United States

Department of 

Agriculture

Forest Service

Northeastern 

Research Station

Northeastern Area

State and Private Forestry

Allegheny National Forest

General Technical

Report NE-339

Analysis of Forest Health 

Monitoring Surveys on 

the Allegheny National Forest 

(1998-2001)

Randall S. Morin

Andrew M. Liebhold

Kurt W. Gottschalk

Chris W. Woodall

Daniel B. Twardus

Robert L. White

Stephen B. Horsley

Todd E. Ristau
Abstract

Describes forest vegetation and health conditions on the Allegheny National Forest (ANF). 

During the past 20 years, the ANF has experienced four severe droughts, several outbreaks 

of exotic and native insect defoliators, and the effects of other disturbance agents. An 

increase in tree mortality has raised concerns about forest health. Historical aerial surveys 

(1984-98), Forest Inventory and Analysis plot data collected in 1989, and FHM plot data 

collected 1998-2001 were analyzed to compare disturbed and undisturbed areas. Tree 

mortality and crown dieback levels were compared between undefoliated areas and areas 

defoliated by cherry scallopshell moth, elm spanworm, and gypsy moth. American beech 

mortality was compared inside and outside the beech bark disease killing front. This study 

illustrates the value of an intensified grid of P3 plots and demonstrates the integration of 

aerial survey and plot data.

Published by: For additional copies:

USDA FOREST SERVICE USDA Forest Service 

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

February 2006 Fax: (740)368-0152

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

The Authors

RANDALL S. MORIN is a research forester with the Northeastern Research Station at 

Newtown Square, PA. ANDREW M. LIEBHOLD and KURT W. GOTTSCHALK are research 

entomologist and research forester, respectively, with the Northeastern Research Station 

at Morgantown, WV. CHRIS W. WOODALL is a research forester with the North Central 

Research Station at St. Paul, MN. DANIEL B. TWARDUS is a forest health specialist with 

Northeastern Area, State and Private Forestry, in Morgantown, WV. ROBERT L. WHITE is a 

forest silviculturist with the Allegheny National Forest at Warren, Pennsylvania. STEPHEN 

B. HORSLEY and TODD E. RISTAU are research plant physiologist and research ecologist, 

respectively, with the Northeastern Research Station at Irvine, PA.

Manuscript received for publication 22 February 2005

Cover Photos

Clockwise from top left: Allegheny Reservoir, by Janeal Hedman, USDA Forest Service; 

Cladonia coniocraea, courtesy of Yale University Press; Logan Falls, by Greg Porter, 

USDA Forest Service; Gypsy moth larva, by John H. Gent, USDA Forest Service, www.

forestryimages.org.

This publication/database reports research involving pesticides. It does 

not contain recommendations for their use, nor does it imply that the 

uses discussed here have been registered. All uses of pesticides must be 

registered by appropriate State and/or Federal, agencies before they can be 

recommended. 

CAUTION: Pesticides can be injurious to humans, domestic animals, desirable plants, and 

fish or other wildlife—if they are not handled or applied properly. Use all pesticides selectively 

and carefully. Follow recommended practices for the disposal of surplus pesticides and 

pesticide containers.

CAUTION:

PESTICIDES
Contents

Executive Summary........................................................................................... 1

Current Forest Conditions................................................................................. 1

Disturbance Processes..................................................................................... 1

Additional Forest Health Indicators................................................................... 2

 Lichen Communities ........................................................................................ 2

 Down Woody Material ...................................................................................... 2

 Vegetation Diversity ......................................................................................... 2

 Ozone Bioindicator Plants ................................................................................ 2

Introduction ......................................................................................................... 3

Objectives ......................................................................................................... 3

Location ............................................................................................................ 3

Climate.............................................................................................................. 4

History............................................................................................................... 5

 Early Timber Removals .................................................................................... 5

 Effects of Deer Density..................................................................................... 6

 Multiple-use Forest Management ...................................................................... 7

 Recent Disturbance Events .............................................................................. 8

Overview Of Analyses ....................................................................................... 9

Description of Data ........................................................................................... 9

 Forest Inventory and Analysis Data ................................................................... 9

 Forest Health Monitoring Data .......................................................................... 9

 Aerial Survey Data..........................................................................................10

Methods..............................................................................................................10

Kriged Surfaces ...............................................................................................10

Species Diversity and Richness.......................................................................11

Effects of Forest Pests.....................................................................................12

Current Forest Conditions................................................................................13

Overstory Conditions .......................................................................................13

 Forest Types ..................................................................................................13

 Tree-Species Abundance ................................................................................13

 Diameter Distribution.......................................................................................14

 Spatial Distribution of Selected Tree Species ....................................................14

Plot Age Class, Stand-Development Class, and Relative Density...................17

 Age-Class Distribution.....................................................................................17

 Distribution by Stand-Development Class .........................................................17

 Relative Density .............................................................................................17

Live-Tree Distribution by Size Class for Indiana Bat Habitat ...........................17

Summary of Crown Condition, Tree Damage, and Standing Dead .................19

 Crown Dieback ...............................................................................................19

 Crown Density................................................................................................21
 Crown Ratio ...................................................................................................21

 Foliage Transparency......................................................................................23

 Tree Damage .................................................................................................23

 Standing Dead Trees ......................................................................................24

Understory Conditions .....................................................................................31

 Seedling and Sapling Counts...........................................................................31

 Sawtimber Plots .............................................................................................31

 Poletimber Plots .............................................................................................33

 Seedling/Sapling Plots ....................................................................................34

 Seedling and Sapling Richness and Diversity....................................................35

Sustainability of Tree Species..........................................................................35

Disturbance Processes on the Allegheny National Forest...........................38

Cherry Scallopshell Moth.................................................................................39

Elm Spanworm.................................................................................................40

Gypsy Moth......................................................................................................43

Beech Bark Disease Complex .........................................................................45

Sugar Maple Decline........................................................................................46

Additional Forest Health Monitoring Indicators..............................................48

Lichen Communities ........................................................................................48

Down Woody Materials ....................................................................................52

 Estimates and Summaries of DWM Fuel Loadings ............................................54

 Ecology of CWD on the ANF............................................................................55

 Summary of DWM ..........................................................................................57

Soils .................................................................................................................58

Vegetation Diversity .........................................................................................62

Ozone Bioindicator Plants................................................................................65

Acknowledgments.............................................................................................68

Literature Cited..................................................................................................68

Appendix I ..........................................................................................................77

1989 FIA Survey Plot Designs .........................................................................77

Common and Scientific Names of Tree Species Found on ANF .....................78

Region 9 Forest Cover Types ..........................................................................79

Distribution of FHM and FIA Forest Type Groups and Forest Types ...............80

Variogram Parameters for Kriged Maps...........................................................81

Lichen Species Sampled on the ANF ..............................................................82

Lichen Species Pollution Tolerances ...............................................................83

DWM Plot Designs (1999 and 2000) ...............................................................84

Quadrat Ground-Cover Variables ....................................................................85

Appendix II.........................................................................................................87

Vegetation Species and Percentage of Subplots Sampled .............................87

Introduced Vegetation Species and Percent of Subplots Sampled ...............101


Executive Summary

Current Forest Conditions

•	 Current conditions on the Allegheny National Forest (ANF) have been shaped largely by

timber removals at the turn of the 19th century, the decline and subsequent rebound of

populations of white-tailed deer, multiple-use management by the USDA Forest Service over

the past 75 years, and disturbance events that have occurred during the past 15 years.

•	 Nearly half of the forest land in the ANF consists of the mixed upland hardwoods and

Allegheny hardwoods forest types.

•	 Black cherry and red maple are the most abundant tree species on the ANF.

•	 A summary of even-aged hardwood stands throughout the ANF revealed an overall inverse

J-shape diameter distribution which usually indicates uneven-aged stands. The abundance

of smaller diameter stems results from the following: 1) the diameter distribution in older

stands (80 to 100 years) is an inverse J shape because faster growing species rapidly outgrew

slower growing species. Thus, the diameter distribution is stratified by species growth rate;

and 2) in younger stands that originated during the past 40 years, species low in food

preference to deer or that are resilient to repeated browsing make up a large proportion of

small-diameter stems.

•	 For most species, the average number of standing dead trees is greater on the ANF than for

other forested portions of Pennsylvania.

•	 Average conditions across the ANF easily meet suitable and optimal habitat requirements for

the Indiana bat.

•	 Overstory tree species richness is higher in the stem exclusion and understory reinitiation

categories than in the stand initiation category.

•	 Because black cherry is abundant in all stand-size categories, this species probably will

increase in dominance on the ANF over the next century.

•	 Due to the overwhelming abundance of non-oak regeneration, little oak forest likely will be

sustained over the long term both in areas where timber harvesting occurs and is prohibited.

Disturbance Processes

•	 The frequency of defoliation by the cherry scallopshell moth was significantly related to the

percentage of black cherry basal area in stands.

•	 The number of years of defoliation by the cherry scallopshell moth was significantly

associated with the percentage of standing dead black cherry. Crown dieback of black cherry

increased with years of defoliation by cherry scallopshell moth, though the relationship

was not statistically significant. Managers should consider suppression activities following a

defoliation episode so that tree damage can be mitigated should another defoliation event

occur.
•	 The frequency of defoliation by the elm spanworm was significantly associated with the

proportion of black cherry in stands.

*	 There was a tendency for greater levels of host crown dieback and tree mortality in stands

defoliated by elm spanworm.

•	 The frequency of defoliation by gypsy moth was significantly related to the percentage of oak

basal area in stands.

•	 The percentage of standing dead American beech was more than twice as great inside than

outside the killing front of beech bark disease.

•	 Most of the basal area of standing dead sugar maple was on upper slopes but mortality was

greater in defoliated than in undefoliated areas regardless of slope position. Crown dieback of

sugar maple also was higher on defoliated than undefoliated trees regardless of slope position.

Additional Forest Health Indicators

Lichen Communities

•	 Fifty-two lichen species were sampled on the ANF. Lichen species that are sensitive to

pollution are uncommon on the ANF.

Down Woody Material

•	 Duff accounted for 64 percent of down woody materials (by weight) on the ANF.

•	 The weights of duff and 100-hr fuels were higher on plots in Pennsylvania outside the ANF.

•	 The weight of 1,000-hr fuels was higher on plots within the ANF than on other forested

plots in Pennsylvania outside the ANF.

Vegetation Diversity

•	 In all, 540 species were sampled in surveys of understory vegetation on the ANF. Another

184 specimens remain unidentified or partially identified; some of these may be additional

species.

•	 Forty nonnative species were identified on the ANF.

Ozone Bioindicator Plants

•	 Nearly half of the plants sampled for ozone (O3

) damage (44.6 percent) showed symptoms

(generally less than 25 percent of leaf area with damage) of O3 injury in 1998. By contrast,

less than 25 percent of the sampled plants showed injury symptoms in 2000, and less than 8

percent showed symptoms in 1999 and 2001.

•	 Blackberry had the most O3 damage as 40 to 60 percent of the sampled plants showed

symptoms of injury (generally less than 25 percent of leaf area) in 1998-2000.
Introduction

In this report we present information collected and

analyzed over a 4-year period as part of a forest health

assessment of the Allegheny National Forest (ANF).

An interim assessment, “Forest Health Conditions on the 

Allegheny National Forest (1989-1999): Analysis of Forest 

Health Monitoring Surveys” (Morin et al. 2001), was

a compilation of aerial pest surveys and data collected

from inventory plots over the first 2 years of the 4-year

assessment. The current report includes results for all

Forest Health Monitoring (FHM) plots established

throughout the ANF as well as the full suite of forest

health indicators, e.g., lichen communities, soils,

down woody materials, ozone bioindicator plants, and

vegetation, which were not included in the 2001 report.

The national FHM program was initiated by the USDA

Forest Service in 1990 to monitor, assess, and report the

status of and trends in forest health across the Nation.

Methods were developed to collect data on forest

health indicators such as tree mortality, damage, and

growth, regeneration, crown condition, plant diversity,

vegetation structure, ozone, lichens, down woody

debris and fuel loadings, and soil chemistry. These

indicators were included in the forest health assessment

for the ANF. Also analyzed in this assessment were pest

data collected during aerial surveys. ANF personnel

collected data at an intensified spatial resolution so that

information could be summarized at the National Forest

scale.

Objectives

The following issues were addressed in the assessment:

•	 Forestwide overstory and understory conditions.

•	 Tree-crown conditions as a reflection of tree and

forest health.

•	 Tree mortality and relationships to possible causes.

•	 Habitat conditions for the endangered Indiana

bat (Myotis sodalis).

•	 Pest-caused damage and relationships to forest

conditions.

•	 Diversity and distribution of lichens.

•	 Soil characteristics and relationships to forest

health.

•	 Down woody material and fuel loadings.

•	 Composition and distribution of herbaceous

vegetation.

•	 Ground level ozone injury.

The aerial pest surveys and the data collection on forest

health plots will continue so that current information on

trends and change is available to planners.

Location

The ANF is located in northwestern Pennsylvania

(Fig. 1) on the unglaciated portion of the Allegheny

Plateau. The Forest comprises portions of Warren, Forest,

McKean, and Elk Counties. The area within the forest

Figure 1.—The Allegheny National Forest in Pennsylvania overlayed 

on percent forested land (1-km grid cells--percent of each cell that 

is forest; NLCD data from Multi-Resolution Land Characteristics 

Consortium).
proclamation boundary is nearly 740,500 acres, about

70 percent of which is federal land (513,000 acres). The

rugged plateau country in which the ANF lies includes

numerous creeks and streams that have created a rolling

and sometimes steep topography (elevation up to 1,300

feet).

Climate

Winters are long and cold while summers are

comparatively short. The growing season is usually

about 148 days. Precipitation is plentiful throughout

the year and averages 40 to 45 inches annually; snowfall

averages 55 to 85 inches annually. Except for January

and February, monthly precipitation usually totals 3 to 4

inches (Fig. 2). Precipitation generally peaks in June with

a mean of nearly 5 inches for that month. Infrequent dry

periods of varying duration and intensity are most likely

during summer and fall. The Palmer Drought Severity

Index (PDSI) (July only) from 1895 to 1940 indicates

drought events (PDSI less than or equal to -1) 4 of every

5 years (Fig. 3). Between 1941 and 1987, PDSI indicates

droughts 1 of every 4 years. There were four significant

MONTH AVG. TEMP. (F) AVG. PRECIP (in.)

JAN 25.5 2.8

FEB 26.5 2.46

MAR 34.6 3.39

APR 46.1 3.68

MAY 57.1 4

JUN 65.9 4.76

JUL 70.2 4.29

AUG 68.5 3.87

SEP 61.9 3.83

OCT 51.1 3.41

NOV 40.1 3.89

DEC 29.5 3.38

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

Precipitation (inches)

0

10

20

30

40

50

60

70

80

Temperature (degrees Fahrenheit)

Precipitation

Temperature

1896 1.47

1897 -0.35

1898 -1.25

1899 -2.07

1900 -2.88

1901 -3.72

1902 1.37

1903 -1.17

1904 -0.29

1905 0.04

1906 0.43

1907 0.44

1908 -0.92

1909 -2.12

1910 -2.9

1911 -3.44

1912 0.52

1913 -1.03

1914 -1.82

1915 0.97

1916 -0.73

1917 0.41

1918 -2.23

1919 -1.56

-8

-6

-4

-2

0

2

4

6

1895189919031907191119151919192319271931193519391943194719511955195919631967197119751979198319871991199519992003

Year (July only)

Palmer Drought Severity Index

Figure 2.—Average monthly precipitation and temperature for Warren, PA, 1926-94, data 

from Pennsylvania State University, College of Earth and Mineral Science, Department of 

Meteorology.

Figure 3.—Palmer Drought Severity Index (July only) for Warren, PA, 1940-2004, data 

from National Climatic Data Center.
drought events on the ANF from 1988 to 2001 following

a relatively drought-free period from 1972 to 1987. The

droughts in the late 1980s and 1990s coincided with

several severe outbreaks of insect defoliators.

History

Conditions on the ANF have changed dramatically

since the early 1800s. Today, the Forest is characterized

by an abundance of black

cherry, sugar and red maple, and

other hardwoods. Most of the

commercial black cherry timber

in the United States is from the

Allegheny Plateau (Marquis 1975).

The original forest was dominated

by eastern hemlock and American

beech (Lutz 1930). Stands of

eastern white pine originated

following numerous catastrophes

in well-defined patches (Marquis

1975) that occurred as a distinct

forest type. These areas measured

in tens rather than hundreds of

acres (Hough and Forbes 1943).

Current forest conditions have

been shaped largely by timber

removals at the turn of the 19th century, the decline and

subsequent rebound of populations of white-tailed deer

(Odocoileus virginianus), multiple-use management by the

Forest Service over the past 75 years, and disturbances

that have occurred during the past 15 years.

Early Timber Removals

The first European settlers reached the area in 1796 and

1797 (Kussart 1938). At first, trees were cut to clear land

for agriculture and provide timber for cabins and barns

(Marquis 1975). Not long after settlement of the area,

forest based industries were developed. In the late 1850s,

the tanning industry began using hemlock bark as a

source of tannin for curing leather (Marquis 1975). The

Civil War created a boom for tanneries because of the

demand for harnesses, military equipment, and industrial

belting. The vast supply of hemlock on the Allegheny

Plateau helped meet this demand. At the end of the 19th

century, the tanning industry was using massive quantities

of hemlock bark from the Plateau. A chute for sliding

hemlock bark down the hillside is shown in Figure 4.

Figure 5 shows a trainload of hemlock tanbark.

Between 1850 and 1900 there was increased demand

for lumber to build homes, stores, and furniture. The

demand for paper and other wood-pulp products

increased. When band saws came into use around 1880,

Figure 4.—A log and bark landing of the Goodyear 

Lumber Company around 1912 (photo from Charles 

Catlin Collection).

Figure 5.—A trainload of hemlock tanbark of the Central Pennsylvania Lumber 

Company in McKean County.
some sawmills could cut more than 100,000 board feet

per day.

Around 1890, a new forest industry, wood chemicals,

began to change forest development. Harvested timber

was procured and refined to make acetic acid, charcoal,

wood alcohol, and other distillation products (Marquis

1975). Over the next 40 years, this industry provided a

market for nearly every size, species, and quality of tree

growing in the area. Piles of chemical wood are shown in

Figure 6.

Harvesting during this era cleared nearly

every tree that was usable. The once large,

contiguous forest on the Allegheny Plateau

was almost completely removed in what must

have been one of the highest records of forest

utilization (Horst and Smith 1969; Taber

1974). Following removal of the original forest,

regeneration to the same species occurred but

in different proportions. Fast-growing, shadeintolerant species such as black cherry and

species intermediate in shade tolerance such as

red maple increased in proportion while slower

growing, shade-tolerant species such as beech

and hemlock decreased. Thus, the secondgrowth forest was essentially even-aged, having

arisen from nearly complete forest removals

over a relatively short period.

Effects of Deer Density

Deer populations have had and continue to

have a major impact on the development of

vegetation on the Allegheny Plateau. At the

turn of the century and following a period of

intensive timber harvesting that supported the

wood chemical industry, deer populations were

low. Unregulated hunting resulted in the near

extirpation of deer from some areas. As a result,

tree seedlings became established and thrived

in most areas where extensive timber harvest

had occurred.

Timber-cutting trends and estimated deer

population on the ANF are shown in Figure 7

(Redding 1995). The regeneration in harvested areas

serves as forage for deer. In the early 20th century, the

deer population rebounded due to the passage of game

laws, restocking of deer, and regulation of antlerless

harvests. Densities increased rapidly in the presence of a

virtually limitless supply of food during the first quarter

of the 20th century. As forest vegetation matured and

grew above browsing height, the food supply dwindled.

Populations crashed twice from 1930 through 1980

following severe winters (early 1940s and late 1970s) but

then recovered. Since 1980, deer densities have been

more constant largely due to efforts by the Pennsylvania

Game Commission to regulate population levels, though

Figure 6.—Bolts of chemical wood at the Otto Chemical Company in 

Sergeant, McKean County.

1911 4 98

1915 9 90

1919 14 81

1923 19 56

1927 22 11

1931 24 5

1935 27 5

1939 43 0 5

1943 22 5

1947 15 8

1951 21 2

1955 25 8

1959 30 13

1963 35 21

1967 40 26

1971 45 35

1975 50 41

1979 32 25

1983 32 54

1987 34 84

1991 26 70

1995 29 48

1999 30 3

0

20

40

60

80

100

120

1907191519231931193919471955196319711979198719952000

Deer/Sq. Mile - Millions of Bd. Ft.

Deer/Sq. Mile

Actual Cut

Estimated Cut

Figure 7.—Deer population and timber cutting trends on the Allegheny 

National Forest (reproduced from Redding 1995 and updated to 2001 

with ANF data).
densities still remain above the level that allow many

species to regenerate. This has been the case since 1990

despite a reduction in forest harvesting on the ANF.

The long-term impact from years of high deer densities

has been the loss of understory and midstory vegetation

over much of the ANF (Marquis and Brenneman 1980;

Tilghman 1989; Horsley et al. 2003). Seedlings of many

species are not abundant, and most understories are

dominated by fern, grass, root suckers of American beech,

or striped maple (USDA For. Serv. 1995). On the ANF,

the most important factor limiting seedling establishment

on the ANF is browsing by white-tailed deer (Marquis

and Brenneman 1980; Tilghman 1989; Horsley et al.

2003). The maximum deer density that allows desirable

tree seedlings to develop in heavily forested areas of

northern Pennsylvania is about 20 per square mile

(Tilghman 1989; deCalesta 1994). From the winter of

2000 to 2001, deer populations in the four-county area

that includes the ANF averaged 31.5 per square mile, a

decrease from estimates for the previous year (R. White,

PA Game Comm., 2001, pers. commun.).

The current overstory on the ANF was established when

the deer herd was minimal. In 1980, the Pennsylvania

Game Commission set a density goal of 20 deer per

square mile for the four-county area. To date, this density

has not been achieved and the current goal for the ANF

is about 20 deer per square mile (USDA For. Serv. 2000).

Horsley et al. (2003) found that tree regeneration can be

diverse, at this deer density, in heavily forested regions

where forest management is practiced.

Multiple-use Forest Management

Since much of the land had been cut over when the ANF

was established in 1923, early management focused on

developing the second-growth forest and reforesting areas

where seedlings failed to develop. The first challenge

facing managers was ensuring the survival of the young

trees growing amid logging slash. Civilian Conservation

Corps enrollees from ANF camps planted trees, built

forest roads, and constructed recreation sites. Protecting

the forest from wildfires and erosion were other major

concerns. Since most stands began developing around

the same time, most of the trees on the ANF are roughly

70 to 100 years old. Today, management on the ANF

emphasizes forest-ecosystem sustainability and multipleuse.

Currently, the range of Forest Service management

and research activities are based on the research and

silvicultural guidelines established by the Northeastern

Research Station. These activities are designed to benefit

vegetation, water, wildlife, and people. For example,

achieving adequate natural regeneration of tree species is

a major concern on the Allegheny Plateau. Efforts have

focused on understanding the growth and development

of Allegheny hardwood and oak stands, particularly with

respect to requirements for regenerating tree seedlings.

On the ANF, forest health and the effect of the deer herd

on the regeneration of species preferred as food have

raised concerns (USDA For. Serv. 2000). During the past

15 years, managers have been increasingly challenged

by native and exotic disturbance agents. The ANF is

responsible for monitoring and describing changes in

health and vigor of stand conditions (USDA For. Serv.

2000).

From 1985 to 1995, tree mortality increased in Allegheny

hardwood forests (McWilliams et al. 1996; 1999).

During this same period nearly 250,000 acres were

sprayed with insecticide to reduce defoliation by the

gypsy moth, elm spanworm, and forest tent caterpillar.

Most of this acreage was sprayed with the biological

insecticide Bacillus thuringiensis (B.t.). Despite this effort,

the ANF has experienced both sudden and gradual tree

mortality (Stout et al. 1995).

Adequate natural regeneration of a variety of species on

the ANF is another major concern. Adequate numbers of

seedlings must be present before a final harvest to assure

satisfactory postharvest seedling stocking or growth.

Species composition of the advance seedlings largely

determines the species composition of the resulting stand

(Marquis et al. 1992). Stout et al. (1995) found adequate

regeneration on only 8 percent of the 12,000-acre

sample, and that understory stocking with ferns exceeded

30 percent on more than 70 percent of the study area.

Marquis et al. (1992) found that adequate regeneration

was nearly impossible when fern stocking exceeded

that percentage. A survey of 6,000 plots on the ANF
revealed interference on 70 percent of the study area, and

interference by ferns was found on 46 percent of the area

(USDA For. Serv. 1995). Striped maple seedlings, beech

root suckers, and grasses are other sources of interference

(Horsley and Bjorkbom 1983; Horsley and Marquis 1983).

Even-age silviculture often is used to reproduce stands

in the cherry-maple type. Grisez and Peace (1973)

reported satisfactory natural regeneration on only 35 of

65 clearcuts from the early 1970s. Because of regeneration

failure, clearcutting was largely abandoned except in

areas with desirable advanced regeneration. Shelterwood

cutting is useful in increasing the number of desirable

advance seedlings (Marquis 1978). Since 1988, nearly 90

percent of the final harvesting (non-salvage harvests) on

the ANF has been shelterwood cuts. When the percentage

of ground cover and/or number of interfering stems

exceeds thresholds, herbicides often are used to control

interfering vegetation such as hay-scented and New York

fern, striped maple, grasses, and root suckers of American

beech (Horsley 1991). Reforestation activities such as

site preparation, fencing, planting, and fertilization and

release treatments also play a role in assuring seedling

establishment and growth. After adequate regeneration is

established, the remaining overstory trees are removed.

Local land managers share similar concerns regarding

future species composition and forest sustainability in

areas where active reforestation or harvest activity is

prohibited. Trees that die may not be replaced through

natural processes by an adequate quantity of tree seedlings

or appropriate species capable of replacing them (USDA

For. Serv. 2001).

Recent Disturbance Events

During the past 15 years, the following native and exotic

disturbance agents have been of particular concern on the

ANF (Stout et al. 1995):

•	 Pear thrips (Taeniothrips inconsequens)

•	 Forest tent caterpillar (Malacosoma distria)

•	 Gypsy moth (Lymantria dispar)

•	 Cherry scallopshell moth (Hydria prunivorata)

•	 Fall cankerworm (Alsophila pometaria)

•	 Elm spanworm (Ennomos subsignarius)

•	 Oak leaftier (Croesia semipurpurana)

•	 Linden looper (Erannis tiliaria)

•	 Beech bark disease complex

•	 Maple decline

•	 Ash dieback

Many factors are involved in the cause-effect relationship

of maple decline, including soil moisture, Armillaria

root rot, sugar maple borer, insect defoliators, and air

pollution (Horsley et al. 2000, 2002; Marçais and Wargo

2000; Bailey et al. 2004). Ash viruses and canker fungi

are factors in ash dieback (Manion 1991).

Since 1985, more than 86 percent of the ANF has been

defoliated at least once. Although gypsy moth defoliation

peaked in the mid-1980s, damage was observed between

1993 and 1995. Trees also were stressed by severe

droughts during the 1988, 1991, 1995, and 1999

growing seasons. Tree mortality was substantial in the oak

type in 1988 and in other forest types in the summer of

1994. Some tree decline has continued since then, but

certain areas with fewer affected crowns have recovered

partially (USDA For. Serv. 2001).

To provide an initial characterization of mortality/decline

in the most heavily impacted areas, McWilliams et al.

(1999) analyzed stand plot-level data collected between

1994 and 1996 in 869 stands (18,876 acres) with

symptoms of decline and mortality. Of the existing basal

area in these stands, 12.3 percent was classified as dead

and 6.4 percent considered at risk. In some stands, dead

and at risk trees constituted a majority; in others, they

were a minor component. Black cherry, sugar maple, and

red maple accounted for more than 83 percent of the

total live basal area prior to decline in the sampled stands.

The dieback and mortality of sugar maple, American

beech, and red maple were the most significant, with

levels of mortality and trees at risk at 43, 20, and 13

percent of the basal area, respectively.

McWilliams et al. (1999) also evaluated understory

vegetation. The number of tree seedlings was adequate

on only 8 percent of the sampled stands. Vegetation that

interferes with tree seedling development and growth

was present in sufficient quantities to require treatment
in 93 percent of the stands examined. McWilliams et

al. (1999) concluded that sparse regeneration and the

abundance of interfering vegetation continue to raise

questions about the sustainability of forest ecosystems

on sites where tree mortality and decline are or may

become most severe.

Overview Of Analyses

Description of Data

The analyses in this report are based primarily on

three sources of data: 1) Forest Service Forest

Inventory and Analysis (FIA) plot data, 2) FHM plot

data, and 3) aerial surveys of defoliation. The objective

was to describe and quantify vegetation characteristics

and insect and disease factors on the ANF, and to

determine the effect of insects and diseases on tree and

stand damage and overall forest health.

Forest Inventory and Analysis Data

Since 1930, the objective of the FIA program has been

to periodically assess the extent and condition of the

Nation’s forests, and report on trends in this important

resource. In the Eastern United States, inventories were

conduted on a state-by-state basis, usually every 5 to

15 years. The first FIA inventory of Pennsylvania was

conducted in 1958. Plots were remeasured in 1968,

1977-78, and 1989-90 (Alerich 1993). Recently, FIA

switched from periodic to annual inventories. For

example, in Pennsylvania, 20 percent of the plots are

measured each year. This plot network within FIA is

known as Phase 2.

Different arrangements of fixed- and variable-radius plots

have been used to select sample trees. For each tree,

several variables are measured, including diameter at

breast height (d.b.h.) for live and dead trees, species, and

variables for estimating volume, growth rate, and quality.

The last periodic forest inventory of the ANF was

conducted in 1988-90 when 168 FIA plots were

measured (Alerich 1993). Usually there is one FIA plot

for every 6,000 acres (Hansen et al. 1992), though

sampling intensity is higher on the ANF (about one plot

for every 3,000 acres). For this survey, two plot designs

were used: remeasured plots were a 10-point cluster of

basal area factor (BAF) 37.5 prism plots while new plots

were fixed radius with variable radius points (Appendix I).

Eighty-nine percent of the plots visited in 1988-90 were

remeasurements from the 1977-78 inventory. The

analyses in this report that are based on 1988-90 FIA

survey data include all 168 plots unless stated otherwise.

Forest Health Monitoring Data

The national FHM program was implemented in New

England in 1990 (Brooks et al. 1992) to monitor, assess,

and report on the long-term status, changes, and trends

in forest ecosystem health at regional and national scales.

FHM was developed due to increasing concerns about

the health of the Nation’s forests with regard to pollution,

insects, diseases, climatic change, and other stressors.

The plot component of FHM (now known as Phase 3

within FIA) is a network of about 4,600 permanent plots

covering all 50 States. A systematic sample of the plots is

measured each year. Each permanent plot has four 1/24-

acre, fixed-area, circular subplots (Fig. 8) (USDA For.

Serv. 1998; 2002).1 All trees 5 inches and larger in d.b.h.

1

U.S. Department of Agriculture, Forest Service. 2002. Forest 

inventory and analysis national core field guide, volume 

2: field data collection procedures for phase 2 plots, 

version 1.6. Internal report on file wth U.S. Department of

Agriculture, Forest Service. Forest Inventory and Analysis, 201 

14th St., Washington, DC.

Subplot:

24.0-foot radius

(7.32 m)

Annular Plot:

58.9-foot radius

(17.95 m)

Distance between

subplot centers is

120-foot (36.6 m)

Microplot:

6.8-foot radius (2.07 m).

Center is 12 ft. (3.7 m)

@ 90 azimuth from

subplot center

0

T1 (30)0

T2 (150)0

T3 (270)0

Figure 8.—FHM field plot design.
10

are measured on these subplots. Seedlings

and saplings are measured on 1/300-acre,

fixed-area, circular microplots offset 12 feet

east of subplot center. Measurements of

forest health-related indicators are taken in

addition to the basic tree-measurement data

collected on Phase 2 FIA plots. A forest

health indicator is defined as any

environmental component that

quantitatively estimates the condition or

change in condition of ecological resources,

the magnitude of stress, or the exposure of a

biological component to stress. Indicators

currently being measured on FHM plots are

tree mortality, damage, growth, regeneration,

crown condition, plant diversity, vegetation

structure, ozone bioindicator plants, lichen

communities, down woody materials, fuel

loading, and soil chemistry.

Throughout most of the country, FHM

plots are located on a hexagonal grid with

one plot per 96,000 acres. An intensified

network of 173 FHM plots was established on the ANF

in 1998. Each plot was measured at least once in 1998,

1999, 2000, and 2001. For this survey, 168 plots were

co-located with the 1988-90 FIA plots and 5 were colocated with newly established FIA plots. The shapes

of plots and specific trees sampled differed due to the

different plot designs. The approximate locations of the

FHM and FIA plots are shown in Figure 9.

Aerial Survey Data

The symptoms of forest stressors often can be detected

remotely by aerial photography and/or satellite imagery.

The survey component of FHM detection monitoring

consists of an aerial survey to detect damage in the form

of canopy defoliation and mortality and thus monitor the

occurrence and/or spread of insect, disease, blowdown,

and other forest disturbances.

Aerial surveys supply a landscape-level overview of forest

health conditions at a relatively low cost (McConnell et

al. 2000). Forest defoliation usually is documented by

a remote sensing technique known as sketch-mapping.

A sketch-map is created while flying in an aircraft

and observing damage and outlining its location on

topographic maps. Sketch-mapping is an acquired and

difficult skill that is somewhat subjective because human

observers must rely on their judgement in identifying and

delineating damaged areas.

The cumulative defoliation frequency (1984-98) for the

ANF is shown in Figure 10. All acreage values in this

report include the entire area within the proclamation

boundary of the ANF (not just public land). The area

defoliated by each major insect pest on the ANF is shown

in Figure 11. There was little defoliation from 1999 to

2001.

Methods

Kriged Surfaces

We used the ordinary kriging procedure (Deutsch and

Journel 1998) to interpolate surfaces of various variables

of interest on the ANF from point measurements (FIA

and FHM plot data). Kriging is a geostatistical method

that provides unbiased estimates at unsampled locations

as weighted averages of values from nearby plot locations

Figure 9.—FIA and FHM plot locations on the ANF (approximate coordinates).
11

(Isaaks and Srivistava 1989). Weights are determined on

the basis of a semivariogram, a statistical model of the

relationship between spatial autocorrelation and distance

between pairs of sampled values. For this analysis we

generated maps from plot data by calculating kriged

estimates on a grid of 1- by 1-km cells. Variography and

kriging were performed using the GSLIB software library

(Deutsch and Journel 1998).

Species Diversity and Richness

Species diversity is the term used to describe the number

of different species present in an area and the distribution

1984 0.54051561 0 0 0

1985 9.596427425 0 0 0

1986 78.68064932 0 0 0

1987 157.5753886 0 0 0

1988 1.234718616 0 0 0

1989 0.751407255 0 0 0

1990 0 0 0 0

1991 3.294975241 0 23.452 0

1992 48.86062955 0 72.67 0

1993 2.046853898 4.367844796 338.876 4.482821744

1994 0 53.0122303 0 20.18592559

1995 0 287.6273524 0 0

1996 0 15.63619762 0 0

1997 0 0 0 0

1998 0 0 0 0

1999 0 0 0 0

2000 0 0 0 0

2001 0 0 0 0

0

50

100

150

200

250

300

350

400

198419851986198719881989199019911992199319941995199619971998199920002001

Defoliation (thousand acres)

Forest tent caterpillar

Elm Spanworm

Cherry scallopshell moth

Gypsy moth

Figure 10.—Years of defoliation (percent of area) by all damaging agents (1984-98); 

percentage of land area and acreage in each category in parentheses.

Figure 11.—Area defoliated by major insect pests since 1984.
12

of individuals among species. Evenness is the term

used to describe how individuals in a community are

distributed among various species. If the number of

individuals is the same for each species, the community is

said to be completely even, though this is rare in nature.

When a species has more individuals than the other

species, it is said to be dominant. Degree of dominance

is another attribute of species diversity. The easiest way

to measure species diversity is to count the number of

species at a site; this measure is termed species richness.

However, species richness does not provide a complete

picture of diversity in an ecosystem because abundance

is excluded. Diversity indices are calculated numeric

values or graphical expressions used to describe species

composition of a community in a single number for

comparison with values from other communities. The

Shannon index is commonly used to describe species

diversity of a site. It emphasizes species richness but also

takes into account the proportional abundance of the

species. It is calculated using the following formula:

H ́=–Spilnpi

where H ́ represents the diversity of a community, pi

represents the proportion of each individual species to

the total, and ln pi represents the natural logarithm of

pi (Magurran 1988). An index of evenness based on the

Shannon index can be calculated using the maximum

value of that index if all of the individuals sampled

were distributed evenly among the species present. The

measure of evenness is derived from the ratio of the

observed Shannon diversity to its maximum, calculated

as:

E = H ́/Hmax = H ́/ln S

where S represents the number of species in the sample.

H ́ is the observed Shannon index and Hmax is the value

of H ́ when the total number of individuals measured

(N) is divided equally among the species encountered

(S). Values of E are forced between 0 and 1 with 1 

representing a situation in which all species are equally

abundant (Magurran 1988).

Measures of dominance are based on the abundance

of the most common species rather than incorporating

species richness. The Berger-Parker index (d) is a simple

dominance measure that represents the degree to which

a community is dominated by one species and can be

useful in describing monocultures. It is calculated as:

d = Nmax/N

where Nmax represents the number of individuals in the

most abundant species, and N is the total number of

individuals of all species. An increase in the value of d

accompanies a decrease in diversity and an increase in

dominance (Magurran 1988).

Effects of Forest Pests

Tree conditions were assessed using tree measurements

taken in 1988-90 as part of the FIA program and

in 1998, 1999, 2000 and 2001 as part of the FHM

program. We used the 1988-90 FIA data to analyze the

effects of gypsy moth defoliation from 1985 to 1987.

The cherry scallopshell moth and elm spanworm analyses

were performed using 1998-2001 FHM data. In the case

of remeasured plots, only the most recent measurement

was used.

The frequency of defoliation at each plot location was

calculated using a geographic information system to

determine coincidence of plot locations with yearly sets

of defoliation polygons. Defoliation layers were compiled

by digitizing sketch-maps of canopy defoliation generated

during aerial surveys conducted yearly from 1984 to

1999.

Oneway analysis of variance (ANOVA) was used to test

both the effect of tree species composition on defoliation

and of defoliation on percent standing dead basal area

and percent crown dieback. A P value is a measure of

probability that a difference between groups during

an experiment happened by chance. For example, a P

value of 0.01 means there is a 1 in 100 chance the result

occurred by chance. Differences between group means

are indicated by the letters a, b, and c. Estimates were

calculated as averages of plot values. To analyze the effect

defoliation on mortality and crown dieback, we excluded

plots with less than 10 percent host-species basal area

because we expected an excessively high sampling error

of mortality and crown-dieback estimates on hosts. In
13

other words, a plot with one tree of a host species that

was dead would have 100 percent mortality, inflating the

estimates. Crown dieback is defined as recent mortality

(3 to 10 years) of branches with fine twigs and reflects the

severity of recent stresses on a tree. However, it may be

measurable only for several years as most dead fine twigs

or branches do not remain on the tree for a long time.

Once they fall, there is no visible indicator of how large

the tree crown should have been, though it likely would

appear smaller than normal for some time depending on

the severity of the dieback. The variable is estimated as a

percentage of the live crown area that is dead for each tree

(USDA For. Serv. 1998).

Current Forest Conditions

Overstory Conditions

In this section we assess current overstory conditions

across the ANF using 4 years of FHM data (1998-2001)

and past overstory conditions using the 1989 FIA survey.

Variables discussed include forest type, tree-species

abundance, diameter distribution, stand age and size class

by plot, tree crown dieback and damage, abundance and

species composition of standing dead trees, and Indiana

bat habitat. All tree species that were sampled in the

FHM and FIA surveys are listed in Appendix I.

Forest Types

The forest types used by the Eastern Region (9) of the

Forest Service are defined in Appendix I. We classified

each FHM plot condition into Region 9 forest types by

calculating the percentage of the live basal area of each

species and combination of species. In some cases there

were no overstory trees on a subplot so seedling/sapling

data were used to determine forest type. The breakdown

of Region 9 forest types on the ANF is shown in Table 1.

Nearly 50 percent of the land area is in mixed upland

hardwood (25 percent) and Allegheny hardwood (24

percent) forest types. Other than red maple (17 percent),

all other forest types account for less than 10 percent of

the land area. Four percent of the land area is classified as

nonforest because trees or seedlings were not sampled,

probably because part of a plot fell on a road, utility

right-of-way, or in an opening. The distribution of plots

on the ANF by FHM and FIA forest type groups and

forest types is shown in Appendix I.

Tree-Species Abundance

Figure 12 shows tree-species abundance expressed as

the average live basal area per acre calculated from the

1989 FIA and 1998-2001 FHM data for the 10 most

abundant species on the ANF. The number of trees

sampled of each species also is shown in Figure 12. The

latter numbers represent only sample size and have no

relation to abundance. Black cherry and red maple were

the two most abundant species, which is consistent with

the forest-type information in Table 1. Black cherry, red

maple, American beech, eastern hemlock, and sweet birch

increased in abundance while sugar maple, northern red

oak, and white ash decreased in abundance. Decreases

in sugar maple likely reflect the effects of elm spanworm

defoliation, drought, and poor soil nutrition (Bailey et al.

2004) while decreases in northern red oak likely reflect

the effects of gypsy moth defoliation and drought (Morin

et al. 2004). Decreases in white ash probably reflect an

observed decline due to multiple stressors.

Table 1.—Current distribution of Region 9 

forest types on the ANF (1998-2001 FHM data)

Forest type Percent of area

Mixed upland hardwoods 25.5

Allegheny hardwoods 23.6

Red maple 17.2

Northern hardwoods 8.5

Hemlock 4.7

White oak/red oak 4.5

Nonforest 4.0

Oak/hardwood transition 3.9

Red oak 1.7

Sugar maple 1.6

White oak 1.2

Black birch/hickory 1.0

White spruce/Norway spruce 0.6

Chestnut oak 0.6

Pin cherry 0.6

Aspen 0.4

Red pine 0.4
14

Figure 13 shows the percentage of total average

basal area per acre for the 10 most abundant tree

species and the total of all other species. Black

cherry and red maple account for more than half

of the total average basal area per acre on the ANF.

Diameter Distribution

The distribution of basal area and number of trees

by diameter across the ANF (80- to 110-year-old

high forest and younger stands regenerated over

the past 40 years) calculated from 1998-2001 

FHM data are shown in Figure 14. Figure 14a

shows the number of trees per acre and basal

area per acre of all species by 5-inch diameter

classes. The number of trees per acre of all species

forms an inverse J-shape curve that is typical of

uneven-aged stands (Oliver and Larson 1996;

Marquis 1992). However, stands on the ANF

are even-aged, having regenerated between 1890

and 1930. The inverse-J diameter distribution

occurs because of the difference in growth rates

of the mix of species that developed following

forest removals at the turn of the 19th century.

Young seedlings of American beech, sugar

maple, eastern hemlock (slow growing), red

maple, and black cherry (fast growing) grew

together before overstory removal. Following

the overstory removal cut, fast-growing species

rapidly outgrew slower growing ones, resulting

in a diameter distribution stratified by species

growth rate (inverse J). In younger stands that

originated during the past 40 years, deer have

had a substantial impact on the species of

regeneration present before overstory removal. Species

that are low in food preference to deer (black cherry) or

that are resilient to repeated browsing (American beech)

make up a large proportion of the regeneration in these

younger stands.

Basal area was highest in the 10- to 15-inch diameter

class. Figure 14b shows the diameter distribution of

basal area for the five most common species and oak spp.

Black cherry basal area increased with diameter while

eastern hemlock, sugar maple, and American beech

decreased with diameter. The highest percentage of red

maple basal area was in the 10- to 15-inch diameter class;

the highest percentage of oak basal area was in the 15- to

20-inch diameter class.

Spatial Distribution of Selected Tree Species

A kriged surface of percent basal area was created for

each of the 10 most abundant species on the ANF. We

generated maps (Figs. 15-16) from FHM plot data by

calculating kriged estimates on a 1-km grid. The kriging

parameters for each surface are listed in Appendix I.

FHM FIA

White ash 1.895665315 2.27671428

White oak 2.460391486 2.376998477

Yellow birch 3.034925521 2.949361647

Sweet birch 5.136962302 3.961160962

Northern red oak 5.634035826 6.561312846

Sugar maple 8.21430938 8.21430938

Eastern hemlock 10.54506786 9.004664591

American beech 11.71303693 10.45137202

Red maple 30.54221883 27.83559258

Black cherry 31.35339938 28.95016698

0 5 10 15 20 25 30 35

White ash

White oak

Yellow birch

Sweet birch

Northern red oak

Sugar maple

Eastern hemlock

American beech

Red maple

Black cherry

Mean Live Basal Area/Acre (ft2

)

1998-2001 FHM surveys

1989 FIA survey

1075 trees

1672 trees

1148 trees

1839 trees

674 trees

995 trees

543 trees

750 trees

464 trees

911 trees

109 trees

286 trees

341 trees

346 trees

170 trees

229 trees

79 trees

154 trees

52 trees

132 trees

Figure 12.—Average live basal area per acre for major tree species 

on the ANF.

27%

25%

9%

9%

8%

7%

5%

4%

2% 2%2%

Black cherry

Red maple

American beech

Eastern hemlock

Other

Sugar maple

Northern red oak

Sweet birch

Yellow birch

White oak

White ash

Figure 13.—Percentage of total live basal area for major tree species 

on the ANF (1998-2001 FHM data).
15

Figure 15 shows kriged surfaces of percent basal area

for black cherry, red maple, American beech, eastern

hemlock, and sugar maple. The southeastern two-thirds

of the ANF had the largest component of black cherry.

The lowest estimated value for percent basal area of black

cherry was zero and the highest was 65. Red maple had a

more uniform distribution, though the southern third of

the ANF has the highest red maple component followed

by the northern third. The red maple component is

lowest in the central portion but no cell was estimated at

less than 6 percent basal area for red maple. The highest

estimated value was 53 percent for basal area of red

maple. American beech is a small component on most of

the ANF; it is most prevalent in a band from the westcentral border to the northeastern corner. The highest

estimate was 42 percent for basal area of beech. Percent

Figure 14.—Diameter distributions by a) number of trees and b) basal area per acre on the ANF.

 Diameter Class

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

basal area all species 12.8 23.3 33.7 29.6 23.4

Black cherry 3.0 2.6 8.1 9.0 9.7

Red maple 1.7 5.3 9.7 8.4 5.8

American beech 3.0 3.1 2.4 1.7 1.2

Eastern hemlock 0.6 3.6 3.3 1.9 1.4

Oak spp. 0.1 0.7 2.7 3.7 2.6

Sugar maple 1.1 3.0 2.6 1.2 0.6

trees/acre all species 389.3 80.5 41.1 18.6 7.8

oak spp. 1.7 2.0 3.2 2.3 0.9

sugar maple 19.9 10.2 3.3 0.8 0.2

eastern hemlock 6.9 12.5 4.2 1.1 0.5

American beech 83.7 12.2 2.9 1.1 0.5

red maple 41.6 17.3 11.7 5.4 2.0

black cherry 116.6 9.4 9.7 5.6 3.2

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

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

Diameter Class (inches)

Trees/Acre (no.)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Basal area/acre (sq. ft.)

Trees/acre

Basal area/acre

a

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Black cherry Red maple American

beech

Eastern

hemlock

Oak spp. Sugar maple

Species

 Mean Basal Area/Acre (ft2)

1-5

5-10

10-15

15-20

20+

b
16

black cherry red maple

American beech eastern hemlock

sugar maple

Figure 15.—Kriged surfaces of percent basal area of black 

cherry, red maple, American beech, eastern hemlock, and 

sugar maple on the ANF; NLCD data from Multi-Resolution 

Land Characteristics Consortium.
17

basal area of eastern hemlock was less than 10 on most of

the ANF. Small areas of greater hemlock density probably

represent concentrations of this species on lower slopes

or bottomlands. The component of sugar maple is higher

in the eastern two-thirds of the ANF. Percent basal area

of less than 10 sugar maple was on most of the ANF; the

highest estimate was 70 percent.

Figure 16 shows kriged surfaces of percent basal area

of northern red oak, sweet birch, yellow birch, white

oak, and white ash. The northern red oak component

is greatest along the northern, western, and southern

boundaries of the ANF in areas bordering the Allegheny

and Clarion Rivers. Estimates of percent of basal area

of red oak ranged from zero to 32. Percent basal area of

sweet birch was estimated at less than 10 on most of the

ANF. Several areas on the western half of the ANF have

a higher component of sweet birch; the highest estimate

for basal area was 19 percent. Percent basal area of yellow

birch was estimated at less than 10 across the ANF. The

white oak component is highest along the northern,

western, and southern borders of the Forest. Estimates of

percent basal area of white oak ranged from zero to 36.

White ash was estimated at less than 10 percent across

most of the ANF.

Plot Age Class, Stand-Development 

Class, and Relative Density

Data describing each plot as a unit (rather than

examining individual trees) was used to characterize tree

age classes, size classes, and relative density across the

ANF.

Age-Class Distribution

The land area in each age class stratified by Eastern

Region forest type is shown in Figure 17. Nearly 70

percent of the land area is characterized by 60- to 100-

year-old forests due to widespread clearcutting at the turn

of the century. Most of this mature forest is in mixed

upland hardwoods, Allegheny hardwoods, and red maple

forest types. The Allegheny hardwood type constitutes

a small portion of the 100+ year age class. Forest types

dominated by longer lived species dominate this oldest

class.

Distribution by Stand-Development Class

Based on stand-development categories described by

Oliver and Larson (1996), we assigned each subplot a

stand development class. The classes were stand initiation

(0 to 14 years), stem exclusion (15 to 49 years), and

understory reinitiation (50+ years). The distribution of

subplots on the ANF for each stand-development class is

shown in Figure 18. More than half of the subplots were

in the understory reinitiation phase (as expected due to a

large percentage of trees on the ANF that are 60 to 100

years old; Fig. 16), and contained 80 to 200 ft2 of basal

area per acre.

Relative Density

Relative plot density was calculated using the method of

Stout and Nyland (1986) (Fig. 19). Plots were classified

as poorly, moderately, or well stocked according to

ANF protocol.2 Fifty-eight percent of the forest in the

understory reinitiation class was classified as moderately

stocked, and about 18 percent was classified as well

stocked. The stand initiation category could not be

included because the method described above was

inappropriate.

Live-Tree Distribution by Size Class for 

Indiana Bat Habitat

Live trees provide important habitat for mammals, birds,

and insects. The Indiana bat, listed as endangered by

the USDI Fish and Wildlife Service, has been a concern

on the ANF. The ANF Forest Plan as amended (USDA

For. Serv. 2000) lists specific factors that can be used to

evaluate the Indiana bat’s roosting habitat:

2

U.S. Department of Agriculture, Forest Service. 2000. Final 

environmental impact statement for the eastwide project.

Warren, PA: U.S. Department of Agriculture, Forest Service,

Allegheny National Forest. 102 p.

Indiana bat requirements

(no. of live trees)

ANF conditions

(no. of live trees/acre)

D.b.h. class Suitable Optimal (Mean ± SE)

> 9 8/acre 16/acre 79.08 ± 3.19

> 20 1/acre 3/acre 7.84 ± 0.66

These include criteria for numbers and sizes of live trees

per acre according to the bat’s habitat suitability index

model (Romme et al. 1995). For habitat to be considered
18

northern red oak sweet birch

yellow birch white oak

white ash

Figure 16.—Kriged surfaces of percent basal area of northern 

red oak, sweet birch, yellow birch, white oak, and white 

ash on the ANF; NLCD data from Multi-Resolution Land 

Characteristics Consortium.
19

 AGE CLASS

FOREST TYPE 0-20 20-40 40-60 60-80 80-100

Sum of above 3.541960405 1.589595376 2.019340318 5.598217197 5.780346821

Hemlock 0 0 1.957173699

Northern hardwoods 0.578034682 0 1.589595376 3.757225434

Red maple 1.584994509 0.144508671 0.578034682 4.759959682 8.670520231

Allegheny hardwoods 1.156069364 1.80234104 2.47563974 7.831696965 7.26066474

Mixed upland hardwoods 0.544102601 1.011560694 3.007775289 12.80343367 6.352337428

0

5

10

15

20

25

30

35

40

0-20 20-40 40-60 60-80 80-100 100+

Stand Age (years)

Percent of Forest Area

Mixed upland hardwoods

Allegheny hardwoods

Red maple

Northern hardwoods

Eastern hemlock

All other forest types

Stand Development Class 0-40 40-80 80-120 120-160 160-200

Stand initiation 3.612716763 2.312138728 1.589595376 0.289017341 0.144508671

Stem exclusion 1.156069364 2.89017341 2.023121387 1.734104046 0.722543353

Understory reinitiation 8.38150289 11.12716763 18.6416185 17.91907514 14.01734104

0

2

4

6

8

10

12

14

16

18

20

0-40 40-80 80-120 120-160 160-200 200-240 >240

Basal Area/Acre ( ft2

)

Percent of Forest Area

Stand initiation

Stem exclusion

Understory reinitiation

Figure 17.—Distribution of stand age by 

forest type.

Figure 18.—Distribution of basal area by 

stand-development class.

suitable, 5 percent of the landscape under consideration

must be forested and meet the criteria in the “suitable”

column. For habitat to be classified as optimal, 30

percent of the landscape must be forested and meet the

criteria in the “optimal” column. The ANF is 94 percent

forested (USDA For. Serv. 2000). The live-tree density

estimates imply that the average condition across the

ANF easily meets both suitable and optimal live-tree

habitat requirements. Nearly 60 percent of the plots

meet both suitable and optimal conditions for both

diameter classes.

Summary of Crown Condition, Tree 

Damage, and Standing Dead

Crown Dieback

The percentage of basal area of major species in crowndieback categories measured during the 1998-2001 

FHM surveys is shown in Table 2. Trees with less than
20

25 percent dieback generally are healthy and usually

recover, those with 25 to 49 percent dieback are in fair

health and might recover, and trees with 50 percent or

more crown dieback are in poor health and probably

will not recover (Gottschalk and MacFarlane 1993).

Standing dead trees (100-percent dieback) are included

in Table 2.

Crown dieback generally was low for most species,

ranging from 50 to 100 percent for 10 to 15 percent of

the basal area of American beech, sugar maple, sweet

birch, yellow birch, white oak, and white ash. Ash

decline is prevalent in the Northeastern United States

(Sinclair et al. 1988). Ash yellows, a disease caused

by phytoplasma-like organisms, has been associated

with dying trees in certain areas where ash is declining

(Sinclair et al. 1996). However, not all dying trees are

infected with these organisms (Matteoni and Sinclair

1985). Currently, ash decline is thought to have multiple

causes (Schlesinger 1990). Northern red oak apparently

has recovered well from gypsy moth defoliation and

stress from drought in the late 1980s.

A kriged surface of percent crown dieback for all species

estimated from the 1998-2001 FHM surveys is shown

in Figure 20. Note that Table 2 provides the percent of

basal area in crown-dieback categories. By contrast, these

maps are estimated values of percent crown dieback

Table 2.—Percent basal area of major tree species on the 

ANF, by crown-dieback class (1998-2001 FHM data)

Crown dieback (%)

Species 0-24.9 25-49.9 50-95 100 (Dead)

Black cherry 88.9 2.4 1.0 7.6

Red maple 91.4 2.5 0.3 5.8

American beech 86.0 3.1 1.5 9.4

Eastern hemlock 91.5 1.9 1.4 5.2

Sugar maple 81.2 2.6 1.3 14.8

Northern red oak 95.2 0.0 0.0 4.8

Sweet birch 88.3 0.0 0.2 11.5

Yellow birch 84.8 1.8 0.0 13.4

White oak 88.4 0.0 0.0 11.6

White ash 85.8 0.0 6.7 7.5

Poorly Stocked (<40% Moderately Stocked (40-79%) Well Stocked (>80%)

Stem exclusion 4.89 6.03

Understory reinitiation 13.14 57.65

0

10

20

30

40

50

60

70

Poorly Stocked (<40%) Moderately Stocked

(40-79%)

Well Stocked (>80%)

Percent of Forest Area

Stem exclusion

Understory reinitiation

Figure 19.—Distribution of stocking levels by stand-development class.

averaged for each plot. Estimated dieback values were

highest on the southeastern two-thirds of the ANF. This

is consistent with the areas that had the most numerous

defoliations (Fig. 10).

Kriged surfaces of crown dieback also were generated for

selected species on the ANF estimated from the 1998-

2001 FHM surveys (Fig. 21). Dieback of black cherry

was greatest in the southeastern two-thirds of the ANF,

while red maple dieback was low in all areas except the

far western corner. Crown dieback of American beech,
21

eastern hemlock, and sugar maple was low over

most of the ANF; white ash dieback was high in the

central portion.

Crown Density

Crown density, an estimate of crown condition in

relation to a typical tree for the site where it is found,

is defined as the amount of crown branches, foliage,

and reproductive structures that block light visibility

through the crown. Crown density can serve as an

indicator of expected growth in the near future.3

Percent basal area of major species in crown-density

categories measured during the 1998-2001 FHM

surveys is shown in Table 3. Trees with higher

densities should be considered healthier and more

vigorous.

American beech had the highest percentage of basal

area (27) in the less than 25 percent crown-density

category followed by sweet birch (25 percent). Black

cherry, eastern hemlock, and northern red oak have

30 to 40 percent of their basal area per acre in the 25

to 50 percent crown-density category, though black

cherry usually has a lower foliage density. Northern

red oak and white oak both had less than 1 percent of

basal area in the lowest category.

Steinman (2000) reported that trees with crown

densities of less than 30 percent are the most likely

to die. Crown density as an indicator of tree health is

useful for long-term monitoring as trends are evaluated

against a baseline measurement. The crown densities

established in this report will serve as that baseline.

Crown Ratio

Live crown ratio is a percentage determined by dividing

live crown height by total tree height. Live crown height

is the distance from the live crown top to the “obvious

live crown” base.3

Percent basal area of major species in crown-ratio

categories measured during the 1998-2001 FHM surveys

is shown in Table 4. Trees in the less than 25 percent

crown-ratio category probably are unhealthy. Where

trees grow close together and self-prune, one would not

expect a high proportion in the 75+ percent crown-ratio

category except for highly shade tolerant species such as

beech, sugar maple, and hemlock.

3

U.S. Department of Agriculture, Forest Service. 2002. Forest 

inventory and analysis national core field guide, volume 

2: field data collection procedures for phase 3 plots, 

version 1.6. Internal report on file with U.S. Department of

Agriculture, Forest Service, Forest Inventory and Analysis, 201 

14th St., Washington, DC.

Figure 20.—Kriged surface of percent crown dieback of all species; 

NLCD data from Multi-Resolution Land Characteristics Consortium.

Table 3.—Percent basal area of major tree species on the 

ANF, by crown-density class (1998-2001 FHM data)

Crown density (%)

Species 0-24.9 25-49.9 50-74.9 75-100

Black cherry 10.4 40.2 47.4 2.0

Red maple 7.0 20.6 63.1 9.2

American beech 27.1 20.5 46.9 5.5

Eastern hemlock 9.0 32.8 49.8 8.3

Sugar maple 16.1 23.5 49.2 11.3

Northern red oak 0.9 29.6 55.6 13.9

Sweet birch 24.8 11.8 50.4 13.0

Yellow birch 9.8 22.0 57.0 11.3

White oak 0.4 18.4 56.5 24.7

White ash 7.9 26.1 55.9 10.1
22

Figure 21.—Kriged surface of percent crown dieback of selected species (1998-2001 FHM data; NLCD data 

from Multi-Resolution Land Characteristics Consortium).

black cherry red maple

American beech eastern hemlock

sugar maple white ash
23

Foliage Transparency

Foliage transparency is the amount of skylight

visible through the live, usually foliated portion

of the crown. The normal range of foliage

transparency varies by species. For example,

black cherry and white ash generally have higher

transparency than many other species. However,

changes in foliage transparency can occur due

to defoliation or reduced foliage resulting from

stresses in preceding years.3

Percent basal area of major species in foliagetransparency categories measured during the

1998-2001 FHM surveys is shown in Table 5. The

transparency for American beech was higher than

would be expected for this shade tolerant species.

Foliage transparency as an indicator of tree health

is useful for long-term monitoring as trends are

evaluated against a baseline measurement. The

transparencies established in this report will serve

as that baseline.

Tree Damage

During FHM surveys, damage was assessed for

each tree beginning at the roots. As many as three

damages can be recorded per tree in the following

order: roots, roots and lower bole, lower bole,

lower and upper bole, upper bole, crownstem,

and branches.1 In some instances, not all of the

damage on a tree is recorded, resulting in an

underestimation of damages on the upper part

of the tree. However, this bias is small as three

damages were only recorded on 2 percent of the

sampled trees. Percent basal area of major tree

species on the ANF that incured damage is shown in

Table 6. The percentage of basal area without signs or

symptoms ranged from 41 for American beech to 89

for white oak. Conks and advanced signs of decay were

the most frequently observed damage, ranging from 8

percent for white oak to 40 percent for yellow birch.

Conks are the fruiting bodies of fungi that cause decay.

Discoloration and decay are the major causes of defect

and loss in wood quality of yellow birch. Nectria galligena

is the most common and damaging stem disease of this

species (Erdmann 1990). Therefore, much of the decay

on yellow and sweet birch may be due to infection by the

Nectria fungus. The extensive conks and advanced decay

reported in northern red oak requires further evaluation.

Decay on American beech is attributed at least partly to

the effects of beech bark disease. Affected trees may live

for several years (Houston 1994).

The observed decay on trees by tree-size class using

the 1998-2001 FHM data is shown in Table 7. Except

for northern red oak and white ash, the percentage of

Table 4.—Percent basal area of major tree species on the 

ANF, by crown-ratio class (1998-2001 FHM data)

Live crown ratio (%)

Species 0-24.9 25-49.9 50-74.9 75-100

Black cherry 7.0 74.6 17.5 0.9

Red maple 1.4 34.5 57.3 6.9

American beech 1.2 17.0 39.9 41.9

Eastern hemlock 1.7 5.5 26.6 66.2

Sugar maple 3.9 27.9 55.1 13.1

Northern red oak 0.1 54.7 41.2 3.9

Sweet birch 0.7 42.5 49.7 7.1

Yellow birch 1.6 31.7 52.8 13.9

White oak 0.0 40.9 54.8 4.4

White ash 8.1 66.2 24.7 0.9

Table 5.—Percent basal area of major tree species on the 

ANF, by foliage-transparency class (1998-2001 FHM data)

Foliage transparency (%)

Species 0-24.9 25-49.9 50-74.9 75-100

Black cherry 58.1 40.4 1.0 0.6

Red maple 80.6 18.9 0.3 0.2

American beech 76.5 22.5 0.7 0.3

Eastern hemlock 93.6 4.8 0.2 1.5

Sugar maple 83.4 15.5 0.2 0.8

Northern red oak 72.8 27.2 0.0 0.0

Sweet birch 86.6 13.4 0.0 0.0

Yellow birch 76.3 23.7 0.0 0.0

White oak 96.2 3.8 0.0 0.0

White ash 66.4 28.0 1.1 4.5
24

basal area with observed decay increased with diameter

class. This trend can be expected as part of normal tree

senescence. Decay observed in northern red oak was

atypical as it was highest in the 5- to 10-inch diameter

class.

Standing Dead Trees

Standing dead trees (at normal background levels),a

natural component of healthy forest ecosystems, play an

important role in nutrient cycling and provide wildlife

habitat. Tree mortality is increasingly affected by factors

such as disease and insect damage asa forest ages (Greif

and Archibold 2000). Standing dead is nota true

measure of mortality becausea dead tree can be removed,

fall over, or remain standing fora number of years.

However, number of standing dead trees can provide an

indirect measure of past mortality.

Live and dead basal area per acre and percentage of

basal area that is standing dead for major tree species

on the ANF are shown in Table 8. Among the five

most dominant species, mortality appeared to be

proportionally greatest in sugar maple. This increase in

percent dead sugar maple likely is due toa general decline

in that species on the unglaciated portion of the northern

Allegheny Plateau (Horsley et al. 2000). Beech bark

disease and elm spanworm defoliation contributed to the

Table 6.—Percent basal area of major tree species on ANF, by damage signs and symptoms (1998-2001 FHM data)

No Open Broken Bole Broken/ Dead Broken/Dead Damaged Buds

Species Damage Canker Decay Wound Resinosis Cracks or Roots Dead Roots Vines Terminal Branches or Foliage Discoloration OtherBlack cherry 59.3 4.7 26.9 3.1 0.3 4.9 0.0 0.1 0.5 1.6 8.4 0.0 0.0 0.0Red maple 55.0 4.1 35.1 2.5 0.0 8.4 0.0 0.0 0.2 1.6 2.0 0.0 0.2 0.0Eastern hemlock 67.5 5.3 15.5 4.8 0.0 4.2 0.0 0.2 0.0 9.5 1.8 0.7 0.0 0.0American beech 41.4 4.8 38.3 7.5 0.0 5.6 1.2 0.0 0.2 5.5 1.4 0.3 0.1 16.3Sugar maple 53.6 11.0 30.8 7.7 0.0 5.8 0.1 0.3 0.1 7.8 1.6 0.0 0.0 0.2Northern red oak 56.9 0.0 34.5 0.0 0.0 5.5 0.0 0.0 0.6 0.2 2.8 0.0 0.0 0.0Sweet birch 61.4 17.4 25.0 6.1 0.0 2.8 0.0 0.0 0.5 0.5 1.8 0.0 0.0 0.0Yellow birch 50.6 12.3 39.8 3.4 0.0 1.7 0.0 0.0 0.0 1.8 1.4 0.0 0.0 0.0White oak 89.1 0.0 8.5 0.0 0.0 1.9 0.0 0.0 0.0 0.5 1.4 0.0 0.0 0.0White ash 82.4 0.0 12.5 0.0 0.0 0.0 1.6 0.0 0.0 2.6 7.7 0.0 0.0 0.0

Table 7.—Percent basal area of major tree species with 

observed decay on the ANF, by diameter class (1998-

2001 FHM data)

Diameter class (inches)

Species 0-4.9 5-9.910-14.915-19.9 >20

Eastern hemlock 0.0 2.5 11.3

21.9 45.1

Red maple16.2 26.4 29.8 35.6 52.8

Sugar maple15.731.3 34.2

15.0 49.3

Yellow birch 0.9 37.2 38.6 46.5 54.5

Sweet birch 2.3

17.3 24.1 52.5 NA

American beech13.9 30.3 26.6 66.6 47.4

White ash 0.0

10.418.3 7.015.8

Black cherry1.514.619.0 29.7 36.6

Northern red oak 0.0 23.2 8.7 6.2 0.0

White oak 0.0 6.7 6.

1 30.3 53.6
25

increase in the percentage of dead American beech. The

high mortality of white oak likely is due to defoliation

by gypsy moth. The effects of these disturbances are

discussed in detail in subsequent sections. Most birch

trees become infected with the Nectria fungus and few

exceed 60 years of age. Once dead, they tend to decay

and fall fairly soon, probably accounting for the decrease

in the basal area of standing dead birch from the 1988-90

survey to the 1998-2001 surveys.

The percent of total standing basal area that is dead by

diameter class for major species on the ANF is shown in

Table 9. Several points can be made from Table 9:

•	 Nearly one-third of the black cherry basal area in

the 5- to 10-inch d.b.h. class was dead. Standing

dead accounted for less than 11 percent in the

other diameter classes. The high percentage of

standing dead in the smallest class likely was due

to self-thinning of this shade-intolerant species.

•	 Percent standing dead red maple was highest

(nearly 10 percent) in the 5- to 10-inch d.b.h.

class, probably due to self-thinning. The

proportion of standing dead was less than 8

percent in the other classes.

•	 The proportion of standing dead American

beech was highest in the largest d.b.h. class.

Twenty-seven percent of the basal area in that

class was dead, probably due to beech bark

disease. Defoliation by gypsy moth and elm

spanworm also contributed to beech mortality.

The basal area of standing dead beech was 15

percent or less in the other diameter classes.

•	 A large proportion of sugar maple basal area

was in standing dead in the 5- to 20-inch d.b.h.

classes. More than 22 percent of the basal

area in the smallest diameter class was dead.

Table 9.—Percent of total standing basal area that is 

dead for major species on the ANF, by diameter class 

(1998-2001 FHM data)

Diameter class (inches)

Species 5-9.9 10-14.9 15-19.9 20+

Northern red oak 33.9 2.2 4.4 3.4

Black cherry 29.1 10.2 6.0 0.9

Sugar maple 22.3 14.8 12.7 0.0

White ash 19.5 21.0 0.0 0.0

Sweet birch 14.6 17.9 6.5 NA

Yellow birch 14.1 21.7 0.0 0.0

White oak 10.5 10.3 9.8 22.3

Red maple 9.5 7.4 4.4 3.1

American beech 7.4 15.0 3.1 27.0

Eastern hemlock 2.7 3.0 7.1 14.7

Table 8.—Live and dead basal area per acre (ft2) and percent basal area that is standing 

dead for major tree species on the ANF

Basal area/acre

1998-2001a

Basal area/acre

1989b

Species Live Dead Percent dead Live Dead Percent dead

Black cherry 32.5 2.7 7.6 29.0 2.4 7.5

Red maple 31 1.9 5.8 27.8 2.1 7.0

American beech 11.4 1.2 9.4 10.5 0.2 1.7

Eastern hemlock 10.7 0.6 5.2 9.0 0.3 3.4

Sugar maple 8.5 1.5 14.8 10.1 1.1 9.4

Northern red oak 5.7 0.3 4.8 6.6 0.6 8.3

Sweet birch 5.2 0.7 11.5 3.9 1.6 28.9

Yellow birch 2.9 0.5 13.4 2.9 1.0 25.1

White oak 2.5 0.3 11.6 2.4 0.3 11.2

White ash 2 0.2 7.5 2.3 0.3 12.3

a

FHM data. b

FIA data.
26

Sugar maple decline is discussed in detail in a

subsequent section.

•	 Nearly 15 percent of the basal area of eastern

hemlock in the largest diameter class was dead.

The proportion of standing dead was less than 8

percent in the other classes. The reasons for this

mortality are not completely understood.

•	 Standing dead northern red oak was highest

(nearly 34 percent) in the 5- to 10-inch class,

most likely from self-thinning. Standing dead

basal area was less than 5 percent in the other

classes. Since most oak mortality on the ANF

occurred more than 12 years ago, a combination

of blowdown and salvage operations since the

gypsy moth outbreaks of 1985-88 might account

for lower proportions of standing dead northern

red oak.

•	 Birch mortality ranged from 14

to 22 percent in the 5- to 15-inch

d.b.h. classes. There was no yellow

birch mortality in the largest classes,

probably because there are few trees

of this species in those classes on the

ANF.

•	 Standing dead basal area of white

oak was highest (22 percent) in

the largest class versus about 10

percent in the other classes. As with

eastern hemlock, the reasons for

this mortality are not completely

understood.

•	 Nearly 20 percent of the standing

basal area of white ash in the 5- to

15-inch d.b.h. classes was dead, likely

due to ash decline that has been

observed locally for several decades.

Spatial Distribution of Dead-Tree Basal Area

A kriged surface of percent standing dead basal area

(all species) estimated from the 1998-2001 FHM data is

shown in Figure 22. The greatest proportion of standing

dead was in the central two-thirds of the ANF. This also

is the area that has been defoliated the most often

(Fig. 10).

Figure 22.—Kriged surface of percent standing dead 

basal area (1998-2001 FHM data; NLCD data from MultiResolution Land Characteristics Consortium).

Table 10.—Number of standing dead trees per acre in the ANF, by 

species and diameter class (1998-2001 FHM data); Pennsylvania 

state averages from McWilliams et. al. (2004)

Diameter class (inches) Pennsylvania

Species >9 >12 >20 Total state average

Black cherry 2.0 0.9 0.0 2.9 1.2

Red maple 1.5 0.8 0.1 2.4 1.7

American beech 0.9 0.4 0.1 1.4 0.7

Eastern hemlock 0.3 0.2 0.1 0.7 0.6

Sugar maple 1.2 0.2 0.0 1.4 0.9

Northern red oak 0.1 0.1 0.0 0.3 1.0

Sweet birch 0.6 0.2 0.0 0.8 0.7

Yellow birch 0.4 0.1 0.0 0.5 0.3

White oaks 0.2 0.2 0.0 0.5 0.8

White ash 0.2 0.1 0.0 0.2 0.4

Aspen 0.6 0.1 0.0 0.7 0.2

Total 8.0 3.4 0.4 11.8 8.5

Number and Distribution 

of Standing Dead Trees Per Acre

Standing dead trees provide structure, nesting, or

roosting sites for numerous species of wildlife, and are

important foraging sites for species that rely on insects for

food. Table 10 shows the number of standing dead trees

per acre by species and d.b.h. class on the ANF as well as
27

state averages as reported by McWilliams et. al. (2004).

For most species (exceptions are northern red oak, white

oak, and white ash), the number of standing dead trees

per acre is greater on the ANF than the average for the

rest of Pennsylvania. For black cherry, American beech,

and aspen the number of standing dead trees is at least

twice the statewide average.

Dead Tree or Snag Longevity

Because dead trees provide habitat for wildlife, the length

of time they remain standing is important. Tree species

vary in the time they remain standing after death. Black

cherry and the oaks generally remain standing longer

than maples and American beech. Sample sizes are

sufficient for red maple, sugar maple, and black cherry

to draw some general conclusions. In Table 11, the first

column shows the percentage of standing dead trees

that fell since the 1988-90 survey (by 2001). Of the 12

red maples that were dead in 1989, all remain standing.

Seventeen percent of the sugar maples had fallen versus

only 3 percent of the black cherrys. Although sample

sizes were small for the remaining dead tree species except

bigtooth aspen, virtually all of these trees are standing

after 10 years. Aspen would be expected to fall more

quickly because its wood is soft.

Indiana bats prefer larger trees (9+ inches d.b.h.) with flaky

bark that they crawl under for shelter (Menzel et al. 2001).

As shown in Table 11, larger dead trees tend to remain

standing longer than smaller trees. Since virtually all

Indiana bat maternity colonies are found under exfoliating

bark, the characteristics of individual snags may be more

important than the species itself (Romme et al. 1995).

Distribution of Dead Trees by Size Class 

for Indiana Bat Habitat

In evaluating habitat for the Indiana bat on the ANF, it is

highly likely that at least 5 percent of the area is suitable as

conditions on the Forest meet the requirements for

optimal-dead tree habitat in the smaller d.b.h. classes:

Table 11.—Comparison of size and percentage of trees that were standing and dead in 

1989 with their status (standing or fallen) during the 1998-2001 reinventory

Fallen trees D.b.h. Mean d.b.h. of

Species Number Percent Range Mean fallen trees

-----Inches----- Inches

Eastern hemlock 3 0 5 to 14 8.1 NA

Red maple 12 0 5.1 to 19.1 8 NA

Sugar maple 23 17.4 5.5 to 23.2 8.3 6.2

Yellow birch 2 0 8.9 to 11.7 10.3 NA

Sweet birch 2 0 6.8 to 9.5 8.2 NA

American hornbeam 1 0 5.6 5.6 NA

American beech 2 0 5.3 to 8.9 7.1 NA

White ash 2 0 7.4 to 9.4 8.4 NA

Bigtooth aspen 3 33.3 5.7 to 17.1 11.3 5.7

Black cherry 32 3.1 5 to 20.8 9.5 9.7

White oak 1 0 12.8 12.8 NA

Northern red oak 2 0 18.3 to 18.8 18.6 NA

Indiana bat requirements

(no. of dead trees)

ANF conditions

(no. of dead trees/acre)

D.b.h. class Suitable Optimal (Mean ± SE)

> 9 3/acre 5/acre 8.34 ± 0.79

> 12 0.1/acre 3.49 ± 0.48

> 20 0.5/acre 0.39 ± 0.11
28

Because the estimated average number of dead trees in

the largest size class is slightly below the threshold for

optimal habitat (0.39 per acre versus 0.5 per acre), it

is less certain that conditions on the ANF meet that

criterion, though it is possible that optimal conditions

would be met as 94 percent of the ANF is forested

(USDA For. Serv. 2000). Thirty-five percent of the plots

met requirements for suitable habitat versus 7 percent for

optimal conditions.

Figure 23 shows kriged surface representations of the

spatial arrangement of the estimated number of dead

trees per acre by d.b.h. class from 1998-2001 FHM data.

Figure 23.—Kriged surfaces of number of 

standing dead trees (1998-2001 FHM data; 

NLCD data from Multi-Resolution Land 

Characteristics Consortium).

< 9′′ d.b.h. > 9′′ d.b.h.

> 12′′ d.b.h. > 20′′ d.b.h.
29

Overstory Species Richness and Diversity

All indices were calculated by plot condition; categories

with fewer than seven plot conditions were excluded

due to small sample sizes. The average species richness,

Shannon index, Shannon evenness, and Berger-Parker

index were calculated for each FHM plot condition

by Eastern Region forest type (Table 12) and stand

development category (Table 13). Among Eastern Region

types, species richness ranged from 2.0 in the sugar

maple type to 6.25 in the white oak/red oak type. The

Shannon index ranged from 0.5 in the sugar maple type

to 1.47 in the white oak/red oak type. Shannon evenness

ranged from 0.32 in the sugar maple type to 0.52 in

the northern hardwoods type. The Berger-Parker index

ranged from 0.4 in the mixed upland hardwoods type to

0.74 in the sugar maple type.

Forest type is a reflection of site conditions favoring one

set of vegetation over another, and site differences might

account for variation in diversity. In defining forest types,

a name is assigned when the defining species represents

more than half of the basal area. Forest types defined as

mixed have inherently more diversity than those defined

as single species or two species. However, it is possible

that species comprising the remaining 50 percent of the

basal area are more diverse in some forest types than in

others.

Differences in species diversity can be related to a

variety of ecological factors and land uses. Silvicultural

practices in certain stands might have reduced the

abundance of some species and favored others. However,

few silvicultural practices completely eliminate

species. Physiography and soils strongly determine the

composition, size, and productivity of vegetation (Barnes

et al. 1992). Soil moisture, nutrients, and pH control

species composition, size, and productivity. Whitney

(1986) observed that hardwoods were common on richer,

Table 12.—Overstory richness and diversity indices for trees at least 5.0 inches d.b.h., by 

Region 9 forest type (1998-2001 FHM data)

Species Shannon Shannon Berger- No. of plot

Forest type richness index evenness parker index conditions

Mixed upland hardwoods 5.59 1.44 0.46 0.40 50

Allegheny hardwoods 3.74 0.98 0.35 0.57 42

Red maple 3.93 0.99 0.34 0.60 30

Northern hardwoods 5.82 1.44 0.52 0.41 17

Hemlock 4.91 1.14 0.39 0.57 11

White oak/red oak 6.25 1.47 0.45 0.45 8

Sugar maple 2.00 0.50 0.32 0.74 8

Oak/hardwood transition 5.86 1.44 0.44 0.46 7

Stand-development

category

Species

richness

Shannon

index

Shannon

evenness

Bergerparker indexNo. of plot

conditions

No. of deer

per mile2

Stand initiation (0-14

years)

2.17 0.58 0.47 0.71 17 25-28

Stem exclusion (15-49

years)

3.67 0.77 0.30 0.71 19 21-50

Understory reinitiation

(50+ years)

4.95 1.26 0.42 0.48 153 0-21

Table 13.—Overstory richness and diversity indices for trees at least 5.0 inches d.b.h., by stand development 

stage (1998-2001 FHM data) and deer density
30

finer textured soils of moraines and ridges in presettlement

pine sites in Michigan. Coarse-textured, excessively

drained soils of ridges favored oaks while finer textured

soils of uplands supported more nutrient-demanding

hardwoods. American beech and sugar maple grew on

coarser textured loams, while hemlock grew on finer

textured loams and clays. In New York, the composition

of overstory species was positively related to soil texture,

stoniness, pH, specific conductance, soil moisture, and

percent organic matter (Seischab and Bernard 1991;

1996; Bernard and Seischab 1995). Differences in soils

were significant among communities at the Waterloo

Barrens in Maine where cation exchange capacity (CEC),

pH, Ca, Mg, P, percent organic matter, and total N

differed significantly among five vegetation community

types identified (Copenheaver et al. 2000). These kinds of

relationships merit further investigation on the ANF.

Among stand-development categories, species richness

ranged from 2.17 in the stand initiation phase to 4.95 in

the understory reinitation phase (Table 13). The Shannon

index ranged from 0.58 in the stand initiation phase (0 to

14 years old) to 1.26 in the understory reinitiation phase

(50+ years old). Shannon evenness ranged from 0.3 in the

stem exclusion phase (15 to 49 years old) to 0.47 in the

stand initiation phase. The Berger-Parker index ranged

from 0.48 in the understory reinitation phase to 0.71 in

the stem exclusion and stand initiation phases.

Disturbance plays an important role in the assembly and

maintenance of plant communities beneath the canopy of

forests. It can be defined as the mechanism(s) that limit

plant biomass by causing its partial or total destruction

(Grime 2001). A forested community usually has an

understory that is stable due to the natural cycling of

small-scale disturbances (Odum 1969). Halpern and

Spies (1995) suggested that there are two classes of

disturbance effects: initial effects that occur as a direct

result of the disturbance and long-term effects on species

recovery. Initial effects consist primarily of destruction

of vegetation and of propagules through modification

of habitat such as seedbed disturbance, changes in

light, temperature, and moisture (Gilliam and Roberts

2003). Long-term effects result from changes in species

composition, rates of stand development, and

competitive interactions.

Table 13 shows that overstory species richness is higher in

the stem exclusion and understory reinitiation categories

than in the stand initiation category. Several factors likely

contributed to this result, particularly deer browsing. 

Species richness of overstory trees is highest where deer

densities were lowest at the time the stands originated,

likely reflecting the ability of deer to selectively remove

species preferred as food. Also, in the stand initiation

phase, there may be as many species present as in the

other phases (or at least more than are reflected in the

species richness value). However, slower growing, more

shade-tolerant species such as American beech and sugar

maple were too small (at least 5 inches d.b.h.) to be

measured. Faster growing, intolerant species such as black

and pin cherry tend to dominate first, attaining larger

diameters faster. As a result, fewer species were measured

in younger stands. Additional research is needed to

confirm the importance of these factors.

Species in the understory can reappear following

disturbance by one or more of four basic mechanisms

summarized by Gilliam and Roberts (2003): 1) Survival

in situ--plants may survive in vegetative form due to the

patchy nature of disturbance and low severity of some

disturbances, 2) Vegetative regeneration--many plants

reproduce vegetatively (Bierzychudek 1982), new shoots

form when the aboveground portion of vegetation is

killed or damaged, 3) Regeneration from the seedbank,

and 4) Regeneration by dispersed propagules. Persistence

of community composition in forest understories is

referred to as stability, resistance, or resilience (Halpern

1989).

Oliver and Larson (1996) described two patterns of

stand development after a disturbance: relay floristics

and initial floristics. Relay floristics is one species after

another invading a site in a “relaylike” manner. By

contrast, according to the initial floristics pattern, species

that predominate later have been present since the

disturbance. The development pattern after a disturbance

usually follows the initial floristics concept.
31

Understory Conditions

Seedling and Sapling Counts

Several studies have reported low stocking of seedlings/

saplings in forested stand understories on various portions

of the ANF (USDA For. Serv. 1995; McWilliams et

al. 1996; 1999; USDA For. Serv. 2000). Browsing

associated with high deer populations for more than 70

years has resulted in a lack of understory conditions as

deer selectively removed herbaceous plants, shrubs, and

tree seedlings. When desired native plants were removed

or died, other vegetation (beech, striped maple, ferns,

and grass) occupied much of the vacant growing space

interfering with the development and growth of tree

seedlings.

Where interfering plants are abundant, it is difficult for

seedlings (and other native plants) to become established.

This has important consequences when catastrophic

removal events (e.g., wind damage) occur. It is difficult for

vigorous young trees to grow from seed, gain dominance

over interfering plants, and replace trees that die.

It is important to quantify densities of seedlings and

saplings to predict future stand composition. FHM plots

are divided into three stand-size classes based on the

average d.b.h. of all live trees that are not overtopped.

Stand sizes are defined as sawtimber (11+ inches d.b.h.),

poletimber (5 to 10.9 inches d.b.h.), and seedling/sapling

(less than 5 inches d.b.h.). For this analysis we calculated

the number of seedlings and saplings per acre for each size

class. The oak types are the most distinctly different forest

types on the ANF, and great concern has been expressed

about how to develop the oak seedling component. This

separation is important because of the distinct differences

between these forest ecosystems and concerns related

to the sustainability of the oak forest type group in the

eastern United States (Johnson et al. 2002). Therefore, we

calculated seedling and sapling density separately for nonoak and oak sawtimber plots. Seedling and sapling data

are collected at 6.8-foot, fixed-radius circular microplots.

A seedling was defined as a tree at least 1 foot tall but

less than 1 inch d.b.h. Saplings were defined as live trees

1 to 4.9 inches d.b.h. It should be noted that seedlings

less than 1 foot tall are not counted according to FHM

protocol; this affects the estimated species distribution.

ANF protocol includes counting all seedlings with a

woody stem (i.e., at least 2 years old) and two normal

leaves (even if less than 1 foot tall) (Marquis et al. 1992).

Sawtimber Plots

Non-Oak Plots

American beech was the most abundant seedling species

on non-oak sawtimber plots followed by black cherry,

striped maple, birch spp., and red maple (Fig. 24). Most

of the beech stems probably originated from root sprouts.

Species with an average of fewer than 100 seedlings per

Species Seedlings/acre

Red maple 110.5603448

Birch spp. 137.0689655

Striped maple 162.2844828

Black cherry 197.1982759

American beech 697.6293103

0 100 200 300 400 500 600 700 800

Red maple

Birch spp.

Striped maple

Black cherry

American beech

Seedlings/Acre (no.)

Figure 24.—Number of seedlings per acre on non-oak sawtimber plots (116 plot conditions).
32

acre included serviceberry,

eastern hophornbeam,

eastern hemlock, American

hornbeam, sassafras, northern

red oak, white oak, blackgum,

chokecherry, cucumbertree,

white ash, Norway spruce, sugar

maple, pin cherry, American

chestnut, eastern white pine,

and American mountain-ash.

American beech was the most

abundant sapling on non-oak

sawtimber plots followed by

sugar maple, red maple, and

serviceberry (Fig. 25). Species

with fewer than 10 saplings

per acre included striped

maple, eastern hemlock,

American hornbeam, eastern

hophornbeam, sweet birch,

yellow birch, white ash, and

eastern white pine.

American beech is more than

twice as abundant as any species

in both the seedling and sapling

classes, with nearly as many

stems/acre in each size class

as all other species combined.

This has raised concerns about

long-term forest sustainability,

particularly as American beech

is susceptible to the beech bark

disease complex. Reforestation

practices should be implemented to encourage the

establishment and development of other tree species.

Oak Plots

Red maple was the most abundant seedling on oak

sawtimber plots followed closely by American beech

and a much lower abundance of birch spp., oak spp.,

black cherry, striped maple, and serviceberry (Fig.

26). Of the oak seedlings, 55 percent were northern

red oak, 24 percent were white oak, 13 percent were

Species Saplings/acre

Serviceberry 10.34482759

Red maple 19.39655172

Sugar maple 23.92241379

American beech 67.88793103

0 10 20 30 40 50 60 70 8

Serviceberry

Red maple

Sugar maple

American beech

Saplings/Acre (no.)

0

Figure 25.—Number of saplings per acre on non-oak sawtimber plots (116 plot conditions).

Species Seedlings/acre

Serviceberry 80.76923077

Striped maple 89.42307692

Black cherry 100.9615385

Oak spp. 109.6153846

Birch spp. 207.6923077

American beech 617.3076923

Red maple 651.9230769

0 100 200 300 400 500 600 700

Serviceberry

Striped maple

Black cherry

Oak spp.

Birch spp.

American beech

Red maple

Seedlings/Acre (no.)

Figure 26.—Number of seedlings per acre on oak sawtimber plots (26 plot conditions).

chestnut oak, and 8 percent were scarlet oak. Species

with fewer than 50 seedlings per acre included white

ash, hawthorn, white oak, sassafras, chestnut oak, scarlet

oak, chokecherry, eastern hophornbeam, pignut hickory,

blackgum, cucumbertree, American chestnut, sugar

maple, and eastern white pine.
33

American beech was the most

abundant sapling on oak

sawtimber plots followed by

red maple, birch spp., oak spp.,

and sugar maple (Fig. 27). Of

the oak saplings, black and

chestnut oak each accounted

for 33 percent while white and

northern red oak each accounted

for 17 percent. Species with

fewer than 10 saplings per acre

included blackgum, hawthorn,

black oak, chestnut oak,

serviceberry, northern red oak,

white oak, cucumbertree, white

ash, hickory spp., and eastern

white pine.

Non-oak seedlings and saplings

are 17 and 15 times times more

abundant, respectively, than all

oak species combined. ANF

silvicultural guidelines reflect

the importance of adequate

numbers of oak seedlings and

saplings in determining future

species composition (Horsley

et al. 1994). Unless this

overwhelming abundance of

non-oak regeneration changes

through natural causes or

reforestation, little oak forest

will be sustained over the

long term, both where timber

harvesting occurs and where it is

prohibited.

Poletimber Plots

No distinction was made between non-oak versus oak

poletimber plots due to the small sample size for oak

poletimber. The distribution of seedlings in poletimber

plots was similar to that for non-oak sawtimber plots.

American beech was the most abundant species followed

by striped maple, birch spp., serviceberry, red maple,

and black cherry (Fig. 28). In contrast to the non-oak

sawtimber plots (Fig. 24), the number of seedlings

per acre is substantially lower on the poletimber plots

primarily due to the decrease in American beech and

black cherry seedlings. This decrease probably is due to

the relatively high density of stems in poletimber versus

sawtimber stands, which, in turn, resulted in a lower

amount of light reaching the forest floor. Species with

fewer than 50 seedlings per acre included sugar maple,

sassafras, pin cherry, American hornbeam, northern

red oak, American basswood, quaking aspen, eastern

hophornbeam, white ash, and eastern hemlock.

Figure 27.—Number of saplings per acre on oak sawtimber plots (26 plot conditions).

Species Saplings/acre

Sugar maple 11.53846154

Oak spp. 17.30769231

Birch spp. 25.96153846

Red maple 66.34615385

American beech 124.0384615

0 20 40 60 80 100 120 140

Sugar maple

Oak spp.

Birch spp.

Red maple

American beech

Saplings/Acre (no.)

Species Seedlings/acre

Black cherry 60.32608696

Red maple 70.10869565

Serviceberry 86.41304348

Birch spp. 96.19565217

Striped maple 110.8695652

American beech 158.1521739

0 20 40 60 80 100 120 140 160 180

Black cherry

Red maple

Serviceberry

Birch spp.

Striped maple

American beech

Seedlings/Acre (no.)

Figure 28.—Number of seedlings per acre on poletimber plots (46 plot conditions).
34

Sweet birch was the most

abundant sapling on poletimber

plots followed by black cherry,

American beech, sugar maple,

red maple, and yellow birch (Fig.

29). Birch spp. had twice as many

saplings per acre as other species.

Species excluded (those with

fewer than 10 saplings per acre)

were serviceberry, striped maple,

eastern hemlock, pin cherry,

blue spruce, sassafras, quaking

aspen, chokecherry, eastern

hophornbeam, and white ash.

Sweet birch saplings were much

more abundant and black cherry

saplings were somewhat more

abundant on poletimber plots

(Fig. 29) than on sawtimber plots

(Fig. 25). This probably reflects

differences in deer density at the

time of stand initiation or the

effects of self-thinning of shadeintolerant black cherry. The

impact of deer was much lower

at the time current sawtimber

stands were initiated. Both

sweet birch and black cherry are

intermediate to low in preference

by deer on the Allegheny Plateau

(Healy 1971). Sugar and red

maple saplings are similar in

abundance.

Seedling/Sapling Plots

Oak and non-oak plots again were combined for

analysis due to small numbers of plots in the separate

types. Black cherry was the most abundant seedling on

seedling/sapling plots followed by American beech, red

maple, birch spp., serviceberry, and pin cherry (Fig. 30).

Species excluded (those with fewer than 90 seedlings

per acre) were striped maple, northern red oak, white

oak, eastern hophornbeam, chokecherry, American

hornbeam, white ash, and eastern hemlock.

Black cherry, red maple, birch, serviceberry, and pin

cherry seedlings were much more abundant on seedling/

sapling plots (Fig. 30) than on sawtimber plots (Fig. 24).

American beech is similar in seedling abundance on

sawtimber and seedling/sapling plots. Again, this reflects

differences in deer impact at the time of initiation of

current sawtimber and seedling/sapling stands.

Black cherry saplings were the most abundant species

on seedling/sapling plots followed by sweet birch, red

Species Saplings/acre

Yellow birch 13.04347826

Red maple 16.30434783

Sugar maple 16.30434783

American beech 57.06521739

Black cherry 65.2173913

Sweet birch 102.7173913

0 20 40 60 80 100 120

Yellow birch

Red maple

Sugar maple

American beech

Black cherry

Sweet birch

Saplings/Acre (no.)

Figure 29.—Number of saplings per acre on poletimber plots (46 plot conditions).

Species Seedlings/acre

Pin cherry 146.4285714

Serviceberry 432.1428571

Birch spp. 492.8571429

Red maple 532.1428571

American beech 592.8571429

Black cherry 1085.714286

0 200 400 600 800 1000 1200

Pin cherry

Serviceberry

Birch spp.

Red maple

American beech

Black cherry

Seedlings/Acre (no.)

Figure 30.—Number of seedlings per acre on seedling/sapling plots (21 plot conditions).
35

maple, pin cherry, and American

beech (Fig. 31). Once black

cherry establishes itself as a

seedling in an opening where it

is free to grow, growth is rapid

and this species dominates the

site. Species with fewer than 60

saplings per acre included striped

maple, chokecherry, yellow

birch, northern red oak, sugar

maple, eastern hemlock, white

oak, eastern hophornbeam, and

serviceberry.

Since black cherry is an

abundant sapling in all the

stand sizes, this species is likely to

increase in dominance on the ANF over the next century.

As stated earlier, the most important factor in the

dominance of black cherry is that it is not preferred by

white-tailed deer. The virtual absence of oak regeneration

indicates that oak-dominated stands may transition

to stands dominated by other species unless steps are

taken to ensure its inclusion. Marquis et al. (1992)

outlined standards for achieving desirable regeneration

in hardwood forests of the Alleghenies based on surveys

using plots. The standards were adjusted to estimate

whether each 6.8-foot-radius FHM microplot would be

considered stocked with desirable regeneration. When

these criteria were used, only 64 of 692 FHM microplots

(9.2 percent) met the standards for adequate stocking of

tree seedlings.

This low percentage underscores both the adverse effect

of deer (Tilgman 1989; Horsley et al. 2003) and the

need for reforestation practices that enhance seedling

development and diversity, e.g., herbicide/fencing

(Horsley et al. 1994) and release treatments (Ristau and

Horsley 1999).

Seedling and Sapling Richness and Diversity

Average species richness, Shannon index, Shannon

evenness, and Berger-Parker index of seedlings and

saplings were calculated for each FHM plot condition

by Eastern Region forest type and stand-development

category (Tables 14-17).

Since development following a disturbance usually follows

the initial floristics concept (Oliver and Larson 1996), sites

in the stand initiation phase contain many species. As the

canopy closes and the stem exclusion phase occurs, only

shade-tolerant species survive in the understory as advance

regeneration, e.g., sugar maple and American beech.

Sustainability of Tree Species

Analysis of survey data collected in 1992 from a 6,000-

plot sample on 60 percent of the ANF raised concerns

about long-term tree species sustainability/diversity

(USDA For. Serv. 1995). Specifically, certain species

represented in the overstory tree tally are not represented

at all or are poorly represented in the tree seedling tally.

Analysis of 1998-2001 FHM data raised similar concerns,

though the FHM analysis was not designed to address

sustainability. Table 18 summarizes the average number of

stems per acre tallied for four size classes--seedling (at least

1 foot tall and less than 0.9 inch d.b.h.), sapling (1 to 4.9

inches d.b.h.), trees 5 to 10.9 inches d.b.h., and trees 11 

or more inches d.b.h. on FHM plots.

Six tree species with overstory trees tallied had no

seedlings or saplings tallied (red pine, shagbark hickory,

Species Saplings/acre

American beech 64.28571429

Pin cherry 67.85714286

Red maple 135.7142857

Sweet birch 175

Black cherry 653.5714286

0 100 200 300 400 500 600 700

American beech

Pin cherry

Red maple

Sweet birch

Black cherry

Saplings/Acre (no.)

Figure 31.—Number of saplings per acre on seedling/sapling plots (21 plot conditions).
36

Table 14.—Seedling richness and diversity indices, by Eastern Region forest type 

(1998-2001 FHM data)

Forest type

Species

richness

Shannon

index

Shannon

evenness

Bergerparker indexNo. of plot

conditions

Mixed upland hardwoods 2.52 0.56 0.28 0.25 50

Allegheny hardwoods 2.62 0.58 0.23 0.22 42

Red maple 2.50 0.56 0.25 0.26 30

Northern hardwoods 1.82 0.46 0.30 0.35 17

Hemlock 2.55 0.50 0.22 0.34 11

White oak/red oak 3.25 0.68 0.42 0.36 8

Sugar maple 5.00 0.71 0.21 0.12 8

Oak/hardwood transition 1.00 0.15 0.12 0.75 7

Table 15.—Seedling richness and diversity indices, by stand development stage (1998-2001 

FHM data)

Stand development stage

Species

richness

Shannon

index

Shannon

evenness

Bergerparker indexNo. of plot

conditions

Stand initiation (0-14 years) 4.53 0.68 0.18 0.11 17

Stem exclusion (14-49 years) 2.89 0.51 0.21 0.33 19

Understory reinitiation (50+ years) 2.37 0.54 0.27 0.30 153

Table 16.—Sapling richness and diversity indices, by Eastern Region forest type (1998-

2001 FHM data)

Forest type

Species

richness

Shannon

index

Shannon

Evenness

Bergerparker indexNo. of plot

conditions

Mixed upland hardwoods 1.50 0.26 0.37 0.86 50

Allegheny hardwoods 1.97 0.46 0.33 0.79 42

Red maple 1.72 0.36 0.33 0.80 30

Northern hardwoods 1.29 0.18 0.30 0.89 17

Hemlock 2.00 0.23 0.16 0.93 11

White oak/red oak 1.43 0.23 0.17 0.88 8

Sugar maple 2.43 0.59 0.27 0.74 8

Oak/hardwood transition 2.00 0.46 0.31 0.79 7

Table 17.—Sapling richness and diversity indices, by stand development stage (1998-2001 

FHM data)

Stand development category

Species

richness

Shannon

index

Shannon

evenness

Bergerparker indexNo. of plot

conditions

Stand initiation (0-14 years) 2.29 0.50 0.18 0.77 17

Stem exclusion (14-49 years) 2.16 0.47 0.28 0.79 19

Understory reinitiation (50+ years) 1.53 0.29 0.35 0.84 153
37

Table 18.—Number of stems per acre by species and size class (1998-2001 FHM data)

Species

Trees 5-11

inches d.b.h.

Trees 12+

inches d.b.h. Saplings Seedlings

Norway spruce 5 1 0 75

white spruce 7 1 0 0

blue spruce 15 0 25 0

slash pine 0 1 0 0

red pine 8 6 0 0

eastern white pine 7 4 12.5 37.5

eastern hemlock 400 127 200 237.5

striped maple 1 0 425 4687.5

red maple 579 473 1200 7387.5

sugar maple 326 92 575 437.5

mountian maple 0 0 0 25

serviceberry 56 3 287.5 4287.5

birch spp. 0 0 125 1712.5

yellow birch 114 33 287.5 487.5

sweet birch 166 58 1462.5 3862.5

American hornbeam 11 0 112.5 312.5

hickory spp. 2 0 12.5 0

pignut hickory 2 0 0 25

shagbark hickory 1 0 0 0

mockernut hickory 4 0 0 0

American chestnut 0 0 0 62.5

hawthorn 0 0 37.5 175

common persimmon 1 0 0 12.5

American beech 376 105 2412.5 19462.5

ash spp. 1 1 0 12.5

white ash 17 33 25 312.5

yellow-poplar 0 14 0 0

cucumbertree 13 23 12.5 87.5

blackgum 11 0 37.5 100

eastern hophornbeam 9 0 100 437.5

sycamore 1 0 0 0

bigtooth aspen 9 11 175 25

quaking aspen 8 4 12.5 37.5

cherry and plum spp. 0 0 0 25

pin cherry 37 1 262.5 650

black cherry 335 471 3362.5 8512.5

chokecherry 0 0 0 112.5

white oak 32 46 12.5 250

scarlet oak 2 5 0 37.5

chestnut oak 9 16 0 62.5

northern red oak 21 85 37.5 637.5

black oak 13 12 0 0

sassafras 12 0 12.5 450

American mountain-ash 0 0 0 12.5

basswood spp. 0 2 0 0

American basswood 8 17 0 37.5
38

mockernut hickory, yellow-poplar, sycamore, and black

oak). Only yellow-poplar had a sufficient number of

overstory trees sampled to justify initial inferences from

the data. Other tree species with substantially fewer

seedlings or saplings tallied than overstory trees include

eastern white pine, eastern hemlock, sugar maple, white

ash, cucumbertree, black gum, bigtooth aspen, quaking

aspen, white oak, scarlet oak, chestnut oak, and American

basswood. The lack of seedlings of these species may be

due to seed crop issues (infrequent or small crops), poor

seedling survival due to overbrowsing by deer, or poor

site quality. All of these conditions raise questions about

sustainability over the long term. When overstory trees

of these species die, are blown down, or are removed,

will they be replaced by adequate numbers of young,

vigorously growing stems of the same species?

Seedlings of some species are ephemeral, particularly

where deer browsing is significant and there is substantial

interference from other plants, e.g., fern, grass, striped

maple, and American beech root suckers. For example,

sugar maple may develop numerous seedlings initially but

few become established and grow to a larger size class. In

Table 18, the sugar maple shown for the sapling and 5- to

11-inch d.b.h. classes generally are the same age as stems

in the greater than 11-inch class; these are stems that

were suppressed by faster growing species following forest

removals at the turn of the 19th century. Red maple has

many small seedlings but often fails to develop wellestablished, larger seedlings or saplings that can become a

codominant component of the next stand. This species is

highly preferred by deer and requires several years of low

browsing impact to become established in the moderate

shade of stands with overabundant numbers of large

saplings and small poles.

Additional research is needed to assess the dynamics of

tree-seedling development and determine the conditions

for successful regeneration. In managed areas, local

research and data from ANF post-reforestation treatment

surveys suggest that tree species and herbaceous diversity

is improved where area fencing (often supplemented by

an herbicide treatment) is used, e.g., seedlings of some

species preferred by deer begin to develop over time. An

assessment of individual species has not been completed.

Disturbance Processes on the 

Allegheny National Forest

The role of nsects and pathogens in natural disturbance

dynamics usually is positive as they cycle nutrients from

foliage to soils, kill weak or noncompetitive trees, and

decompose dead trees (Haack and Byler 1993). Most

insects and diseases rarely reach epidemic levels, but some

insect pests cause significant damage at outbreak levels

(Mason 1987). Between 1991 and 1996, native insects

that reached outbreak levels on the ANF included cherry

scallopshell moth, elm spanworm, forest tent caterpillar,

and oak leaftier. Collectively, these caterpillars defoliated

611,000 acres.

Exotic organisms are a serious threat to the ecological

balance that has evolved through thousands of years

of coexistence among native insects, pathogens, and

host-tree species (Haack and Byler 1993; Liebhold et

al. 1995). Non-native pest species have more frequent

outbreaks due to their lack of natural enemies. Damaging

exotic organisms on the ANF include gypsy moth, beech

bark disease, and pear thrips. The gypsy moth reached

outbreak levels on the ANF from 1986 to 1988 and 1991 

to 1993. Moreover, the beech scale Nectria complex has

been expanding its range southward from New England

and New York for many years. It reached the northern

portion of the ANF in the early 1980s. Beech mortality

associated with this disease first became evident on

the ANF in 1986. The disease complex involves the

interaction of the European scale insect Cryptococcus 

fagisuga with the exotic canker fungus Nectria coccinea

var. faginata or the native Nectria galligena. Nectria 

coccinea var. faginata is now thought to be an introduced

organism because of its pattern of occurrence in beech

scale-infested areas and absence in uninfected forests

(Houston 1994).

Natural climatic disturbance (particularly drought) has

played a substantial role in shaping the forest ecosystems

on the Allegheny Plateau. Four significant drought events

between 1988 and 1999 coincided with outbreaks of

insect defoliators. Storm activity also has affected the

ANF; in 1985, nearly 10,600 acres of forest land were

damaged severely by several large tornados.
39

Cherry Scallopshell Moth

The native cherry scallopshell moth, Hydria prunivorata

(Lepidoptera: Geometridae), distributed widely in the

Eastern United States and Canada, is not considered a

serious pest in most areas. Larvae (Fig. 32) form shelters

by fastening together the margins of leaves. Larvae

aggregate within these shelters and feed

on the upper epidermis of the leaves. As

larvae grow, the shelters are enlarged or

reformed on new, undefoliated branches

(Craighead 1950). This progressive feeding

often defoliates entire trees, reducing radial

growth the following year. Decline in some

stands can occur if repeated defoliations

or other stresses occur in successive years

(Shultz and Allen 1975; USDA For. Serv.

1979) or in the same year. The cherry

scallopshell moth has one generation per

year. Pupae overwinter in the leaf litter or

in the upper soil layer and adults emerge

in late spring to early summer. Females

begin laying eggs in late June and continue

through midsummer. Pyramid-shape egg

masses are laid one to four layers deep on

the undersides of leaves (USDA For. Serv.

1979).

The most recent cherry scallopshell moth

outbreak on the ANF occurred from 1993

through 1996 (Figs. 33-34). Outbreaks have

occurred about every 10 years on the northern

Allegheny Plateau. Previous outbreaks occurred

from 1972 to 1974 and 1982 to 1984 (Bonstedt

1985). Outbreaks usually last 2 to 3 years and tree

decline/mortality can follow after an outbreak.

On the ANF, decline has been observed when

repeated defoliation or other stresses, e.g., severe

drought, occur concurrently or in successive years.

Figure 34 shows the area defoliated by the cherry

scallopshell moth on the ANF during the last

outbreak. The 1995 defoliation affected 290,000

acres (205,000 on federal land), mostly on the

southeastern two-thirds of the ANF, despite an

important component of black cherry scattered

throughout the northern portion.

Because black cherry is the preferred host of cherry

scallopshell moth larvae (USDA For. Serv. 1979), and

stands dominated by this species were defoliated most

often during outbreaks (Table 19); there was a significant

Figure 32.—Cherry scallopshell moth larva (photo by James B. 

Hanson, USDA Forest Service, www.forestryimages.org).

Figure 33.—Frequency of cherry scallopshell moth defoliation from 1993 

to 1996 on the ANF.
40

relationship between percent black cherry basal area and

years of defoliation (Table 19). The number of years

of defoliation was significantly associated with percent

standing dead black cherry (Table 19) (Morin et al.

2004). Crown dieback of this species increased with

years of cherry scallopshell moth defoliation, though

the relationship was not significant (Table 19). With

respect to crown condition and mortality, black cherry

is affected only by two or more defoliation events,

so managers should consider suppression activities

following an initial defoliation event.

Elm Spanworm

The elm spanworm, Ennomos subsignarius (Lepidoptera:

Geometridae), is a native species that is found

throughout the Eastern United States and a portion of

Canada. Outbreaks are relatively uncommon, though

major multiyear outbreaks have occurred in Connecticut

and North Carolina. This pest is responsible for periodic

severe defoliations of hardwoods such as ash, hickory,

walnut, beech, black cherry, elm, basswood, red maple,

sugar maple, and yellow birch. Two consecutive summers

of defoliation can cause dieback and even mortality

when an invasion of secondary pests occurs (USDA For.

Serv. 1979).

Elm spanworm has one generation per year. Females

lay eggs in small groups on the underside of branches;

after overwintering, eggs usually hatch in May or June.

Larvae (Fig. 35) feed on the lower surface of leaves

but eventually consume everything but the veins (Fig.

36) (USDA For. Serv. 1979). This species is highly

polyphagous and nearly all major hardwood species

on the ANF are hosts except for yellow-poplar and

cucumbertree.

Table 19.—Mean stand characteristics averaged over FHM plots grouped by years of 

defoliation by cherry scallopshell moth (1998-2001 FHM data); number of plots in 

parentheses (P values given for one-way analyses of variance)

Characteristic for Years of defoliation

black cherry (%) 0 1 2 or 3 P value

Basal area 16.33a (81) 30.62b (80) 38.87b (12) 0.0001*

Mortality 5.45a (30) 5.65a (58) 17.38b (9) 0.0209*

Dieback 7.24 (30) 6.86 (58) 8.87 (9) 0.7530

* Significant at a =0.05 level.

years cherry scallop shell defoliation (thousands of acres) percent of area

1984 0 0

1985 0 0

1986 0 0

1987 0 0

1988 0 0

1989 0 0

1990 0 0

1991 0 0

1992 0 0

1993 4.367844796 0.589850749

1994 53.0122303 7.158977758

1995 287.6273524 38.84231633

1996 15.63619762 2.11157294

1997 0 0

1998 0 0

0

50

100

150

200

250

300

350

198419851986198719881989199019911992199319941995199619971998

Defoliation (thousand acres)

Figure 34.—Acres defoliated by cherry scallopshell moth on the ANF.
41

The most recent elm spanworm outbreak on the ANF

occurred from 1991 through 1993 (Figs. 37-38). The

insect population collapsed early in 1994. It is interesting

that the western one-third of the ANF had little

defoliation even though preferred host tree species were

relatively abundant there. In 1993, the area defoliated by

the elm spanworm within the proclamation boundary

of the ANF covered nearly 340,000 acres (Fig. 38). In

1992, defoliation was substantial following

the aerial mapping survey, so the acreage

reported in Figure 38 does not reflect total

defoliation for that year.

The most highly preferred hosts of elm

spanworm on the ANF are black cherry,

red maple, sugar maple, and American

beech, though the frequency of defoliation

was significantly associated only with

the proportion of black cherry in stands

(Table 20).

As with the cherry scallopshell moth,

there was a tendency for greater levels of

host crown dieback and tree mortality

in stands defoliated by elm spanworm

(Table 20). The relationship between

sugar maple mortality and years of elm

spanworm defoliation was not significant

even though mortality was high. This

result probably reflects the considerable heterogeneity in

site characteristics (e.g., soil nutrition) and low sample

size. Also, it may be that crown dieback was low for two

or three years of defoliation because many trees already

had died. Generally, sugar maple on poor sites die if they

suffer more than one defoliation. On good sites sugar

maple might survive two or more defoliation events.

Figure 35.—Elm spanworm larva (photo by Arnold T. Drooz, 

USDA Forest Service, www.forestryimages.org).

Figure 36.—Elm spanworm defoliation (photo by Arnold T. 

Drooz, USDA Forest Service, www.forestryimages.org).

Figure 37.—Frequency of elm spanworm defoliation from 1991 to 1993 on the ANF.
42

year elm spanworm defoliation (thousands of acres) percent of area

1984 0 0

1985 0 0

1986 0 0

1987 0 0

1988 0 0

1989 0 0

1990 0 0

1991 23.452 3

1992 72.67 10

1993 338.876 46

1994 0 0

1995 0 0

1996 0 0

1997 0 0

1998 0 0

0

50

100

150

200

250

300

350

400

198419851986198719881989199019911992199319941995199619971998

Defoliation (thousand acres)

Table 20.—Mean stand characteristics averaged over FHM plots grouped by years of 

defoliation by elm spanworm (1998-2001 FHM data); number of plots in parentheses 

(P values given for one-way analyses of variance)

Years of defoliation

Stand characteristic (%) 0 1 2 or 3 P value

Black Cherry

Basal area 15.35a (75) 31.46b (86) 31.85b (12) 0.0001*

Mortality 8.94 (31) 7.54 (59) 15.36 (10) 0.3782

Dieback 5.29 (31) 7.79 (59) 7.7 (10) 0.3185

Red Maple

Basal area 22.93 (75) 23.79 (86) 23.57 (12) 0.9641

Mortality 4.71 (58) 4.95 (56) 9.93 (8) 0.2891

Dieback 3.82 (58) 4.41 (56) 4.92 (8) 0.634

Sugar Maple

Basal area 6.39 (75) 10.29 (86) 7.12 (12) 0.1978

Mortality 8.22 (15) 15.84 (31) 31.05 (4) 0.1119

Dieback 3.68 (15) 11.11 (31) 1.33 (4) 0.1420

American Beech

Basal area 9.65 (75) 10.4 (86) 7.83 (12) 0.7823

Mortality 3.6 (28) 11.5 (27) 9.56 (5) 0.237

Dieback 5.06 (28) 8.76 (27) 6.19 (5) 0.2565

* Significant at a =0.05 level.

Figure 38.—Acres defoliated by elm spanworm on the ANF.
43

Gypsy Moth

The gypsy moth, Lymantria dispar L. (Lepidoptera:

Lymantriidae), was introduced from Europe around 1868

near Boston, Massachusetts. Outbreaks began to occur in

that area about 10 years later. Its range has continued to

expand and now extends as far west as Wisconsin (Fig. 39).

The current estimated rate of spread is about 13 miles per

year (Liebhold et al. 1992).

Gypsy moths spend the winter in the egg stage. In April or

early May, the eggs hatch and larvae (Fig. 40) feed until June.

After feeding is complete, the larvae pupate and emerge as

adult moths after about 2 weeks. The adults then mate and

the female lays eggs, usually on tree trunks. The gypsy moth

has one generation per year (USDA For. Serv. 1979).

WWWWWest est est est estVVVVVVVVVirginia irginia irginia irginia

District District District Districtof of of of ofColumbia Columbia Columbia Columbia

Ohio Ohio Ohio

Kentucky Kentucky Kentucky

Pennsylvania Pennsylvania Pennsylvania

New Jersey Jersey Jersey Jersey

Maryland Maryland Maryland

Delaware Delaware Delaware

Connecticut Connecticut Connecticut

Michigan Michigan Michigan

Wisconsin Wisconsin Wisconsin

North North North NorthCarolina Carolina Carolina Carolina

VVVVVVVermont ermont ermont ermont

TTTTTTTTTennessee ennessee ennessee ennessee

New

Hamp Hamp Hamp Hampshire shire shire shire

Massachusett Massachusett Massachusett Massachusettsssssssss

Rhode Rhode Rhode RhodeIsland Island Island Island

Illinois Illinois Illinois Indiana Indiana Indiana

Maine Maine Maine

New YYYYYYYYYork ork ork ork ork

VVVVVVVVirginia irginia irginia irginia

Portion of County Quarantined

Entire County Quarantined

Figure 39.—Gypsy moth quarantine of USDA Animal and Plant Health Inspection Service, 2004.

Figure 40.—Gypsy moth larva (photo by John H. 

Gent, USDA Forest Service, www.forestryimages.org).
44

The most recent major outbreak of gypsy moth on the

ANF occurred from 1984 to 1989. A second, less intense

outbreak occurred from 1991 to 1993 (Figs. 41-42).

Figure 41 shows the area and frequency of defoliation

from 1984 to 1993. This frequency distribution is

similar to the geographic distributions for northern red,

white, and other oaks (Fig. 16). The southern portion

of the ANF, just north of the southern boundary, shows

little repeated gypsy moth defoliation even though this

Figure 41.—Frequency of gypsy moth 

defoliation from 1984 to 1993 on the ANF.

year gypsy moth defoliation (thousands of acres) percent of area

1984 0.54051561 0

1985 9.596427425 1

1986 78.68064932 11

1987 157.5753886 21

1988 1.234718616 0

1989 0.751407255 0

1990 0 0

1991 3.294975241 0

1992 48.86062955 7

1993 2.046853898 0

1994 0 0

1995 0 0

1996 0 0

1997 0 0

1998 0 0

0

20

40

60

80

100

120

140

160

180

198419851986198719881989199019911992199319941995199619971998

Defoliation (thousand acres)

area has a substantial oak component. Gypsy moth

populations peaked in that region later than on the rest

of the ANF. Areas in the southern portion that were

threatened with repeated severe defoliation were treated

more aggressively with aerial applications of the biological

insecticide B.t. to avoid the higher rates of tree mortality/

decline observed elsewhere on the ANF where treatment

was less intense. The amount of acreage defoliated by

gypsy moth is shown in Figure 42. The worst year during

Figure 42.—Acres defoliated by gypsy 

moth on the ANF.
45

the first episode was 1987 when nearly 160,000 acres

were defoliated; nearly 50,000 acres were defoliated

during the second outbreak in 1992.

The gypsy moth defoliates primarily hardwood trees,

especially oak, aspen, and beech, though large larvae feed

on other species, particularly during outbreaks (Liebhold

et al. 1995). Small larvae feed readily on beech but large

larvae avoid this species because its leaves are tough.

On the ANF, plots with a higher

percentage of oak were defoliated

more often by gypsy moth (Table

21). The average amount of basal

area of standing dead oak appeared

to increase with frequency of

defoliation but this relationship was

not significant (Table 21).

Beech Bark Disease 

Complex

Beech bark disease (BBD), an

insect-fungus complex that

consists of the European scale

insect (Cryptococcus fagisuga) and

the exotic canker fungus Nectria 

coccinea var. faginata or the native

Nectria galligena (Houston 1994),

results when a Nectria fungus

infects the bark of American

beech through the feeding wounds

caused by beech scale insects. The

beech scale was introduced into

Nova Scotia from Europe around

the turn of the century. It has since

spread southwestward into New England, New York,

Pennsylvania, and West Virginia (Manion 1991).

BBD was first detected in Pennsylvania in 1958.

Currently, the killing front is moving from the northeast

corner toward the southwest corner of the ANF (Fig. 43).

The killing front is the area in which trees are infested

with both the European scale insect and Nectria fungus,

Table 21.—Mean stand characteristics averaged over FIA 

plots grouped, by years of defoliation by gypsy moth (1998-

2001 FHM data); number of plots in parentheses (P values 

given for one-way analyses of variance)

Stand characteristic Years of defoliation

of all oaks (%) 0 1 or 2 P value

Basal area 2.18a (129) 32.83b (35) 0.0001*

Mortality 2.85 (6) 9.39 (22) 0.1164

* Significant at a =0.05 level.

Figure 43.—The 2001 beech bark disease killing front on the ANF from surveys 

conducted by the USDA Forest Service; NLCD data from Multi-Resolution Land 

Characteristics Consortium.
46

and mortality is occurring. Beech mortality on the ANF

was first reported in 1985.

Table 22 shows percent basal area of dead American

beech inside and outside of the killing front. Percent

standing dead beech was more than twice as great inside

than outside the killing front. Beech mortality likely was

even higher than reported here since dead beech trees

often decay and snap quickly, and thus would not have

been measured as standing dead.

An estimated surface of percent standing dead American

beech was generated using only FHM plots where this

species made up at least 10 percent

of the total basal area (Fig. 44).

Beech mortality was higher within

the killing front though there was

significant mortality from other

causes (e.g., blowdown) outside of

the killing front. Eastern hemlock

has shown the greatest increase

in relative dominance following

the loss of beech to BBD (Runkle

1990; Twery and Patterson 1984;

Le Guerrier et al. 2003).

Sugar Maple Decline

Sugar maple dominates the northern

hardwood forest, accounting for

half or more of the basal area. The

largest contiguous area of this forest

type extends from northern Ohio

and Pennsylvania through southern

Ontario and Quebec and eastward

through northwestern Massachusetts

into western Maine (Nyland 1999).

Numerous reports of sugar maple decline or dieback

have been recorded over the last 50 years. Houston

(1999) found that crown dieback and death result when

at least one predisposing stress event reduces resistance

to invasion by opportunistic, secondary organisms that

kill tissues. Maple dieback/decline has been associated

with insect defoliation, drought, unbalanced nutrition

(particularly of Ca, Mg, and K), stand density, and

midwinter thaw/freeze events (Long et al. 1997;

Houston 1999; Horsley et al. 2000).

Table 22.—Percentage of basal area of standing dead American beech within and outside of 

killing front of beech bark disease (number of plots in parentheses)

2001 front 1989 front

Survey data Inside Outside Inside Outside

1989 FIA 1.7 (50) 0.8 (73) 2.9 (17) 0.9 (106)

1998-2001 FHM 10.2 (58) 4.1 (64) 9.5 (19) 6.5 (103)

Figure 44.—Kriged surface of percent standing dead American beech basal area 

(1998-2001 FHM data; NLCD data from Multi-Resolution Land Characteristics 

Consortium).
47

Sugar maple can occupy a variety of

sites but grows best on moderately

fertile and well-drained soils (Godman

1957). It is particularly abundant on

lower slopes or coves enriched by leaf

litter, colluvium, or nutrient-rich water

moving from upslope (Leak 1982;

Pregitzer et al. 1983; Smith 1995). In

the early to mid-1980s, foresters in

northern Pennsylvania observed sugar

maple decline in the form of decreased

crown vigor, crown dieback, and

higher mortality of large trees. Sugar

maple growing on the lower slopes of

unglaciated sites and in every position

on glaciated sites seemed unaffected or

only slightly affected. Trees on the upper

slopes of unglaciated sites were affected

the most. Defoliated stands were more

likely to be unhealthy, though not all

sites that were defoliated had unhealthy

sugar maple. It was concluded that some

factor(s) must be involved in making

some sites more resistant to decline

after defoliation. This resistance was

attributed to foliar chemistry (Horsley et

al. 2000). Unhealthy sugar maple were

found on sites where trees had low foliar

Mg and Ca and high Mn (Bailey et al.

2004). Horsley et al. (2000) suggested

that sugar maple decline occurs on sites

with an imbalance of Mg nutrition and

excessive defoliation stress.

The 1998, 2000, and 2001 FHM data

were used to compare the basal area of dead sugar maple

with the topographical position of the trees and whether

they had been defoliated by elm spanworm. The 1999

data were excluded from the analysis because data on

topographical position was not collected. In Figure 45,

most of the basal area of standing dead sugar maple

was on upper and middle slopes whether or not the

areas were defoliated. Horsley et al. (2000) reported a

similar relationship between sugar maple mortality and

slope position, though mortality generally was lower on

the lower middle slope. In the data presented here, the

middle-slope category was not split between upper and

lower. Less nutrient-rich upper slopes are less suitable for

sugar maple. Mortality was greater in defoliated than in

undefoliated areas regardless of slope position.

Surprisingly, average crown dieback for sugar maple

from plots defoliated by elm spanworm was similar for

all slope positions (Fig. 46). Crown dieback was higher

on all defoliated trees regardless of slope position.

SLOPE POSITION DEFOLIATED # OF PLOTS % DEAD BA SE

Bottom no 3 0 0

Bottom yes 9 5.674919896 4.097482794

Middle no 18 7.408384856 5.545027909

Middle yes 30 25.65017802 5.782010719

Ridge no 3 12.61843284 12.61843284

Ridge yes 12 24.43553523 7.425542836

0

5

10

15

20

25

30

35

Bottom Middle Ridge

Slope Position

Dead Sugar Maple Basal Area (%)

Not defoliated

Defoliated

N=3

N=18

N=9

N=30

N=3

N=12

Figure 45.—Mean standing dead sugar maple basal area per acre on FHM plots 

grouped by slope position (1998, 2000, and 2001 FHM data) and defoliation status.

SLOPE POSITION DEFOLIATED # OF TREES MEAN CROWN DIEBACK SE

Bottom no 19 0.789473684 0.429735043

Bottom yes 32 9.423076923 2.859894068

Middle no 86 3.881578947 0.96244701

Middle yes 170 5.952 1.062489833

Ridge no 12 3.125 1.315260702

Ridge yes 91 9.051724138 1.483203481

0

2

4

6

8

10

12

14

Bottom Middle Ridge

Slope Position

Dieback on Sugar Maple Trees (%)

Not defoliated

Defoliated

N=19

N=32

N=12

N=86

N=170

N=91

Figure 46.—Mean crown dieback of sugar maple trees grouped by slope position 

(1998, 2000, and 2001 FHM data) and defoliation status.
48

Additional Forest 

Health Monitoring 

Indicators

Lichen Communities

Lichens are composite, symbiotic

organisms from as many as three

kingdoms. The dominant partner

is a fungus. Because fungi cannot

catabolize their own nutritional

reserves, they usually act as parasites

or decomposers. Lichen fungi

(kingdom Fungi) cultivate partners

that manufacture carbohydrates by

photosynthesis. Partners can be algae

(kingdom Protista), cyanobacteria

(kingdom Monera), formerly called

blue-green algae. Some fungi exploit

both at the same time (Brodo et

al. 2001). Data on lichens were

collected from 1999 to 2001 on all

173 ANF FHM plots to determine

the presence and abundance of

lichen species on woody plants and to obtain samples.

Although lichens occur are found on different substrates,

e.g., rocks, sampling was restricted to standing trees

or branches/twigs that recently fell to the ground. The

samples were sent to experts on lichens for species

identification.

There is a close relationship between lichen communities

and air pollution, especially sulfur dioxide (SO2

) and

acidifying or fertilizing nitrogen- and sulfur-based

pollutants. Lichens are particularly sensitive to air

quality because they must rely on atmospheric sources of

nutrition. By contrast, trees may be indicators of chronic

air pollution but all other influences on tree growth

make it difficult to measure responses to pollutants

(McCune 2000). Lichens also are important components

of biodiversity in forest ecosystems. Seven lichen genera

and 52 lichen species were sampled in the 1999-2001 

FHM data (Appendix I). A list of lichen species sampled

on the ANF that could be assigned a pollution tolerance

also is included in Appendix I. The pollution tolerance

scale is a provisional and qualitative ordinal scale

developed by Dr. Susan Will-Wolf (FIA Lichen Indicator

Advisor) (Showman 1990; 1997; McCune et al. 1997;

Showman and Long 1992; McCune 1988; Wetmore

1983; Nash 1975).

A kriged surface of lichen species richness scores is shown

in Figure 47. They are called scores because the samples

are timed, that is, the scores are not absolute richnesses.

Showman and Long (1992) reported that mean lichen

species richness was significantly lower in areas of high

sulfate deposition (6.3 and 5.4) than in low deposition

areas (11.5 and 10.6) in north-central Pennsylvania. The

mean species richness score of lichens was calculated

by Eastern Region forest type (Table 23) and standdevelopment stage (Table 24). The oak/hardwood

transition forest type had the highest mean species

richness score (9.6 species) followed closely by northern

hardwoods and white oak/red oak. Mean species richness

was much lower on sites in the stand initiation phase,

reflecting the lag time for lichen recolonization after

harvest or disturbance. Selva (1994) reported that lichen

floras become richer over time.

Figure 47.—Kriged surface of lichen species richness on the ANF.
49

The most common lichen species (present on at least

10 percent of plots) are shown in Figure 48. Their

respective pollution tolerances (Appendix I) are in

parentheses. Each bar represents 100 percent of the

plots and bar segments represent the percentage of

plots on which a species was present in each abundance

class. The per-plot abundance classes are none, rare

(fewer than 3 individuals), uncommon (4 to 10

individuals), common (more than 10 individuals

but less than half of the boles and branches contain

that species), and abundant (more than half of boles

and branches have that species). Species classified as

sensitive to pollution were uncommon in the 1999-

2001 lichen surveys.

The four most common lichen species on the ANF

are shown in Figures 49-52. Parmelia sulcata (Fig.

49), extremely widespread in the Northern and

Western United States (Brodo et al. 2001), has been

described as being resistant to SO2 (Showman and

Long 1992), and, conversely, as a nearly ideal indicator

of air pollution (De Wit 1983). The growth and

death of this species is measured in The Netherlands,

and dying off has been shown to increase at higher

Table 23.—Species richness of lichen species, by 

Eastern Region forest type (1998-2001 FHM data)

Forest type

Mean species

richness

Number

of plots

Oak/hardwood transition 9.6 7

Northern hardwoods 8.6 15

White oak/red oak 8.3 8

Mixed upland hardwoods 6.8 45

Red maple 6.4 28

Hemlock 6.0 9

Allegheny hardwoods 5.7 37

Sugar maple 3.0 7

Table 24.—Species richness of lichen species, by stand 

development stage (1998-2001 FHM data)

Stand development stage

Mean species

richness

Number

of plots

Stand initiation (0-14 years) 3.7 15

Stem exclusion (15-49 years) 7.1 14

Understory reinitiation (50+ years) 7.0 142

 Abundance Class

Species 0 1 2 3

Cladonia parasitica 89.01734104 1.156069364 1.156069364 8.092485549

Evernia mesomorpha (I) 88.43930636 6.358381503 5.202312139 0

Parmelia squarrosa (I) 87.86127168 3.468208092 2.89017341 5.202312139

Phaeophyscia pusilloides (T) 87.28323699 1.734104046 6.358381503 4.624277457

Melanelia subaurifera (I) 80.34682081 5.202312139 9.248554913 5.202312139

Punctelia rudecta (T) 79.76878613 1.156069364 9.248554913 9.248554913

Cetraria oakesiana (T) 78.61271676 5.202312139 5.780346821 10.40462428

Cladonia caespiticia 76.87861272 1.734104046 5.780346821 15.60693642

Cladonia bacillaris (T) 72.83236994 0.578034682 6.358381503 19.65317919

Hypogymnia physodes (T) 57.22543353 8.670520231 14.45086705 18.49710983

Physcia millegrana (VT) 53.17919075 2.89017341 10.40462428 33.52601156

Punctelia subrudecta (T) 53.17919075 7.514450867 15.60693642 23.69942197

Phaeophyscia rubropulchra (VT) 43.35260116 2.89017341 16.76300578 35.26011561

Cladonia coniocraea (T) 35.83815029 2.312138728 5.202312139 50.86705202

Flavoparmelia caperata (T) 32.94797688 10.98265896 31.21387283 24.27745665

Parmelia sulcata (VT) 15.02890173 2.89017341 10.40462428 68.20809249

0 20 40 60 80 100

Cladonia parasitica

Evernia mesomorpha (I)

Parmelia squarrosa (I)

Phaeophyscia pusilloides (T)

Melanelia subaurifera (I)

Punctelia rudecta (T)

Cetraria oakesiana (T)

Cladonia caespiticia

Cladonia bacillaris (T)

Hypogymnia physodes (T)

Physcia millegrana (VT)

Punctelia subrudecta (T)

Phaeophyscia rubropulchra (VT)

Cladonia coniocraea (T)

Flavoparmelia caperata (T)

Parmelia sulcata (VT)

Percent of Plots

Abundant

Common

Uncommon

Rare

Not present

Figure 48.—Lichen distribution by abundance class for the 16 most common species in 

the ANF (tolerance classes: I = intolerant, T = tolerant, VT = very tolerant).
50

SO2 concentrations (De Wit 1983). Flavoparmelia 

caperata (Fig. 50), distributed widely in the Eastern and

Southwestern United States, was sensitive to SO2 near a

powerplant in Ohio (Showman 1975). F. caperata has been

used to monitor the influence of air pollution and city

climate on the lichen flora of Long Island (Brodo et al.

2001). Cladonia coniocraea (Fig. 51), distributed widely in

the East, Northwest, and Pacific Coast, is found most often

near tree bases but also grows along the sides of trees when

moisture is adequate. Phaeophyscia rubropulchra (Figure 52)

is distributed widely in the Eastern United States.

Figure 49.—Parmelia sulcata (photo courtesy of Yale University Press).

Figure 50.—Flavoparmelia caperata (photo 

courtesy of Yale University Press).

Figure 51.—Cladonia coniocraea (photo 

courtesy of Yale University Press).

Figure 52.—Phaeophyscia rubropulchra (photo courtesy of Yale 

University Press).
51

Indicator kriged surfaces of the probability of presence

were generated for these four lichen species (Fig. 53).

That these species differ in spatial distributions across the

ANF possibly reflects different ecological and/or habitat

preferences.

Sulfate deposition was most severe in Eastern Region

compared to other Forest Service regions (Fig. 54)

(USDA For. Serv. 2002). To assess the response of

lichen species to pollutants across the ANF, a pollution

sensitivity index was determined for each FHM plot.

Figure 53.—Kriged probability surfaces of the four 

most common species on the ANF; NLCD data from 

Multi-Resolution Land Characteristics Consortium.

Parmella sulcata Flavoparmelia caperata

Cladonia coniocraea Phaeophysicia rubropulchra
52

The index is calculated as the number of sensitive and

intermediate species divided by the total number of rated

species using the sensitivity ratings listed in Appendix

I. A kriged surface of the pollution

sensitivity index was generated (Fig. 55)

to display the spatial variation across the

ANF landscape. The proportion of sensitive

lichen species was highest on the western

edge and in two areas in the central part

of the ANF. The severity of the pollution

response of lichens requires additional study.

Down Woody Materials

Down woody materials (DWM), defined

as dead matter on the ground in various

stages of decay, are important components

of forest ecosystems because they affect

or otherwise influence the quality and

structure of wildlife habitats, structural

diversity, fuel loading and fire behavior,

carbon sequestration, and storage and

cycling of water.3

Figure 54.—Sulfate deposition in 1999 and the largest sulfur dioxide point sources; data from National Acid 

Deposition Program.

Figure 55.—Kriged surface of the lichen pollution sensitivity index; 

NLCD data from Multi-Resolution Land Characteristics Consortium.
53

Components measured by the DWM indicator include

coarse woody debris (CWD), fine woody debris (FWD),

duff, litter, herbs/shrubs height, and fuelbed depth.

CWD is dead wood 3 inches or larger in diameter

(1,000-hr fuels); FWD is dead wood 0.1 to 2.9 inches

in diameter (1-,10-, and 100-hr fuels). Litter is the loose

plant material on top of the forest floor where little

decomposition has occurred. Duff is the layer just below

the litter consisting of decomposing leaves and other

organic material.

CWD and FWD are sampled using line-intersect

sampling methodologies. DWM sample transects

begin at each subplot center extending 24 feet to the

subplot border. CWD and FWD are sampled along

transects occurring in accessible forest land. Three

CWD transects are established at azimuths of 30, 150,

and 270 degrees. One FWD transect is established at

an azimuth of 150 degrees. The depth of the duff and

litter layers are important components of fire models

used to estimate the behavior, spread, and effects of fire,

and smoke production. Litter and duff were measured

on microplots in 1999 and 2000 and at the 24-foot

location on each transect in 2001.

3 An alternative to

reporting mean herb/shrub height and coverage is

incorporating them into a single measure of height

known as integrated fuel depth (Woodall and Williams

2005).

In 1999 and 2000, the DWM indicator was a pilot

sample design tested by the FHM program. The ANF

was one of a few selected areas where sampling was

conducted in 1999 and 2000. In 2001, the FIA program

adopted FHM’s pilot sample design and started the

DWM indicator. The DWM plot design for 2001 is

shown in Figure 56; plot designs from 1999 and 2000

are shown in Appendix I. Designs were sufficiently

similar to combine the years for analysis.

N

4

150°

270°

3

2

1

FWD < 0.25 inch

and 0.26-0.99 inch

FWD 1.00 -

2.99 inches

CWD

3.00 inches

10 ft s.d.

20 ft s.d.

58.9 ft h.d.

s.d.= slope dist., h.d.=horizontal dist.

Subplot

Microplot

CWD Tran.

FWD Tran.

Annular plot

30°

150°

150°

270°

30°

150°

Dist. between subplots(2, 3, and 4) and subplot center (1): 120 ft at angles (deg.) 0, 120, and 

240, respectively; dist. between subplot center and microplot center: 12 ft; shrubs/herbs 

sampled on microplot. Duff/litter sampled at 14- and 24-foot slope dist. on each CWD transect.

Figure 56.—2001 DWM plot layout for sampling CWD, FWD, and fuels.
54

Estimates and Summaries of DWM 

Fuel Loadings

The extensive wildfires that have occurred across

the nation in recent years has focused attention

on DWM and its potential to sustain large

wildfires. Data from the DWM indicator were

used to estimate fuel loadings (mass) of CWD,

FWD, and total DWM. For the ANF, estimates

of DWM vary by fuel class (duff, litter, and 1-,

10-, 100-, and 1,000-hr fuels). These fuel classes

area based on the approximate time it takes for

the fuel class to experience moisture fluctuations

(Deeming et al. 1977). For instance, fine fuels

in the 0 to 0.25-inch transect diameter fuel

class dry out quickly and thus are called 1-hr

fuels. By contrast, a 10-inch log might take

more than 1,000 hours to change in moisture

content. The majority of the tonnage per acre

of DWM on the Allegheny National Forest

(64 percent) consisted of duff (Fig. 57).

A comparison of DWM between plots within

the ANF and the rest of Pennsylvania reveals

obvious differences in DWM estimates (Fig.

58). Mean tonnage per acre of duff and 100-

hr fuels was higher on plots outside of the

ANF. However, the mean tonnage per acre of

1,000-hr fuels was higher on ANF plots. Mean

tonnage per acre of litter, 1-hr fuels, and 10-hr

fuels was similar on plots inside and outside of

the ANF.

DWM loadings in tons per acre

were broken down by Eastern

Region forest type (Table 25). Duff

reflected the highest range (13.7

tons per acre) in mean values of all

the fuel classes and had the greatest

tonnage per acre. Mean tons per

acre of duff was highest in the

northern hardwoods forest type

and lowest in the sugar maple forest

type. Levels of litter and fine woody

debris were similar among forest

types except that the mean value for

litter in oak types was substantially

10 hr (FWD) 0.969857368

100 hr (FWD) 1.564764932

1,000 hr (CWD) 5.560928633

Duff

64%

Litter

6%

1 hr (FWD)

1%

10 hr (FWD)

3%

100 hr (FWD)

6%

1,000 hr (CWD)

20%

Duff

Litter

1 hr (FWD)

10 hr (FWD)

100 hr (FWD)

1,000 hr (CWD)

Figure 57.—Total DWM (percent of weight) on the ANF by debris category.

dwm ANF MEAN ANF SE PA MEAN PA SE

Duff 17.75687283 1.114957966 23.7474026 1.642882466

Litter 1.737776445 0.07467089 1.625324675 0.187122925

1 hr (FWD) 0.235846233 0.013307425 0.165324675 0.02147467

10 hr (FWD) 0.969857368 0.059532457 0.575194805 0.085680025

100 hr (FWD) 1.564764932 0.073853975 3.306363636 0.894781329

1,000 hr (CWD) 5.560928633 0.35252053 2.811558442 0.408587336

0

5

10

15

20

25

30

Duff Litter 1 hr (FWD) 10 hr

(FWD)

100 hr

(FWD)

1,000 hr

(CWD)

Down Woody Material

Tons/Acre (no.)

ANF plots

Non-ANF PA plots

Figure 58.—Mean values of DWM variables on the ANF 

versus the rest of Pennsylvania.

Table 25.—Mean values of DWM variables (tons/acre), by Eastern Region 

forest type (1999-2001 FHM data)

Forest type N Duff SE Litter SE FWD SE CWD SE

Mixed upland hardwoods 45 15.9 2.2 1.7 0.1 2.6 0.2 5.6 0.7

Allegheny hardwoods 37 17.4 1.5 1.6 0.1 3.0 0.3 5.2 0.7

Red maple 28 20.1 2.9 2.0 0.2 2.7 0.3 5.4 0.8

Northern hardwoods 15 24.6 7.2 1.5 0.2 2.9 0.4 6.2 1.1

All conifers 11 21.6 3.9 1.6 0.2 3.2 0.5 4.7 1.3

Hemlock 9 20.1 4.4 1.5 0.3 3.3 0.6 5.2 1.5

White oak/red oak 8 20.3 3.9 2.6 0.3 2.5 0.2 4.1 0.8

Oak/hardwood transition 7 16.8 5.3 2.4 0.4 2.6 0.4 5.3 2.1

Sugar maple 7 10.9 3.2 1.3 0.2 2.7 0.5 6.5 1.7
55

higher than that for litter in the other

forest types. Levels of coarse woody

debris were similar among forest

types and lowest in the white oak/red

oak forest type.

DWM fuel components vary by

tree density (basal area per acre) and

stand age (Figs. 59-60) on the ANF.

Duff tended to increase with stand

age and with basal area to some

extent. As stands progress through

the latter stages of development

and experience higher levels of

density/competition, it follows that

years of cumulative leaf fall would

create deep duff conditions. Also,

the denser overstories may shade

the duff, thereby reducing decay

rates. CWD seems to decrease with

increasing stand basal area and stand

age (up to age 80), possibly due

to CWD recruitment from large

stand disturbances (stand replacing

event). CWD begins to recover

through individual tree mortality as

a stand ages. Litter and FWD levels

were similar among age and density

classes.

As shown in Figure 61, integrated

fuel depth (herb/shrub height and

coverage) tended to decrease with

basal area per acre and stand age.

A more developed herb and shrub

layer would be expected in younger, less dense stands

due to an increase in available light (less shade). This also

might reflect the long-term effects of deer herbivory on

understory vegetation in older stands.

Ecology of CWD on the ANF

CWD might be most important ecological attribute of

the DWM indicator on the ANF. It represents potential

fire hazards and also is an important structural attribute

of forest ecosystems (Harmon et al. 1986). CWD creates

numerous ecological niches and serves as habitat for

plants, animals, protists, bacteria, and fungi (Harmon

et al. 1986). The volumes and weights of CWD on the

ANF are well within the range of CWD habitat cited

in past regional CWD research (Muller and Liu 1991).

Although duff accounts for the majority of DWM

weight on the ANF (Fig. 57), CWD weight (1,000-

hr) is the second heaviest DWM component. The

amount of CWD for the average FIA plot on the ANF

is nearly twice that for the rest of Pennsylvania (Fig. 58).

Basal Area n Duff stderr Litter stderr FWD stderr CWD stderr Shrub/Herbs stderr

0.0-75.0 28 12.93 1.72 1.53 0.18 2.98 0.35 7.72 1.34 5.71 2.15

75.1-150.0 98 19.01 1.57 1.73 0.1 2.84 0.15 5.39 0.42 4.61 0.65

150.1+ 47 18.01 2.21 1.87 0.14 2.49 0.14 4.64 0.45 3.32 0.89

0

5

10

15

20

25

0.0-75.0 75.1-150.0 150.1+

Basal Area (ft2

/acre)

Tons/Acre (no.)

Duff

Litter

FWD

CWD

Figure 59.—Mean values of DWM variables on the ANF in basal area/acre classes.

Stand Age n Duff stderr Litter stderr FWD stderr CWD stderr Shrub/Herbs stderr

0-40 27 13.09 2.09 1.51 0.18 2.97 0.36 7.84 1.25 6.14 2.33

41-80 78 17.75 1.63 1.97 0.13 2.46 0.16 4.21 0.42 5.46 0.83

81+ 68 19.62 1.93 1.56 0.09 3.04 0.18 6.2 0.52 2.6 0.48

0

5

10

15

20

25

0-40 41-80 81+

Stand Age (years)

Tons/Acre (no.)

Duff

Litter

FWD

CWD

Figure 60.—Mean values of DWM variables on the ANF in stand age classes.
56

Therefore, data from the DWM inventory indicate that

CWD habitat on the ANF may be sufficient. However,

as future DWM data are acquired, additional estimates of

CWD diameters, decay classes, and species composition

might elucidate the attributes of CWD on the ANF.4

The relationship between CWD and estimates of

stand attributes/disturbance history may allow a

reinterpretation of CWD dynamics. The relationship

between CWD and standing vegetation often is given

little attention, but it could be a driving force in CWD

accumulation in eastern forests (Muller and Liu 1991;

Rubino and McCarthy 2003). On the ANF, CWD

seems to decline with increasing stand basal area (Fig.

59). McCarthy and Bailey (1994) and Muller and Liu

(1991) reported similar findings and attributed the

4

Woodall, C.W.; Leutscher, B. Extending and intensifying the

FIA inventory of down forest fuels: Boundary Water Canoe

Area (MN) and the Pictured Rocks National Lakeshore (MI).

In preparation.

Basal Area Shrub/Herbs stderr

0.0-75.0 5.71 2.15

75.1-150.0 4.61 0.65

150.1+ 3.32 0.89

Stand Age Shrub/Herbs stderr

0-40 6.14 2.33

41-80 5.46 0.83

81+ 2.6 0.48

0

1

2

3

4

5

6

7

8

9

0.0-75.0 75.1-150.0 150.1+

Basal Area (ft2

/acre)

Integrated Fuel Depth (ft)

a

0

1

2

3

4

5

6

7

8

9

0-40 41-80 81+

Stand Age (years)

Integrated Fuel Depth (ft)

b

Figure 61.—Mean values of integrated fuel depth on the ANF in (a) basal area/acre classes and 

(b) stand age classes.
57

presence of additional CWD in stands with lower basal

area relative to logging activity or other disturbance, e.g.,

insect defoliation. If most of the CWD volumes reside in

recently treated stands, this small-diameter logging slash

may decompose quickly and may not be as ecologically

desirable as larger diameter pieces (McCarthy and Bailey,

1994). This result is affirmed by the relationship between

stand age and CWD densities (Fig. 60; Table 26). CWD

densities generally follow a U shape over time (Hagan

and Grove 1999). The youngest stands have the most

CWD on the ANF (Fig. 60; Table 26). Preliminary

investigations of CWD levels on the ANF revealed low

volumes of CWD in second-growth stands compared

to the amount found in the old growth Tionesta Scenic

and Research Natural Areas. The volume of CWD in

old-growth stands ranged from 866 to 1,659 ft3

/acre

(R. White, D. deCalesta and C. Nowak 1998, pers.

commun.). By contrast, CWD volumes were much lower

in all but the youngest stands on the ANF (Table 26).

The relationship between CWD amounts and forest type

also might help identify trends. Research has suggested

that oak forests contain more CWD than maple forests

(McCarthy et al. 2001) possibly because oak logs

may decay more slowly than maple logs (MacMillan

1988). Unfortunately, the standard errors of the DWM

inventory preclude conclusions except that most CWD

masses (Table 25) and volumes (Fig. 62) seem fairly

constant across all forest types on the ANF.

Summary of DWM

Initial results from the DWM inventory indicate that fuel

loadings do not differ substantially from those of forest

ecosystems in the rest of Pennsylvania. CWD weights

(1,000 hr) on the ANF may be twice that for the rest

of the State, though this does not present a significant

fire hazard and may benefit forest diversity and habitat

accumulation. The weights of the fine woody fuels (flash

fuels) are associated with a considerable proportion of

fire behavior. The relationship between CWD and stand

attributes suggests that less dense younger stands that

Table 26.—Mean values of CWD volumes (ft3/acre), by stand 

development stage (1999-2001 FHM data)

Stand development stage N CWD volume SE

Stand initiation (0-14 years) 14 1294.3 248.92

Stem exclusion (15-49 years) 14 504.2 124.92

Understory reinitiation (50+ years) 140 542.6 39.48

R9 TYPE N VOLUME SE

White oak/red oak 8 492.86 112.6591498

Allegheny hardwoods 36 504.0455556 68.44133681

Red maple 28 539.5157143 79.67864219

Oak/hardwood transition 7 561.7342857 216.3208392

Northern hardwoods 14 567.1435714 134.4330518

Mixed upland hardwoods 44 595.7577273 79.21809753

Sugar maple 7 745.6571429 259.7460275

Eastern hemlock 9 794.8344444 259.2023224

0 200 400 600 800 1000 1200

White oak/red oak

Allegheny hardwoods

Red maple

Oak/hardwood transition

Northern hardwoods

Mixed upland hardwoods

Sugar maple

Eastern hemlock

R9 Forest Type

CWD Volume (ft3/acre)

Figure 62.—Mean values of CWD volumes by Eastern Region forest types.
58

may have experienced recent disturbances contain the

most CWD. CWD recruitment by logging activities over

long periods may not establish a sufficient diversity of

CWD sizes and decay classes. Data from future DWM

inventories should allow more sophisticated hypothesis

testing with respect to CWD recruitment and attributes.

Kriged surfaces were generated to depict the spatial

arrangement of fuels and debris (Fig. 63). The area in the

southwestern section of the ANF had both high FWD

and CWD loadings and a high percentage of standing

dead basal area (Fig. 21).

Soils

The vitality, species composition, and hydrology of

forest ecosystems are potentially influenced by any

environmental stressor that changes the natural function

of the soil.3 Soil samples are collected from the forest

floor (at subplots 2, 3, and 4) and the underlying mineral

Figure 63.—Kriged surfaces of DWM on the ANF; NLCD data from Multi-Resolution Land Characteristics Consortium.
59

soil layers (at subplot 2). Once the forest floor has been

removed, mineral soils are sampled from 0 to 10 and

10 to 20 cm. The texture of each layer is assigned in the

field as organic, loamy, clayey, sandy, or coarse sandy.

The physical and chemical properties of the soil layers

are determined in a regional laboratory.3 The protocol

for data collection has been changed frequently since the

first year that data were obtained on the ANF. Therefore,

in instances where all years of data were not available,

missing years are listed in tables and/or figures.

The physical properties of soils from FHM plots on

the ANF are shown in Table 27. Moisture content was

substantially higher in the forest-floor layer than in the

mineral-soil layers. Soil-water content is expressed as a

percentage of the dry weight of the soil (Thompson and

Troeh 1973). Consequently, it is not unusual for the

values for soil-moisture content to exceed 100 percent,

especially in samples of high organic matter, e.g., the

forest floor. Due to the high sorption capacities of soils

high in organic matter, the forest-floor layer probably

can absorb many times its own weight in water. On

the basis of the forest types sampled, the following

observations can be made with respect to moisture

content (Table 27):

•	 The white oak type had the lowest average

values for all three soil layers. One reason

for this may be that it also had the lowest

mean forest-floor thickness of the forest types

sampled. White oak grows better on somewhat

droughty sites than many other species.

•	 The eastern hemlock, northern hardwoods, and

white oak/red oak types had the highest average

values for the forest-floor layer.

•	 The eastern hemlock, northern hardwoods, red

maple, and black birch/hickory types had the

highest average values for the mineral layers.

Chemical properties from FHM plots on the ANF are

shown in Tables 28 and 29. Sugar maple decline tends

to be more prevalent on sites with poor nutrition of

calcium (Ca) and magnesium (Mg) (Bailey et al. 2004).

By contrast, black cherry, red maple, and northern red

oak do not seem to have a high requirement for Ca and

Mg. Our data appear to confirm this, i.e., Mg and Ca are

higher in the northern hardwoods and sugar maple types

than in the Allegheny hardwoods, red maple, and oak

types) (Table 28). Iron (Fe) levels are nearly twice as high

in the mineral layer (0 to 10 cm) for the eastern hemlock

type than for other types (Table 29). For the mineral

layer (10 to 20 cm), Fe is highest for both the hemlock

and sugar maple forest types.

On the basis of the forest types sampled, the following

observations can be made concerning pH, extractable

phosphorous, total nitrogen, and exchangeable cations

(Table 28):

•	 The pH of the mineral (0 to 10 cm) layer ranges

from 3.9 to 4.3.

•	 The pH of the mineral (10 to 20 cm) layer

ranges from 4.1 to 4.4.

•	 The white oak/red oak type had the highest

average values for extractable phosphorous.

•	 Total mean nitrogen had the highest average

values (1.4 percent) in the forest-floor layer.

•	 Total mean nitrogen generally was higher (0.2

percent) in the 10- to 20-cm layer than the 0- to

10-cm layer (0.1 percent).

•	 The northern hardwoods type generally had

much higher mean values for exchangeable

cations than the other types.

Additional research is needed to determine the

importance of these observations, how our results

compare with those for the rest of Pennsylvania, and

whether there are additional relationships among soil

characteristics, vegetation, ecological land-type groups,

and forest health. Analysis might be limited by the

shallow depth of the soil sampling. For the Allegheny

Plateau in New York and Pennsylvania, Bailey et al.

(2004) demonstrated the value of sampling the upper

and lower portions of the B horizon, which extends well

below 20 cm. Relationships were better correlated when

the entire B horizon was considered.
60

Table 27.—Mean values for physical soil properties from FHM plots on the ANF, by forest type 

(1998-2001 FHM data; NA = not applicable)

Moisture

Number of content (%) Coarse Forest floor

samples (oven-dry basis)a fragments (%)b thickness

---cm---

Eastern hemlock 24 NA 193.5 NA 6.4

White oak 6 NA 40.1 NA 4.9

Hardwood/oak transition 16 NA 135.7 NA 7.1

White oak/red oak 11 NA 181.1 NA 7.1

Red maple 56 NA 166.2 NA 8.1

Northern hardwoods 18 NA 187.3 NA 6.2

Allegheny hardwoods 64 NA 158.1 NA 6.4

Sugar maple 11 NA 105.6 NA 6.2

Mixed upland hardwoods 77 NA 159.6 NA 7.7

Average for all types 283 NA 147.5 NA 6.7

Eastern hemlock 40 organic, loamy, clayey 33.0 12.5 NA

White spruce/Norway spruce 24 loamy, sandy No data 2.9 NA

White oak 8 loamy 9.7 23.5 NA

Northern red oak 28 loamy, clayey 14.9 17.2 NA

Hardwood/oak transition 32 loamy, sandy 22.2 10.8 NA

White oak/red oak 64 loamy, clayey, sandy 22.9 17.2 NA

Red maple 168 loamy, clayey, sandy 31.8 13.2 NA

Northern hardwoods 72 loamy, clayey 35.8 13.5 NA

Allegheny hardwoods 232 loamy, clayey, sandy 26.7 11.7 NA

Sugar maple 36 loamy, sandy 18.4 16.4 NA

Beech 12 loamy, clayey No data 2.6 NA

Black birch/hickory 16 loamy, sandy 38.7 21.6 NA

Mixed upland hardwoods 280 loamy, clayey, sandy 29.3 12.3 NA

Average for all types 1012 NA 25.8 NA NA

Eastern hemlock 40 loamy, clayey 28.0 10.8 NA

White spruce/Norway spruce 24 loamy, clayey No data 4.2 NA

White oak 8 clayey 7.4 6.1 NA

Northern red oak 28 loamy, clayey 11.5 14.9 NA

Hardwood/oak transition 32 loamy, clayey, sandy 22.4 6.4 NA

White oak/red oak 64 loamy, clayey, sandy 20.2 16.0 NA

Red maple 168 loamy, clayey, sandy 33.0 12.1 NA

Northern hardwoods 72 loamy, clayey, sandy 27.9 12.5 NA

Allegheny hardwoods 232 loamy, clayey, sandy 25.5 11.9 NA

Sugar maple 36 loamy 22.8 15.4 NA

Beech 12 loamy, clayey No data 2.6 NA

Black birch/hickory 16 loamy, coarse sandy 31.4 14.7 NA

Mixed upland hardwoods 280 loamy, clayey, sandy 26.9 10.4 NA

Average for all types 1012 NA 23.4 NA NA a

1999 and 2001 only.

b

2000 and 2001 only.

Mineral layer (0-10 cm)

Mineral layer (10-20 cm)

Forest type Texture

Forest floor
6

1

Table 28.—Mean values for chemical properties (including exchangeable cations and extractable sulfur) from FHM plots on the ANF, by forest type (1998-2001 FHM data)

Number 

of Extractable

Number 

of

samples H O CaCl 2 2 carbon nitrogen phosphorus Na K Mg Ca Alb ECECab sulfurb samplesb

---percent--- ---percent--- ---mg/kg--- ---mg/kg---

Eastern hemlock 25 NA NA 39.4 1.6 NA NA NA NA NA NA NA NA NAWhite oak 6 NA NA 42.7 1.6 NA NA NA NA NA NA NA NA NANorthern red oak 6 NA NA 28.1 1.2 NA NA NA NA NA NA NA NA NAOak/hardwood transition 17 NA NA 30.7 1.3 NA NA NA NA NA NA NA NA NAWhite oak/red oak 14 NA NA 30.4 1.2 NA NA NA NA NA NA NA NA NARed maple 69 NA NA 39.3 1.7 NA NA NA NA NA NA NA NA NANorthern hardwoods 25 NA NA 35.1 1.6 NA NA NA NA NA NA NA NA NAAllegheny hardwoods 76 NA NA 36.5 1.7 NA NA NA NA NA NA NA NA NASugar maple 12 NA NA 31.0 1.4 NA NA NA NA NA NA NA NA NABlack birch/hickory 6 NA NA 23.0 1.1 NA NA NA NA NA NA NA NA NAMixed upland hardwoods 91 NA NA 34.6 1.5 NA NA NA NA NA NA NA NA NAAverage for all types 347 NA NA 33.7 1.4 NA NA NA NA NA NA NA NA NAEastern hemlock 10 4.1 3.8 4.4 0.2 3.9 4.7 28.3 27.0 137.2 488.0 707.1 27.2 9Oak/hardwood transition 6 4.3 3.8 2.3 0.1 5.1 7.2 64.0 10.1 26.1 276.7 384.1 28.9 6White oak/red oak 9 4.1 3.7 2.7 0.1 22.8 6.6 31.8 7.2 34.2 270.3 360.1 25.8 8Red maple 30 3.9 3.6 3.3 0.2 13.3 7.1 40.3 12.5 31.1 442.4 536.5 31.9 29Northern hardwoods 12 4.1 3.8 3.7 0.2 3.7 14.6 54.6 82.5 471.6 430.2 1110.1 29.2 11Allegheny hardwoods 41 4.0 3.7 3.1 0.2 3.3 7.1 37.1 11.6 41.7 491.7 616.4 36.6 32Sugar maple 6 3.9 3.6 2.7 0.2 2.6 6.5 44.5 13.1 67.5 386.3 517.7 27.0 6Mixed upland hardwoods 43 4.1 3.8 3.1 0.2 2.6 6.7 36.8 22.0 106.0 422.0 626.7 30.5 36Average for all types 157 4.1 3.7 3.1 0.2 7.2 7.6 42.2 23.2 114.4 400.9 607.3 29.6 17.1Eastern hemlock 10 4.4 4.1 2.2 0.1 1.7 4.9 36.2 20.6 73.8 542.7 693.4 29.0 9Oak/hardwood transition 6 4.4 4.0 1.1 0.1 8.9 5.5 30.3 6.8 25.1 284.2 351.9 27.8 6White oak/red oak 9 4.3 3.9 1.5 0.1 17.4 10.3 29.3 7.2 23.4 219.5 299.7 33.9 8Red maple 30 4.3 4.0 2.0 0.1 7.2 7.0 32.1 8.2 22.6 401.7 474.1 31.1 29Northern hardwoods 12 4.4 4.0 1.9 0.1 3.8 12.9 45.2 89.2 453.6 281.7 928.8 24.4 11Allegheny hardwoods 41 4.2 4.0 2.3 0.2 2.4 6.2 29.0 5.8 19.6 420.6 499.7 35.3 32Sugar maple 6 4.1 3.7 2.5 0.1 2.0 9.1 42.3 10.0 54.5 501.4 617.3 26.1 6Mixed upland hardwoods 43 4.3 4.0 2.8 0.2 9.4 7.1 31.2 16.0 73.0 370.2 521.5 30.5 36Average for all types 157 4.3 4.0 2.0 0.1 6.6 7.9 34.5 20.5 93.2 377.7 548.3 29.8 17.1

aECEC=effective cation exchange capacityb2000 and 2001 only

Forest floor

Mineral layer (0-10 cm)

Mineral layer (10-20 cm)

Extractable Exchangeable cations

---cmol/kg---

Forest type

pH Organic Total
62

Vegetation Diversity

The objectives of the FHM vegetation indicator are

to assess forest ecosystem health with respect to the

diversity, abundance, and rate of change of native and

nonnative vascular plant species, as well as the vertical

layering of vegetation within a forest3 (Stapanian et al.

1998). Chronic stressors such as discrete site degradation,

climate change, and pollution can change species

composition and lead to the decline or local extinction

of sensitive species and to an increase in opportunistic

species, i.e., invasive and nonnative plants.3 The

abundance and layering of vegetation is a good predictor

of wildlife habitat and the severity of damage that might

develop when fire occurs. Individual species also are

important indicators of a site’s potential productivity,

economic value, and wildlife forage and shelter.3

A vegetation survey requires multiscale sampling because

different plant communities have different spatial

patterns of species richness, so a single plot size is an

arbitrary sample of species diversity. Figure 64 shows

the plot design for the FHM vegetation indicator. The

following protocols for data collection apply to data

collected in 2000 and 2001. Species and cover data for

vascular plants are collected on two plot sizes at each

subplot point: three 3.28-ft2 (1-m2

) quadrats, and the

24-foot-radius subplot. On the quadrats, species and

cover data are collected for the 0- to 6-foot layer. On each

subplot, a time-constrained search of all species in all

Table 29.—Mean values for extractable micronutrients and trace metals from FHM plots on 

the ANF, by forest type (2000-2001 FHM data only)

Forest type

Number of

samples Mn Fe Ni Cu Zn Cd Pb

---mg/kg---

Mineral Layer (0-10 cm)

Eastern hemlock 9 49.6 120.3 0.5 0.1 3.1 0.1 3.1

Oak/hardwood transition 6 103.8 10.2 0.2 0.1 2.5 0.1 2.1

White oak/red oak 8 85.9 15.3 0.3 0.1 2.8 0.1 2.1

Red maple 29 70.1 68.8 0.5 0.1 2.5 0.1 4.1

Northern hardwoods 11 106.5 26.6 1.5 0.2 5.2 0.1 4.3

Allegheny hardwoods 32 120.2 41.1 0.6 0.3 2.9 0.1 3.4

Sugar maple 6 92.1 40.2 0.4 0.1 2.1 0.0 2.2

Mixed upland hardwoods 36 80.1 61.6 1.0 0.1 3.2 0.1 3.4

Average for all types 137 88.5 48.0 0.6 0.1 3.0 0.1 3.1

Mineral Layer (10-20 cm)

Eastern hemlock 9 23.2 58.6 0.6 0.1 3.8 0.1 2.2

Oak/hardwood transition 6 20.0 8.4 1.6 0.1 1.8 0.6 1.3

White oak/red oak 7 52.3 4.0 1.1 0.1 2.0 0.0 1.1

Red maple 28 27.9 37.3 1.4 0.1 2.2 0.0 1.8

Northern hardwoods 13 73.3 17.9 3.1 0.1 2.8 0.1 1.4

Allegheny hardwoods 29 52.4 18.9 1.7 0.1 2.1 0.0 2.0

Sugar maple 6 35.9 60.9 1.7 0.1 2.0 0.0 1.5

Mixed upland hardwoods 36 37.1 33.4 1.9 0.1 2.4 0.0 1.6

Average for all types 134 40.3 29.9 1.6 0.1 2.4 0.1 1.6
63

height strata is conducted and cover is estimated. Total

cover of all vegetation in four height layers (0 to 2, 2 to

6, 6 to 16, and 16+ feet) is estimated on each subplot.2

(USDA For. Serv. 2002). Due to protocol updating, only

data on species presence on subplots were collected in all

years.

Average percent cover on quadrats of dung, stream,

lichens, litter/duff, moss, road, rock, root/bole, soil,

trampling, trash/junk, water, and dead wood by Eastern

Region forest type and stand-development class are

shown in Tables 30-31. The definitions of the quadrat

ground cover variables are listed in Appendix I.3 Litter/

duff coverage was the highest overall component (61 

percent). Dung was surprisingly high (21 percent),

particularly in the pin cherry (84 percent) and white

spruce/Norway spruce (53 percent) forest types. Bare soil

was low on average on all quadrats. Stream coverage was

highest in the red pine forest type, possibly a function of

where red pine was planted during the 1930s, i.e., areas

that failed to regenerate naturally after the turn of the

century harvests.

All of the species of vegetation sampled on the ANF (397

native and 48 nonnative to the ANF) and the percentage

of plots on which they were found are listed in Appendix

II. An additional 184 specimens remain unidentified

or partially identified because they were collected when

identification characteristics, e.g., flowers and seeds, were

absent. Some of these may be additional species.

Mean species richness of sampled vegetation by forest

types (Table 32) ranged from 44 species in the oak/

hardwood transition forest type to 81.4 species in the

white oak/red oak type. Species richness ranged from 57

to 69 in all other forest types, except for the sugar maple

type. Mean species richness of vegetation by standdevelopment phase (Table 33) ranged from 61.3 species

in the stand initiation phase to 75.7 species in the stem

exclusion stage.

All nonnative vegetation species sampled on the ANF

(48) and the percentage of plots on which they were

found also are listed in Appendix II. Five species from

the ANF list of invasive plant species of concern were

Figure 64.—Layout of FHM subplot showing location of vegetation quadrants.
64 Table 30.—Percentage of quadrats with ground-cover variables, by Eastern Region forest type

Forest type No. of quads Dung Lichen Litter/duff Moss Road Rock Root/bole Soil Stream/lake Trampling Trash Water Wood

Red pine 10 0.0 0.1 81.9 4.7 0.0 0.0 1.8 0.0 10.0 1.0 0.0 0.0 1.5Hemlock 113 8.5 2.9 70.1 11.9 1.8 1.2 0.6 2.6 1.2 0.6 0.9 4.0 1.2White spruce/Norway spruce 15 53.3 45.5 2.1 0.6 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Chestnut oak 12 0.0 0.2 92.1 5.8 0.0 0.5 0.2 0.0 0.0 1.0 0.0 0.0 1.2White oak 24 0.0 0.6 93.0 3.0 0.0 0.0 0.6 0.3 0.0 1.1 0.0 0.0 2.5Red oak 36 28.7 4.2 63.2 1.9 0.4 0.0 0.7 0.1 0.0 0.8 0.0 0.0 2.8Oak/hardwood transition 81 8.3 6.7 73.4 1.8 0.6 2.3 1.1 0.2 0.6 0.3 0.0 6.4 1.0White oak/red oak 93 26.2 12.1 57.6 2.4 0.7 0.9 0.6 0.8 0.2 0.5 0.0 0.7 0.9Red maple 298 21.7 5.7 65.5 3.1 1.3 1.3 0.7 0.8 0.4 0.5 0.0 2.6 1.8Northern hardwoods 181 21.9 10.0 59.2 4.0 1.1 2.5 1.0 1.1 0.1 0.6 0.0 1.1 0.7Pin cherry 7 84.4 13.6 0.7 1.7 0.1 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0Allegheny hardwoods 397 20.3 6.9 67.4 3.0 0.7 0.7 1.2 0.5 0.2 0.7 0.1 2.0 1.2Sugar maple 69 24.7 6.2 63.9 3.4 0.2 0.4 1.0 0.0 0.3 0.5 0.0 3.0 3.5Beech 19 19.8 45.4 28.7 1.2 5.6 2.2 0.4 0.1 0.6 0.1 0.1 3.3 0.0Black birch/hickory 36 0.1 0.0 85.8 7.3 0.0 0.9 2.2 0.1 0.0 1.0 0.1 0.0 3.4Mixed upland hardwoods 501 19.4 3.8 70.0 2.0 1.3 1.3 1.0 0.6 0.3 0.6 0.0 2.9 0.7Total 1892 21.1 10.2 60.9 3.6 0.9 0.9 0.8 0.5 0.9 0.6 0.1 1.6 1.4

Table 31.—Percentage of quadrats with ground-cover variables present, by stand development stage

Stand development stage No. of quads Dung Lichen Litter/Duff Moss Road Rock Root/bole Soil Stream Trampling Trash Water Wood

Stand initiation (0-14 years) 150 4.0 7.1 74.9 0.7 1.4 1.1 1.2 0.4 0.5 0.7 0.0 9.4 4.1Stem exclusion (15-29 years) 166 24.5 7.4 58.6 3.6 2.0 1.0 1.0 0.4 0.3 0.5 0.1 2.0 2.0Understory reinitiation (50+ years) 1595 20.4 6.4 66.5 3.6 1.0 1.2 0.9 0.8 0.4 0.6 0.1 1.9 0.9
65

sampled in the vegetation surveys. Glossy buckthorn

(Frangula alnus) was sampled on 25 subplots followed by

multiflora rose (Rosa multiflora) (7), Japanese barberry

(Berberis thunbergii) (3), canary reed grass (Phalaris 

arundinacea) (2), and crown vetch (Coronilla varia) (1).

A kriged surface of introduced species richness is shown

in Figure 65. It was estimated that the areas near the

ANF boundary have more nonnative plant species.

Ozone Bioindicator Plants

Ozone (O3) is a byproduct of industrial development

and is found in the lower atmosphere. It forms when

nitrogen oxides and volatile organic compounds react in

the presence of sunlight (Krupa et al. 2001). Groundlevel O3 has a detrimental effect on forest ecosystems

and certain plant species show visible, easily diagnosed

foliar symptoms. O3 stress in a forest environment

can be detected and monitored using these plants as

bioindicators. The FHM program uses O3 bioindicator

plants to monitor changes in air quality across a region

and to evaluate the relationship between O3 air quality

and the indicators of forest condition.3 Black cherry

Table 32.—Mean species richness of vegetation on the 

ANF, by Eastern Region forest type (1998-2001 FHM 

data)

Forest type

Number

of plots

Species

richness SE

White oak/red oak 8 81.4 14.5

Sugar maple 7 75.0 14.2

Northern hardwoods 13 69.5 6.9

Mixed upland hardwoods 45 67.8 6.6

Allegheny hardwoods 35 66.7 5.0

Eastern hemlock 9 66.1 11.1

Red maple 29 57.4 5.6

Oak/hardwood transition 7 44.1 8.3

Table 33.—Mean species richness of vegetation on the ANF, 

by stand development stage (1998-2001 FHM data)

Stand development stage

Number

of plots

Species

richness SE

Stand initiation (0-14 years) 15 61.3 7.9

Stem exclusion (15-49 years) 15 75.7 11.7

Understory reinitation (50+ years) 143 66.2 2.9

Figure 65.—Kriged surface of exotic vegetation species richness; 

NLCD data from Multi-Resolution Land Characteristics Consortium.
66

and blackberry are two abundant species on the ANF

that are sensitive to O3

. This report provides results

from the expanded O3 biomonitoring project conducted

at the recommendation of the Allegheny Air Quality

Assessment (USDA For. Serv. 2002).

O3 plot data from 1998-2001 are summarized in

Table 34. The data showed a high degree of temporal

heterogeneity. Nearly half of the sampled plants (44.6

percent) showed some symptoms of O3 injury in 1998,

though most of the injury was on less than 25 percent

of their foliage. By contrast, less than 25 percent of the

sampled plants showed symptoms in 2000, and less than

8 percent showed injury symptoms in 1999 and 2001.

Smith et al. (2003) reported that even when ambient

O3 exposures are high, the percentage of injured plants

can be reduced sharply in dry years, e.g., 1999 and 2001 

on the ANF. Differences in the amount of O3 injury

between years probably are due to precipitation levels

rather than ambient O3 exposure levels.

The number of plants sampled by species with percent

injured in parentheses is shown in Table 35. The

scientific names of the bioindicator species are listed in

Appendix II. Blackberry had the highest occurrences of

O3 damage with 40 to 60 percent of the sampled plants

showing symptoms of damage in 1998-2000 versus

only 19 percent in 2001. Nearly 60 percent of the black

cherry plants sampled showed injury symptoms in 1998,

but less than 16 percent showed symptoms between

1999 and 2001. Damage was minimal to other species

sampled except in 2000 when one-third of the pin cherry

plants sampled showed injury symptoms. Even for

species with high occurrences of O3 injury, the severity

was low (Table 35).

Table 34.—Percentage of sampled plants showing ozone injury (1998-2001 FHM data)

No. of plots No. of plants Percent of leaves with injury

evaluated sampled No injury 1-6 7-25 26-50 51-75 >75

1998 6 244 55 13 17 7 5 2

1999 10 648 93 1 4 2 1 0

2000 7 417 75 6 7 5 4 3

2001 9 971 92 2 2 3 1 0

Note: Rows may not add to 100 due to rounding.

Table 35.—Number of plants evaluated for ozone injury on the 

ANF (1998-2001 FHM data; percent injured in parentheses)

Species 1998 1999 2000 2001

Blackberry 118 (59) 106 (41) 118 (54) 196 (19)

Black cherry 90 (61) 283 (2) 167 (16) 257 (10)

Milkweed 31 (6) 90 (0) 29 (0) 131 (6)

Pin cherry 36 (14) 31 (0) 30 (33) 75 (1)

Sassafras -- -- 26 (8) 60 (0)

Spreading dogbane 37 (0) 84 (0) -- 165 (0)

White ash -- 30 (0) 16 (0) 73 (4)

Yellow-poplar -- 24 (0) 11 (0) --
67

Coulston et al. (2003) created an estimated surface

of biosite index using block kriging procedures to

determine which tree species in the Northeastern United

States are sensitive to O3 (Fig. 66). Biosite index was the

average score (amount times severity) for each species

averaged across all species on an FHM ozone plot

multiplied by 1,000 to allow risk categories to be defined

by integers. The central portion of the ANF is in the

moderate-risk category.

A typical summer O3 exposure pattern for Eastern

Region is shown in Figure 67 (USDA For. Serv. 2002).

The term SUM06 is defined as the sum of all valid

hourly O3 concentrations that equal or exceed 0.06

ppm. Controlled studies have found that high O3 levels

(shown in orange and red) can lead to measurable

growth suppression in sensitive tree species (Chappelka

and Samuelson 1998).

Figure 66.—Estimated surface of ozone biosite index in the Northeastern 

United States (from Coulston et al. 2003).
68

Acknowledgments

Thanks to the ANF field crew members for the collection

of the Forest Health Monitoring (P3) data: Robert

Purfield and Todd Roffe (1998); Renae Essenmacher,

Eric Mosbacher, and Aaron Wells (1999); and Ann

Zurbriggen, Donald Lepley, John Rohm, Thomas

Gabriel, Thomas Hebda, and Ava Turnquist (2000

and 2001). Thanks to other ANF staff members who

participated in this process, and to FIA field crew

members for collecting the P2 portion of the plot data:

Todd Renninger, Mitch Pennabaker, Richard Starr, and

Jason Gould. Thanks to the FIA staff members who

assisted in training the ANF crews: Mike Whitehill, Mike

Effinger, and Brian LaPoint, Will McWilliams and Jim

Steinman for reviewing this report, Barbara O’Connell

for providing data and analytical support, Susan WillWolf for reviewing the lichens portion of the report,

Gretchen Smith for reviewing the ozone damage portion,

Ted Huffman for reviewing the soils and ozone damage

portion, and April Moore for reviewing the vegetation

portion.

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77

New 5-point fixed- and variable-radius cluster

Appendix I

1989 FIA Survey Plot Designs

7

8 6

1

2

3

4

9 5

Azimuth between points: 10

1-2 0

2-3 0

3-4 120

4-5 180

5-6 180

6-7 240

7-8 300

8-9 0

9-10 0



















The distance between any point

to any adjacent point is 70 ft

Remeasured 10-point cluster (37.5 BAF)
78

American basswood

American beech

American chestnut

American elm

American hornbeam

apple species

ash species

bigtooth aspen

birch species

black cherry

blackgum

blue spruce

chestnut oak

chokecherry

cucumbertree

eastern hemlock

eastern hophornbeam

eastern redbud

eastern white pine

hawthorn species

mountain maple

northern red oak

oak species

pignut hickory

pin cherry

quaking aspen

red maple

red pine

sassafras

scarlet oak

serviceberry

slippery elm

silver maple

striped maple

sugar maple

sweet birch

white ash

white oak

white spruce

yellow birch

yellow-poplar

Tilia americana

Fagus grandifolia

Castanea dentata

Ulmus americana

Carpinus caroliniana

Malus spp.

Fraxinus spp.

Populus grandidentata

Betula spp.

Prunus serotina

Nyssa sylvatica

Picea pungens

Quercus prinus

Prunus virginiana

Magnolia acuminata

Tsuga canadensis

Ostrya virginiana

Cercis canadensis

Pinus strobus

Crataegus spp.

Acer spicatum

Quercus rubra

Quercus spp.

Carya glabra

Prunus pensylvanica

Populus tremuloides

Acer rubrum

Pinus resinosa

Sassafras albidum

Quercus coccinea

Amelanchier spp.

Ulmus rubra

Acer saccharinum

Acer pensylvanicum 

Acer saccharum

Betula lenta

Fraxinus americana

Quercus alba

Picea glauca

Betula alleghaniensis

Liriodendron tulipifera

Common and Scientific Names of Tree Species Found on ANF
79

Region 9 Forest Cover Types

Ø	Red pine: 50 percent or more of the basal area composed of red pine.

Ø	White pine: 50 percent or more of the basal area composed of white pine.

Ø	Hemlock: 50 percent or more of the basal area composed of eastern hemlock.

Ø	White spruce/Norway spruce: 50 percent or more of the basal area composed of white

and Norway spruce.

Ø	Chestnut oak: 50 percent or more of the basal area composed of chestnut oak.

Ø	White oak: 50 percent or more of the basal area composed of white oak.

Ø	Red oak: 50 percent or more of the basal area composed of northern red oak.

Ø	Oak/hardwood transition: 50 percent or more of the basal area composed of the

combination of oaks, red maple, and black cherry (oaks comprise at least 25 percent of

the basal area).

Ø	White oak/red oak: 50 percent or more of the basal area composed of white and

northern red oak.

Ø	Red maple: 50 percent or more of the basal area composed of red maple.

Ø	Mixed lowland hardwoods: 50 percent or more of the basal area composed of red

maple, ash species, sycamore, silver maple, and oak species.

Ø	Northern hardwoods: 50 percent or more of the basal area composed of sugar maple,

beech, yellow birch, or hemlock.

Ø	Allegheny hardwoods: 50 percent or more of the basal area composed of black cherry,

yellow-poplar, and white ash.

Ø	Pin cherry: 50 percent or more of the basal area composed of pin cherry.

Ø	Sugar maple: 50 percent or more of the basal area composed of sugar maple.

Ø	American beech: 50 percent or more of the basal area composed of American beech.

Ø	Black birch/hickory: 50 percent or more of the basal area composed of black birch and

hickory.

Ø	Mixed upland hardwoods: 50 percent or more of the basal area composed of red maple,

black cherry, yellow-poplar, white ash, basswood, cucumbertree, and black birch.

Ø	Aspen: 50 percent or more of the basal area composed of bigtooth and quaking aspen.
80

1998-2001 FHM Data 1989 FIA Data

Forest type/group Percent of plots Percent of plots

Maple/beech/birch 72.8 73.2

Red maple/upland hardwoods 34.3 19.6

Black cherry 29.8 38.6

Sugar maple/beech/yellow birch 6.9 15

Cherry/ash/yellow-poplar 1.2 0

Hard maple/basswood 0.6 0

Oak/hickory 15 18.5

Mixed upland hardwoods 8 15.1

White oak/red oak/hickory 4.2 2

White oak 1 0.8

Northern red oak 1.2 0

Red maple/oak 0.6 0

Chestnut oak 0 0.6

White/red/jack pine 8.3 1

Eastern hemlock 7.9 0.9

Red pine 0.4 0

White pine 0 0.1

Nonforest 3.7 0

Nonstocked 0.5 5.1

Exotic Softwoods 0.6 0

Other exotic softwoods 0.6 0

Intermediate 0.6 0

Aspen/birch 0.4 0.4

Aspen 0.4 0.4

Spruce/fir 0 1.1

White spruce 0 1.1

Oak/pine 0 0.7

White pine/red oak 0 0.7

Distribution of FHM and FIA Forest Type Groups and Forest Types

The majority of the FHM plots on the ANF were in the maple/beech/birch foresttype group. The forest types within this group include red maple upland hardwoods,

black cherry, sugar maple/beech/yellow birch, cherry/ash/yellow-poplar, and hard

maple/basswood. The red maple/upland hardwoods and black cherry forest types

were the most common on the ANF, covering about 35 and 30 percent, respectively.

No other forest type accounted for more than 10 percent of the plots surveyed.
81

Variogram Parameters for Kriged Maps

Figure 15—black cherry: exponential model, nugget = 300, sil l= 350, range = 4,500;

red maple: exponential model, nugget = 350, sill = 120, range = 5,000; American beech:

exponential model, nugget = 0, sill = 140, range = 4,000; eastern hemlock: exponential model,

nugget = 50, sill = 210, range = 5,000; sugar maple: exponential model, nugget = 0, sill = 220,

range = 3,000.

Figure 16—northern red oak: spherical model, nugget = 50, sill = 200, range = 60,000; sweet

birch: spherical model, nugget = 90, sill = 40, range = 15,000; yellow birch: spherical model,

nugget = 20, sill = 6, range = 20,000; white oak: spherical model, nugget = 20, sill = 65, range =

50,000; white ash: exponential model, nugget = 0, sill = 13, range = 5,000.

Figure 20—exponential model, nugget = 7, sill = 15, range = 5,000.

Figure 21—black cherry: exponential model, nugget = 10, sill = 160, range = 12,000; red

maple: spherical model, nugget = 0, sill = 30, range = 13,000; American beech: exponential

model, nugget = 30, sill = 20, range = 8,000; eastern hemlock: exponential model, nugget =

0, sill = 37, range = 15,000; sugar maple: pure nugget model, nugget = 175; white ash: pure

nugget model, nugget = 175.

Figure 22—exponential model, nugget = 0, sill = 110, range = 1,500.

Figure 23—< 9 inches d.b.h.: spherical model, nugget = 160, sill = 35, range = 10,000; > 9

inches d.b.h.: pure nugget model, nugget = 110; > 12 inches d.b.h.: pure nugget model, nugget

= 35; > 20 inches d.b.h.: exponential model, nugget = 0.3, sill = 1.5, range = 6,000.

Figure 44—exponential model, nugget = 0, sill = 290, range = 5,000.

Figure 47—pure nugget model, nugget = 16.

Figure 53—Parmelia sulcata: pure nugget model, nugget = 0.15; Flavoparmelia caperata: pure

nugget model, nugget = 0.23; Cladonia coniocraea: exponential model, nugget = 0.19, sill =

0.05, range = 15,000; Phaeophyscia rubropulchra: exponential model, nugget = 0.22, sill = 0.04,

range = 30,000.

Figure 55—spherical model, nugget = 0.009, sill = 0.005, range = 20,000.

Figure 63—FWD: pure nugget model, nugget = 2.5; CWD: exponential model, nugget = 8,

sill = 16, range = 10,000; integrated depth: exponential model, nugget = 25, sill = 40, range =

8000; total DWM: exponential model, nugget = 100, sill = 150, range = 5,000.

Figure 65—exponential model, nugget = 2, sill = 0.75, range = 20,000.
82

Genus Species

Percent of

plots sampled

Anaptychia palmulata 0.6

Candelaria concolor 0.6

Canoparmelia texana 2.3

Cetraria aurescens 0.6

Cetraria oakesiana 0.6

Cladonia 8.1

Cladonia bacillaris 21.4

Cladonia caespiticia 0.6

Cladonia chlorophaea 0.6

Cladonia coniocraea 1.7

Cladonia cristatella 27.2

Cladonia cylindrica 23.1

Cladonia floerkeana 3.5

Cladonia grayi 64.2

Cladonia macilenta 4.0

Cladonia ochrochlora 5.8

Cladonia parasitica 1.2

Cladonia pyxidata 0.6

Cladonia ramulosa 1.7

Cladonia squamosa 1.2

Cladonia squamosa 2.9

Cladonia peziziformis 0.6

Cladonia furcata 4.0

Cladonia rei 8.1

Cladonia incrassata 11.0

Cladonia decorticata 0.6

Cladonia farinacea 0.6

Evernia mesomorpha 1.7

Flavoparmelia caperata 1.7

Heterodermia speciosa 1.2

Lichen Species Sampled on the ANF

Genus Species

Percent of

plots sampled

Hypogymnia physodes 11.6

Imshaugia aleurites 67.1

Melanelia subaurifera 0.6

Myelochroa aurulenta 0.6

Parmelia 42.8

Parmelia squarrosa 0.6

Parmelia sulcata 0.6

Parmeliopsis 2.9

Parmeliopsis hyperopta 19.7

Parmeliopsis capitata 7.5

Parmotrema 2.9

Parmotrema margaritatum 12.1

Phaeophyscia pusilloides 85.0

Phaeophyscia rubropulchra 0.6

Physcia adscendens 0.6

Physcia aipolia 0.6

Physcia millegrana 1.2

Physcia stellaris 0.6

Physciella melanchra 1.2

Physconia 0.6

Physconia detersa 0.6

Punctelia 12.7

Punctelia missouriensis 56.6

Punctelia rudecta 1.2

Punctelia subrudecta 1.7

Pyxine sorediata 46.8

Usnea 4.0

Usnea hirta 0.6

Usnea strigosa 0.6
83

Lichen Species Pollution Tolerances

Genus Species Pollution tolerancea

Anaptychia palmulata S

Candelaria concolor T

Canoparmelia texana S

Cetraria oakesiana T

Cladonia bacillaris T

Cladonia coniocraea T

Cladonia pyxidata I

Evernia mesomorpha I

Flavoparmelia caperata T

Heterodermia speciosa S

Hypogymnia physodes T

Imshaugia aleurites I

Melanelia subaurifera I

Myelochroa aurulenta T

Parmelia squarrosa I

Parmelia sulcata VT

Parmotrema margaritatum S

Phaeophyscia pusilloides T

Phaeophyscia rubropulchra VT

Physcia adscendens I

Physcia millegrana VT

Physcia stellaris T

Physconia detersa I

Punctelia rudecta T

Punctelia subrudecta T

Pyxine sorediata I

Usnea hirta S

Usnea spp. S

a S=sensitive, I=intermediate, T=tolerant, VT=very tolerant.
84

DWM Plot Designs (1999 and 2000)

1999

N

4

30°

150°

270°

3

2

1

FWD < 0.25 inch

and 0.26 -0.99 inch

FWD 1.00 -

2.99 inches

CWD 

3.00 inches

7 ft s.d.

14 ft s.d.

55 ft h.d.

s.d.= slope dist., h.d.=horizontal dist.

Subplot

Microplot

CWD Tran.

FWD Tran.

Dist. between subplots - (2, 3, and 4) and subplot - center (1): 120 ft at angles (deg.) 0, 120, and 

240, respectively; dist. between subplot center and microplot center: 12 ft; depth/coverage of 

shrubs/herbs/duff/litter estimated on microplot.

Annular plot

30°

150°

270°

30°

150°

270°

30°

150°

270°

2000

N

4

30°

150°

270°

3

2

1

FWD < 0.25 inch

& 0.26-0.99 inch

FWD 1.00-

2.99 inches

CWD 

3.00 inches

7 ft s.d.

14 ft s.d.

55 ft h.d.

s.d.= slope dist., h.d.=horizontal dist.

Subplot

Microplot

CWD Tran.

FWD Tran.

Annular plot

30°

150°

270°

30°

150°

270°

30°

150°

270°

Dist. between subplots (2, 3, and 4) and subplot center (1): 120 ft at angles (deg.) 0, 120, and 

240, respectively; dist. between subplot center and microplot center: 12 ft.; depth/coverage of 

shrubs/herbs/duff/litter estimated on microplot.
85

Quadrat Ground-Cover Variables

Ø	Dung

Ø	Lichen

Ø	Litter/duff – a continuous layer of accumulated organic matter over forest mineral soil

Ø	Moss

Ø	Road – any constructed portion of a maintained road, and other areas compacted and

unvegetated from regular use by motorized vehicles

Ø	Rock – rocks, boulders, or accumulations of gravel or pebbles

Ø	Root/bole – living roots at the base of trees or exposed at the ground surface, and crosssectional area of live tree boles at the ground line

Ø	Soil – physically weathered soil parent material that may or may not also be chemically

or biologically altered

Ø	Stream/lake – body of water contained within banks

Ø	Trampling

Ø	Trash

Ø	Water – ponding or flowing water that is not contained within banks

Ø	Wood – logs and slash, stumps, branches, and limbs

87

Appendix II

Vegetation Species and Percentage of Subplots Sampled

Scientific name Common name Percent of subplots present

Acer 0.72

Acer pensylvanicum striped maple 33.05

Acer rubrum red maple 74.43

Acer saccharum sugar maple 43.97

Acer spicatum mountain maple 0.14

Achillea millefolium common yarrow 0.57

Actaea 0.57

Actaea pachypoda white baneberry 0.43

Adiantum pedatum northern maidenhair 0.43

Agrimonia rostellata beaked agrimony 0.14

Agrostis 1.01

Agrostis canina velvet bentgrass 0.14

Agrostis capillaris colonial bentgrass 0.14

Agrostis gigantea redtop 0.72

Agrostis hyemalis winter bentgrass 0.43

Agrostis perennans upland bentgrass 9.91

Agrostis scabra rough bentgrass 3.16

Agrostis stolonifera creeping bentgrass 0.43

Allium tricoccum wild leek 1.29

Ambrosia artemisiifolia annual ragweed 0.14

Amelanchier 37.93

Amelanchier arborea common serviceberry 9.63

Amelanchier bartramiana oblongfruit serviceberry 0.29

Amelanchier laevis Allegheny serviceberry 1.72

Amelanchier X intermedia intermediate serviceberry 0.14

Amphicarpaea bracteata American hogpeanut 0.86

Anaphalis 0.43

Anaphalis margaritacea western pearly everlasting 0.57

Anemone quinquefolia nightcaps 2.59

Antennaria 0.14

Antennaria neglecta field pussytoes 0.14

Anthoxanthum odoratum sweet vernalgrass 2.44

Apocynum 0.29

Apocynum cannabinum Indianhemp 0.14

Arabis hirsuta hairy rockcress 0.14

Aralia nudicaulis wild sarsaparilla 2.87

Aralia spinosa devil’s walkingstick 3.3

Arctium minus lesser burrdock 0.29
88

Appendix II continued

Scientific name Common name Percent of subplots present

Arisaema triphyllum Jack in the pulpit 16.67

Arisaema triphyllum ssp. triphyllum Jack in the pulpit 0.29

Asarum 0.14

Asplenium platyneuron ebony spleenwort 0.14

Aster 2.01

Athyrium 0.14

Athyrium filix-femina common ladyfern 1.29

Atriplex cristata crested saltbush 0.14

Berberis 0.29

Berberis thunbergii Japanese barberry 0.57

Betula 9.63

Betula alleghaniensis yellow birch 29.02

Betula lenta 41.67

Bidens 0.29

Bidens connata purplestem beggarticks 0.29

Bidens frondosa devil’s beggartick 0.72

Bidens tripartita threelobe beggarticks 0.14

Boehmeria 0.43

Boehmeria cylindrica smallspike false nettle 0.43

Botrychium 0.43

Botrychium dissectum cutleaf grapefern 0.14

Botrychium multifidum leathery grapefern 0.29

Botrychium oneidense bluntlobe grapefern 0.14

Botrychium virginianum rattlesnake fern 0.14

Brachyelytrum erectum bearded shorthusk 61.35

Bromus pubescens hairy woodland brome 0.14

Bulbostylis capillaris densetuft hairsedge 0.29

Calamagrostis canadensis bluejoint 0.29

Calamagrostis coarctata arctic reedgrass 0.14

Calamovilfa longifolia prairie sandreed 0.14

Caltha palustris yellow marsh marigold 0.57

Cardamine 1.01

Cardamine diphylla crinkleroot 1.44

Cardamine impatiens narrowleaf bittercress 0.14

Cardamine pensylvanica Pennsylvania bittercress 1.15

Cardamine rotundifolia American bittercress 0.14

Carex 47.7

Carex aestivalis summer sedge 7.47

Carex albicans whitetinge sedge 0.43

Carex annectens yellowfruit sedge 0.14
89

Scientific name Common name Percent of subplots present

Carex appalachica Appalachian sedge 1.44

Carex arctata drooping woodland sedge 2.16

Carex baileyi Bailey’s sedge 2.01

Carex blanda eastern woodland sedge 2.44

Carex bromoides bromelike sedge 0.72

Carex brunnescens brownish sedge 1.44

Carex communis fibrousroot sedge 13.22

Carex crinita fringed sedge 1.01

Carex debilis white edge sedge 51.44

Carex debilis var. pubera white edge sedge 0.14

Carex debilis var. rudgei white edge sedge 1.29

Carex deweyana Dewey sedge 1.87

Carex digitalis slender woodland sedge 2.44

Carex disperma softleaf sedge 1.01

Carex folliculata northern long sedge 0.14

Carex gracillima graceful sedge 2.01

Carex gynandra nodding sedge 1.58

Carex hirtifolia pubescent sedge 0.29

Carex hitchcockiana Hitchcock’s sedge 0.29

Carex hystericina bottlebrush sedge 0.43

Carex intumescens greater bladder sedge 11.64

Carex laxiculmis spreading sedge 6.32

Carex laxiculmis var. laxiculmis spreading sedge 0.14

Carex laxiflora broad looseflower sedge 6.61

Carex leptalea bristlystalked sedge 0.29

Carex lucorum Blue Ridge sedge 0.43

Carex lurida shallow sedge 1.15

Carex normalis greater straw sedge 0.14

Carex novae-angliae New England sedge 0.86

Carex ormostachya necklace spike sedge 0.14

Carex pallescens pale sedge 0.29

Carex pensylvanica Pennsylvania sedge 8.91

Carex plantaginea plantainleaf sedge 4.31

Carex prasina drooping sedge 1.44

Carex projecta necklace sedge 1.72

Carex radiata eastern star sedge 1.58

Carex scabrata eastern rough sedge 2.01

Carex scoparia broom sedge 2.73

Carex seorsa weak stellate sedge 0.14

Carex stipata owlfruit sedge 0.57

Appendix II continued
90

Appendix II continued

Scientific name Common name Percent of subplots present

Carex striatula lined sedge 0.43

Carex swanii Swan’s sedge 8.91

Carex tenera quill sedge 0.14

Carex torta twisted sedge 0.14

Carex trisperma threeseeded sedge 1.44

Carex vulpinoidea fox sedge 0.86

Carpinus caroliniana American hornbeam 7.04

Carya 0.14

Carya alba mockernut hickory 0.14

Carya cordiformis bitternut hickory 1.01

Carya glabra pignut hickory 0.86

Carya ovalis red hickory 0.43

Carya ovata shagbark hickory 1.01

Castanea dentata American chestnut 0.86

Caulophyllum thalictroides blue cohosh 1.15

Centaurium pulchellum branched centaury 0.14

Cerastium fontanum ssp. vulgare big chickweed 0.14

Chelone glabra white turtlehead 0.14

Chrysosplenium americanum American golden saxifrage 1.15

Cinna 1.72

Cinna arundinacea sweet woodreed 0.86

Cinna latifolia drooping woodreed 14.51

Circaea 1.29

Circaea alpina small enchanter’s nightshade 5.75

Circaea alpina ssp. alpina small enchanter’s nightshade 0.14

Circaea lutetiana broadleaf enchanter’s nightshade 0.14

Circaea lutetiana ssp. Canadensis broadleaf enchanter’s nightshade 0.14

Cirsium 0.14

Claytonia 0.14

Clematis virginiana devil’s darning needles 0.72

Clinopodium vulgare wild basil 0.57

Clintonia borealis bluebead 0.14

Conopholis 0.43

Conopholis americana American cancer-root 0.14

Coptis trifolia threeleaf goldthread 8.48

Cornus 0.72

Cornus alternifolia alternateleaf dogwood 1.44

Cornus florida flowering dogwood 0.14

Coronilla varia purple crownvetch 0.14

Corylus 0.14
91

Appendix II continued

Scientific name Common name Percent of subplots present

Corylus americana American hazelnut 0.57

Corylus cornuta beaked hazelnut 0.57

Crataegus 5.17

Crataegus intricata Copenhagen hawthorn 0.14

Crataegus pruinosa waxyfruit hawthorn 1.15

Crataegus punctata dotted hawthorn 0.14

Cypripedium 0.29

Cypripedium acaule moccasin flower 1.72

Cystopteris fragilis brittle bladderfern 0.29

Dactylis glomerata orchardgrass 1.15

Dalibarda repens robin runaway 8.33

Danthonia 7.9

Danthonia compressa flattened oatgrass 29.89

Danthonia spicata poverty oatgrass 2.87

Dennstaedtia 0.57

Dennstaedtia punctilobula eastern hayscented fern 64.94

Deparia acrostichoides silver false spleenwort 2.01

Deschampsia caespitosa tufted hairgrass 0.14

Diarrhena obovata 0.14

Dichanthelium acuminatum tapered rosette grass 1.72

Dichanthelium acuminatum var. fasciculatum western panicgrass 0.43

Dichanthelium clandestinum deertongue 12.64

Dichanthelium dichotomum cypress panicgrass 0.43

Dichanthelium latifolium broadleaf rosette grass 0.14

Diervilla lonicera northern bush honeysuckle 1.01

Digitaria ischaemum smooth crabgrass 0.14

Dioscorea 1.01

Dioscorea villosa wild yam 0.43

Disporum lanuginosum yellow fairybells 1.29

Doellingeria 0.14

Doellingeria umbellata parasol whitetop 7.76

Doellingeria umbellata var. umbellata parasol whitetop 0.14

Drosera rotundifolia roundleaf sundew 0.14

Dryopteris 15.23

Dryopteris campyloptera mountain woodfern 1.72

Dryopteris carthusiana spinulose woodfern 2.16

Dryopteris intermedia intermediate woodfern 56.75

Dryopteris marginalis marginal woodfern 1.44

Dryopteris X triploidea triploid woodfern 0.29

Echinochloa muricata var. microstachya rough barnyardgrass 0.14
92

Appendix II continued

Scientific name Common name Percent of subplots present

Elymus hystrix eastern bottlebrush grass 1.29

Elymus riparius riverbank wildrye 0.29

Elymus virginicus Virginia wildrye 0.14

Epifagus virginiana beechdrops 7.18

Epigaea repens trailing arbutus 1.01

Epilobium leptophyllum bog willowherb 0.14

Equisetum 0.57

Equisetum arvense field horsetail 0.14

Equisetum sylvaticum woodland horsetail 0.14

Erechtites hieraciifolia American burnweed 3.16

Eurybia divaricata white wood aster 16.24

Eurybia macrophylla bigleaf aster 1.29

Euthamia graminifolia flat-top goldentop 1.01

Fagus grandifolia American beech 72.41

Festuca subverticillata nodding fescue 1.87

Festuca trachyphylla hard fescue 0.14

Fragaria 0.29

Fragaria virginiana Virginia strawberry 0.14

Frangula alnus glossy buckthorn 3.59

Fraxinus 0.29

Fraxinus americana white ash 18.97

Galeopsis bifida splitlip hempnettle 0.14

Galium 1.01

Galium aparine stickywilly 0.57

Galium asprellum rough bedstraw 0.57

Galium circaezans licorice bedstraw 0.57

Galium obtusum bluntleaf bedstraw 0.29

Galium odoratum sweetscented bedstraw 0.29

Galium palustre common marsh bedstraw 0.14

Galium tinctorium stiff marsh bedstraw 0.72

Galium triflorum fragrant bedstraw 6.61

Gaultheria procumbens eastern teaberry 10.49

Gaylussacia baccata black huckleberry 1.87

Geranium 0.14

Geranium maculatum spotted geranium 0.14

Geum 0.43

Geum canadense white avens 1.29

Glyceria 0.86

Glyceria melicaria mannagrass 8.05

Glyceria striata fowl mannagrass 2.3
93

Appendix II continued

Scientific name Common name Percent of subplots present

Goodyera tesselata checkered rattlesnake plantain 0.14

Gratiola neglecta clammy hedgehyssop 0.14

Gymnocarpium 0.72

Hackelia virginiana beggarslice 0.14

Hamamelis virginiana American witchhazel 16.67

Hepatica nobilis hepatica 1.58

Hepatica nobilis var. acuta sharplobe hepatica 0.86

Hepatica nobilis var. obtusa roundlobe hepatica 0.29

Hieracium 0.57

Hieracium lachenalii common hawkweed 0.14

Hieracium paniculatum Allegheny hawkweed 0.14

Hieracium scabrum rough hawkweed 0.14

Hieracium venosum rattlesnakeweed 0.14

Hieracium X marianum hawkweed 0.14

Holcus lanatus common velvetgrass 1.44

Houstonia caerulea azure bluet 0.57

Huperzia 0.57

Huperzia lucidula shining clubmoss 9.63

Hydrocotyle americana American marshpennywort 0.86

Hydrophyllum canadense bluntleaf waterleaf 0.29

Hydrophyllum virginianum Shawnee salad 0.29

Hypericum 1.44

Hypericum ellipticum pale St. Johnswort 0.29

Hypericum mutilum dwarf St. Johnswort 0.43

Hypericum perforatum common St. Johnswort 0.43

Hypericum punctatum spotted St. Johnswort 0.29

Ilex montana mountain holly 23.71

Ilex verticillata common winterberry 0.29

Impatiens 5.75

Impatiens capensis jewelweed 1.87

Juncus bufonius toad rush 0.29

Juncus effusus common rush 4.89

Juncus effusus var. decipiens lamp rush 0.14

Juncus tenuis poverty rush 1.58

Kalmia latifolia mountain laurel 4.17

Laportea canadensis Canadian woodnettle 1.15

Leersia 0.14

Leersia oryzoides rice cutgrass 0.43

Leersia virginica whitegrass 3.16

Leucanthemum vulgare oxeye daisy 0.72
94

Appendix II continued

Scientific name Common name Percent of subplots present

Lilium philadelphicum wood lily 0.14

Linaria vulgaris butter and eggs 0.14

Lindera benzoin northern spicebush 1.15

Liriodendron tulipifera tuliptree 9.77

Lobelia inflata Indian-tobacco 1.29

Lobelia spicata palespike lobelia 0.14

Lolium arundinaceum tall fescue 0.14

Lolium perenne perennial ryegrass 0.14

Lolium pratense meadow ryegrass 0.29

Lonicera canadensis American fly honeysuckle 0.14

Lotus corniculatus birdfoot deervetch 1.01

Luzula 0.43

Luzula acuminata hairy woodrush 0.43

Luzula multiflora common woodrush 0.72

Lycopodiella 0.14

Lycopodium 6.75

Lycopodium annotinum stiff clubmoss 5.03

Lycopodium clavatum running clubmoss 8.91

Lycopodium dendroideum tree groundpine 4.17

Lycopodium digitatum fan clubmoss 4.02

Lycopodium obscurum rare clubmoss 23.99

Lycopodium tristachyum deeproot clubmoss 0.14

Lycopus 2.16

Lycopus uniflorus northern bugleweed 3.88

Lycopus virginicus Virginia water horehound 0.72

Lysimachia ciliata fringed loosestrife 0.14

Lysimachia quadrifolia whorled yellow loosestrife 4.02

Magnolia acuminata cucumber-tree 26.01

Maianthemum canadense Canada mayflower 48.85

Maianthemum racemosum feathery false lily of the vally 1.44

Malus 0.72

Medeola virginiana Indian cucumber 40.23

Melampyrum lineare narrowleaf cowwheat 0.14

Mentha arvensis wild mint 0.14

Milium effusum American milletgrass 1.58

Mitchella repens partridgeberry 50.86

Mitella diphylla twoleaf miterwort 0.14

Monotropa uniflora Indianpipe 11.49

Muhlenbergia sylvatica woodland muhly 0.29

Myosotis arvensis field forget-me-not 0.14
95

Appendix II continued

Scientific name Common name Percent of subplots present

Myosotis scorpioides true forget-me-not 0.14

Nyssa sylvatica blackgum 4.45

Oclemena acuminata whorled wood aster 5.75

Onoclea sensibilis sensitive fern 5.46

Oryzopsis 0.14

Oryzopsis asperifolia roughleaf ricegrass 1.01

Osmorhiza claytonii Clayton’s sweetroot 0.43

Osmunda 4.02

Osmunda cinnamomea cinnamon fern 12.79

Osmunda claytoniana interrupted fern 3.74

Ostrya virginiana hophornbeam 10.06

Oxalis 0.72

Oxalis corniculata creeping woodsorrel 0.14

Oxalis grandis great yellow woodsorrel 0.29

Oxalis montana mountain woodsorrel 36.35

Oxalis stricta common yellow oxalis 4.31

Packera aurea golden ragwort 0.29

Panax quinquefolius American ginseng 0.14

Panicum 0.43

Panicum dichotomiflorum fall panicgrass 0.14

Parthenocissus quinquefolia Virginia creeper 0.43

Phalaris arundinacea reed canarygrass 0.29

Phleum pratense timothy 0.86

Phytolacca americana American pokeweed 0.72

Picea abies Norway spruce 0.57

Picea glauca white spruce 0.29

Pilea pumila Canadian clearweed 5.17

Pinus resinosa red pine 0.43

Pinus strobus eastern white pine 3.45

Plantago 0.29

Plantago rugelii blackseed plantain 0.14

Plantago virginica Virginia plantain 0.14

Platanthera 0.43

Platanthera clavellata small green wood orchid 0.29

Platanthera hookeri Hooker’s orchid 0.14

Platanthera macrophylla greater roundleaved orchid 0.14

Platanthera orbiculata lesser roundleaved orchid 0.86

Platanus occidentalis American sycamore 0.14

Poa 0.72

Poa alsodes grove bluegrass 4.45
96

Appendix II continued

Scientific name Common name Percent of subplots present

Poa compressa Canada bluegrass 0.29

Poa nemoralis wood bluegrass 1.15

Poa pratensis Kentucky bluegrass 0.72

Poa saltuensis oldpasture bluegrass 1.01

Poa sylvestris woodland bluegrass 0.14

Poa trivialis rough bluegrass 0.86

Podophyllum peltatum mayapple 2.59

Polygala paucifolia gaywings 0.72

Polygonatum 1.44

Polygonatum biflorum smooth Solomon’s seal 0.86

Polygonatum pubescens hairy Solomon’s seal 4.45

Polygonum 0.43

Polygonum amphibium water knotweed 0.29

Polygonum caespitosum oriental ladysthumb 0.43

Polygonum cilinode fringed black bindweed 4.74

Polygonum hydropiperoides swamp smartweed 0.43

Polygonum persicaria spotted ladysthumb 0.29

Polygonum punctatum dotted smartweed 0.43

Polygonum sagittatum arrowleaf tearthumb 2.87

Polygonum virginianum jumpseed 0.14

Polypodium 0.14

Polypodium virginianum rock polypody 0.57

Polystichum acrostichoides Christmas fern 15.95

Populus 0.29

Populus grandidentata bigtooth aspen 2.87

Populus tremuloides quaking aspen 3.88

Potentilla 0.43

Potentilla norvegica Norwegian cinquefoil 0.14

Potentilla simplex common cinquefoil 6.61

Prenanthes 3.16

Prenanthes alba white rattlesnakeroot 0.29

Prenanthes altissima tall rattlesnakeroot 0.43

Prenanthes trifoliolata gall of the earth 0.29

Prunella vulgaris common selfheal 2.16

Prunus 0.29

Prunus americana American plum 0.14

Prunus pensylvanica pin cherry 10.34

Prunus serotina black cherry 68.97

Prunus virginiana chokecherry 0.57

Pteridium 1.01
97

Appendix II continued

Scientific name Common name Percent of subplots present

Pteridium aquilinum western brackenfern 8.48

Pycnanthemum 0.14

Pyrola 0.57

Quercus 0.72

Quercus alba white oak 9.63

Quercus coccinea scarlet oak 1.44

Quercus prinus chestnut oak 2.01

Quercus rubra northern red oak 20.11

Quercus velutina black oak 5.03

Ranunculus 2.3

Ranunculus hispidus bristly buttercup 0.29

Ranunculus hispidus var. caricetorum bristly buttercup 0.14

Ranunculus recurvatus blisterwort 0.29

Ranunculus repens creeping buttercup 0.57

Rhododendron 0.57

Rhododendron prinophyllum early azalea 0.14

Rhus hirta staghorn sumac 0.14

Ribes 1.15

Ribes cynosbati eastern prickly gooseberry 0.29

Ribes glandulosum skunk currant 0.43

Ribes hirtellum hairystem gooseberry 0.14

Ribes lacustre prickly currant 0.14

Ribes rotundifolium Appalachian gooseberry 1.72

Rosa 0.72

Rosa carolina Carolina rose 0.29

Rosa multiflora multiflora rose 1.44

Rubus 19.4

Rubus allegheniensis Allegheny blackberry 35.34

Rubus flagellaris northern dewberry 2.73

Rubus hispidus bristly dewberry 11.21

Rubus idaeus American red raspberry 5.46

Rubus occidentalis black raspberry 0.86

Rubus pubescens dwarf red blackberry 0.29

Rumex 0.14

Rumex acetosella common sheep sorrel 1.15

Rumex crispus curly dock 0.29

Rumex obtusifolius bitter dock 0.29

Salix 0.43

Salix sericea silky willow 0.29

Sambucus 2.3
98

Appendix II continued

Scientific name Common name Percent of subplots present

Sambucus nigra ssp. canadensis common elderberry 0.72

Sambucus racemosa red elderberry 0.43

Sassafras albidum sassafras 5.03

Schizachne purpurascens false melic 0.29

Schizachyrium scoparium little bluestem 0.14

Scirpus 0.43

Scirpus atrovirens green bulrush 1.58

Scirpus cyperinus woolgrass 1.29

Scirpus georgianus Georgia bulrush 0.29

Scirpus polyphyllus leafy bulrush 0.43

Scutellaria 0.14

Scutellaria lateriflora blue skullcap 1.01

Sisyrinchium 0.14

Sisyrinchium angustifolium narrowleaf blue-eyed grass 0.14

Smilax 2.73

Smilax glauca cat greenbrier 1.01

Smilax herbacea smooth carrionflower 1.01

Smilax rotundifolia roundleaf greenbrier 3.16

Smilax tamnoides bristly greenbrier 0.86

Solanum dulcamara climbing nightshade 0.29

Solidago 1.58

Solidago caesia wreath goldenrod 0.43

Solidago caesia var. curtisii mountain decumbent goldenrod 0.14

Solidago canadensis Canada goldenrod 0.14

Solidago canadensis var. scabra Canada goldenrod 0.57

Solidago flexicaulis zigzag goldenrod 0.57

Solidago rugosa wrinkleleaf goldenrod 16.24

Solidago rugosa ssp. aspera wrinkleleaf goldenrod 0.43

Solidago rugosa ssp. rugosa var. villosa wrinkleleaf goldenrod 0.14

Sorbus 0.29

Sorbus aucuparia European mountain ash 1.01

Spiraea tomentosa steeplebush 0.86

Stellaria 0.14

Stellaria borealis boreal starwort 0.29

Stellaria graminea grasslike starwort 0.14

Stellaria longifolia longleaf starwort 0.29

Streptopus lanceolatus var. roseus twistedstalk 2.44

Symphyotrichum lanceolatum white panicle aster 0.29

Symphyotrichum lateriflorum calico aster 0.57

Symphyotrichum lateriflorum var. lateriflorum calico aster 0.14
99

Appendix II continued

Scientific name Common name Percent of subplots present

Symphyotrichum novae-angliae New England aster 0.14

Symphyotrichum novi-belgii New York aster 0.14

Symphyotrichum pilosum hairy white oldfield aster 0.43

Symphyotrichum prenanthoides crookedstem aster 1.87

Symphyotrichum racemosum smooth white oldfield aster 0.57

Symplocarpus foetidus skunk cabbage 0.72

Taraxacum officinale common dandelion 1.58

Thalictrum dioicum early meadow-rue 0.14

Thelypteris noveboracensis New York fern 57.33

Tiarella cordifolia heartleaf foamflower 10.92

Tilia americana American basswood 4.17

Toxicodendron radicans eastern poison ivy 0.43

Trientalis borealis starflower 26.58

Trifolium 0.29

Trifolium pratense red clover 0.14

Trifolium repens white clover 0.14

Trillium 15.23

Trillium erectum red trillium 1.87

Trillium undulatum painted trillium 8.48

Tsuga canadensis eastern hemlock 31.9

Tussilago farfara coltsfoot 0.14

Typha angustifolia narrowleaf cattail 0.14

Ulmus americana American elm 0.14

Ulmus rubra slippery elm 0.29

Urtica dioica stinging nettle 0.29

Uvularia perfoliata perfoliate bellwort 0.29

Uvularia sessilifolia sessileleaf bellwort 25.72

Vaccinium 0.72

Vaccinium angustifolium lowbush blueberry 7.76

Vaccinium myrtilloides velvetleaf huckleberry 0.14

Vaccinium pallidum Blue Ridge blueberry 4.31

Vaccinium stamineum deerberry 1.58

Veratrum viride green false hellebore 0.72

Veronica 0.14

Veronica officinalis common gypsyweed 3.02

Viburnum 0.72

Viburnum acerifolium mapleleaf viburnum 5.03

Viburnum dentatum southern arrowwood 0.57

Viburnum dentatum var. lucidum southern arrowwood 0.29

Viburnum lantanoides hobblebush 0.86
100

Appendix II continued

Scientific name Common name Percent of subplots present

Viburnum nudum var. cassinoides withe-rod 0.57

Vicia 0.14

Vicia sativa garden vetch 0.14

Viola 54.74

Viola blanda sweet white violet 8.19

Viola blanda var. palustriformis sweet white violet 1.72

Viola hastata halberdleaf yellow violet 0.57

Viola hirsutula southern woodland violet 0.29

Viola macloskeyi small white violet 0.43

Viola macloskeyi ssp. pallens smooth white violet 0.29

Viola pubescens downy yellow violet 0.72

Viola rostrata longspur violet 0.29

Viola rotundifolia roundleaf yellow violet 0.57

Viola sagittata arrowleaf violet 0.29

Viola sororia common blue violet 0.72

Viola tricolor johnny jumpup 0.14

Vitis 3.3

Vitis aestivalis summer grape 0.14

Waldsteinia fragarioides Appalachian barren strawberry 0.72
101

Introduced Vegetation Species and Percent of Subplots Sampled

Scientific name Common name Percent of subplots present

Achillea millefolium common yarrow 0.57

Agrostis capillaris colonial bentgrass 0.14

Agrostis gigantea redtop 0.72

Anthoxanthum odoratum sweet vernalgrass 2.44

Arctium minus lesser burrdock 0.29

Berberis thunbergii Japanese barberry 0.57

Cardamine impatiens narrowleaf bittercress 0.14

Centaurium pulchellum branched centaury 0.14

Cerastium fontanum ssp. vulgare big chickweed 0.14

Coronilla varia purple crownvetch 0.14

Dactylis glomerata orchardgrass 1.15

Digitaria ischaemum smooth crabgrass 0.14

Festuca trachyphylla hard fescue 0.14

Frangula alnus glossy buckthorn 3.59

Galeopsis bifida splitlip hempnettle 0.14

Galium odoratum sweetscented bedstraw 0.29

Hieracium lachenalii common hawkweed 0.14

Holcus lanatus common velvetgrass 1.44

Hypericum perforatum common St. Johnswort 0.43

Leucanthemum vulgare oxeye daisy 0.72

Linaria vulgaris butter and eggs 0.14

Lolium arundinaceum tall fescue 0.14

Lolium perenne perennial ryegrass 0.14

Lolium pratense meadow ryegrass 0.29

Lotus corniculatus birdfoot deervetch 1.01

Myosotis arvensis field forget-me-not 0.14

Myosotis scorpioides true forget-me-not 0.14

Phleum pratense timothy 0.86

Picea abies Norway spruce 0.57

Poa compressa Canada bluegrass 0.29

Poa pratensis Kentucky bluegrass 0.72

Poa trivialis rough bluegrass 0.86

Polygonum caespitosum oriental ladysthumb 0.43

Polygonum persicaria spotted ladysthumb 0.29

Ranunculus repens creeping buttercup 0.57

Rosa multiflora multiflora rose 1.44

Rumex acetosella common sheep sorrel 1.15

Rumex crispus curly dock 0.29

Rumex obtusifolius bitter dock 0.29
102

Scientific name Common name Percent of subplots present

Solanum dulcamara climbing nightshade 0.29

Sorbus aucuparia European mountain ash 1.01

Stellaria graminea grasslike starwort 0.14

Taraxacum officinale common dandelion 1.58

Trifolium pratense red clover 0.14

Trifolium repens white clover 0.14

Tussilago farfara coltsfoot 0.14

Typha angustifolia narrowleaf cattail 0.14

Viola tricolor johnny jumpup 0.14

Introduced Vegetation Species and Percent of Subplots Sampled continued.
Printed on Recycled Paper

Morin, Randall S.; Liebhold, Andrew M.; Gottschalk, K.W.; Woodall, Chris, W.; Twardus, 

Daniel B.; White, Robert L.; Horsley, Stephen B.; Ristau, Todd E. 2006. Analysis of 

forest health monitoring surveys on the Allegheny National Forest (1998-2001).

Gen. Tech. Rep. NE-339. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northeastern Research Station. 102 p.

Describes forest vegetation and health conditions on the Allegheny National Forest 

(ANF). During the past 20 years, the ANF has experienced four severe droughts, several 

outbreaks of exotic and native insect defoliators, and the effects of other disturbance 

agents. An increase in tree mortality has raised concerns about forest health. Historical 

aerial surveys (1984-98), Forest Inventory and Analysis plot data collected in 1989, 

and FHM plot data collected 1998-2001 were analyzed to compare disturbed and 

undisturbed areas. Tree mortality and crown dieback levels were compared between 

undefoliated areas and areas defoliated by cherry scallopshell moth, elm spanworm, and 

gypsy moth. American beech mortality was compared inside and outside the beech bark 

disease killing front. This study illustrates the value of an intensified grid of P3 plots and 

demonstrates the integration of aerial survey and plot data.

Keywords: forest health, crown condition, cherry scallopshell moth, elm spanworm, 

gypsy moth, beech bark disease
Headquarters of the Northeastern Research Station is in Newtown Square, 

Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

Burlington, Vermont, in cooperation with the University of Vermont

Delaware, Ohio

Durham, New Hampshire, in cooperation with the University of New Hampshire

Hamden, Connecticut, in cooperation with Yale University

Morgantown, West Virginia, in cooperation with West Virginia University

Parsons, West Virginia

Princeton, West Virginia

Syracuse, New York, in cooperation with the State University of New York, 

College of Environmental Sciences and Forestry at Syracuse University

Warren, Pennsylvania

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