United States Office of Research and EPA/620/R-94/010
Environmental Protection Development May 1994
Agency Washington DC 20460
Forest Health
Monitoring 1992
Annual Statistical
Summary
Environmental Monitoring and
Assessment Program
-------
EP A/620/R-94/010
May 1994
FOREST HEALTH MONITORING
1992 ANNUAL STATISTICAL SUMMARY
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
Printed on Recycled Paper
-------
FOREST HEALTH MONITORING
1992 ANNUAL STATISTICAL SUMMARY
Approved by
OA
Joseph E. Barnard Samuel A. Alexander
National Program Manager Technical Director
Forest Health Monitoring Forest Health Monitoring
-------
NOTICE
The information in this document has been funded in part by the U.S. Environmental Protection
Agency under Interagency Agreement number DW12935103-01 with the USDA Forest Service and
Interagency Agreement number DW14935509-01 with the USDI Bureau of Land Management. It has
been subject 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.
This report represents data from one year of field operations of the Environmental Monitoring
and Assessment Program (EMAP). Because the probability-based scientific design used by the EMAP
necessitates 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. Please note that this report
contains data from demonstration studies in four Standard Federal Regions. Appropriate precautions
should be exercised when using this information for policy, regulatory or legislative purposes.
This report should be cited as follows:
Forest Health Monitoring. 1994. Forest Health Monitoring 1992 Annual Statistical Summary. EPA/
/ / . U.S. Environmental Protection Agency, Washington, D.C.
v
-------
EXECUTIVE SUMMARY
Introduction
In 1990, the United States Department of Agriculture (USDA) Forest Service and the United
States Environmental Protection Agency (EPA) initiated a cooperative national program to monitor the
condition of the nation's forests. This multi-agency effort, within the framework of the EPA's
Environmental Monitoring and Assessment Program (EMAP), is called the Forest Health Monitoring
(FHM) program.
On FHM Detection Monitoring plots, a set of indicators is used to classify forest health status.
These indicators collectively represent many components of forest health and are generally responsive
to many types of stresses. The indicators are measured at various sites which are selected statistically
so that regional forest populations are represented.
This report summarizes the data that were collected as a result of the Detection Monitoring
activities. Chapter two provides a brief overview of forest health monitoring. The remaining chapters
summarize the data for tree species and stand density, tree crown condition, tree species diversity, and
air pollution bioindicator plants. An overview of indicator development, the plot network, plot design,
and data analysis procedures are presented in several appendices.
Methods
In 1992, Detection Monitoring activities were conducted in twelve eastern states: Alabama;
Connecticut; Delaware; Georgia; Maine; Maryland; Massachusetts; New Hampshire; New Jersey;
Rhode Island; Vermont; and Virginia. Detection Monitoring was also conducted in California and
Colorado. Those data, however, will be included in the 1993 Annual Statistical Summary as part of the
western data analysis.
The cumulative distribution function (CDF) methods used in the analyses provide a statistical
summary of most measurements. Tabular summaries were also prepared in some cases. Where
possible, indices have been used in the CDF analysis.
Results
Mensuration
Standard Federal Regions 1 and 2 combined have more dead trees (on a per-area basis) than
either Federal Region 3 or Federal Region 4.
This is apparently due to a noticeably larger number of dead trees (per area) across the major
forest type groupings of spruce-fir forests ami maple/beech/birch forests.
It would be premature to assume that this reflects significantly increased mortality and reduced
regeneration in these major forest type groupings without additional information on changes
over time.
Basal area per hectare shows roughly the same distribution across ail three Standard Federal
Regions.
vi
-------
Crown Assessments
The defoliation of tree crowns was examined through analysis of 3 ecological groups (species,
forest types, and crown groups) that were found on 45 or more plots within any of the four
geographical regions (SFRs 1 and 2 combined, 3, 4, and 1-4 combined).
The crown variables dieback and transparency were aggregated into a plot-level indicator that
evaluated the defoliation of the outer and inner portions of the tree crowns.
Less than 10 percent of any population for any ecological group fell within the subnominal
category, and less than 3 percent of any subnominal population proportion was found in the
poor category.
The only ecological groups that deserve a cursory investigation due to the low proportions of
populations in a subnominal or poor condition are one species (White ash) and two crown
groups (Cedar-Juniper; miscellaneous).
No forest types had any significant proportions of the population in the subnominal or poor
condition.
Species Diversity of Trees and Saplings
Species density was used as a measure of species diversity of trees and saplings in Standard
Federal Regions 1 and 2 combined, Federal Region 3, and Federal Region 4. Two species
per unit area was used as a preliminary subnominal threshold.
Standard Federal Region 4 had a significantly higher proportion of plots with subnominal tree
species density than Federal Region 3. Federal Regions 1 and 2 combined and Federal
Region 3 did not differ significantly for these proportions.
Standard Federal Regions 1 and 2 combined had a significantly higher proportion of plots with
subnominal sapling species density than either Federal Region 3 or Federal Region 4.
Air pollution Bloindlcator Plants
Field crews established biomonitoring sites, for determining the presence or absence of ozone
injury conditions, at 39 of the 212 forested plots in New England.
Based on data from 39 biomonitoring sites, an estimated 27% ± 13% of the forested population
covered by these sites showed foliar symptoms indicating the presence of ozone injury.
Most of the plots rated positive for ozone injury were located in rural areas that are known to
be contaminated by air masses moving into the region from urban-industrial areas to the south
of New England.
vii
-------
TABLE OF CONTENTS
Section/Title Page
Executive Summary vi
List of Figures .. i ix
List of Tables x
Acknowledgements xi
Authors xil
1. introduction 1-1
2. Monitoring Methods 2-1
2.1 Sampling Design and Measurements 2-1
2.2 Training, Logistics, Quality Assurance, and Data Collection 2-1
2.3 Data Analysis 2-2
3. Mensuration 3-1
3.1 Summary 3-1
3.2 Methods 3-1
3.3 Results and Discussion 3-1
4. Crown Condition 4-1
4.1 Summary 4-1
4.2 Introduction 4-1
4.3 Methods.: 4-2
4.3.1 Aggregation of Variables into Plot-level Indices 4-2
4.3.2 Thresholds 4-3
4.4 Results and Discussion 4-3
5. Species Diversity of Trees and Saplings 5-1
5.1 Summary 5-1
5.2 Introduction 5-1
5.3 Methods 5-1
5.4 Results and Discussion 5-2
6. Air Pollution Bfoindlcator Plants 6-1
6.1 Summary 6-1
6.2 Introduction 6-1
6.3 Methods 6-1
6.4 Results and Discussion 6-2
7. References 7-1
Appendix A FHM Indicator Development and Evaluation A-1
Appendix B Interpreting Cumulative Distribution Function Graphs B-1
Appendix C FHM Plot Netwoik and Plot Design C-1
vHi
-------
LIST OF FIGURES
Section/Title Page
Figure 1-1 Sites in which Detection Monitoring occurred in 1992 1-2
Figure 3-1 Cumulative distribution function for basal area per hectare in
Standard Federal Regions 1, 2, 3, and 4 combined 3-2
Figure 3-2 Cumulative distribution function for overstory dead per hectare in
Standard Federal Regions 1, 2, 3, and 4 combined 3-2
Figure 3-3 Cumulative distribution function for overstory dead per hectare in
Standard Federal Regions 1 and 2 combined 3-4
Figure 3-4 Cumulative distribution function for overstory dead per hectare in
Standard Federal Region 3 3-4
Figure 3-5 Cumulative distribution function for overstory dead per hectare in
Standard Federal Region 4 3-5
Figure 3-6 Cumulative distribution function for overstory dead per hectare for
the spruce-fir forest type group 3-5
Figure 3-7 Cumulative distribution function for overstory dead per hectare for
the maple/beech/birch forest type group 3-6
Figure 5-1 Cumulative distribution function for tree species density in
Standard Federal Regions 1 and 2 combined 5-3
Figure 5-2 Cumulative distribution function for tree species density in
Standard Federal Region 3 5-3
Figure 5-3 Cumulative distribution function for tree species density in
Standard Federal Region 4 5-4
Figure 5-4 Cumulative distribution function for sapling species density in
Standard Federal Regions 1 and 2 combined 5-5
Figure 5-5 Cumulative distribution function for sapling species density in
Standard Federal Region 3 5-5
Figure 5-6 Cumulative distribution function for sapling species density in
Standard Federal Region 4 5-6
Figure 6-1 Distribution of FHM detection plots in New England evaluated for
ozone injury in 1992 6-2
Figure B-1 Interpreting a hypothetical CDF B-2
Figure C-1 The national grid ol monitoring locations C-1
Figure C-2 The FHM field plot design C-3
ix
-------
LIST OF TABLES
Section/Title Page
Table 4-1 Species, forest types, and crown groups used in CDF analysis -
Number of plots by Standard Federal Region .. 4-4
Table 4-2 Population proportions from CDF analysis for SPECIES in
combined SFRs 1, 2, 3, and 4 4-6
Table 4-3 Population proportions from CDF analysis for FOREST TYPES in
combined SFRs 1, 2, 3, and 4 4-7
Table 4-4 Population proportions from CDF analysis for CROWN GROUPS in
Combined SFRs 1, 2, 3, and 4 4-7
Table 4-5 Population proportions from CDF analysis for SPECIES in
combined SFRs 1 and 2 4-8
Table 4-6 Population proportions from CDF analysis for CROWN GROUPS in
combined SFRs 1 and 2 4-9
Table 4-7 Population proportions from CDF analysis for SPECIES in SFR 3 4-9
Table 4-8 Population proportions from CDF analysis for CROWN GROUPS in SFR 3 .... 4-10
Table 4-9 Population proportions from CDF analysis for SPECIES in SFR 4 4-10
Table 4-10 Population proportions from CDF analysis for FOREST TYPES in SFR 4 4-11
Table 4-11 Population proportions from CDF analysis of CROWN GROUPS in SFR 4 .... 4-11
x
-------
ACKNOWLEDGEMENTS
The authors thank the external reviewers of this document, Dr. Daniel Binkley (Colorado State
University), Dr. George Furnival (Yale University), and Dr. William Smith (North Carolina State
University), for their time in reading the document and their constructive comments.
The authors gratefully acknowledge the additional review comments from William Bechtold, David
Cassell, John Hazard, Marjorie Holland, Laura Jackson, Leon Liegel, Rick Linthurst, and Robert
Loomis.
Appreciation goes to Barbara Conkting and Timothy Lewis for their contributions as editors.
Appreciation also goes to the following persons: Robert Binns for his assistance with graphics
programming; Elizabeth Eastman for her assistance with graphics and word processing; Patrick Green
for his assistance with spatial displays; and Jane Larson for her assistance with word processing.
xi
-------
Authors
(listed alphabetically by organization, location, and name)
Bureau of Land Management, Corvallls, OR
D.L. Cassell (Chapters 2, 3, 5, and Appendices B and C)
M.R. Stapanian (Chapters 1 and 5)
Bureau of Land Management, Las Vegas, NV
S. P. Cline (Chapter 5)
Bureau of Land Management, Research Triangle Park, NC
T.E. Lewis (Chapter 2, Appendix A)
North Carolina State University, Raleigh, NC
R.N. Binns (Chapter 4)
B.L. Conkling (Chapters 1,2, and Appendices B and C)
USDA Forest Service, Southeast Forest Experiment. Station, Research Triangle Parte, NC
K.W. Stolte (Chapter 4)
Duke University, Durham, NC
T.R. Stockton (Chapter 4)
University of Massachusetts, Amherst, MA
G.C. Smith (Chapter 6)
U.S. Environmental Protection Agency, Research Triangle Park, NC
S A Alexander
xii
-------
Introduction
CHAPTER 1
INTRODUCTION
In 1990, the United States Department of Agriculture (USDA) Forest Service (FS) and the
United States Environmental Protection Agency (EPA) initiated a cooperative national program to
monitor the condition of the nation's forests. This multi-agency effort, within the framework of the EPA's
Environmental Monitoring and Assessment Program (EMAP), is called the Forest Health Monitoring
(FHM) program. The FHM program is jointly managed and largely funded by the FS and EPA in
cooperation with other program participants. FHM partners provide additional financial and personnel
support and include participating State Forestry agencies, the USDI Bureau of Land Management, the
Tennessee Valley Authority, and the USDA Soil Conservation Service. Other Cooperators include
universities, and three USDI agencies - U.S. Fish and Wildlife Service, U.S. Geological Survey, and
the National Park Service. The National Association of State Foresters provides essential program
support, guidance, and assistance.
The health of the United States' forest ecosystems has attracted popular attention in recent
years due to increasing environmental concerns about air pollution, add rain, global climate change
and long-term resource management. On FHM Detection Monitoring plots, a set of indicators is used
to describe forest health status. The indicators collectively represent many components of forest health
and are generally responsive to many types of stresses. The indicators are measured at sites which
are selected statistically so that regional forest populations are represented.
The FHM program objectives include the following:
1. Estimate with known confidence, the current status, changes, and trends in selected indicators
of forest ecosystem condition on a regional basis,
2. identify associations between changes or trends in indicators of forest ecosystem condition and
indicators of natural and human-caused stressors, including changes in forest extent and
distribution, and
3. Provide information on the health of the nation's forest ecosystems in annual statistical
summaries and periodic interpretive reports for use in policy and management decisions.
In 1992, Detection Monitoring activities were conducted in twelve eastern states: Alabama;
Connecticut; Delaware; Georgia; Maine; Maryland; Massachusetts; New Hampshire; New Jersey;
Rhode Island; Vermont; and Virginia (Figure 1-1). Detection Monitoring was also conducted in
California and Colorado. Those data, however, will be included in the 1993 Annual Statistical Summary
as part of the western data analysis.
1-1
-------
Introduction
"9L
m
i^ir
Regions 1&2
Region 3
.7*-
Ragion 4
Ffgura 1-1. Situ in which ~¦taction Monitoring occurad in 1992.
Standard Fadarai Ragiona ara autiinad by haavy linaa.
1-2
-------
Introduction
The Standard Federal Regions used in this report are also indicated in Figure 1-1. Standard
Federal Regions 1 and 2 combined included plots from Maine, Vermont, New Hampshire, Rhode
Island, Connecticut, Massachusetts, and New Jersey. These plots are in the Northeastern Highlands
and Coastal Zone Ecoregions (Omernik 1987). Standard Federal Region 3 included plots from Virginia,
Maryland and Delaware. These plots are located predominantly in the Northern Piedmont and
Mid-Atlantic Coastal Plain Ecoregions. Standard Federal Region 4 included plots from Georgia and
Alabama, predominantly in the Southeastern Plain and Southern Coastal Plain Ecoregions.
This report summarizes the data that were collected as a result of the Eastern 1992 Detection
Monitoring activities. Chapter two provides a brief overview of forest health monitoring and the
methods used to collect data on a plot network. The remaining sections summarize the data for tree
species and stand density, tree crown condition, tree species diversity, and air pollution bioindicator
plants. Tree and sapling damage data were also collected, however, the damage indicator will be
included in the 1993 Annual Statistical Summary. Additional indicators will be added to Detection
Monitoring activities as the FHM program continues to develop and mature.
1-3
-------
Monitoring Methods
CHAPTER 2
MONITORING METHODS
This chapter provides a brief overview of the Forest Health Monitoring (FHM) procedures used
in 1992. An overview of indicator development in FHM is presented in Appendix A. The FHM plot
network, plot design, and data analysis procedures are discussed in Appendices B and C. Additional
information about sampling methodologies and statistical analyses used by the Environmental
Monitoring and Assessment Program (EMAP) and FHM may be found in Overton et al. (1990) and
Conkling and Byers (1992).
2.1 Sampling Design and Measurements
The EMAP emphasizes the need for statistical rigor at the Standard Federal Region (SFR) and
the Ecologically Meaningful Regional (EMR) scales of plot network. However, to evaluate whether or
not regional estimates can be obtained with known confidence, the FHM statistical approach requires
that measurements at the plot level meet well-defined measurement quality objectives and that
measurements adequately address most of the indicator development criteria as outlined in Knapp et.
al. (1990).
2.2 Training, Logistics, Quality Assurance, and Data Collection
All FHM field crew members completed a rigorous training session to become certified in each
indicator for which they collected data. The training sessions included classroom and field instruction
and a certification examination. Pre-training workshops for the trainers were held prior to the field crew
training so that methods were taught consistently across all regions.
Quality assurance (QA) is a vital part of the FHM program. The OA procedures followed in
1992 are documented in the 1992 FHM Quality Assurance Project Plan.
Portable data recorders (PDRs), which are compact, hand-held, field computers, were used by
FHM field crews for data collection. An interagency information management team of data managers
and analysts, computer programmers, and crew persons have written and tested the necessary
programs. The field data collection system is continually updated and improved by the interagency
team. The PDRs provide faster data collection and processing as well as improved data quality and
real-time data verification.
2-1
-------
Monitoring Methods
2.3 Data Analysis
In this report, CDFs were generated for the data. The estimated CDF, at a value x, is the
estimated proportion of the areal extent of the real resource being examined which has values less than
or equal to x. The Horvitz-Thompson estimate of the CDF at x is given by (Horvitz and Thompson
1952):
£7
A ^ '
Px =
£7
M */
where
y, ¦ 1 if the variable is not greater than x, and 0 otherwise
itj » inclusion probability for plot i
n = number of plots in this subset of the data.
This calculation can be viewed as a weighted average of the observations, wherein the weights are
equivalent to the area of forest that any selected ground plot represents.
The estimate of the standard error for this proportion is based on standard estimation
guidelines forthe EMAP program (Overton et al. 1990). The approximate 90 percent confidence limits
for the CDFs were calculated by assuming that the CDF estimates could be reasonably approximated
by normal distributions. The confidence regions were obtained from the estimated CDF value, plus or
minus 1.645 times the estimated standard error of the CDF value. When the estimated proportion was
near zero or one, a truncated normal distribution was used because confidence intervals for a
proportion should not extend above one or below zero. Additional information about how to interpret
a CDF is found in Appendix B.
2-2
-------
Mensuration
CHAPTER 3
MENSURATION
3.1 Summary
Standard Federal Regions 1 and 2 combined have more dead trees (on a per-area basis) than
either Federal Region 3 or Federal Region 4.
This is apparently due to a noticeably larger number of dead trees (per area) across the major
forest type groupings of spruce-fir forests and maple/beech/birch forests.
It would be premature to assume that this reflects significantly increased mortality and reduced
regeneration in these major forest type groupings without additional information on changes
over time.
Basal area per hectare shows roughly the same distribution across all three Standard Federal
Regions.
3.2 Methods
The basal area was computed as the sum of the basal areas (in mz) for individual trees (DBH
> 12.7 cm) in a condition class. Overstory dead was computed as the count of all standing dead
(snags) and fallen dead (dead and down) trees (DBH > 12.7 cm) in a condition class. Overstory dead
was normalized to a per-hectare basis by multiplying by the appropriate expansion factor.
For plots which were not completely in a forested land use, the proportion of the plot on land
classified as forested was calculated. For these plots, the normalization term was the usual expansion
factor divided by the proportion of the condition class on forested land. In this way, for a plot that was
50 percent forested land, the values of basal area and overstory dead were corrected to a per hectare
basis.
For those plots with more than one forested condition class, the plot-level versions of the
reported variables were obtained by adding across condition classes to obtain summary values of the
basal area, overstory dead, and proportion of area. The per-hectare estimates were then obtained for
the plot, the same as they were calculated for each individual condition class.
3.3 Results and Discussion
Figures 3-1 and 3-2 show the distribution of basal area per hectare and overstory dead per
hectare for the entire forest population sampled by the FHM program.
3-1
-------
Mensuration
1.00
0.80
o 0.60
¦3 0.40
0.00
0.00 13.00 26.00 39.00 52.00 65.00
Basal Area, sq m / ha
Figure 3-1. Cumulative distribution function for basal area per
hectare in Standard Federal Regions 1, 2, 3, and
4 combined.
1.00
0.80
S 0.60
J 0.40
0.00
0.00 78.00 156.00 234.00 312.00 390.00
Overstory Dead per Hectare
:igure 3-2. Cumulative distribution function for overstory deac
per hectare in Standard Federal Regions 1, 2, 3,
and 4 combined.
3-2
-------
Mensuration
No thresholds are drawn on these COFs, because these variables can be difficult to interpret
without auxiliary data. When these plots are visited again, new estimates of these variables will be
made. It will then be feasible to calculate basal area growth (the change in basal area per hectare)
and mortality rate (the change in counts of overstory dead per hectare). These new variables will
provide directly interpretable indicators of forest ecosystem processes.
The CDF for basal area per hectare within each of the Standard Federal Regions (SFRs) looks
much the same as that of the entire population. Across each region, a similar pattern of stocking
exists. As each region showed the same pattern, additional CDFs by Standard Federal Region were
not included.
Figure 3-2 shows the distribution of overstory dead for the three-region area. Note that 45
percent of the population (90 percent confidence interval of 39 percent to 51 percent) had no overstory
dead in an area the size of the sampled part of the FHM plot. Most areas of this size would be
expected to have some standing or fallen dead. This condition is not bad in itself; the presence of
snags is important to many animals, and fallen trees serve as nurse logs and wildlife habitat.
The distribution of overstory dead per area differed noticeably between SFRs. Figures 3-3
through 3-5 illustrate these differences. Figure 3-3 shows the distribution of this variable for SFRs 1
and 2 combined, while Figure 3-4 shows the same variable for SFR 3, and Figures 3-5 shows this for
SFR 4. Note that the CDFs for SFRs 3 and 4 are fairly similar, showing noticeably less overstory dead
than the CDF for SFR 1 and 2 combined.
In SFR 1 and 2 combined, only 17 percent (confidence interval: 11 to 25 percent) of the
forested areas were without any overstory dead. More of the population had large numbers of
overstory dead.
Examination of CDFs for the major forest type groupings revealed that the spruce-fir forest type
grouping and the maple/beeclVbirch forest type grouping showed similar patterns (Figures 3-6 and 3-7).
The typical areas in each forest type grouping showed larger numbers of overstory dead. Further
examination of additional CDFs showed no patterns due to presence of disturbance, type of
disturbance, or stand origin.
These patterns In the spruce-fir forest type may be related to patterns previously observed in
spruce-fir forests of the eastern U.S. However, it would be premature to assume that the counts of
overstory dead are equivalent to unbiased data on the process of forest mortality. Overstory dead
depends not only on the mortality rate of trees, but also on the rates of decay and fall of the dead
trees. These rates can be affected by tree species, age classes, climate, and cause of death. The
data reported here only provide an indication of regional patterns that warrant further study. The next
statistical summary will address this problem by providing data on mortality rate instead.
3-3
-------
Mensuration
1.00
0.80
/j/s'''
if,-'
&
(J;
i
|0.60
m
i*4T j
- Ik
it
I
rTl
;jT
§0.40
' ;/ :
E
;(
3
o
:/
'/
0.20
ft*
r
/
0.00
0.00 78.00 156.00 234.00 312.00 390.00
Overstory Dead per Hectare
igure 3-3. Cumulative distribution function for overstory dead
per hectare in Standard Federal Regions 1 and 2
combined.
1.00
0.80
¦80.60
0.00
0.00 52.00 104.00 156.00 208.00 260.00
Overstory Dead per Hectare
Figure 3-4. Cumulative distribution function for overstory dead
per hectare in Standard Federal Region 3.
3-4
-------
Mensuration
1.00
0.80
0.20
0.00
0.00 24.00 48.00 72.00 96.00 120.00
Overstory Dead per Hectare
Figure 3-5. Cumulative distribution function for overstory dead
per hectare in Standard Federal Region 4.
1.00
0.80
iS 0.40
0.00
0.00 72.00 144.00 216.00 288.00 360.00
Overstory Dead per Hectare
Figure 3*6. Cumulative distribution function for overstory dead
per hectare for the spruce-fir forest type group.
3-5
-------
Mensuration
1.00
0.80
3
-g 0.60
0.20
0.00
0.00 81.00 162.00 243.00 324.00 405.00
Overstory Dead per Hectare
:igure 3-7. Cumulative distribution function for overstory dead
per hectare for the maple/beech/birch forest type
group.
3-6
-------
Crown Condition
CHAPTER 4
CROWN CONDITION
4.1 Summary
The defoliation of tree crowns was examined through analysis of 3 ecological groups (species,
forest types, and crown groups) that were found on 45 or more plots within any of the four
geographical regions (SFRs 1 and 2 combined, 3,4, and 1-4 combined).
The crown variables dieback and transparency were aggregated into a plot-level indicator that
evaluated the defoliation of the outer and inner portions of the tree crowns.
Less than 10 percent of any population for any ecological group fell within the subnominal
category, and less than 3 percent of any subnominal population proportion was found in the
poor category.
The only ecological groups that deserve a cursory investigation due to the low proportions of
populations in a subnominal or poor condition are one species (White ash) and two crown
groups (Cedar-Juniper; miscellaneous).
No forest types had any significant proportions of the population in the subnominal or poor
condition.
4.2 Introduction
Morphological determinations of crown 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 about the growth, vitality, and appearance of trees.
Information was collected about the crown position, ratio, density, transparency, dieback, and diameter.
A brief description of the crown variables is presented to give a general impression of the nature of the
variables. Additional information about these variables can be found in previous reports and white
papers (Stolte et al. 1992; Anderson and Millers 1992; Millers et al. 1992).
4-1
-------
Crown Condition
Crown position refers to the relative position of the tree crown in a stand of trees. The five
crown positions are: open grown (crown exposed); dominant (crown above canopy)codominant
(crown the same height); intermediate (crown below); and suppressed (crown far below). Crown
position can be used to stratify analyses; the crown condition of open grown, dominant, and codominant
trees can be compared to the crown condition of intermediate and suppressed trees. All plot-level
cumulative distribution function (CDF) analyses were calculated on crown variables aggregated from
all five crown positions. Crown ratio is the percentage of the entire tree bole that supports living,
foliated canopy. Crown density is the two-dimensional appearance of fullness of the crown when ideally
viewed against the sky. It basically estimates how much of the sky is blocked from view by the upper
tree bole, branches, foliage, and any reproductive structures (Belanger 1991). Crown transparency
refers to the amount of sunlight that is blocked by only the foliated portions of the tree crown. 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. Crown diameter is the average of the widest part of the
crown and the 90° perpendicular axis to the widest part of the crown.
4.3 Methods
The Crown Defoliation Indicator developed for the 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 [CDI], composed of dieback and transparency).
4.3.1 Aggregation of Variables Into Plot-Level Indices
The data were post-stratified by species, forest type, and crown group (e.g., long needle pines,
[Millers et al. 1992]). Data analysis was performed using CDFs based upon plot-level averages which
were weighted using basal area. Populations described by CDFs (forest types, spedes, crown groups)
were delineated into categories of optimal, nominal, subnominal, and poor by thresholds estimated from
information available from the scientific literature or expert opinion. These thresholds were based on
the relationship between crown condition and growth or survivorship, or in some cases, aesthetics.
Once the population was delineated into the four crown condition categories (optimal, nominal,
subnominal, or poor), proportions of populations that fell into each category were identified.
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
4-2
-------
Crown Condition
variables are a reflection of the tree foliage condition, both within the crown (transparency) and in the
sun-exposed outer crown foliage. At this point in time, the variables in the COI have equal weights:
CDI - (Transparency + Dieback) / 2 (1)
4.3.2 Thresholds
Thresholds delineate populations into various condition classes and can serve as standards
to evaluate the distribution of the indicator in the population. In this way they are used as a yardstick
to measure changes in COFs. Thresholds delineate proportions of populations that are representative
of a species and are relatively comparable across species. A detailed discussion on the use of
thresholds in crown assessments can be found in Stolte et al. (1992). When analyzing the 1992 data,
we used three thresholds that are indirectly based on the relationship of the crown variables to tree
growth or survival (Anderson et ai. 1992):
Nominal/Subnominal threshold: The threshold where it is felt that the tree(s) may begin to be
stressed to a degree that is detrimental and ultimately not sustainable.
Optimal threshold: The point or zone where a nominal portion of the population is considered
to be of superior condition and highly sustainable.
Poor threshold: The point or zone where the subnominal portion of the population is
considered to be in serious condition, and the probability of mortality is high. These population
proportions typically are highly vulnerable to natural forest stressors and are not sustainable.
4.4 Results and Discussion
The COFs were generated for those ecological group strata found on more than 50 plots. The
most common strata (species, forest types, and crown groups) are listed in Table 4-1.
4-3
-------
Crown Condition
TABLE 4-1. SPECIES, FOREST TYPES, AND CROWN GROUPS USED IN CDF ANALYSIS -
NUMBER OF PLOTS BY STANDARD FEDERAL REGION
Species, Forest Type, or
SAF
Number of
Plots
Crown Group
Number
SFRs 1,2,3,4
SFR1&2
SFR3
SFR4
SDecies:
Balsam Fir
12
102
102
*
~
Red Spruce
97
89
89
*
*
Shortleaf Pine
110
62
*
*
49
Eastern White Pine
129
87
75
*
*
Loblolly Pine
131
158
*
131
Northern White Cedar
241
47
47
*
Eastern Hemlock
261
59
59
*
~
Red Maple
316
273
143
63
67
Sugar Maple
318
89
80
*
Paper Birch
375
90
90
#
~
Hickory sp.
400
89
*
*
61
American Beech
531
87
63
White Ash
541
58
54
*
Sweetgum
611
148
117
Yellow Poplar
621
115
#
48
65
Black Gum
693
85
*
*
57
Black Cheny
762
59
*
*
White Oak
802
133
53
63
Scarlet Oak
806
55
*
~
*
Southern Red Oak
812
66
*
*
49
Water Oak
827
109
96
Chestnut Oak
832
57
*
~
*
Post Oak
835
49
*
*
«
Northern Red Oak
833
91
47
~
*
Black Oak
837
45
*
*
*
(Continued)
'The number of plots in the region was less than 45, and no CDF analysis was performed.
4-4
-------
Crown Condition
Species, Forest Type, or
SAF
Number of Plots
Crown Group
Number
SFRs 1,2,3,4
SFR1&2
SFR3
SFR4
Forest Tvoe:
Loblolly Forest
310
69
*
*
54
White Oak-Red Oak-Hickory
530
73
*
Crown Gtoud:
Spruce-Fir
1
115
115
Cedar-Juniper
2
79
46
«
*
Pine-Long Needle
3
214
*
166
Hemlock
5
60
59
*
*
Hardwoods-Large Leaf-
Closed Canopy
6
402
169
80
153
Hardwoods-Large Leaf-
Open Canopy
7
123
*
52
65
Hardwoods-Small Leaf
8
378
177
68
133
Hardwoods-Compound Leaf
. 9
145
61
69
Hardwoods-Oak
10
299
51
87
161
Miscellaneous Species1
11
242
149
*
56
*The number of plots in this region was less than 45, and no CDF analysis was performed.
'Miscellaneous is an assemblage of diverse species that do not fit into any of the other crown groups.
The remainder of the tables (Tables 4-2 to 4-11) present the results from the CDF analysis that
was performed on the various strata. The numbers in parentheses represent the lower and upper
confidence bounds on the estimate of the proportion of the population contained within a given category
(optimal, nominal, etc.). The optimal and poor categories are subsets of the nominal and subnominal
categories, respectively.
A cursory evaluation of the results of the CDF analysis indicates that most tree species, all
forest types, and most crown groups are in good condition for the CDI, with high percentages of the
populations In a nominal or optimal classification. A few species and crown groups appear to have
relatively high percentages of the population in a subnominal or poor condition. There is particular
concern about any ecological group that has a relatively high subnominal (> 10 percent) or poor (> 0
percent) percentage. These values identify the thresholds of concern, providing early warning signals
of potential forest health problems.
4-5
-------
Crown Condition
The tables for species, forest types, and crown groups are organized by Standard Federal
Region. The species, forest types, and crown groups that warrant further consideration based on
relatively high proportions of the population in the subnominal or poor classification are listed after each
table.
Crown CDF Analysis in Standard Federal Regions 1,2,3, and 4 Combined (East Region)
Subnominal
Nominal
Optimal
1.00
0.99(0.87 1.00^ 0.01
1-00
1.00
0.99 <0.92 1.00) ::;;::.0.01
oP tiie^numencal ot data Elected
ireshoid is
TABLE 4-2. POPULATION PROPORTIONS FROM CDF ANALYSIS FOR SPECIES IN COMBINED
SFRs 1, 2,3, AND 4 (Numbers represent percentage of population classified as poor,
subnominal, nominal, or optimal. Numbers in parentheses are lower and upper bounds
of the CDF 90% confidence interval.)
Specie?
Balsam Fir 0.
Red Spruce 0.
Shortleaf Pine 0.
Eastern White Pine 0.
Loblolly Pine 0.
Northern White Cedar 0.
Eastern Hemlock 0.
Red Maple 0.
Sugar Maple 0.
Paper Birch 0.
Hickory sp. 0.
American Beech 0.
White Ash 0.
Sweetgum 0.
Yellow Poplar 0.
Black Gum 0.
Black Cherry 0.
White Oak 0
Scarlet Oak 0
Southern Red Oak 0.
Water Oak 0
Chestnut Oak 0
Northern Red Oak 0
Post Oak 0
0
93 (0.80 0.99)
91 (0.76 0.99)
77 (0.62 0.91)
90 (0.76 0.98)
95 (0.85 0.99)
64 (0.48 0.81)
87 (0.71 0.98)
84 (0.77 0.92)
89 (0.75 0.98)
65 (0.52 0.77)
99 (0.88 1.00)
86 (0.71 0.97)
76 (0.60 0.92)
95 (0.84 0.99)
99 (0.86 1.00)
96 (0.88 0.99)
70 (0.52 0.86)
97 (0.85 0.99)
69 (0.54 0.85)
91 (0.75 0.98)
93 (0.80 0.99)
96 (0.77 0.99)
81 (0.68 0.94)
96 (0.76 0.99)
00 (0.00 0.03)
00 (0.00 0.05)
01 (0.00 0.06)
00 (0.00 0.05)
4-6
-------
Crown Condition
The species within the threshold of concern in the combined SFRs 1, 2, 3, and 4 based upon
a relatively high proportion of the population in the poor classification is:
White ash: 0.01 (0.00, 0.06).
TABLE 4-3.
POPULATION PROPORTIONS FROM CDF ANALYSIS FOR FOREST TYPES IN
COMBINED SFRs 1, 2, 3, AND 4 (Numbers represent percentage of population
classified as poor, subnominal, nominal, or optimal. Numbers in parentheses are lower
and upper bounds of the CDF 90% confidence interval.)
Species Optimal
Loblolly Pine 0.97 (0.81 0.99)
White Oak-Red 0.94 (0.78 0.99)
Oak-Hickory
* This threshold is outside of the numerical range of data collected.
No forest types are within the threshold of concern in combined SFRs 1, 2, 3, and 4.
TABLE 4-4.
POPULATION PROPORTIONS FROM CDF ANALYSIS FOR CROWN GROUPS IN
COMBINED SFRs 1, 2, 3, AND 4 (Numbers represent percentage of population
classified as poor, subnominal, nominal, or optimal. Numbers in parentheses are lower
and upper bounds of the CDF 90% confidence interval.)
Species
Spruce-Fir
Cedar-Juniper
Pine-Long Needles
Hemlock
Hardwoods-Closed
Canopy\Large Lvs.
Hardwoods-Open
Canopy\Large Lvs.
Hardwoods-Small
Lvs.
Hardwoods-Cmpd.
Lvs.
Hardwoods-Oaks
Miscellaneous
Optimal
0.93 (0.81 0.99)
0.74 (0.61 0.86)
0.91 (0.83 0.98)
0.87 (0.71 0.98)
0.88 (0.81 0.94)
0.99 (0.86 1.00)
0.78 (0.72 0.84)
0.97 (0.85 0.99)
0.91 (0.84 0.97)
0.84 (0.76 0,92)
Poor
(0.01 0.17)
(0.00 0.02)
(0.00 0.08)
(0.00 0.11)
* This threshold is outside of the numerical range of data collected.
4-7
-------
Crown Condition
The crown groups within the threshold of concern in combined SFRs 1, 2, 3, and 4 based upon
relatively high proportions of the population in the poor classification, are:
Cedar-juniper: 0.02(0.01,0.17)
Miscellaneous: 0.01 (0.00,0.11).
Crown CDF Analysis in Standard Federal Regions 1 & 2 Combined
TABLE 4-5. POPULATION PROPORTIONS FROM CDF ANALYSIS FOR SPECIES IN COMBINED
SFRs 1 AND 2 (Numbers represent percentage of population classified as poor,
subnominal, nominal, or optimal. Numbers in parentheses are lower and upper bounds
of the CDF 90% confidence interval.)
Species
Optimal
Nominal Sal
Poor
Balsam Fir
Red Spruce
0.93 (0.80 0.99)
0.91 (0.76 0.99)
1.00
Mmm
*
*
Eastern White Pine
0.90 (0.76 0.98)
1.00
*
Northern White Cedar
0.64 (0.48 0.81)
0.99^0.92/1.00) 0.01 (0-00 0,08), -
~
Eastern Hemlock
0.87 (0.71 0.98)
t.oo - 1 11 bbibbim -
Red Maple
0.84 (0.77 0.92)
0.99 {0.90 1.00), O.Ot (0;00:fcG9) -
0.00 (0.00 0.03)
Sugar Maple
0.89 (0.75 0.98)
1 (Wl /G 9^11 00) 0 00 tO 00 0 07V
*
Paper Birch
0.65 (0.52 0.77)
0.98 {0.83 0.99) 0.02(0,01^7)
0.00 (0.00 0.05)
American Beech
0.86 (0.71 0.97)
0.99 (0.82 0.99) 0.01 (0.01 0*18)
' ' w ' ' ''M* *A. / W'.
^ ; \ . j ,
*
White Ash
0.76 (0.60 0.92)
0.01 (0.00 0.06)
Northern Red Oak
0.78 (0.65 0.92)
0.99 {0.85 1.00), , 0.01s(OJ)0,0;i%'
* This threshold is outside of the numertcal range of data collected.
The species within the threshold of concern in the combined SFRs 1 and 2 based on the
relatively high proportion of the population in the poor classification, is:
White ash: 0.01 (0.01, 0.17).
4-8
-------
Crown Condition
TABLE 4-6. POPULATION PROPORTIONS FROM CDF ANALYSIS FOR CROWN GROUPS IN
COMBINED SFRs 1 AND 2 (Numbers represent percentage of population classified
as poor, subnominal, nominal, or optimal. Numbers in parentheses are lower and
Species
Optimal
Nomina? SubnoninaJ
Poor
Spruce-Fir
0.93 (0.81 0.99)
, : ... . .
4 AFl *
1 ,Uv
*
Cedar-Juniper
Hemlock
Hardwoods-Closed
\Large Lvs.
0.74 (0.61 0.86)
0.87 (0.71 0.98)
0.88 (0.81 0.94)
0.9& {0.83 0.99) 0.02/OJJ1 0,17)
*1 00 *
0.99 {0.92 1.00) 0.01 (0-00 0.08)
W? - .. * / " '
0.02 (0.01 0.17)
*
0.00 (0.00 0.02)
Hardwoods Small
Lvs.
0.78 (0.72 0.84)
"0+09 \0*91 1 <00) 0*01 [u+uO 0*09}
0.00 (0.00 0.08)
Hardwoods Oaks
0.91 (0.84 0.97)
1 00 {Q 93 1001 0 00 (0 00 0.07)
0.97 {0.88 1.00) 0.03 (0.00 0.12)
Miscellaneous
0.84 (0.76 0.92)
0.01 (0.00 0.11)
This threshold is outside of the numerical range of data collected.
The crown groups within the threshold of concern in combined SFRs 1 and 2 based on the
relatively high proportion of the population in the poor classification, are:
Cedar-Juniper: 0.02(0.01,0.17)
Miscellaneous: 0.01 (0.00,0.11).
Crown CDF Analysis in Standard Federal Region 3
TABLE 4-7. POPULATION PROPORTIONS FROM CDF ANALYSIS FOR SPECIES IN SFR 3
(Numbers represent percentage of population classified as poor, subnominal, nominal,
or optimal. Numbers in parentheses are lower and upper bounds of the CDF 90%
confidence interval.)
Species Optimal
. ¦ ¦ ¦
WzMM sffTiiTTiTil 1 ti iRmmgM
Poor
Red Maple 0.84 (0.77 0.92)
Yellow Poplar 0.99 (0.86 1.00)
White Oak 1.0
...... :
0.00 (0.00 0.03)
* This threshold is outside of the numerical range of data collected.
No species are within the threshold of concern in SFR based upon
population in the subnominal or poor classification.
of the
4-9
-------
Crown Condition
TABLE 4-8.
POPULATION PROPORTIONS FROM CDF ANALYSIS FOR CROWN GROUPS IN
SFR 3 (Numbers represent percentage of population classified as poor, subnominal,
nominal, or optimal. Numbers in parentheses are lower and upper bounds of the CDF
90% confidence interval.)
Species Optimal
Nominal Subnominal Poor
Hardwoods-Closed 0.88 (0.81 0.94)
Canopy\Large Lvs.
Hardwoods-Open 0.99 (0.86 1.00)
CanopyVLarge Lvs.
Hardwoods-Small Lvs. 0.78 (0.72 0.84)
Hardwoods-Cmpd. 0.97 (0.85 0.99)
Lvs.
Hardwoods-Oaks 0.91 (0.84 0.97)
- "'"A"
0,99 (0.92 1.00) 0.01 {0,00 0.08) 0.00 (0.00 0.02)
0.99 (0.91 1.00) 0.01 (0.00 0.09) 0.00 (0.00 0.08)
1.00
* This threshold is outside of the numerical range of data collected.
No crown groups are within the threshold of concern in SFR 3 based on the proportion of the
population in the subnominal or poor classification.
Crown CDF Analysis in Standard Federal Region 4
TABLE 4-9. POPULATION PROPORTIONS FROM CDF ANALYSIS FOR SPECIES IN SFR 4
(Numbers represent percentage of population classified as poor, subnominal, nominal,
or optimal. Numbers in parentheses are lower and upper bounds of the CDF 90%
confidence interval.)
Species
Optimal
' Nominal '
Subnorrinai'
Poor
Shortleaf Pine
0.77 (0.62 0.91)
O.OtffcOO 033}'
#
Loblolly Pine
0.95 (0.85 0.99)
*
Red Maple
0.84 (0.77 0.92)
A OQ'/A
0.99 (U.9U T,00}
O.Ot (0.00 0.09),
0.00 (0.00 0.03)
Hickory sp.
0.99 (0.88 1.00)
I AM)
1 -
*
Sweetgum
0.95 (0.84 0.99)
1.00 (0.931.00}
0-00(0,00 0.07)
*
Yellow Poplar
Black Gum
0.99 (0.86 1.00)
0.96 (0.88 0.99)
; ¦
~
*
White Oak
0.97 (0.85 0.99)
1.00 <033 T.D0),
0.00(0.00 0.07)
*
Southern Red Oak
0.91 (0.75 0.98)
1.00/
mm
Water Oak
0.93 (0.80 0.99)
too-
*
* This threshold is outside of the numerical range of data collected.
4-10
-------
Crown Condition
No species are within the threshold of concern in SFR 4 based upon proportions of the
population in the subnominal or poor classification.
TABLE 4-10 . POPULATION PROPORTIONS FROM CDF ANALYSIS FOR FOREST TYPES IN SFR
4 (Numbers represent percentage of population classified as poor, subnominal,
nominal, or optimal. Numbers in parentheses are lower and upper bounds of the CDF
90% confidence interval.)
Species Optimal
Sli. Nominal \ ' Subnominal
Poor
Loblollv Pine 0.97 (0.81 0.99)
* This threshold is outside of the numerical
ange of data collected.
No forest types are within the threshold of concern in SFR 4 based on the population
proportions in the subnominal or poor classification.
TABLE 4-11. POPULATION PROPORTIONS FROM CDF ANALYSIS OF CROWN GROUPS IN SFR
4 (Numbers represent percentage of population classified as poor, subnominal,
nominal, or optimal. Numbers in parentheses are lower and upper bounds of the CDF
90% confidence interval.)
'
Species Optimal
Poor
Pine-Long Needles 0.91 (0.83 0.98)
Hardwoods-Closed 0.88 (0.81 0.94)
CanopyVLarge Lvs.
Hardwoods-Open 0.99 (0.86 1.00)
CanopyVLarge Lvs.
Hardwoods- 0.78 (0.72 0.84)
Small Lvs.
Hardwoods- 0.97 (0.85 0.99)
Cmpd. Lvs.
Hardwoods-Oaks 0.91 (0.84 0.97)
Miscellaneous 0.84 (0.76 0.92)
*
0.00 (0.00 0.02)
0.00 (0.00 0.08)
*
~
0.01 (0.00 0.11)
* This threshold is outside of the numerical range of data collected.
The crown group within the threshold of concern in SFR 4 based upon a relatively high
proportion of the population in the poor classification, is:
Miscellaneous: 0.01(0.00,0.11).
4-11
-------
Crown Condition
The determination of crown health or vigor has at least three factors: (1) the condition of the
resource at the time of the evaluation; 2) our ability to accurately and precisely quantify the condition
of the resource; and (3) the anthropogenic values ascribed to the meaning of the estimated condition.
The first factor is determined by the sampling design and the relevance of the indicators used. The
second factor involves the methods used to quantify the indicators, and the measurement error
associated with collecting the data. The third factor depends on our ability to analyze the data, and
the knowledge of humans to define and quantify a healthy tree, stand, or forest.
For this report, the CDI thresholds were set based on the general values developed by
Anderson et. al. (1992). Differences in this approach are discussed in the Forest Health Monitoring
Southeast Loblolly/Shortleaf Pine Demonstration Interim report. In this report a single value for each
threshold is used as a "yardstick" marker to assess changes in the population distribution of the Crown
Defoliation Indicator. At this time the detailed information necessary to estimate a "threshold range"
has not been obtained, and it is not readily apparent that this might be a more precise way of
evaluating population proportions. The need to obtain more information about the relationship between
tree crown condition, tree growth (shoots and roots), and survivability is recognized.
In this report it has been determined that there was no regional defoliation of common tree
species, forest types, or crown groups. More specifically, none of the ecological groups on a regional
level had major defoliation of both the outer and inner portions of the crown. Only a few species and
forest types had small proportions of the population in a subnominal or poor classification. White ash
was the only species within the threshold of concern.
4-12
-------
Specie* Diversity of Trees and Saplings
CHAPTER 5
SPECIES DIVERSITY OF TREES AND SAPLINGS
5.1 Summary
Species density was used as a measure of species diversity of trees and saplings in Standard
Federal Regions 1 and 2 combined, Federal Region 3, and Federal Region 4. Two species
per unit area was used as a preliminary subnominal threshold.
Standard Federal Region 4 had a significantly higher proportion of plots with subnominal tree
species density than Federal Region 3. Federal Regions 1 and 2 combined and Federal
Region 3 did not differ significantly for these proportions.
Standard Federal Regions 1 and 2 combined had a significantly higher proportion of plots with
subnominal sapling species density than either Federal Region 3 or Federal Region 4.
5.2 Introduction
Conservation and maintenance of animal and plant species diversity is an important public
value. The Forest Health Monitoring Program (FHM) plans to monitor regional changes in habitat
structure, and the corresponding status and trends of plant species diversity in forests of the United
States (U.S. EPA 1990). Habitat structure, including species composition and age structure of the
vegetation, strongly influences wildlife diversity (e.g., Wlilson 1974; Harper 1977; Deuser and Shugart
1978; August 1983). Many vegetation or habitat indicators could be measured, depending on the
objective and scale of interest. A complete set would permit assessing the heterogeneity and complexity
of vegetation and habitat structure. This section's objective is to characterize tree and sapling species
diversity for the 1992 field measurements. A well-known indicator of species diversity is used.
5.3 Methods
Data for trees and saplings were separated into three Standard Federal Regions. Separate
analyses in each region were performed for (1) trees > 12.7 cm diameter breast height (DBH)
measured on forested subplots and (2) sapflngs < 12.7 cm DBH and > 30 cm in height
A simple index of species diversity is the number of species present on a plot. However,
species counts are comparable only when based on the same sample area. For this reason, species
density was used in the analysis. For trees, species density was the number of species per 1/iS ha
(the total area of a plot). For saplings, species density was the number of species per 55 m2 (the total
5-1
-------
Species Diversity of Trees and Saplings
area of the microplots). Clearly, the number of species on a plot (S) and species density were equal
for those plots that were 100 percent forested. For each plot, species density (R) was calculated as
R = S/X
where X =» the proportion of the subplots (for trees) or microplots (for saplings) that were forested.
Although the relationship between the number of species and area is often nonlinear and dependent
on habitat heterogeneity and isolation (e.g., MacArthur and Wilson 1963,1967; Simpson 1964; Cook
1969; Brown 1971), previous study suggests that the linear modei is an excellent first-order
approximation for these plots.
The regional status of tree and sapling species density was assessed by using cumulative
distribution functions (CDFs). For each SFR (1 and 2 combined, 3, and 4), a CDF was constructed for
R. Each CDF, therefore, characterizes the regional variation in species density.
In previous work two species were selected per plot as a preliminary subnominal-nominal
threshold for trees (Cline 1992). This threshold is extended to < 2 species per unit area for both trees
and saplings. The condition, number, and species composition of saplings is one measure of forest
regeneration. Therefore, extending this threshold to saplings is reasonable. Zero species means that
a site is devoid of trees and/or saplings. A predominance of this condition for trees and/or saplings on
a regional scale is clearly not acceptable for forests. One species indicates a monoculture. From the
standpoint of biological diversity, this condition is obviously species-poor. Although monocultures
provide important products for American life, there would be cause for concern if such sites were
widespread in a region, even though the tree species may differ among sites. Many forest types in the
United States are characterized by the dominance of two or three tree species (e.g., spruce-fir,
oak-hickory, beech-maple). Furthermore, presence of four to six tree species is not uncommon in
American forests. A threshold value greater than one and less than four species makes sense from
an ecological standpoint. Therefore, a preliminary selection of a threshold value of < 2 species per unit
area is reasonable and conservative.
5.4 Results and Discussion
Most plots were 100 percent forested. Therefore, species density was identical to the number
of species found on the plots in most cases. The CDFs for tree species density are shown in Figures
5-1 through 5-3. The proportion of plots in which tree species density was subnominai was significantly
greater in SFR 4 (25 percent ± 5 percent) than in SFR 3 (9 percent ± 5 percent). The proportion of
subnominai plots in SFRs 1 and 2 combined (18 percent ± 12 percent) did not differ significantly from
those of the other SFRs.
5-2
-------
Spscles Diversity of Traas and Saplings
1.00
0.80
0.00 ' ¦ ' ¦ ¦
0.00 3.00 6.00 9.00 12.00 15.00
Number of Species per Area
Figure 5-1. Cumulative distrftxjtion function for tree species
density in Standard Federal Regions 1 and 2
combined.
1.00
0.80
ss
0.00 L""* I . . .
0.00 4.00 8.00 12.00 16.00 20.00
Number of Species per Area
Figure 5-2. Cumulative distribution function for tree species
density in Standard Federal Region 3.
5-3
-------
Spec las Diversity of Trass and Saplings
1.00
1
« r'fc"'
0.80
i r**
" ' iP
|5>
Jp
i :Jr
| 0-60
£
I
to 0.40
i
1 1*
i .+*¦
E
3
o
0.20
T
XfJL
P
r
i
0.00
> i
0.00 5.00 10.00 15.00 20.00 25.00
Number of Species per Area
Figure 5-3. Cumulative distribution function for tree species
density in Standard Federal Region 4.
The proportion of plots in which sapling species density was subnominal was significantly
higher in SFRs 1 and 2 combined (48 percent ± 7 percent) than in either SFR 3 (10 percent ± 6
percent) or SFR 4 (15 percent ± 5 percent) (Figures 5-4 through 5-6). The proportions observed for
SFRs 3 and 4 were not significantly different.
5-4
-------
Spacias Diversity of Trass and Saplings
1.00
0.80
"§ 0.60
0.40
0.00
0.00 2.00 4.00 6.00 8.00 10.00
Number of Species per Area
Figure 5-4. Cumulative distribution function for sapling species
density in Standard Federal Regions 1 and 2
combined.
1.00
0.80
0.60
0.00 « '- ' 1
0.00 5.00 10.00 15.00 20.00 25.00
Number of Species per Area
Figure 5-5. Cumulative distribution function for sapling species
density in Standard Federal Region 3.
5-5
-------
Species Diversity of Trees and Saplings
1.00
0.80 ¦
0.60
^ 0.40
0.20
0.00
0.00 8.00 16.00 24.00 32.00 40.00
Number of Species per Area
-igure 5-6. Cumulative distribution function for sapling species
density in Standard Federal Region 4.
The high proportion of subnominal plots for sapling species density in SFRs 1 and 2 combined
warrants further investigation. The results suggest proportionately more subnominal sites for tree
species density in the forests of SFR 4 than in the forests of SFR 3. Furthermore, the results suggest
proportionately more subnominal sites for sapling species density in the forests of SFRs 1 and 2
combined than in those of either SFR 3 or SFR 4.
5-6
-------
Air Pollution Bioindicator Plants
CHAPTER 6
AIR POLLUTION BIOINDICATOR PLANTS
6.1 Summary
Field crews established biomonitoring sites, for determining the presence or absence of ozone
injury conditions, at 39 of the 212 forested plots in New England.
Based on data from 39 biomonitoring sites, an estimated 27% ± 13% of the forested population
covered by these sites showed foliar symptoms indicating the presence of ozone injury.
Most of the plots rated positive for ozone injury were located in rural areas that are known to
be contaminated by air masses moving into the region from urban-industrial areas to the south
of New England.
6.2 Introduction
This indicator is intended primarily for the determination of the presence or absence of ozone
injury conditions on the FHM plots. When ozone contaminates the environment, certain plants,
bioindicators, show a visible response (Krupa and Manning 1988). Foliar symptoms on bioindicator
plants indicate that ozone is present at concentrations that will cause injury and that other necessary
"conditions" for injury (e.g., adequate soil moisture) are also present. For a particular geographical area
and time period, we can obtain a biologically meaningful measure of air quality by observing how
bioindicators respond in ambient air. Over time, changes in bioindicator response can be used to
detect and quantify changes and trends in ozone injury conditions on the FHM plots.
6.3 Methods
On the FHM Detection Monitoring plots, the 1992 field crew procedures included the selection
of a suitable site for symptom evaluation, identification of one to three known ozone-sensitive species
at the site, and the identification of ozone injury on the foliage of up to 30 plants of each of three
species. Field crews recorded informational data about the location and size of the opening used for
biomonitoring, and numerical data about the number of plants evaluated and the number of plants
injured for each species. In addition, quality assurance/quality control (QA/QC) personnel collected
foliar injury data to enhance the interpretation of the data collected by the field crews, and to test
QA/QC objectives.
6-1
-------
Air Pollution Bloindlcator Plants
6.4 Results and Discussion
In 1992, field crews identified 39 suitable sites for biomonitoring out of a total of 212 forested
plots in New England. Of these 39 sites, 11 were scored as positive (+) for ozone injury. On the (+)
ozone plants, the sample mean percent injured plants per plot was 17.6 percent with a range of 2.1
percent to 100 percent. Blackberry and milkweed were the species most often reported with ozone
injury.
Figur« 61. Distribution ol FHU deiiclion plots in He* Englond
fvaluoltd for ozoni injury in 1992.
V«F kf* VS9A ftrut Sirtlii, lifdiiiliri irt«
fiU 6ri«f. Iff3)
6-2
-------
Air Pollution Bioindicator Plants
Foliar symptoms of ozone injury were first recorded on July 13 by the QA/QC staff and on July
14 by the field crews. The July 13 date sets the beginning of the evaluation window for this indicator;
only those plots evaluated after the onset date can be included in a regional analysis. At this time, the
number or distribution of the plots that were visited before July 13 and which, according to the field
protocol, were not evaluated for this indicator, is not known. Something is known, however, about the
distribution of the plots identified as (+) ozone after the onset date (Figure 6-1). It is known, for
example, that most of the (+) ozone plots were located in rural areas in the Connecticut River Valley
and just inland from Acadia National Park in Maine, areas that are known to be impacted by winds
carrying pollutants up the east coast from points South (Pinkerton and Lefohn 1987). Air quality
monitoring stations in these rural areas routinely record the highest ozone concentrations region-wide.
The bioindicator approach has been used successfully and repeatedly in other field studies
(Anderson et al. 1989; Duchelle and Skelly 1981; Jacobson and Feder 1974; Kohut et al. 1992; Renfro
1988). The limitations of the data set are well understood. In 1993, with improvements in methodology
and quality assurance, bioindicator evaluations will be fully implemented on all forested plots in the
Northeast.
6-3
-------
RafarancM
CHAPTER 7
REFERENCES
Anderson, R.L., C. Huber, R. Belanger, and others. 1989. Recommended survey procedures for
assessing ozone injury on bioindlcator plants in Region 8. Class I Wilderness Areas. USDA Forest
Service, Region 8, FPM Report 89-1-36. 6 pp.
Anderson, R.L, W.Q. Burkman, I. Millers, and W.H. Hoffard. 1992. Visual crown rating model for
upper canopy trees in the eastern United States. USDA Forest Service, Southeastern Region, Forest
Pest Management. 15 pps.
Anderson, R. L. and I. Millers. 1992. Tree groups based on foliage and crown characteristics. USDA
Forest Service white paper. Asheviile, North Carolina. 7pp.
August, P.V. 1983. The role of habitat complexity and heterogeneity in structuring tropical mammal
communities. Ecology 64:1495-1507.
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.
Brown, J.H. 1971. Mammals on mountaintops: nonequillbrium insular biogeography. Amer. Naturalist
105:467-478.
Chojnacky, 0. 1991. Eastern Forest Health Monitoring: Field Measurements Guide. USDA Forest
Service, Forest Survey, Intermountain Research Station, Ogden, UT.
Cline, S. 1992. Characterization of regional overstory tree species diversity on FHM plots. In Forest
Health Monitoring 1991 Statistical Summary. Forest Health Monitoring. EPA/ / /. U.S. Environmental
Protection Agency, Washington DC.
Conkling, B.L. and G.E Byere (eds.). 1992. Forest Health Monitoring Field Methods Guide. Internal
Report EPA/600/X-92/073. U.S. Environmental Protection Agency, Las Vegas, NV.
Cook, R.E. 1969. Variation in species density in North American birds. Systematic Zoology 18:63-84.
Deuser, R.D. and H.H. Shugait 1978. Microhabitats in a forest- floor small mammal fauna. Ecology
59:89-98.
Duchelle, S.F. and J.M. SkeUy. 1981. Response of common milkweed to oxidant pollution in the
Shenandoah National Park in Virginia: Plant Disease 65:661-663.
Haiper, J.L 1977. Population biology of plants. Academic Press, London.
Horvitz, D.G. and D.J. Thompson. 1952. A generalization of sampling without replacement from a finite
univeree. J. American Stat. Assoc. 47:663-685.
7-1
-------
References
Jacobson, J.S. and W.A. Feder. 1974. A regional network for environmental monitoring: Atmospheric
oxidant concentrations and foliar injury with tobacco indicator plants in the eastern United States. Mass.
Agric. Exper. Station Bull. 64.
Knapp, C.M., D.R. Marmorek, J.P. Baker, K.W. Thornton, J.M. Klopatek, D.P. Charies. 1990. The
indicator development strategy for the Environmental Monitoring and Assessment Program. DRAFT
DOCUMENT, U.S. EPA, Environmental Research Laboratory - Corvallis.
Kohut, R.J., R.T. Eckert, and others. 1992. Sensitivity to ozone of the vegetation of the Acadia National
Park. Proc: Effects of Air Pollution on Terrestrial and Aquatic Ecosystems in New England and New
York. October 19-21, Waterville Valley, NH. pp. 31-33.
Krupa, S.V. and W.J. Manning. 1988. Atmospheric ozone: Formation and effects on vegetation.
Environ. Pollut. 50:101-137.
MacArthur, R.H. and E.O. Wilson. 1963. An equilibrium theory of insular zoogeography. Evolution
17:373-387.
MacArthur, R.H. and E.O. Wilson. 1967. The theory of island biogeography. Princeton University Press,
Princeton, NJ.
Millers, I., R.L. Anderson, W.G. Burkman, and W.H. Hoffard. 1992. Definitions, Acquisition, and Use
of Crown Measurements. USDA Forest Service, State and Private Forestry, Northeastern Area, Forest
Health Protection. 19 pps.
Omemik, J.M. 1987. Ecoregions of the coterminous United States. Ann. Amer. Geog. 77:118-125.
Overton, W.S., D. White, and D.L Stevens. 1990. Design report for EMAP (Environmental Monitoring
and Assessment Program). EPA/600/3-91/053. U.S. Environmental Protection Agency, Office of
Research and Development. Washington, DC.
Pinkerton, J.E. and A.S. Lefohn. 1987. The characterization of ozone data for sites located in forested
areas of the eastern United States. JAPCA 37(9): 1005-1010.
Renfro, J.R. 1988. Evaluating the effects of ozone on the plants of the Great Smokey Mountains
National Park. Park Sci. 822-23.
Simpson, G.G. 1964. Species density of North American recent mammals. Systematic Zoology
13-57-73.
Stolte, K.W., D.M. Duriscoe, E.R. Cook, and S.P. Cline. 1992. Chapter 7 - Methods of assessing
responses of trees, stands, and ecosystems to air pollution. In: Binkiey DB, Olson RL, and Bohm M,
Pollution impacts on forest ecosystems in western United States. Springer-Verlag, New York. 65 pps.
US EPA. 1990. Threats to biological diversity in the United States. Office of Policy, Planning and
Evaluation. PM-223X. 57 p.
Willson, M.F. 1974. Avian community organization and habitat structure, Ecology 55:1017-1029.
7-2
-------
FHM Indicator Development and Evaluation
APPENDIX A
FHM INDICATOR DEVELOPMENT AND EVALUATION
Forest Health Monitoring indicators progress from candidate to pilot to demonstration before
they are considered ready for national implementation in Detection Monitoring. The progression
includes many steps, which culminate in the selected indicator satisfactorily meeting development
criteria.
The selected indicators provide quantitative or qualitative links to key ecosystem processes and
components. An indicator thus serves as a metric of society's concerns and its perceptions about
forest health. Quantitative links between indicators and assessment questions wiil be derived by
analysis of the relationship between indicators and response variables closely associated with defined
assessment questions. Some information may be found in relevant scientific literature, but in many
cases off-frame research will be required to obtain this information.
Indicators are not intended to demonstrate cause-and-effect relationships, but will do so in
certain instances. The preponderance of evidence obtained from monitoring activities may be
convincing enough to implicate or clarify certain causal hypotheses, but additional off-frame data wiil
usually be required to verify or nullify these hypotheses.
The indicator development strategy does not intend to address all the processes and
components that interact to determine forest health. The conceptual model wilt point out gaps in our
knowledge and help us to understand whether the addition of a new indicator is critical to assessing
forest health. As a national program, FHM will add and/or drop indicators based on their ability to
address certain criteria Existing or new indicators may be able to pass the indicator criteria test
admirably, but may be redundant, may be a measure of a process or component that is not critical to
forest health assessment, or may not actually measure the process or component they were originally
intended to measure. In such cases, an indicator may be dropped from further consideration. The
number of indicators to be measured will be strictly limited and prioritized according to the value it adds
to characterizing ecosystem status and trends.
Although an indicator may meet the specified performance standards, it may: (1) be redundant,
(2) measure a process or component that is not critical to forest health assessment, or (3) not actually
measure the process or component it was originally thought to measure. In such cases, the indicator
may be dropped from further consideration.
Redundancy among indicators can be identified by evaluating their respective roles in the
conceptual model. Redundancy will be perpetuated only long enough to evaluate the relative values
of the indicators.
A-1
-------
FHM Indicator Development and Evaluation
The current set of indicators was chosen based on peer review, expert opinion, and literature
reviews. Each indicator addresses one or more assessment questions which pertain to certain values
that are easily interpretable by other scientists, policy makers, and the general public.
-------
Interpreting Cumulative Distribution Functions
APPENDIX B
INTERPRETING CUMULA WE DISTRIBUTION FUNCTION GRAPHS1
Many monitoring programs around the world use this simple, powerful, and informative
graphical technique for presenting data summaries. Portions of the following description closely follow
the discussion in Linthurst et al. (1986).
Using a CDF graph 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.
Each CDF graph consists of a variable X (a measurement or an index) on the
horizontal axis and the cumulative frequency of plots in the population being examined on the vertical
axis. To find the estimated proportion of the population that falls below some critical value of X, use
this procedure (see Figure B-1):
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.
In the example, a critical value of 42 in step 1 leads to the interpretation that about 67 percent
of the population has a value of X less than 42. Different critical values in step 1 will result in different
population estimates in step 4. The critical value is really a criterion by which subnominai conditions
wiil be identified. By experimenting with critical values, the reader may see how data interpretations
change with the criterion chosen to signify subnominai conditions.
There are some important things to remember when using the above procedure. First, this
graphical technique is suitable tor rough estimates, but precise computation requires the use of data
biases and appropriate algorithms. .Second, the CDF cannot be used (strictly) to get estimates of
proportions greaterxhan some value of X Instead, a new chart (of 1-F(X)) must be prepared for that
purpose, again using data bases and appropriate algorithms.
*Much of the information in this section is from Chapter 2 of the FHM 1991
Summary.
B-1
-------
Interpreting Cumulative Distribution Functions
e d
e
1.00
¦*
I b,..-
1 x .*
K a/ i /
.i f i /
.80
! \ f 1 /
if bf-T'"
' r Y
4 3 /
r i * i
^ J
I s
J 1 / »
/ L«« 1
."fiS
3 -60
//
1 1
/« '
.
J9
s
* /
1 1
a.
J / /
1 1
/ :
1 1
>
/
| |
| .40
/ i
1 1
I
«¦*
// /
J ~*
1 1
o
« / /
V |
/ ~
, J
1 1
1 1
1 |
.20
: // 2+
1 >
1 1
1 1
0.00
1 1
1 1
: . :
0.00 14.00 28.00 42.00 56.00 70.00
Index value
Figure B-1. Interpreting a hypothetical CDF.
The CDFs in this report have solid and dashed lines charted as shown in the example (Figure
B-1). The solid line, labeled a, yields the population estimate as described above. The dashed lines,
labeled b, yield the lower and upper 90 percent confidence limits for the population estimate.
Some authors have defined one or more COF thresholds for their indices. Thresholds delineate
the population into acceptable (nominal) and unacceptable (subnominal) categories. Thresholds are
shown on the example COF graph as the dashed lines labeled c and d. The confidence intervals for
the thresholds are represented by the hash-mark lines labeled a in the example. Note that this
hypothetical COF has two thresholds (c and d) and therefore has two confidence intervals (e) which
overlap. This type of CDF was used to generate the tabular values in Chapter 4, Crown Condition, of
this report. The establishment of these thresholds is based on literature, peer reviews, and public
opinion, and should address relevant environmental values. Some FHM indicators may address
multiple environmental values, in which case the location of a threshold may vaiy for each
environmental value. The threshold may also be related to the poststratification procedure applied to
the data. For example, if data are stratified by forest type, the crown density threshold for spruce-fir
may be drastically different than that for oak-hickory.
B-2
-------
FHM Plot Network and Plot Design
APPENDIX C
FHM PLOT NETWORK AND PLOT DESIGN
The plot network consists of a systematic grid of sampling points covering the entire United
States (Figure C-1). Approximately 12,600 sampling points on the grid, excluding Alaska, are equally
spaced about 27 km apart.
A crucial step in calculating the indicator information is selecting the field plots so that the data
represent a probability sample. The current method of FHM site selection is equivalent to selection of
a single USDA Forest Service Forest Inventory and Analysis (FIA) photo point. This permits a
statistical linkage to the FIA program. The FIA photo point grid for a region is overlain on a 40-km
hexagon. 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 was done with 1992
Detection Monitoring plots in all of the eastern states except Alabama.
Figure C-1. The national grid of monitoring locations.
C-1
-------
FHM Plot Network and Plot Design
Because the selected photo point is the one closest to the center of the 40-km hexagon and
the photo point grid is statistically independent of the EMAP hexagon grid, the probability of selecting
that particular photo point is proportional to the area that photo point "covers." The coordinates of the
photo points around each hexagon center are entered into a Geographic Information System (GIS) data
base and plotted in an equal-area projection. Then, for the selected photo point, the GIS system
computes the area of all land cover closer to this point than any other photo point. The larger the area,
the more likely it is that the hexagon center will be included. The inclusion probabilities are calculated
as a constant multiplied by the area.
Inclusion probabilities are proportionally related to the area represented by the FIA photo point
in the photo point grid. Thus the inclusion probabilities can be used to provide population estimates
for ail of the variables of interest. However, this assumes that the coordinates of the photo points and
hence the ground plots, are calculated using the best current methods. Due to time and funding
limitations, it was not feasible to have all the necessary photo points fully digitized using the best
current methods. Therefore, some of the inclusion probabilities are only first-order estimates. The
variance component resulting from the estimates is not included in the statistical estimation procedures.
These inclusion probabilities will be corrected for the next report.
Another alternative is feasible in places where FIA photo points are not currently available or
are difficult to obtain. The ground plot can be located at a random offset and azimuth from the center
of the 40-km hexagon. These points can then be added to the list of FIA photo points for
photointerpretation.
Once defined, each plot location is fixed. In any 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. A plot is established (or remeasured) at every
forested location and the plot condition further defines the sampling roles for future measurements.
Systematic sampling designs such as this have proven to be extremely efficient for large-scale forest
inventories; this design is expected to be equally efficient for monitoring forest health.
The FHM plot design is shown in Figure C-2. Each one-hectare area plot has a fixed radius
and contains a four-subplot cluster. All mensuration, crown condition, and species diversity
measurements taken in 1992 were done on the subplots. Hie air pollution bioindicator measurements
were taken in association with the plots although not necessarily on the subplots. Specific sampling
protocols are documented in the 1992 FHM Field Methods Guide (Conkling and Byers 1992).
Modifications will be made to the plot design to accommodate specific sampling situations (i.e.,
redwoods or sequoias) in which the usual subplots may not be adequate to obtain a representative
sample. Microplots may also be used for small trees or seedlings to reduce the total number of trees
to be measured on a single plot.
C-2
-------
FHM Plot Network and Plot Design
Azimuth 1-2360°
Azimuth 1-3120°
Azimuth 1-4 240°
Subplot.
240* radus (7.32m). \
N
© *
, OfatanoslwtwMn points
'taiarpMm).
SMFSia.-,
«t*piot cantors (346m).
Figure C-2. The FHM field plot design.
~ u.«. GOVERNMENT PRINTING OFFICEi MM - MMIIrtBfl
C-3
------- |