real prices of delivered black cherry (a), red oak (b), and 

white oak (c) sawlogs in Pennsylvania from 1987 to 1997, by low, 

medium, and high grades, corresponding to USFS grades F3, F2, 

and F1, respectively. *mbf = thousand board feet

Source: Chapter 15, Figures 75, 76, and 77.
25

second highest exported species to Europe. The demand 

for veneer—whether for cabinet, furniture, or architectural 

millwork—also had increased. Of the total amount of 

black cherry available in the region, higher quality timber 

was relatively scarce. Increasing concern for sustainable 

management of this environmentally and economically 

important species contributed to declining timber harvests 

in the Kene National Forest in Pennsylvania, resulting in 

less cherry timber available in the region and more pressure 

on the supply. Black cherry has a thin margin: a small 

increase in demand can affect pricing significantly, even 

though the total demand volume does not change much. 

All of these factors, i.e., greater demand in both domestic 

and international markets, along with reduced supply, 

combine to make black cherry a very valuable commodity 

in Pennsylvania. 

The demand for sawlogs comes from the demand for 

products made from them. An understanding of the demand 

for lumber improves our understanding of the behavior of 

sawlog prices. Since the early 1970s, red oak has become 

an important furniture lumber and the dominant species 

for kitchen cabinets and millwork Prices for species of red 

oak and white oak sawlogs also increased significantly in 

Pennsylvania from 1987 to 1997, and prices for higher-grade 

sawlogs increased faster than for lower grades. High-grade 

red oak and high-grade white oak grew 5.0 percent and 7.1 

percent per year, respectively, compared to medium-grade 

red oak (4.2 percent), medium-grade white oak (4.4 percent), 

and low-grade red oak (4.9 percent); all indicated an overall 

increase in demand for most grades of both red and white 

oaks. Considerable demand for white oak lumber in the 

international market may have accounted for the increasing 

prices of white oak sawlogs in Pennsylvania. In 1981 almost 

equal volumes of red and white oak were exported to Europe 

from the U.S.; but by 1990 white oak exports were more 

than 3.5 times greater than red oak exports. White oak was 

the most widely exported species to the European market 

in 1990, and individual market shares for white oak ranged 

from under 20 percent in Denmark and Italy to more than 

80 percent in Spain. Red oak was in high demand in some 

countries, and accounted for roughly 40 percent of the 

market in Luxembourg and Belgium, 30 percent in France, 

and a very small amount in most Scandinavian countries.

Mill prices for softwood sawlogs from North Carolina 

and Virginia increased about 2.1 and 3.9 percent per year, 

respectively, from 1977 to 1997, and pine pulpwood prices 

declined during the same period at an annual rate of 0.74 

percent per year in western North Carolina, and 1.08 percent 

per year in central and far eastern North Carolina. 

Total pulpwood production in Pennsylvania increased by 

about 40 percent between 1968 and 1988. Pulp processors 

relied heavily on chipped-residues to handle production 

increases, aided by increased sawlog portions that made 

manufacturing residue more available. The chipped-residue 

portion of total pulpwood production increased from 23 

to 37 percent from 1968 to 1988. This implied decreased 

demand for higher and medium-grade pulpwood that 

resulted in lower prices. However, in 1990, hardwood 

pulpwood production declined by almost 34 percent 

compared to 1988 production. And, even though softwood 

pulpwood increased by about 11 percent in 1990 compared 

to 1988, the production of hardwood and softwood 

pulpwood together declined by 32 percent in 1990. 

Pine pulpwood production and harvesting increased in 

North Carolina from 1986 to 1992. Almost all of this 

growth was in southwestern North Carolina, where 

softwood pulpwood production expanded by 53 percent. 

Pulping capacity in that region was relatively small, 

however, suggesting that mills in Georgia and Tennessee 

were drawing increasing amounts of material from this 

region. This implied that hauling distances and zones 

of procurement for pine pulpwood were expanding, 

foreshadowing increasing demand for pulpwood timber. 

Pine pulpwood prices increased in Virginia between 

1977 and 1997. They increased at an annual rate of 1.45 

percent in western Virginia and 1.73 percent in eastern 

Virginia. Eighty-four percent of roundwood products cut 

for pulpwood were retained for processing at Virginia pulp 

mills. Imports of nearly 49 million cubic feet exceeded 

exports by 64 percent, making the State a net importer of 

pulpwood, and suggested that pine pulpwood was relatively 

scarce in Virginia during those two decades. 

Forest Industry

To quantify the economic importance of forest-based 

industries in the MAIA region, we examined employment 

and income generated in the following sectors based on 

Standard Industry Classifications (SIC): lumber and wood 

products (SIC 24), furniture and fixtures (SIC 25), and paper 

and allied products (SIC 26).

Forest industry has been an important contributor to the 

economy of the MAIA region, producing an average 

of a quarter million jobs, or 2.04 percent of all wage 

employment, and generating $4.5 billion in wages and 

salaries each year between 1975 and 1995 (table 9). 

However, with the exception of lumber and woods products 

in Delaware, West Virginia, and Pennsylvania, and furniture 
26

State/sector

Average 

employment 

Average share

of total economy 

employment

Average annual

rate of change 

--thousands-- -----------------percent------------------

Total MAIA region 

 All sectors 11969.8 100 1.93

 SIC 24a 83.6 0.70 1.32

 SIC 25b 73.1 0.61 0

 SIC 26a 87.4 0.73 0

 Total SIC 24+25+26 244.1 2.04

Delaware 

 All sectors 248.3 100 2.75

 SIC 24 0.82 0.33 3.36

 SIC 25 0.5 0.20 5.8

 SIC 26 2.5 1.01 0

 Total 

SIC 24+25+26 3.82 1.54

Maryland

 All sectors 1455 100 2.62

 SIC 24 3.8 0.26 0

 SIC 25 3.3 0.23 -1.23

 SIC 26 9.4 0.65 -0.84

 Total 

SIC 24+25+26 16.50 1.14

New Jerseyd

 All sectors 1441.2 100 2.24

 SIC 24 2.6 0.18 -1.44

 SIC 25 1.8 0.12 0

 SIC 26 6.8 0.47 -1.62

 Total 

SIC 24+25+26 11.20 0.77

New Yorkd

 All sectors 972.7 100 1.93

 SIC 24 4.7 0.48 0

 SIC 25 6.6 0.68 1.15

 SIC 26 4.2 0.43 0.84

 Total 

SIC 24+25+26 15.50 1.59

North Carolinad

 All sectors 1439 100 2.46

 SIC 24 16.8 1.17 0

 SIC 25 16.7 1.16 -1.06

 SIC 26 6.5 0.45 1.34

 Total 

SIC 24+25+26 40.00 2.78

Pennsylvania

 All sectors 4042.6 100 0.97

 SIC 24 25.4 0.63 2.98

 SIC 25 19.3 0.48 0

 SIC 26 41.3 1.02 -0.43

 Total 

SIC 24+25+26 86.0 2.13

Virginia

 All sectors 1893.3 100 3.22

 SIC 24 23.3 1.23 0

SIC 25 24 1.27 -0.95

a

 SIC 24 = lumber and wood products 

b

 SIC 25 = furniture and fixtures

c

 SIC 26 = paper and allied products 

d Figures for NJ, NY and NC are totals for the subset of counties in the MAIA 

region. 

Source: Department of Labor, unemployment insurance database ES-202; 

http://workforcesecurity.doleta.gov/unemploy/finance.asp.

Table 9—Average employment (wage and salary) and rate of change 

in all Standard Industry Classification (SIC) sectors in MAIA region 

1975 to 1995 (Source: Chapter 15, Table 24)
27

a

 SIC 24 = lumber and wood products 

b

 SIC 25 = furniture and fixtures

c

 SIC 26 = paper and allied products 

d Figures for NJ, NY and NC are totals for the subset of counties in the MAIA 

region. 

Source: Department of Labor, unemployment insurance database ES-202; 

http://workforcesecurity.doleta.gov/unemploy/finance.asp.

 All sectors 972.7 100 1.93

 SIC 24 4.7 0.48 0

 SIC 25 6.6 0.68 1.15

 SIC 26 4.2 0.43 0.84

 Total 

SIC 24+25+26 15.50 1.59

North Carolinad

 All sectors 1439 100 2.46

 SIC 24 16.8 1.17 0

 SIC 25 16.7 1.16 -1.06

 SIC 26 6.5 0.45 1.34

 Total 

SIC 24+25+26 40.00 2.78

Pennsylvania 

 All sectors 4042.6 100 0.97

 SIC 24 25.4 0.63 2.98

 SIC 25 19.3 0.48 0

 SIC 26 41.3 1.02 -0.43

 Total 

SIC 24+25+26 86.0 2.13

Virginia 

 All sectors 1893.3 100 3.22

 SIC 24 23.3 1.23 0

 SIC 25 24 1.27 -0.95

 SIC 26 15.5 0.82 1.21

 Total SIC 24+25+26 62.80 3.32

West Virginia 

 All sectors 477.7 100 0

 SIC 24 6.2 1.30 2.3

 SIC 25 0.97 0.20 -2

 SIC 26 1.3 0.27 -1.92

 Total SIC 24+25+26 8.47 1.77

Table 9—(Continued) Average employment (wage and salary) and 

rate of change in all Standard Industry Classification (SIC) sectors in 

MAIA region 1975 to 1995 (Source: Chapter 15, Table 24)

and fixtures in Delaware, the forest industry sector has not 

been growing as rapidly as the rest of the MAIA region 

economy. Several States had even experienced negative rates 

of growth in some forest industry sectors (e.g., Maryland 

and New Jersey). As a result, the share of employment in 

forest industries declined during the last two decades in all 

states except West Virginia and Delaware. 

Real wage and salary income for the entire Region’s 

economy averaged $222.3 billion per year between 1975 

and 1995, and about 2.02 percent ($4.50 billion) of that 

total came from forest industries. The average wage per job 

increased in all forest industries, despite slight decreases in 

wages in the early 1980s, and all other sectors between 1975 

and 1995 (fig. 21). 

The average real wage per job for the entire economy of the 

MAIA region between 1975 and 1995 was about $18,000—

growing from about $16,000 in 1975 to $21,000 in 1995 

(an increase of 31 percent). The average wage per job in 

SIC 24 and SIC 25 was $14,816 and $15,497, respectively; 

0

5

10

15

20

25

30

35

197519761977197819791980198119821983198419851986198719881989199019911992199319941995

Year

Dollars

(thousands)

 Lumber

 Furniture

 Paper

 All

Figure 21—Real wages per job in lumber and wood products (SIC 

24), furniture and fixtures (SIC 25), paper and allied products (SIC 

26), and all sectors of the MAIA economy 1975 to 1995. 

Source: Chapter 15, Figure 88.
28

earnings below the MAIA regional average by 18 and 14 

percent, respectively. The average wage per job in SIC 

26 was $24,000, higher than the regional average for the 

entire MAIA economy by almost 33 percent. The wage per 

job in SIC 26 increased by 52 percent between 1975 and 

1995, compared to a 40 percent increase in SIC 24 and a 39 

percent increase in SIC 25.

Wages in SIC 24 increased at the highest average annual rate 

of 3.34 percent per year, SIC 25 followed with 1.35 percent, 

and SIC 26 with 2.16 percent for the whole MAIA region, 

compared to average annual rate of 3.81 percent for all other 

non-forest sectors. The lumber and wood products sector 

(SIC 24) produced an average of $1.25 billion per year in 

wages and salaries, SIC 25 generated $1.13 billion, and SIC 

26 an average $2.12 billion. 

If recent trends continue, the forest industry will continue to 

be an important source of employment and income for parts 

of some States in the MAIA region—but forest industry’s 

importance relative to the entire Mid-Atlantic economy 

will probably continue to decline in the 21st century. Even 

so, more than half of the total forest area in the MAIA 

region now contains sawtimber-size stands, which, by 

timber market standards, represent a valuable resource in 

a harvestable condition. Most of these harvestable stands 

were found in Pennsylvania, Virginia, and West Virginia. 

game Species

Trends in game species populations are also indicators of 

overall forest health and sustainability. Negative changes 

in animal populations usually reflect changes in habitat 

available for wildlife, harvesting pressures, or stress from 

exotic competitors, diseases, etc. It is imperative that rates 

of harvest do not exceed rates of population increases so a 

growing, viable population is present to withstand unforeseen 

environmental stresses. Excessive harvest rates, combined 

with degradation of habitat, often lead to catastrophic crashes 

in wildlife populations. Wildlife in the MAIA region, similar 

to wildlife populations throughout the eastern U.S., have 

been recovering from the severe over-harvesting and habitat 

degradation that led to the near extinction of many species by 

the beginning of the 20th century. 

Current population estimates and rates of harvest of primary 

game species in the MAIA region indicated populations 

were robust and in some cases increasing at greater than 

harvest rates. For example, populations of black bear 

increased from about 8,000 bears in 1975 to about 23,000 

bears in 1993, while harvest levels only increased from 

70 75 80 85 90 95

0

500

1000

1500

2000

2500

3000

3500

4000

(thousands)

White- tailed deer

Harvest

Population

Year

Figure 22—White-tailed deer population and harvest for 1975 to 

1993 for all States in the MAIA region. Estimates include counties 

outside of the MAIA region for New York, New Jersey, and North 

Carolina.

Source: Chapter 16, Figure 91. 

about 2,000 bears in 1975 to about 5,000 in 1993. While 

increased estimates of bear populations may be partially an 

artifact of improved tracking technologies, it is apparent 

that bear populations are not in danger of being depleted by 

current hunting practices. Turkey populations also increased 

at rates greater than harvest, with a population estimated at 

300,000 in 1993 compared with a harvest of about 110,000 

for the same year. Squirrel populations have remained 

relatively constant at 11 million for the same period, while 

harvest of squirrels has decreased from about 4 million 

to 2 million per year. The stability of squirrel population 

estimates, even with declining harvest pressures since 1980, 

suggests that this species has probably reached the carryingcapacity of environments to support their populations, and 

populations are more controlled by habitat and other factors 

then by hunting pressures. White-tailed deer populations 

exploded from about 2 million in 1980 to about 3.6 million 

in 1990, while deer harvest levels only increased from 0.5 

million to about 1.5 million during the same period (fig. 

22). Hopefully, the leveling-off of white-tail deer population 

at about 3.6 million animals in 1990, despite increases in 

the number of deer harvested after 1990, may indicate this 

species has also reached or exceeded the carrying capacity 

of the environment. The excessive browsing of large 

populations of white tail deer are causing very negative 

effects on forests primarily because of the selective feeding 

on seedlings and saplings of tree species, thus affecting 

the composition, structure, and function of future forests 

throughout much of the eastern United States. 
29

CONSeRVATION OF SOIL AND WATeR SYSTeMS

Soils

Soils are one of the most essential components of healthy 

and sustainable forest ecosystems. Soils anchor plants to 

the ground, supply water and essential nutrients, and protect 

roots from harsh environmental conditions. Soils provide 

five of the six environmental components that trees and 

other plants are dependent: mechanical support, heat, air, 

water, and nutrients. Most of the crucial mineral exchange 

between the biosphere (world of living creatures) and 

the inorganic world (rock) occurs in the soil. Dead plants 

and animals decompose and return mineral nutrients and 

organic material to the soil. The numerous small organisms 

responsible for decomposition are abundant in the surface 

layers of the soil where dead organic matter is most 

plentiful. The activities of these organisms contribute to 

the development of soil properties in a surface-to-bottom 

direction, while physical and chemical decomposition of 

bedrock contribute to soil development in a bottom-tosurface direction. Both surface and bedrock processes are 

important for a healthy soil system. Human-induced threats 

to degradation of forest soils are erosion, acidification 

through atmospheric deposition, loss of nutrients, 

accumulation of toxins, and compaction.

Most of the soil is relatively inert, and most of the chemical 

and biological functions in soil are determined by the clay 

and organic matter components. Soil analyses in the MAIA 

region focused on the biological, chemical, and physical 

processes of mineral soils and organic matter in the upper 

20 inches of soil, with most emphasis on the upper 8 inches. 

This top 8 inches often contains the litter layer, O-horizon, 

and the A-horizon layers. 

Generally the nutrient calcium and magnesium levels in 

surface soils in the MAIA region were marginal; however 

there were scattered plots where magnesium levels were 

relatively low (0.04 to 0.60 milliequivalents per 100 grams). 

However, the acidity levels (pH values) of the 0 to 4 inch 

surface and 4.1 to 8 inch subsurface mineral soils were very 

low at pH values of 3.1 to 4.5 in many areas, indicating high 

vulnerability to any further acidifications that would reduce 

nutrient cation availability, increase the availability of 

toxic aluminum, and result in leaching of nutrient and toxic 

chemicals into associated aquatic systems.

Water

The primary concern for the quality of fresh water systems 

in the MAIA region are the conversion of forested lands 

to other uses and the resultant increases in soil erosion 

and stream sedimentation, increases in water flows during 

storm events, increases in toxic elements, and subsequent 

changes in stream habitat and biology. Conversion of 

forest to other land uses was found to degrade stream 

quality on short-term scales of 2 to 5 years at local spatial 

scales, but there was no evidence of cumulative effects on 

stream quality at regional scales from local-scale forest 

management practices. 

The condition of first, second, and third order streams in 

mountainous watersheds were evaluated as part of EPA’s 

Environmental Monitoring and Assessment Program 

Mid-Atlantic Highlands stream monitoring program. In 

first order streams, 97 percent of the area was forested, 

compared to 80 percent forested for second order streams, 

and 75 percent forested for third order streams. Thus 

the total non-forest land use in 1st order streams was 3 

percent, compared to 20 percent for 2nd order, and 25 

percent for 3rd order streams. Agriculture accounted for 

about 20 percent of the land area for second and third order 

streams. Stream orders 1, 2, and 3 all had significant fish 

populations, with respectively 74 percent, 92 percent, and 

93 percent respectively of the stream miles having either 

fish or sport fish present. The 26 percent of first-order 

stream miles without fish did not imply stream degradation, 

but may have simply been inadequate habitat for fish 

due to steep topography, low flows, and other factors. 

Highly forested watersheds with better water quality 

also had better biological condition based on the widelyused indicators of stream quality, the presence of the 

benthic insects in the Orders Ephemeroptera, Plecoptera, 

and Trichoptera (EPT). EPT scores were higher for all 

stream orders in low land use watersheds, but were only 

statistically higher in third-order streams (figure 23).

The effects of land use on stream physical and chemical 

quality was also evaluated by comparing stream orders 1, 

2, and 3 in watersheds with greater than 95 percent forested 

(low landuse) with watersheds less than 95 percent forested 

(high landuse). The following variables were averaged 

across stream orders 1, 2, and 3 and compared by ratios of 

high landuse to low landuse. Thus ratio values of 1.0 meant 

that there was no difference between watersheds with high 

landuse compared to low landuse; ratio values of 2.0 meant 

that watersheds with high landuse had twice the value found 

in low landuse. The high landuse to low landuse watershed 

ratios were: road density = 2.3; total suspended solids = 

2.4; percent fine clays and silts = 8.6; nitrate = 3.3; and 

total phosphorus = 2.8. Thus highly forested watersheds 

contained streams with better physical and chemical water 

conditions and water quality. 
30

The general result was that conversion of forests to other 

land uses, particularly agriculture, appears to have mainly 

affected 3rd order streams or higher, with little measurable 

effects on 2nd or 1st order streams. Thus there was no 

evidence of regional effects on stream condition from 

conversion of forest land to other uses, but local effects at 

the scale of 3rd order streams or higher.

CARBON CYCLINg

Carbon is the biological building block of life on earth, 

and thus the cycling of carbon is the essential fundamental 

process in all ecosystems. Changes in expected variances 

in carbon cycling patterns can reflect major alterations in 

forest ecosystems. Plants incorporate carbon into biological 

systems through photosynthesis—energy from sunlight is 

used to combine carbon dioxide with water and produce 

simple sugars (molecular chains of carbon atoms) and 

gives off oxygen as a waste product. Some of this fixed

(chemically reduced) carbon is burned (chemically oxidized) 

to produce energy for most biological processes; some 

is sequestered as part of above and below ground plant 

biomass; some is used by insects, animals, bacteria, and 

other organisms for growth, a process known as secondary 

productivity; and some is incorporated as part of the upper 

soil horizons as organic matter. 

Part of the carbon stored in forest systems later is released 

into the atmosphere as organic matter decomposes over time. 

Both forest biomass and forest soils serve as large carbon 

sinks (carbon deposits) and are, therefore, an essential 

component of a stable ecosystem and global carbon cycles. 

Approximately one-half of the carbon harvested as biomass 

is stored for long periods as wood products. A net gain in 

carbon is the result of high stand-growth rates, relatively low 

mortality volumes, efficient utilization of harvest trees and 

salvage of mortality, or some combination thereof. 

The amount of carbon stored or lost annually for MD, VA, 

and WV was estimated for variable periods from 1991 to 

1998. Average carbon sequestration rates in woody biomass 

for this part of the MAIA region was 1,600 lbs per acre 

per year. Carbon sequestration rates in the MAIA region 

were highest (greater than 60 ft3

 per acre per year) in the 

middle and lower Atlantic Coastal Plain of the Southeast; 

moderately high (40 to 60 feet3

 per acre per year) in the 

western and northwestern parts of the region; lower (less 

then 40 feet3 per acre per year) in the central and north 

Piedmont areas and western mountain areas; and lowest in 

the northwestern part of the region. 

These patterns of carbon storage are not surprising, because 

the rate of carbon sequestration in a given area is a function 

of inherent site quality (high moisture, soil fertility, and 

moderate temperatures), seral stage of development, and 

intensity of forest management. The southeastern Coastal 

Plain has some of the best conditions for tree growth in the 

U.S., and includes a high proportion of managed forest pine 

plantations with harvest rotation cycles set to get maximum 

growth rates. 

BIOLOgICAL DIVeRSITY

Biological diversity, sometimes shortened to biodiversity, 

is of interest over a wide range of spatial and temporal 

scales from genotypes within a species to mixes of forest 

types and plant communities at regional, national, and 

international levels. It can also include the physical or 

structural arrangement of species within a forest stand. 

There are numerous ways of assessing biological diversity 

that are generally based on the number of species, the 

relative abundance of each species, and the distribution of 

species within some defined ecological strata. 

The amount of biodiversity is always constrained by 

the conditions found in any environment. Physical and 

biological factors create potential habitats or spaces 

for species to occupy that are called niches. The 

primary physical drivers are moisture and temperature; 

secondarily are topography, geology, and other factors; 

and thirdly are cyclic disturbances like storms, rockslides, 

and other events. Environments that are inherently harsh 

for plants typically have low biodiversity, for example 

Median EPT score

Stream order

Forest land use Mixed land use

0 5 10 15

3rd

1st

2nd

Figure 23—Median Ephemeroptera, Plecoptera, and Trichoptera 

(EPT) scores by stream order. EPT scores are based on type and 

number of these three groups of stream insects.

Source: Chapter 18, Figure 101.
31

boreal forests; and environments that are milder, for 

example tropical forests, often have relatively high 

biodiversity. A fourth factor in the biodiversity framework 

is that biology itself creates additional niches where more 

species can be added. For example, a tree species creates 

micro-environmental conditions that are habitat for 

epiphytic lichen species, animals, and understory plants. 

The numbers of niches represent biodiversity potential, 

and if and how these niches are occupied by living 

organisms is the actual amount of biological diversity. 

Because any environment has a potential for some 

maximum expression of biological diversity that 

is generally limited by the existing environmental 

conditions, comparisons of biodiversity are only 

reasonable within specific ecological strata. Maximum 

biodiversity for any ecosystem is often realized in the 

absence of non-historic disturbances that sometimes 

result from human activities.

Biological diversity is often reported as the number 

of species (richness), the number or area occupied by 

individuals (abundance), or by indices that characterize 

various relationships of numbers, abundances, and 

distributions of species. While species richness is not 

per se the whole story of diversity, it is often used as an 

indicator of diversity because it is a simple, direct, and 

understandable measure of how many species are present, 

and is often a good indicator of stable but not static 

habitat conditions. 

The eastern deciduous forests of the MAIA region are 

among the world’s most floristically diverse regions, 

home to numerous endemic (common but found nowhere 

else) as well as rare, endangered, and threatened plant 

species. This diversity is the result of a relatively mild, 

moist climate and varying geological parent rock and 

topographical features. Moving from east to west, the 

coastal plains with their broadleaf and bottomland forests 

rise slightly to the clayey Piedmont section with its mixed 

conifer-hardwood forests, and then rise more abruptly 

to the rich oak–hickory forests of the western valleys 

and foothills of the north-south trending Blue Ridge 

Mountains, Allegheny Mountains, and the Cumberland 

Mountains. The rounded tops of these mountains are 

crowned with Northern firs and spruce that have been able 

to extend southward because of the higher elevations. The 

MAIA region is truly an area of high diversity at multiple 

scales, with a rich mix of landscapes, forest types within 

landscapes, tree species within forest types, and structural 

diversity.

Landscape Diversity

At the landscape scale, the forests of the MAIA region 

are highly fragmented, with few large contiguous tracts 

of forests remaining. Forest fragments in the region 

were interspersed mainly with agriculture, urban, and 

other developed land uses. The watersheds closest to the 

Chesapeake Bay remain the most highly deforested and 

urbanized, while the watersheds in the western mountains 

are relatively contiguous. 

Urbanization and fragmentation continue to be the greatest 

threats to the health and sustainability of the forests in the 

MAIA region. Increases in human population numbers 

and densities have resulted in the conversion of forest land 

to urban development and agricultural. Agriculture lands 

sometimes revert to forest, but are more often converted to 

urban land; urban land almost never reverts to forest. As 

the quantity of forest land is reduced, the remaining forest 

becomes more fragmented. The negative effects of forest 

fragmentation include loss of wildlife habitat, increased 

sedimentation and changes in the quality of aquatic systems, 

changes in microclimatic conditions within the forest, 

increased opportunities for human-induced wildfires, 

increased access for exotic species invasions, loss of species 

diversity, and species extinction. 

Forest Type Diversity

The dominant forest type was oak–hickory containing oak, 

hickory, red maple, yellow-poplar, other tree species that 

was dispersed throughout and covering 32 percent of the 

MAIA region (fig. 24). 

There are significant concerns that oak will continue to be 

the dominant tree species in this region because of wildfire 

suppression, heavy deer browsing, and other factors that 

favor the regeneration of other tree species—especially red 

maple and tulip poplar—at the expense of oak regeneration. 

The second most common forest type is maple–beech–

birch (composed primarily of sugar maple, red maple, 

American beech, yellow birch, sweet birch, black cherry, 

white ash, and eastern hemlock), found mostly in northern 

Pennsylvania, southern New York, and western mountain 

areas that covers 12 percent of the land. The loblolly–

shortleaf pine forest type (loblolly pine, shortleaf pine, 

sweet gum, red maple, and red oak species) covered about 

6 percent mostly in eastern Virginia and North Carolina. 

Other forest types were more restricted to local distributions, 

including other conifer types (eastern hemlock, white pine, 

pond pine, pitch pine); and oak–gum–cypress (swamp 
32

tupelo, bald cypress, and mesic white oak species) found in 

wet, poorly drained areas near the Atlantic coast.

Structural Diversity

The size and biomass distribution of trees is a measure of the 

structural diversity of the forests and their potential uses, as 

well as a measure of the resiliency of the forests to stressors. 

In the MAIA region, over 75 percent of the trees were found 

to be 1 to 5 inches in diameter, but accounted for less than 

20 percent of the biomass (fig. 25). 

Size distribution of trees on a biomass-basis indicated that 5 

to 10 and 10 to 15 inch diameter classes accounted for about 

55 percent of the total biomass, suggesting the beginning 

of a more stable system where the majority of the biomass 

is found in the mid-sized and larger trees. The largest trees 

in the MAIA region (greater than 20 inches in diameter) 

accounted for only 10 percent of the total biomass.

Species Diversity

Tree genera and Species

The high diversity of forest types also entailed a high 

diversity of tree species. Tree species diversity—important 

for recreation, wildlife habitat, timber, and non-timber 

commodities—is influenced by climate, soils, topography, 

cyclic disturbances, and natural successional patterns. The 

diversity of tree species in the MAIA is unique, with more 

than 60 hardwood or softwood species found in the forests 

of the region, 25 of these with a relative abundance of 1 

percent or more. More than 8 genera and 15 tree species 

were found in most of West Virginia and parts of western 

Pennsylvania. Red maple was the most common species 

in the MAIA region, accounting for 12 percent of all trees; 

followed by the red oak group (black oak, northern red oak, 

scarlet oak) at 12 percent of all trees (table 10). Loblolly 

pine accounted for 7 percent of all trees; most were 

relatively small (less than 10 inches d.b.h) and occurred on 

plantations managed for timber production.

Chestnut oak, white oak, yellow-poplar, and sugar maple 

each represented 5 or 6 percent of all trees, with white oak 

and yellow-poplar common in the greater-than-15 inches 

diameter size class. Black cherry and American beech were 

also in the top ten most abundant species. Sweet gum, black 

gum, and dogwood were common as saplings (less than 5 

inches in diameter) in the understory of many forests. Even 

the numerous pine plantations in the Piedmont ecoregion 

sections of Virginia and North Carolina contained 5 to 7 

genera of tree species. Tree diversity was lowest in the very 

Forest 

(percent) 

100

80

0

20

40

60

Tree diameter class (inches)

1 - 5 5 - 10 10 - 15 15 - 20 20 +

Number of trees

Biomass

Figure 25— Distribution of numbers of trees and biomass in five 

size-classes in the MAIA region.

Source: Chapter 22, Figure 116.

Acres

(millions)

35

30

25

0

20

10

15

5

DL MD NJ NY NC PA VA WV

States

Figure 24— Forest cover type by MAIA region state.

Source: Chapter 21, Figure 107.
33

wet coastal plains, which are primarily oak-gum-cypress 

swamps, and on the very dry ridges of the mountains which 

contained relatively few oak and hickory species. 

Lichen Species

Particularly sensitive to changes in air quality and climatic 

conditions, lichens are good indicators of air quality, stand 

age, disturbance, and other factors. Biologically unique— 

they are epiphytes that grow on tree boles, branches, rocks, 

etc. and get moisture and nutrients from the atmosphere—

lichens are an important component of a healthy forest that 

can provide fixed nitrogen, food, and habitat for a variety of 

animals, and substrate for the germination of other epiphytic 

species. 

Table 10—Relative importance (rank) of the twenty most 

common tree species by abundance (number of trees) in 

five d.b.h. size classes circa 2000. Column 1 

(1 to 15+ inches d.b.h.) determined the overall rank of 

each species. Columns 2 to 5 are the ranks of species in 

other d.b.h. classes (Source: Chapter 23, Table 29)

Diameter at breast height (inches)

Species 1 - 15+ 1 - 5 5 -10 10 -15 15+ 

-----------------species rank---------------------

Red maple 1 1 1 2 3 

Red oaksa

 2 3 2 1 1 

White oak 3 9 4 3 4 

Yellow-poplar 4 10 7 5 2 

Chestnut oak 5 16 5 4 5 

Loblolly pine 6 8 3 6 6 

Sugar maple 7 2 6 7 8 

Sweetgum 8 4 10 10 15 

American beech 9 7 14 12 7 

Black cherry 10 11 13 9 9 

Blackgum 11 5 12 11 10 

Hickory 12 12 8 8 13 

Ash 13 14 15 13 14 

Birch 14 13 9 15 16 

Virginia pine 15 17 11 14 20 

Hemlock 16 18 16 16 12 

Black locust 17 19 17 17 17 

White pine 18 22 20 18 11 

Sourwood 19 15 18 27 29 

Dogwood 20 6 21 32 32 

a

Mostly black oak, northern red oak, and scarlet oak 

Source: USFS Forest Inventory and Analysis program; 

www.fia.fs.fed.us.

The number of lichen species was highest (18 to 32 species) 

in the southwestern areas of the MAIA region, and lowest 

(2 to 12 species) in the northern half of the region, as well as 

most of the Coastal Plain and Piedmont regions (fig. 26). 

While some of the relatively low numbers of species 

can be attributed to new stand development, frequentlyrotated plantations, influence of sea air, etc. in coastal 

and Piedmont areas, the very low numbers of species in 

the more mature and heavily forested areas of western 

Pennsylvania are anomalous. Further evaluation of the 

stand, climatic, and atmospheric influences on lichen 

species in these areas is needed. 

Bird Species

Breeding Bird Survey data indicated that the number of 

bird species have increased significantly in many MAIA 

region watersheds from 1975 to 1990. In 1975 most MAIA 

watersheds had 8.5 to 28.6 bird species (fig. 27 a.), and in 

1990 that had increased to 18.6 to 38.6 (fig. 27 b.). 

24 - 32

18 - 23

2 - 7

8 - 12

13 - 17

Lichen species richness

Ecoregion sections

MAIA boundary

Figure 26— Lichen species richness in the MAIA region.

Source: Chapter 23, Figure 118.
34

The increased numbers of bird species were found mostly 

in Pennsylvania, south-central North Carolina, and parts of 

West Virginia. Two northeastern watersheds in Pennsylvania 

in 1990 averaged 38.7 to 58.9 species—more species of 

birds than were found anywhere in the MAIA region in 

8.5 – 18.5

18.6 – 28.6 

28.7 – 38.6

38.7 – 48.7

Species richness (1975)

HUC 8 watersheds

8.5 – 18.5

18.6 – 28.6 

28.7 – 38.6

38.7 – 48.7

Species richness (1990)

HUC 8 watersheds

(A)

(B)

Figure 27—(a) Species richness of forest birds in HUC-8 MAIA 

region watersheds in 1975 based on Breeding Bird Survey data.

 (b) Species richness of forest birds in HUC-8 MAIA region 

watersheds in 1990 based on Breeding Bird Survey data.

Source: Chapter 24, Figures 120 and 121.

1975. We cannot say, however, whether observed increases 

in the number of bird species was due to increases in native 

bird species as a result of habitat improvement marked 

by the maturity of forests in these areas, or to an influx of 

exotic, invasive bird species.No differentiation was made 

between native or exotic invasive species. The lowest 

numbers of bird species, both in 1975 and in 1990, occurred 

primarily in eastern watersheds above the Chesapeake and 

Delaware Bays. Although species richness characterizes 

forest bird communities, as well as other biota, it should be 

emphasized that there is no threshold for what are acceptable 

or desirable and not acceptable or undesirable values for 

species richness. 

Relative richness of bird species was used to evaluate the 

stability of bird communities over time, rather then just 

the absolute number or richness of species. This required 

developing a historical reference species list in 1992 that 

contained all bird species present on each target BBS 

route within distinct ecological strata based on ecological 

classifications detected in any past years of Breeding 

Bird Surveys. Evaluations were made within an 80-km 

radius centered on the route of interest and within the 

physiographic stratum in each State where the route was 

located. The ratio of the number of reference species to 

the number of same observed species from BBS surveys 

corresponds to relative richness, evaluated on a 0 to 1 

ratio scale, where higher numbers indicated higher relative 

richness, which is ecologically most desirable. 

Relative richness values for bird species were highest (0.75 

to 0.94) indicating bird species population were most stable 

in watersheds near the Chesapeake Bay, and high relative 

richness values (0.565 to 0.75) were found in watersheds 

of northern Pennsylvania, and parts of northwestern 

West Virginia. Low relative richness ratios of 0.0 to 0.38, 

indicating that bird species had changed greatly, were found 

in watersheds of central Maryland and also in Pennsylvania 

where relatively large increases in species richness from 

1975 to 1990 were found on BBS routes. 

The BBS data and the analyses of relative species richness 

derived from the data did not indicate any consistent 

spatial patterns at survey-route scales. Some of the lowest 

and highest relative richness values for bird species were 

common on BBS routes in northern Pennsylvania where 

large increases in BBS data of bird species richness 

occurred from 1975 to 1990. Therefore there was no strong 

spatial relationship between increases in bird species 

richness from 1975 to 1990 and relative species richness 

evaluated in 1992. 
35

Long-Term Monitoring of MAIA Forests 

Humans have caused major changes in forests in the 

MAIA region over the past three centuries, and will 

undoubtedly cause more in the future. Continued increases 

in human population with subsequent expansion of urban 

developments into forests will further fragment the forest 

landscape, reduce wildlife habitat, increase introductions 

of more exotic, invasive species, increase concentrations of 

some air pollutants, and degrade or threaten aquatic systems. 

Climate change effects on temperature, precipitation, and 

the severity of storms will add even more stress or forest 

ecosystems. 

The value of continuous, standardized, long-term, qualityassured data sets cannot be overstated for assessing the 

condition and trends in forest ecosystem health and 

sustainability so land managers and policy makers can 

provide the best protection for natural resources. This 

type of information leads to quality assessments of current 

forest conditions and problems, identifies possible causal 

agents, and provides a sound database to monitor future 

directions in forest ecosystems. Continuous data sets allow 

researchers to identify deviations from expected patterns, to 

focus remedial activities on the most serious problems, and 

monitor the effects of those remedial actions. The continued 

monitoring and assessment of forest condition in the MAIA 

region is essential to protect an invaluable resource that 

provides timber, paper, furniture, recreation, esthetic values, 

and other resources to the millions of people living and 

working in this region.
36

List of Figures for Summary Report

Figure 1—The Mid-Atlantic Integrated Assessment (MAIA) 

region includes all of Maryland, Virginia, West Virginia, 

Pennsylvania, Delaware, District of Columbia, and whole or 

partial counties in northeast North Carolina (~47), western 

New Jersey (~12), and southeastern New York (~25).

Source: Chapter 2, Figure 1.

Figure 2—Bailey’s ecoregion sections within the MAIA 

region. The U.S. Forest Service’s Forest Health Monitoring, 

Forest Inventory and Analyses, and other programs 

data were frequently averaged at these spatial scales for 

comparison of forests within the MAIA region. 

Source: Chapter 3, Figure 5. 

Figure 3—Major 4-Digit Hydrologic Unit Code (HUC4) 

watersheds in the MAIA region. Some data from the U.S. 

Forest Service’s Forest Health Monitoring program was 

averaged at HUC4 spatial scales to assess differences 

with ecoregion section values and provide potential for 

comparisons with water condition.

Source: Chapter 3, Figure 6.

Figure 4—Metropolitan and urban areas in the MAIA 

region.

Source: Chapter 4, Figure 8.

Figure 5—Potential biomass loss in black cherry in 1990 

where the distribution of this species overlapped areas with 

phytotoxic ozone exposures in the MAIA region.

Source: Chapter 6, Figure 17.

Figure 6— Average annual precipitation pH in the MAIA 

region from 1979 to 1995. Data from this 16-year period 

were averaged and interpolated at 5-km grid scale to 

estimate average annual wet deposition at ecoregion section 

scales.

Source: Chapter 6, Figure 20.

Figure 7—Condition classes of changes in current fire 

regimes from historic fire regimes in forested areas in the 

MAIA region. Higher numbered condition classes indicate 

increasing amounts of silvicultural treatments would be 

needed to restore historic fire regimes. 

Source: Chapter 7, Figure 22.

Figure 8—Dominant forest types under circa 2000 

climate regimes, and potential forest type distributions 

under Canadian Climate Center and Hadley doubled CO2

equilibrium climate scenarios. 

Source: Chapter 8, Figure 23.

Figure 9—Gypsy moth infested areas in MAIA counties 

through 1998 with gypsy moth caterpillar photograph 

(inset). 

Source: Chapter 9, Figure 24. 

Figure 10—(a) Population density in MAIA region in 1990. 

(b) Change in population density 1970 to 1990. 

Source: Chapter 11, Figures 31 and 33. 

Figure 11—(a) Forest cover within 1457 acre landscape 

units in the MAIA region circa 1990. The fine-scale (0.22 

acres per pixel) land cover map from EPA’s MRLC was 

generalized to show the proportion of forest cover within 

the 1457 acre units. Areas with more than 90 percent forest 

cover are highly interior forested landscapes; in contrast 

to highly fragmented units with less than 10 percent forest 

cover. A 60 percent cover threshold separates interior 

forests from more fragmented forests. (b) Type and 

distribution of Landscape Pattern Types (LPTs) in the MAIA 

region circa 1990. LPTs are labeled according to the type 

and relative amount of forest, agriculture, and developed 

areas within the surrounding 590-hectare units. The 19 

MRLC LPT defined categories were condensed into 7 LPT 

aggregates to simplify regional patterns.

Source: Chapter 4, Figures 12 and 13.

Figure 12— Land cover in five MAIA region states in 

1997. Total agriculture is the combination of crop lands and 

pasture lands.

Source: Chapter 11, Figure 37. 

Figure 13— Differences in major land cover types in five 

MAIA region states 1982 to 1997. Total agriculture (AG1) is 

the combination of crop and pasture lands. 

Reference: Chapter 11, Figure 41.

Figure 14— Change in number of acres per owner of forest 

land by State and MAIA region 1978 to 1994. 

Source: Chapter 11, Figure 43. 

Figure 15—Mean and standard deviation of percent 

forested, agricultural-herbaceous and residential-commercial 

land cover of sites in condition categories determined by the 

Bird Community Index (BCI).

Source: Chapter 12, Figure 49.
37

Figure 16— Average annual change in percent foliar 

transparency of hardwood tree for the period of record for 

each State by ecoregion section (colored polygons), derived 

from the average foliar transparency of hardwood crowns at 

each FHM plot (solid black dots) in each ecoregion section 

in 1998. The plot value was the actual value if the plot was 

measured in 1998, and an estimated value based on previous 

plot measurements otherwise. Note legend also shows 

annual percent change in hardwood foliar transparency 

for ecoregion sections outside of the MAIA region for 

perspective. Data collected 1991-1998 in Delaware, 

Maryland, New Jersey, and Virginia; in Pennsylvania in 

1995 and 1998; in North Carolina in 1998.

Source: Chapter 13, Figure 53.

Figure 17—Average percent foliar transparency of softwood 

trees in 1998 by ecoregion section (colored polygons). The 

black circles show the average softwood foliar transparency 

at each FHM plot in 1998; the plot value is the actual 

value if the plot were measured in 1998 and an estimated 

value based on previous measurements otherwise. Data 

collected 1991-1998 in Delaware, Maryland, New Jersey, 

and Virginia; in Pennsylvania in 1995 and 1998; in North 

Carolina in 1998.

Source: Chapter 13, Figure 56.

Figure 18—Tree mortality volume by ecoregion section 

expressed as the ratio of annual mortality volume to annual 

growth volume (colored polygons). Mortality ratio volumes 

of 1.0 indicate that there was no net gain in tree volume on 

the plot. Closed black circles represent plot-level values of 

the ratio of the average diameter of trees that died to the 

average diameter of the surviving trees on each plot (DDLD 

ratios). DDLD ratios of 1.0 indicate that on average the 

trees that died were as large as the surviving trees. Data 

collected 1991-1998 in Delaware, Maryland, New Jersey, 

and Virginia; in Pennsylvania in 1995 and 1998.

Source: Chapter 13, Figure 66. 

Figure 19—Forest stand volume in MAIA counties as 

average tree volume in cubic feet per acre prior to 2000. 

Source: Chapter 14, Figure 69.

Figure 20—Real prices of delivered black cherry (a), 

red oak (b), and white oak (c) sawlogs in Pennsylvania 

from 1987 to 1997, by low, medium, and high grades, 

corresponding to USFS grades F3, F2, and F1, respectively. 

*mbf = thousand board feet

Source: Chapter 15, Figures 75, 76, and 77.

Figure 21—Real wages per job in lumber and wood 

products (SIC 24), furniture and fixtures (SIC 25), paper 

and allied products (SIC 26), and all sectors of the MAIA 

economy 1975 to 1995. 

Source: Chapter 15, Figure 88.

Figure 22—White-tailed deer population and harvest for 

1975 to 1993 for all States in the MAIA region. Estimates 

include counties outside of the MAIA region for New York, 

New Jersey, and North Carolina.

Source: Chapter 16, Figure 91. 

Figure 23—Median Ephemeroptera, Plecoptera, and 

Trichoptera (EPT) scores by stream order. EPT scores are 

based on type and number of these three groups of stream 

insects.

Source: Chapter 18, Figure 101.

Figure 24— Forest cover type by MAIA region state.

Source: Chapter 21, Figure 107.

Figure 25— Distribution of numbers of trees and biomass in 

five size-classes in the MAIA region.

Source: Chapter 22, Figure 116.

Figure 26— Lichen species richness in the MAIA region.

Source: Chapter 23, Figure 118.

Figure 27—(a) Species richness of forest birds in 

HUC-8 MAIA region watersheds in 1975 based on Breeding 

Bird Survey data. (b) Species richness of forest birds in 

HUC-8 MAIA region watersheds in 1990 based on Breeding 

Bird Survey data.

Source: Chapter 24, Figures 120 and 121.
38

List of Tables for Summary Report

Table 1—Rank of forest health stressors, response, and 

condition topics from two MAIA Stakeholder Workshops in 

Annapolis, Maryland in 1997 (Source Chapter 2, Table 1)

Table 2—Land cover types in MAIA States circa 2000 

(Source: Chapter 2, Table 2)

Table 3—Tree cover in metropolitan areas (MA) and urban 

areas (UA) by State area and tree cover in MAIA region and 

the U.S. circa 2000 (Source: Chapter 5, Table 6)

Table 4—Exotic plant species in MAIA region States circa 

2000 (Source: Chapter 10, Table 10)

Table 5—Annual hardwood and softwood sawlog and 

pulpwood production in MAIA region States in the 1970s, 

80s, and 90s (Source: Chapter 15, Table 16)

Table 6—Average annual sawlog removals of species and 

species-groups in MAIA region and States in the 1980s 

(Source: Chapter 15, Table 17)

Table 7—Hardwood and softwood growing stock and 

sawtimber volume in MAIA region States circa 2000 

(Source: Chapter 15, Table 18)

Table 8—Growing stock and sawtimber volume removals, 

growth, and mortality in MAIA region States circa 2000 

(Source: Chapter 15, Table 20)

Table 9—Average employment (wage and salary) and rate of 

change in all Standard Industry Classification (SIC) sectors 

in MAIA region 1975 to 1995 (Source: Chapter 15, Table 

24)

Table 10—Relative importance (rank) of the twenty most 

common tree species by abundance (number of trees) in five 

d.b.h. size classes (inches) circa 2000. Column 1 (1 to 15+ 

inches d.b.h.) determined the overall rank of each species. 

Columns 2 to 5 are the ranks of species in other d.b.h. 

classes (Source: Chapter 23, Table 29)
Stolte, Kenneth W. 2012. State of mid-atlantic region forests in 2000-Summary Report. Gen. 

Tech. Rep. SRS–163. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern 

Research Station. 37 p.

Wet and warm climate, mountainous topography, and deep rich soils produced one of the 

most magnificent and diverse temperate forests in the world. In 1650 the Mid-Atlantic forests 

covered 95 percent of the region, but were greatly reduced in 1900 by extensive tree harvesting, 

and conversion to farms and pastures. Settlement of forests also led to severe wildfires, soil 

erosion, and destruction of wildlife. Recovery began in the early 1900s, and later improvements 

in agricultural allowed millions of acres to return to forest cover. Suppression of catastrophic 

wildfires reduced flooding and watershed degradation, and wildlife management returned native 

animal and fish populations. Forest management improvements led again to productive and diverse 

forests in more mature stages of development. By the end of the 20th century, the Mid-Atlantic 

forests covered 61 percent of the land area and produced numerous products that brought social 

and economic benefits to people. Continuing pressures from urbanization and fragmentation; 

selective species harvests; air pollution; exotic invasive species; wildlife habitat loss; historic fire 

regime changes; stream degradation; and climate change still affect and threaten these forests, and 

require enlightened management and policy decisions to ensure sustainability of healthy, diverse, 

and productive forests.

Keywords: forest health, forest economics, indicators, stressors, sustainability, Mid-Atlantic 

forests, Montreal Process Criteria and Indicators 

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