United States Office of Research and EPA/620/R-94/028
Environmental Protection Development November 1994
Agency Washington DC 20460
Forest Health
Monitoring
1991 Statistical
Summary
>
/
Environmental Monitoring and
Assessment Program
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EPA/620/R-94/028
November 1994
FOREST HEALTH MONITORING
1991 STATISTICAL SUMMARY
Contract 68-DO-0106
Project Officer
Ralph Baumgardner
Atmospheric Research and Exposure Assessment Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Technical Director
Samuel A. Alexander
U.S. Environmental Protection Agency
Environmental Monitoring and Assessment Program
EMAP Center
Research Triangle Park, NC 27709
and
Program Manager
Joseph E. Barnard
U.S.D.A. Forest Service
U.S. Forest Service Laboratory
Research Triangle Park, NC 27709
Atmospheric Research and Exposure Assessment Laboratory - Research Triangle Park, NC 27711
Environmental Research Laboratory - Corvallis, OR 97333
Environmental Monitoring Systems Laboratory - Las Vegas, NV 89119
Printed on Recycled Paper
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FOREST HEALTH MONITORING
1991 STATISTICAL SUMMARY
Approved by
Joseph E. Barnard Samuel A. Alexander
National Program Manager Technical Director
Forest Health Monitoring Forest Health Monitoring
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Notice
Funding for this research was provided by the Environmental Protection Agency to the U.S. Forest
Service through Interagency Agreement Number DW12934170-5, to ManTech Environmental
Technology, Inc. through Contract Numbers 68-DO-0106 (RIP) and 68-C8-0006 (Corvallis), to
Lockheed Engineering & Sciences Company, Las Vegas, NV through Contract Number 68-CO-0049,
to the University of Nevada-Las Vegas through Cooperative Agreement Number CR818526-01-0, to
the Duke University through Cooperative Agreement Number CR-819634-01, to North Carolina State
University through Cooperative Agreement Number 58-6645-0-002, to Statistical Consulting Service
through P.O. Number 2B0270NASA, to the National Oceanic and Atmospheric Administration through
Interagency Agreement, to Bionetics Corporation through Contract Number 68-03-3532. It has been
subjected to the Agency's peer and administrative review, and it has been approved for publication as
an EPA document.Mention of trade names or commercial products does not constitute endorsement
or recommendation for use.
Mention of trade names is for the information of the reader and does not constitute endorsement by
the U.S. Government.
^ This report represents data from one year of field operations of the Environmental Monitoring and
Assessment Program (EMAP). Because the probability-based scietific design used by EMAP
K necesitates multiple years of sampling, there is uncertainty associated with these data. This uncertainty
will decrease as the full power of the approach is realized. Similarly, temporal changes and trends
« cannot be reported, as these require multiple years of observation. Appropriate precautions should be
{ exercised when using this information for policy, regulatory or legislative purposes.
^
v Proper citation of this document is:
^ Forest Health Monitoring. 1992. Forest Health Monitoring 1991 Statistical Summary. EPA/xxx/
^- / . U.S. Environmental Protection Agency, Washington, DC.
in
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Forward
This document presents a statistical view of Forest Health Monitoring program data for 1991. It is a
limited summary of a few measurements in a few areas of the United States; its scope and content are
defined by the current (1991) stage of implementation and therefore it does not necessarily reflect how
data collection or analyses will be conducted when the program is fully implemented. Because Forest
Health Monitoring is not fully implemented and because 1991 was the first year of data collection,
summaries such as this are necessarily incomplete. However, responses to an early summary such
as this will help to define needed improvements to Forest Health Monitoring.
Forest Health Monitoring is an interagency program that has headquarters at the U.S. Forest Service,
Southeast Forest Experiment Station, Box 12254, Research Triangle Park, NC 27709. Comments
concerning the program are encouraged and should be forwarded to the program management staff.
Program Manager:
Joseph E. Barnard
U.S. Forest Service
Technical Director-
Samuel A. Alexander
U.S. Environmental Protection Agency
Deputy, Applications:
Robert C. Loomis
U.S. Forest Service
Deputy, Research:
Kenneth W. Stotte
U.S. Forest Service
Deputy Technical Director:
Craig J. Palmer
U.S. Environmental Protection Agency
National Program Management Coordinators:
James Colby and James Francis, Bureau of Land Management
Thomas Martin, Fish and Wildlife Service
Joe Abrell, National Park Service
Robert Smith, Soil Conservation Service
Elizabeth Smith, Tennessee Valley Authority
The following individuals are recognized as the U. S. EPA Work Assignment Managers:
Ralph Baumgardner, Atmospheric Research and Exposure Assessment Laboratory, Research
Triangle Park, NC.
Craig Palmer, Environmental Monitoring Systems Laboratory, Las Vegas, NV.
Spencer Peterson, Environmental Research Laboratory, Corvallis, OR.
IV
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Executive Summary
Introduction
In response to the diverse and growing public concerns about potential human impacts on our
environment, the U.S. Forest Service and the U.S Environmental Protection Agency have initiated a
cooperative national program of Forest Health Monitoring (FHM). Although not established in all parts
of the nation, FHM has already provided valuable information about forest "health" in some areas. The
intention is to provide a many-sided view of forest health that will assist the public and other decision-
makers in setting priorities and making informed choices aimed at reducing the ecological risks of
human impacts.
The purposes of this report are: to describe the current approach and activities; to summarize
the data that were collected in 1991, and; to outline some new directions that are being explored for
possible implementation later. This report is also a test of an assessment process by which data from
many sources will be brought together for analyses. It is anticipated that suggestions received from
the readers will enable FHM to tailor future reports to better meet the needs of information users. The
appendices to this report contain tables and charts so that readers may explore particular items of
interest. A description of the statistical procedures used to prepare this report is also provided.
Procedures
There are now 925 plots in the FHM national network, of which 628 plots are forested. This
is about 16% (excluding Alaska) of the projected total number of forested plots that will be installed
nationwide over the next several years. The installed plots are located systematically, using a
probability sampling design, throughout the forests of 12 states in the eastern United States. During
the late summer of 1991, these plots were visited by trained field crews to make selected
measurements of forest health. Over 45,000 trees and seedlings of more than 100 species in 10 major
forest types were measured by state and federal personnel. Through a rigorous quality assurance
program, these data were found to meet data quality objectives. The data and supporting
documentation have been entered into the FHM databases.
At each one-hectare forested plot location, four fixed-radius (7.32 meters) subplots were
established. Within these subplots, forest condition measurements were made of tree size, species,
frequency, damage, and crown condition. Additional measurements, such as slope and aspect, were
made to characterize the forested plot locations. Forest condition measurements were analyzed by
using statistical procedures, and the results were expressed in tables, charts, and cumulative
distribution functions. The cumulative distribution functions of the crown condition measurements were
further analyzed to estimate the proportions of the sampled populations with different health status.
Data summaries and maps were also prepared for three types of off-plot data: climate, forest
insects and diseases, and air pollution. For these off-plot data types, selected measurements that are
made by other monitoring programs were summarized in a way that will permit future analyses of the
possible determinants of forest health as indicated by the on-plot measurements. It was not possible
to utilize the FHM statistical estimators for the off-plot data types; summary procedures that were
appropriate for each particular off-plot database were used instead.
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Results and Discussion - On-Plot Data
Although conclusions about regional forest health are tenuous when they are based on just a
few measurements taken in just one year, the following general findings emerged from statistical
analyses of the 1991 on-plot data.
Tree Size and Stand Density
Tree size and stand density measures are important for many analyses of forest health and
productivity. This report summarizes stand density (expressed as basal area per unit area) and tree
frequency (number of stems per unit area) for selected subpopulations that were measured in 1991.
Results are presented in the form of cumulative distribution functions. These data will be the basis for
assessments of tree and stand growth after the plots are remeasured in later years.
Tree Crown Conditions
Tree damage and crown condition was measured on all trees > 12.7 cm dbh on forested
subplots. This report presents summary tables and cumulative distribution functions of selected
measurements, aggregated by species, by forest type, and by crown group. Subpopulations of species,
forest types, and crown groups with a sufficient sample size were also delineated into categories of
optimal, nominal, subnominal, and poor condition for selected measurements, based on expert
judgement or published "threshold" values for those categories.
The measurement procedures are described in detail in the report. Briefly, the following crown
measurements were made.
Crown transparency A measure of the amount of sunlight that passes through foliated
portions of a crown.
Crown dieback A measure of the mortality of relatively young branches that are in the
upper, sunlight-exposed portions of the crown.
Crown position A measure of the relative physical location of a tree crown in a stand
of trees.
Crown density A measure of the two-dimensional appearance of crown fullness and
symmetry.
Crown ratio A measure of the amount of an entire'tree bole that is foliated.
A Crown Defoliation Indicator (CDI) model was also used to describe overall crown condition as a
function of the crown transparency and crown dieback measurements.
The interpretation of tree crown condition measurements depends not only on the ecological
significance of a particular measured value, but also on the perceived importances of relatively high or
relatively low values. The approach taken here to setting the threshold values of condition was
conservative. Where possible, the setting of threshold values considered expected differences among
species, size classes, crown positions, and typical habitat characteristics.
A cursory evaluation of the cumulative distribution function analysis results indicated that most
of the tree species, all examined forest types, and most crown groups examined were in good condition.
It was recognized that since most of the population for species and crown groups were found to be
nominal, that the thresholds might be too high. If this is true, then the percentages of the population
which were in a subnominal condition should be of greater concern.
VI
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Research both off the Detection Monitoring plots and using data from the Detection Monitoring
plots is needed to set consistent and tailored threshold values for each species, forest type, and/or
crown group.
Tree Species Diversity
This report quantifies the tree species diversity of the overstory community by using three well-
known diversity indices -- the number of species present, the natural exponent of Shannon's index, and
the reciprocal of Simpson's index. Data for trees > 12.7 cm diameter at breast height were used to
calculate the diversity indices. Cumulative distribution functions of the indices were used to quantify
the 1991 regional status of overstory tree species diversity in the northeast and southeast United
States.
The cumulative distribution functions of the diversity indices were not significantly different
within either geographic region. Using the exponent of Shannon's index to illustrate the results for the
northeast region, cumulative probability values of 0.25, 0.50, and 0.75 were obtained when the index
was 2.2, 3.6, and 4.6, respectively. The corresponding index values for the southeast region were 1.6,
2.9, and 4.6, respectively.
Although statistical relationships do not imply cause and effect, some associations were
explored among forest characteristics and the values of the exponent of Shannon's index. In the
southeast region, high overstory tree species diversity values were most closely associated with an oak
forest type, natural stand origin, saw limber size class, no recent disturbances, and more than one plot
condition code. In contrast, low values were most closely associated with a pine forest type, planted
stand origin, seedling/sapling size class, recent cutting disturbance, and one plot condition code.
Interpretations of these statistical associations were difficult because of confounding or correlations
among the forest characteristics.
In the northeast region, the possible associations of diversity values and forest characteristics
were even more difficult to investigate because there was relatively little variation in forest
characteristics. For example, the northern hardwoods forest type had sample plots distributed over the
full range of diversity index values, but stand origins and size classes were the same for most of those
plots. Although few pine plots were sampled in the northeast, their proportion decreased as diversity
values increased. In contrast to the southeast region, recent disturbances were not associated with
diversity values in the northeast region.
The cumulative distribution functions of the two geographic regions were most different at lower
diversity values. The difference was not statistically significant, and was explainable by higher
proportions of the pine forest type and planted stand origin in the southeast in comparison to the
northeast. The number of species was more sensitive than the exponent of Shannon's index to regional
differences.
Results and Discussion -- Oil-Plot Data
Three types of off-plot information were introduced into the FHM databases in 1991. Data from
the Forest Service (forest insects and diseases), the Environmental Protection Agency (ozone and wet
deposition), and the National Oceanic and Atmospheric Administration (precipitation, temperature, and
significant weather events) were summarized by FHM analysts. In future reports, these and other
summaries will be used to explore possible regional associations between on-plot measurements and
particular stresses that are of concern. The highlights of the auxiliary data summaries follow.
vii
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Major Forest Insects and Diseases (Eastern U.S.)
A compilation of information from various State and Federal sources identified four problems
of special interest. A new disease (cause unknown) of blackgum has been found in the Appalachian
mountains in three states. Thousands of cabbage palms have died from unknown causes along the
Florida Gulf coast. Populations of the black twig borer (an introduced ambrosia borer) are increasing
and affecting a large number of tree species. Dogwood anthracnose, first discovered in 1987, is
expanding rapidly throughout the range of flowering dogwood.
The report summarizes the status of a large number of common forest insects and diseases.
Detailed insect information is provided for the hemlock wooly adelgid, the hemlock loopers, the eastern
spruce budworm, the southern pine beetle, and the gypsy moth. Detailed information is also provided
for fusiform rust, the littleleaf disease syndrome, the oak decline syndrome, and the beech bark
disease. Observations of other insects and diseases are organized according to forest types. Evidence
of damage from weather events and ozone is mentioned.
Climate
The climate report summarizes selected climate conditions and events which are known from
experience to impact forest health. Data from a variety of sources were summarized into databases
and maps of precipitation, hurricane occurrence, high wind events, ice storms, and late spring hard
freezes. The period from October, 1990 through September, 1991 is summarized for the regions where
FHM plots were measured in 1991.
Highlights include severe tropical storms in the fall, a record warm winter with low snowfall, an
early spring, a severe spring ice storm, and a long and hot summer punctuated by Hurricane Bob.
Air Pollution
Maps of interpolated ozone data for low elevations in the eastern U.S. and maps and regional
summary statistics for selected ions in precipitation revealed the general trends described below.
Emissions data, dry deposition, and pollutants that are typically point source-oriented were not included
in this report.
A summary of ozone data for the years 1985-1989 was made using the seasonal (April through
October) 'W126 index1 that places an emphasis on peak ozone hourly average concentrations. In
general, ozone concentrations vary among years, but over the five-year period there were two regions
that tended to give higher values of the W126 index. One region extends continuously along the mid-
Atlantic seacoast and west to the Appalachian Mountains. The second region is smaller and is
centered over the Ohio River valley near the Ohio-Indiana border. Ozone data sources included the
EPA Aerometric Information Retrieval System, the National Dry Deposition Network, and the Mountain
Cloud Chemistry Program.
Data from the National Atmospheric Deposition Program were used to prepare summaries of
(precipitation-weighted) mean pH, sulfate deposition, nitrate deposition, and ammonium deposition for
1990 (January through December). Precipitation was most acidic, and sulfate and nitrate deposition
was highest, in a corridor that extended roughly from eastern Michigan and southern Indiana to New
York and southern New England. Precipitation acidity and deposition of sulfate and nitrate generally
decrease with distance away from this corridor. This general pattern is similar to that observed in
earlier years. Nearly the entire eastern U.S. receives precipitation that is more acidic than 'normal
rainfall'. There was not a clear spatial pattern of ammonium deposition over the study area.
viii
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Conclusion
This Forest Health Monitoring Statistical Summary describes the current approach and
activities, and summarizes the data that were collected in 1991. Looking to the future, FHM plans the
following events and improvements by 1994:
Agreements will be reached with additional State and Federal agencies to increase the
scope of FHM participation, and to improve the efficiency of data collection and the
depth of data analysis.
The plot network will be expanded from 12 to 18-24 states, including states from the
western and north-central regions of the Nation.
The core set of measurements made at each plot location will be expanded so that
more aspects of forest condition can be monitored. New measurements must first
pass a rigorous peer-review process, and candidates include measures of soil, wildlife
habitat, and foliage chemistry.
The collection of off-plot forest insect and disease information will be standardized.
The set of off-plot databases will be augmented to include information from satellite
sensors and soil surveys.
The assessment capabilities will be developed further and made available to data
analysts around the nation. Annual statistical summaries will be prepared, and the
data will be made available for in-depth interpretive reports to address specific forest
health issues.
IX
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Table of Contents
Notice iii
Forward iv
Executive Summary v
List of Figures xii
List of Tables xv
Acronyms xviii
Acknowledgements xx
1. Introduction 1-1
The Challenge of Monitoring Forest Health 1-3
Current FHM Research 1-7
Looking to the Future 1-12
2. Monitoring Methods 2-1
Technical Objectives 2-1
Sampling Design and Measurements 2-1
Data Analysis 2-8
3. Characterization of Stand Density and Number of Trees
on FHM Plots 3-1
Introduction 3-1
Objective 3-1
CDFs of Basal Area by Major Forest Group for the Northeast
and Southeast 3-1
Method 3-1
Figures 3-2
CDFs of Seedlings, Sapling, Trees and Stems for the Northeast
and Southeast 3-6
Method 3-6
Seedlings 3-6
Saplings 3-6
Trees 3-6
Stems (seedlings, saplings and trees combined) 3-6
4. Crown Condition of Forest Trees on FHM Plots 4-1
Introduction 4-1
Crown Defoliation Indicator 4-2
Thresholds in CDFs 4-2
Results 4-5
Discussion 4-9
5. Characterization of Regional Overstory Tree Species Diversity
on FHM Plots 5-1
Background 5-1
Data Set Characteristics 5-1
Index Selection and Calculation 5-1
Analysis 5-4
Results and Discussion 5-4
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6. Selected Climatic Data Summaries 6-1
Introduction 6-1
Synopsis 6-1
Precipitation 6-1
Hurricanes 6-4
High Wind Events 6-4
Ice Storms 6-9
Last Hard Spring Freeze (-2.2 C) 6-9
Data and Information Sources 6-9
7. Status of Major Forest Insects and Diseases in the Eastern
United States, 1991 7-1
Introduction 7-1
New Problems of Special Interest 7-1
Eastern White-Red-Jack Pine (includes Eastern Hemlock) 7-1
Eastern Spruce-Fir 7-2
Southern Pines 7-4
Oak-Hickory 7-5
Northern Hardwoods 7-16
Aspen-Birch 7-18
Special Topics 7-18
Acknowledgements 7-23
8. Selected Air Quality and Deposition Data Summaries 8-1
Ozone 8-1
Wet Deposition 8-8
9. References 9-1
Appendix A. Statistical Design A-1
Appendix B. Quality Assurance Program for Forest Health
Monitoring for 1991 B-1
Appendix C. Nomenclature of Species in Text and Forest Type
and Species Codes C-1
Appendix D. Conversion Factors for Common Measurement Units D-1
Appendix E. Supplemental Tabular Summaries E-1
XI
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List of Figures
Figure 1-1. Distribution of forests in the United States 1-4
Figure 1-2. The 1990 and 1991 plot design and logistics studies 1-8
Figure 1-3. The 1992 regional projects 1-10
Figure 1-4. Landscape pattern types for northern Georgia 1-11
Figure 2-1. The national grid of monitoring locations 2-2
Figure 2-2. The locations of sampling points that were classified (all or in part)
as forested 2-4
Figure 2-3. Typical layout of an FHM plot 2-5
Figure 2-4. Interpreting a CDF 2-11
Figure 3-1. Cumulative distribution function of (a) basal area for the pine group
in the NE, and (b) basal area for the spruce-fir group in the NE 3-3
Figure 3-2. Cumulative distribution function of (a) basal area for the hardwoods
and miscellaneous group in the NE, and (b) basal area for the pine
group in the SE 3-4
Figure 3-3. Cumulative distribution function of (a) basal area for the oak-pine
group in the SE, and (b) basal area for the hardwoods and miscellaneous
group in the SE 3-5
Figure 3-4. Cumulative distribution function of (a) number of seedlings/hectare in
the NE, and (b) number of seedlings/hectare in the SE 3-7
Figure 3-5. Cumulative distribution function of (a) number of saplings/hectare in
the NE, and number of saplings/hectare in the SE 3-8
Figure 3-6. Cumulative distribution function of (a) number of trees/hectare in
the NE, and (b) number of trees/hectare in the SE 3-9
Figure 3-7. Cumulative distribution function of (a) total number of stems/hectare
in the NE, and (b) total number of stems/hectare in the SE 3-10
Figure 4-1. The three established thresholds for the Crown Defoliation Indicator 4-4
Figure 4-2. Standard Federal Regions used for the analyses 4-10
xii
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Figure 5-1. Forest Health Monitoring ecological assessment model for biotic diversity .... 5-2
Figure 5-2. Cumulative distribution function of (a) regional overstory tree
diversity in the NE using species richness, and (b) regional
overstory tree diversity in the SE using species richness 5-5
Figure 5-3. Cumulative distribution function of (a) regional overstory tree
diversity in the NE using Simpson's Index, and (b) regional
overstory tree diversity in the SE using Simpson's Index 5-6
Figure 5-4. Cumulative distribution function of (a) regional overstory tree
diversity in the NE using the Shannon-Weiner method, and (b) regional
overstory tree diversity in the SE using the Shannon-Weiner method 5-7
Figure 6-1. Deviation of annual climate division precipitation (October 1990 -
September 1991) from 1961-1990 average conditions 6-2
Figure 6-2. Probability of a climate division receiving less precipitation than the
October 1990 - September 1991 total 6-3
Figure 6-3. County frequency of hurricane contact, October 1984 - September 1991 6-5
Figure 6-4. County hurricane contact, October 1990 - September 1991 6-6
Figure 6-5. County frequency of high wind events containing wind speeds in
excess of 70 km/h, October 1984 - September 1991 6-7
Figure 6-6. County frequency of high wind events containing wind speeds in
excess of 70 km/h, October 1990 - September 1991 6-8
Figure 6-7. County frequency of ice storm events, October 1984 - September 1991 .... 6-10
Figure 6-8. County frequency of ice storm events, October 1990 - September 1991 .... 6-11
Figure 6-9. Deviation (days) of 1991 date of last hard spring freeze (24-hour
minimum temperature equal to or less than -2.2° C) from 30-year
median conditions 6-12
Figure 7-1. Hemlock wooly adelgid distribution in Virginia (Forest Pest
Management, R-8) 7-3
Figure 7-2. Fusiform rust hazard for loblolly pine in 1991 (Forest Pest
Management, R-8) 7-7
Figure 7-3. Fusiform rust hazard for slash pine in 1991 (Forest Pest
Management, R-8) 7-8
Figure 7-4. Counties with outbreaks of southern pine beetle in 1991 (Forest
Pest Management, R-8) 7-9
Figure 7-5. Historic range of littleleaf disease (Forest Pest Management, R-8) 7-10
XIII
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Figure 7-6. Gypsy moth defoliation, 1984-1991 (Forest Pest Management,
R-8 and MA) 7-13
Figure 7-7. Bottomland oak FIA plots affected by oak decline in the southern
region (Forest Pest Management, R-8) 7-14
Figure 7-8. Upland oak FIA plots affected by oak decline in the southern region
(Forest Pest Management, R-8) 7-15
Figure 7-9. Dogwood anthracnose occurrence in the Eastern United States
(Forest Pest Management, R-8 and NA) 7-19
Figure 7-10. Black gum disease occurrence (Forest Pest Management, R-8) 7-21
Figure 8-1. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1985 ... 8-2
Figure 8-2. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1986 ... 8-3
Figure 8-3. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1987 ... 8-4
Figure 8-4. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1988 . . . 8-5
Figure 8-5. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1989 . . . 8-6
Figure 8-6. An example of a 'gradient' analysis showing one way that kriged ozone
information across years can be aggregated 8-7
Figure 8-7. The 1990 annual levels of precipitation-weighted mean pH for the
eastern United States 8-9
Figure 8-8. The 1990 annual levels of precipitation-weighted mean sulfate
deposition for the eastern United States 8-10
Figure 8-9. The 1990 annual levels of precipitation-weighted mean nitrate
deposition for the eastern United States 8-11
Figure 8-10. The 1990 annual levels of precipitation-weighted mean ammonium
deposition for the eastern United States 8-12
XIV
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List of Tables
Table 2-1. Schedule of descriptive measurements and associated information
collected for FHM plots in 1991 2-7
Table 2-2. Schedule of mensuration measurements for FHM plots in 1991 2-7
Table 2-3. Overstory crown condition and tree damage measurements made
on large trees within subplots in 1991 2-8
Table 3-1. Basal areas associated with cumulative distribution probabilities
of 0.25, 0.05, and 0.75 for Figures 3-1 to 3-3 3-2
Table 4-1. CDF concern, optimal, and poor thresholds for the crown
variables dieback, and transparency 4-4
Table 4-2. Population proportions for species in the East U.S. Region from
CDF analysis 4-5
Table 4-3. Population proportions for crown groups in the East U.S. Region
from CDFanalysis 4-6
Table 4-4. Population proportions for species in Standard Federal Regions I and II
from CDF analysis 4-6
Table 4-5. Population proportions for crown groups in Standard Federal Regions I and II
from CDF analysis 4-7
Table 4-6 Population proportions for species in Standard Federal Region III
from CDF analysis 4-7
Table 4-7. Population proportions for crown groups in Standard Federal Region III
from CDF analysis 4-8
Table 4-8 Population proportions for species in Standard Federal Region IV
from CDF analysis 4-8
Table 4-9 Population proportions for crown groups in Standard Federal Region IV
from CDF analysis 4-9
Table 5-1. Definition and calculation of plot-level values for overstory
tree species richness and diversity 5-3
Table 6-1. Particularly severe icing events in recent history 6-9
Table 7-1. Defoliation by eastern spruce budworm by state for 1990
and 1991 7-4
Table 7-2. Area and percent of host type infected with fusiform rust by
state, 1991 7-6
xv
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Table 7-3.
Table 7-4.
Table 7-5.
Table 7-6.
Table 7-7.
Table 8-1.
Table B-1.
Table B-2.
Table B-3.
Table E-1.
Table E-2.
Table E-3.
Table E-4.
Table E-5.
Table E-6.
Table E-7.
Table E-8.
Table E-9.
Table E-10.
Table E-11.
Table E-12.
Southern Pine Beetle Outbreak Area 1990-1991 7-6
Area and number of countries within the historic range of
littleleaf disease
7-11
Area of defoliation by gypsy moth by state, 1990 and 1991 7-11
Incidence and impact of oak decline in the Southeast, 1991 7-12
Area affected by Dogwood Anthracnose in the Southeast 1988-91 7-20
Median values of annual summary statistics, 1979-1990 8-13
FHM measurements subject to 1991 QA requirements B-2
Format for quality assurance for implementation measurement
documentation for QAPjP B-3
Personnel responsibilities in 1991 FHM monitoring QA activities B-4
Number of subplots by state and land use E-2
Number of subplots by state and major forest type E-3
Number of forested subplots by major forest type and stand
size class E-4
Number of forested subplots by major forest type and stand origin E-5
Number of forested subplots by major forest type and utilization
disturbance E-6
Number of forested subplots by major forest type and care
disturbance
E-7
Number of forested subplots by major forest type and natural
disturbance E-8
Number of subplots by major forest type and terrain position E-9
Number of subplots by major forest type and elevation class E-10
Number of live trees greater than 12.7 cm DBH by species and
crown position on subplots E-11
Number of trees tallied by species and size class (seedlings,
saplings, and large trees > 12.7 cm DBH) E-14
Number of live trees > 12.7 cm DBH tallied on subplots, by
species and crown ratio class E-18
XVI
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Table E-13. Number of live trees > 12.7 cm DBH tallied on subplots, by
species and crown density class E-21
Table E-14. Number of live trees > 12.7 cm DBH tallied on subplots, by
species and crown dieback class E-24
Table E-15. Number of live trees > 12.7 cm DBH tallied on subplots, by
species and crown transparency class E-27
Table E-16. Number of live trees > 2.5 cm DBH tallied on microplots, by
species and crown ratio class E-30
Table E-17. Frequency of visible tree damage types on all live trees greater
than 12.7 cm DBH, by species E-32
Table E-18. Numbers of seedlings and saplings tallied by species and vigor
class on microplots
E-41
XVII
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Acronyms
AIRS - Aerometric Information Retrieval System (U.S. EPA)
CDF - cumulative distribution function
DBH - diameter at breast height (1.37 meters)
DQO - data quality objective
EMAP - Environmental Monitoring and Assessment Program
EPA - U.S. Environmental Protection Agency
EQO - ecosystem-level data quality objective
FIA - Forest Inventory and Analysis
FHM - Forest Health Monitoring
CIS - Geographic Information System
ha - hectare
IQO - indicator-level data quality objective
LPT - landscape pattern type
m - meter
MGCP - Mountain Cloud Chemistry Program
MQO - measurement quality objective
NADP - National Atmospheric Deposition Program
NASF - National Association of State Foresters
NE - northeast region of the United States
NDDN - National Dry Deposition Network
NE - Northeast region
PDR - Portable Data Recorder
QA - Quality Assurance
QAPjP - Quality Assurance Project Plan
xviii
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QC
RQO
SAP
SAMAB
SE
SOP
USDA
VCR
Quality Control
resource-level data quality objectives
Society of American Foresters
Southern Appalachian Man and Biosphere program
Southeast region of the United States
standard operating procedure
United States Department of Agriculture
visual crown rating
XIX
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Acknowledgements
Forest Health Monitoring is an interagency program including the Forest Service, the Environmental
Protection Agency, the Tennessee Valley Authority, the U.S. Soil Conservation Service, the National
Park Service, the Bureau of Land Management, the Fish and Wildlife Service, the National Association
of State Foresters, and individual State forestry authorities.
This document was conceived and prepared by the members of the Forest Health Monitoring national
assessment group. The following lists the current membership of the group.
Bob Anderson
Chuck Barnett
Bill BechtoW
Bill Burkman
Jerry Byers
David Cassell
Steve Cline
Barbara Conkling
Ellen Cooler
Terry Droessler
Beth Eastman
Karl Hermann
Eric Hyatt
Chuck Liff
Bob Loomis
Craig Palmer
Kurt Riitters
Victoria Rogers
Doug Shadwick
Luther Smith
Tom Stockton
Ken Stolte
Larry Truppi
Jim Wickham
USDA Forest Service, Asheville, NC
USDA Forest Service, Radnor, PA
USDA Forest Service, Asheville, NC
USDA Forest Service, Radnor, PA
Lockheed Engineering & Sciences Co., Las Vegas, NV
ManTech Environmental Technology, Inc., Corvallis, OR
ManTech Environmental Technology, Inc., Corvallis, OR
North Carolina State University, Raleigh, NC
U. S. Environmental Protection Agency, Research Triangle Park, NC
ManTech Environmental Technology, Inc., Corvallis, OR
North Carolina State University, Raleigh NC
ManTech Environmental Technology, Inc., Research Triangle Park, NC
US Environmental Protection Agency, Research Triangle Park, NC
University of Nevada-Las Vegas, Las Vegas, NV
USDA Forest Service, Research Triangle Park, NC
U. S. Environmental Protection Agency, Las Vegas, NV
ManTech Environmental Technology, Inc., Research Triangle Park, NC
Lockheed Engineering & Sciences Co., Las Vegas, NV
ManTech Environmental Technology, Inc., Research Triangle Park, NC
ManTech Environmental Technology, Inc., Research Triangle Park, NC
Duke University, Durham, NC
USDA Forest Service, Research Triangle Park, NC
U. S. Environmental Protection Agency, Research Triangle Park, NC
Bionetics Corp., Warrenton, VA
The following members of the Forest Health Monitoring Information Management group are recognized:
David Dickson, Andy Gillespie, Doug Griffith, Larry Royer, Mark Rubey, and Art Walmsley.
The authors thank the external reviewers of this document, Dr. Daniel Binkley and Dr. George Furnival,
for their time in reading the document and their constructive comments.
The authors gratefully acknowledge the additional review comments from Roger Blair, David Cassell,
Gardner Evans, Tony Olson, Don Stevens, and John Vissage.
Appreciation goes to Kurt Riitters and Barbara Conkling for their contributions as editors.
Appreciation also goes to the following persons: Elizabeth Eastman for her assistance with editing, Karl
Hermann and Tom Stockton for their graphics assistance, and Cheryl Simmons for her word processing
assistance.
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1. Introduction
In 1990, the U.S. Forest Service and
the U.S Environmental Protection Agency
initiated a cooperative national program of
Forest Health Monitoring, known as FHM.
Participants include the National Association of
State Foresters, individual State forestry
agencies, the Tennessee Valley Authority, the
Soil Conservation Service, the Bureau of Land
Management, the Fish and Wildlife Service,
and the National Park Service. Although FHM
is not established in all parts of the nation, it
has already provided valuable information
about forest health in some areas. Today, as
more Federal and State organizations are
joining the program, FHM is becoming a focal
point for many efforts to understand forest
health and to assess the impacts of natural and
man-made stresses. In response to the
diverse and growing public concerns about
human impacts on our environment, the
objective of FHM is to give a many-sided view
of forest health that will assist the public in
setting priorities and making informed choices
aimed at reducing the environmental risks of
human activities.
The Forest Health Monitoring program
is designed to provide information to help
protect, manage, and use forest resources
wisely. But monitoring alone can only provide
information about the status and trends of
forest health, and that is why FHM also
supports more intensive research that is
intended to discover specific cause and effect
relationships. When these relationships are
well-understood, then specific actions may be
taken to mitigate or prevent impacts, and
monitoring can again be used to measure the
effectiveness of those actions. Some stresses
which operate at regional scales can cause
subtle and slow, yet widespread, changes in
forest health that can only be detected by using
data from a long-term and large-scale
monitoring program such as FHM. For the
sake of efficiency, FHM is designed to
complement and utilize the results of ongoing
; iPA% Environmental Monitoring ant* Assessment Program
The Environmental Monitoring and Assessment Program (EMAP) is an interagency program to
monitor the condition of the nation's ecological resources. EMAP is the foundation of EPA's comprehensive
ecological risk assessment program and it utilizes forest condition information from the Forest Health
Monitoring program. Other interagency efforts will supply monitoring data for lakes, streams, wetlands,
agricultural lands, deserts, and estuaries. Together, these data bases will permit scientists to make
comprehensive analyses of ecological resource conditions at regional scales.
In a sense, EMAP is a 'consortium without walls' where members of the consortium share common
objectives for ecological monitoring. The varied efforts of the agencies in the consortium, made necessary
because of the variety of ecological resources in the nation, are tied together by EMAP's common statistical
sampling design and approach to regional assessments. Because EMAP represents a substantial
commitment of resources, it has become a focal point for both scientific discussions and agency planning
as many groups work to deploy an integrated ecological monitoring network. EMAP is fostering interagency
cooperation and the development of tools and procedures for regional ecological risk assessments. EMAP
also permits the EPA to target its own resources where its scientists can make the highest contribution to
the overall effort.
For more information, contact:
Director, Environmental Monitoring and Assessment Program
U.S. Environmental Protection Agency, RD-680
401 M Street, SW
Washington, DC 20460
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***
State Forestry Agencies and the National Association of State Foresters (NASF) -- through its Forest Health
Committee -- are key partners for planning and implementing Forest Health Monitoring. Contact the NASF
at (202) 624-5415 for more information on state forestry activities.
The USDA Forest Service has several programs which relate to FHM. Some of these currently participate
in FHM and others may contribute or use FHM information in the future. Information about the following
programs can be obtained from the Forest Service National Headquarters Office at (202) 205-1760.
Forest Inventory, Economics, and Recreation Research - Forest Inventory and Analysis Program
Forest Pest Management
Watershed and Air Management - Air, Water, and Soils Programs
Timber Management ~ Timber Inventory Program
Resources Program and Assessment
Land Management Planning
***
environmental monitoring and research that is
carried out by other Federal and State
organizations. In these ways, FHM makes a
unique and valuable contribution to
comprehensive assessments of the health
status and trends of the nation's forests.
Forest Health Monitoring is being
improved during its initial stages based on
suggestions received from information users
and environmental scientists. The scientific
community has been involved in the planning
and initial deployment of the program. It is now
time to present FHM in some detail to
the public. The main purposes of this report
are: 1) to describe the current approach and
activities; 2) to summarize the limited amount
of data that has been collected to date; and 3)
to outline some new directions that are being
explored. This report is itself part of a test of
an assessment process by which data from
many sources will be brought together for
analyses. As sufficient data are available,
FHM will produce a series of annual 'statistical'
reports and periodic 'interpretive' reports.
Improvements to the assessment process will
be based on reactions to the tables and charts
that appear here as prototypes.
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This report is organized as follows.
This introductory section provides a brief
overview of forest health monitoring. The next
section describes briefly the methods used by
FHM to collect data on a network of plots. This
is followed by a brief characterization of the
plots in the network at the end of 1991. The
final sections provide statistical summaries and
some interpretations of the data that were
collected in 1991, and summaries of the
auxiliary data bases that were made available
in 1991. Detailed tables and charts, and
additional methodological information, are
appended to this report.
The Challenge of Monitoring Forest
Health
Forests are an important part of the
American economy, culture, and ecology.
Overall, about one-third (296 million hectares)
of the United States is forested (Figure 1-1).
Of this total, about 200 million hectares help to
support forest products-related industries that
employ over 18 million people and contribute
over 5% of the gross national product. Forests
everywhere provide important wildlife habitat,
watershed protection, and recreational
opportunities. These amenities are difficult to
evaluate in economic terms, but their value is
at least substantial and in some cases
priceless. Forests occur in a variety of
environments ranging from near-deserts to
wetlands, and they are a part of many national
parks, wilderness areas, and community parks
and greenways. Some forests are virtually
untouched, while others are completely
artificial. Forest lands are owned by the public,
by businesses, and by private individuals.
Nearly everyone has had some personal
experience with forests that helps to shape his
or her expectations and therefore perceptions
of what constitutes 'good forest health'.
American forests are an integral part of a
global ecosystem that permits and sustains the
richness and productivity of life on our planet.
What is'normal'
Your doctor can monitor your general health status with a few measurements such as temperature
and blood pressure. These simple tests are inexpensive and non-specific and therefore, they efficiently
screen a broad range of possible health problems. The test results are taken to be indicators of health
because 'normal' values are well-known and interpretations are not subject to much debate. Similarly, it
is believed that a few key measurements will efficiently and effectively detect changes in forest condition.
But unlike human health standards, forest health standards are not well-defined. The measurements can
be interpreted in different ways because 'normal' forest health means different things to different people.
Different indicators can yield different results if only because they measure different components of forest
health. Furthermore, if populations of forests are normally more diverse than populations of people, it is
possible that different standards will apply to different situations. Finally, foresters consider broad
definitions of wellness that go beyond the normality of physiological processes.
The FHM program relies on the research results of forest scientists everywhere to help define
biological expectations of normality. But lacking a complete biological understanding, FHM can still use
the accumulated monitoring data to identify statistical patterns that indicate relative forest health. The
assessment process will permit people to apply different value judgements to the measurements, further
defining what is meant by a normal forest condition. Meanwhile, interpretations are still necessary because
most people want to know whether or not the forests of the nation are healthy. FHM uses the experiences
of scientists who have studied forest health for many years to guide data interpretation. All interpretations
will be made stronger as the operational definitions of normality become more rigorous and generally
accepted.
***
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Alllkt
Hoilii
Figure 1-1. Distribution of forests in the United States (digitized by S. Azevedo).
1-4
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How can FHM best help to resolve
current and potential threats to forest health?
The public discussion of environmental threats
tends to be issue-oriented: global climate
changes, acid rain, deforestation, and non-point
source pollution are current examples. The
FHM program can contribute to this discussion
in at least two ways. First, FHM can provide
objective statistical information that can be
used to evaluate real and perceived threats
from many viewpoints, including the regulatory
or management viewpoints of the agencies that
sponsor FHM, Second, FHM can contribute
thoughtful and understandable, issue-oriented
interpretations that show a scientific basis for
why forest health is as it appears to be.
Forest health monitoring is not a simple
task. Forests are continually exposed to a
changing array of natural and man-made
stresses. For this reason, it is not usually
possible to point to a single factor that is the
sole determinant of forest health. Forest
function may be viewed as a complex set of
interactions among physical factors such as
weather or disturbance, chemical factors such
as nutrient or pollution levels, and biological
factors such as growth or competition. Often,
the specific response of a forest to some stress
will depend very much on the local
circumstances. Against this backdrop of poorly
understood and interacting factors, the regional
status and trends of forest health can be subtle
and difficult to identify.
The multi-tiered FHM approach to
regional monitoring directs the immediate
objective to identifying those forest regions
where conditions are poor or are changing, for
better or worse. On FHM plots, a set of
'leading indicators' is used to classify the health
status of forests. Because the indicators are
not diagnostic, the specific reasons for
changing conditions may not be obvious at first.
Instead, the indicators collectively represent
many dimensions of forest health and they are
generally responsive to many types of stresses.
The indicators are measured at locations that
are selected by statistical procedures to
systematically represent populations of forests
on a regional basis. The off-plot data bases
may also be used to quantify status and trends
in some cases.
When the measurements indicate
changes in forest health, follow-up analyses
are made in an attempt to associate the
changes with particular classes or types of
stresses. Forest Health Monitoring plot data in
combination with auxiliary data are used for
this task. The resulting picture of forest health
is sharper and guides the next steps of the
assessment process. Sometimes, the
exploratory analysis may conclude that the
observed changes are 'normal', or perhaps not
important enough to warrant further
investigation. At other times, exploratory
analysis may suggest research needed to fully
understand the observed changes. In the rare
cases when FHM measurements include
diagnostic tests of specific cause-effect
relationships, the exploratory analysis can
determine the effectiveness of health mitigation
or stress reduction actions.
The multi-tiered approach to monitoring
preserves options by not focusing on specific
cause-effect relationships at the beginning. For
example, if FHM set out to monitor the effects
of a particular stress such as global climate
changes, then the effects of other equally
important stresses could be missed entirely.
Furthermore, an assessment of the possible
effects of global climate changes on forests
would not be able to separate the effects of
any other interacting stresses. In other words,
forest health monitoring must not be issue-
oriented even though forest health
assessments may be. In another example, if
FHM focused measurements on only one
attribute of the forest, for example biodiversity,
then an effect on another forest attribute, say
productivity, could be missed, and
assessments would then be unable to address
the full range of public concerns. In practice,
the basic FHM design goal is to focus later
research by distinguishing among effects
caused by broad classes of stresses such as
weather, land management, and air pollution.
Another challenge for FHM comes from
the realization that regional forest health is not
just the sum of the individual forest tracts or
1-5
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species. The health of a regional population
depends in part on the spatial context and
functional arrangement of forests. Hunters
know that some grouse species use aspen
forests of different age classes for food or
shelter; the health of the grouse population
depends on a 'healthy' spatial arrangement of
a population of aspen forests. In another
example, a fire that occurs in a remote forest
can have different social consequences than
one that occurs in a municipal watershed. The
historical context is also important. Consider,
for example, that this century has seen the
virtual disappearance of the once-dominant
American chestnut tree from the eastern forest
(caused by an imported fungus), and yet to
many people the eastern forest appears
'healthy* today. This does not imply that
species extinctions are a healthy occurrence,
but rather that interpretations of current health
are always contextual.
Finally, there is the challenge of
actually doing forest health monitoring on a
national scale. There will never be enough
funds to deploy all possible monitoring
Historically, the EPA's science programs have focused primarily on single pollutants, and on their
chemical compositions and impacts on a single medium (e.g., forests). Assessments have been made
primarily in terms of concentrations of that pollutant, and its ultimate transport and fate. Although this
single-medium, stressor-based approach allows us to set regulatory standards for specific pollutants, it may
not be the most effective approach for problems like global warming, stratospheric ozone depletion, habitat
destruction, loss of biodiversity, tropical deforestation, and extinction of amphibian and neotropical migrant
bird species.
One alternative recommended by the EPA Science Advisory Board is an environmental health-
oriented, assessment-driven, ecological monitoring and assessment program which can tell us with known
confidence the actual condition of our environmental resources. Scientifically rigorous information in a
multi-media context would allow decision-makers to focus resources where they could make the greatest
difference. Though somewhat simplified, this approach to achieving the "greatest bang for the
environmental buck" is generally known as risk-based decision making.
Risk-based decision-making uses the relatively new science of ecological risk assessment. The
beginnings of the EPA ecological risk assessment program exist in the Environmental Monitoring and
Assessment Program (EMAP), and in the Forest Health Monitoring program which supplies the forestry
component of EMAP. The goal of EMAP is to use long-term environmental monitoring data for
consolidated assessments that will 'make a difference'. The Environmental Monitoring and Assessment
Program is intended to allow EPA to manage for environmental results as called for by EPA's Administrator
William Reilly. In EPA, EMAP is one of several innovative techniques such as risk-based strategic
planning, regional comparative risk, and ecological risk assessment programs that will be used, to assist
Agency decision-makers in solving environmental problems.
For more information, contact:
Project Officer for EMAP-lntegration and Assessment
U.S. Environmental Protection Agency
Atmospheric Research and Exposure Assessment Laboratory MD-75
Research Triangle Park, NC 27711
***
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procedures, or to measure every acre of forest
land, but a statistical framework and a higher
measurement efficiency mean that fewer trade-
offs have to be made. The operational
problems of logistics, quality assurance, and
data management are not trivial for a national
network of plots. Effective forest health
monitoring requires a substantial investment in
a plot network and in a cadre of forest
monitors. The approach taken by FHM is to
utilize existing resources whenever possible,
and to test procedures in medium-scale pilot
tests before deployment. Much attention has
also been given to optimizing the statistical
designs. Uniform training and quality
assurance programs ensure that the data are
comparable and of sufficient quality for the
intended use. The data are managed almost
entirely by electronic means, from hand-held
computers used for data collection to an
interagency network of large computers for
data analysis and assessments. Some
features of the implementation of FHM will be
highlighted in later sections of this report.
Despite the difficulties, forest health
monitoring is an activity that needs to be
started now. The full value of such monitoring
will be realized when the network has been
operating for several decades, but useful
information can be obtained in the short term
also. Imagine how data from decades of
monitoring could be used today, a time when
regional forest health assessments are
sometimes rendered anecdotal by a lack of
statistical baseline information. The nation
recently spent more than one hundred million
dollars to assess acid rain impacts in forests ~
without the benefit of baseline data that
monitoring could provide. The National
Academy of Sciences, the President's Council
on Environmental Quality, and the EPA
Administrator's Science Advisory Board have all
concluded that knowledge of regional status
and trends will greatly improve this kind of
assessment. The legacy of our efforts today is
a generation of Americans better-equipped to
make informed decisions that affect the
environment.
Current FHM Research
Scientific monitoring is not a matter of
just looking at a few trees here and there. The
FHM program has been started with a sound
scientific basis, and a wide range of research
and development projects are underway to
improve monitoring. The current monitoring
procedures emphasize trees because they are
an important element of forests and because
most monitoring experience is with trees.
When peer-review panels agree, and when
logistically feasible, new monitoring and
assessment procedures can be deployed. The
panels represent many scientific disciplines and
have been selected by, for example, the
National Academy of Sciences, the EPA's
Science Advisory Board, and the American
Statistical Association. Every major plan and
report produced by FHM is peer-reviewed by
scientists from the relevant disciplines.
Change is inevitable because science will
progress, and so FHM has been designed to
accommodate new information.
One exciting research opportunity is
provided by the national FHM network of plot
locations. Whereas most ecological research
is confined to a few plots, or to single
watersheds, the FHM plot network is being
used to test regional-scale hypotheses that
could not be addressed in most other studies.
This type of research complements work that is
conducted by others at experimental forests
and watersheds around the nation. Forest
Health Monitoring supports research to improve
the set of monitoring measurements, and to
increase the sampling and logistical efficiency
of monitoring operations.
The Plot Design and Logistics Study
done in 1990 was a test of the basic plot
design and monitoring procedures in eastern
forest types (Figure 1-2). The primary
objectives were to optimize the sampling
designs and logistical procedures that are now
used. Also included were tests of a few
measurements that are not yet routine, for
example, soil chemistry and vegetation
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Landscape patterns: Research to improve monitoring.
When the earth's surface is viewed from an airplane, a 'landscape' can be represented as a
heterogeneous composition of ecosystems and land uses. Ecologists are learning to classify these patterns
in many ways. EMAP-Landscape Characterization and FHM are testing the use of landscape pattern types
(LPTs) as a means to analyze forest data measured on ground plots in a landscape context.
The LPTs are mapped by visual interpretation of satellite images according to the land cover types
(e.g., forest, agriculture)present and the pattern that these cover types form. The land cover categories
used to map LPTs include forest, clearcut, agriculture, wetland, water, residential, and urban. The pattern
categories are based on the dominance or evenness of the land cover categories. Like a ground-based
measurement, the dynamics of LPTs can indicate large-scale changes in forest condition. The LPTs can
also be used to organize summaries of the FHM ground plot data.
Figure 1-4 shows LPTs for northern Georgia. Landscape pattern type mapping and analysis is part
of current research designed to bring landscape analysis into FHM assessments.
For more information, contact:
Project Officer for EMAP-Landscape Characterization
U.S. Environmental Protection Agency
Environmental Photographic Interpretation Center
Building 166, Bicher Road, Vint Hill Farms Station
Warrenton, VA22186
***
structure. The Georgia Demonstration and the
Western Pilot projects done in 1991 were
similar tests that extended results to different
ecological regions and more types of forests
(Figure 1-2).
Apart from optimizing data collection
after a measurement is decided upon,
improving efficiency also means optimizing the
set of measurements that are made. A suite of
measurements is needed that characterizes
forest health, broadly defined, and that signals
changes in forest health from the wide variety
of possible causes. Ideally, the suite is chosen
such that analyses also give clues about
plausible causes of change, and hence suggest
the next logical steps in the assessment
process. Starting from several conceptual
models of forest health, FHM scientists are
conducting regional demonstration projects to
test the efficiency and efficacy of alternate sets
of measurements.
The Southeast Loblolly/Shortleaf Pine
Demonstration project (1992 - 1993) is one
such test of a set of measurements, in this
case for a single forest type over a large
geographic region (Figure 1-3). The Southern
Appalachian Man and Biosphere (SAMAB)
program in cooperation with FHM is conducting
the SAMAB Demonstration project (1992 -
1993) to make the same kind of test in multiple
forest types in a somewhat smaller geographic
region (Figure 1-3). In California and Colorado,
the Western Pilot project (1992) is also the first
step towards implementing a basic set of
measurements in western forests (Figure 1-3).
Where appropriate, FHM also sponsors
research projects that are not based solely on
the regional plot network. The interagency
approach and FHM peer-review process are
used, and teams of researchers are formed to
collaborate on parallel studies in their local
areas. For example, to develop methods to
measure forest canopy light transmittance, a
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matrix
Forest matrix with patch
Dominated by forest dearcutting
Residentid dominated
Urban dominated
Water
Dominated by Agriculture
Agriculture mosaic
Clouds
Figure 1-4. Landscape pattern types for northern Georgia.
1-11
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team is made up of researchers from the
Forest Service, the Tennessee Valley Authority,
the University of Wisconsin-Madison, the
Michigan Technological University, and the
Environmental Protection Agency. Other teams
are being formed to develop field and
laboratory methods for soil and foliage analysis,
and to learn to use satellite sensors in
conjunction with high-resolution photography
and the global positioning system to augment
ground-based measurements. In this fashion,
FHM gains the benefits of cost-sharing and the
wisdom of researchers who are experts for
particular new measurements. It is prudent to
explore the latest technology at smaller scales
than would be required for regional-scale tests.
Looking to the Future
Forest Health Monitoring plans the
following events and improvements by 1994.
• Agreements will be reached with additional
State and Federal agencies to increase the
scope of FHM participation and to improve
the efficiency of data collection and the depth
of data analysis.
• The plot network will be expanded from 12 to
18-24 states, including states from the
western and north-central regbns of the
nation.
• The core set of measurements made at each
plot location will be expanded so that more
aspects of forest condition can be monitored.
New measurements must first pass a
rigorous peer-review process; candidates
include measures of soil, wildlife habitat, and
foliage chemistry.
• The collection of off-plot forest pest
information will be standardized.
• The set of off-plot auxiliary data bases will be
augmented to include information from
satellite sensors and soil surveys.
• The assessment capabilities will be
developed further and made available to data
analysts around the nation. Annual statistical
summaries will be prepared and the data will
be made available for in-depth interpretive
reports to address specific forest health
issues.
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2. Monitoring Methods
The objective of this chapter is to give
a brief overview of the forest health monitoring
procedures that were used in 1991. A
summary will be given of the basic sampling
methods, measurements, and data analysis
procedures. Later chapters will consider
particular classes of measurements in some
detail. Other references for sampling and
statistical analysis include Appendix A of this
document, Overton et al. (1990), and Stevens
et al. (in preparation). Monitoring operations
and measurements are also described in a
series of methods guides (Conkling and Byers
1991; Byers and Van Remortel 1991;
Chojnacky 1991; Conkling and Byers 1992).
Technical Objectives
Three technical objectives determine
the methods of forest health monitoring. The
first objective is to estimate the status,
and change over time, of a set of regional
forest condition and stress indicators. The
second is to explore some possible
associations between regional trends of
condition and stresses. The third is to provide
statistical and interpretive summaries of the
observations that are made.
Sampling Design and Measurements
The technical objectives stress the
need for statistical rigor at the regional scale of
the plot network. The statistical theme is also
emphasized for the measurements that are
made at every plot location and for analysis of
the data.
The Plot Network
The plot network is defined by a
regular grid of sampling points that is laid out
over the entire nation (Figure 2-1). There are
about 12,500 sampling points on the grid,
1
Forest Bsafch Indicator Development and Evaluation
Experience has shown that there is no single 'best' way to measure the health of a particular tract
of forest. The main reason for the differences is the lack of consensus about what 'forest health' means.
For example, some view forest health in terms of long-term soil productivity while others see it in terms of
aesthetic features. These valid differences of perspective can lead to very different sets of measurements
for monitoring. Even with a consensus view, however, there would still be alternatives for making particular
measurements. For example, tree crown density can be gauged by eye or by instrument, and instruments
can be located in space or on the ground. Indicator development and evaluation refers to the process of
sorting out the options and choosing the measurements and the methods best suited to regional monitoring.
There is a defined procedure for identifying candidate measurements and moving them through
a series of tests prior to their acceptance or rejection. This mechanism ensures that the selected
measurements represent a good cross-section of scientific and social perspectives about forest health.
More measurements are possible than can ever be implemented. It follows that measurement testing and
implementation should also be aimed at improving the efficiency of the overall measurement effort so that
assessments can be made using as many definitions of health as is possible within the logistical
constraints. Inevitable advances in biological understanding and technological capabilities will lead to
changes in the details of measurements over time. The indicator development and evaluation strategy
ensures that the changes do not disrupt the continuity or value of any long-term records that have
accumulated.
Contact the FHM Technical Director or Program Manager for information on indicator evaluation and
selection.
***
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excluding Alaska, equally spaced about 27 km
apart. There is a sampling rule for choosing a
random plot location in the vicinity of each grid
point (Appendix A). Once defined, each plot
location is fixed. In a given year, plot locations
are classified as forested or non-forested
(Chojnacky 1991). Non-forested plot locations
are noted, but no field measurements are made
at those locations in that year (Figure 2-2). A
plot is established (or remeasured) at every
forested location and the plot further defines
the sampling rules for particular measurements
(see below).
This type of systematic sampling
design has proven to be extremely efficient for
large-scale forest inventory and is expected to
be equally efficient for monitoring forest health.
The design provides an unbiased sample of the
nation's forests at any point in time, because
different types of forests are sampled in
proportion to their actual occurrence and
distribution at that time. Land-use conversions
from forested to non-forested, or vice versa,
are expected and will not disrupt the design.
The relative simplicity of this design ensures
that it will be possible to use more
sophisticated statistical methods during data
analysis. For example, post-stratification can
be used to group plots of different types for
more in-depth analyses, or spatial statistical
methods can be applied to estimate trends over
an entire region. The particular triangular grid
design also facilitates the use of
interpenetrating sampling methods and
increasing the numbers of plots in specific
areas without losing the strengths of the
design. Other approaches that are not based
on a grid were evaluated and rejected because
they do not provide such a combination of
flexibility and efficiency.
A Typical FHM Plot
An FHM plot defines the sampling rules
that are used at a given location (Chojnacky
1991). The FHM plot is presently defined as a
cluster of four subplots that are contained
within a one-hectare area, centered on the plot
center (Figure 2-3). All measurements made in
1991 were made within these four subplots. In
the future, other measurements may be made
outside the four subplots, but they will still be
referenced to the same plot center to preserve
the overall design. It is also possible to define
sub-subplots within an FHM plot. This
procedure is commonly used for measurements
of small trees and seedlings, to reduce the
numbers that would otherwise have to be
measured on the larger subplots.
The FHM plot is now being used in
most forest types. In the future, some
alterations will be necessary in forests that
have exceptionally large trees which occupy a
high percentage of the plot area (redwood and
sequoia forests), and in forests that naturally
have a very low density of trees (pinyon-juniper
forests). Consistency is highly desirable to
maintain comparability of data among regions
and forest types, but the flexible plot design
permits modifications to deal with these special
situations.
Although the plot locations are
considered to be permanent, there are no
physical indications that would permit a casual
observer to identify an FHM plot on the ground.
This is important in maintaining an unbiased
sample and the courtesy of the landowner.
Landowners who are reluctant to have plots on
their land have the opportunity to refuse the
entry of field crews. These locations are
considered 'non-respondents' for the current
year, and landowner permission is again
requested at the next scheduled measurement.
Measurements in 1991
It is beyond the scope of this chapter
to describe the measurements in detail.
Appropriate references for methods include
Chojnacky (1991) and Conkling and Byers
(1992). The former reference applies strictly to
the 1991 procedures, and the latter is a more
comprehensive edition of procedures that is
being used for the 1992 field measurements.
Subsequent chapters of this report also define
measurements as necessary. Some
measurements and observations are made for
purely descriptive purposes and others are
made to characterize the health of the forest at
a plot location. Still other measurements can
2-3
-------
Figure 2-2. The locations of sampling points that were classified (all or in part) as forested are
shown.
2-4
-------
Distance between points is 120 ft.
Azimuth 1-2 360*
Azimuth 1-3 120*
Azimuth 1-4 240*
Subplot
24.0' radius (7.32 m)
Annular Ptet
58.9' radius (17.95 m)
' Micro-plot
6.8' radius (2.1 m)
12' @ 90' az. from
subplot center (3.66 m)
Figure 2-3. The FHM field plot design.
2-5
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, «**> Ct^y Assurance
A large program such as Forest Health Monitoring requires a substantial amount of coordination
to ensure that all field operations are completed in a timely and high-quality fashion. Our field crew
candidates come from many agencies with various backgrounds, and each must complete a rigorous
training session to become certified 'forest health monitors.' Every year, the actual field work is preceded
by inter-regional, pre-training workshops to ensure that methods are being taught consistently across all
regions. In 1991, the formal training sessions were held in Durham, NH and Asheville, NC.
At the training sessions, the final logistical preparations are also made and the field equipment is
distributed to the crews. To the field logistics specialists, this is just one more step in a year-round process
of procurement, maintenance, inventory, and planning. All logistical arrangements and personnel are
coordinated at the national and regional levels to ensure consistency and efficiency of the field operations
and staffing. A logistics plan specifies 'how the job will get done', and it is safe to say that without logistical
support, very little could be accomplished.
Quality assurance (QA) is not just a 'buzz-word' for the Forest Health Monitoring program. A QA
program is required by several of the participating agencies for all data collection activities, and there are
very specific guidelines to folbw. But QA is also more than just an agency requirement. Without QA, the
data that are collected would be of unknown quality and therefore much less useful for assessments of
forest health. Few rf any forest monitors think that their data are of poor quality, and they welcome a QA
program to document just how good they are!
Contact the FHM Technical Director or Program Manager for information on training, logistics, and quality
assurance.
***
Poiiabte Data i$ft&»«i#B* Sp«<>d Field Operations
Portable data recorders (PDRs) are the compact and rugged field'computers that Forest Health
Monitoring crews use to record the observations and measurements they make. An interagency team of
computer programmers, data analysts, and crew persons collaborated to design, test, and deploy PDRs
for data capture.
From the start, FHM has used PDRs because they speed up data collection and processing, and
because they improve the quality of the data. No longer is data processing deferred until the end of the
field season; every several days the field data are transmitted to a larger computer where it is verified and
entered into the data bases. At the same time, crew status and location, equipment needs, and weekly
plans are transmitted so that field operations can be fine-tuned during the season. The use of PDRs
improves data quality by providing real-time verification. This ensures that only valid data codes are used
and that all the measurements are made on each tree or plot. In some cases, apparently extreme values
are brought to the attention of the operator for on-site verification.
Contact the FHM Technical Director or Program Manager for information on portable data recorders and
information management.
***
2-6
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be used either as indicators or as classification
variables depending on the particular analysis.
It is convenient to group the descriptive
measurements and observations into three
categories depending on their level of detail at
a given plot location (Table 2-1). The plot-level
category consists of measurements of the plot
location as a whole. The subplot-level category
has measurements that are made at the center
of each of the four subplots. The microplot-
level category has measurements that apply to
the sub-subplots, the microplots, where small
trees are measured. The descriptive
measurements are used to characterize the
sample of plots.
Each subplot may be made up of very
different sub-areas. For example, a subplot
may include both forested and non-forested
land uses. This within-plot variation must be
accounted for when aggregating the data for
analyses. In the field, the transitions between
land uses, forest types, stand origins, stand
sizes, and past disturbances are mapped for
each subplot. In the following chapters, these
recognizable categories are referred to as
condition categories (Chojnacky 1991).
Table 2-1. Schedule of descriptive
measurements and associated information
collected for FHM plots in 1991. See text for
definition of information categories.
Plot-level information
Plot number
State
County
Old plot status
(Aerial) photo year
Date of measurement
Elevation
Terrain position
Crew identification
Subplot-level information
Land use class
Forest type class
Stand origin class
Stand size class
Past disturbances
Slope correction
Slope percent
Aspect
Microrelief
Subplot map
Microplot-level information
Land use class
Forest type class
Stand origin class
Stand size class
Past disturbances
A set of mensuration measurements
(Table 2-2) is made partly for descriptive
purposes and partly to estimate growth and
related indicators following remeasurements in
later years. In 1991, all large trees (stem
diameter > 12.7 cm) within each subplot, and
all saplings (stem diameter between 2.5 cm
and 12.7 cm) within each microplot were tallied
separately. Seedlings (stem height > 30.5 cm
and stem diameter < 2.5 cm) within each
microplot were tallied as frequency by species,
condition class, and crown class. The large
tree mensuration data were also used for an
analysis of tree species diversity (see Chapter
5).
Table 2-2. Schedule of mensuration
measurements for FHM plots in 1991. See text
for explanation of tree categories.
Large trees and Saplings
Tree number
Location on the plot
Tree history (e.g., live, ingrowth, cut)
Species
Stem diameter
Condition class
Crown class
Seedlings
The frequency of seedlings is tallied by:
Tree species
Condition class
Crown class
2-7
-------
A final set of crown classification and
tree damage measurements were made on
large trees (Table 2-3). In addition to the
measurements shown for large trees on
subplots in Table 2-3, the frequency of
seedlings by vigor class, and the vigor class,
damage, and crown ratio of saplings was
measured on microplots. This set of
measurements is intended to partially represent
forest health in terms of visual evidence of
stress responses and damage, especially to
overstory trees. The majority of measurements
are not diagnostic indicators but rather help to
characterize overall tree condition. The crown
classification measurements are reported in
Chapter 4.
Table 2-3. Overstory crown condition and
tree damage measurements made on large
trees within subplots in 1991. See text for
measurements of seedlings and saplings.
Crown measurements
Crown ratio
Crown diameter width
Crown density
Crown dieback
Foliage transparency
Damage measurements
Up to three 'significant' (in terms of obvious
impact on tree health) damages to the tree
are noted, and the following information is
recorded for each:
Condition (e.g., dead, open wound,
deformed, watersprouts)
Location on the sample tree
Probable cause (only if obvious; e.g.,
insect, fire, logging)
Data Analysis
The data were summarized in several
ways for this report. A statistical summary of
most measurements is based upon cumulative
frequency distribution methods described below
(see also Appendix A). The methods of
indicator analysis in this report are under
continuous review as part of the FHM indicator
development and selection process. Reviewers
of this report have suggested alternative
methods of data analysis for several of the
indicators, which may improve the interpretation
of the results. The FHM team has accepted
these comments and will be studying these
suggestions for data analysis of existing and
new indicators. Tabular summaries were also
prepared in some cases. Finally, indicator
leaders sometimes use innovative techniques
to highlight particular aspects of their analyses.
At the first stage of data analysis, the
emphasis is on providing objective data
summaries that will be valuable for looking at
forest health from a variety of perspectives.
The techniques for these purely statistical
summaries were peer-reviewed (during the
design of FHM and EMAP) before they were
used in this report. The value of this rather
conservative approach is that there will be
much more consistency over time and among
regions, in comparison to an ad hoc approach
every year. Once a reader learns how to
interpret the summary of one measurement,
then it is a simple matter to interpret the
summary of every measurement, every year.
The drawback is that, at this stage of program
development, only a limited set of statistical
summary procedures have been agreed upon.
In later stages of analysis, the
measurements are interpreted from more than
just a statistical point of view. Here is where
value judgements become extremely important.
It is recognized that not everyone's value
judgement is exactly the same, and so not
everyone will necessarily agree with any single
interpretation. Where interpretations are made
in this report, the analysts state carefully their
assumptions and the scientific basis of their
interpretation.
Rather than being of a purely statistical
or purely interpretive style, this report is a
hybrid. In time, two types of reports will be
produced when the amount of data becomes
sufficient. For this report, the basic statistical
summaries appear because their preparation
2-8
-------
forced the annual process of data verification,
validation, and data base documentation to a
timely conclusion. Individual indicator leaders
interpreted their particular results based upon
available data and accepted interpretive
techniques. This middle ground was chosen
because we know in advance that we cannot
fully interpret all the measurements, or forest
health in general, when the program has not
been fully implemented. On the other hand,
the more common measurements should be
interpreted in ways that have been acceptable
historically. The intention is to provide the most
information possible from the limited data, and
at the same time, to acknowledge differences
of opinion and preserve options for other
analyses and conclusions.
This report was subject to a review
process to validate the methods that were
used. As experience grows with particular
measurements, it may be possible to routinely
include interpretive procedures that were
originally tenuous but which have become
more accepted over time.
This first report makes few attempts to
analyze many measurements at one time. This
is mainly because there are not many accepted
methods for doing so at present. There is also
not a sufficient breadth of measurements
available for comprehensive, biological model-
based analyses of forest conditions. Where
feasible, measurements have been condensed
into index values which were statistically
summarized and interpreted. The choice of an
index is itself an interpretation, and so these
indices were also subject to the peer review
process.
HOW to Interpret Cumulative Distribution Functions
The cumulative distribution function (CDF) graphical technique that is used extensively in this report
is unfamiliar to many readers. For the casual reader, this section will describe how to interpret a CDF.
Appendix A supplies additional details for those who require a more rigorous mathematical treatment. The
effort to learn the technique is modest, and the reward is substantial; many monitoring programs around
the world use this simple, powerful, and informative graphical technique for presenting data summaries.
Parts of the following description follow closely the discussion in Linthurst et al. (1986).
A CDF is better than a tabular summary for presenting an objective view of FHM data. Specifically,
'poor1 forest health is not unequivocally or universally defined (see sidebar on "What is Meant by 'Good'
Forest Health?"). The use of a CDF permits the reader to choose a 'critical value' of a measurement, and
to see what proportion of the sampled population is estimated to fall below the threshold for that
measurement. Tabular summaries summarize data according to a critical value defined by the data
analyst, but the reader is unable to see how an interpretation could change if a different value was chosen.
If needed, a tabular summary with confidence intervals can be prepared from CDFs for any particular
critical values.
Each CDF is identified by the variable X (a measurement or an index, on the horizontal axis) and
by the subset of plots in the population being examined. For example, X could be a crown rating score
and a subset of the population could be all oak trees found in oak-pine forests. To find the estimated
proportion of the population that falls below some critical value of X, use this procedure (see Figure 2-3):
1. Find the chosen critical value (X) on the horizontal axis.
2. Draw a line straight up to meet the solid line that is charted on the CDF.
3. Draw a line straight left to meet the vertical axis.
4. Read the estimated proportion where the line meets the vertical axis.
5. The proportion is the fraction of the population that is estimated to have a value of X that is less
than the critical value.
2-9
-------
In the example, a critical value of 17 in step 1 leads to the interpretation that about 47% of the
population has a value of X less than 17. Different critical values (in step 1) will yield different population
estimates (in step 4). The critical value is really a criterion by which 'poor health' will be gauged. By
experimenting with critical values, the reader may see how data interpretations change with the criterion
chosen to signify 'poor health1. For example, a different critical value of 10 in step 1 leads to the
interpretation that about 38% of the population is in 'poor health' (this example is not shown in Figure 2-3).
There are some important things to remember when using the above procedure. First, this
graphical technique is suitable for rough estimates, but precise computation requires the use of data base
and appropriate algorithms (see Appendix A). Second, the CDF cannot be used (strictly) to get estimates
of proportions greater than some value of X. Instead, a new chart (of 1 - F(X)) must be prepared for that
purpose, again using the data base and appropriate algorithms.
The CDFs in this report have solid and dashed lines charted. The solid line yields the population
estimate as described above. The dashed lines yield the lower and upper 90% confidence limits for the
population estimate. A good statistical text and Appendix A should be consulted for a discussion of what
the confidence limits mean.
In summary, the CDF technique is a flexible format for presenting an unbiased view of FHM data.
It permits any reader or analyst to impose a value judgement of what 'poor" health means, and to see what
fraction of the population is estimated to be in 'poor" health according to that criterion. The CDF does not
change the data in any way , it simply presents it in a non-tabular fashion. Despite the simplicity of the
approach, some readers will never be comfortable with the CDF technique. For these readers, only the
possibility of making one's own interpretation will be lost; it is always possible to accept (or argue) the
interpretations that other analysts will make in later chapters or in other reports.
***
2-10
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Population: Oak species in oak-pine forests
F(x)
10
20
30
40
50
Hypothetical Crown Rating Score
Figure 2-4. Interpreting a CDF.
2-11
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3. Characterization of Stand
Density and Number of
Trees on FHM Plots
T. D. Droessler
Introduction
The mensuration statistical summary
presents baseline estimates of basal area for
trees 12.7 cm DBH and larger and numbers of
stems/ha for seedlings, saplings, and trees,
both separately and combined. Tables of the
number of subplots by state, land use, major
forest type, stand size, stand origin, elevation,
natural and anthropogenic disturbance history,
terrestrial position, elevation class, subplot
characteristics, and size class of tally trees are
also provided (Appendix E). Point estimates of
mensurational characteristics can be used to
help interpret other measurements. For
example, the probability of a forest pest
infestation is frequently a function of the density
(basal area or otherwise) of combinations of
tree species.
Objective
The objective is to present cumulative
distribution functions (CDFs) with 90%
confidence bounds of basal area per hectare by
major forest group over the subplots, and
numbers of seedlings, saplings, and trees per
hectare over the microplots, separately and
combined, for the Northeast (NE) and
Southeast (SE) United States. Other
characteristics of the subplot/microplot tally are
presented in tables. Minimal commentary
regarding the CDFs or tables is provided.
CDFs of Basal Area by Forest
Group for the Northeast and
Southeast
Method
The data from the 12 states was
divided into Northeast and Southeast states.
The Northeast states were Maine, New
Hampshire, Vermont, Massachusetts, Rhode
Island, and Connecticut. The Southeast states
were New Jersey, Delaware, Maryland,
Virginia, Georgia, and Alabama.
The Northeast and Southeast data sets
were divided into major forest groups. The
major forest groups were partitioned as follows:
the pine group consisted of the white, red, and
jack, longleaf-slash, and loblolly-shortleaf pine
groups; the spruce-fir group consisted ol
spruce and fir; and the hardwoods/
miscellaneous group took in the remaining
categories. The species groups were originally
further subdivided (e.g., the longleaf-slash pine
group), but the sample sizes were too small to
be meaningful.
Only forested land use portions of
subplots (wholly or partially) were included in
the calculations. Each subplot was considered
a sampling unit. Basal area by forest group
was first calculated by subplot and then
expanded to a hectare basis before calculating
the mean for the plot. If basal area of a
particular forest group was present on only one
of the four subplots, the basal area values for
that forest group on the other three subplots
were included as zeros in the calculation of the
mean basal area. The calculations were
conducted as follows.
First the basal area (m2) was calculated
by forest group for each subplot. The condition
number was used for subsetting to a forest
group. Next the forest group basal area was
expanded to a per hectare value by multiplying
by the subplot per hectare expansion factor
(approximately 59.48).
Next, a weighted mean basal area
(m2/ha) was calculated by forest group over the
four subplots. The weight used in the
numerator was the fractional area of each
forest group for each subplot calculated by
summing the fractional area percents (area
percent divided by 100) by condition number.
A weight of 1.0 was used if the whole subplot
had a forested land use and was of a particular
forest group. It was possible for a plot to have
zero basal area if it was classified as a
3-1
-------
forested land use with a forest type identified,
but there were no trees greater than 12.7 cm
DBH present.
The denominator in the weighted mean
was the total area over the four subplots
classified as forested land use (maximum of
4.0). The sum of the weights in the numerator
for a particular forest group equalled the
denominator only if all forested area on all
subplots was of a particular forest group.
The mean basal area for a forest group
represented an observation for calculating a
CDF. The CDFs can be used to determine the
cumulative probability of the relevant forested
population that has a basal area equal to or
less than a specified level.
Figures
Figures 3-1 through 3-3 present CDFs
with 90% confidence bounds of basal area
(m2/ha) by major forest groups for the
Northeast and Southeast states. The maximum
basal area ranged from 0 to 60 m*/ha for any
forest group in the Northeast and Southeast.
Figure 3-1 shows CDFs of basal area
in m2/ha for the pine group and the spruce-fir
group in the Northeast. The 90% confidence
region for the pine group CDF is large because
the sample size used to calculate the CDF was
24 hexes.
Figure 3-2 shows CDFs of basal area
in m2/ha for the hardwoods and miscellaneous
group in the Northeast and for the pine group
in the Southeast.
Figure 3-3 shows CDFs of basal area
in m2/ha for the oak-pine group and the
hardwoods and miscellaneous group in the
Southeast.
As an example of how the CDFs can
be used, consider the basal area associated
with specified cumulative distribution functions
(Table 3-1).
Looking at NE Pine, 75 percent of the
population have a basal area equal to or less
than 31 m2/ha. For SE Pine, 75 percent of the
population have a basal area equal to less than
12 m2/ha. The interpretation of these
differences must be based on other
characteristics of the population which is
beyond the scope of this example.
Table 3-1. Basal areas associated with cumulative distribution probabilities of 0.25, 0.50, and 0.75 for
Figures 3-1 to 3-3.
Figure
Cumulative Probability
0.25
0.50
0.75
1 (a) NE Pine
1 (b) NE Spruce-fir
2 (a) NE Hardwoods + miscellaneous
2(b) SEPine
3 (a) SE Oak-pine
3 (b) SE Hardwoods + miscellaneous
18.
7.
3.
0.
1.5
2.
27.
18.
13.
3.
6.5
10.
31.
30.
22.
12.
15.
21.
3-2
-------
(a)
0.0
0.0
10.0 20.0 30.0 40.0 50.0
60.0
(b)
Basal Area ( sq m / ha )
0.0
10.0 20.0 30.0 40.0 50.0 60.0
Basal Area (sq m / ha
Figure 3-1. Cumulative distribution function of (a) basal area for the pine group in the NE, and
(b) basal area for the spruce-fir group in the NE.
3-3
-------
(a)
1.0 T
0.8 -•
Rx),
0.2
O.Q
0.0 10.0 20.0 30.0 40.0
Basal Area ( sq m / ha )
(ft)
1.0 T
QiS -•
F(x)
0.4 -•
0.2 -,
0.0
1 1 1 1—
0.0 10.0 20.0 30.0 40.0
Basal Area (sq m / ha )
50.0 60.0
-t
50.0 60.0
Figure 3-2. Cumulative distribution function of (a) basal area for the hardwoods and
miscellaneous group in the NE, and (b) basal area for the pine group in the SE.
3-4
-------
(a)
1.0 T
0.0
10.0 20.0 30.0 40.0
Basal Area ( sq m / ha )
(b)
1.0 T
F(x) 0.6-•
0.0
0.0 10.0 20.0 30.0 40.0
Basal Area (sq m / ha )
50.0 60.0
50.0
60.0
Figure 3-3. Cumulative distribution function of (a) basal area for the oak-pine group in the SE,
and (b) basal area for the hardwoods and miscellaneous group in the SE.
3-5
-------
CDFs of Seedlings, Saplings,
Trees and Stems for the
Northeast and Southeast
Method
The hex data from the 12 states were
divided into Northeast and Southeast states in
the same way as was done for basal area
above. The seedling and sapling count for
each microplot was expanded to a per hectare
basis and a mean calculated over the
microplots on the hex (maximum of four). The
mean count for a hex was used as an
observation to calculate the CDF. Microplots
with zero seedlings or saplings were included
in the mean as long as they had a forested
land use classification. Although it was
possible to have more than a single condition
class on a microplot, only two microplots (out of
more than 2000) were split. Therefore,
condition class was ignored. Tree count was
calculated as a weighted mean over the
subplots using the forested area on each
subplot as a weight. Stem count was
calculated as the sum of the seedlings and
saplings on the microplot and trees on the
subplot, using the appropriate expansion
functions to bring the estimates to a hectare
basis.
Figures 3-4 through 3-7 show
seedlings, saplings, trees and stems per
hectare for the Northeast and Southeast states.
Seedlings
Figure 3-4 presents CDFs of seedlings
per hectare for the Northeast and Southeast.
The mean number of seedlings per hectare is
greater in the Northeast than the Southeast
throughout the range of seedling density.
Saplings
Figure 3-5 presents CDFs of saplings
per hectare for the Northeast and Southeast.
The mean number of saplings per hectare is
greater in the Northeast than the Southeast
throughout the range of sapling density.
Trees
Figure 3-6 presents CDFs of trees per
hectare for the Northeast and Southeast. The
mean number of trees per hectare is greater in
the Northeast than the Southeast throughout
the range of tree density.
Stems (seedlings, saplings and trees
combined)
Figure 3-7 presents CDFs of stems per
hectare for the Northeast and Southeast.
Consistent with Figures 3-4 through 3-6, the
mean number of stems per hectare is greater
in the Northeast than the Southeast throughout
the range of stem density.
3-6
-------
(a)
F(x)
1.0 T
0.8 -•
0.6 -
0.4 -•
0.2 -•
0.0
(b)
1.0 T
20000 40000 60000
Number of Seedlings per Hectare
20000 40000 60000
Number of Seedlings per Hectare
Figure 3-4. Cumulative distribution function of (a) number of seedlings/hectare in the NE, and (b)
number of seedlings/hectare in the SE.
3-7
-------
(a)
1.0
0.8
F(x) 0.6
0.4
0.2 -
0.0
^ r
f r
2000 4000 6000 8000
Number of Saplings per Hectare
(b)
1.0 T
0.8 -
F(x) 0.6-
0.4 -•
0.2 -
0.0
0
2000 4000 6000 8000
Number of Saplings per Hectare
10000
10000
Figure 3-5. Cumulative distribution function of (a) number of saplings/hectare in the NE, and (b)
number of saplings/hectare in the SE.
3-8
-------
(a)
1.0 T
0.0
200 400 600 800 1000
Number of Trees per Hectare
1200 1400
(b)
1.0 T
0.8 -•
F(x) 0-6 -•
0.4 -•
0.2 -
0.0
-t-
-f-
-\
0 200 400 600 800 1000 1200 1400
Number of Trees per Hectare
Figure 3-6. Cumulative distribution function of (a) number of trees/hectare in the NE, and (b)
number of trees/hectare in the SE.
3-9
-------
(a)
1.0 T
0.0
20000 40000 60000
Total Number of Stems per Hectare
1
20000
-i-
40000
60000
Total Number of Stems per Hectare
Figure 3-7. Cumulative distribution function of (a) total number of stems/hectare in the NE, and
(b) total number of stems/hectare in the SE.
3-10
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4. Crown Condition of Forest
Trees on FHM Plots
K. Stolte, R. Anderson, W. Burkman,
T. Stockton and R. Binns
Introduction
The crown portion of trees has always
been an indicator of forest tree health. Long
before scientists attempted to add the scientific
process and rigor to the estimation or
measurement of crown variables, humans have
used tree crown condition as an instantaneous
visual estimation of the vigor of individual trees,
tree stands, and sometimes entire watersheds.
With the possible exception of root pathologies,
healthy tree crowns can be equated with good
condition of trees. Conversely, unhealthy tree
crowns are clear indicators of threats to tree and
stand productivity, biodiversity, and sustainability.
Additionally, when natural or anthropogenic
stresses are affecting a forest, the first signs of
deterioration are often observed in the tree
crowns. Since tree crowns form the basic
structural architecture of a forest ecosystem, they
directly affect the composition, processes, and
vigor of the understory floral and faunal
components of the forest.
Morphological determinations of crown
and bole condition were made on all forest trees
with diameters at breast height greater than 12.7
cm. Measurement and estimation of variables to
describe tree crown condition have provided
reliable information on the growth characteristics
of trees. In the eastern United States, crown
diameter has been related to the size of
hardwood trees (Francis 1986; Sprinz and
Burkhart 1987), and crown density has been
related to the growth of loblolly pines (Grano
1957; Anderson and Belanger 1991; Anderson et
al. 1992; Belanger and Anderson 1991). In the
western United States, crown ratio and crown
density have been related to growth and
survivorship of conifers (Dolph 1988). Other
crown variables, such as dieback, transparency,
and density, can be related to insect defoliation
and subsequent growth and survivorship effects
on both conifers and hardwoods (Kulman 1971;
Schmitt et al. 1984).
Crown transparency refers to the amount
of sunlight that passes through the foliated
portions of the tree crowns ignoring holes in the
tree crown caused by lack of branches.
Transparency partially overlaps with crown
density; the difference is in the emphasis on
foliage. For example, a tree could have only one
branch with thick foliage on that branch. This
tree would score poorly for crown density (and
live crown ratio), but would score well for crown
transparency (i.e., it would have a low crown
transparency). Crown transparency is estimated
in five percent classes, with a range of 0-100%.
Trees with a low crown transparency score
increase the potential for carbon fixation and
nutrient storage, and indicate a lack of defoliating
agents that attack new and older foliage.
Crown dieback refers to the mortality of
relatively new branches (less than 2.5 cm in
diameter) that are in the upper, sunlight-exposed
portions of the crown. The premise is that these
branches have died from some stress other than
normal branch mortality which occurs from
shading. On open-grown trees, crown dieback
can occur on most of the crown. On codominant
trees, crown dieback is confined primarily to the
top and upper sides of the crown that are
exposed to direct sunlight. Crown dieback is
estimated in five percent classes, with a range of
0-100%. Trees with a low crown dieback score
increase the potential for carbon fixation and
nutrient storage, and indicate a lack of defoliating
agents that attack new foliage.
Crown position refers to the relative
position of the tree crown in a stand of trees.
The five crown positions often utilized to stratify
crowns are: open grown (crown exposed to
sunlight on all sides); dominant (tree crown
above the general height of the stand; some of
the sides of the crown in direct sunlight);
codominant (tree crown about the same height as
the general height of the stand; top of crown
exposed to direct sunlight); intermediate (tree
crown generally below height of the stand; very
top of crown may be exposed to some direct
sunlight); and suppressed (tree crown far below
4-1
-------
the general height of the stand; tree crown may
never be exposed to direct sunlight). Only grown,
dominant, and codominant trees are used in the
analysis of crown data.
Crown density is the two-dimensional
appearance of crown fullness when ideally
viewed against the sky. It estimates how much
of the sky is blocked from view by the upper tree
bole, branches, foliage, and any reproductive
structures (e.g., cones). Crown diameter is the
measured average of the crown's widest part and
the 90° perpendicular axis to the widest part of
the crown. It is measured by standing under the
estimated drip line of the crown at the two axes
described.
Crown ratio is the percentage of the
entire tree bole, from the ground to the top of the
tree, that supports living, foliated canopy
contributing to the vigor and growth of the tree.
It is length of the bole that is foliated (X), divided
by the total length of the bole (Y). These
variables have been shown to be important
measures of growth and survivorship, and will be
represented by appropriate indicators in future
statistical summaries.
Crown assessments directly address
productivity, sustainability, aesthetics, and more
indirectly biodiversity. Contamination is partially
addressed by crown ratio, density, and dieback.
Gaseous pollutants are known to accelerate leaf
senescence, reduce production of new foliage,
and cause mortality of lower or upper branches
(Stolte et al. 1992). Air pollution can produce
thinner crowns (density) that are reduced in size
(ratio) and that may have dead branches
(dieback) in susceptible species and individuals.
Forest extent is not addressed by crown
assessments. Deterioration of tree crowns
affects the sustainability of forests. Nutrient
cycles, light regimes, and climatic habitats are
altered.
Crown Defoliation Indicator
The Crown Defoliation Indicator
developed for the FHM 1992 Statistical Summary
is an additive index. This index and its CDF
thresholds discussed below have evolved from
some of the initial work on crown indices by
Anderson et al. (1992). This indicator addresses
the defoliation of the tree crown (Crown
Defoliation Index [GDI], composed of dieback and
transparency). The crown variables transparency
and dieback were aggregated into a plot-level
index (indicator) (Equation 1), which reflects the
nature of the tree crown response to stressors.
These variables reflect the tree foliage condition,
both within the crown (transparency) and in the
sun-exposed outer crown foliage (dieback). At
this point in time, the variables in the GDI are not
scaled by species and have equal weights:
GDI = (Transparency + Dieback) / 2
(1)
The focus of the FHM sampling design is
regional assessments of forest health, focused on
aggregating plot-level data into a single datum
point for analyses using cumulative distribution
functions. The data were post-stratified for
analysis in three ways: by species, forest type,
and crown group (species with similar crown
characteristics such as long needle pines [Millers
et al. 1992]). Cumulative distribution functions
were based upon plot-level averages which were
weighted using basal area. Each stratum must
contain enough plots to calculate CDFs. Based
on consultation with the EMAP Statistical Team
(Cassell and Hazard pers. comrn.), 50 plots has
been chosen as the minimum for calculation of
CDFs in this report.
Thresholds in CDFs
Determining the points in a variable's
population distribution that are considered as
anthropogenic valuation-based thresholds is a
key component of assessing forest health. In
general, thresholds delineate a population into
acceptable (nominal and optimal subclasses) and
undesirable (subnominal and poor subclasses)
categories and should be based on reasonable
and consistent criteria for all species, forest
types, or condition classes. There are several
possible approaches for determining CDF
thresholds, but in all cases, any criteria
developed should address a relevant societal
4-2
-------
value. Two relevant approaches for setting
thresholds for crown variables with respect to
forest health are:
1. Tree growth/survivorship relationship to crown
condition (addresses productivity and
biodiversity):
• Based on experimental data relating tree
height or basal area growth to the condition of
the tree crown.
• Based on expert opinion ( >10 years exper-
ience in research/monitoring tree crowns and
tree vigor).
2. Visual appearance of the tree (addresses
aesthetics):
• Public perception of healthy trees.
Choosing the method for delineating
thresholds in CDFs depends on the intended
assessment or endpoint requirements of the user
information. Industry may be interested in forest
productivity; thresholds based on growth would
be the most meaningful. Alternately, the public
may be primarily concerned with aesthetics and
thresholds based on the aesthetic appearance of
the crown may be more meaningful. Another
group, such as foresters, may be interested in
both growth and the visual appearance of the tree
crowns. A limitation to both approaches is the
lack of data about the relationship between crown
condition and growth or appearance that may be
currently available from the literature. In
comparing thresholds set for productivity and
those set for aesthetics, it must be recognized
that large differences in the perception of forest
health may occur depending on the type of
threshold used. For most species and forest
types, it is likely that thresholds for aesthetics will
be more conservative than thresholds for growth;
the visual appearance of the tree may become
undesirable well in advance of any negative,
discernible growth effects. In general, it is
surmised that aesthetic thresholds will be more
rigorous than growth thresholds. In this report
the thresholds were set based on known or
suspected relationships between crown condition
and tree growth and/or survival (Anderson et al.
1992; Belanger and Anderson 1991).
Ultimately FHM would like to have data
about the growth-crown condition relationships for
all common species (identified in the frequency
distribution analyses), since forest growth is
probably a better estimate of resiliency than is
visual appearance (aesthetics). The thresholds
based on growth effects may be determined by:
• Specific growth studies in which crown
condition estimates were compared to tree
growth rates or tree DBH size.
• Existing models of crown condition and
growth; values set for variables in those
models.
• Peer-reviewed literature.
• Estimates by scientists weighted by years of
experience working with the species of
concern.
In this report three thresholds have been
used in the CDFs: poor, concern, and optimal
(Figure 4-1). Since each threshold value
intersects the CDF and the confidence bounds for
the CDF, each threshold also defines a proportion
of the population in a specific category and the
confidence range for that proportion (Figure 4-1).
A brief discussion of the purpose of each
threshold and its effects on the population
follows:
• Concern Thresholds-A concern threshold is
the nominal-subnominal break point at which
one becomes concerned about the values
measured or estimated for a variable. It is the
threshold based on growth data or expert
opinion, at which the tree(s) may begin to be
stressed to a degree that is detrimental to
growth or survival. Delineation of a concern
threshold divides the population into nominal
and subnominal portions.
• Optimal Thresholds-An optimal threshold is
the breakpoint which indicates a portion of
the nominal population that is considered to
be in superior condition. On plots considered
optimal, the condition of the crown variable is
outstanding relative to values that can be
expected for individuals or stands of a
species, forest type, or crown group.
4-3
-------
020
OX
000
Delotcmon indicator
Lotlolyflne
f
• ssx^
400
800 1200
Index value
Figure 4-1. The three established thresholds
for the Crown Defoliation Indicator.
Delineation of an optimal threshold
highlights an optimal proportion of the
nominal population.
• Poor Thresholds-The poor threshold is the
breakpoint which delineates the
subnominal portion of the population that is
considered to be in serious condition. The
trees on these plots are considered either
to be susceptible to mortality as a direct
result of a particular stress or to be
exceptionally vulnerable to other forest
stresses because of that particular stress.
Delineation of a poor threshold highlights
the poor proportion of the subnominal
population.
The degree of uncertainty in delineating
the population further depends on where the
thresholds are placed in relation to the curve of
the CDF. If a threshold occurs where the slope
of the CDF curve is steepest, then the uncertainty
in delineating the population segments will be
increased (i.e., the confidence bounds on the
proportion of the population are wider).
The values selected for CDF thresholds
in this report are derived from the visual crown
rating (VCR) classification model developed by
Anderson et al. (1992). This model is based on
relationships between crown condition and
growth, derived from the available literature and
expert opinion on crown condition relationships to
tree growth and mortality for a limited number of
species. The determination of tree crown health
of other species is based on expert opinion of
foresters with many years of experience working
with the species found in the eastern forests, and
their evaluation of crown health reflects what is
normal for each species. They recognize that
some species normally have less dense crowns,
small live crown ratios, etc. Although the
thresholds proposed for the CDFs in this report
are not solely based on functional relationships
between crown condition and tree growth/survival,
they do broadly reflect those relationships.
Thresholds set to delineate the subnominal
proportion of the population are intended to infer
reduced growth for those proportions of the
species, forest type, or crown group population
under consideration.
Table 4-1. CDF concern, optimal, and poor
thresholds for the crown variables
dieback and transparency. For
dieback and transparency, only one
value represents all species, forest
types, and crown groups.
CDF
Crown
Variable
Transparency
Dieback
Thresholds(%
Optimal
20
5
)
Concern
45
20
Poor
70
30
We decided to use a single value for each
threshold since these values are intended to
represent the mode for a range of values for a
variety of species. We did not feel we had
sufficient data to calculate any confidence bounds
for the threshold values and question the
appropriateness of confidence bounds when
these values are intended to be generic and
represent a wide range of species. We recognize
the need to obtain much more information about
the relationship between tree crown condition and
tree growth (shoots and roots) and survivability.
By setting thresholds, we were able to make
4-4
-------
inferences concerning the status of the forests in
12 eastern states based on the 1991 data and
will be able to track changes in the status of
these forest over time.
Results
The CDFs for the Crown Defoliation Index
were generated for the most common species,
forest types, and crown groups based on
frequency analysis of occurrence on Detection
Monitoring plots (Tables 4-2 through 4-9). The
Standard Federal Regions are shown in Figure
4-2.
A cursory evaluation of the CDF analysis
results indicates that most tree species, all
examined forest types, and most crown groups
examined are in good condition, with high
percentages of the population in a nominal or
optimal classification. There are some species
that do not appear in the following tables even
though they are on more than 50 plots because
we could not estimate the proportions in a given
class with known confidence. No species or
crown groups examined have high percentages of
the population in the subnominal classification. In
most cases the entire population could be
TABLE 4-2. POPULATION PROPORTIONS FOR SPECIES IN THE EAST U.S. REGION FROM CDF
ANALYSIS. Numbers represent the percentages of the population classified as poor, subnominal,
nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF 90%
confidence interval.
Species
Balsam Fir
Eastern White
Pine
Virginia Pine
Northern
White Cedar
Red Maple
Yellow Birch
Paper Birch
American
Beech
White Ash
Yellow Poplar
Black Cherry
White Oak
Scarlet Oak
Southern Red
Oak
Northern Red
Oak
Optimal
0.80 (0.68 0.92)
0.73 (0.60 0.86)
0.72 (0.55 0.89)
0.56(0.400.71)
0.75 (0.68 0.82)
0.87 (0.73 0.97)
0.54 (0.43 0.66)
0.73 (0.60 0.86)
0.63 (0.47 0.78)
0.92 (0.81 0.99)
0.66 (0.50 0.82)
0.83 (0.72 0.94)
0.65(0.500.81)
0.88 (0.73 0.98)
0.66 (0.53 0.79)
Nominal
0.99 (0.84 0.99)
0.99(0.91 1.00)
0.98 (0.77 0.99)
0.99 (0.89 1 .00)
0.98 (0.90 1 .00)
0.99 (0.87 1 .00)
0.98 (0.83 0.99)
0.96 (0.81 0.99)
0.96 (0.77 0.99)
0.97 (0.85 0.99)
0.94 (0.75 0.99)
0.99 (0.88 1 .00)
0.98(0.871.00)
0.99 (0.88 1 .00)
0.97 (0.82 0.99)
Subnominal
0.01 (0.01 0.16)
0.01 (0.00 0.09)
0.03 (0.01 0.23)
0.01 (0.000.11)
0.02(0.000.10)
0.01 (0.000.13)
0.02(0.01 0.17)
0.04(0.01 0.19)
0.04 (0.01 0.23)
0.03 (0.01 0.15)
0.06 (0.01 0.25)
0.01 (0.000.12)
0.02(0.000.13)
0.01 (0.000.12)
0.03(0.01 0.18)
Poor
*
*
*
*
0.00 (0.00 0.07)
0.01 (0.00 0.09)
0.01 (0.00 0.08)
0.00 (0.00 0.05)
*
*
*
*
*
*
0.01 (0.000.16)
This threshold is outside the numerical range of data collected
4-5
-------
TABLE 4-3. POPULATION PROPORTIONS FOR CROWN GROUPS IN THE EAST U.S. REGION FROM
CDF ANALYSIS. Numbers represent the percentages of the population classified as poor, subnominal,
nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF 90%
confidence interval.
Crown Group Optimal Nominal Subnominal Poor
Pine-Short
Needles 0.70 (0.54 0.87) 0.98 (0.78 0.99) 0.02 (0.01 0.22)
Spruce-Fir 0.90 (0.78 0.98) 0.99 (0.86 0.99) 0.01 (0.01 0.14)
Cedar-Juniper 0.71(0.590.84) 0.98(0.830.99) 0.02(0.010.17)
Hardwoods-
Closed Canopy
Large Leaves 0.81 (0.75 0.87) 0.99 (0.92 1.00) 0.01 (0.00 0.08)
Hardwoods-
Open Canopy
Large Leaves 0.92 (0.81 0.99) 0.97 (0.85 0.99) 0.03 (0.01 0.15)
Hardwoods-
Small Leaves 0.74(0.670.80) 0.98(0.911.00) 0.02(0.000.09) 0.00(0.000.03)
Hardwoods-
Compound
Leaves 0.85(0.750.95) 0.99(0.881.00) 0.01(0.000.12) 0.00(0.000.04)
Hardwoods-
Oaks 0.80 (0.73 0.87) 0.99 (0.91 1.00) 0.01 (0.00 0.09)
Miscellaneous 0.72 (0.64 0.80) 0.96 (0.87 0.99) 0.04 (0.01 0.13) 0.01 (0.00 0.10)
* This threshold is outside the numerical range of data collected
TABLE 4-4. POPULATION PROPORTIONS FOR SPECIES IN STANDARD FEDERAL REGIONS I AND
II FROM CDF ANALYSIS. Numbers represent the percentages of the population classified as poor,
subnominal, nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF
90% confidence interval.
Species
Eastern White
Pine
Balsam Fir
Red Maple
Paper Birch
American
Beech
White Ash
Yellow Birch
* This threshold
Optimal
0.68
0.80
0.64
0.54
0.60
0.65
0.87
(0.55
(0.68
(0.55
(0.43
(0.46
(0.48
(0.73
is outside the
0.81)
0.92)
0.74)
0.66)
0.74)
0.81)
0.97)
numerical
Nominal
0.99 (0.91
0.99 (0.84
0.97 (0.86
0.98 (0.83
0.94 (0.77
0.95 (0.76
0.99 (0.87
Subnominal
1.00)
0.99)
0.99)
0.99)
0.99)
0.99)
1.00)
0.01
0.01
0.03
0.02
0.06
0.05
0.01
(0.00 0
(0.01 0
(0.01 0
(0.01 0
(0.01 0
(0.01 0
(0.00 0
09)
16)
14)
17)
23)
24)
13)
Poor
*
*
0.01
0.01
0.00
*
0.01
(0.00
(0.00
(0.00
(0.00
0.10)
0.08)
0.06)
0.09)
range of data collected
4-6
-------
TABLE 4-5. POPULATION PROPORTIONS FOR CROWN GROUPS IN STANDARD FEDERAL REGIONS
I AND II FROM CDF ANALYSIS. Numbers represent the percentages of the population classified as
poor, subnominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF
90% confidence interval.
Crown Groups
Spruce-Fir
Cedar-Juniper
Hardwoods-
Closed Canopy
Large Leaves.
Hardwoods-
Small Leaves.
Miscellaneous
Optimal
0.90 (0.78 0.98)
0.56 (0.40 0.72)
0.71 (0.62 0.80)
0.60 (0.52 0.69)
0.61 (0.51 0.70)
Nominal
0.99 (0.86 0.99)
0.99 (0.89 1 .00)
0.98(0.88 1.00)
0.96 (0.86 0.99)
0.94 (0.83 0.99)
Subnominal
0.01 (0.01 0.14)
0.01 (0.000.11)
0.02 (0.00 0.12)
0.04 (0.01 0.14)
0.06(0.01 0.17)
Poor
*
*
*
0.00 (0.00 0.05)
0.01 (0.000.12)
* This threshold is outside the numerical range of data collected
TABLE 4-6. POPULATION PROPORTIONS FOR SPECIES IN STANDARD FEDERAL REGION III FROM
CDF ANALYSIS. Numbers represent the percentages of the population classified as poor, subnominal,
nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF 90%
confidence interval.
Species Optimal Nominal Subnominal Poor
Red Maple 0.76(0.620.91) 0.99(0.871.00) 0.01(0.000.13)
Yellow Poplar 0.92 (0.74 0.99) 0.97 (0.78 0.99) 0.03 (0.01 0.22)
* This threshold is outside the numerical range of data collected
4-7
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TABLE 4-7. POPULATION PROPORTIONS FOR CROWN GROUPS IN STANDARD FEDERAL REGION
III FROM CDF ANALYSIS. Numbers represent the percentages of the population classified as poor,
subnominal, nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF
90% confidence interval.
Crown Groups Optimal Nominal Subnominal Poor
Hardwoods-
Oaks 0.62(0.500.74) 0.99(0.901.00) 0.01(0.000.10)
Hardwoods-
Closed Canopy
Large Leaves 0.79(0.660.91) 0.99(0.921.00) 0.01(0.000.08)
Hardwoods-
Open Canopy
Large Leaves 0.93(0.760.99) 0.98(0.790.99) 0.02(0.010.21)
Hardwoods-
Small Leaves 0.75(0.620.89) 1.00(0.971.00) 0.00(0.000.03)
Hardwoods-
Compound
Leaves 0.72(0.570.86) 0.98(0.820.99) 0.02(0.010.18) 0.00(0.000.06)
This threshold is outside the numerical range of data collected
TABLE 4-8. POPULATION PROPORTIONS FOR SPECIES IN STANDARD FEDERAL REGION IV FROM
CDF ANALYSIS. Numbers represent the percentages of the population classified as poor, subnominal,
nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF 90%
confidential interval.
Species
Yellow Poplar
White Oak
Southern Red
Oak
Optimal
0.93 (0.77 0.99)
0.96 (0.80 0.99)
0.95 (0.75 0.99)
Nominal
0.98 (0.81 0.99)
0.99 (0.91 1 .00)
0.98 (0.86 1 .00)
Subnominal
0.02 (0.01 0.19)
0.01 (0.00 0.09)
0.02 (0.00 0.14)
Poor
*
*
*
* This threshold is outside the numerical range of data collected
4-8
-------
TABLE 4-9. POPULATION PROPORTIONS FOR CROWN GROUPS IN STANDARD FEDERAL REGION
IV FROM CDF ANALYSIS. Numbers represent the percentages of the population classified as poor,
subnominal, nominal, and optimal. Numbers in parenthesis are the lower and upper bounds of the CDF
90% confidence interval.
Crown Groups
Hardwoods-Oaks
Miscellaneous
Hardwoods-
Open Canopy
Large Leaves
Hardwoods-
Small Leaves
Optimal
0.92
0.95
0.92
0.87
(0.83
(0.76
(0.77
(0.77
0.99)
0.99)
0.99)
0.96)
Nominal
1.00
1.00
0.98
0.99
(0.94
(0.91
(0.82
(0.87
1.00)
1.00)
0.99)
1.00)
Subnominal
0.00
0.00
0.02
0.01
(0.00
(0.00
(0.01
(0.00
0
0
0
0
06)
09)
18)
13)
Poor
*
*
*
*
* This threshold is outside the numerical range of data collected
characterized as nominal and optimal. In most
cases where portions of the population fell into a
subnominal category, it represented less than three
percent of the population. It is not possible to infer
causality for any crown condition status observed in
Detection Monitoring.
Discussion
The importance of setting realistic CDF
thresholds should be apparent from the approach
presented in this section; the determination of
nominal and subnominal proportions is dependent
on the accuracy of the CDF thresholds. We have
set thresholds on the best information available to
us today. However, if the nominal-subnominal
thresholds for defoliation are too low, then a larger
percentage of the population will be in a
subnominal condition. If they are too high, a larger
percentage of the population will be in a nominal
condition. Since we found most of the population
for species and crown groups to be nominal, our
interest is in whether or not the thresholds are too
high (i.e., everything looks good). To address this
possibility, we should then give greater weight to
the even smaller percentages of the population that
are in a subnominal condition ( i.e., they are
subnominal even when the threshold is very
lenient). If future research information indicates
that the thresholds should be more robust, then
those proportions of the population that are now
subnominal will become poor.
Monitoring techniques research on-frame (on
the Detection Monitoring plots), off-frame, and
computer research are needed to set consistent
and tailored threshold values for each species,
forest type, and/or crown group. At a minimum,
one to three representative species in each crown
group should be intensely evaluated to precisely
determine threshold values for both growth and/or
survivability (primarily) and aesthetics (secondarily).
Future analyses should have the flexibility to
evaluate tree crown condition both on the basis of
visual appearance and on growth. We should have
threshold values specific for both growth and
aesthetics, however growth is considered more
important because the productivity and
sustainability of species and forest types are
dependent on growth and not on human evaluation
of desired appearance.
4-9
-------
Standard Federal Regions (SFR) Boundaries
SFR l&li
SFR III
Figure 4-2. Standard Federal Regions used for the analyses.
4-10
-------
5. Characterization of Regional
Overstory Tree Species
Diversity on FHM Plots
S. Cline
Background
Conservation and maintenance of
animal and plant species diversity is an
important public value. Physical alteration or
destruction of habitats is a preeminent threat to
the maintenance of animal and plant species
diversity (EPA 1990). Forest Health Monitoring
(FHM) plans to monitor the status and trend in
condition of vegetation and habitat structure.
Through this approach, vegetation diversity can
be assessed directly, and animal diversity can
be assessed indirectly through its relationship
with vegetation and habitat structure.
Depending upon the specific objective
and scale of interest, many vegetation or
habitat indicators could be measured (Figure
5-1). A complete set of measurements would
permit assessing the heterogeneity and
complexity of vegetation and habitat structure,
but the limited set of measurements that were
made in 1991 do support some preliminary
analyses. The objective of this chapter is to
present quantitative baseline estimates of
overstory tree species diversity as indicators of
forest condition for specific forest types in
twelve states in the northeast and southeast
United States.
Data Set Characteristics
Only data for trees >12.7 cm diameter
breast height (DBH) measured on forested
subplots in 1991 were used to calculate tree
species diversity (see Chapter 2). As such,
each plot data set represents a segment of the
whole tree community composed of similarly
(large) sized, mature trees that interact in the
forest overstory, in contrast to tree sapling and
seedling communities, which are smaller in
stature, and interact more directly in lower,
environmentally distinct, strata. With this
analytical approach, diversity measures
indicate the community structure of organisms
similar in habitat or microhabrtat, size, life
history traits, and resource utilization (Hurlbert
1971).
A total of 925 sample plots were
separated into two regional data sets:
Northeast (NE) and Southeast (SE). The NE
data set represents forests of northern
glaciated ecoregions (Omernik 1987). It is
made up of the 202 forested (out of 245 total)
plots in Maine, New Hampshire, Vermont,
Rhode Island, Connecticut and Massachusetts.
These plots were dominated by the northern
hardwoods forest type and the spruce-fir forest
type groups. The most frequently recorded
tree species on NE sample plots were red
maple, balsam fir, and red spruce.
The SE data set represents forests of
southern, humid ecoregions (Omernik 1987).
It includes the 420 forested (out of 680 total)
plots from New Jersey, Delaware, Maryland,
Virginia, Georgia, and Alabama. These plots
were dominated by the oak forest type and the
pine forest type groups. The most frequently
recorded tree species on SE sample plots were
loblolly pine, sweet gum, and red maple.
Index Selection and Calculation
Hill's (1973) series of diversity
measures was selected. The series consists of
diversity measures based on the number of
species present (S), the exponent of Shannon's
index (eHl, Shannon and Wiener 1949), and the
reciprocal of Simpson's index (1/D, Simpson
1949), and represents various symbolic
measures of the number of species present
based on different levels of analysis of their
rarities (Hill 1973, Table 5-1). The highest
diversity values are measured using S,
because it includes species presence without
regard to rarity. The lowest diversity values
are derived from 1/D because it weighs the
abundant species more heavily than rarer
5-1
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5-3
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species. Intermediate diversity values are
obtained using eH> because it weighs rarer
species more heavily than 1/D.
Overstory tree species diversity values
based upon S, eH>, and 1/D were calculated for
each plot. Species richness (S) was expressed
as species density, the number of tree species
>12.7 cm DBH per 672 m2 (the total area of a
plot). The Shannon (H') and Simpson (D)
indices were calculated from the relative
abundance (p1) of each tree species >12.7 cm
DBH on the plot. In this analysis, the pi values
were the weighted mean basal area in m2/ha.
First the basal area of each tree >12.7 cm DBH
was calculated in m2, which was then multiplied
by an expansion factor (ca 59.48) to estimate
basal area in m2/ha. Next, basal area of each
tree was summed by species on each subplot
and multiplied by a weighting factor based on
the percent subplot area forested. Then the
weighted basal areas of each tree species
were summed across subplots, and divided by
the total percent area forested. Finally, each
species-specific mean weighted basal area was
divided by the mean weighted basal area of all
trees on the plot. When trees >12.7 cm DBH
were absent on the plot (i.e., basal area was
zero), S, eH>, and 1/D were all set to zero.
Both Hl and D are sensitive to changes
in species richness (S) and to the evenness of
the distribution of p1 values among species. H'
increases with species richness from a
minimum value of 0 when only one species is
present. For a given number of species, the
maximum value of H> is obtained when all
species are equally abundant; H'max equals
ln(S). The value eH> is the number of equally
abundant species equivalent to the sample
diversity H>. In contrast, D increases as species
richness decreases. The maximum value of D
= 1.0 is obtained when one species is present.
Similarly, the value 1/D is the number of
equally abundant species in a sample with a
dominance of D, and is comparable to eH>. In
comparison to H', D is less sensitive to changes
in the abundance of rare species of the
community.
Analysis
The baseline status of overstory tree
species diversity in NE and SE regions was
expressed using cumulative distribution
functions (CDF; see Chapter 2 and Appendix
A). A CDF characterized the regional variation
in overstory tree species diversity in 1991. In
each region a CDF was constructed based
upon the diversity values using S, eH>, and 1/D.
The CDFs were analyzed for intra- and inter-
regional patterns.
While a statistical summary such as
this cannot be used to infer cause and effect
relationships, associations among variables can
be explored. For example, what are the
predominant characteristics of the forest
population with relatively high overstory tree
species diversity values? How do these
features contrast with conditions associated
with low diversity values? Consequently, an
exploratory analysis of the association of eH>
diversity values and forest type group, stand
origin, stand size class, disturbance history,
and condition code was conducted in each
region (see Chapter 2). First the range of eH'
diversity values was divided into five categories
(range in eH> values): none (0), very low (>
0-1), low (> 1-3), medium (> 3-5), high (> 5).
Then for each variable, the proportion of
sample plots within each diversity and forest
condition category were determined. The
trends based upon eH' diversity as
representative of trends based upon S and 1/D
diversity values.
Results and Discussion
Intra-Regional Patterns
Southeast Region-Comparison of
Diversity Indices
The SE CDFs for S, eHl, and 1/D had
similar shapes, but the cumulative probabilities
at any given diversity value differed
systematically as S < eHl < 1/D (Figures 5-2
(b), 5-3 (b), 5-4 (b)). For example, when
5-4
-------
(a)
1.0 T
(b)
1 T
0.8 -
F(x) 0.6-•
0.4 -
0.2 -
0
4 6 8 10
Regional Overstory Tree Diversity
12
—I 1 1 I—
4 6 8 10
Regional Overstory Tree Diversity
12
14
14
Figure 5-2. Cumulative distribution function of (a) regional overstory tree diversity in the NE using
species richness, and (b) regional overstory tree diversity in the SE using species richness.
5-5
-------
(a)
0.0
(b)
0.0
2.0 4.0 6.0 8.0
Regional Overstory Tree Diversity
2.0 4.0 6.0 8.0
Regional Overstory Tree Diversity
10.0
10.0
Figure 5-3. Cumulative distribution function of (a) regional overstory tree diversity in the NE using
Simpson's Index, and (b) regional overstory tree diversity in the SE using Simpson's Index.
5-6
-------
(a)
1.0 T
0.0 2.0 4.0 6.0 8.0 10.0
Regional Overstory Tree Diversity
(b)
1.00 T
0.80-
F(x) 0-60 -
0.40 -
0.20 -
0.00
-t-
12.0
^
Figure 5-4.
0.00 2.00 4.00 6.00 8.00 10.00 12.00
Regional Overstory Tree Diversity
Cumulative distribution function of (a) regional overstory tree diversity in the NE using the
Shannon-Weiner method, and (b) regional overstory tree diversity in the SE using the
Shannon-Weiner method.
5-7
-------
overstory tree species diversity equalled 2, the
cumulative probabilities (± 90% confidence
limit) were 0.26 (0.05), 0.33 (0.05), and 0.41
(0.05) based upon S, eH>, and 1/D, respectively.
Diversity values of eH> equalling 1.6, 2.9, and
4.6 defined the respective 0.25 (0.04), 0.50
(0.05), and 0.75 (0.05) cumulative probabilities
of the SE forest population.
Differences in the cumulative
probabilities were expected because of
systematic differences in the calculation of Hill's
(1973) series of diversity indices, as noted
earlier. Although these differences among
diversity indices were not statistically significant
and tended to decrease as diversity values
increased, the tendency for diversity values to
be index-dependent suggests that only the
same index should be compared to assess
community changes or environmental effects.
Southeast Region-Forest Conditions
Associated with High and Low Overstory Tree
Species Diversity
Qualitative results of an exploratory
analysis of the association of eH> diversity
values and forest condition variables in the SE
region follow.
As overstory tree species diversity increased:
• The predominant forest type group changed
from pine to oak. Both pine and oak types
had eHl values in all diversity categories, but
the proportion of the pine type decreased
while that of the oak increased with higher
diversity values.
• The predominant stand origin condition
changed from planted softwoods to naturally
regenerated. The planted softwoods
condition dominated the "none" overstory
diversity category, but the proportion of
naturally regenerated and planted stand
origin were approximately equal in the "very
low" diversity category. However, plots with
the planted softwoods conditbn were absent
from the "high" diversity category.
The predominant stand size class changed
from seedling/sapling to sawtimber. One
might expect all seedling/sapling to exhibit
low or no tree diversity because fewer (or
no) individual trees > 12.7 cm DBH are
present compared to the sawtimber size
class; however, results showed that both size
classes had eH' values in all diversity
categories. Instead, this trend appeared to
be more closely associated with changing
forest conditions within each size class.
Specifically, within the seedling/sapling size
class, a planted softwood origin/pine forest
type group condition predominated at no and
low diversity values, but the proportion of this
stand condition type decreased as stand size
increased.
The proportion of plots with a disturbance
history condition of "none" increased while
the proportion with disturbance history coded
as "cutting" decreased. However only the
proportion of plots in different diversity
categories changed with disturbance
conditions, since disturbance condition
"none" and "cutting" both had eH' values in
all diversity categories.
The proportion of plots with > 1 condition
code increased while the proportion of plots
with 1 condition code decreased. As noted
in Chapter 2, condition code is a composite
of land use, forest type, size class, stand
origin, and disturbance history; condition
code on a plot changes if any one of these
variables changes. Multiple condition codes
on a plot indicates multiple habitats and thus
increased diversity was expected as the
number of conditions on a plot increased.
However, even though the proportion of plots
with 1 condition decreased as overstory
diversity increased, it was the predominant
condition code in all diversity categories.
Northeast Region-Comparison of
Diversity Indices
The NE CDFs for S, eH', and 1/D also
had similar shapes, and as in the SE, the
cumulative probabilities at any given diversity
value differed systematically as S < eH' < 1/D
5-8
-------
(Figures 5-2 (a), 5-3 (a), 5-4 (a)). For example,
when overstory tree species diversity equalled
2, the cumulative probabilities (± 90%
confidence limit) were 0.17 (0.07), 0.20 (0.08),
and 0.32 (0.08) based upon S, eH>, and 1/D,
respectively. Diversity values of eH> equalling
2.2, 3.6, and 4.6 defined the respective 0.25
(0.08), 0.50 (0.08), and 0.75 (0.08) cumulative
probabilities of the NE forest population.
Northeast Region-Forest Conditions
Associated with High and Low Overstory Tree
Species Diversity
Qualitative results of an exploratory
analysis of the association of eH> diversity
values and forest condition variables in the NE
region follow.
As overstory tree species diversity increased:
• The proportion of plots in the spruce-fir forest
type group increased while the proportion in
the northern hardwoods and pine forest type
group decreased. Only the pine type had
eH' values in all diversity categories, and
although the proportion of the spruce-fir type
increased with higher diversity categories,
the northern hardwoods type was
predominant in all diversity categories but
"none".
• There was no trend in the stand origin
condition. Results indicated that nearly all
stands encountered were naturally
regenerated, with the exception of a few
planted softwoods stands.
• The predominant stand size class changed
from seedling/sapling to sawtimber. This
trend was not as clear here as in the SE
because in the NE, few seedling/sapling
stands were sampled in comparison to the
sawtimber class. Similar to the SE, though,
both size classes were present in a range of
diversity categories. In contrast to the SE,
this trend in the NE could not be closely
associated with specific changing forest
conditions within each size class.
• There was no trend in the disturbance history
condition. The eH> diversity values of
disturbance conditions "none" and "cutting"
were both distributed in about the same
proportion across all diversity categories.
This contrasted with the SE result, where the
proportion of plots in different diversity
categories changed with disturbance
conditions.
• The proportion of plots with > 1 condition
code increased while the proportion of plots
with 1 condition code decreased, matching
the trend found in the SE. Similarly, even
though the proportion of plots with 1
condition decreased as overstory diversity
increased, it was the predominant condition
code in all diversity categories in theNE.
Plots with > 1 condition code were absent
from the none and very low diversity
categories.
Inter-Regional Patterns
Regional Differences
The CDFs of the regions were most
clearly distinguishable at low diversity values
(e.g. < 3). However, even here the regional
CDFs were not statistically different. All three
diversity indices showed that the SE forest
population had more low overstory tree
diversity values compared to the NE. This
difference was associated more closely with
the predominance of a particular combination
of forest conditions (i.e., a higher proportion of
the pine forest type group by planted softwood
stand origin in the SE than in the NE) than with
regional differences in the relative proportions
of the stand size classes.
Index Sensitivity
The diversity value S was the most
sensitive indicator of differences in overstory
tree species diversity between regions because
the CDFs based on S were more distinct
between regions over a wider range of diversity
values than the CDFs based on either eH> or
1/D. While the CDFs for S, eHl, and 1/D all
5-9
-------
showed similar differences between the NE and
SE at low diversity values, only S showed a
difference beyond diversity values of 6. For
example, at a diversity of 2, the differences in
cumulative probabilities between regions were
0.08, 0.13, and 0.09 based on S, eH>, and 1/D,
respectively. In contrast, at a diversity of 6, the
differences in cumulative probabilities between
regions were 0.07, 0.01, and 0 based on S, eHr,
and 1/D, respectively.
5-10
-------
6. Selected Climatic Data
Summaries
E. J. Cooler and L Truppi
Introduction
This chapter summarizes selected
climate conditions and events that, through
experience, experimentation or research, are
known to impact forest ecosystem status and
health. Data have been assembled and
processed from a variety of sources listed at
the end of this chapter. Every effort has been
made to ensure information quality and
consistency, but it must be recognized that this
information represents regional summaries and,
in many cases, does not reflect conditions at
particular places within the region.
Synopsis
October, 1990 through September,
1991 was marked by severe tropical storms, a
record warm winter with far below normal
snowfall, early springtime warmth, a severe
spring ice storm, a long, hot summer, and a
visit from Hurricane Bob.
In October, 1990, the remnants of
tropical storms Marco and Klaus were among
several storm systems that brought excessive
rainfall to the eastern United States. States
from Pennsylvania to Georgia were affected.
The storms produced heavy rain and high
winds in Maryland, Virginia, and North Carolina.
November and December were generally warm
and quiet.
January 1991 was the second wettest
in 96 years of record in the Southeast. Florida,
Louisiana, Alabama and North Carolina were
particularly hard hit.
Nationwide, February was the second
driest and third warmest February since 1895.
Only 1947 was drier and only 1932 and 1954
were warmer. Particularly warm temperatures
were reported February 1-10.
By March, it was being said that the
Middle Atlantic states and Southern New
England had experienced an almost winterless
1990-1991. Boston reported the warmest six-
month winter (October-March) in the last 121
years. In sharp contrast, an ice storm,
described as the worst in 30 years, was
reported in New York.
The fifth warmest April in the last 96
years was reported in the Southeast. It was
the sixth warmest April in the Northeast.
Record numbers of April tornados were
reported in New York (4) and Pennsylvania
(10).
Connecticut, New Hampshire, New
Jersey and Rhode Island experienced their
warmest Mays ever, while Florida,
Massachusetts, New York, Pennsylvania,
Vermont and Virginia experienced their second
warmest Mays.
June and July were wet but uneventful.
August witnessed the passage of Hurricane
Bob. Although the path of Hurricane Bob
impacted Massachusetts, Rhode Island and
northeastern Maine most directly, severe
weather associated with the accompanying
trough of low pressure was responsible for
more wide spread damage. A new record for
August tornados was set in North Carolina
(10).
Precipitation
Climate division precipitation data for
October, 1990 through September, 1991
indicate most of the eastern seaboard and
southeast received well above average rainfall
(Figure 6-1). The greatest excess was
reported in Alabama, where the 1991 total was
60 cm more than the 30-year division average.
The exception to these wet conditions was the
Mid-Atlantic states. Here, precipitation totals
as much as 24 cm below 30-year division
averages were recorded. Figure 6-2 gives
these precipitation values an historical
perspective. The 1991 conditions ranged from
the wettest 10% of each division's 30-year
record (northwestern Alabama, northern
6-1
-------
Precipitation Deviation
(in centimeters)
Figure 6-1. Deviation of annual climate division precipitation (October 1990 - September 1991)
from 1961-1990 average conditions. Positive values represent above-average rainfall
and negative values represent below-average rainfall.
6-2
-------
Cumulative Frequency 1961-1990
fflH Greater Than 90%
f§§§§§§§§§| 90% To 75%
BBSS 75% To 50%
WMh 50% To 25%
25% To 10%
Less Than 10%
Figure 6-2. Probability of a climate division receiving less precipitation than the October 1990 -
September 1991 total. Probabilities are based on 1961-1990 climatological records.
6-3
-------
Florida, central and eastern Georgia, South
Carolina, Maine, and portions of New
Hampshire and Massachusetts) to the driest
10% (southern Maryland). The excessively wet
areas are largely the result of decaying tropical
storms in October, 1990 and Hurricane Bob in
August, 1991. The dry area lies along the
eastern edge of a severe drought region
extending along the upper Ohio Valley.
Hurricanes
Figure 6-3 highlights counties that have
experienced hurricane passages since October,
1984. The most numerous reports come from
Eastern Texas (Cass County) and Northern
Louisiana (Caldwell County). Figure 6-3 was
constructed by counting the number of times a
plotted hurricane path passed through or
touched a county. This explains the narrow,
well defined patterns of most impact zones and
generates a conservative estimate of stressed
area. There have been 14 hurricanes during
this period whose paths have crossed the
assessment area. Only those storms with
reported winds in excess of 119 km/h (class 1
hurricane) have been plotted. Two extreme
hurricane events were reported during this
period. The first, Hurricane Hugo, passed
through North and South Carolina during
September, 1989. The second, Hurricane Bob,
occurred in August of 1991. Both storms were
class 3 hurricanes, containing winds in excess
of 178 km/h.
Figure 6-4 shows hurricane contact for
October, 1990 through September 1991. Only
one storm, Hurricane Bob was reported to pass
through the assessment area. In August, 1991
Hurricane Bob and its accompanying trough of
low pressure brought destruction to eastern
New England and excessive rains to most of
the Northeast. The hurricane center passed 25
km east of Cape Hatteras with winds of 181
km/h. At these wind speeds, foliage can be
torn from trees and large trees blown down.
The eye passed east of Delaware Bay and
moved northeast, missing Long Island. It
passed over Block Island and made landfall on
the Rhode Island shore, near Narragansett
Bay. The storm moved east of Providence and
Boston and later crossed northeastern Maine.
Excessive rainfall accompanied the western
periphery of the rather narrow northeastward
track. Up to 229 mm of rain were reported in
Maine. Massachusetts and Rhode Island
reported the greatest amount of wind damage.
Historically, Bob resembled the September
"Gale" of 1869 and the "Enigma" storm of
August, 1788 which impacted New Jersey,
central Hudson Valley and western New
England.
High Wind Events
Figure 6-5 contains the frequency of
report by county of winds in excess of 70 km/h.
All severe weather analysis must be interpreted
with care. To be entered into the data base, all
events must be verified. This means that the
number of reports is highly dependent on
population density. For instance, most of the
counties reporting the greatest frequency of
high wind events also coincide with major
metropolitan areas, e.g., Dallas-Ft Worth, TX,
Shreveport, LA, Birmingham, AL, and
Pittsburgh, PA. Still, general areas of
widespread activity can be determined from
these data. Key areas during this five year
period are: 1) the Texas, Louisiana, Arkansas
boarder area; 2) most of Alabama; 3) most of
South Carolina and eastern North Carolina; 4)
the southwestern edge of the Florida peninsula;
and 5) Pennsylvania, New York and
Massachusetts.
Figure 6-6 shows areas of activity
during the 1991 sampling year. The Florida
area is associated with a decaying tropical
storm in April. Sustained winds of 128 km/h for
a full hour were reported, with gusts of 169
km/h reported in Tampa. Many considered this
event to be worse than Marco (October 1990)
and in some places, worse than Hurricane
Elena (September 1985). While much of the
Northeast sustained high winds associated with
the passage of Hurricane Bob, those that are
highlighted are largely the result of winds
associated with severe ice and snow storms
during March, 1991.
6-4
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Ice Storms
No geographically detailed, digitized
record of ice and freezing rain events currently
exists. A county-scale data base has been
created for this assessment. Published NOAA
records describing the events were reviewed.
Minor events (identified by reports of
occasionally slick roads) were not included in
the digitized set. National Weather Service
forecast zones were used when specific county
lists were not provided. Rgure 6-7 highlights
active areas between October, 1984 and
September, 1991. The largest number of ice or
freezing rain events are reported in New York,
Vermont, New Hampshire and Maine. A
second active area is the western mountains of
South Carolina. As with winds, it appears that
these reports are influenced by population and
level of vehicular traffic, so Figure 6-7 should
be taken as an indication of icing regions rather
than an exhaustive list of point events.
Particularly severe icing events are listed in
Table 6-1. Figure 6-8 shows those areas
reporting icing conditions during the 1991
season. The most frequently impacted areas
lie in New York, New Hampshire, Vermont, and
Maine. New York state, in particular, was
impacted by a severe ice storm in March, 1991
which was described as the worst in 30 years.
Nineteen counties in western and northern New
York were declared disaster areas.
Last Hard Spring Freeze (-2.2? C)
The Middle Atlantic states and
southern New England experienced an almost
winterless 1990-1991 season. This record
warmth is reflected in the extremely early dates
of last hard freeze shown in Figure 6-9. Areas
reporting their last hard freeze for Winter, 1991
in January, included Mississippi, New York and
Pennsylvania. Although last spring frost dates
as much as 17 weeks early were reported (as
compared to median date), most locations
recorded last freeze dates two to three weeks
ahead of median dates. This, combined with
the severe March ice storm discussed earlier,
could have resulted in significant damage to
first flush foliage. Florida is the only state in
the study region to report spring frost dates
later than the median date. Temperatures at or
below -2.2°C occurred two to seven weeks
late.
Data and Information Sources
Synopsis:
Weatherwise, American Meteorological Society,
Boston, MA
Table 6-1. Particularly severe icing events in recent history.
Date
Place
December, 1986
January, 1987
February, 1987
March, 1987
January, 1988
February, 1989
March, 1991
North Carolina
Pennsylvania
North Carolina and South Carolina
New York
Alabama, Georgia, North Carolina and South Carolina
Georgia
New York
6-9
-------
I ce Frequency , 1984-1991
CZJ 0
6 To 10
11 To 15
Greoter Than 16
Figure 6-7. County frequency of ice storm events, October 1984 - September 1991.
6-10
-------
Ice Frequency, 1990-1991
0
Greater Than 16
Figure 6-8. County frequency of ice storm events, October 1990 - September 1991.
6-11
-------
Dev i o t i on
(Number of Days)
Earlier than 60
-60 To -45
-45 To -30
-30 To -15
-15 To 0
0 To 15
15 To 30
Later than 30
Figure 6-9. Deviation (days) of 1991 date of last hard spring freeze (24-hour minimum
temperature equal to or less than -2.2° C) from 30-year median conditions. A positive
value indicates a late spring freeze and a negative value indicates an early spring
freeze.
6-12
-------
Precipitation:
Time biased corrected divisional temperature-
precipitation-drought Index, TD9640, National
Climatic Data Center, Asheville, NC.
Hurricanes:
Weatherwise (1991 hurricane tracks and storm
description).
Jarinen, B.R., C.J. Neuman and MAS. Davis,
1984. "A tropical cyclone Data Tape for the
North Atlantic Basin, 1886 - 1983: Contents,
Limitations and Uses." NOAA Technical
Memorandum NWS NHC 22 and data tape
updated through 1990, National Climatic Data
Center, Asheville, NC.
High Wind Events:
Mr. Preston Leftwich, National Weather
Service, National Severe Storm Forecast
Center, Kansas City, MO.
Ice Storms:
Storm Data, National Climatic Data Center,
Asheville, NC.
Last Hard Freeze:
National Weather Service, Cooperative
Observer Network, Tape TD3200, summary of
day, National Climatic Data Center, Asheville,
NC.
6-13
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7. Status of Major Forest
Insects and Diseases in the
Eastern United States, 1991
R. L. Anderson, W. G.
Burkman, and W. H. Hoffard
Introduction
This is a summary of significant pest
activity observed in the eastern United States
in 1991. The information is organized by major
forest type group and by pest within forest type
group. Detailed information is provided for
hemlock woolly adelgid, hemlock loopers,
eastern spruce budworm, fusiform rust,
southern pine beetle, littleleaf disease, gypsy
moth, oak decline, beech bark disease, and
dogwood anthracnose. Brief summaries are
given for other pests. The information was
compiled from state forestry and agriculture
agency reports, from Forest Service research,
inventory, and Forest Pest Management data
bases such as the Southern Pine Beetle
Information System and Forest Inventory and
Analysis, and from Forest Hearth Protection
information.
New Problems of Special Interest
A new disease of blackgum (cause
unknown) has been found in the Appalachian
mountains, damaging trees in a three-state
area. Thousands of cabbage palms have been
found dead (cause unknown) along the Florida
Gulf coast. The black twig borer, an introduced
ambrosia borer, is on the increase, affecting a
large number of tree species. Hurricane Bob
struck New England and caused widespread
devastation.
Eastern White-Red-Jack
(includes Eastern Hemlock)
Pine
The redheaded jackpine sawfly caused
2,000 ha of defoliation in Wisconsin and
resulting mortality is expected to be heavy in
1992.
The jack pine budworm defoliated
approximately 20,000 ha in the upper peninsula
of Michigan and 190,000 ha in the lower
peninsula. Populations in Michigan are
expected to stabilize or decrease. There were
200 ha defoliated in Pine, Beltrami, and
Hubbard counties in Minnesota.
The jack pine sawfly defoliated 2,800
ha in northern Minnesota.
Red pine adelgid populations were
unchanged in 1991 in Connecticut,
Massachusetts, New York, and Rhode Island.
The red pine scale has shown
increased activity after several years of minimal
activity, and threatens to damage 400 ha of red
pine in southeastern Connecticut. Populations
are reported to be stable in southeastern New
York, due to reduced numbers of host trees.
Heavy defoliation and mortality continued in
southern Rhode Island and is expected to
spread throughout the range of red pine within
the state.
Red pine beetles caused mortality in
scattered pockets on 175 ha in Baraga County,
Michigan.
The white pine weevil affects eastern
white pine, jack pine, red spruce, and white
spruce. Statewide damage continued to occur
in Maine, with 170,000 ha affected. Levels are
expected to remain high but stable. No heavy
damage was observed in Vermont, where
levels were similar to those in 1990.
The elongated hemlock scale was
found in association with the hemlock woolly
adelgid in Connecticut. There were severe
infestations in southeastern New York,
increasing in some stands and dying out in
areas where hemlock woolly adelgid
populations were high.
Hemlock looper refers to two species,
Lambdina athasaria and L. fiscellaria. In
Connecticut, L athasaria caused about 600 ha
7-1
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of defoliation, and populations appeared to be
increasing. The infestation of L fiscellaria
expanded to 130,000 ha of moderate to heavy
defoliation. About 1,000 ha had greater than
50% tree mortality. Lambdina athasaria
affected 4,600 ha in Maine with light defoliation.
Defoliation from L. athasaria occurred on 300
ha in Massachusetts and 1,100 ha in New
Hampshire. Populations of L fiscellaria in
Vermont increased dramatically in 1991. Moths
were commonly seen throughout the area, but
no defoliation occurred. Damage from L
athasaria was found on 600 ha in southern
Vermont compared with 4 ha in 1990.
Scleroderris canker was unchanged at
several isolated places in Maine. In the upper
peninsula, the canker was found on 100 ha and
there has not been significant mortality for 14
years. The disease was not found in any new
locations for the fifth consecutive year in
Vermont, but about 400 ha are infected in the
northern part of the state.
White pine blister rust was reported
throughout Maine. In Vermont, significant
mortality and top dieback occurred in a mature
stand and heavy damage occurred in young
plantations in the southern part of the state.
Small infestations were reported in Indiana.
Active blister rust cankers were found at 10 of
the 52 grid points surveyed in West Virginia.
The hemlock woolly adelgid is an
introduced insect which attacks both eastern
hemlock and Carolina hemlock. It is
established in the Blue Ridge Mountains in the
vicinity of the Shenandoah Valley and is
spreading south (Figure 7-1). In the northeast,
the adelgid is reported in Connecticut,
Maryland, Massachusetts, New Jersey, New
York, Pennsylvania, Rhode Island, and
Vermont. Hemlock stands in previously
infested areas have been re-infested. The first
occurrences in native hemlock stands were
reported from Maryland. The infestation
appears to be spreading in western
Massachusetts, but it is still contained in the
Springfield area. There are unconfirmed
reports of the insect in the eastern part of the
state. New Jersey reports moderate
infestations of native hemlock. In southeastern
New York, moderate to heavy mortality was
reported on about 6,000 ha. The infestation is
intensifying and spreading, and hemlock
mortality is increasing. The hemlock woolly
adelgid was reported from five new locations in
Pennsylvania. The infestation increased to
three of the five counties in Rhode Island,
spreading mostly in suburban areas along the
coastline. Some areas reported heavy
defoliation. The insect was introduced to
Vermont in 1990. Eradication efforts continued
at the infected site where several additional
seedlings were found infected. No evidence of
the pest was found in native hemlock stands in
the valley below the infected site.
Eastern Spruce-Fir
Eastern spruce budworm (Table 7-1)
caused light to moderate defoliation on
approximately 44,000 ha in Minnesota during
1991. This was a decline from the 80,000 ha
recorded in 1990. No defoliation was reported
from any of the other Lake States (Michigan
and Wisconsin). For the second year in a row,
no defoliation was reported from Maine, New
Hampshire, New York, or Vermont. However,
trap catches in both New York and Vermont
were higher than last year.
Spruce and fir insects of the Southern
Appalachian Mountains have been the subject
of much controversy over the past decade.
Most questions raised concern the cause of
catastrophic tree mortality occurring in this
high-elevation ecosystem. An evaluation of the
standing dead tree losses was made in
1984-85 through a combination of aerial
photography and ground sampling. A 100
percent photographic coverage was made over
the Great Smoky Mountains National Park,
Roan Mountain, Grandfather Mountain, the
Black Mountains, Balsam Mountains, Mount
Rogers, and the White Top Range. A total of
232 photo plots and 123 ground plots were
established over all of these areas with the
exception of Mount Rogers and White Top
Mountain which were experiencing significant
tree mortality at the time of the survey.
The survey showed that approximately
26,500 ha of host type was present in these
7-2
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Table 7-1. Defoliation by eastern spruce bud worm by state for 1990 and 1991.
State
Hectares defoliated
1990 1991
81,000 44,000
Percentage of the
spruce/fir type defoliated
1990 1991
Maine
Michigan
Minnesota
New Hampshire
New York
Vermont
Wisconsin
0
1,000
80,000
0
0
0
0
0
0
44,000
0
0
0
0
0
0.1
6.7
0
0
0
0
0
0
3.7
0
0
0
0
1.2
0.6
areas and that 74% of the total host type
occurred in the Great Smoky Mountains
National Park. The survey also showed a
range of 3 to 14% mortality in red spruce and
44 to 91% mortality of Fraser fir. The balsam
woolly adelgid was indicated as a major cause
of fir mortality.
Balsam woolly adelgid populations
were high again this year. Mortality continues
throughout the range of Fraser fir in this area.
A large number of trees are exhibiting thinning
crowns and dieback in the southern
Appalachian mountains of Tennessee, North
Carolina, and Virginia. The insect was also
found throughout most of central and southern
Maine, but populations have declined there and
in southern Vermont following several years of
continued low populations.
Within New Hampshire, New York, and
Vermont, the conifer swift moth is still
considered to be a factor in high elevation
spruce-fir stands. High elevation, permanent
plots continued to be monitored for spruce
mortality, but no increase in occurrence was
reported.
The spruce beetle affects white, red,
and black spruces. Heavy infestation continued
on about 3,600 ha in northern and western
areas of Maine and mortality in spruce was
over 25% in those areas. Scattered damage
was identified on over 400,000 ha. The attack
appears to have peaked in 1990 and 1991 in
much of this area and is expected to be stable.
New York reported infestation on over 3,200 ha
in Hamilton and Fulton counties. Mortality was
moderate on 1,200 ha and heavy on 600 ha.
Stillwell's Syndrome, a decline of
balsam fir that is associated with Armillaria root
disease, is present throughout Maine. The
general trend is expected to remain at low
levels unless a new stress factor, such as a
spruce budworm outbreak, occurs. Scattered,
low numbers of afflicted fir were noted on over
2,800,000 ha of the spruce-fir forest type. No
significant concentrations of the syndrome were
detected and tree mortality was minimal.
Cytospora canker is present throughout
the range of red spruce.
Southern Pines
Fusiform rust continues as the most
damaging disease of the loblolly and slash pine
types in the South. Almost 1 in 3 ha in these
forest types show 10% or more trees infected
with potentially lethal cankers. Georgia is the
7-4
-------
most heavily affected state (2 million ha)
accounting for 30% of all infected lands.
Alabama and Mississippi also have over
800,000 ha each infected. Together, these
three states account for nearly 60% of all
affected areas. Table 7-2 summarizes
infections of fusiform rust, and Figures 7-2 and
7-3 show the fusiform rust hazard ratings for
slash pine and loblolly pine.
Southern pine beetle infested nearly
four million ha in 1991, a 150% increase over
the previous year (Figure 7-4). Heaviest
activity shifted from the western Gulf states to
the east, with Alabama accounting for over
37% of all outbreak acreage. The outbreak in
the Appalachian mountains has collapsed, but
populations have expanded in the piedmont of
Georgia, South Carolina, and North Carolina.
A total of 6 ha were newly infested in 1991 in
Maryland and is the first report since 1987.
Southern pine beetle populations
continue to cause significant mortality,
particularly in national forest wilderness areas
where the beetle poses a threat to colonies of
the red-cockaded woodpecker, a federally listed
endangered species. This bird nests in trees
which are highly susceptible to attack by the
southern pine beetle.
Table 7-3 compares outbreak acreage
across the South in 1990 and 1991 by state.
Arkansas, Florida, Kentucky, Oklahoma, and
Tennessee were not epidemic in either year.
Littleleaf is a decline disease affecting
shortleaf pine and, to a lesser extent, loblolly
pine. It results from the interactions of feeder
root disease, poor internal drainage of clay
soils eroded after abandonment of agricultural
lands, and older, physiologically mature trees.
Bark beetles, especially the southern pine
beetle, preferentially attack littleleaf-affected
stands. The range of littleleaf disease at its
height in the 1940's and '50's encompassed
165 counties with a total area of 19.6 million ha
in the Piedmont Plateau from Mississippi to
Virginia, and it included areas in Tennessee
and Kentucky (Figure 7-5, Table 7-4).
Littleleaf disease is probably less
prevalent today due to regional trends in
forestry such as increases in industrial forest
land ownership with associated lower rotation
ages, preference for the less susceptible
loblolly pine over shortleaf pine for
reforestation, and urbanization. However,
specific information on current disease
distribution is lacking. A regional analysis of
littleleaf vulnerability is underway, utilizing
existing information on historical disease
distribution, soil properties, and the distribution
of susceptible forest types.
Pine engraver beetles populations were
active and widespread throughout the South.
In nearly all cases, there was an association
with drought.
Oak-Hickory
Gypsy moth populations declined in
1991 throughout much of the eastern United
States, where approximately 1.6 million ha
were defoliated compared to more than 2.8
million ha in 1990 (Table 7-5). The sharpest
decline occurred in Pennsylvania, where
defoliation was 0.5 million ha compared to 1.4
million ha in 1991. Defoliation increased
significantly in the New England states of
Maine, New Hampshire, and Massachusetts
and continued to expand into new areas in
Michigan, Ohio, and West Virginia. In 1990,
approximately 6% of the susceptible host type
was defoliated, but in 1991 it was only 3%.
In 1991, approximately 332,000 ha of
cooperative State and Federal lands were
treated to protect forested communities,
recreation areas, parks, and high-value forests
from the gypsy moth. This represents about
one-third less than was treated in 1990.
Private landowners may have treated another
0.2 million ha.
Although it is early, suppression
estimates for 1992 suggest that at least
342,000 ha will be treated with large state
suppression projects in Maryland, Michigan,
Pennsylvania, and West Virginia. National
Forest and other Federal suppression is again
expected to be between 20 and 30 thousand
ha.
7-5
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Table 7-2. Area and percent of host type infected with fusiform rust by state, 1991.
State (survey yr.)
Alabama (82)
Arkansas (88)
Florida (87)
Georgia (89)
Louisiana (84)
Mississippi (87)
North Carolina (90)
Oklahoma (86)
South Carolina (86)
Texas (86)
Virginia (86)
TOTALS
Area infected
(ha)
1,061,243
124,445
3,949
2,016,985
722,490
817,209
452,047
9,119
745,160
252,961
28,556
6,234,164
Percent of
susceptible type
34
8
23
53
30
32
29
6
40
12
4
30
Table 7-3. Southern Pine Beetle Outbreak Area 1990 and 1991.
State
Alabama
Arkansas
Georgia
Florida
Kentucky
Louisiana
Mississippi
N.Carolina
Oklahoma
S.Carolina
Tennessee
Texas
Virginia
1990
0
0
0
0
0
0
0
45,084
0
939,540
0
728,745
0
1991
1,659,919
0
130,180
0
0
568,097
458,119
40,067
0
977,179
0
164,972
14,188
TOTAL
1,713,369
3,988,876
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Figure 7-5. Historic range of littleleaf disease (Forest Pest Management, R-8).
7-10
-------
Table 7-4. Area and number of counties within the historic range of Irttleleaf disease.
State
Hectares
(million)
Counties
(number)
AL
GA
KY
MS
NC
SC
TN
VA
5.3
4.7
0.3
0.1
3.0
2.8
1.3
2.1
Total 19.6
29
59
3
1
25
18
13
17
165
Table 7-5. Area of defoliation by gypsy moth by state, 1990 and 1991.
State
Hectares defoliated
1990 1991
Connecticut
Delaware
Massachusetts
Maryland
Maine
Michigan
New Hampshire
New Jersey
New York
Ohio
Pennsylvania
Rhode Island
Vermont
Virginia
Washington, D.C.
West Virginia
TOTAL
71,488
1,534
33,844
53,871
109,487
145,076
53,927
174,589
143,385
47
1,764,251
0
25,506
240,486
4
137,144
2,954,640
20,305
5,455
114,228
30,444
248,789
253,720
73,227
68,785
71,239
140
498,002
0
1,456
249,474
51
45,709
1,752,263
7-11
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The gypsy moth continues its westward
and southward spread (Rgure 7-6). In Illinois,
1,442 moths were trapped, the second largest
catch ever recorded. Moth catches continued
to increase in northern Indiana counties and
limited ground control efforts took place in
Kosciusko County. In Minnesota, 51 male
moths were trapped from 31 sites, down from
a high of 126 in 1990. In Iowa, 60 male moths
were trapped from 41 traps in seven counties
and in Missouri, statewide trapping continues.
Male moths were trapped in several cities, 25
of 36 in St. Louis. Most moths in Iowa and
Missouri were associated with infested
ornamental spruce shipped from Pennsylvania.
Mortality caused by gypsy moth varies
between stands, with some stands experiencing
high losses. Severe summer drought and
secondary pests contributed to this mortality.
Still, the majority of stands remain adequately
stocked.
Oak decline is a complex, slow-acting
disease syndrome involving the interactions of
predisposing factors such as climate, site
quality, or tree age; inciting stress such as
drought or insect defoliation; and contributing
organisms of secondary action such as
Armillaria root disease and the two-lined
chestnut borer. Decline is characterized by a
gradual but progressive dieback of the crown.
Mortality commonly results among susceptible
trees but usually not until after dieback has
been present for several years. Mature
overstory trees are most often affected. Oak
decline and mortality is widely distributed over
the eastern half of the United States and has a
long history. Episodes of damage have been
reported for more than 130 years. Since the
turn of the century, at least 26 episodes have
been estimated to have occurred; some in
each decade except the 1940's.
Forest Inventory and Analysis data
from 12 Southern states were used to assess
oak decline on a regional scale (Table 7-6).
The dieback pest/damage code was used to
key on plots with decline damage. Average
annual mortality volume per unit area (oaks) on
affected and unaffected plots was then
compared as a measure of decline severity
(Figures 7-7 and 7-8).
Table 7-6. Incidence and impact of oak decline in the Southeast, 1991.
State
Affected
Affected Area Incidence
Unaffected
Dead Volume
Dead Volume
(ha) (%) (%)
Alabama
Arkansas
Florida
Georgia
Louisiana
Mississippi
N. Carolina
Oklahoma
S. Carolina
Tennessee
Texas
Virginia
TOTAL
107,566
152,964
67,091
111,144
11,385
45,733
288,853
7,400
34,824
274,416
44,753
440,441
1,586,569
6.87
6.38
18.65
7.82
2.32
3.48
19.63
0.92
5.49
12.02
4.43
19.13
9.88
1.08
1.22
2.43
1.36
1.90
0.88
1.30
2.00
2.49
1.71
2.20
1.53
1.49
1.00
0.97
1.90
1.02
1.09
0.78
1.00
1.35
1.26
1.15
1.55
0.86
1.04
7-12
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Figure 7-6. Gypsy moth defoliation, 1984-1991 (Forest Pest Management, R-8 and NA).
7-13
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In the northeast, oak decline was
reported in Iowa, Michigan, Minnesota,
Missouri, and Wisconsin. There were 300,000
ha of scattered mortality caused by attacks of
the two-lined chestnut borer and armillaria root
disease, predisposed by the droughts of
1987-1989.
Variable oak leaf caterpillar defoliated
over 400,000 ha in northeast Texas in 1991.
Damage is more spectacular than serious and
trees normally recover.
Fall webworm populations in
Massachusetts were heavy in 1991, especially
in areas with high proportions of black cherry.
There were 1.2 million ha affected statewide in
Missouri. Moderate to heavy defoliation
occurred in scattered areas in New Hampshire,
especially along the seacoast. There were
isolated occurrences of complete defoliation in
New York. Defoliation was widespread in
Vermont, especially along roadsides. Incidence
has noticeably increased over 1990, with
complete defoliation occurring in some areas.
Oak leaftier population levels were low
in Maine with no reportable defoliation.
Conditions are expected to remain the same in
1992. In Vermont, very few moths were
collected in pheromone traps except in
Rochester where numbers increased
substantially from last year. No damage was
reported anywhere in the state.
Oak skeleton izer populations caused
heavy leaf skeletonizing on shingle oak
throughout Missouri. The last outbreak was in
1983, with the next outbreak predicted in 1999.
The orangestriped oakworm caused
heavy defoliation of 1,400 ha in Montcalm and
Ionia counties in the lower peninsula of
Michigan. The insect was also very prevalent
in the Southern Appalachians.
Northern Hardwoods
Beech bark disease (Nectria coccinea
var. faginata in association with beech scale)
seemed to be down in Maine, although heavy
mortality and dieback occurred from the West
Virginia disease complicated by a severe May
freeze. Scattered dieback and chlorosis
occurred in southern Vermont with an increase
in Nectria fruiting. Northeastern Vermont had
a decrease of occurrence and the Champlain
Valley had a slight increase of the disease.
Despite a decrease in the area infested by the
scale, about 1,600 ha in New York still showed
heavy infection (70-100%). Infection levels
were static in Connecticut. The disease also
continued to cause dieback and mortality
throughout the other New England states. The
disease was widespread in northern
Pennsylvania counties. In West Virginia, the
presence of the disease continued to be
reported on the Monogahela National Forest
though expansion in 1991 may have been
limited due to dry weather.
Elm spanworm defoliated 12,065 ha in
Pennsylvania including areas of the Allegheny
National Forest.
Sugar and red maple were affected by
maple decline in Maine, Michigan,
Pennsylvania, and Vermont. Maple stands
continued to be monitored throughout the
region. Symptoms remained static in western
Maine with less than 10% crown dieback. In
the upper peninsula of Michigan, dieback and
mortality occurred on sites with poor drainage,
heavy or shallow soils, and subjected to forest
tent caterpillar defoliation during the droughts
of 1987-1989. Some reports of damage in
Pennsylvania may possibly be associated with
pear thrip activity. In northern Vermont, 1,347
ha of decline and mortality were observed in
scattered locations. In general, trees looked
healthier than in previous years due to vigorous
spring flush. Dry summer conditions may lead
to some decline in 1991.
Pear thrips affected sugar maple
throughout Maine, Iowa, Maryland, Michigan,
Minnesota, Pennsylvania, Rhode Island,
Vermont, and Wisconsin. Population levels
continued to be low in central and southern
Maine with some light damage observed. An
area of 1,170 ha was surveyed for the pest.
Populations were very low throughout Maryland
and Pennsylvania. First report of pear thrips in
7-16
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Minnesota was found in Hennepin and Carver
counties. It was also found in Wisconsin,
Michigan, and Iowa in 1991. Some light
damage was reported on survey plots in Rhode
Island. Damage in Vermont declined in 1991
and was reported light throughout the state and
no defoliation was aerially mapped.
Overwintering soil counts decreased about 90%
from the previous year and counts were also
reported lower than 1990. Spring weather
conditions which promoted rapid leaf expansion
contributed to lower damage levels. Dieback
continued in one southern Vermont maple
stand that was severely defoliated in 1988 and
1989 and also stressed by drought conditions.
Sapstreak affected some sugarbushes
in the northern part of New York.
Maple trumpet skeletonizer caused
defoliation in several counties in northern New
York, an increase from last year. Damage in
Vermont was scattered and variable throughout
the state.
The maple leafcutter defoliated
approximately 4,000 ha in northern New York.
About 1,200 ha of defoliation were observed in
southern Vermont. Lighter damage could be
found throughout the region, especially on
regeneration. Damage was also found on 824
ha in northern Vermont and populations are
increasing.
Ash dieback (ash yellows) has been
reported in stands of green, blue, black, white,
and brown ash in Indiana, Iowa, Maine, New
York, Pennsylvania, and Vermont. Ash decline
and ash yellows are epidemic in northeastern
Indiana and can be found throughout the
northern half of the state. Annual mortality was
estimated at 3% of the ash population. The
disease increased in urban trees in Iowa in
1991. Maine reported 800 ha affected in
Aroostook county, particularly along streams
and other wet areas. This was a slight
increase from 1990. Approximately 300 ha
were severely affected (70-100% of trees
symptomatic or dead), and it appeared to be
something other than ash yellows. The trend is
expected to increase. Ash decline continued to
be associated with ash dieback in New York.
There were nine counties in Pennsylvania that
reported symptoms: Lehigh, Adams,
Cumberland, Mifflin, Clarion, Erie, Perry, Junita,
and Clinton. In Vermont, dieback remained
common and stable especially in the
Champlain Valley area and other low elevation
sites with a high stocking of pole size trees.
Butternut canker is widespread
throughout the range of butternut. Up to 100%
mortality has occurred in some stands and
large geographic areas such as Virginia and
North Carolina have lost 77% of their butternut
over the last 20 years. The canker was found
in Vermont, with new infections detected in
Orange, Lamoille, and Chittenden Counties.
Twenty-eight counties are now affected in
Wisconsin.
Eastern tent caterpillar nests were very
common along roadsides in Connecticut. In
Massachusetts, damage has been increasing
over the last several years, especially in the
eastern portion of the state. In Michigan, tow
to moderate defoliation occurred on over
800,000 ha in the southern lower peninsula.
Occurrence was widespread throughout
northern Vermont, similar to last year's level.
However, in southern Vermont, the tents were
less common.
Forest tent caterpillar was reported with
widespread light defoliation in eastern North
Carolina and with moderate defoliation along
the Roanoke River drainage. Light scattered
defoliation was observed in Tennessee and
Virginia. Severe defoliation occurred in
western Mississippi and throughout Louisiana,
but overall population levels are declining.
Build-ups occurred in Texas and Arkansas.
There was no reported defoliation in Indiana
this year. Low populations were reported in
Maine and Vermont with only scattered
individuals and no defoliation observed. In
Michigan, 79,202 ha of defoliation occurred in
the upper peninsula. This was a decline from
265,600 ha in 1990, and appears to signal the
end of an outbreak that began in 1988. Only
the extreme eastern upper peninsula may
sustain defoliation in 1992. In Minnesota, light
to moderate defoliation occurred on 49,800 ha
and heavy defoliation on 168,000 ha. This was
the third successive year of some defoliation
on over 1,740,000 ha. However, the intensity
7-17
-------
has declined and is expected to continue to
decline in 1992. In New York, over 8,000 ha of
moderate to heavy defoliation was reported in
the Adirondack region. On over half of the
area affected, the trees did not refoliate.
Populations are expected to increase in 1992.
In Wisconsin, 19,560 ha were defoliated in
Marinette and Oconto counties. This appears
to be the end of an outbreak that began in
1986. Defoliation, together with drought,
resulted in widespread oak mortality.
Fall cankerworm caused light
defoliation on Cape Cod where populations
appear to be increasing.
Cherry scallop shell moth-caused
damage was heavy within stands composed of
a high percentage of black cherry in central
and western Massachusetts. The infestation
occurred on 2,544 ha and the population is
expected to continue to cause damage in the
next year or two. This is the second
consecutive year of damage in some areas in
northern New York. There was some heavy
defoliation in Vermont, where the pest was
common in the southern portion of the state.
Saddled prominent populations are
stable at low levels and are not expected to
change in the near future. Numbers of moths
caught in pheromone traps were minimal.
Infestation was reported to be heavy on 800 ha
in Berkshire County in western Massachusetts.
Presence of a disease in the population was
indicated. In Vermont, populations declined
and no defoliation was aerially mapped.
Larvae were occasionally observed and small
numbers of moths were caught in light and
pheromone traps. A fungal pathogen was
suspected to have caused the decline.
Aspen-Birch
Birch dieback continued in Maine
affecting 13,890 ha in the Rangeley region and
20,000 ha in Washington county. Defoliation
was light throughout the area. In an annual
plot survey, 59-66% of the paper birch
exhibited top dieback and 3-9% died. Tree
health as reflected by crown condition improved
from 1990. In northern Vermont, trees
appeared to be recovering, but scattered
mortality was still occurring. Widespread birch
defoliation and dry summer weather is
expected to cause dieback in southern
Vermont in the future.
Scattered defoliation caused by the
birch casebearer was recorded on over 40,000
ha in central and eastern portions of Maine.
Populations were higher than expected in
coastal areas. The insect was also present in
the northern counties.
Two species of birch leafminers
affected gray, paper, and yellow birches in
Vermont and Maine. Populations were high in
some locations in central and eastern coastal
portions of Maine affecting over 48,000 ha.
Damage from this pest was widespread in
southern Vermont with 2,000 ha of defoliation.
Over 10,000 ha were moderately to heavily
defoliated in northern Vermont.
The birch skeletonizer, which caused
moderate to severe late season defoliation on
4,000 ha in Maine, was most visible on higher
elevation paper birch sites in the western
portion of the state. Defoliation was up
noticeably from last year. Over 4,500 ha were
affected in the Adirondack region of New York
with about half of the area heavily defoliated.
In Vermont, there were scattered occurrences
of moderate to heavy defoliation.
The bronze birch borer continued to
attack paper birch in the northern regions of
Michigan, Minnesota, and Wisconsin where the
effects of the 1987-89 drought linger.
Hypoxylon canker is a common
occurrence throughout the range of aspen.
Special Topics
Dogwood anthracnose
Since first discovered in the South in
northern Georgia (1987), dogwood anthracnose
continues to expand rapidly throughout the
range of the tree. To date, 120 counties,
encompassing some 2.3 million ha in seven
southeastern states, have shown confirmed
infections (Figure 7-9). The disease is more
prevalent in moist, cool sites such as
7-18
-------
Figure 7-9. Dogwood anthracnose occurence in the eastern United States (Forest Pest
Management, R-8 and NA).
7-19
-------
Table 7-7. Area affected by Dogwood Anthracnose in the Southeast, 1988-91
State
1988
1989
1990
1991
Alabama
Georgia
Kentucky
N.Carolina
S.Carolina
Tennessee
Virginia
.
62,298
0
50,003
64,086
102,499
5,438
149,561
0
215,275
119,706
265,572
140,258
337,215
0
615,291
298,452
531 ,656
487,876
429,013
0
758,593
325,732
827,489
1,461,651
Notes:
(1) Alabama data were not available for this report.
(2) Overall estimate in the text does not equal the sum of the states in the table because the values are
based on the total areas for all states combined.
north-facing slopes and beneath dense
overstories.
The disease was present throughout
Connecticut and Maryland. Infections occurred
in eastern Massachusetts and southeastern
New Hampshire. The disease was identified
infecting dogwood throughout Long Island and
all along the southern tier of New York. It was
also present in Monroe County which is along
Lake Ontario. The disease was present in
most counties in Pennsylvania. All 55 counties
in West Virginia had dogwood anthracnose
reported.
Table 7-7 shows the estimated
cumulative acreage increase by state by year
since first quantified in 1988 in the South.
Cabbage Palm Mortality
The Florida Department of Agriculture
reported thousands of cabbage palms dead or
dying along the Gulf coast (cause unknown).
The area affected is approximately 20 km long
and 1 to 3 km wide between Crystal River and
Cedar Key. Palms of all ages, but primarily
older ones, are affected. Trees on islands as
well as along the coastline are afflicted.
Black Gum Disease
Black gum disease (cause unknown)
intensified in the Southern Appalachian
mountains, principally in the tri-state area of
Tennessee, Georgia, and North Carolina
(Figure 7-10). This condition appears to have
great potential significance. Its occurrence in
frequent association with dogwood anthracnose
suggests that the two might have a similar
etiology.
Dutch Elm Disease
Dutch elm disease remains common
throughout the range of American elm with the
more virulent strain causing mortality in
previously unaffected trees. It is considered to
be the most important forest and shade tree
problem in many states.
Pitch Pine Looper
Pitch pine looper (Lambdina spp.)
caused the first year of noticeable defoliation
from L pellucidaria in Massachusetts since the
mid 1970's. About 200 ha were defoliated and
the infestation is expected to increase.
Approximately 81,000 ha were defoliated by L.
7-20
-------
op
CC
-------
athasaria in the Pine Barrens in New Jersey.
Some tree mortality may occur. L pellucidaria
caused moderate to heavy damage on about
250 ha on Long Island in New York.
Eastern Larch Mortality
Eastern larch mortality was still
common throughout most of Maine. Up to
100% mortality occurred in some stands
infested with the eastern larch beetle. New
attacks were most common in previously
infected stands. However, new localized
outbreaks occurred in eastern counties. Only
8 ha of mortality were detected in Vermont.
European larch canker reported no
increases in 1991. The area affected was
approximately 2,600 ha and remains relatively
unchanged. Infection was heavy at two coastal
epicenters and light to moderate elsewhere.
No major increases are expected in the future.
The quarantine was reviewed and remains in
effect.
General Stressors
Armillaria root rot-caused mortality in
northern red oak, northern pin oak, scarlet oak,
and red pine was scattered over 200,000 ha in
Wisconsin, Minnesota, Iowa, and Michigan
related to regional drought in previous years.
Anthracnose was especially prevalent
this year in the Appalachian mountains, due no
doubt to the unusually wet spring. Maples
were perhaps most conspicuously affected, but
various other hardwoods were also damaged.
Light damage was observed in Vermont and
Maine on American sycamore, maple and other
hardwoods, down dramatically from 1990.
Annosus root disease continues as the
most serious root disease of the South.
Southern pine beetle, Ips spp., and black
turpentine infestations frequently occur in
infected stands. Trace to light infections were
present in red pine plantations in Maine. The
trend was expected to gradually increase.
Decay continues as one of the most
important problems in hardwoods. The
condition is widespread and is often associated
with fire, logging injury, or storm damage.
Cyclic Hardwood Insects
Incidence of the black twig borer, a
recently introduced ambrosia borer, is on the
increase. It is sometimes found in association
with fusarium canker, especially in shade trees.
Buck moth defoliation was prominent in
Tennessee on ridge tops and on the upper
slopes of forest stands of the Western
Highlands Rim. Scattered defoliation was also
reported throughout Virginia. Moderate to
severe defoliation occurred in the New Orleans
metropolitan area. Populations have been
building in Delaware for the past few years.
About 65,000 ha were defoliated by the
fruit tree leafroller in the Atchafalaya basin of
south central Louisiana.
Brood XIV of the periodical cicada
caused considerable twig damage to various
hardwood species in western North Carolina.
Eastern Tennessee and the Cumberland
Plateau also incurred moderate to heavy
damage.
Ozone
Ozone damage symptoms appeared
throughout the South in 1991. Tipburn was
observed in some eastern white pine families.
Indicator plants were used to assess ozone
levels in wilderness areas in the South, with
light to moderate damage on sensitive native
species. Ozone symptom surveys continued to
be conducted in Wilderness Areas on the
White and Green Mountain National Forests.
Evaluations showed light damage occurred on
sensitive native plants.
Windstorms
In Wisconsin, 8,715 ha in Chippewa,
Lincoln, Langlade, and Oneida counties were
damaged by a storm on May 29, 1991. There
were 28,700 ha in Rusk, Barren, Chippewa,
and Price counties affected by another storm
on June 7.
7-22
-------
Drought
Residential and rural oaks were
affected in Delaware. Light mortality was
reported in Indiana. The species most affected
were yellow poplar, sugar maple, white pine,
and black cherry. Drought affected jack, red,
and eastern white pine in Iowa. There were 89
ha of tree mortality in small pockets in
plantations. This mortality was related to
lingering effects of 1987, 1988, and 1989
droughts. Scattered mortality occurred to
balsam fir and paper birch in Itasca,
Koochiching, and St. Louis counties in
Minnesota. Lake and Carlton counties also
had scattered birch mortality. Drought has also
affected forests in central and western New
York. Drought conditions in June and July led
to leaf scorch, chlorosis, leaf curling and
dropped leaves on hardwoods in Vermont.
Symptoms are expected to show for the next
couple of years. In West Virginia, drought
conditions were worst in northern counties.
Hurricane Bob
Coastal areas were hard hit with salt
spray damage, conifers being most susceptible.
Forest trees and ornamentals were broken or
blown down in areas throughout the coastal
New England region. Cape Cod,
Massachusetts was the hardest hit with pockets
of severe damage occurring throughout the
area. Most susceptible were black locust,
pines, older oaks, and other stressed trees
being uprooted and broken. Remnants of the
damage are expected to remain for years.
Many trees were permanently tilted in certain
larch plantations in Maine.
Ice Storms
In Indiana, 40,000 ha of jack pine in
forest, fence row, and yard trees in 13 counties
were damaged. Damage included uprooted
and broken limbs and tops. Up to an inch of
ice damaged trees in western and central New
York. Monroe County reported the most
damage with 30% of the trees severely broken
and another 30-50% moderately damaged.
Most species affected were ash, red and silver
maples, pines, larch, willow, and other stressed
or weakened trees. The storm also caused
limbs to droop in the summer and internal
injuries which may take more time to detect.
Salvage of timber and pruning, removal, and
planting of ornamental trees is being done.
Over three million cubic yards of debris was
cleared from Monroe County. A severe storm
in March, 1991 snapped trees on over 1,740
ha acres in Wisconsin.
Spring Visits
As a part of the Forest Health
Monitoring initiative, crews in Alabama,
Georgia, and Virginia are making spring visits
to the FHM plots. Activity in all states was
minimal this year. Only some minor
frost/freeze damage was reported, principally in
Alabama.
Aerial Photography of Georgia FHM Plots
Photointerpretation of Georgia Forest
Health Monitoring plots showed that in the vast
majority of cases (about 98.5%), tree mortality
averaged 1% or less per stand in which the
FHM plot was located. The most significant
single cause of mortality in any one stand (6%)
was attributed to fire.
Acknowledgements
The information from the Northeastern
Area was compiled by personnel from USDA
Forest Service, Forest Health Protection:
Margaret Miller-Weeks, Susan Cox, and
Florence Peterson of the Durham, NH Field
Office; Dan Twardus of the Morgantown, WV
Field Office; Manfred Mielke of the St. Paul,
MN Reid Office; and Charles Hatch of the Area
Office in Radnor, PA.
Information from the Southeast was
compiled by personnel of the USDA Forest
Service, Forest Pest Management offices in
Asheville, NC, Pineville, LA, and Atlanta, GA:
James D. Ward (Gypsy moth); Jose F. Negron
(hemlock woolly adelgid); Steven W. Oak
(littleleaf disease); Dale Starkey (oak wilt);
Nolan Hess (annosus root rot); John Taylor,
Walter Salazar, Carol Scott (Georgia FHM Plot
Photointerpretation); Wes Nettleton (southern
7-23
-------
pine beetle); and Steve Holtzman (GIS
support).
The hemlock woolly adelgid distribution
map is a collaborative effort by the
Southeastern Forest Experiment Station (Forest
Inventory and Analysis), the Department of the
Interior (Shenandoah National Park and Blue
Ridge Parkway), and the Department of
Entomology, Virginia Polytechnic Institute and
State University.
Additional information was contributed
by personnel from the following state
organizations:
Alabama Forestry Commission (Jim Hyland,
FHM Coordinator)
Arkansas Forestry Commission
Connecticut Department of Environmental
Protection, Bureau of Forestry
Delaware Department of Agriculture
Florida Division of Forestry
Georgia Forestry Commission (Terry Price and
Richard Jernigan, FHM Coordinators)
Illinois Department of Conservation, Division of
Forest Resources
Illinois Natural History Survey
Louisiana Forestry Commission
Maine Forest Service
Massachusetts Division of Forests and Parks
Maryland Department of Agriculture
Michigan Department of Agriculture
Michigan Department of Natural Resources,
Division of Forestry
Minnesota Department of Natural Resources,
Division of Forestry
Mississippi Forestry Commission
Missouri Department of Conservation, Forestry
Division
New Hampshire Division of Forests and Lands,
Department of Resources and Economic
Development
New Jersey Department of Agriculture
New York State Department of Environmental
Conservation, Division of Lands and Forests
North Carolina Forest Service
Ohio Department of Natural Resources,
Division of Forestry
Oklahoma Forestry Division
Pennsylvania Bureau of Forestry, Division of
Pest Management
Rhode Island Division of Agriculture and
Marketing
South Carolina Forestry Commission
Tennessee Division of Forestry
Texas Forest Service
Vermont Division of Forests, Parks, and
Recreation, Forest Resource Protection
Virginia Division of Forestry (Tim Tigner, FHM
Coordinator)
West Virginia Department of Agriculture, Pest
Industry Division
Wisconsin Department of Agriculture
Wisconsin Department of Natural Resources,
Forestry Division
7-24
-------
8. Selected Air Quality and
Deposition Data Summaries
D. S. Shadwick and L. A.
Smith
This chapter presents maps of
interpolated ozone data for low elevation
regions and maps and regional summary
statistics for ions found in precipitation. The
selection of air quality and deposition data for
inclusion in this report has been motivated by
several concerns. First, the air constituents
that have been summarized are suspected to
be in high enough concentrations to adversely
affect vegetation immediately or in the future.
For example, this is the reason that gaseous
NO2 is not included. Second, only data from
monitors that cover a broad geographic area
and that come from a sufficiently dense
network are included. This criterion excluded,
for example, maps of dry deposition
constituents. Finally, pollutants that are
typically point source oriented (for example,
SO2 and fluoride) have not been included. The
review by Garner et al. (1989) is a good
overview of possible pollutant effects on forests
in the eastern U.S.
Ozone
The information consists of interpolated
(i.e., kriged) maps of the seasonal ozone W126
index (ppm-h):
—
l+4403exp(-126 x
where O, is the Ith hourly average ozone
concentration (in ppm) and the index includes
all hourly average concentrations for the
months April-October, inclusively. The rationale
for the emphasis on peak ozone hourly
average concentrations and examples of
applications of the index are in Lefohn et al.
(1992).
Maps of the interpolated W126 values
and the relative errors (interpolation standard
deviation divided by the interpolated value)
were prepared for the years 1985-1989.
The area covered by the maps includes all
states east of, and adjacent to, the
Mississippi River (approximately 96° west
longitude). The maps are of kriged values
on a 1/2°x 1/z° (latitude by longitude) grid.
Figures 8-1, 8-2, 8-3, 8-4, and 8-5 show for
each year, smooth contours of the gridded
values at 10 ppm-h intervals. The
seasonal W126 values shown on the maps
are generally lower than values observed
at sites in southern California where ozone
has been linked to forest damage. For
example, seasonal W126 values for the
Lake Gregory, CA ozone monitor (AIRS ID
060710005) are generally in excess of 200
ppm-h.
The ozone data sources were the
U.S. EPA's Aerometric Information
Retrieval System (AIRS), National Dry
Deposition Network (NDDN), and Mountain
Cloud Chemistry Program (MCCP) data
bases. All data used in the analysis have
passed through the appropriate quality
assurance programs (AIRS, Office of the
Federal Register 1988; NDDN 1989;
MCCP 1987). Only those sites with land
use designations 'suburban' or 'rural' in the
AIRS data base were used. Furthermore,
only data for which the actual data capture
was at least 75% of all possible hourly
average values for the April-October
'ozone season' (at least 3852 actual out of
5136 possible hourly average values) were
included. Details on corrections for the
seasonal W126 index to account for less
than 100% data capture and the kriging
procedures used in the analysis of the
ozone data can be found in Lefohn et al.
(1992).
To suggest ways that the kriged
ozone information across years can be
aggregated, an example of a 'gradient'
analysis is shown in Figure 8-6. The
method of aggregating the kriged
information to produce the figure is
explained in Lefohn et al. (1992) for the
'Student An a I y s i s'. This illustrative
8-1
-------
70.00
Figure 8-1. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1985.
8-2
-------
E in p pm h
0.00 -
0.00 -
20.00 -
30.00 -
40.00 -
50.00 -
60.00 -
10.00
20.00
30.00
40.00
50.00
60.00
70.00
Over 70.00
Figure 8-2. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1986.
8-3
-------
E in p pm h
1
2
3
4
5
6
0
0
0
0
0
0
0
0
V
.0
.0
.0
.0
.0
.0
.0
e r
0 -
0 -
0 -
0 -
0 -
0 -
0 -
70
1
2
3
4
5
6
7
.0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
0
0
0
0
0
0
0
Figure 8-3. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1987.
8-4
-------
ONE in p pm h
1
2
3
4
5
6
0
0.
0.
0.
0.
0.
0.
0.
v e
0
0
0
0
0
0
0
r
0 -
0 -
0 -
0 -
0 -
0 -
0 -
70.
1
2
3
4
5
6
7
0
0.
0.
0.
0.
0.
0.
0.
0
00
00
00
00
00
00
00
Figure 8-4. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1988.
8-5
-------
Over 70.00
Figure 8-5. Map of kriged seasonal ozone W126 index at 10 ppm-h intervals for 1989.
8-6
-------
Figure 8-6. An example of a 'gradient' analysis showing one way that kriged ozone information
across years can be aggregated.
8-7
-------
example may or may not be the 'best'
method of analysis for other questions that
could be asked of the data. The analysis was
designed to identify regions where seasonal
ozone W126 values were high (or low), not
only within a year, but also across years
relative to other regions. Noteworthy features
of the map are the regions where ozone is
relatively high each year. One such region
extends continuously along the Atlantic
seacoast and extends roughly to the
Appalachian Mountains. A second region of
smaller area is centered over the Ohio River
Valley near the Ohio-Indiana border.
Wet Deposition
This section summarizes the analysis
of wet deposition of hydrogen, sulfate, nitrate,
and ammonium ions for the calendar year
1990. Data sources included the National
Atmospheric Deposition Program (NADP)
(National Atmospheric Deposition
Program/National Trends Network 1991).
Aubertin et al. (1990) describe the NADP
quality assurance program. To ensure that
representative values were used, the same
data completeness criteria adopted by NADP
were applied here before an annual value was
accepted as valid (NADP 1991).
The value of pH=5.0 is chosen here as
the benchmark for natural precipitation in
accord with the suggestion by Charison and
Rodhe (1982) that pH=5.0 is a reasonable
midpoint for their hypothetical range of natural
precipitation pH values from pH=4.5 to pH=5.6.
The value of pH=5.0 is also in good agreement
with values actually monitored at remote
locations (Galloway et. al. 1982; Galloway and
Gaudry 1984).
Figures 8-7, 8-8, 8-9, and 8-10 display
the 1990 annual levels of precipitation-weighted
mean pH, sulfate deposition, nitrate deposition,
and ammonium deposition, respectively, for the
eastern United States. Figures 8-7 through 8-9
indicate that in 1990, precipitation was most
acidic and sulfate and nitrate deposition levels
highest in an area extending roughly from
eastern Michigan, down to southern Indiana,
across Ohio, West Virginia, and Pennsylvania,
and into New York and southern New England.
Levels generally decrease in all directions
away from this area. This general pattern is
consistent from year to year for each of these
ions. Figure 8-7 indicates that almost the
entire eastern United States experiences
precipitation more acidic than would be
expected from natural processes; that is, nearly
all the eastern United States has precipitation
with an average pH less than 5.
Ammonium deposition does not have a
clear spatial pattern (Figure 8-10). In 1990,
ammonium levels tended to increase with
distance from the coast. However, this same
picture is not necessarily obtained for other
years (results not shown).
Wet deposition data from certain areas
of special interest to Forest Health Monitoring
(FHM) were examined. These areas were:
Alabama-Georgia, mid-Atlantic (Virginia,
Maryland, Delaware, and New Jersey), and
New England (Maine, Vermont, New
Hampshire, Massachusetts, Connecticut, and
Rhode Island). A few sites that are near, but
outside, these areas were also included. For
example, a few sites were used that are
located just north of the Alabama-Tennessee
border and in eastern New York. Table 8-1
shows median annual values over the entire
period of monitoring for which data are
available (1979-1990). The physiographic
regions listed in Table 8-1 are based upon
those outlined in The National Atlas of the
United States of America (US Geological
Survey 1970) and a geography text by Wheeler
etal. (1969).
Some physiographic regions had
relatively little data. The statistics in Table 8-1
roughly agree with Figures 8-7 through 8-10, in
that more acidic precipitation and higher
deposition levels occur in the mid-Atlantic area
than in the other two areas. Within each of the
three areas, acidity and deposition levels
appear to be lower in the coastal areas than in
the other physiographic regions.
8-8
-------
Figure 8-7. The 1990 annual levels of precipitation-weighted mean pH for the eastern United
States.
8-9
-------
Figure 8-8. The 1990 annual levels of precipitation-weighted mean sulfate deposition for the
eastern United States.
8-10
-------
Figure 8-9. The 1990 annual levels of precipitation-weighted mean nitrate deposition for the
eastern United States.
8-11
-------
Figure 8-10. Thel 990 annual levels of precipitation-weighted mean ammonium deposition for
the eastern United States.
8-12
-------
Table 8-1. Median values of annual summary statistics, 1979-1990.
AL-GA:
coastal plain
Piedmont
mountains
uplands
mid-Atlantic:
coastal plain
Piedmont
mountains
New England:
lower
highlands
Number
of
site years"
34
10
12
4
27
10
26
19
74
-fid.
4.69
4.54
4.56
4.51
4.40
4.26
4.36
4.49
4.38
sulfate
dep."
14.20
18.82
22.42
20.29
21.23
25.63
30.14
20.64
21.19
nitrate
dep.
kg/ha
7.65
8.87
10.89
10.36
12.31
16.23
16.49
11.74
15.22
ammonium
dep.
1.43
1.79
2.08
1.97
2.12
2.34
2.69
1.48
1.99
a A site-year is one year of monitoring (subject to meeting data completeness criteria) at one site.
b Deposition
8-13
-------
9. References
Anderson, R.L and R.P. Belanger. 1987. A
crown rating method for assessing tree vigor of
loblolly and shortleaf pines. In: Phillips DR,
comp. Proceedings of the fourth biennial
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November 4-6, 1986; Atlanta, GA. General
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Forest Experiment Station, Asheville, NC. 538-
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Anderson, R.L, W.G. Burkman, (.Millers, and
W.H. Hoffard. 1992. Visual crown rating
model for upper canopy trees in the eastern
United States. USDA Forest Service,
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Anderson, R. L and I. Millers. 1992. Tree
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characteristics. USDA Forest Service white
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Aubertin, G.M., D.S. Bigelow, and B.A. Malo
[eds.J. 1990. Quality Assurance Plan:
NADP/NTN Deposition Monitoring. National
Atmospheric Deposition Program. Natural
Resource Ecology Laboratory, Colorado State
University, Fort Collins, CO.
Belanger, R.P. and R.L Anderson. 1991. A
guide for visually assessing crown densities of
loblolly and shortleaf pines. USDA Forest
Service, Research Note SE-352. 1 p.
Byers, G.E., and R.D. Van Remortel (eds.).
1991. Environmental Monitoring and
Assessment Program Forests: Laboratory
Methods Manual for the Forest Health
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Agency, Las Vegas, NV.
Charlson, R.J. and H. Rhode. 1982. Factors
controlling the acidity of natural rainwater.
Nature 295:683-685.
Chojnacky, D. 1991. Eastern Forest Health
Monitoring:Field Measurements Guide. USDA
Forest Service, Forest Survey, Intermountain
Research Station, Ogden, UT.
Cochran, W.G. 1977. Sampling Techniques,
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Inc.
Conkling, B.L, and G.E. Byers (eds.). 1991.
Environmental Monitoring and Assessment
Program - Forests: Field Methods Manual for
the Forest Health Monitoring Demonstration
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Environmental Protection Agency, Las Vegas,
NV.
Conkling, B.L and G.E. Byers (eds.). 1992.
Forest Health Monitoring Field Methods Guide,
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Dolph, K.E. 1988. Predicting height increment
of young-growth mixed conifers in the Sierra
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Einhaus, R.L, D.M. McMullen, R.L Graves,
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EPA. 1990. Threats to biological diversity in the
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Francis, J.K. 1986. The relationship of bole
diameters and crown widths of seven
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1986. 3 pps.
Galloway, J.N. and A. Gaudry. 1984. The
composition of precipitation on Amsterdam
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Galloway, J.N., G.E. Likens, W.C. Keene, and
J.M. Miller. 1982. The composition of
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Garner, J.H.B., T. Pagano, and E.B. Cowling.
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deposition, and other airborne pollutants in the
forests of eastern North America. Gen. Tech.
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Ashevilte, NC. 172 pp.
Grano, C.X. 1957. Growth of loblolly pine
seed trees in relation to crown density. Journal
of Forestry, 55(11):852.
Hill, M.O. 1973. Diversity and evenness: a
unifying notation and its consequences.
Ecology 54:427-432.
Horvitz, D.G. and D.J. Thompson. 1952. A
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approach to statitics. John Wiley & Sons, New
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Kutman, H.M. 1971. Effects of insect
defoliation on growth and mortality of trees.
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Lefohn, A.S., C.M. Benkovitz, R.L. Tanner, D.S.
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and K.A. Hermann. 1992. Surface-level ozone
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Linthurst, R.S., D.H. Landers, J.M. Eilers, D.F.
Brakke, W.S. Overton, E.P. Meier, and R.E.
Crowe. 1986. Characteristics of lakes in the
Eastern United States. Volume I: Population
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W.H. Hoffard. 1992. Definitions, Acquisition,
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19 pps.
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7)/National Trends Network. 1991. Tape of
Weekly Data: July, 1978 - April, 1991.
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coterminous United States. Ann. Amer. Geog.
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9-2
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Overton, W.S., D. White, and D.L Stevens.
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assessing responses of trees, stands, and
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9-3
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Appendix A. Statistical Design
D. L. Cassell
Design Objectives
The design objectives can be stated as
follows. First, we wish to be able to statistically
estimate the current status and extent, as well
as changes and trends in, indicators of the
condition of the nation's forest ecosystems.
Furthermore, this is to be done on a regional
and a national basis, with known statistical
confidence in our estimates. Second, we want
to monitor indicators of pollutant exposures and
habitat condition, and seek associations
between human-induced stresses and the
ecological condition of the forests. Third, we
want to be able to provide yearly statistical
summaries and periodic interpretive reports on
ecological status and trends to resource
managers and the public.
Specific design criteria were essential
to achieve these objectives. There had to be a
consistent and realistic representation of all
ecological resources and environmental entities
through the use of probability samples. The
design had to be flexible to accommodate post-
stratification and aggregation for many
alternative subpopulations such as states or
other areas of special interest. There had to
be a provision for responding quickly to new
questions or issues. The spatial distribution of
the sample of any resource needed to be
arranged according to the population
distribution of the resource. Periodic revisiting
of all sampling sites was also required.
These requirements led directly to the
EMAP/FHM design (Overton et al. 1990). This
design has been peer reviewed by a panel of
the American Statistical Association. The
design has also been extensively reviewed by
Forest Service and EPA statisticians, among
others.
The Monitoring Grid
The design uses a triangular grid so
that the nation is tesselated (tiled) with
hexagons, each covering approximately 635
km2. Within each 635 km2 hexagon, another
hexagon of approximately 40 km2 represents a
1-in-16 sample of the resource. Each such
'40-hex' represents the EMAP Tier I sample
that will be characterized using remote-sensing
techniques. Each 40-hex is uniformly offset
from the center of the larger hexagon. Within
a 40-hex, a point can be located on the ground
where one plot, or a constellation of plots, can
be physically established. On these plots,
measurements are made to gauge the selected
forest condition and stress indicators. To give
some idea of the scale of this monitoring, the
distance between ground plot locations is about
27 km. For the U.S., the grid yields about
12,600 potential ground plot locations. Of
these, about 4,000 are expected to be forested.
The national design provides for
measurements at four-year intervals. The plots
measured in any one year are selected so that
the spatial structure of the plots is preserved; in
other words, so that the samples are spread
uniformly over time and space. This
arrangement of sites across space and time is
often referred to as an 'interpenetrating' design,
or a rotating panel design. The design permits
analysis using traditional Horvttz-Thompson
procedures (Horvitz and Thompson 1952;
Overton et al. 1990).
Benefits of the Triangular Grid
The triangular grid is systematic with a
random start. The centers of the larger
hexagons have been randomly located by
selecting a single random point in space and
moving the entire pattern so that an arbitrary
hexagon center is on that point. Then, a
random point is located within that hexagon,
and the entire systematic grid of smaller 40-
hexes is fixed by centering one of them on that
point. The triangular grid is spatially compact,
provides uniform spatial coverage, and is very
flexible. The grid density can be altered easily,
without destroying the overall temporal and
spatial structure of the measurements. In
addition, a triangular grid is less likely than a
square grid to coincide with artificially linear
features such as state boundaries.
A-1
-------
In forestry, regular spacing of sample
points generally leads to population estimates
with smaller sampling variances than simple
random sampling. The uniform spatial
coverage means that for a given density,
resources in the landscape are sampled in
proportion to their abundance, area, and spatial
pattern. Use of an interpenetrating sample and
a fixed remeasurement cycle also means that
this is achieved every year. The triangular grid
allows a wide variety of grid magnifications and
reductions, and it can handle special cases
such as subpopulations of special interest.
Evaluating Status and Change
There are several key issues in
monitoring across both time and space.
Current status is best estimated by including as
many population units as possible, because
greater coverage enhances the identification of
subtle subpopulation differences. But detection
of trends is best done with repeated
observations of the same units over time, with
the interval between observations chosen on
the basis of the signal-to-noise ratio of the
measurements. Observer effects can be
important; too long a time between visits may
mean that field measurements cannot be
properly calibrated, while too short a time may
lead to unwanted impacts such as trampling of
the understory.
To answer questions about the
condition of forest ecosystems, it is necessary
to specify explicitly what set of forests will be
analyzed. This set becomes the target
subpopulation, the areal extent of forested
ecosystem about which estimates of conditions
will be made. Target subpopulations can be
defined for any region or attribute. They serve
two main purposes: 1) to increase the precision
of condition and trend estimates by controlling
extraneous variation, and 2) targeting specific
sets of resources for reporting and assessment.
Stratification
The sample design currently used does
not employ stratification. This topic continues
to be debated because stratification is usually
very useful for reducing sample variances, but
no clear choice of stratification method has
emerged from the debate. One argument
against stratification is that if it is based on
current resources, then distinctly non-optimal
sample designs would be forced upon any
future study of the same forest resource
because forests normally change over time. In
many cases, insufficient data are available to
perform the proposed stratifications at the
desired scale, and classification errors as small
as 20 percent would erase any potential gains
in precision. It is still an open question
whether stratification can be applied effectively
at larger scales in the immediate vicinity of
ground plots.
Selection of Ground Plots
The field sampling involves the
collection of EMAP Tier II measurements that
will be used to calculate values for forest
condition indicators. One key feature is the
statistical selection of ground plots so that the
data represent a probability sample. in
comparison with current and well-known Forest
Service Forest Inventory and Analysis (FIA)
procedures, the FHM method of Tier II site
selection is equivalent to selection of a single
FIA photo point (i.e., stage one of the FIA
multi-stage sample). This permits a linkage to
the FIA statistical design. The FIA photo point
grid for a region is overlaid on a 40-hex. Then
the closest FIA photo point is selected. In
cases where the selected photo point is
already an FIA plot, the FHM plot will be offset
from the photo point. This procedure was used
in all but one of the states implemented in
1991.
Because the selected photo point is the
one closest to the center of the 40-hex, and
because the photo point grid is statistically
independent of the EMAP/FHM hexagon grid,
the probability of selecting that particular photo
point is proportional to the area that photo point
represents in the FIA sample design. To
estimate this probability, the coordinates of the
photo points around each hex center are
entered into a data base and plotted on an
equal-area projection map. Then, for the
A-2
-------
selected photo point, a geographic analysis
system computes the area of ail land closer to
this point than any other photo point. The
larger this area, the more likely it is that the
hex center would fall in this area. The
inclusion probabilities are calculated as a
constant multiple of this area.
Thus, inclusion probabilities are
proportional to the area that the FIA photo point
represents in the photo point grid. This means
that the inclusion probabilities can be used to
provide population estimates for all of our
variables of interest. In 1991, it was not
feasible to complete the necessary analysis,
and some inclusion probabilities are only
approximately correct. The additional variance
component due to the approximation is not
included in the statistical estimation procedures
used in this report. Strictly correct inclusion
probabilities will be available for the next report.
Another approach is needed for the
regions where FIA photo points are not
available. In these regions, the ground plot can
be located at a random offset and azimuth from
the center of the 40-hex. These points are
then added to the list of photo points that are
interpreted by the FIA process. This 'reversed'
procedure was used in the one state that did
not start with the FIA photo points as a
sampling basis. Since the same probabilistic
procedure was performed within each 40-hex,
all ground plots in that state have the same
inclusion probability.
Ground Plot Structure
Another key feature of the Tier II
measurements is the flexible design of ground
plots for multiple categories of measurements.
The plot designs used in EMAP/FHM have
been developed jointly by the Forest Service
and the EPA, based on an optimally cost-
effective plot design for FIA mensuration
measurements (Scott 1991). A circular, one-
hectare plot contains four fixed-radius (7.32
meters) subplots for field measurements. The
subplots are arranged in an equilateral triangle
with an additional subplot at the center of the
triangle. The centers of the outer subplots are
36.6 meters from the center of the central
subplot.
This basic plot design has been
evaluated for many indicators (Riitters et al.
1991) using sampling theory (Cochran 1977) to
estimate optimal numbers of plots, subplots,
and observations within subplots. The results
suggested that the plot design was adequate
for current FHM indicators, in the areas where
FHM has been implemented. Furthermore, the
indicators evaluated on the FHM plots are thus
implicitly defined as being sampled over a
specific area of forested land.
Fixed-radius plots have several
advantages over the variable-radius plots that
are used often in forest inventory. Variable-
radius plots are more efficient for measuring
the current status of characteristics that are
correlated with tree size. But fixed-radius plots
are generally more efficient for measuring
quantities that are not correlated with tree size,
which is the case for most FHM indicators.
Another advantage is that change over time is
more easily estimated by using fixed-area
plots.
A subset of the total ground plot is
sometimes used for a given measurement. In
those cases, a factor was calculated to show
what proportion of the plot was used in the
analysis. This provided an expansion factor so
that all variables could be summarized on an
equal-area basis. Since only areal estimates
were computed, this was combined with the
inclusion probability for the plot to give an
inclusion probability for the subset. Another
alternative is to use multiple-stage sampling
procedures.
For all estimates computed over the
forested portion of the plot, the expansion
factor was the inverse of the proportion of plot
in forested conditions. For estimates computed
over specific forest type groupings or crown
groups, the expansion factor was the inverse of
the proportion of the plot in that classification.
For estimates based on individual tree species,
the expansion factor was the inverse of the
proportion of plot in forested conditions,
multiplied by the inverse of the proportion of
A-3
-------
the plot's basal area covered by the specified
species.
Statistical Procedures for this Report
In this report, cumulative distribution
functions (CDFs) were generated for the data
collected at the ground plot locations. The
estimated CDF at a value of, for example x,
equals the estimated proportion of the areal
extent of the real resource being examined that
has values less than or equal to x. The
Horvitz-Thompson estimate of the CDF at x is
given by:
gU
(Cochran 1977) and approximate joint inclusion
probabilities (Stevens et al. in review). The
approximation to ^ is given by:
The approximate 90% confidence bounds
about each CDF were calculated by assuming
that the CDF estimates approximated normal
distributions. The confidence regions were
obtained as the estimated CDF value, plus or
minus 1.645 times the estimated standard error
of the CDF value. The standard error was
computed as the square root of the estimated
variance of the CDF value, where the
estimated variance was calculated by:
(-
Tlj
where
Vi = 1 if the variable is not greater than x, and
0 otherwise
it, = inclusion probability for plot i
n = number of plots in this subset of the data.
For example, in the CDF of basal area for the
spruce-fir forest type group in the New England
area, the height of the CDF at x = 20 m2ha"1 is
the estimated percent of the underlying
population of spruce-fir forests in New England
with basal area less than 20 m2ha"1. The CDF
rises monotonically to 100% at the maximum
observed value of x.
The estimated variance of this
percentage is based on both the first-order
inclusion probability (it,) and the joint inclusion
probability (jt,j). The first-order inclusion
probability was described earlier. The joint
inclusion probability is the probability that both
sites / and j are selected for inclusion in the
sample. The joint inclusion probabilities must
be non-zero to obtain unbiased variance
estimates. In the systematic sampling design
of EMAP/FHM, some joint inclusion
probabilities are zero or even unknown. An
approximate variance estimate was obtained by
using the Yates-Grundy variance estimate
where
A = V —
In application, the variance estimates assumed
a fixed sample size within each analyzed
subset. The analyzed subset may have been
the region as a whole, the area of a major
forest type grouping within a region, or the area
within a region covered by a particular tree
species. For such subsets, the sample size is
a random variable depending on the position of
the sampling grid and the selection of the
major forest type grouping or tree species.
This variance component has not been
incorporated into the estimation procedures.
A-4
-------
Appendix B. Quality
Assurance Program for Forest
Health Monitoring for 1991
G. E. Byers and C. J. Palmer
Quality Assurance Within EMAP
The potential temporal and geographic
scope of EMAP is immense, compared to most
environmental monitoring programs.
Information from EMAP will be used in risk
assessments that are integrated across
ecological resource types. To provide
information that is of sufficient quality (and thus
usefulness) to address the program's
objectives, a comprehensive and integrated
quality assurance (QA) program is required for
both EMAP as a whole and for the Forests
component of EMAP.
EMAP participates in the Environmental
Protection Agency's mandatory quality
assurance program (Stanley and Verner 1985).
The overall policies, organization objectives,
and functional responsibilities associated with
the EMAP QA program are documented in the
quality assurance program plan (QAPP)
(Einhaus et al. in preparation). The part of the
QA program that is implemented by the Forest
Health Monitoring (FHM) program is
documented by an annual quality assurance
project plan (QAPjP). It describes the plan for
achieving data quality objectives established for
all data acquisition activities. It also serves as
a reference for all QA activities within FHM.
The 1991 FHM Quality Assurance
Project Plan
The purpose of the QAPjP is to
describe the organization and objectives of the
FHM program and the QA activities needed to
achieve certain data quality requirements. The
data quality requirements for many FHM
measurements cannot be specified with
experience using those measurements for
assessments. During the formative stage of
FHM, the QAPjP is being used to assess and
control measurement errors that may enter the
system during the field measurement phase,
sample preparation, and sample analysis. The
QAPjP attempted to identify all environmental
measurements that are being made and to
identify specific processes within each
measurement that could introduce possible
sources of error or uncertainty in the resulting
data. Methods, materials, and schedules for
assessing the error contributed by each
process were also be addressed in some
cases. The 1991 QAPjP also described the
QA activities and assessment criteria that were
implemented to ensure that the data bases met
all data quality objectives (DQOs) that had
been established, using defined criteria and
procedures for assessing statistical control for
each measurement parameter in many cases.
Because of the complexity and variety of data
acquisition activities, the organization of the
QAPjP was modified from that recommended
by EPA's Quality Assurance Management
Staff.
Table B-1 summarizes the 14
measurements, including mensuration and
visual damage, for which data were collected
during the summer of 1991. The
implementation of mensuration and visual
damage measurements have been designated
the responsibility of specific indicator leaders.
There is close cooperation between indicator
leaders, the indicator development coordinator,
the QA coordinator for EMAP-Forests, and the
logistics coordinators. Following a format
provided by the coordinators (Table B-2), each
indicator leader specified appropriate QA
procedures for their measurements.
The standard operating procedures
(SOPs) for each indicator are not included in
the 1991 QAPjP, but are printed in separate
methods manuals. The QAPjP will be revised
as necessary to reflect changes in procedures.
To ensure proper interactions among the
various data acquisition and management
components of FHM, all project personnel are
familiar with the policies and objectives outlined
in the QAPjP and other FHM documents.
B-1
-------
Table B-1. FHM measurements subject to 1991 QA requirements.
Mensuration Data
Visual Damage Indicator
Soil Productivity Indicator
Foliar Chemistry Indicator
Elemental Analysis of Tree Cores Indicator
Evaluation of Tree Branch Damage
Evaluation of Root Disease
Photosynthetically Active Radiation Indicator
Vegetation Structure Indicator
Root Sampling for Evaluation of Root Disease and Mycorrhizae
Wildlife Habitat and Population Measurements
Tree Height
High Resolution Areal Photography
Global Positioning System
Methods Manuals for Standard
Operating Procedures
Forest Health Monitoring utilizes SOPs
which are also called "methods" or "protocols"
(ConMing and Byers 1991; Byers and Van
Remortel 1991; and Chojnacky 1991).
Environmental monitoring SOPs are devised for
sampling and analysis, data management, QA,
reporting activities, accounting, project finance
and contracts, and in analysis and integration
phases of the project. The use of written SOPs
helps to ensure consistency in planning,
implementation, and analysis activities over
time and among personnel for routine activities
within an organizational unit. To ensure
consistency of data among the FHM program
measurements, SOPs must be cooperatively
developed.
The FHM managers are responsible for
determining which activities require SOPs and
for ensuring that they are developed, reviewed,
and implemented. The research personnel
closest to the actual implementation of an
activity (indicator leaders) develop specific
SOPs. The QA coordinator has responsibilities
in SOP identification, consistency, evaluation of
method or protocol status, and training.
Due to the many and varied data
gathering activities and the existence of current
Forest Service manuals for mensurational
methods for FHM 1991, the program managers
deemed it appropriate to produce separate
documents for methods manuals. These
documents are an extension of the QAPjP.
This separate document approach is
appropriate because of the needs for stand-
alone methods manuals for field measurements
and laboratory analyses, and because of the
sheer amount of all required documents.
Quality Assurance Responsibilities
in 1991 FHM Program
The success of the FHM program
depends on the willingness of all participating
agencies to cooperate as full partners in QA.
The roles and responsibilities have been
identified to encourage cooperation and
successful implementation. In general, the
EPA is responsible for preparing planning
documents. Field activities are coordinated by
the Forest Service. Evaluation of the results is
a shared activity. The individuals who are
most responsible for the success of QA in FHM
are the indicator leaders, regardless of the
agency from which they come. Table QA-3
lists persons responsible for QA activities in
1991.
B-2
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Table B-2. Format for quality assurance for implementation measurement documentation for QAPjP.
Field Operations Plan
Plot locations
Personnel (field crew requirements, etc.)
Measurement activities
Description of measurement site
Description of measurement procedure
Table of parameters and measurement techniques
Data entry and reporting
Documents available
Data qualifiers
Reporting units
Evaluation of measurement quality data
Communications
Data custody
Documentation
QA Operations Plan
Training
Batch analysis
Independent measurement quality samples (test trees, etc.)
Measurement error samples (control duplicates, reference samples, calibration check samples,
reagent blanks, detection check samples)
Sources of measurement error (field spatial variability, temporal variability, between-crew, within-
crew, wrthin-site)
Measurement quality objectives (MQOs) for DPARCC (detectability, precision, accuracy,
representativeness, completeness, comparability)
Audits (on-site crew or tab audits)
QA Implementation Plan
Training
Control of data quality
Field sampling/measurement and characterization (PARCC)
On-site systems and crew audits
Data verification - describing, defining, implementing
Field measurement
Data Quality Assessment and Reporting Plan
DPARCC
Validation procedures
QA reports to management
Status reporting
Formal reports
References
B-3
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Table B-3. Personnel Responsibilities in 1991 FHM Monitoring QA Activities.
Strategy Document
Study Plan
QA Program Plan
QA Project Plan
Methods Manuals:
Field Research
Sample Preparation and Analysis
Eastern FHM Monitoring
Plot Reconnaissance:
New England, Mid-Atlantic
South and Southeast
Pretraining Workshops:
New England
Mid-Atlantic
South and Southeast
Training Workshops:
New England
Mid Atlantic
South and Southeast
Audit Crews:
New England, Mid-Atlantic
Georgia
Virginia
Alabama
Indicator Leaders:
Mensuration
Visual Damage
Design
Information Management
Other Forest Service Coordinators
C. Palmer
C. Palmer, J. Barnard
R. Graves
G. Byers
B. Conkling, G. Byers
G. Byers, R. Van Remortel
D. Chojnacky
W. Burkman
W. Bechtold
I. Millers. M. Miller-Weeks
W. Jackson, D. Twardus
W. Anderson, W. Hoffard
W. Burkman
W. Burkman
S. Alexander, R. Kucera, M. Baldwin
W. Burkman
W. Bechtoid, N. Snyder
J. Pemberton
D. May
W. Burkman, I. Millers
S. Alexander, W. Hoffard, R. Anderson
J. Hazard, D.Cassell
C. Liff
C. Eagar, N. Cost, J. Hyland, R. Brooks,
R. Beltz, V. Few, C. Scott
Data Quality Objectives (DQOs)
The EPA's Quality Assurance
Management Staff has developed a process for
establishing DQOs that can be applied to the
FHM QA program. Modifications to the process
are expected to be developed over time. At
present, DQOs are considered to be specific
statements of the level of uncertainty a data
user (presently defined as the respective
indicator leaders) is willing to accept in a body
of environmental data, with respect to the kind
of scientific or policy question that motivated
the data collection activity. Data Quality
Objectives are definitive, quantitative or
qualitative statements developed jointly by data
users (e.g., scientists, policy makers, interest
groups) in conjunction with the QA staff.
The DQO process is an iterative
approach that balances costs versus
uncertainty to achieve a desired or acceptable
level of quality. This information can also be
used to allocate resources to specific
monitoring phases in order to generate data of
sufficient quality to support management
decisions or answer specific scientific
questions.
B-4
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Data quality, and therefore DQOs, may
be defined for several levels of FHM data
collection. The first level is measurement-level
DQOs (MQOs) for specific measurement
parameters, estimated using existing or initial
baseline data. These DQOs may define
acceptance criteria for detectivity, precision,
accuracy, representativeness, completeness,
and comparability in field and laboratory
measurement data (Byers et al. 1990). Beyond
this, another criterion may be to optimize
measurement uncertainty with respect to non-
measurement sources of uncertainty (e.g., due
to sampling design constraints or naturally-
occurring spatial and temporal variability that
often is confounded within environmental data).
Other levels are recognized at a higher
ecosystem level but they have not yet been
addressed seriously. They are Indicator-level
DQOs (IQOs) derived from aggregated
parameter data for ecological indicators:
Resource-level DQOs (RQOs) derived from
aggregated indicator data for the EMAP-
Forests Resource Group, and; Ecosystem-level
DQOs (EQOs) from aggregated resource data
for overall ecosystem assessments.
Throughout the DQO-setting process,
there should be communication among
program management, policy-makers, program
coordinators, resource scientists, data analysts,
and scientists involved in the actual data
collection activities. Acceptance criteria
established during the DQO development
process serve as benchmarks for satisfying
data user requirements. In FHM, DQOs are
being established for several levels of data
collection, e.g., sample measurement system,
measurement parameter, or indicator level for
various indicators. Included in the DQO
assessment are four quantitative attributes:
detectability, precision, accuracy, and
completeness. Also included are two
qualitative attributes; representativeness, and
comparability.
Field QA for Measurements
Implemented in 1991
Several QA issues were of concern for
the 1991 field measurements of mensuration
and visual damage. Their resolution for 1991
was based partly on logistical and budgetary
constraints. The issues were:
• How to assess field crew precision,
accuracy, and comparability within a
region.
• How to assess field crew precision,
accuracy, and comparability between
regions.
• How to assess trainer crew precision,
accuracy, and comparability between
regions.
• What are the logistical constraints using
different crews on the application ofQA
techniques in the relatively large Forest
Service regions, such as the Northeastern
region and Southeastern/Southern regions
of the U.S.
• How beneficial and cost-effective will be
the different possible QA techniques.
Accuracy
1. Two-person field crews were provided
by the Forest Service or by state
forestry authorities. An adequate
number of field crews were hired to
ensure that the planned 1991
measurement program was
accomplished in the available time.
2. Reference plots (also called 'standard'
or 'accuracy' plots) were established at
training sites in Durham, NH, and
Asheville, NC. All trainers measured
the reference plots to establish 'true'
values. The field crews also measured
the reference plots as part of the
training program, and were judged
under the same standard.
B-5
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3. Specific test and training trees were
used for training and testing the field
crews' abilities to make the visual
crown measurements. An expert team
selected approximately 40 trees at
each training site that reflected a broad
range of visual damage conditions
found in the forests. The field crews
were trained on these trees after
classroom instruction. Then the field
crews were tested on another set of 20
test trees, both individually as crew
members and as two-man crews.
4. Expert trainers were used to form
trainer crews for each of the regions
for 1991. All measurement data
used as standards on the reference
plots were established by these
Forest Service personnel who are
trained and experienced in the
measurement techniques. The four
trainers for the regions crossed
over to each other's regions during
pre-training sessions to establish the
national standards for the
measurements.
5. Two-person audit crews visited field
plots randomly during measurements
to observe protocol application and to
provide real-time input to the field
crews to reduce inaccuracy in the
measurements. The audit crews
worked with the field crew, assessed
its performance, and discussed
deviations from protocol when they
occurred. The audit crews performed
these audits about 1 to 4 weeks after
the start of data collection.
Precision
Each field crew remeasured two plots
that it had previously measured. These plots
were selected about 1/3 and 2/3 through the
data collection period. This permitted
estimation of within-crew variability. Between-
crew variability was assessed from the
reference plot measurements.
Comparability
Each field crew collected data from the
reference plots at training in June and also at
a debriefing session in October to establish a
basis for comparability among crews within a
region. Inter-regional comparability is also of
great concern, and a technique was developed
in 1991, but it was not deemed proper
(financially, logistically, and legally) for all field
crews to collect data inter-regionally, nor to
travel to inter-regional training sessions.
However, two field crews from the northeast
region were able attend the debriefing session
in Asheville, NC, in October. The southeast
and the northeast trainers both measured the
reference plots (mensuration and visual
measurements) to reestablish accuracy data for
the plots that might have changed from the
pretraining sessions and to compare
measurements among trainers. Detailed
results are not yet available due to limited QA
resources, but preliminary analyses suggest
acceptable within-crew and between-crew
precision.
Representativeness, Completeness,
and Detectibility
Representativeness criteria are
established by the plot design and plot sample
selection established for 1991. Completeness
and detectivity criteria have been established
for some of the components of the
implementation indicators. Measurement
parameters for some indicators were very
qualitative, e.g., merely presence or absence
with no detectivity criteria.
Data Verification
Verification is the act of determining
and controlling the quality of data. Verification
can be accomplished manually, electronically,
or through remeasurements. A systematic
approach to data verification ensures that all
data are subjected to basic standards of
accuracy that verify the authenticity, but not
necessarily the validity, of the data.
Verification is accomplished by comparing data
at each level of processing to established data
quality criteria. Most of the data collected were
B-6
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entered into portable data recorders in the field.
Computer programs performed real-time logic
checks of most entries, automatically
determining if entries were valid and logically
correct.
The 1991 FHM Quality Assurance
Report
A summary of quality assurance
information collected during the 1991 field
season is currently being prepared. It will be
available for distribution in the late fall, 1992.
The report will provide information on data
quality attributes for the indicators included in
this annual statistical summary as well as
indicators undergoing testing in pilot and
demonstration studies.
B-7
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Appendix C. Nomenclature of Species in Text and Forest Type and
Species Codes
Tree Species Nomenclature
American basswood
American beech
American chestnut
American elm
American holly
American hornbeam
American mountain-ash
American sycamore
apple
ash
Atlantic white-cedar
Austrian pine
baldcypress
balsam fir
balsam poplar
bear oak
bigleaf magnolia
bigtooth aspen
birch
bitternut hickory
black ash
black cherry
black locust
black oak
black spruce
black walnut
black willow
blackgum
blackjack oak
blue ash
blue spruce
bluebeech
bluejack oak
boxelder
brown ash
buckeye
butternut
cabbage palm
Carolina hemlock
catalpa
cherry
cherrybark oak
chestnut oak
Chinaberry
chinkapin oak
Tilia americana L.
Fagus grandifolia Ehrh.
Castanea dentata (Marsh.) Borkh.
Ulmus americana L.
Ilex opaca Ait.
Carpinus caroliniana Walt.
Sorbus americana Marsh.
Platanus occidentalis L.
Malus spp.
Fraxinus spp.
Chamaecyparis thyoides (L.) B.S.P.
Pinus nigra Arnold
Taxodium distichum (L.) Rich.
Abies balsamea (L.) Mill.
Populus balsamifera L.
Quercus ilicifolia Wangenh.
Magnolia macrophylla Michx.
Populus grandidentata Michx.
Betula spp.
Carya cordiformis (Wangenh.) K. Koch
Fraxinus nigra Marsh.
Prunus serotina Ehrh.
Robinia pseudoacacia L.
Quercus velutina Lam.
Picea mariana (Mill.) B.S.P.
Juglans nigra L.
Salix nigra Marsh.
Nyssa sylvatica Marsh.
Quercus marilandica Muenchh.
Fraxinus quadrangulata Michx.
Picea pungens Engelm.
Carpinus caroliniana Walt.
Quercus incana Bartr.
Acer negundo L.
Fraxinus nigra Marsh.
Aesculus spp.
Juglans cinerea L.
Sabal palmetto (Walt.) Lodd.
Tsuga caroliniana Engelm.
Catalpa spp.
Prunus spp.
Quercus falcata var. pagodaefolia Ell.
Quercus prinus L.
Melia azedarach L.
Quercus muehlenbergii Engelm.
C-1
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choke cherry (common)
cucumbertree
dwarf live oak
dwarf post oak
eastern cottonwood
eastern hemlock
eastern hophombeam
eastern larch
eastern redbud
eastern redcedar
eastern white pine
elm
European larch
Florida maple
flowering dogwood
Fraser fir
giant sequoia
gray birch
green ash
hackberry
hackberry (common)
hawthorn
hemlock
hickory
honey locust
jack pine
juniper
larch (introduced)
laurel oak
live oak
loblolly pine
longleaf pine
lowland blackgum
magnolia
maple
mockernut hickory
mountain maple
mulberry
northern pin oak
northern red oak
northern white-cedar
Norway spruce
Ohio buckeye
osage-orange
overcup oak
paper birch
Paulownia
pawpaw
pecan
persimmon
pignut hickory
pin cherry
Prunus virginiana L.
Magnolia acuminata L.
Ouercus virginiana Mill.
Ouercus stellata var. margaretta
Populus deltoides Bartr.
Tsuga canadensis (L.) Carr.
Ostrya virginiana (Mill.) K. Koch
Lam laricina (Du Roi) K. Koch
Cercis canadensis L.
Juniperus virginiana L.
Pinus strobus L.
Ulmus spp.
Larix decidua Mill.
Acer barbatum Michx.
Cornus florida L.
Abies fraseri
Sequoia gigantea (Lindl.) Decne.
Betula populifolia Marsh.
Fraxinus pennsylvanica Marsh.
Celtis spp.
Celtis occidental L.
Crataegus spp.
Tsuga spp.
Carya spp.
Gledftsia triacanthos L.
Pinus banksiana Lamb.
Juniperus spp.
Larix decidua Mill.
Quercus laurifolia Michx.
Quercus virginiana Mill.
Pinus taeda L.
Pinus palustris Mill.
Nyssa sylvatica var. biflora (Walt.) Sarg.
Magnolia spp.
Acer spp.
Carya tomentosa Nutt.
Acer spicatum Lam.
Morus spp.
Quercus ellipsoidalis E. J. Hill
Quercus rubra L.
Thuja occidentalis L.
Picea abies (L.) Karst.
Aesculus glabra Willd.
Madura pomifera (Raf.) Schneid.
Quercus lyrata Walt.
Betula papyrifera Marsh.
Paulownia tomentosa (Thunb.) Steud.
Asimina tritoba (L.) Dunal
Carya illinoensis (Wangenh.) K. Koch
Diospyros virginiana L.
Carya glabra (Mill.) Sweet
Prunus pensylvanica L.
C-2
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pin oak
pinyon
pitch pine
pondcy press
pond pine
post oak
prunus
quaking aspen
red maple
red mulberry
red pine
red spruce
redbay
redwood
river birch
rock elm
sassafras
scarlet oak
Scots pine
scrub oak (several)
serviceberry
shagbark hickory
shingle oak
shortleaf pine
Shumard oak
Sitka spruce
silver maple
silverbell
slash pine
slippery elm
sourwood
southern magnolia
southern red oak
sparkleberry
spruce pine
striped maple
sugar maple
swamp chestnut oak
swamp tupelo
swamp white oak
sweet birch
sweetbay
sweetgum
table-mountain pine
tamarack
tulip-poplar
turkey oak
upland blackgum
Virginia pine
Quercus palustris Muenchh.
Pinus cembroides Zucc.
P. edulis Engelm.
P. monophylla Torr. and Frem.
P. quadrifolia Part.
Pinus rigida Mill.
Taxodium distichum var. nutans (Ait.) Sweet
Pinus serotina Michx.
Quercus stellata Wangenh.
Prunus spp.
Populus tremuloides Michx.
Acer rubrum L.
Moms rubra L.
Pinus resinosa Ait.
Picea rubens Sarg.
Persea borbonia (L.) Spreng.
Sequoia sempervirens (D. Don) Endl.
Betula nigra L.
Ulmus thomasii Sarg.
Sassafras albidum (Nutt.) Nees
Quercus coccinea Muenchh.
Pinus sylvestris L.
Quercus spp.
Amelanchier spp.
Carya ovata (Mill.) K. Koch
Quercus imbricaria Michx.
Pinus echinata Mill.
Quercus shumardii Buckl.
Picea sitchensis (Bong.) Carr.
Acer saccharinum L.
Halesia spp.
Pinus elliottii Engelm.
Ulmus rubra Muhl.
Oxydendrum arboreum (L.) DC.
Magnolia grandiflora L.
Quercus falcata Michx.
Vaccinium arboreum Marsh.
Pinus glabra Walt.
Acer pennsylvanicum L.
Acer saccharum Marsh.
Quercus michauxii Nutt.
Nyssa sylvatica var. biflora (Walt.) Sarg.
Quercus bicolor (Willd.)
Betula lenta L.
Magnolia virginiana L
Liquidambar styraciflua L
Pinus pungens Lamb
Larix laricina (Du Roi) K. Koch
Liriodendron tulipifera L.
Quercus laevis Walt.
Nyssa sylvatica Marsh.
Pinus virginiana Mill.
C-3
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water hickory Carya aquatica (Michx. f.) Nutt.
water oak Quercus nigra L.
water tupeto Nyssa aquatica L
white ash Fraxinus americana L
white basswood Tilia heterophylla Vent.
white oak Quercus alba L.
white pine Pinus strobus L
white spruce Picea glauca (Moench) Voss
willow Salix spp.
willow oak Quercus phellos I.
winged elm Ulmus alata Michx.
yellow birch Betula alleghaniensis Britton
yellow buckeye Aesculus octandra Marsh.
yellow-poplar liriodendron tulipifera L.
yellowwood Cladrastis lutea (Michx. f.) K. Koch
C-4
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Insects Nomenclature
balsam woolly adelgid
beech scale
black twig borer
birch casebearer
birch leafminers
birch skeletonizer
bronze birch borer
buck moth
cherry scallop shell moth
conifer swift moth
eastern larch beetle
eastern spruce budworm
eastern tent caterpillar
elm spanworm
elongated hemlock scale
fall cankerworm
fall webworm
forest tent caterpillar
fruittree leafroller
gypsy moth
hemlock looper
jack pine budworm
jack pine sawfly
maple leafcutter
maple trumpet skeletonizer
oak leaftier
oak skeletonizer
orangestriped oakworm
pear thrips
periodical cicada
pine engraver beetles
pitch pine looper
redheaded jackpine sawfly
red pine adelgid
red pine beetle
red pine scale
saddled prominent
southern pine beetle
spruce beetle
variable oak leaf caterpillar
white pine weevil
Adelges piceae
Cryptococcus fagisuga
Xylosandais compactus
Coleophora serratella
Fenusa pusilla, and Messa nana
Bucculatrix canadensisella
Agrilus anxius
Hemileucia maia
Hydria prunivorata
Korscheltellus gracilis
Dendroctonus simplex
Choristoneura fumiferana
Malacosoma americanum
Ennomos subsignarius
Fiorinia extema
Alsophila po met aria
Hyphantria cunea
Malacosoma disstria
Archips argyrospila
Lymantria dispar
Lambdina athasaria and L. fiscellaria
Choristoneura pinus
Neodiprion pratti banksianae
Paraclemensia acerifoliella
Epinotia aceriella
Croesia semipurpurana
Bucculatrix spp.
Anisota senatoria
Taeniothrips inconsequens
Magicicada septendecim
Ips spp.
Lambdina pellucidaria, and L. athasaria
Neodiprion rugifrons
Pineus borneri
Ips pini
Matsucoccus resinosae
Heterocampa guttivitta
Dendroctonus frontalis
Dendroctonus rufipennis
Heterocampo manteo
Pissodes strobi
C-5
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Diseases Nomenclature
annosus root disease Heterobasidium anno sum
anthracnose Gloeosporium spp., Discula spp., Apiognomonia venta, and Gnomonia
spp.
armillaria root rot Armillaria spp.
butternut canker Sirrococcus clavigignenta-juglandacearum
cytospora canker Valsa kunzei (Cytospora kunzei)
dogwood anthracnose Discula destrucliva
Dutch elm disease Ceratocystis ulmi
European larch canker Lachnellula wilkommii
fusiform rust Cronartium quercuum f. sp. fusiforme
hypoxyton canker Hypoxyton mammatum
sapstreak Ceratocystis coerulescens
scteroderris canker Ascocalyx abietina (Gremmeniella abietina)
white pine blister rust Cronartium ribicola
C-6
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Eastern Forest Type Codes
Code Definition
0000 WHITE/RED/JACK PINE GROUP
0010 Jack pine
0020 Red pine
0030 White pine
0040 White pine/hemlock
0050 Hemlock
0060 Scotch pine
0070 Ponderosa pine
0100 EASTERN SPRUCE/FIR GROUP
0110 Balsam fir
0120 Black spruce
0130 Red spruce/balsam fir
0140 Northern white-cedar
0150 Tamarack (eastern larch)
0160 White spruce
0170 Norway spruce
0180 Larch (introduced)
0190 Red spruce
0200 LONGLEAF/SLASH PINE GROUP
0210 Longleaf pine
0220 Slash pine
0300 LOBLOLLY AND SHORTLEAF PINE GROUP
0310 Loblolly pine
0320 Shortleaf pine
0330 Virginia pine
0340 Sand pine
0350 Eastern redcedar
0360 Pond pine
0370 Spruce pine
0380 Pitch pine
0390 Table-mountain pine
0400 OAK/PINE GROUP
0410 White pine/northern red oak/white ash
0420 Eastern redcedar/hardwood
0430 Longleaf pine/scrub oak
0440 Shortleaf pine/oak
0450 Virginia pine/southern red oak
0460 Loblolly pine/hardwood
0470 Slash pine/hardwood
0480 Scarlet oak
0490 Other oak/pine
0500 OAK/HICKORY GROUP
C-7
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0510 Post, black, or bear oak
0520 Chestnut oak
0530 White oak/red oak/hickory
0540 White oak
0550 Northern red oak
0560 Yellow-poplar/white oak/northern red oak
0562 Sweetgum/yellow-popular
0564 Yellow-popular
0570 Southern scrub oak
0580 Black locust
0590 Mixed central hardwood
0592 Sassafras/persimmon
0594 Central hardwoods reverting from field
0600 OAK/GUM/CYPRESS GROUP
0610 Swamp chestnut oak/cherrybark oak
0620 Sweetgum/Nuttall oak/willow oak
0630 Sugarberry/American elm/green ash
0650 Overcup oak/water hickory
0660 Atlantic white-cedar
0670 Bakfcypress/water tupelo
0680 Sweetbay/swamp tupelo/red maple
0690 Palm/mangrove/other tropical
0692 Mangrove
0694 Palm
0696 Other tropical
0700 ELM/ASH/RED MAPLE GROUP
0710 Black ash/American elm/red maple
0720 River birch/sycamore
0730 Cottonwood
0740 Willow
0750 Sycamore/pecan/American elm
0800 MAPLE/BEECH/BIRCH GROUP
0810 Sugar maple/beech/yellow birch
0820 Black cherry
0830 Black walnut
0840 Red maple/northern hardwoods
0850 Red maple/central hardwoods
0880 Northern hardwood reverting from field
0890 Mixed northern hardwoods
0900 ASPEN/BIRCH GROUP
0910 Aspen
0920 Paper birch
0930 Gray birch
0990 INDETERMINANT/NONSTOCKED
0998 Indeterminant
0999 Nonstocked
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Tree Species Codes
Code Common Name
Genus
Species
010
011
012
014
015
016
017
018
019
020
021
022
041
042
043
051
052
060
058
059
061
062
063
064
065
066
067
068
069
070
071
072
073
081
090
091
092
093
094
095
OUIIWUUUS
fir sp.
Pacific silver fir
balsam fir
bristlecone fir
white fir
Fraser fir
grand fir
corkbark fir
subalpine fir
California red fir
Shasta red fir
noble fir
Port-Orford -cedar
Alaska-cedar
Atlantic white-cedar
Arizona cypress
Baker cypress
Redcedar/Juniper
Pinchot juniper
Red berry juniper
Ashe juniper
California juniper
Alligator juniper
Western juniper
Utah juniper
Rocky Mountain juniper
southern redcedar
eastern redcedar
one seed juniper
larch (introduced)
tamarack (native)
subalpine larch
western larch
incense-cedar
spruce
Norway spruce
Brewer spruce
Engelmann spruce
white spruce
black spruce
Abies
Abies
Abies
Abies
Abies
Abies
Abies
Abies
Abies
Abies
Abies
Abies
Chamaecyparis
Chamaecyparis
Chamaecyparis
Cupressus
Cupressus
Juniperis
Juniperis
Juniperis
Juniperis
Juniperis
Juniperis
Juniperis
Juniperis
Juniperus
Juniperus
Juniperus
Juniperis
Larix
Larix
Larix
Larix
Libocedrus
Picea
Picea
Picea
Picea
Picea
Picea
sp.
amabilis
balsamea
bracteata
concolor
fraseri
grandis
lasiocarpa var.
lasiocarpa
magnifica var.
magnifica var.
procera
lawsoniana
nootkatensis
thyoides
arizonica
bakeri
sp.
pinchotii
erythrocarpa
ashei
California
deppeana
occidentalis
osteosperma
scopulorum .
sillcicola
virginiana
monosperma
sp.
laricina
lyallii
occidentallis
decurrens
sp.
abies
breweriana
engelmannii
glauca
mariana
arizonica
magnflfca
shastensis
C-9
-------
Code Common Name
Genus
Species
096
097
098
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
201
202
ouuwuuuo
blue spruce
red spruce
Sitka spruce
whitebark pine
bristlecone pine
knobcone pine
foxtail pine
jack pine
common pinyon
sand pine
lodgepole pine
coulter pine
shortleat pine
slash pine
Apache pine
limber pine
Mexican white pine
spruce pine
Jeffrey pine
Sugar pine
Chihuahua pine
Western white pine
bishop pine
longleaf pine
ponderosa pine
table mountain pine
monterey pine
red pine
pitch pine
Digger pine
pond pine
eastern white pine
Scotch pine
loblolly pine
Virginia pine
Singleleaf pinyon
Border pinyon
Arizona pine
southwestern white pine
washoe pine
four-leaf pine
Austrian pine
Mexican pinyon pine
bigcone Douglas-fir
Douglas-fir
Picea
Picea
Picea
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pinus
Pseudotsuga
Pseudotsuga
pungens
rubens
sitchensis
albicaulis
aristata
attenuate
batfouriana
banksiana
edulis
clausa
contorta
coulter!
echinata
elliottii
engelmannii
flexilis var. reflexa
flexilis var. reflexa
glabra
Jeffrey!
lambertiana
leiophylla
monticola
muricata
palustris
ponderosa
pungens
radiata
resinosa
rigida
sabiniana
serotina
strobus
sylvestris
taeda
virginiana
monophylla
discolor
ponderosa var. arizonica
strobiformis
washoensis
quadrifolia
nigra
cembroides
macrocarpa
menziesii
211 redwood
Sequioa
sempervirens
C-10
-------
Code Common Name
Genus
Species
-Softwoods
212 giant sequoia
221
222
baldcypress
pondcypress
231 Pacific yew
241 northern white-cedar
242 western redcedar
250 California torreya
260 hemlock
261 eastern hemlock
262 Carolina hemlock
263 western hemlock
264 mountain hemlock
Sequoiadendron
Taxodium
Taxodium
Taxus
Thuja
Thuja
Torreya
Tsuga
Tsuga
Tsuga
Tsuga
Tsuga
giganteum
distichum
distfchum var. nutans
brevifolia
occidentalis
plicata
Califomica
sp.
canadensis
caroliniana
heterophylla
mertensiana
C-11
-------
Code Common Name
Genus
Species
300
310
311
312
313
314
315
316
317
318
319
321
322
330
331
332
333
341
350
351
352
353
354
355
356
361
367
370
371
372
373
374
375
376
377
378
379
381
nciiuwuuus
acacia
maple
Florida maple
bigleaf maple
boxelder
black maple
striped maple
red maple
silver maple
sugar maple
mountain maple
Rocky Mountain maple
Bigtooth Maple
buckeye, horsechestnut
Ohio buckeye
yellow buckeye
buckeye(except 331 ,332)
ailanthus
Alder
red alder
white alder
Sitka alder
thinleaf alder
European alder
serviceberry
Pacific madrone
pawpaw
birch sp.
yellow birch
sweet birch
river birch
water birch
paper birch
western paper birch
Alaska paper birch
northwestern paper birch
gray birch
chittamwood.gum bumelia
Acacia
Acer
Acer
Acer
Acer
Acer
Acer
Acer
Acer
Acer
Acer
Acer
Acer
Aesculus
Aesculus
Aesculus
Aesculus
Ailanthus
Alnus
Alnus
Alnus
Alnus
Alnus
Alnus
Amelanchier
Arbutus
Asimina
Betula
Betula
Betula
Betula
Betula
Betula
Betula
Betula
Betula
Betula
Bumelia
sp.
sp.
barbatum
macrophyllum
negundo
nigrum
pennsylvanicum
rubrum
saccharinum
saccharum
spicatum
glabrum
grandidentatum
sp.
glabra
octandra
sp.
altissima
sp.
rubra
rhombifolia
sinuata
tenuifolia
glutinosa
sp.
menziesii
triloba
sp.
alleghaniensis
lenta
nigra
occidentalis
papyrifera
papyrifera var. commutata
papyrifera var. neoalaskana
papyrifera var. subcordata
populifolia
lanuginosa
C-12
-------
Code Common Name
Genus
Species
391
400
401
402
403
404
405
407
408
409
421
422
423
430
450
451
452
460
461
462
463
471
475
476
477
478
479
481
491
500
521
531
540
541
543
narowooas
American hornbeam,
musclewood
hickory sp.
water hickory
bittemut hickory
pignut hickory
pecan
shellbark hickory
shagbark hickory
black hickory
mockernut hickory
American chestnut
Allegheny chinkapin
Ozark chinkapin
chinkapin
catalpa
southern catalpa
northern catalpa
hackberry sp.
sugarberry
hackberry
netleaf hackberry
eastern redbud
curfleaf mountain-mahogany
alder-leaf mountain-mahogany
hairy mountain-mahogany
birchleaf mountain-mahogany
dwarf mountain-mahogany
yellowwood cladrastis
flowering dogwood
hawthorn
common persimmon
American beech
ash
white ash
black ash
Carpinus
Carya
Carya
Carya
Carya
Carya
Carya
Carya
Carya
Carya
Castanea
Castanea
Castanea
Castanopsis
Catalpa
Catalpa
Catalpa
Celtis
Celtis
Celtis
Celtis
Cercis
Cercocarpus
Cercocarpus
Cercocarpus
Cercocarpus
Cercocarpus
Cladrastis
Cornus
Crataegus
Diospyros
Fagus
Fraxinus
Fraxinus
Fraxinus
caroliniana
sp.
aquatica
cordiformis
glabra
illinoensis
laciniosa
ovata
texana
tomentosa
dentata
pumila
ozarkensis
sp.
sp.
bignonioides
speciosa
sp.
laevigata
occidentalis
reticulata
canadensis
ledifolius
montanus
brevrflorus
betuloides
intricatus
kentukea
florida
sp.
virginiana
grandifolia
sp.
americana
nigra
C-13
-------
Code Common Name
Genus
Species
544
545
546
551
552
555
571
580
581
591
601
602
611
621
641
650
651
652
653
654
660
661
680
681
682
691
692
693
694
701
711
712
naiuwwua
green ash
pumpkin ash
blue ash
waterlocust
honey locust
loblolly-bay
Kentucky coffeetree
Mountain silvertell
Carolina silverbell
American holly
butternut
black walnut
sweetgum
yellow-poplar
Osage-orange
magnolia sp.
cucumbertree
southern magnolia
sweetbay
bigleaf magnolia
apple sp.
Oregon crab apple
mulberry sp.
white mulberry
red mulberry
water tupelo
ogeechee tupelo
blackgum
swamp tupelo
eastern hophornbeam,
ironwood
sourwood
paulownia, empress tree
Fraxinus
Fraxinus
Fraxinus
Gleditsia
Gleditsia
Gordonia
Gymnocladus
Halesia
Halesia
Ilex
Juglans
Juglans
Liquidambar
Liriodendron
Madura
Magnolia
Magnolia
Magnolia
Magnolia
Magnolia
Malus
Malus
Morus
Morus
Morus
Nyssa
Nyssa
Nyssa
Nyssa
Ostrya
Oxydendrum
Paulownia
C-14
pennsylvanica
profunda
quadrangulata
aquatica
triacanthos
lasianthus
dioicus
sp.
Carolina
opaca
cinerea
nigra
styraciflua
tulip ifera
pomifera
sp.
acuminata
grandiflora
virgirriana
macrophylla
sp.
fusca
sp.
alba
rubra
aquatica
ogeche
sylvatica
sylvatica var. biflora
virginiana
arboreum
tomentosa
-------
Code Common Name
Genus
Species
721
722
731
740
741
742
743
744
745
746
747
748
749
752
755
760
761
762
763
764
765
766
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
narowooas
redbay
water elm, planer tree
sycamore
cottonwood
balsam poplar
eastern cottonwood
bigtooth aspen
swamp cottonwood
plains cottonwood
quaking aspen
black cottonwood
fremont poplar
narrow/leaf cottonwood
silver poplar
mesquite
cherry, plum spp.
pin cherry
black cherry
chokecherry
plums, cherries,
except 762
Canada plum
wild plum
Oak
California live oak
white oak
Arizona white oak, Gray oak
swamp white oak
canyon live oak
scarlet oak
blue oak
durand oak
northern pin oak
emery oak
engelmann oak
southern red oak
cherrybark oak,
swamp red oak
gambel oak
Oregon white oak
bear oak, scrub oak
shingle oak
California black oak
Persea
Planera
Platanus
Populus
Populus
Populus
Populus
Populus
Populus
Populus
Popufus
Populus
Populus
Populus
Prosopis
Prunus
Prunus
Prunus
Prunus
Prunus
Prunus
Prunus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
borbonia
aquatica
occidentalis
spp.
balsamifera
deltoides
grandidentata
heterophylla
sargentii
tremuloides
trichocarpa
fremontii
angustifolia
alba
sp.
sp.
pensylvanica
serotina
virginiana
sp.
nigra
americana
spp.
agrifolia
alba
arizonica, grisea
bicolor
chrysolepsis
coccinea
douglassi
durandii
ellipsoidalis
emoryi
engelmannii
falcata var. falcata
falcata var. pagodaefolia
gambelii
garryana
ilicifolia
imbricaria
kelloggii
C-15
-------
Code Common Name
Genus
Species
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
848
849
899
901
902
920
921
922
928
931
935
936
950
951
952
970
971
972
naruwuuus
turkey oak
laurel oak
California white oak
overcup oak
bur oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
nuttall oak
Mexican blue oak
pin oak
willow oak
chestnut oak
northern red oak
shumard oak
post oak
delta post oak
black oak
live oak
interior live oak
dwarf post oak
dwarf live oak
bluejack oak
silverleaf oak
western Oak (Deciduous)
western Oak (Evergreen)
scrub oak
black locust
New Mexico locust
willow
peachleaf willow
black willow
diamond willow
sassafras
American mountain-ash
European mountain-ash
basswood
American basswood
white basswood
elm
winged elm
American elm
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercus
Quercua
Quercus
Quercus
Quercus
Quercus
Robinia
Robinia
Salix
Salix
Salix
Salix
Sassafras
Sorbus
Sorbus
Tilia
Tilia
Tilia
Ulmus
Ulmus
Ulmus
laevis
laurifolia
lobata
lyrata
macrocarpa
marilandica
michauxii
muehlenbergii
nigra
nuttalii
oblongifolia
palustris
phellos
prinus
rubra
shumardii
stellata
stellata var. Mississippiensis
velutina
virginiana
wislizenii
stellata var stellata
sp.
incana
hypoleucoides
spp.
spp.
sp.
pseudoacacia
neomexicana
sp.
amygdaloides
nigra
eriocephala
albidum
americana
aucuparia
sp.
americana
heterophylla
sp.
alata
americana
C-16
-------
Code Common Name
Genus
Species
973
974
975
976
977
981
990
991
992
993
994
995
996
998
999
naruwoous
cedar elm
Siberian elm
slippery elm
September elm
rock elm
California laurel
Arizona ironwood
salt cedar
sparkleberry
chinaberry
Chinese tallowtree
tung-oil tree
smoketree
Other tree species
unknown or not listed
Ulmus
Ulmus
Ulmus
Ulmus
Ulmus
Umellularia
Olneya
Tamarisk
Vaccinium
Melia
Sapium
Aleu rites
Cotinus
crassifolia
pumila
rubra
serotina
thomasii
califomica
tesota
sp.
arboreum
azedarach
sebiferum
fordii
obovatus
C-17
-------
Appendix D. Conversion Factors for Common Measurement Units
Metric unit
English equivalent
1 centimeter (cm)
1 meter (m)
1 kilometer (km)
Length
0.39370 inches (in)
3.28083 feet (ft)
0.62137 miles (mi)
Area
1 square centimeter (cm2)
1 square meter (m2)
1 hectare (ha)
1 square kilometer (km2)
1 m2/ha
0.15500 square inches (in2)
0.001076 square feet (ft2)
10.76387 square feet (ft2)
2.471044 acres (ac)
0.0038610 square miles (mi2)
247.104 acres (ac)
0.38610 square miles (mi2)
4.3560 ft2/ac
D-1
-------
Appendix E. Supplemental Tabular Summaries
By the end of 1991,925 potential plot locations had been visited at least once in 12 eastern states,
and 628 plots had been established. Considering trees larger than 12.7 cm DBH, 14,307 trees of 109
identified species were measured in 1991. Among the smaller trees, 4,438 saplings larger than 2.5 cm
DBH, and 26,688 seedlings larger than 30.5 cm tall were tallied in 1991, and eleven additional species were
identified.
The objective of this Appendix is to describe the sample of plots and trees that were measured in
1991. This information can be used to help interpret the forest health indicators that are summarized in the
main body of the report. This information does not constitute population estimates for the forests, states,
or other categories that were sampled. Population estimates must be based on specific statistical
procedures (Appendix A), and these tables are presented as background information only.
A simplification was made to prepare the summaries of plot characteristics. Each subplot was
placed into separate categories so that the sampling effort could be expressed in terms of the numbers of
subplots. In the field, however, there is not a single category for any given plot or subplot. For example,
each plot may be made up of subplots of different land use categories, and a forested subplot may border
a plantation-natural forest boundary. Field crews used a set of rules to identify and map distinctly different
land use categories or conditions within a subplot. The rules recognize different conditions if any one of
the following situations was different with a subplot: land use, forest type, stand origin, predominant size
class, or site disturbance. The summary tables (in this Appendix only) assigned each subplot to the
category of the most prevalent condition encountered on each subplot. The alternative of summarizing
according to the area of each condition sampled was not used because there is not a simple procedure for
re-expressing the resulting area statistics as numbers of subplots.
Table E-5 through E-7 report the evidence of disturbance in the last five years. In Table E-5,
utilization disturbance includes evidence of harvest, commercial thinning, selective cutting, and grazing.
In Table E-6, care disturbance includes site preparation, artificial regeneration, prescribed burning, and
miscellaneous silvicultural treatments such as herbicide or fertilizer application. In Table E-7, natural
disturbance includes evidence of damage by insects, disease, weather, and wildfire.
E-1
-------
Table E-1. Number of subplots by state and land use.
Land Use
State
AL GA VA MD DE NJ CT Rl MA VT NH ME Total
(no. of subplots)
Forest land
Crop land
Improved pasture
Natural rangeland
Idle farmland
Other farmland
Urban or developed
Marsh (non-forest)
Water
Access denied
Unsafe conditions
Data lost
512
74
70
0
37
12
100
0
13
0
14
0
553 374
173 43
29 77
0 0
12 17
2 4
61 85
21 3
12 13
33 12
4 0
36 12
58
48
4
0
0
0
30
4
4
0
0
0
5
11
0
0
0
0
12
4
0
0
0
0
57
12
4
0
1
0
35
8
0
0
3
0
41
0
4
0
0
0
19
0
4
0
0
0
8
0
4
0
0
0
4
0
0
0
0
0
59
0
7
0
4
0
27
8
3
0
0
0
83
20
8
0
1
4
8
0
0
0
0
0
120
2
0
1
0
0
10
3
0
0
0
0
484
15
4
0
15
0
22
0
0
0
0
0
2354
398
211
1
87
22
413
51
49
45
21
48
Total
832 936 640 148 32 120 68 16 108 124 136 540 3700
E-2
-------
Table E-2. Number of subplots by state and major forest type.
Major Forest type
State
AL GA VA MD DE NJ CT Rl MA VT
(no. of subplots)
NH ME Total
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
Non-forest
Total
0
0
23
140
171
111
53
7
0
0
7
320
832
1
0
112
167
83
104
79
4
0
0
3
383
936
5
0
0
67
44
237
1
8
8
0
4
266
640
0
0
0
3
6
33
7
0
5
0
4
90
148
0
0
0
0
0
1
4
0
0
0
0
27
32
7
0
0
13
6
11
5
0
11
0
4
63
120
4
0
0
0
0
23
0
6
7
1
0
27
68
0
0
0
0
0
0
0
4
0
4
0
8
16
1
0
0
0
14
4
0
0
37
3
0
49
108
16
11
0
0
0
0
0
0
48
4
4
41
124
27
2
0
0
5
0
0
4
81
1
0
16
136
35
250
0
0
20
0
0
0
161
17
1
56
540
96
263
135
390
349
524
149
33
358
30
27
1346
3700
E-3
-------
Table E-3. Number of forested subplots by major forest type and stand size class.
Major forest type
Stand size class
Seedling/ Non-
Sawtimber Poletimber sapling stocked
(no. of forested subplots)
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech -birch
Aspen-birch
Other
63
125
63
123
193
333
108
21
221
12
3
32
106
43
105
65
88
20
7
100
10
8
1
32
26
158
91
100
21
5
37
8
5
0
0
0
0
0
3
0
0
0
0
11
Total
1265
584
484
14
E-4
-------
Table E-4. Number of forested subplots by major forest type and stand origin.
Stand Origin
Major forest type Natural Planted
(no. of forested subplots)
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
82
259
83
203
325
521
149
33
358
30
27
14
4
52
187
24
3
0
0
0
0
0
Total 2070 284
E-5
-------
Table E-5. Number of forested subplots by major forest type and utilization disturbance.
Evidence of
Utilization disturbance
Major forest type No Yes
(no. of forested subplots)
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
88
235
101
332
292
446
125
31
294
26
13
8
28
34
58
57
78
24
2
64
4
14
Total 1983 371
E-6
-------
Table E-6. Number of forested subplots by major forest type and care disturbance.
Major forest type
Evidence of
care disturbance
No Yes
(no. of forested subplots)
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
96
260
77
296
330
503
146
33
349
30
24
0
3
58
94
19
21
3
0
9
0
3
Total
2144
210
E-7
-------
Table E-7. Number of forested subplots by major forest type and natural disturbance.
Major forest type
Evidence of
natural disturbance
No Yes
(no. of forested subplots)
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
96
263
132
377
341
486
138
32
358
30
27
0
0
3
13
8
38
11
1
0
0
0
Total
2280
74
E-8
-------
Table E-8. Number of subplots by major forest type and terrain position.
Major forest type
Terrain position
Slope Flatland Bottomland
(no. of subplots)
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
Non-forest
Total
82
247
8
167
168
362
15
14
323
26
11
1229
2652
14
16
115
194
160
144
55
11
35
4
12
92
852
0
0
12
29
21
18
79
8
0
0
4
25
196
E-9
-------
Table E-9. Number of subplots by major forest type and elevation class.
Elevation class (ft)
Major forest type <500 500-1500 1500-2500 >2500 Missing
White/red/jack pine
Eastern spruce-fir
Longleaf/slash pine
Loblolly/shortleaf pine
Oak-pine
Oak-hickory
Oak-gum-cypress
Elm-ash-red maple
Maple-beech-birch
Aspen-birch
Other
85
251
123
243
225
238
132
26
337
27
18
4
12
12
139
91
175
16
0
12
3
3
2
0
0
4
9
65
0
0
0
0
5
4
0
0
4
20
42
0
4
8
0
0
1
0
0
0
4
4
1
3
1
0
1
Non-forest 1035 217 71 14 9
Total 2740 684 156 96 24
E-10
-------
Table E-10. Number of live trees greater than 12.7 cm DBH by species and crown position on
subplots.
Species
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortleaf pine
slash pine
spruce pine
longleaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white
-------
Species
hackberry (common)
eastern redbud
yellowwood
flowering dogwood
persimmon
American beech
ash species
white ash
black ash
green ash
American holly
butternut
black walnut
sweetgum
yellow-poplar
osage-o range
magnolia species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupelo
blackgum
swamp tupelo
eastern hophombeam
sourwood
Paulownia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
black oak
northern pin oak
southern red oak
cherrybark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
Open -grown
0
0
0
0
0
0
0
3
0
1
0
0
0
3
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
2
0
3
0
2
0
0
0
0
0
0
0
1
0
Dominant
2
0
0
4
6
15
2
23
1
4
3
1
2
120
112
0
2
0
0
14
0
1
0
0
5
24
19
0
1
1
0
4
3
3
11
33
0
10
0
81
0
58
0
0
28
2
3
3
2
1
0
Crown position
Co-dominant
13
0
1
7
6
171
5
118
16
11
16
4
8
250
185
1
2
10
1
38
3
1
0
4
27
66
70
5
14
0
5
4
6
1
27
111
2
66
5
203
0
86
0
6
99
4
3
17
5
14
4
Intermediate
8
3
0
25
1
76
4
21
5
11
24
0
3
179
73
1
1
4
10
32
2
1
0
4
20
65
40
19
42
0
4
3
0
0
7
11
2
30
2
112
2
14
2
1
43
1
1
11
0
7
0
Suppressed
0
2
0
33
0
32
2
7
1
2
13
0
2
53
19
0
2
1
0
9
2
0
1
1
5
35
13
3
35
0
0
2
0
0
0
2
0
6
0
41
2
3
0
0
11
0
1
3
1
3
0
Total
23
5
1
69
13
294
13
172
23
29
56
5
15
605
390
2
7
15
12
93
7
3
1
9
57
190
142
27
92
1
9
13
9
4
45
160
4
114
7
440
4
163
2
7
181
7
8
34
8
26
4
E-12
-------
Species
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
black locust
black willow
sassafras
American basswood
white basswood
elm species
winged elm
American elm
slippery elm
unknown / other
Open-grown
0
2
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
Crown position
Dominant
0
50
9
73
64
0
12
23
3
0
12
0
2
1
0
2
0
1
0
4
Co-dominant
1
144
21
220
209
1
30
65
16
0
10
3
15
8
0
6
11
9
3
6
Intermediate
3
57
6
96
46
0
23
37
1
1
9
3
13
5
0
1
6
3
5
0
Suppressed
0
18
4
26
14
0
11
8
0
2
5
0
5
2
1
0
2
0
3
2
Total
4
271
40
415
334
1
76
133
21
3
36
6
35
17
1
9
19
13
11
12
E-13
-------
Table E-11. Numbers of trees tallied by species and size class (seedlings, saplings, and large trees >12.7
cm DBH).
Species
balsam fir
Atlantic white-cedar
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortleaf pine
slash pine
spruce pine
longleaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
po ndcy press
northern white-cedar
hemlock species
eastern hemlock
maple species
Florida maple
boxelder
striped maple
red maple
sugar maple
mountain maple
horsechestnut species
Ohio buckeye
yellow buckeye
serviceberry
pawpaw
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
Seedlings
3017
7
60
0
3
29
91
0
529
15
44
2
5
0
1
6
0
220
0
636
105
0
8
1
244
0
254
2
76
61
940
3292
1696
236
17
1
20
42
40
5
367
215
0
640
194
Saplings
400
0
23
0
17
18
76
0
145
24
33
0
9
0
1
13
0
57
0
325
28
0
1
0
36
1
58
0
0
2
85
506
118
6
0
0
0
8
3
11
59
30
2
80
49
Trees
585
0
49
1
16
78
21
2
654
278
455
13
119
11
12
103
14
613
4
1814
471
1
47
3
350
1
338
0
2
18
23
1463
464
0
0
0
6
1
0
22
257
92
16
309
64
Total
4002
7
132
1
36
125
188
2
1328
317
532
15
133
11
14
122
14
890
4
2775
604
1
56
4
630
2
650
2
78
81
1048
5261
2278
242
17
1
26
51
43
38
683
337
18
1029
307
E-14
-------
Species
American hornbeam
hickory species
water hickory
brtternut hickory
pignut hickory
pecan
shagbark hickory
mockernut hickory
American chestnut
catalpa species
hackberry species
hackberry (common)
eastern redbud
yellowwood
flowering dogwood
hawthorn species
persimmon
American beech
ash species
white ash
black ash
green ash
honeylocust
silverbell species
American holly
butternut
black walnut
sweetgum
yellow-poplar
osage-orange
magnolia species
cucumbertree
southern magnolia
sweetbay
bigleaf magnolia
apple species
Chinaberry
mulberry species
red mulberry
water tupelo
blackgum
swamp tupelo
eastern hophornbeam
sourwood
Paulownia
redbay
American sycamore
Seedlings
341
477
0
6
18
4
2
148
71
1
1
7
85
0
1248
31
196
523
19
467
32
213
2
6
229
0
3
1307
516
0
34
21
11
185
2
8
2
5
11
51
787
79
156
300
0
29
1
Saplings
70
88
0
0
2
0
2
31
2
0
2
0
19
1
231
1
12
117
6
47
4
18
0
0
38
0
0
286
78
0
10
5
3
61
0
6
0
0
0
8
153
33
42
47
0
4
3
Trees
57
246
2
1
31
0
19
82
1
0
1
23
5
1
69
0
13
294
13
172
23
29
0
0
56
5
15
605
390
2
7
15
12
93
0
7
3
1
9
57
190
142
27
92
1
9
13
Total
468
811
2
7
51
4
23
261
74
1
4
30
109
2
1548
32
221
934
38
686
59
260
2
6
323
5
18
2198
984
2
51
41
26
339
2
21
5
6
20
116
1130
254
225
439
1
42
17
E-15
-------
Species
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
cherry, plum species
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
bear oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
pin oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
dwarf live oak
scrub oak
black locust
willow species
black willow
sassafras
American mountain-ash
American basswood
white basswood
elm species
winged elm
American elm
slippery elm
rock elm
Seedlings
27
0
16
202
7
171
695
17
519
15
96
50
2
381
8
73
19
234
0
41
7
17
853
4
212
244
396
0
137
191
29
0
48
81
36
1
17
612
31
14
1
75
110
54
31
2
Saplings
0
0
3
28
2
39
84
0
73
1
10
7
0
51
0
1
5
61
0
4
0
0
147
0
23
22
53
0
20
21
3
3
1
3
2
2
1
18
0
1
0
3
25
6
5
0
Trees
9
4
45
160
0
4
114
7
440
4
163
2
7
181
7
0
8
34
8
26
4
4
271
0
40
415
334
1
76
133
21
3
0
0
36
0
6
35
0
17
1
9
19
13
11
0
Total
36
4
64
390
9
214
893
24
1032
20
269
59
9
613
15
74
32
329
8
71
11
21
1271
4
275
681
783
1
233
345
53
6
49
84
74
3
24
665
31
32
2
87
154
73
47
2
E-16
-------
Species Seedlings Saplings Trees Total
sparkleberry 18 6 0 24
unknown / other 138 50 12 200
total 26,688 4438 14,407 62,681
E-17
-------
Table E-12. Number of live trees > 12.7 cm DBH tallied on subplots, by species and crown ratio class.
Species
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortteaf pine
slash pine
spruce pine
longteaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yellow buckeye
service berry
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yedowwood
flowering dogwood
persimmon
American beech
ash species
white ash
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
4
1
0
0
1
0
0
0
8
12
0
1
0
0
2
0
14
0
17
27
0
1
0
7
0
1
0
0
0
8
0
1
0
0
2
0
0
1
1
0
2
0
0
0
0
0
0
0
0
0
0
1
0
1
0
3
3
29
1
0
2
2
2
0
24
52
64
2
4
0
0
2
0
86
0
120
101
0
6
0
11
0
8
0
0
2
75
12
1
0
5
10
2
0
34
4
1
5
0
0
0
0
1
0
0
1
0
0
1
0
8
1
31
4
56
2
0
0
6
3
0
104
72
148
4
16
1
2
20
3
104
0
354
154
0
10
1
18
0
27
0
0
1
225
54
1
0
4
22
13
1
85
15
6
27
0
0
4
0
6
0
0
8
2
1
12
4
24
4
49
Crown
5
74
7
0
3
12
1
1
135
75
158
3
45
6
2
15
3
116
1
495
116
1
9
1
35
0
33
1
3
6
436
97
1
0
4
56
19
6
96
18
10
51
1
0
9
3
23
0
0
2
2
0
7
4
46
8
41
Ratio Class*
6 7
115
7
1
2
24
3
0
160
43
68
3
35
2
5
33
0
99
0
402
36
0
8
1
51
0
49
1
4
8
338
115
0
0
4
69
30
3
46
18
19
59
1
1
9
6
24
0
0
3
0
0
10
2
57
0
18
78
8
0
4
12
7
1
98
14
5
0
14
1
0
18
5
60
1
215
18
0
6
0
77
0
55
0
4
2
199
105
0
0
1
43
19
4
27
6
11
45
0
0
5
3
13
0
1
2
0
0
10
2
49
0
18
8
79
5
0
2
7
1
0
63
5
0
1
4
1
2
9
3
40
0
123
11
0
1
0
45
0
51
0
2
3
105
52
1
0
3
30
3
1
16
1
7
31
0
0
4
5
8
0
0
2
0
0
14
1
31
0
6
9
70
9
0
1
11
3
0
48
5
0
0
0
0
1
3
0
42
2
66
3
0
4
0
63
1
50
0
2
0
51
20
1
1
1
19
4
0
4
1
3
17
0
0
0
1
6
1
0
1
1
0
10
0
41
0
5
10
52
4
0
1
1
1
0
15
2
0
0
0
0
0
1
0
28
0
17
4
0
2
0
36
0
40
0
3
1
22
9
0
0
0
6
2
0
0
0
0
6
0
0
0
1
1
0
0
4
0
0
3
0
24
0
1
11
28
5
0
1
2
0
0
7
2
0
0
0
0
0
0
0
24
0
5
1
0
0
0
7
0
24
0
0
0
2
0
0
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
13
0
0
No.
trees
585
49
1
16
78
21
2
654
278
455
13
119
11
12
103
14
613
4
1814
471
1
47
3
350
1
338
2
18
23
1463
464
6
1
22
257
92
16
309
64
57
246
2
1
31
19
82
1
1
23
5
1
69
13
294
13
172
E-18
-------
Species
black ash
green ash
American holly
butternut
black walnut
sweetgum
yetow-poplar
osage -orange
magnola species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupelo
blackgum
swamp tupelo
eastern hophombeam
sourwood
Pautownia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
black locust
black willow
sassafras
1
0
0
0
0
0
2
2
0
0
0
0
1
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
0
1
0
0
0
1
0
0
2
1
0
0
0
1
10
3
0
0
0
0
2
0
0
0
0
0
3
3
0
0
0
1
0
1
0
2
2
1
3
0
2
0
2
0
0
2
0
0
0
0
0
0
0
3
1
2
2
0
1
0
0
0
1
0
0
3
5
2
0
0
1
35
5
0
0
0
0
3
0
0
0
0
1
14
15
0
4
0
0
1
1
2
6
30
0
14
1
9
1
10
0
2
7
0
0
1
0
1
0
0
5
1
14
11
0
4
3
0
0
14
0
4
4
10
4
0
2
1
101
63
0
0
0
0
19
0
0
0
5
20
23
36
4
22
0
0
2
1
2
14
70
2
33
2
33
0
18
0
1
27
2
1
4
0
5
0
2
27
4
42
70
0
7
11
3
0
8
1
8
Crown
5
3
7
2
2
6
150
130
0
2
2
2
28
0
0
0
1
22
54
41
6
31
0
1
3
1
0
16
39
1
35
2
90
2
50
1
1
54
2
2
8
2
11
0
0
59
10
114
107
1
24
34
8
1
7
0
13
Ratio Class'
6 7
2
6
9
1
2
136
97
0
5
2
2
23
4
1
0
1
10
45
30
7
21
0
4
1
2
0
5
15
0
18
2
129
0
54
1
1
51
3
2
7
4
4
0
2
74
9
110
79
0
18
42
5
0
2
4
6
0
6
9
0
2
86
55
1
0
4
5
14
1
1
0
1
3
28
8
6
10
0
2
4
2
0
2
2
0
7
0
98
0
11
0
2
27
0
2
2
2
2
1
0
39
5
62
38
0
12
26
1
0
1
1
3
8
1
2
9
0
1
43
22
0
0
4
2
0
1
1
0
1
0
11
7
3
1
0
1
2
1
0
0
0
0
3
0
34
1
11
0
0
5
0
1
5
0
1
2
0
27
3
40
19
0
7
9
2
0
1
0
0
9
1
2
15
0
1
27
7
1
0
3
1
3
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
2
0
0
0
33
0
2
0
0
• 6
0
0
5
0
0
1
0
19
3
22
7
0
2
6
2
1
1
0
1
10
0
0
8
0
0
11
3
0
0
0
0
0
0
0
1
0
1
2
1
1
1
1
0
0
0
0
0
0
0
0
0
8
0
5
0
0
2
0
0
1
0
0
0
0
15
2
7
1
0
0
2
0
1
0
0
0
11
0
0
4
0
0
4
3
0
0
0
0
0
1
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
1
0
1
0
0
3
2
0
0
0
0
0
21
0
0
0
0
No.
trees
23
29
56
5
15
605
390
2
7
15
12
93
7
3
1
9
57
190
142
27
92
1
9
13
9
4
45
160
4
114
7
440
4
163
2
7
181
7
8
34
8
26
4
4
271
' 40
415
334
1
76
133
3
36
6
35
E-19
-------
Species
American basswood
white basswood
elm species
winged etm
American elm
slippery elm
unknown / other
1
0
0
0
0
0
0
0
2
0
0
0
0
1
0
0
3
1
0
0
0
1
2
2
4
3
1
1
1
1
1
2
Crown Ratio Class*
567
4
0
4
2
2
1
4
6
0
2
6
6
5
2
1
0
0
3
0
1
2
8
2
0
1
3
0
1
0
9
0
0
1
1
2
0
0
10
0
0
0
3
0
0
0
11
0
0
0
0
0
0
0
No.
trees
17
1
9
19
13
T1
12
Total 19 166 943 2393 3502 3077 1867 1070 756 364 150 14307
•Crown ratio classes, in percent:
1=0 7 = 51 - 60
2 = 1 -10 8 = 61 - 70
3 = 2-20 9 = 71-80
4 = 21-30 10 = 81-90
5 = 31-40 11 =91 -99
6 = 41 - 50
E-20
-------
Table E-13. Number of live trees > 12.7 cm DBH tallied on subplots, by species and crown density class.
Species
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortteaf pine
slash pine
spruce pine
longteaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yellow buckeye
serviceberry
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yellowwood
flowering dogwood
persimmon
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
2
4
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
5
0
3
3
0
0
0
9
0
0
0
0
0
5
1
1
0
0
1
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
3
9
0
0
2
1
1
0
5
9
21
0
0
0
0
0
0
19
0
48
29
0
2
1
14
0
8
0
0
0
32
5
0
0
0
5
0
0
2
3
1
0
0
0
0
0
1
0
0
0
0
0
1
0
4
26
3
0
2
3
3
2
26
33
133
3
14
2
0
2
0
52
0
169
87
0
4
1
40
0
19
1
1
0
97
9
2
0
3
6
1
1
11
8
2
7
0
1
1
0
3
0
0
0
1
0
4
3
Crown Density Class*
567
89
8
0
4
9
0
0
78
119
198
6
41
5
2
8
1
93
0
551
163
0
12
1
70
0
74
1
3
4
248
54
1
0
2
34
15
4
34
16
11
40
0
0
4
1
17
0
0
13
2
0
11
7
94
18
1
1
11
6
0
116
79
71
3
43
4
1
19
10
144
0
600
119
1
15
0
61
0
90
0
9
2
384
110
1
1
5
48
22
7
56
15
20
84
2
0
16
6
21
0
0
6
0
1
20
2
130
6
0
2
22
7
0
171
19
30
1
19
0
6
23
3
132
2
324
46
0
9
0
77
0
70
0
3
7
352
127
1
0
3
58
31
2
79
13
13
67
0
0
7
7
28
0
1
4
2
0
21
1
8
121
8
0
4
18
1
0
164
13
2
0
2
0
2
24
0
104
0
92
15
0
3
0
57
0
46
0
2
5
228
89
0
0
0
50
13
1
77
6
8
34
0
0
2
5
8
1
0
0
0
0
5
0
9
80
3
0
1
12
3
0
76
3
0
0
0
0
1
24
0
55
2
26
9
0
1
0
20
1
20
0
0
4
98
56
0
0
7
46
9
1
41
3
2
12
0
0
1
0
2
0
0
0
0
0
2
0
10
28
1
0
0
1
0
0
17
2
0
0
0
0
0
3
0
8
0
1
0
0
1
0
2
0
8
0
0
1
16
12
0
0
2
9
1
0
5
0
0
1
0
0
0
0
2
0
0
0
0
0
1
0
11
4
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
3
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
No.
trees
585
49
1
16
78
21
2
654
278
455
13
119
11
12
103
14
613
4
1814
471
1
47
3
350
1
338
2
18
23
1463
464
6
1
22
257
92
16
309
64
57
246
2
1
31
19
82
1
1
23
5
1
69
13
E-21
-------
Species 1
American beech
ash species
white ash
black ash
green ash
American holy
butternut
black walnut
sweetgum
yellow-poplar
osage-orange
magnolia species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupeto
blackgum
swamp tupeto
eastern hophombeam
sourwood
Pautownia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
black locust
black willow
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
0
1
0
0
0
0
0
2
9
0
1
2
1
0
0
0
3
1
0
0
0
0
2
0
0
0
0
0
0
1
0
0
0
0
0
0
1
8
5
0
2
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
3
11
0
8
3
2
0
0
0
12
1
0
0
0
0
1
0
0
0
1
0
5
4
2
2
0
1
0
0
0
3
23
0
4
0
12
0
2
0
0
6
0
0
0
0
2
0
0
3
0
2
6
0
2
3
0
0
3
0
Crown Density Class*
4567
19
2
5
4
3
0
2
1
54
6
1
0
0
0
9
0
0
0
2
1
8
25
1
9
0
2
4
1
1
0
16
1
14
0
22
1
10
0
1
15
0
3
1
3
6
0
0
19
3
35
32
0
14
10
1
2
14
3
45
4
31
5
7
2
2
5
135
64
0
1
0
2
26
2
3
0
4
12
42
54
6
31
0
2
2
0
2
6
31
1
16
0
82
2
47
1
2
33
1
2
3
2
4
0
2
70
3
120
90
0
28
26
5
0
6
1
62
2
38
5
7
14
1
4
194
138
1
1
5
3
35
3
0
1
1
34
54
49
9
28
0
2
5
3
0
7
22
1
27
2
128
0
55
0
3
66
3
1
10
3
5
1
1
90
18
151
71
1
19
52
7
1
5
2
61
2
36
3
5
23
0
5
138
113
0
1
3
6
13
0
0
0
1
9
43
7
6
12
0
1
2
3
0
10
28
1
23
2
124
1
29
1
0
37
1
1
14
0
6
1
1
57
11
74
73
0
10
27
6
0
5
0
8
62
3
29
0
4
13
0
0
43
48
0
4
5
1
7
1
0
0
0
1
23
1
1
5
1
0
0
2
0
3
21
0
15
1
50
0
15
0
1
22
2
1
6
0
1
1
0
26
5
23
44
0
2
9
2
0
2
0
9
17
0
20
1
0
4
0
0
19
17
0
0
2
0
0
1
0
0
0
0
11
0
1
3
0
1
0
0
0
3
13
0
10
1
19
0
2
0
0
1
0
0
0
0
1
1
0
6
0
7
17
0
0
5
0
0
1
0
10
7
0
4
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
3
0
1
0
0
0
0
0
0
5
1
0
3
1
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
11
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
No.
trees
294
13
172
23
29
56
5
15
605
390
2
7
15
12
93
7
3
1
9
57
190
142
27
92
1
9
13
9
4
45
160
4
114
7
440
4
163
2
7
181
7
8
34
8
26
4
4
271
40
415
334
1
76
133
21
3
36
6
E-22
-------
Species
sassafras
American basswood
white basswood
elm species
winged elm
American elm
slpperyelm
unknown / other
Total
1
0
0
0
0
0
0
0
0
17
2
0
0
0
0
0
1
0
0
82
3
0
0
0
1
0
0
1
0
345
4
3
1
0
1
1
0
1
0
1179
Crown Density Class*
567
9
10
0
3
8
3
4
4
3168
12
1
1
1
5
6
3
3
3828
5
2
0
2
2
2
2
0
2977
8
3
3
0
1
1
1
0
3
1728
9
3
0
0
0
2
0
0
2
812
10
0
0
0
0
0
0
0
0
155
11
0
0
0
0
0
0
0
0
16
No.
trees
35
17
1
9
19
13
11
12
14307
•Crown density classes, in percent:
1=0 7 = 51 - 60
2 = 1 -10 8 = 61 - 70
3-11-20 9 = 71-80
4 = 21 - 30 10 = 81 - 90
5 = 31-40 11=91-99
6 = 41 - 50
E-23
-------
Table E-14. Number of live trees > 12.7 cm DBH tallied on subplots, by species and crown dieback class.
Species
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortteaf pine
slash pine
spruce pine
longleaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yellow buckeye
service berry
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yellowwood
flowering dogwood
persimmon
American beech
ash species
1
373
28
1
10
63
14
2
465
215
398
6
96
9
5
15
13
432
1
1446
314
1
25
2
173
1
228
1
5
7
477
189
1
1
9
91
53
4
60
15
22
93
2
0
22
13
36
1
0
0
2
1
38
8
83
12
2
194
18
0
4
15
6
0
185
53
51
6
23
2
7
76
1
161
3
348
140
0
20
1
153
0
97
0
12
13
879
256
3
0
11
157
39
11
222
30
31
145
0
1
7
6
42
0
1
22
1
0
22
5
173
1
3
8
1
0
0
0
0
0
2
7
4
1
0
0
0
12
0
13
0
14
12
0
2
0
10
0
7
1
1
2
66
11
0
0
2
5
0
1
9
15
3
6
0
0
2
0
3
0
0
1
1
0
4
0
12
0
4
4
0
0
1
0
0
0
0
2
1
0
0
0
0
0
0
2
0
1
1
0
0
0
8
0
4
0
0
1
15
3
0
0
0
2
0
0
10
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
8
0
Crown Dieback Class'
5 6
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
1
0
0
0
0
2
0
1
0
0
0
10
2
0
0
0
0
0
0
0
3
0
1
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
0
0
0
5
0
0
0
0
1
0
0
3
1
0
1
0
0
0
0
1
0
0
0
0
0
1
0
3
0
7
2
1
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
2
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
9
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
2
0
1
0
0
1
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
1
• o
6
0
10
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
3
0
1
0
0
0
0
1
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
11
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
3
0
No.
trees
585
49
1
16
78
21
2
654
278
455
13
119
11
12
103
14
613
4
1814
471
1
47
3
350
1
338
2
18
23
1463
464
6
1
'22
257
92
16
309
64
57
246
2
1
31
19
82
1
1
23
5
1
69
13
294
13
E-24
-------
Species 1
white ash 50
black ash 6
green ash 7
American holly 21
butternut 1
black walnut 5
sweetgum 343
yellow-poplar 276
osage-orange 0
magnolia species 3
cucumbertree 12
southern magnolia 3
sweetbay 50
apple species 5
Chinaberry 3
mutoerry species 0
red mulberry 2
water tupeto 49
blackgum 107
swamp tupeto 73
eastern hophombeam 14
sourwood 34
Paulownia 1
redbay 4
American sycamore 8
balsam poplar 0
eastern cottonwood 1
bigtooth aspen 8
quaking aspen 21
pin cherry 1
black cherry 36
choke cherry (common) 5
white oak 177
swamp white oak 0
scarlet oak 23
bluejack oak 0
northern pin oak 3
southern red oak 66
cherrybark oak 0
turkey oak 2
laurel oak 22
overcup oak 5
blackjack oak 2
swamp chestnut oak 3
chinkapin oak 4
water oak 105
willow oak 15
chestnut oak 87
northern red oak 57
Shumard oak 1
post oak 29
black oak 29
live oak 7
dwarf post oak 1
black locust 9
black willow 2
sassafras 8
2
101
5
16
35
4
8
228
106
1
4
3
8
40
2
0
0
5
8
75
54
10
49
0
2
5
7
3
32
115
2
64
2
235
4
105
2
3
105
7
5
11
3
15
1
0
141
22
298
243
0
40
90
12
1
19
3
24
3
11
3
3
0
0
0
16
5
0
0
0
1
0
0
0
0
2
0
8
8
1
5
0
1
0
1
0
2
12
1
9
0
19
0
23
0
1
5
0
0
0
0
5
0
0
17
2
21
29
0
3
9
2
0
3
1
3
Crown Dieback Class*
4567
2
4
0
0
0
0
8
0
0
0
0
0
0
0
0
1
0
0
0
2
0
1
0
0
0
0
0
1
3
0
4
0
6
0
4
0
0
1
0
0
1
0
1
0
0
3
0
4
4
0
1
4
0
0
0
0
0
5
2
2
0
0
1
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
2
0
0
0
1
0
4
0
0
1
0
0
0
0
2
0
0
1
0
0
1
0
0
1
0
0
1
0
0
2
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
3
0
0
0
0
0
2
0
0
0
0
1
0
0
0
0
0
1
0
2
0
0
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
2
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
1
0
0
8
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
3
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
9
0
0
1
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
10
1
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
11
0
0
0
0
0
1
2
2
0
0
0
0
2
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
2
0
0
No.
trees
172
23
29
56
5
15
605
390
2
7
15
12
93
7
3
1
9
57
190
142
27
92
1
9
13
9
4
45
160
4
114
7
440
4
163
2
7
181
7
8
34
8
26
4
4
271
40
415
334
1
76
133
21
3
36
6
35
E-25
-------
Species
American basswood
white basswood
elm species
winged elm
American elm
slippery elm
unknown / other
1
9
1
6
7
5
7
6
2
8
0
2
10
7
4
6
3
0
0
0
1
0
0
0
Crown Dieback Class'
4567
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
8
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
11
0
0
0
0
0
0
0
No.
trees
17
1
9
19
13
11
12
Total 7429 6074 471 120 55 44 24 17 23 27 23 14307
'Crown dieback classes, in percent:
1=0 7 = 51 - 60
2 = 1 -10 8 = 61-70
3 = 11 - 20 9 = 71 - 80
4 = 21-30 10 = 81-90
5 = 31 - 40 11 = 91 - 99
6 = 41 - 50
E-26
-------
Table E-15. Number of live trees > 12.7 cm DBH tallied on subplots, by species and crown transparency
class.
Species
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortteaf pine
slash pine
spruce pine
longleaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
babcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yetow buckeye
service berry
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yelowwood
flowering dogwood
persimmon
American beech
1
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
3
2
264
23
0
3
38
14
0
325
136
328
3
54
3
8
34
8
157
3
863
136
1
14
0
59
1
96
0
15
12
604
269
1
1
12
131
39
3
71
4
27
180
2
0
24
15
69
1
1
8
2
1
60
9
119
3
258
18
1
7
32
7
1
292
119
93
8
62
5
3
46
6
292
1
870
240
0
33
2
151
0
131
2
2
8
602
177
2
0
7
107
42
13
171
22
20
55
0
1
7
3
11
0
0
14
0
0
6
3
118
Crown Transparency Class*
4567
45
2
0
3
8
0
0
28
18
30
2
1
2
0
8
0
90
0
54
49
0
0
1
88
0
71
0
1
1
160
14
1
0
3
14
8
0
51
27
6
3
0
0
0
1
2
0
0
1
2
0
0
1
27
10
4
0
2
0
0
0
5
3
3
0
1
1
1
4
0
30
0
18
16
0
0
0
39
0
26
0
0
0
56
2
0
0
0
1
2
0
11
8
3
6
0
0
0
0
0
0
0
0
0
0
1
0
13
4
0
0
0
0
0
0
2
1
0
0
0
0
0
5
0
19
0
1
10
0
0
0
8
0
7
0
0
1
19
0
0
0
0
2
0
0
4
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
2
1
0
0
0
0
1
1
1
1
0
0
0
0
2
0
9
0
1
8
0
0
0
2
0
3
0
0
1
11
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
2
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
6
0
0
6
0
0
0
2
0
1
0
0
0
4
1
1
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
9
1
0
0
1
0
0
0
0
0
0
0
0
0
0
2
0
5
0
0
3
0
0
0
1
0
3
0
0
0
2
1
0
0
0
• o
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
10
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
1
2
0
0
0
0
0
0
0
0
0
2
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
No.
trees
585
49
1
16
78
21
2
654
278
455
13
119
11
12
103
14
613
4
1814
471
1
47
3
350
1
338
2
18
23
1463
464
6
1
22
257
92
16
309
64
57
246
2
1
' 31
19
82
1
1
23
5
1
69
13
294
E-27
-------
Species
ash species
white ash
black ash
green ash
American holly
butternut
black walnut
sweetgum
yeHow-poplar
osage-orange
magnolia species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupeto
blackgum
swamp tupeb
eastern hophombeam
sourwood
Paulo wnia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
black locust
black willow
1
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
2
0
0
1
0
0
0
0
2
7
55
2
10
37
2
7
490
312
1
6
14
11
72
1
1
0
6
45
128
63
0 13
60
1
8
0 6
2
0 0
11
17
1
43
0 6
260
2
65
1
6
121
7
1
25
6
15
0 4
4
164
29
202
122
1
49
59
6
0
8
3
3
6
86
17
6
19
3
6
99
63
1
0
1
1
19
4
0
0
3
12
48
58
6
23
0
0
5
7
2
19
73
2
46
1
131
2
72
1
1
41
0
3
8
2
7
0
0
97
8
113
136
0
20
55
12
1
11
3
Crown Transparency Class3
4567
0
21
1
6
0
0
1
9
7
0
1
0
0
1
2
1
0
0
0
10
18
5
7
0
1
0
0
1
5
36
1
17
0
29
0
17
0
0
10
0
3
0
0
2
0
0
9
0
62
45
0
4
12
3
1
11
0
0
6
2
4
0
0
1
2
2
0
0
0
0
0
0
1
1
0
0
2
1
1
1
0
0
1
0
0
0
8
0
5
0
5
0
3
0
0
8
0
1
0
0
0
0
0
1
1
16
15
0
2
2
0
0
2
0
0
1
0
1
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
3
0
0
0
7
0
4
0
0
0
0
0
0
0
1
0
0
0
0
7
5
0
0
2
0
1
4
0
0
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
2
0
1
0
1
0
2
0
0
0
0
0
0
0
0
0
0
0
0
6
5
0
0
2
0
0
0
0
8
0
1
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
1
3
0
0
0
0
0
0
0
0
9
0
0
0
1
0
0
0
1
1
0
0
0
0
1
0
0
0
0
0
0
0
2
0
0
0
0
0
0
1
15
0
1
0
3
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
10
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
4
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
2
0
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
6
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
No.
trees
13
172
23
29
56
5
15
605
390
2
7
15
12
93
7
3
1
9
57
190
142
27
92
1
9
13
9
4
45
160
4
114
7
440
4
163
2
7
181
7
8
34
8
26
4
4
271
40
415
334
1
76
133
21
3
36
6
E-28
-------
Species
sassafras
American basswood
white basswood
elm species
winged elm
American elm
slippery elm
unknown / other
1
0
0
0
0
0
0
0
2
20
0 6
1
4
9
7
7
6
3
15
8
0
2
7
3
3
6
Crown Transparency Class*
4567
0
0
0
2
2
2
0
0
0
1
0
1
1
1
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
10
0
1
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
No.
trees
35
17
1
9
19
13
11
12
Total
29 6909 5475 1188 364
130
72
39
54
27
20 14307
"Crown transparency classes, in percent:
1=0 7 = 51 - 60
2 = 1-10 8 = 61-70
3 = 11 - 20 9 = 71 - 80
4 = 21-30 10 = 81-90
5 = 31-40 11 =91 -99
6 = 41 - 50
E-29
-------
Table E-16. Number of live trees > 2.5 cm DBH tallied on microplots, by species and crown ratio class.
Species
balsam fir
eastern redcedar
tamarack
white spruce
black spruce
red spruce
shortleaf pine
slash pine
longteaf pine
red pine
pitch pine
eastern white pine
loblolly pine
Virginia pine
baldcypress
northern white-cedar
hemlock species
eastern hemlock
boxelder
striped maple
red mapte
sugar maple
mountain maple
serviceberry
pawpaw
birch species
yellow birch
sweat birch
river birch
paper bjrch
gray birch
American hornbeam
hickory species
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
haofcberry species
eastern redbud
yeflowwood
flowering dogwood
hawthorn species
persimmon
American beech
ash species
white ash
black ash
green ash
American holy
sweetgum
yellow-poplar
magnolia species
cucumbertree
southern magnolia
sweetbay
1
45
0
4
3
10
22
0
0
0
1
0
5
1
0
0
3
0
4
0
8
33
13
2
0
0
0
8
0
0
10
9
0
1
0
0
0
0
0
0
0
0
0
0
6
0
5
0
0
0
0
1
0
0
0
1
2
3
0
1
0
2
2
0
4
0
0
0
1
22
1
1
1
0
1
0
0
19
2
0
0
0
0
0
2
0
0
0
1
5
1
0
0
1
0
1
0
7
0
0
2
0
1
0
1
0
6
1
0
0
0
5
3
23
1
2
0
3
8
4
4
3
0
0
3
25
7
0
2
0
1
0
1
50
6
1
2
0
1
4
2
1
9
9
4
4
0
1
4
0
0
5
1
18
0
1
5
0
4
0
4
0
20
3
0
0
0
10
4
29
2
5
0
7
5
2
8
0
0
1
7
42
2
0
5
0
7
0
24
85
16
2
1
0
3
4
3
1
14
13
15
8
0
0
4
0
0
2
0
44
0
3
6
0
11
0
1
3
31
8
1
3
1
15
Crown Ratio Class*
567
41
0
1
1
15
13
8
2
3
0
0
4
35
5
0
2
0
3
1
7
109
21
1
2
0
0
7
9
0
14
8
14
16
0
0
7
0
1
3
0
58
0
2
8
2
6
2
5
3
51
13
1
0
0
17
60
2
2
2
12
21
4
0
3
0
6
9
43
2
0
9
0
5
0
19
85
22
0
1
0
3
12
7
0
12
7
11
17
1
1
8
0
0
6
0
42
1
4
18
2
10
1
5
4
49
20
4
1
1
12
34
6
1
1
10
15
4
3
0
0
2
6
36
2
0
2
0
4
1
7
63
10
0
0
0
2
7
4
0
13
3
16
12
0
0
4
0
1
2
0
25
0
1
21
1
3
1
1
6
45
10
2
0
0
0
8
23
4
0
0
6
14
1
3
0
0
1
5
48
2
0
3
0
5
0
12
33
11
0
0
0
1
12
2
0
4
0
4
7
0
0
4
0
0
0
0
18
0
0
11
1
3
0
1
5
41
11
0
1
1
0
9
51
4
0
0
5
18
0
3
0
0
2
4
40
5
0
3
0
9
0
5
24
13
0
2
1
0
2
0
0
4
0
3
12
0
0
0
1
0
0
0
12
0
1
19
0
4
0
0
7
26
7
1
0
0
0
10
48
2
0
4
2
14
0
6
0
0
1
7
16
1
0
1
0
9
0
2
4
3
0
0
1
1
3
1
0
0
0
2
6
0
0
0
0
0
0
0
4
0
0
16
0
0
0
0
7
12
2
0
0
0
1
11
43
2
1
7
4
13
1
0
0
0
0
6
17
1
0
5
1
10
0
0
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
5
0
0
0
0
3
5
2
1
0
0
0
No.
trees
400
23
17
18
76
145
24
33
9
1
13
57
325
28
1
36
1
58
2
85
506
118
6
8
3
11
59
30
2
80
49
70
86
2
2
31
2
2
19
1
231
1
12
117
6
47
4
18
38
286
78
10
5
3
61
E-30
-------
Crown Ratio
Species
apple species
water tupelo
blackgum
swamp tupelo
eastern hophombeam
sourwood
redbay
American sycamore
bigtooth aspen
quaking aspen
cherry, plum species
pin cherry
black cherry
white oak
swamp white oak
scarlet oak
bluejack oak
southern red oak
bear oak
turkey oak
laurel oak
blackjack oak
water oak
willow oak
chestnut oak
northern red oak
post oak
black oak
live oak
dwarf post oak
dwarf live oak
scrub oak
black locust
willow species
black willow
sassafras
American basswood
elm species
winged elm
American elm
slippery elm
sparkle berry
unknown / other
Total
1
0
0
0
0
3
0
0
0
0
10
0
9
4
5
0
0
0
0
0
0
2
0
1
0
0
5
1
1
0
0
0
0
0
1
0
1
0
0
1
1
0
1
0
241
2
0
0
4
2
0
3
0
0
0
1
0
0
3
1
0
0
2
2
0
1
1
0
0
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
121
3
0
0
13
5
2
6
1
0
1
2
0
4
8
3
0
1
0
1
0
1
3
0
9
3
2
7
1
2
0
0
0
1
1
0
1
6
0
0
3
0
1
0
17
361
4
1
3
28
10
5
7
1
2
0
3
0
10
15
11
0
3
0
9
0
1
15
0
28
2
4
10
3
2
1
0
0
0
0
0
0
1
0
1
4
0
0
1
13
638
5
1
2
33
8
10
14
1
0
0
1
2
8
19
10
0
2
0
7
0
0
13
1
22
2
3
12
3
4
0
2
0
0
1
1
0
6
0
1
3
1
2
1
7
724
6
3
1
32
3
7
7
0
0
1
4
0
3
13
15
0
1
1
11
0
0
12
0
29
4
2
11
3
2
2
0
0
1
0
0
0
2
1
0
5
1
0
3
5
751
Class"
7
1
1
16
3
2
4
1
0
0
2
0
3
10
13
1
2
3
7
1
0
7
0
13
4
5
1
4
3
0
1
0
0
0
0
0
2
0
0
3
3
1
0
3
507
8
0
1
12
1
9
2
0
0
1
0
0
0
9
5
0
0
0
6
0
2
1
1
19
2
3
3
2
3
0
0
0
0
0
0
0
0
0
0
5
0
0
0
2
387
9
0
0
11
1
2
2
0
0
0
3
0
1
2
5
0
1
1
7
0
0
4
2
16
2
0
3
2
2
0
0
1
0
0
0
0
0
0
• 0
0
0
1
0
0
357
10
0
0
1
0
2
2
0
0
0
2
0
0
1
4
0
0
0
1
0
0
3
0
10
2
2
0
1
1
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
210
11
0
0
3
0
0
0
0
1
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
141
No.
trees
6
8
153
33
42
47
4
3
3
28
2
39
84
73
1
10
7
51
1
5
61
4
147
23
22
53
20
21
3
3
1
3
2
2
1
18
1
3
25
6
5
6
50
4438
'Crown ratio classes, in percent:
1 =0
2 = 1-10
3= 11 -20
4 = 21 - 30
5 = 31 - 40
7 = 51
8 = 61
9 = 71
10 = 81
11 =91
-60
-70
-80
-90
-99
6 = 41 - 50
E-31
-------
Table E-17. Frequency of visible tree damage types on all live trees greater than 12.7 cm DBH, by
species.
Total no.
Species trees damages
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortteaf pine
slash pine
spruce pine
longteaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yellow buckeye
service berry
birch species
yeUow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yellowwood
flowering dogwood
persimmon
585
49
1
16
78
21
2
654
278
455
13
119
11
12
103
14
613
4
1814
471
1
47
3
350
1
338
2
18
23
1463
464
6
1
22
257
92
16
309
64
246
2
1
31
19
82
1
1
23
5
1
69
13
76
22
0
2
3
0
0
46
75
94
9
28
4
7
22
5
90
0
438
197
0
8
0
59
0
69
1
7
11
588
140
4
0
7
64
21
7
87
26
5730
140
2
1
13
3
90
1
0
21
6
1
67
7
0
513
31
1
14
75
21
2
610
205
368
7
96
7
5
85
9
532
4
1416
297
1
41
3
298
1
276
1
11
13
975
351
2
1
15
206
74
11
233
42
33
134
0
0
20
16
20
0
1
7
1
0
22
8
1
3
1
0
1
0
0
0
2
0
1
0
0
0
0
1
0
6
0
4
0
0
0
0
9
0
3
0
0
0
31
3
1
0
0
4
0
1
6
0
1
3
0
0
1
0
4
0
0
0
2
0
6
0
Damage type'
2 3
number
17
6
0
0
2
0
0
16
9
12
1
3
1
0
2
0
12
0
39
16
0
0
0
28
0
9
0
0
7
188
63
0
0
1
22
8
3
39
3
7
26
0
1
1
0
5
0
0
10
2
0
8
1
3
3
0
0
0
0
0
1
5
40
0
4
0
0
9
2
1
0
179
59
0
2
0
0
0
0
0
0
1
40
15
0
0
2
8
1
1
7
0
2
12
0
0
0
0
6
1
0
0
2
1
2
1
4
2
1
0
0
1
0
0
2
2
2
0
6
0
6
0
0
1
0
22
11
0
1
0
1
0
7
0
1
0
21
2
0
0
0
0
0
0
1
0
0
18
0
0
2
0
35
0
0
2
0
0
1
0
5
21
4
0
0
0
0
0
8
1
1
3
3
0
0
0
0
2
0
10
5
0
0
0
5
0
4
0
0
2
48
12
0
0
2
8
2
0
15
4
1
8
0
0
0
0
5
0
0
0
0
0
1
0
6
12
1
0
0
0
0
0
7
1
2
0
2
0
0
6
0
15
0
13
3
0
2
0
4
0
6
0
0
1
42
5
0
0
1
6
1
0
2
2
4
9
1
0
2
0
4
0
0
0
0
0
3
0
7
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
1
1
0
16
4
0
0
0
0
0
0
0
0
0
4
2
0
0
0
2
2
0
3
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
E-32
-------
Total no.
Species trees damages
American beech
ash species
white ash
black ash
green ash
American holly
butternut
black walnut
sweetgum
yellow-poplar
o sage-orange
magnolia species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupeto
blackgum
swamp tupelo
eastern hophornbeam
sourwood
Paubwnia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
pin cherry
black cherry
294
13
172
23
29
56
5
15
605
390
2
7
15
12
93
7
3
1
9
57
190
142
27
92
1
9
13
9
4
45
160
4
114
choke cherry (common) 7
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
440
4
163
2
7
181
7
8
34
8
26
4
4
271
40
415
334
1
76
133
21
3
139
9
44
2
30
9
5
12
317
216
2
8
4
1
75
1
0
1
15
22
128
70
10
88
0
4
8
3
1
16
39
1
58
5
285
1
149
0
1
111
5
2
10
7
22
1
0
175
24
431
125
1
54
92
15
1
0
178
5
135
21
7
48
1
8
347
207
1
0
11
11
36
6
3
0
1
35
102
85
21
30
1
5
7
6
3
31
124
3
71
3
224
3
60
2
6
101
2
6
27
3
11
3
4
139
21
135
230
0
36
64
8
2
1
3
0
4
1
4
0
0
3
15
2
0
0
0
0
2
0
0
1
0
0
3
1
0
6
0
1
0
0
0
2
2
0
6
0
12
0
14
0
0
5
0
0
1
1
1
0
0
2
0
19
5
0
4
6
1
0
Damage type'
2 3
number
59
2
15
0
5
5
2
1
62
31
0
2
2
0
10
0
0
0
2
2
21
13
1
13
0
0
1
2
0
4
6
1
5
3
27
0
9
0
0
6
0
0
1
1
1
0
0
18
2
27
11
0
6
20
2
0
7
0
1
0
3
1
1
0
40
14
0
1
2
0
8
0
0
0
0
4
9
12
1
3
0
0
0
0
0
0
3
0
3
1
31
0
21
0
0
10
0
0
1
1
2
1
0
25
2
12
9
0
6
8
3
0
4
8
0
0
0
1
0
0
0
10
14
0
0
0
0
3
0
0
0
0
1
2
1
2
2
0
0
4
0
0
0
0
0
2
0
21
0
29
0
0
13
0
0
0
3
2
0
0
7
3
81
13
0
3
9
0
0
5
16
0
5
0
1
0
0
1
18
9
0
1
0
0
3
0
0
0
0
0
5
0
0
2
0
0
0
0
0
1
2
0
4
0
34
0
7
0
0
16
2
0
0
0
3
0
0
10
4
31
15
1
2
6
1
0
6
4
0
2
0
1
0
1
0
22
7
0
0
0
0
4
0
0
0
1
1
12
4
1
1
0
0
1
1
0
0
1
0
7
0
9
0
2
0
0
1
0
0
0
0
2
0
0
5
2
14
4
0
4
1
0
0
7
3
0
0
1
0
0
0
1
3
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
3
0
3
0
2
0
2
0
0
0
0
0
0
0
0
0
0
4
0
1
1
0
0
0
0
0
E-33
-------
Total no.
Species trees damages
0
Damage type*
1 2 3
4
5
6
7
number
black locust
black willow
sassafras
American basswood
white basswood
ekn species
winged elm
American elm
slippery elm
unknown / other
36
6
36
17
1
9
19
13
11
12
45
4
18
11
1
10
10
4
8
6
8
3
22
7
0
2
11
9
5
7
4
0
4
0
0
0
0
1
0
0
7
2
4
2
0
0
2
0
0
1
2
0
1
0
1
2
0
1
0
1
5
0
0
1
0
1
2
0
0
0
2
0
0
1
0
0
0
0
1
2
1
0
2
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
Total 14307 5571 9829 230 987 664 391 381 276 66
E-34
-------
Table E-17, continued
Species
Damage type
10 11 12 13
number
14
15
16
17
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
shortteaf pine
slash pine
spruce pine
longteaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
lobloly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yellow buckeye
servfceberry
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yeHowwood
flowering dogwood
persimmon
American beech
2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
3
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
3
0
3
6
0
0
0
0
0
31
0
1
0
9
0
1
0
0
0
0
0
1
1
0
5
0
0
0
0
6
0
0
0
0
0
3
1
6
0
0
0
0
0
0
0
4
1
17
0
4
0
0
0
0
10
0
9
5
0
0
0
0
0
0
1
1
0
3
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
27
10
2
3
2
0
1
2
27
0
76
66
0
2
0
2
0
3
0
2
0
74
9
0
0
0
1
1
0
2
1
4
14
1
0
0
1
16
0
0
6
0
0
6
2
1
0
2
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
5
1
0
0
0
0
0
0
0
0
0
11
4
0
0
0
1
0
1
0
0
0
6
0
0
0
0
0
0
0
1
0
0
3
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
6
1
0
1
0
0
0
0
0
0
0
5
0
0
0
0
1
0
0
0
0
1
3
0
0
1
0
0
0
0
1
0
0
2
0
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
5
0
0
0
1
1
0
2
0
0
14
0
0
0
1
3
0
0
0
0
0
8
0
2
2
0
0
0
0
0
0
0
5
0
1
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
2
1
1
0
0
0
0
0
0
0
5
0
0
0
0
0
0
3
0
0
0
39
13
0
0
0
6
2
1
8
1
4
7
0
0
3
0
1
0
0
0
0
0
9
0
5
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
E-35
-------
Species
ash species
white ash
black ash
green ash
American holy
butternut
black walnut
sweetgum
yellow-poplar
osage -orange
magnolia species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupeb
blackgum
swamp tupeb
eastern hophombeam
sourwood
Paukjwnia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtooth aspen
quaking aspen
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
bluejackoak
northern pin oak
southern red oak
cherry bark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
black locust
8
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
1
0
0
0
9
0
0
0
2
0
0
0
1
4
0
0
0
0
1
0
0
0
0
0
5
4
2
1
0
0
0
0
1
8
19
0
1
0
9
0
9
0
0
0
0
0
0
0
1
0
0
1
1
77
12
0
2
11
0
0
4
10
0
0
0
0
0
0
0
4
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
1
1
0
0
0
0
0
0
15
0
1
4
0
0
1
0
0
0
11
5
3
0
5
0
0
4
55
79
0
2
0
0
15
0
0
0
1
12
38
11
0
40
0
2
1
0
0
0
0
0
16
1
26
0
9
0
1
11
0
0
2
0
1
0
0
25
2
89
15
0
6
6
1
0
7
Damage type
12 13
number
0
0
0
2
0
0
0
8
4
0
1
0
0
7
0
0
0
3
0
2
10
0
2
0
1
0
0
0
0
0
0
1
0
11
0
11
0
0
3
0
1
1
0
1
0
0
4
0
8
3
0
1
2
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
4
0
0
0
2
0
2
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
2
0
2
0
0
1
0
0
0
0
14
1
2
0
1
0
0
0
48
24
0
0
0
0
5
0
0
0
0
0
17
6
0
2
0
0
0
0
0
0
0
0
3
0
56
0
9
0
0
26
0
0
3
1
1
0
0-
33
4
26
16
0
9
4
2
0
0
15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
0
3
0
1
1
0
0
13
3
1
1
0
0
6
0
0
0
5
1
5
4
1
3
0
0
0
0
0
0
1
0
2
0
20
0
15
0
0
13
2
0
1
0
6
0
0
15
1
17
6
0
7
9
0
0
0
17
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
E-36
-------
Species
black willow
sassafras
American basswood
white basswood
elm species
winged elm
American elm
slippery elm
unknown / other
8
0
0
0
0
0
0
0
0
0
9
0
0
4
0
1
0
0
0
0
10
0
0
0
0
0
0
0
0
0
11
1
6
1
0
0
1
1
2
2
Damage type
12 13
number
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
14
0
0
2
0
3
0
0
2
0
15
0
0
0
0
0
0
0
0
0
16
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
0
0
Total 14 265 96 871 125 49 362 13 277
E-37
-------
Table E-17, continued.
Species
balsam fir
eastern redcedar
larch (introduced)
tamarack
white spruce
black spruce
blue spruce
red spruce
snortleaf pine
slash pine
spruce pine
tongteaf pine
table-mountain pine
red pine
pitch pine
pond pine
eastern white pine
Scots pine
loblolly pine
Virginia pine
Austrian pine
baldcypress
pondcypress
northern white-cedar
hemlock species
eastern hemlock
Florida maple
boxelder
striped maple
red maple
sugar maple
yellow buckeye
serviceberry
birch species
yelow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
water hickory
bittemut hickory
pignut hickory
shagbark hickory
mockemut hickory
American chestnut
hackberry species
hackberry (common)
eastern redbud
yeUowwood
flowering dogwood
persimmon
American beech
18
0
0
0
0
0
0
0
0
1
5
0
2
1
0
1
0
2
0
5
5
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
1
19
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
3
0
0
2
1
2
0
0
0
0
0
0
0
0
20
7
0
0
0
0
0
0
4
5
2
0
0
0
1
0
0
0
0
12
9
0
0
0
9
0
3
0
2
0
26
2
0
0
1
2
2
0
1
14
5
4
0
0
1
0
0
0
0
1
0
0
11
0
2
21
2
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
0
0
0
0
1
0
0
0
0
0
0
0
0
Damage type
22 23
number
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
3
0
0
0
0
0
0
0
2
0
8
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
25
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
26
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
27
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
99
1
1
0
0
0
0
0
0
1
0
0
0
0
0
1
0
5
0
16
1
0
0
0
0
0
0
0
0
0
11
2
0
0
0
2
1
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
1
0
16
E-38
-------
Species
ash species
white ash
black ash
green ash
American holly
butternut
black walnut
sweetgum
yelow-poplar
o sage-orange
magnolia species
cucumbertree
southern magnolia
sweetbay
apple species
Chinaberry
mulberry species
red mulberry
water tupeto
blackgum
swamp tupelo
eastern hophombeam
sourwood
Paulo wnia
redbay
American sycamore
balsam poplar
eastern cottonwood
bigtootn aspen
quaking aspen
pin cherry
black cherry
choke cherry (common)
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
turkey oak
laurel oak
overcup oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
willow oak
chestnut oak
northern red oak
Shumard oak
post oak
black oak
live oak
dwarf post oak
black locust
18
1
0
0
0
0
0
1
1
2
0
0
0
0
0
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
0
0
0
4
0
2
0
0
0
0
0
0
0
0
0
0
0
2
12
2
0
0
3
0
0
4
19
0
2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Q
0
0
0
0
7
0
0
0
0
2
0
0
0
0
0
0
0
1
0
1
0
0
1
0
0
0
0
20
0
2
0
2
0
1
1
5
2
0
0
0
0
6
1
0
0
1
1
3
0
0
8
0
0
1
0
0
0
0
0
4
0
3
0
1
0
0
2
0
0
0
0
0
0
0
4
0
8
5
0
2
3
4
0
1
21
0
1
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Damage type
22 23
number
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
24
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
3
0
0
0
0
0
0
0
0
0
0
0
1
3
1
0
0
0
0
0
0
26
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
27
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
99
0
3
0
0
2
0
0
3
3
0
0
0
1
1
0
0
0
0
0
1
2
0
1
0
0
0
0
0
0
0
0
0
0
7
1
0
0
0
1
0
1
0
0
0
0
0
4
0
0
3
0
0
2
1
1
6
E-39
-------
Species
black willow
sassafras
American basswood
white basswood
elm species
winged elm
American elm
slippery elm
unknown / other
18
0
0
0
0
0
0
0
1
0
19
0
0
0
0
0
0
0
0
0
20
1
1
0
0
1
1
0
0
0
21
0
0
0
0
0
0
0
0
0
Damage type
22 23
number
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
24
0
0
0
0
0
0
0
0
0
25
0
0
0
0
2
0
0
0
0
26
0
0
0
0
0
0
0
0
0
27
0
0
0
0
0
0
0
0
0
99
0
0
0
0
0
0
1
0
0
Total
72
30
201
23
28
13
22
106
'Damage types (Chojnacky 1991):
0 none
1 dead part of a live tree
2 open wound
3 closed wound
4 small holes or pinhotes
5 crack or seam
6 broken part
7 conk or fruiting bodies or fuzz on surface or mold
8 removed or missing part
9 defoliation
10 resinosis or bleeding
11 crook or sweep
12 swelling
13 excessive branching
14 abundance of epteormic branches or water sprouts on trunk or base
15 abundance of seeds or cones
16 rotten branch stubs or excessive swelling at base of dead branches
17 stunted or dwarfed woody stem
18 deformed or twisted or curled woody stem
19 imbedded foreign object
20 leaning
21 general discoloration
22 pale green foliage
23 yellow-green foliage
24 leaves spotted
25 damaged leaves
26 distorted foliage
27 stunted foliage
99 other damage not listed above
E-40
-------
Table E-18. Numbers of seedlings and saplings tallied by species and vigor class on microplots.
Species
balsam fir
Atlantic white-cedar
eastern redcedar
tamarack
white spruce
black spruce
red spruce
shortteaf pine
slash pine
spruce pine
longteaf pine
red pine
pitch pine
eastern white pine
loblolly pine
Virginia pine
baldcy press
pondcypress
northern white-cedar
hemlock species
eastern hemlock
maple species
Florida maple
boxelder
striped maple
red maple
sugar maple
mountain maple
total
3017
7
60
3
29
91
529
15
44
2
5
1
6
220
636
105
8
1
244
0
254
2
76
61
940
3292
1696
236
horsechestnut species 1 7
Ohio buckeye
yellow buckeye
serviceberry
pawpaw
birch species
yellow birch
sweet birch
river birch
paper birch
gray birch
American hornbeam
hickory species
bittemut hickory
pignut hickory
pecan
shagbark hickory
mockemut hickory
American chestnut
catalpa species
hackberry species
hackberry (common)
eastern redbud
yellowwood
flowering dogwood
hawthorn species
persimmon
1
20
42
40
5
367
215
0
640
194
477
6
18
4
2
148
71
1
1
7
85
0
1248
31
196
Seedlings
vigor class*
1 2
2947
6
31
1
27
91
510
8
42
2
4
1
6
196
512
57
2
1
238
-
218
0
65
9
841
2283
1536
216
7
1
12
23
15
1
351
150
-
622
110
34175
190
1
7
0
2
54
19
0
0
3
23
-
549
14
82
45
1
25
0
0
0
10
7
2
0
1
0
0
17
105
41
6
0
6
-
34
2
9
37
85
847
135
16
1
0
1
14
25
4
11
55
-
16
78
152
237
4
11
4
0
68
48
0
0
2
54
-
603
17
99
3
25
0
4
2
2
0
9
0
0
0
0
0
0
7
19
7
0
0
0
-
2
0
2
15
14
162
25
4
9
0
7
5
0
0
5
10
-
2
6
14
50
1
0
0
0
26
4
1
1
2
8
-
96
0
15
Saplings
total vioor class
1 2
400
0
23
17
18
76
145
24
33
0
9
1
13
57
325
28
1
0
36
1
58
0
0
2
85
506
118
6
0
0
0
8
3
11
59
30
2
80
49
70
88
0
2
0
2
31
2
0
2
0
19
1
231
1
12
321
-
12
11
15
62
117
13
17
-
5
0
12
39
184
9
0
-
23
1
43
-
-
0
66
262
85
4
-
-
-
5
0
9
46
12
0
61
31
29
20
-
1
-
1
8
1
-
1
-
7
0
102
1
3
33
-
8
1
0
3
3
11
14
-
4
0
1
10
106
13
0
-
9
0
9
-
-
2
11
169
17
0
-
-
-
3
3
2
4
15
1
7
7
38
57
-
0
-
1
21
0
-
1
-
10
0
106
0
9
3
1
-
3
1
0
1
3
0
2
-
0
0
0
3
34
6
1
-
1
0
2
-
-
0
1
42
3
2
-
-
-
0
0
0
1
3
1
2
2
3
11
-
1
-
0
2
1
-
0
-
2
1
23
0
0
E-41
-------
Species
American beech
ash species
white ash
black ash
green ash
honeybcust
sitverbeU species
American holy
black walnut
total
523
19
467
32
213
2
6
229
3
sweetgum 1307
yellow-poplar
magnolia species
cucumbertree
southern magnolia
sweetbay
bigteaf magnolia
apple species
Chinaberry
mulberry species
red mulberry
water tupeto
blackgum
swamp tupeto
eastern hophombeam
sourwood
redbay
American sycamore
balsam poplar
bigtooth aspen
quaking aspen
cherry, plum species
pin cherry
black cherry
516
34
21
11
185
2
8
2
5
11
51
787
79
156
300
29
1
27
16
202
7
171
695
choke cherry (common) 17
white oak
swamp white oak
scarlet oak
bluejack oak
northern pin oak
southern red oak
cherrybark oak
bear oak
turkey oak
laurel oak
blackjack oak
swamp chestnut oak
chinkapin oak
water oak
pin oak
willow oak
chestnut oak
northern red oak
post oak
black oak
live oak
dwarf post oak
519
15
96
50
2
381
8
73
19
234
41
7
17
853
4
212
244
396
137
191
29
0
Seedlings
vigor class*
1 2
384
16
394
31
92
2
6
88
0
835
254
27
14
6
107
2
8
0
3
9
41
382
18
67
171
13
1
15
9
176
2
126
427
15
232
0
33
43
1
216
2
72
6
161
29
1
4
581
1
103
62
212
76
88
18
-
115
3
68
1
110
0
0
116
1
422
225
7
7
5
72
0
0
0
1
2
10
351
58
84
116
14
0
7
6
11
4
45
233
2
261
5
59
7
1
147
6
1
10
63
9
5
9
235
3
92
158
165
55
83
9
-
3
24
0
5
0
11
0
0
25
2
50
37
0
0
0
6
0
0
2
1
0
0
54
3
5
13
2
0
5
1
15
1
0
35
0
26
10
4
0
0
18
0
0
3
10
3
1
4
37
0
17
24
19
6
20
2
-
Saplings
total vioor class
1 2
117
6
47
4
18
0
0
38
0
286
78
10
5
3
61
0
6
0
0
0
8
153
33
42
47
4
3
0
3
28
2
39
84
0
73
1
10
7
0
51
0
1
5
61
4
0
0
147
0
23
22
53
20
21
3
3
77
3
39
4
2
-
-
14
-
144
33
4
2
0
23
-
4
-
-
-
2
48
7
19
11
1
0
-
0
16
2
16
43
-
36
0
2
2
-
15
-
1
0
49
2
-
-
76
-
11
10
28
6
5
0
1
28
3
2
0
11
-
-
22
-
118
39
5
2
3
30
-
0
-
-
-
5
90
23
20
33
2
2
-
3
2
0
13
30
-
26
1
7
3
-
33
-
0
4
7
2
-
-
66
-
8
7
20
12
12
3
2
3
6
0
1
0
5
-
-
2
-
24
5
1
1
0
8
-
2
-
-
-
1
15
3
0
3
1
1
-
0
0
0
1
7
6
0
1
2
-
3
-
0
1
3
0
-
-
5
-
4
5
0
2
4
0
0
E-42
-------
Species
dwarf live oak
scrub oak
black locust
willow species
black willow
sassafras
total
48
81
36
1
17
612
American mountain-ash 31
American basswood 14
white basswood
elm species
winged elm
American elm
slippery elm
rock elm
sparkteberry
unknown / other
Total
1
75
110
54
31
2
18
138
26688
Seedlings
vigor class'
1 2
37
52
21
1
16
289
18
7
0
62
31
33
10
1
1
110
18632
11
25
13
0
1
286
13
6
1
10
71
20
19
0
9
15
6914
3
0
4
2
0
0
37
0
1
0
3
8
1
2
1
8
13
1142
Saplings
total vigor class
1 2
1
3
2
2
1
18
0
1
0
3
25
6
5
0
6
50
4438
1
1
0
1
0
3
-
1
-
1
10
2
1
-
2
31
2451
0
2
1
0
1
10
-
0
-
2
14
3
3
-
4
15
1463
3
0
0
1
0
0
5
-
0
-
0
1
0
1
-
0
4
294
•Vigor classification (Chojnacky 1991):
Class 1 plants have:
Class 2 plants have:
Class 3 plants have:
more than 1/3 of the crown in foliage
no dieback in the upper 1/2 of the crown
• more than 4/5 of the foliage normal
• any amount of crown in foliage
• any amount of dieback, anywhere
• between 1/5 and 4/5 of the foliage normal
• any amount of crown in foliage
• any amount of dieback, anywhere
• less than 1/5 of the foliage normal
E-43
•ft U.S. GOVERNMENT PRINTING OFFICE: 199« - 650-006/00219
------- |