United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-20

Ozone Bioindicator 

Sampling and Estimation 

Gretchen C. Smith

William D. Smith

John W. Coulston
Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies:

USDA FOREST SERVICE USDA Forest Service 

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

 Delaware, OH 43015-8640

December 2007 Fax: (740)368-0152

Abstract

Ozone is an important forest stressor that has been measured at known phytotoxic levels at 

forest locations across the United States. The percent forest exhibiting negative impacts from 

ozone air pollution is one of the Montreal Process indicators of forest health and vitality. The 

ozone bioindicator data of the U.S. Forest Service Forest Inventory and Analysis Program 

(FIA) are the only source of information available that documents plant injury from air 

pollution using consistent protocols. This document introduces the FIA ozone indicator and 

describes the sampling and estimation procedures of the national biomonitoring program. 

We provide background material on ozone, examples of bioindicator summary statistics, a 

description of spatial interpolation, and methods to estimate status and change in forested 

areas with respect to the occurrence of ozone injury from ambient ozone concentrations. 

The goal is to provide guidance to analysts and researchers on ways to incorporate ozone 

bioindicator data into reports and research studies. Periodic recommendations to analysts 

on improved analytical techniques will be made. Analysts are encouraged to consult the 

companion user guide for additional guidance on interpreting the ozone biomonitoring data 

and reporting on the issue of ozone and forest health for the FIA program.

Cover Photos

Front: 

Jay Lackey, Forest Health Specialist at the Vermont Department of Forests and Parks, is 

shown evaluating bioindicator species for ozone injury in a typical open area suitable for 

ozone biomonitoring in eastern forest types.

Back: 

Top left: Classic upper-leaf surface ozone stipple on black cherry. In the field, injury severity 

ranges from slight to severe on the same and different plants depending on genotype, leaf 

age, and ozone exposure.

Top right: Black cherry leaves showing obvious ozone injury and other symptoms. Three 

injured leaves are collected at random from individual trees of each injured species on each 

biomonitoring site.

Bottom: A plant press is used in the field to preserve the leaf samples with ozone injury. 

Pressed leaf samples are mailed to a regional ozone expert for microscopic examination and 

validation of the ozone symptom.
CONTENTS

Introduction ...................................................................................................1

 Overview of the Ozone Indicator ............................................................1

 Detection Monitoring ...............................................................................2

 Evaluation Monitoring .............................................................................2

 Key References ........................................................................................3

Biomonitoring and Data Collection ............................................................4

Point-In-Time Estimation .............................................................................5

 Plant-level Estimates ...............................................................................5

 Biosite Index and Proportion Injured Plants .........................................6

 Status Estimation .....................................................................................6

 Spatial Interpolation of the Biosite Index ..........................................7

 Estimating Status for Forested Areas ...............................................8

Discussion .................................................................................................. 11

References ..................................................................................................12

Appendices .................................................................................................17

 Appendix I: Output Tables and Maps ...................................................18

 Appendix II: Ozone Sensitivity of Tree and Shrub Species ..............24

 Appendix III: Documents for Ozone Information Management .........27
1

INTRODUCTION

Overview of the Ozone Indicator

Ozone interacts with forest ecosystems, causing visible injury and alterations 

in species composition and pest interactions (Chappelka and Samuelson 

1998, Miller and others 1996). It is the only regional gaseous air pollutant 

that has been measured at known phytotoxic levels at both remote and 

urbanized forest locations (U.S. EPA 1996a,b). The importance of ozone as 

a forest stressor is illustrated by its inclusion in the Montréal Process Criteria 

and Indicators (Montreal Process 1995) in which the percent forest exhibiting 

negative impacts from air pollutants such as ozone is an indicator of the 

overall forest health and vitality. Coulston and others (2004) point out that 

the ozone biomonitoring data of the USDA Forest Service Forest Inventory 

and Analysis Program (FIA) are the only source of information available 

that documents plant injury from air pollution using consistent protocols. 

The goal of our document is to describe the ozone bioindicator and suggest 

analytical techniques appropriate for FIA ozone biomonitoring data.

The ozone bioindicator provides a biological index of ozone stress to plants 

using consistent protocols on a nationwide system of biomonitoring sites. 

Ozone biomonitoring is part of the FIA phase 3 sample (USDA Forest 

Service 2005) and is based on the documentation of visible foliar injury to 

known ozone-sensitive plant species under conditions of ambient exposure. 

The fi eld methods, site variables, and site-level biosite index were developed 

with support from the scientifi c research community (Smith 1995), and the 

sampling procedures and analytical techniques have been reviewed in the 

scientifi c literature (Coulston and others 2003, Smith and others 2003).

Although ozone biomonitoring is part of the phase 3 (forest health) sample, the 

biomonitoring grid and the FIA base grid are independent and information 

is collected on a different population from other FIA phase 2 (P2) and phase 

3 (P3) measurements (see Biomonitoring and Data Collection, page 4). One 

primary use of these data is Detection Monitoring (DM), which identifi es 

forested areas that may be at risk from some stressor or disturbance (Detection 

Monitoring section, page 2). These areas may then become candidates for 

Evaluation Monitoring (EM) (see Evaluation Monitoring section, page 

2). Vose (2000) defi ned risk analysis as the process of quantifying, either 

qualitatively or quantitatively, the probability and the potential impacts 

of some risk. DM analyses using the ozone biomonitoring data are both 

qualitative and quantitative and fall under the category of risk analysis.

Throughout this document the word “site” or “biosite” will be used to refer 

to the ground location where the FIA fi eld crews collect ozone indicator data 

and the word “plot” will be used to refer to the FIA ground sample plots 

for which the estimation procedures apply. Our objective here is to provide 

guidance on presenting annual summaries of the ozone biomonitoring data 

and performing risk analysis for DM.

GRETCHEN C. SMITH, 

FIA National Ozone Advisor, University 

of Massachusetts, Department of Natural 

Resources Conservation, Amherst, MA.

gcsmith@nrc.umass.edu

WILLIAM D. SMITH, 

Assessment Coordinator, U.S. Forest 

Service, Southern Research Station, 

Research Triangle Park, NC.

JOHN W. COULSTON, 

Supervisory Research Forester, U.S. Forest 

Service, Southern Research Station, 

Knoxville, TN.
2

Detection Monitoring

The FIA ozone biomonitoring program is designed to detect and monitor plant-damaging 

concentrations of ozone in the natural environment. Information gathered at ozone biomonitoring 

sites identifi es whether conditions exist (ozone, light, moisture, relative humidity) for plant injury to 

occur. This information can be used to report national and regional trends in ozone injury to plants 

and to identify areas of concern for closer evaluation. The biomonitoring grid is independent of the 

FIA base grid (Fig. 1). Therefore, spatial interpolation is one method to predict potential risk of ozone 

injury on the ground plots. Interpolated biosite values are classifi ed into four response categories that 

are used to defi ne and describe possible impact (i.e., risk) to the forest resource from ambient ozone 

exposure (Table 1). These categories also provide an indication of ozone relative air quality with 

respect to a plant receptor. The categorizations of the biosite index are qualitative, but have received 

positive reviews in the scientifi c literature (Coulston and others 2003, Smith and others 2003).

The categorized and interpolated biosite values, along with the presence and abundance of ozonesensitive tree species found on FIA P2/P3 ground plots, are used to develop species-specifi c risk maps 

of ozone stress. These maps are then used to identify localized areas of moderate to high risk where 

EM studies are warranted. This approach is documented in Coulston and others (2003).

Evaluation Monitoring

When regional or national analyses of detection-level data indicate areas of potential impact on forest 

productivity and sustainability, these areas are evaluated through the implementation of additional 

studies on an intensifi ed grid. For example, the biomonitoring data may indicate a band of high 

ozone stress across a state or region, which may prompt the regional analyst to ask if the fi nding is 

real or some artifact of data collection. The next logical step is an evaluation study to take a closer 

look at the area of concern. For the ozone indicator, an evaluation study should include an intensifi ed 

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Figure 1.—FIA ozone biomonitoring grid developed from the Environmental Monitoring 

and Assessment Program (EMAP) base grid (White and others 1992). The grid has four 

sampling intensities based on sensitive species and ambient ozone concentrations 

(see Smith and others 2001 for more details). 
3

sampling grid and an analysis of air quality and environmental data that infl uence plant response to 

ozone. It may also be important to examine species distribution maps available from FIA regional 

archives. An example of DM identifying a potential problem area and EM verifying the problem is 

illustrated by Coulston and others (2003) and Skelly and others (2003), respectively.

Key References

Biomonitoring: Biomonitoring has been used since the 1960s in numerous smaller fi eld studies 

to assess pollutant stress. What is new and signifi cant about the FIA biomonitoring program is its 

national scope and the successful implementation of national standards for training, fi eld procedures, 

quality assurance, data analysis, and reporting.

Key references: Berry and Hepting 1964, Chappelka and others 1997, Feder 1978, Heck 1968, 

Hildebrand and others 1996, Kohut and others 1997, Lewis and Conkling 1994, Neufeld and others 

1992, Pronos and Vogler 1981.

Bioindicator species: The FIA list of ozone bioindicator species was gleaned from various sources 

including the peer-reviewed scientifi c literature, interagency reports, and communications with 

Federal and university researchers experienced in ozone biomonitoring. Selected species are common 

across a variety of forest types, easy to identify and distinguish from similar species, and sensitive to 

ozone based on a combination of fi eld evidence and causative fumigation experiments. The eastern 

bioindicator species have a long history of application in ozone fi eld studies. The western bioindicator 

species have not been as well tested under natural conditions of ozone exposure, but have all received 

enough testing to justify inclusion in the FIA program.

Key references: East: Krupa and others 1998, Skelly and others 1987, Skelly 2000. West: Brace 

and others 1999, Campbell and others 2000, Duriscoe and Temple 1996, Mavity and others 1995, 

Temple 2000. East and West: U.S. Department of the Interior 2003.

Biosite index

Bioindicator 

response

Assumption 

of risk Possible impact

Relative air 

quality2

0 - < 5 Little or no foliar 

injury

None Visible injury to 

highly sensitive 

species, e.g., 

black cherry

Good

5 - < 15 Light to moderate 

foliar injury

Low Visible injury 

to moderately 

sensitive species, 

e.g., tulip poplar

Moderate

15 - < 25 Moderate to 

severe foliar 

injury

Moderate Visible and 

invisible injury

Tree-level 

response3

Unhealthy 

for sensitive 

species

≥ 25 Severe foliar 

injury

High Visible and invisible 

injury. Ecosystemlevel response3

Unhealthy

Table 1.—Classification scheme for the FIA biosite index1

1

The categorizations of the biosite index are subjective and based solely on expert opinion.

2

Relative ozone air quality from a plant’s perspective. See: EPA-456/F-99-002 July 1999; 

http://www.airnow.gov. 

3

According to the EPA’s Proposed Guidelines for Ecological Risk Assessment (Federal Register 

61(175): 47552-47631).
4

Biosite index: The Horsfall-Barrett (HB) rating scale used to assess ozone injury in the fi eld is based 

on a technique developed for plant disease research. Since the 1940s, it has been used repeatedly in 

the fi eld evaluation of ozone-induced foliar injury. Details on the formulation of the site-level biosite 

index (BI) are presented in Biosite Index and Proportion Injured Plants, page 6. The index developed 

for the FIA program is new, but it has been widely adopted by cooperating researchers at various 

institutions and published in the scientifi c literature.

Key references: Horsfall and Barrett 1945, Horsfall and Cowling 1978, Smith and others 2003.

Classifi cation scheme for the biosite index: The classifi cation scheme for the FIA biosite index has 

been reviewed in the scientifi c literature and applied in a published assessment of ozone injury to 

eastern forest tree species.

Key references: Coulston and others 2003, Smith and others 2003.

Interpolation techniques: Plot-level attributes required for population estimates are developed by 

spatial interpolation of the biosite data. Spatial interpolation techniques are widely used in the 

analysis of air pollution, environmental, and ecological data. The approach used for the ozone 

indicator has been reviewed in the scientifi c literature and applied in a published assessment of ozone 

injury to eastern forest tree species.

Key references: Coulston and others 2003, Cressie 1993, Cressie and Ver Hoff 1993, Isaaks and 

Srivastava 1989, Lefohn and Pinkerton 1988.

Status and change estimation: There are several references for valid estimation techniques; however, 

Bechtold and Patterson (2005) provide a review and recommendations specifi cally for FIA data.

Key reference: Bechtold and Patterson (2005)

Biomonitoring and Data Collection

Ozone sampling occurs on a unique national grid independent of the P2 and P3 plot system. The 

ozone grid enhances quality assurance for this indicator by allowing greater fl exibility in site location 

on the ground and greater sampling intensity in areas believed to be at high risk for ozone impact 

(Smith and others 2001). The grid design generates differing sampling intensities across the landscape 

based on the best available information on air quality regimes.

The ozone grid is purposive both at the grid level and at the biosite. Biosite location on the ground 

is deliberately chosen fi rst for ease of access and second for optimal size, species, and plant counts. 

The ozone biomonitoring sites vary in size and do not have set boundaries. They are defi ned by the 

presence of ozone-sensitive bioindicator species indigenous to each FIA region. There must be one 

biosite per polygon on the national ozone biomonitoring grid. Some states use an intensifi ed ozone 

grid so two or more biosites may be located in each polygon on the base grid. Biosite locations are 

mapped, geographic coordinates are recorded, and the same sites are evaluated every year. Ozone 

injury and our ability to detect that injury increase over the course of the fi eld season. For this reason, 

the sampling window for the ozone indicator is limited to 3 weeks (from late-July to mid-August) 

within which the indicator is considered stable. This minimizes variability and the error associated 

with the data collection system.
5

The sampling rules for the ozone biosites are as follows. Biosites are wide-open areas, at least one 

acre in size, within or alongside forested areas. Each site must contain at least 30 individual plants of 

at least two bioindicator species. If not enough plants are available at one location, two nearby open 

areas, within 3 miles of each other, may be combined to maximize plant counts. Biosite locations 

must be easy to access, and they must be free of signifi cant soil compaction and other human-made 

disturbance. Additional guidelines are available in the Field Methods Guide (USDA Forest Service 

2000).

The characteristics of each site are described in terms of the size of the open area, elevation, terrain 

position, aspect, soil drainage, soil depth, and site disturbance. If characteristics vary signifi cantly 

across the biosite, then the area where most of the bioindicator species are growing is described 

and variations are recorded on the site map and notes. When two nearby open areas are used, each 

location is described separately.

Up to 30 plants of each species are randomly selected for injury evaluation. Plants less than 12 inches 

in height, suppressed, shaded, or with more than half the crown out of reach are not evaluated. The 

approximate locations of the plants used for evaluations are drawn on the site map so that the same 

population of plants is evaluated on return visits to the biosite. The entire open area is sampled until 

30 plants of two (ideally, three or more) species have been evaluated.

Quality assurance (QA) procedures dictate that the ozone injury symptom must be verifi ed for each 

injured species on each site. Crews collect a minimum of three injured leaves from a random sample 

of individual plants that show obvious ozone injury, and they mail pressed leaf samples to a regional 

expert for review. Three leaves from each injured species are subject to microscopic examination. 

Injury is validated for all samples that show a characteristic color and injury pattern for ozone and 

that are otherwise free of confounding signs and symptoms of other mimicking stress agents (e.g., 

insects, disease, mites, or weather). If the symptoms are not typical of ozone injury, then the fi eld data 

associated with the invalidated leaf voucher are zeroed out. Furthermore, if a leaf voucher is missing 

and unable to be validated, then the fi eld data associated with the missing voucher are fl agged so they 

cannot be used in data summaries or analyses.

The ozone indicator is included in the FIA National QA Plan (USDA Forest Service 2004). Just 

before the sampling window, ozone training and certifi cation sessions are held in each region. A 

minimum of 10 biosites per region are blind checked every year (5 to 6 percent of the total biosites 

in each region). The ozone remeasurement data have been evaluated on two occasions, once in 1999 

and again in 2003 (Pollard and Smith 2001, Pollard 2004). Inconsistent results with two eastern 

bioindicator species reported in Pollard and Smith (2001) were corrected by improvements to the 

ozone training session. Results from the 2003 review indicate the biosite data are robust and fi eld 

crews in all regions are able to meet data quality expectations for the ozone indicator.

POINT-IN-TIME ESTIMATION

Plant-level Estimates

At each ozone biosite, 30 individual plants of two bioindicator species and between 10 and 30 

individual plants of additional bioindicator species are evaluated for ozone injury. Each plant is rated 

for the proportion of leaves with ozone injury (injury amount) and the mean severity of symptoms 

(injury severity) using a modifi ed Horsfall-Barratt scale with break points at 0, 6, 25, 50, 75, and 100 
6

percent (Horsfall and Cowling 1978, USDA Forest Service 1999). This scale uses class break points 

that correspond to the ability of the human eye to distinguish gradations of healthy and unhealthy 

leaf tissue.

Biosite Index and Proportion Injured Plants

For each biosite, the percent injured plants and a biosite index are calculated based on the injury 

amount and severity scores. The proportion injured is Ip=ni

/nt where Ip is the proportion of plants 

injured, ni

 is the number of injured plants (i.e., amount of injury ≠ 0), nt=the total number of plants 

evaluated. The biosite index is the average score (amount * severity) for each species averaged across 

all species on the biosite multiplied by 1,000 to allow risk categories to be defi ned by integers (Table 

1). The biosite index is calculated

10

1 1

1 1

1000

j m n

j pj pj

j p

BI m n a s

p

 

= =

 ¬  ­ =  ­

 ­

 ­­  ®

 

where

BI = biosite index

m = number of species evaluated

nj

 =number of plants of the j

th species evaluated

apj =proportion of injured leaves on the p

th plant of the jth species

s

pj =average severity of injury on the p

th plant of the jth species

Biosite summary statistics on the ozone indicator are generated annually and loaded to three ozone 

data summary tables in each FIA region. Tabular data include species and site counts and calculated 

mean injury indices from the fi rst year each state implemented the ozone indicator up to the current 

year. For some regions it is important to group states with similar air quality regimes together and 

keep them separate from neighboring states with distinctly different air quality regimes. A map of 

biosite-level values will also be produced for illustration. Tables and fi gures illustrating these types of 

products are included in Appendix I.

Status Estimation

FIA plot-level attributes required for population estimates can be developed by spatial interpolation 

of data collected from the biosites. Each ozone season is unique, infl uenced by variable ozone levels, 

weather, windfl ow, and precipitation patterns. Therefore, it is important to use 5-year averages of the 

biosite index to generate a truly representative estimate of ozone stress. Thus, a 5-year moving average 

is used:

0

( 1)

ˆ t

t

t n

B B =  n =  where

ˆ

Bt = the 5-year average estimate for a biosite value at time t

Bt

 = biosite value at time t

t = time in years ranging from t = 0 (the current year) to year t – n

n = number of biosite measurements (maximum of 5)

ˆ

Bt will be used in the spatial interpolation. Spatial interpolation techniques are widely used (e.g., 

see Cressie and Ver Hoff 1993) and air pollution variables are commonly interpolated. For example, 

Lefohn and Pinkerton (1988) interpolated ambient ozone concentrations to characterize forested 
7

areas of the United States. Coulston and others (2003) interpolated data from ozone biomonitoring 

sites to characterize risk to northeastern tree species. For a review of spatial statistics, see Cressie (1993).

Spatial Interpolation of the Biosite Index

Many spatial interpolation techniques are available. In this document we discuss the procedures 

for kriging, inverse distance weighting, and cross-validation, but as work proceeds with the 

ozone indicator, other methods such as universal kriging, splining, and spatial regression may 

be implemented. Spatial interpolation is performed to create a map of ozone risk to plants. This 

map is used to, among other things, classify ozone injury risk for FIA P2/P3 plots. Many spatial 

interpolation techniques require analysts to make assumptions (e.g., stationarity). We assume analysts 

are aware of both the theoretical and practical considerations associated with each interpolation 

technique.

Using kriging, a standard interpolation technique, requires at least three steps to interpolate a 

surface. First, the empirical semivariogram is calculated. Second, the empirical semivariogram is 

modeled. With parameters from the modeled semivariogram, the kriging equations can be used. 

The semivariance between values for a particular lag distance h is γ(h)=1/(2N(h))Σ(vi

-vj )

2

 where N

is the number of pairs (i, j) and vi

-vj is the difference between the values of pair (i, j). It is one-half 

the average squared difference between values a particular distance apart. Several model types can 

be used to model the empirical semivariogram. They include the Gaussian model, wave model, and 

exponential model. Gaussian models tend to account for strong spatial relationships at short distances 

while wave models account for periodicity in spatial relationships. Matern class models may also be 

used when fl exibility near h=0 is desired. See Hoeting and others (2006) for more information on 

semivariogram model selection.

After the semivariogram has been modeled, ordinary kriging can be used to interpolate between 

values. Ordinary kriging is a weighted average such that 0

1

ˆ n

i i

i

V wV == where 0 Vˆ is the estimate at 

unmeasured location 0, w is the weight of each i observation, and Vi

 is the value of each i observation. 

The weights sum to 1 and are determined by minimizing the estimation error. The estimation 

variance is S2

0=wi

γ(si-s0 ) + λ where γ(si-s0 ) is the modeled semivariance for the distance between 

s

i

 and s

0; λ is the Lagrange multiplier from solving the linear system of equations for minimum 

estimation error.

Analysts may choose to use the inverse distance squared weighting interpolation method (IDW). 

Estimates are made by: 

2

0

1

0

2

0

1

ˆ

n

i i

i

n

i

i

d v

V

d



=



=

=





where 0 Vˆ is the estimate at unmeasured location 0, d0i is the distance from the i

th biosite to location 0, 

and vi

 is the value at biosite i. Under the assumption of intrinsic stationarity, the estimation variance 

is:

0

2 2 2

2 0 0 0

22 2 0

00 0

( ) 2( ) j i i

ij i

ij i ij i

i

d d d

S ss ss

dd d H H

  

 

 ¬  ¬ ¬  ­  ­  ­ ­  ­ =   ­ ­ +  ­

­   ­  ® ®  ­  ®­­

    .
8

Cross-validation is a method to quantify and compare various models (e.g., kriging and IDW). It 

can also be used to decide among variogram models (e.g., spherical, Gaussian). The cross-validation 

technique is implemented sequentially by removing each vi

 one at a time and then estimating 

vi

 based on the spatial model (e.g., IDW) and the remaining n-1 observations. If this is done 

sequentially for all i =1,.,.,.,n observations in the sample, the estimates can then be compared to the 

actual values using several standard summary statistics (Prediction error sum of squares – PRESS 

statistics).

The PRESS statistics are the values analysts may use to decide on which interpolation model 

performs the best for their particular situation. One PRESS statistic is the average squared deviation 

= 1 2 ( ) ˆ i i

i

n vv 

   where ˆ i v is the prediction of vi

 from the rest of the data. This value should be 

relatively small if the model fi ts well. Another summary statistic is the mean of standardized PRESS 

residuals = 1 2

( ) ( )/ ˆ ii i

i

n vv S 

    where 2

( )i S  is the estimation variance for ˆ i v . This quantity should 

be close to zero if the model fi ts well. The root mean squared prediction residuals also provide a 

measure of model aptness. This is calculated by ( )2 1 2

( ) ˆ / ii i

i

n vv S 

    and will be approximately 

one if the spatial model fi ts well. An analyst 

should create several interpolated maps using 

the various options for the IDW and kriging. 

For example, analysts may choose to create an 

IDW map based on the 12 nearest neighbors 

rather than all neighbors. Analysts may 

also decide to try several variogram models 

(e.g., spherical, Gaussian) with the kriging 

technique. The resultant maps can then be 

compared based on the PRESS statistics, 

and the analysts can decide on the most 

appropriate map.

Once an appropriate spatial model has been 

selected, biosite index values will be estimated 

for all P2 and P3 plots by intersecting the 

map of interpolated values with P2 and P3 

plot locations (e.g., Fig. 2). This will result in 

a biosite index value estimate for each P2 and 

P3 plot (e.g., Table 2).

Estimating Status for Forested Areas

Bioindicator attributes will be estimated yearly for all FIA plots, using the procedures described 

above. The attributes will then be merged with the other plot attributes. Population estimates 

include (1) the proportion of forest land in each biosite index category by region, ecoregion, and 

state; (2) the acres of forest land in each biosite index category by region, ecoregion, and state; and 

(3) the volume of ozone-susceptible species in each biosite index category by region, ecoregion, and 

state. Population estimates will be made using the procedures presented by Bechtold and Patterson 

(2005).

Table 2.—Example of interpolated biosite index 

values for P2 and P3 plots 

Plot number Biosite index

Injured

plants (%)

27120110311029 13.8 15.8

27120110311156 16.1 18.0

27120110319064 0.1 1.1

27120110319251 20.8 40.8

27120110319361 19.9 25.0

27120110319385 9.7 10.4

27120110712093 1.7 3.8

27120110712438 8.4 8.0

27120110712720 6.1 7.9

27120110712907 24.5 30.8

27120110713096 10.6 10.0

27120110713107 20.9 14.5

27120110713459 14.0 12.9

27120110759099 13.6 6.8

27120110759237 2.8 5.0
9

The following description uses the same equations and terminology as used in “Sample-Based 

Estimators Used by the Forest Inventory and Analysis National Information System” (chapter 4 in 

Bechtold and Patterson 2005). In general terms, each P2 plot (and tree) will be assigned a biosite index 

category (Table 1) based on spatial interpolation that will be considered a plot (and tree) attribute. 

Each plot is assigned to one stratum from phase 1 (e.g., forest, nonforest). To estimate the proportion 

of forest land in biosite index category 4, an indicator function (δ) would be used. In this case, the 

indicator function would equal 1 if the attribute (biosite index category) is in the domain d (biosite 

index = 4) of interest, or 0 otherwise. The portion of each plot in the domain of interest is then

4 Khij

mhijk hijkd

j k

hid

m mh

a

P

a p

E

=

 where

Phid = proportion of plot i in the domain of interest d, for plots assigned to stratum h, adjusted for 

stratum h plots that overlap the population boundary 

Figure 2.—Example of block kriging and intersecting Environmental Monitoring and 

Assessment Program (EMAP) hex centers.
10

amhijk = mapped area (acres) of subplot (macroplot) j covering condition k on plot i assigned to 

stratum h. (Area is computed using the largest area mapped, which is the subplot except in the Pacifi c 

Northwest (PNW) where the macroplot or 1-ha circle is used.)

δhijkd = zero-one domain indicator function, which is 1 if condition k on subplot (macroplot) j of plot 

i assigned to stratum h belongs to the domain of interest d

Khij = the number of conditions that exist on subplot (macroplot) j of plot i assigned to stratum h

am = total area of the largest plot on which area attributes are mapped (i.e., four times the subplot or 

macroplot area)

mh p = mean proportion of stratum h mapped plot areas falling within the population ( mh p is 

generally 1 unless the plot is partially outside the population. If this situation arises, see Bechtold and 

Patterson (2005)).

The estimated proportion of forest land in strata h and domain d is simply the average of the plot 

values. h n

hid

i

hd

h

P

P

n =



The total area in the domain of interest is then 

ˆ H

d T h hd T d

h

A A WP AP = =  where

AT = total area in the population in acres

Pd = estimated proportion of the population in the domain of interest d

Wh

 = weight for stratum h, that is, the proportion of the population area, AT, that is in stratum h

A similar procedure is used to estimate the volume of susceptible tree species in each ozone biosite 

index category. Tables 1 and 2 in the section Ozone Sensitivity of Tree and Shrub Species provide 

a preliminary list of tree and shrub species susceptible to ozone injury. As an example, suppose the 

attribute of interest was the total volume of loblolly pine in biosite index category 4. The following is 

used to estimate the attribute of interest on a per unit area basis: 4

hijt hijtd

j t

hid

o oh

y

y

a p

E

=

 where

yhijt = attribute of interest for tree t on macroplot, subplot or microplot j of plot i assigned to stratum h

δhijtd = zero-one domain indicator function, which is 1 if tree t on subplot j of plot i assigned to 

stratum h belongs to the domain of interest d

ao

 = total area normally used to observe the attribute of interest on a plot, that is, four times the 

microplot, subplot, or macroplot area

4 4 1 n n h h K K hij hij

ohijk hijk

oh ohijk hijk

i jk i jk oh oh

a

p a

an an

E = =   E = mean proportion of stratum h observed-plot areas 

falling within the population where, aohijk = area normally used to observe the attribute of interest 

(microplot, subplot or macroplot j) covering condition k on plot i assigned to stratum h.
11

These values are then averaged across each i plot

h n

hid

i

hd

h

y

Y

n =



The total for the attribute of interest in the domain of interest is then

ˆ H

d T h hd T d

h

Y A WY AY = = 

See Output Tables and Maps section for example output.

DISCUSSION

Here we present one method to perform DM by classifying each FIA plot based on an interpolated 

map of ozone injury risk. The purpose of this activity is to identify candidate areas for EM. As with 

other DM activities, there is a high noise to signal ratio and there may be a relatively high rate of false 

positives. For this reason, EM is an essential part of the process. The map of ozone injury risk does 

have unquantifi ed error. However, other maps used to classify FIA plots (e.g., ecoregion sections, 

counties) also have unquantifi ed error. When the information is used at its intended resolution, 

unquantifi ed errors may be overlooked. For the ozone bioindicator, error propagation can be 

overlooked for DM activities. However, error propagation cannot be overlooked if one is trying to 

make a statistical inference about the relationship between growth rates and ozone injury.

The purpose of this document is to describe the analytical techniques used with the ozone indicator. 

We provided background material on ozone, examples of biosite summary statistics, a description of 

spatial interpolation, and methods to estimate status and change in forested areas with respect to the 

occurrence of ozone injury from ambient ozone concentrations. Appendix I includes examples of each 

expected output. Appendix II provides a current list of tree and shrub species susceptible to ozone. 

Appendix III contains several additional documents to assist FIA analysts with ozone data access and 

management. The list in Appendix II will be updated as more information becomes available. The 

interpolation techniques may be improved over time, and other methods of estimating change (e.g., 

spatio-temporal kriging) may also be investigated. Periodic recommendations to analysts will be made 

as results for QA analyses become available. There is also a companion ozone bioindicator user guide 

(Smith and others, in review) that analysts are encouraged to consult for additional guidance on 

interpreting ozone biomonitoring data and reporting on the issue of ozone and forest health for the 

FIA program.

ACKNOWLEDGMENTS

The ozone biomonitoring program began in the early 1990s and has succeeded due to the efforts 

and direction of numerous investigators, regional trainers, and state forest health coordinators. 

The authors wish to extend special thanks to the following individuals: Ed Jepsen, WI Department 

of Natural Resources; Dan Stratton and John Simpson, U.S. Forest Service, Southern Research 

Station; Barbara O’Connell, U.S. Forest Service, Northern Research Station; Sally Campbell, U.S. 

Forest Service, Pacifi c Northwest Research Station; and Patrick Temple, Ecological Systems Group, 

Riverside, CA. We also thank the regional fi eld crews and quality assurance staff for their efforts and 

commitment to data quality. Without them the national ozone biomonitoring program would not be 

possible.
12

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14

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16

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17

APPENDICES

The appendices include supplementary information on the ozone indicator. Appendix I, 

Output Tables and Maps, includes examples of output tables and maps suitable for 

FIA state reports. Appendix II, Ozone Sensitivity of Tree and Shrub Species, includes 

ozone sensitivity tables for trees and shrubs, information that is needed for risk 

assessment analysis, and Appendix III, Documents for Ozone Information Management, 

provides information on ozone data in the FIA national information management 

system (NIMS) and FIA public database (FIADB), as well as contact information for 

individuals most familiar with the ozone biomonitoring program in FIA.
18

Appendix I: Examples of Output Tables and Maps for Annual and 

Multiyear Summary Reports

 Table 3. State-level summary statistics

 Table 4. Region-level summary statistics

 Table 5. Example of summary statistics using real data

 Table 6. County-level population estimates

 Figure 3. National map of ozone risk to plants. 

 Figure 4. Example of State-level population estimates using real data.

Table 3.—State-level summary statistics

State X -Biomonitoring Program

Parameter 1994 1995 1996 1997 1998 1999 2000 2001 2002

Number of biosites evaluated xx xx xx xx xx xx xx xx xx

Number of biosites with injury x xx x x xx x x xx xx

Average biosite injury score1 x.x x.x x.x x.x x.x x.x x.x x.x x.x

Percent biosites with BI = 0 to 4.92 xx xx xx xx xx xx xx xx xx

Percent biosites with BI = 5 to 14.9 xx xx xx xx xx xx xx xx xx

Percent biosites with BI = 15 to 24.9 xx xx xx xx xx xx xx xx xx

Percent biosites with BI >= 25 xx xx xx xx xx xx xx xx xx

Average number of species per biosite xxxxxxxxx

Number of plants evaluated xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxx

Number of plants injured x xx xx xx xxx x x xxx x

Percent sample plants by HB category3

0 = no injury xx xx xx xx xx xx xx xx xx

1 = 1 to 6% xx xx xx xx xx xx xx xx xx

2 = 7 to 25% x x x x x x x x x

3 = 26 to 50% x x x x x - x x x

4 = 51 to 75% - x - x x - x x -

5 = >75% - x - x - - x x -

Number of plants evaluated by species

Species 1 (#injured in parentheses) x (x) x(x) x(x) x(x) x(x) - x(x) x(x) x(x)

Species 2, etc. - x x(x) x(x) x(x) x xx x(x) x

1

The biosite index is based on the average injury score (amount*severity) for each species averaged across all species on the biosite. 2

Biosite categories represent a relative measure of tree-level response to ambient ozone exposure (see table 1 in the main body of the 

text).

3

HB = injury severity is an estimate of the mean severity of symptoms on injured foliage (0 = no injury; 1=1-6%; 2 = 7- 25%; 3 = 26-50%;

 4 = 51-75%; 5 >75%). Calculated percents are rounded to the nearest whole number. Terms are further described in the text.

*Standard errors can be presented, as needed, for the calculated variables.
19

Table 4.—Region-level summary statistics

ABC Region - Biomonitoring Program

Parameter 1994 1995 1996 1997 1998 1999 2000 2001 2002

Number of biosites evaluated xx xx xx xx xx xx xx xx xx

Number of biosites with injury x xx x x xx x x xx xx

Number of plants evaluated xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxx

Number of plants injured x xx xx xx xxx x x xxx x

Average biosite injury score1 x.x x.x x.x x.x x.x x.x x.x x.x x.x

Percent biosites with BI = 0 to 4.92 xx xx xx xx xx xx xx xx xx

Percent biosites with BI = 5 to 14.9 xx xx xx xx xx xx xx xx xx

Percent biosites with BI = 15 to 24.9 xx xx xx xx xx xx xx xx xx

Percent biosites with BI >= 25 xx xx xx xx xx xx xx xx xx

Average number of species per biosite xxxxxxxxx

Number of plants evaluated xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxx

Number of plants injured x xx xx xx xxx x x xxx x

Percent sample plants by HB category3

0 = no injury xx xx xx xx xx xx xx xx xx

1 = 1 to 6% xx xx xx xx xx xx xx xx xx

2 = 7 to 25% x x x x x x x x x

3 = 26 to 50% x x x x x - x x x

4 = 51 to 75% - x - x x - x x -

5 = >75% - x - x - - x x -

Number of plants evaluated by species

Species1 (#injured in parentheses) x (x) x(x) x(x) x(x) x(x) - x(x) x(x) x(x)

Species2,etc. - x x(x) x(x) x(x) x xx x(x) x

1

The biosite index is based on the average injury score (amount*severity) for each species averaged across all species on the biosite. 2

Biosite categories represent a relative measure of tree-level response to ambient ozone exposure (see table 1 in the main body of the 

text).

3

HB = injury severity is an estimate of the mean severity of symptoms on injured foliage (0 = no injury; 1=1-6%; 2 = 7- 25%; 3 = 26-50%;

 4 = 51-75%; 5 >75%). Calculated percents are rounded to the nearest whole number. Terms are further described in the text.

*Standard errors can be presented, as needed, for the calculated variables.

Note: Tables 3 and 4 provide an example of site-level summary statistics from State X and Region ABC. These 

two tables are core products for the ozone indicator. The summarized values show the base data used to generate 

the plot-level and population-level estimates as described in the text of this document. Individual states may 

choose to use the regional table as a basis of comparison to their summary statistics. Smaller states may choose to 

use the regional table for reports.
20

Table 5.—Number of biomonitoring sites evaluated for ozone-induced foliar symptoms, number of plants 

sampled, and percent of sampled plants in each injury severity category by year and subregion in FIA-North

Subregion and year1

No. of biosites 

evaluated

No. of plants 

sampled

Injury severity categories2

0 1 2 3 45

Percent of sampled plants

Lake States

 1996

 1997

 1998

 1999

 2000

95

104

160

143

160

3,880

4,584

9,012

10,949

12,647

99

99

97

97

97

1

1

2

2

2

<1

<1

1

1

1

<1

<1

<1

<1

<1

0

0

<1

<1

<1

0

<1

<1

<1

<1

New England

 1996

 1997

 1998

 1999

 2000

92

91

98

96

87

4,245

4,248

5,460

5,057

4,850

89

93

90

97

96

5

3

4

1

2

4

3

4

1

2

2

1

2

<1

<1

<1

<1

 <1

<1

<1

<1

<1

<1

<1

0

North Central

 1996

 1997

 1998

 1999

 2000

8

19

36

45

131

589

1,180

1,580

3,387

8,688

67

77

72

90

92

6

3

5

4

4

7

9

10

3

3

4

6

9

2

1

7

4

4

1

<1

9

<1

<1

<1

<1

Mid-Atlantic

 1996

 1997

 1998

 1999

 2000

34

60

170

191

182

1,244

2,908

6,384

10,941

12,762

82

93

78

97

93

5

2

5

1

2

5

2

7

1

2

5

2

5

1

1

2

1

3

<1

1

1

<1

2

<1

<1

Note: Table 5 is an example of site-level summary statistics using real data. It is sometimes useful to summarize 

ozone bioindicator data by multi-state groupings. For example, in the Northeast, it is informative to separate 

New England and New York from the Mid-Atlantic States because they tend to have dramatically different 

air quality regimes. Reporting a single regionwide injury index may mask gradations in air quality across the 

landscape and make it diffi cult to assess changes in the ozone indicator over time.

1 Subregions are defi ned as follows: Lake States = MI, MN, WI; New England = CT, ME, MA, NH, RI, VT; North Central = IL, IN, IA, MO; MidAtlantic = DE, MD, NJ, OH, PA, WV.

2

Injury severity is an estimate of the mean severity of symptoms on injured foliage (0 = no injury; 1=1-6%; 2 = 7- 25%; 3 = 26-50%; 4 = 51-

75%; 5 >75%). Calculated percents are rounded to the nearest whole number. 
21

Table 6.—Population estimates for the ozone indicator including acres of forest land and volume of ozonesusceptible tree species in each biosite index category by state and county in Region ABC. Real data for 

Delaware (2002) are presented.

State Cnty

Biosite Index1

<5 5 - <15 15 - <25 ≥25 <5 5 - <15 15 - <25 ≥25

Acres of forest land Volume of susceptible species

10 1 0 97,356 56,338 11,119 xxx xxx xxx xxx

10 3 0 3,954 71,164 69,434 xxx xxx xxx xxx

10 5 0 7,166 97,356 35,088 xxx xxx xxx xxx

State 2 1 xxx xxx xxx xxx xxx xxx xxx xxx

State 2 2 xxx xxx xxx xxx xxx xxx xxx xxx

State 2 3 xxx xxx xxx xxx xxx xxx xxx xxx

State 3 1 xxx xxx xxx xxx xxx xxx xxx xxx

State 3 2 xxx xxx xxx xxx xxx xxx xxx xxx

State 3 3 xxx xxx xxx xxx xxx xxx xxx xxx

1

The biosite index is based on interpolated values (see Plant-level Estimates, page 5, for details). Biosite categories represent a 

relative measure of tree-level response to ambient ozone exposure (see Table 1). 
22

Note: This map of ozone risk to plants is a core product for the ozone indicator. In this example, 

biosite index values were averaged across the 4-year sampling period from 1999 to 2002 and then 

geostatistical procedures were used to create an interpolated bioindicator response surface across 

the landscape (see Status Estimation, page 6). The interpolated data are classifi ed into color-based 

gradations of response representing low risk of probable ozone injury to forests (green), moderate risk 

(yellow), and high risk (red). These categories also provide an indication of ozone relative air quality 

with respect to a plant receptor (see Table 1). Intensifi ed sampling is recommended where high ozone 

stress coincides with the spatial distribution of ozone-sensitive tree species. Refer to Appendix II for 

more information on the ozone sensitivity of tree species.

The ozone risk map is used to estimate bioindicator attributes for all FIA plots using the procedures 

described in the section on Spatial Interpolation of the Bioside Index, page 7. BI attributes are 

merged with other FIA plot attributes to generate population estimates using the procedures 

described in the section on Estimating Status for Forested Areas, page 8.

Figure 3.—National map of ozone risk to plants. Categorized values are derived from the 

1999-2002 biosite data.
23

Note: The data presented in Figure 4 provide an example of ozone risk estimation at the State level. 

Biosite categories on the x axis represent the risk of probable ozone injury to ozone-sensitive tree 

species in South Carolina in 2002. More than 16 million cubic feet of tree volume was categorized for 

risk of ozone injury. Approximately 6 million cubic feet falls into the no risk category while just over 

10 million cubic feet of tree volume falls into the low to moderate risk categories. Fifty-three percent 

of the total categorized tree volume includes tree species that are ozone sensitive.

In this example, estimates are presented in terms of tree volume. However, other useful population 

estimates include the proportion of forest land and the acres of forest land in each biosite index 

category. Refer to Status Estimation, page 6 for the procedures used to estimate bioindicator 

attributes for forested areas.

Figure 4.—Total tree volume, tree volume for ozone-sensitive trees, and sensitive 

volume as a percent of total volume by ozone risk category for South Carolina (2002). 

Biosite Index and Risk Estimation

SOUTH CAROLINA

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

N o R isk Lo w R isk M o derate R isk T o tals

Biosite Index Categories

Tree Volume 

(million cu.ft.)

0

10

20

30

40

50

60

Percent of 

Total Volum

e

Total Volume Sensitive Volume Sensitive as P ercent of Total
24

Appendix II: Ozone Sensitivity of Tree and Shrub Species

The abbreviations used to assign sensitivity in the following tables are as follows: Sen = ozone 

sensitive, ModSen = moderately sensitive, InSen = ozone insensitive, Unk = unknown ozone 

sensitivity because there is evidence from different observers that is confl icting. Regional analysts 

should review both tables because species listed as eastern may be found in limited areas in Western 

States and visa versa. Additional ozone sensitivity listings of non-woody, forest species can be found 

at: http://www2.nature.nps.gov/air/Pubs/pdf/BaltFinalReport1.pdf.

Table 7. List of eastern tree and shrub species and their ozone sensitivity.

Table 8. List of western tree and shrub species and their ozone sensitivity.

Table 7.—List of eastern tree and shrub species and their ozone sensitivity

Eastern species Sensitivity Citation

balsam fi r Abies balsamea InSen1 Smith 1981

boxelder Acer negundo ModSen1 Smith 1981

striped maple Acer pensylvanicum Unk

red maple Acer rubrum Sen Eckert et al. 1999

silver maple Acer saccharinum Unk USDI 2003

sugar maple Acer saccharum InSen Renfro 1987-1992

mountain maple Acer spicatum Unk

Ohio buckeye Aesculus glabra Unk USDI 2003

yellow buckeye Aesculus octandra Sen2 USDI 2003

tree-of-heaven Ailanthus altissima Sen2 USDI 2003

speckled alder Alnus rugosa Sen2 USDI 2003

serviceberry Amelanchier arborea Sen Renfro 1987-1992

Allegheny seviceberry Amelanchierlaevis Unk USDI 2003

pawpaw Asimina triloba Unk 

yellow birch Betula alleghaniensis Sen Renfro 1987-1992

sweet birch Betula lenta Unk

paper birch Betula papyifera ModSen Eckert et al. 1999

gray birch Betula populifolia ModSen Eckert et al. 1999

bitternut hickory Carya cordiformis Unk

pignut hickory Carya glabra Unk

shagbark hickory Carya ovata Unk

hickory sp. Carya sp. Unk

mockernut hickory Carya tomentosa Unk

hackberry Celtis occidentalis Unk

common buttonbush Cephalanthus occidentalis Unk USDI 2003

eastern redbud Cercis canadensis ModSen, Sen2 Renfro 1987-1992, USDI 2003

yellowwood Cladrastis lutea Unk USDI 2003

Virgin’s bower Clematis virginiana Sen2 USDI 2003

fl owering dogwood Cornus fl orida ModSen Renfro 1987-1992

American hazelnut Corylus americana Sen2 USDI 2003

Hawthorn Crataegus sp. Sen3 Krupa et al. 1998 

common persimmon Diospyros virginiana Unk

American beech Fagus grandifolia Unk

white ash Fraxinus americana Sen Skelly 2000

black ash Fraxinus nigra Sen3 Krupa et al. 1998 

green ash Fraxinus pennsylvanica Sen Krupa and Manning 1988 

black huckleberry Gaylussacia baccata Sen2 USDI 2003

witch-hazel Hamamelis virginiana Unk USDI 2003

Continued
25

American holly Ilex opaca InSen1 Smith 1981

black walnut Juglans nigra Unk

eastern redcedar Juniperus virginiana Unk

tamarack (native) Larix laricina Unk

sweetgum Liquidambar stryracifl ua Sen Krupa et al. 1998

spicebush Lindera benzoin Unk USDI 2003

yellow-poplar Liriodendron tulipifera Sen Krupa and Manning 1988 

maleberry Lyonia ligustrina Sen2 USDI 2003

cucumbertree Magnolia acuminata Unk

apple sp. Malus sp. Unk

blackgum Nyssa sylvatica ModSen Renfro 1987-1992

sourwood Oxydendrum arboreum ModSen Renfro 1987-1992

Virginia creeper Parthenocissus quinquefolia Sen2 USDI 2003

sweet mock orange Philadelphus coronarius Sen2 USDI 2003

Norway spruce Picea abies InSen1 Smith 1981

white spruce Picea glauca InSen1 Smith 1981

black spruce Picea mariana Unk

red spruce Picea rubens InSen Eckert et al. 1999

Jack pine Pinus banksiana Sen2 USDI 2003

shortleaf pine Pinus echinata ModSen1 Smith 1981

table mountain pine Pinus pungens Sen Renfro 1987-1992

red pine Pinus resinosa InSen1 Smith 1981

pitch pine Pinus rigida InSen, Sen2 Eckert et al. 1999, USDI 2003

eastern white pine Pinus strobus Sen Krupa and Manning 1988 

Scotch pine Pinus sylvestris ModSen1 Smith 1981

loblolly pine Pinus taeda Sen Taylor 1994

Virginia pine Pinus virginiana ModSen, Sen2 Renfro 1987-1992, USDI 2003

American sycamore Platanus occidentalis Sen Krupa and Manning 1988 

balsam poplar Populus balsamifera Sen3 Krupa et al. 1998 

eastern cottonwood Populus deltoides Sen3 Krupa et al. 1998 

bigtooth aspen Populus grandidentata Sen3 Krupa et al. 1998 

quaking aspen Populus tremuloides Sen Krupa and Manning 1988 

wild plum Prunus americana Unk USDI 2003

pin cherry Prunus pensylvanica ModSen Renfro 1987-1992

black cherry Prunus serotina Sen Krupa and Manning 1988 

choke cherry Prunus virginiana ModSen Renfro 1987-1992

white oak Quercus alba InSen Renfro 1987-1992

scarlet oak Quercus coccinea ModSen1 Smith 1981

northern pin oak Quercus ellipsoidalis ModSen1 Smith 1981

southern red oak Quercus falcata Unk

shingle oak Quercus imbricaria InSen1 Smith 1981

bur oak Quercus macrocarpa InSen1 Smith 1981

pin oak Quercus palustris ModSen1 Smith 1981

willow oak Quercus phellos Unk

chestnut oak Quercus prinus Unk

northern red oak Quercus rubra InSen Eckert et al. 1999

post oak Quercus stellata Unk

black oak Quercus velutina ModSen1 Smith 1981

winged sumac Rhus copallina Sen2 USDI 2003

black locust Robina pseudoacacia ModSen, Sen2 Renfro 1987-1992, USDI 2003

Allegheny blackberry Rubus allegheniensis Sen2 USDI 2003

thornless blackberry Rubus canadensis Sen2 USDI 2003

Table 7.—continued

Eastern species Sensitivity Citation

Continued
26

sand blackberry Rubus cuneifolius Sen2 USDI 2003

black willow Salix nigra Unk

American elder Sambucus canadensis Sen2 USDI 2003

sassafras Sassafras albidum Sen Krupa et al. 1998 

common snowberry Symphoricarpos albus Sen2 USDI 2003

northern white-cedar Thuja occidentalis InSen Eckert et al. 1999

American basswood Tilia americana InSen1 Smith 1981

Chinese tallow Triadica sebifera Sen2 USDI 2003

eastern hemlock Tsuga canadensis InSen Renfro 1987-1992

American elm Ulmus americana Unk

slippery elm Ulmus rubra Unk

northern fox grape Vitis labrusca Sen2 USDI 2003

Table 7.—continued

Eastern species Sensitivity Citation

1

Based on relative sensitivity to acute ozone exposure. 2

Based on sensitivity to ambient ozone concentrations in the fi eld and exposure chamber. 3

Based on relative sensitivity of genus, not species.

Table 8.—List of western tree and shrub species and their ozone sensitivity

1

Pinus contorta var. latifolia. 2

Pinus ponderosa var. ponderosa. 3

Based on relative sensitivity to acute ozone exposure.

4

Based on sensitivity to ambient ozone concentrations in the fi eld and exposure chamber. 5

Based on relative sensitivity of genus, not species.

Western species Sensitivity Citation

red alder Alnus rubra Sen3 Brace et al. 1996

Sitka alder Alnus sinuata Sen Brace et al. 1996

western serviceberry Amelanchier alnifolia ModSen3 Brace et al. 1996

single-leaf ash Fraxinus anomala Sen4 USDI 2003

twinberry Lonicera involucrata Sen4 USDI 2003

lodgepole pine Pinus contorta1 ModSen3 Brace et al. 1996

Jeffrey pine Pinus jeffreyi Sen Miller et al. 1996

ponderosa pine Pinus ponderosa2 Sen Smith 1981

Monterey pine Pinus radiata Sen4 USDI 2003

Pacifi c ninebark Physocarpus capitatus Sen3 Brace et al. 1996

mallow ninebark Physocarpus malvaceus Sen3 Brace et al. 1996

Fremont cottonwood Populus fremontii Sen4 USDI 2003

quaking aspen Populus tremuloides Sen Smith 1981

black cottonwood Populus trichocarpa ModSen3 Brace et al. 1996

Douglas-fi r Pseudotsuga menziesii ModSen3 Brace et al. 1996

California black oak Quercus kelloggii ModSen Miller et al. 1996

skunk bush Rhus trilobata Sen Temple 2000

thimbleberry Rubus parvifl orus Sen4 USDI 2003

Gooding’s willow Salix gooddingii Sen4 USDI 2003

Scouler’s willow Salix scouleriana Sen4 Brace et al. 1996

willow sp. Salix sp. ModSen5 Krupa and Manning 1988

blue elderberry Sambucus mexicana Sen Temple 2000

red elderberry Sambucus racemosa ModSen3 Brace et al. 1996

common snowberry Symphoricarpos albus Sen4 USDI 2003

snowberry sp. Symphoricarpos sp Sen5 Smith 1981

western hemlock Tsuga heterophylla ModSen3 Brace et al. 1996

huckleberry Vaccinium membranaceum Sen4 USDI 2003

huckleberry sp. Vaccinium sp. ModSen3 Brace et al. 1996
27

Appendix III: Documents for Ozone Information Management

Documents to assist FIA regional analysts with ozone information management:

1. Flow of Ozone Data from the Field to the FIA Information Management System

2. Ozone Standard Summary Tables in the FIA Data Base (FIADB)

3. Formulation of the Biosite Index

4. SAS Code for Biosite Tables and Maps

5. Contact List for Ozone Data Management

To obtain the following documents, email Gretchen Smith at gcsmith@nrc.umass.edu

1. Computation specifi cations for derived ozone data in FHM

2. Ozone bioindicator attribute defi nitions for FIADB

3. Ozone data collection start dates by state and year

4. Crosswalk tables for tracking changes to the ozone sample from 1994 to the present

5. Sample biosite fi eld map

6. National ozone risk map for the sampling period 1994-1998

7. National ozone risk map for the sampling period 1999-2002

8. National map of the 7-year average ozone exposure 1996-2002
28

Flow of Ozone Data from the Field to FIA Information Management

The goals of ozone information management are to clean up the ozone data fi les collected by the fi eld crews, correct the regular 

crew and QA crew data fi les so they are compatible with the leaf voucher data, and generate ozone summary statistics suitable 

for further analysis and reporting. The summary statistics are used to generate an ozone risk map and population metrics as 

described in the main body of this document.

Step 1:

Each Regional Analyst works with the raw data fi le entered by the fi eld crew and the validation fi le created by the National 

Indicator Advisor.

Step 2: 

The Regional Analyst/P3 Data Processor in each region takes the raw data fi les entered by the fi eld crew and the validation 

fi le created by the National Indicator Advisor and loads both into NIMS (National Information Management System). The 

P3 LAB system-checker program determines errors between the validation fi le and the raw data. Differences between these 

two fi les must be resolved at the regional level through direct communication between the National Indicator Advisor and the 

Regional Analyst. Error resolution requires changes to both the raw data fi le and the validation fi le.

Note: It is sometimes helpful to resolve differences between these two fi les before loading the data into NIMS. Software to 

assist with this process is available. Once the data are loaded, the checker program is used as a fi nal edit.

Step 3: 

The Regional Analyst/P3 Data Processor in each region runs the P3 LAB system-report program on the validated ozone data. 

The report program creates three ozone standard summary tables: OZONE_PLOT_SUMMARY, OZONE_SPECIES_

SUMMARY, and OZONE_BIOSITE_SUMMARY.

Step 4: 

The Regional Analyst/P3 Data Processor contacts Brian Cordova, FIA-IM, at: cordovab@unlv.nevada.edu. Each region’s data 

are captured and placed on the national NIMS Web site. Sensitive information is stripped (NULLED), and the remaining 

information is posted on the national FIA database (FIADB) P3 Web site and the FIADB Data Mart, which is the data 

distribution system to the public.

Note: Step 2 instructs the Regional Analyst to load the data into NIMS. Until the new TALLY program is completed, 

the raw TALLY fi les are parsed using TALLY Cracker and inserted into the LOAD tables in NIMS. The data are moved 

from the LOAD tables to the NIMS tables through the front-end, which is a graphical interface used to load and drop 

data, run computations and reports, etc. The front-end is again used to load the validation fi le and create a report of any 

errors. In the future, Step 4 will be a direct upload to the NIMS FIA-P3 Web site via the front-end interface.

Additional Steps:

The NIMS ozone summary tables provide biosite summary statistics suitable for preliminary reports at the State and regional 

levels (see Output Tables and Maps). SAS routines are available that generate additional summary statistics from the validated 

ozone fi les. For example, one routine generates all the necessary values to create a summary table that presents numbers of 

biosites evaluated, number of plants sampled by species, and percentage of sampled plants in each injury severity category. 

Another SAS routine is available that generates a biosite list with presence or absence of ozone injury to use for an ozone site 

distribution map. This map is useful for tracking changes over time in the number and distribution of plus ozone sites across a

State or region.

The OZONE_BIOSITE_SUMMARY table includes the biosite-level ozone injury index referred to as the Biosite Index (BI). 

Using a 5-year rolling average of the BI, an FIA Spatial Data Analyst creates the national risk map of probable ozone injury. A

new map is produced every year. This map surface is stored in the FIADB Data Mart so that it can be extracted by Regional 

Analysts, in whole or in part, as needed. FIA Spatial Data Services uses the national map to generate an estimated BI value for

every P2 ground plot. This biosite attribute is added to the larger P2 table of plot attributes in the FIADB Data Mart. This will 

allow FIA analysts to examine relationships between bioindicator attributes and other indicators of tree growth, forest health,

and condition. FIA Spatial Data Services also maintains a master list of geographical coordinates for the ozone sampling grid 

and crosswalk tables that link biosites on the FHM-P3 grid (1994-2001) to biosites on the FIA Ozone Grid (2002-present).

Analysts responsible for 5-year reports or comprehensive regional reports should refer to the main body of the text of this 

document for detailed guidance on the analytical techniques used to generate FIA P2 plot-level metrics of the ozone data. 

The companion user guide for the ozone indicator provides (1) examples of output tables and maps using real data, and (2) 

additional interpretive guidance on the issues associated with ozone air quality and forest health.
29

Three Ozone Standard Summary Tables in the FIA Data Base

Ozone Species Summary Table 

 Statecd number(2) not null

 countycd number(2) not null

 p3hex number(7) not null

 p3plot number(1) not null

 plot number(8) not null

 measyear number(4) not null

 bio_species_cd number not null

 amount_maximum number not null

 amount_minimum number not null

 amount_mean number not null

 severity_maximum number not null

 severity_minimum number not null

 severity_mean number not null

 plants_inj_cnt number not null

 plants_eval_cnt number not null

 plants_ratio number not null

 bio_species_sum number not null

 bio_species_index number not null

 elev number not null

 pltsize number not null

 aspect number not null

 terrpos number not null

 soildpth number not null

 soildrn number not null

 soildrn number not nu

 plotwet number not null

 pltdstrb number not null

Ozone Plot Summary Table

 Statecd number(2) not null

 countycd number(2) not null

 p3hex number(7) not null

 p3plot number(1) not null

 plot number(8) not null

 measyear number(4) not null

 species_eval_cnt number not null

 biosite_index number not null

 elev number not null

 pltsize number not null

 aspect number not null

 terrpos number not null

 soildpth number not null

 soildrn number not null

 soildrn number not null

 plotwet number not null

 pltdstrb number not null

Ozone Biosite Summary Table

 Statecd number(2) not null

 countycd number(2) not null

 p3hex number(7) not null

 location_cnt number(1) not null

 plot number(8) not null

 measyear number(4) not null

 plant_inj_cnt number not null

 plant_eval_cnt number not null

 plant_ratio number not null

 species_eval_cnt number not null

 biosite_index*** number not null

 svrty_class_zero number not null

 svrty_class_one number not null

 svrty_class_two number not null

 svrty_class_three number not null

 svrty_class_four number not null

 svrty_class_fi ve number not null

***See notes on the formulation of the biosite_index on the following page.
30

Formulation of the Biosite Index (BI)

Note: The ozone indicator site-level biosite index was formulated with the assistance of David Randall, Statistician for 

the USDA Forest Service, Northeastern Area, Washington Offi ce.

Notes on the formulation:

There are 3 components to the formulation: (1) the amount of injury, (2) the severity of injury, and (3) the incidence of 

injury on the site. The formulation selected associates these three components at the individual plant level. This suggests 

that the ozone injury response of each individual plant is important. This is biological reality and better than lumping 

all species together.

The calculation is intuitive. A mean value is calculated that truly represents a proportion of the population at both the 

plant level and the species level. An arithmetic mean is then taken for the “n” species on the plot.

Notes on method:

Each plant observed by the fi eld crew is rated for the percent of the plant that is injured (i.e., injury amount) and the 

average severity of injury (i.e., injury severity) using a modifi ed Horsfall-Barrett scale with breakpoints at 6, 25, 50, 75, 

and 100 percent. This information is used to calculate an injury value for each plant, a mean value for each species, and 

an overall site mean. The incidence of injury on the site is also considered. The formulation is based on the fact that 

each individual plant has a unique response to ozone that is dependent on the genotype and microhabitat at the time of 

exposure.

For each plant:

AMT = injury amount

SEV = injury severity

For each species:

N1

 = the number of injured plants

N2 = the number of evaluated plants

A = N1 / N2

B = ∑[(AMT) (SEV)] / N1

bio_species_index = (A)(B)

For each biosite (hexagon number):

N3 = the number of evaluated species

biosite_index = ∑(bio_species_index) / N3

Notes on transforming crew values from the ordinal scale to the percent scale:

In the fi eld, the crews estimate the percent injury to the plant and then assign ordinal values that refl ect 5 broad classes 

of injury as follows: 0 = no injury; 1 = 1-6% injury; 2 = 7-25% injury; 3 = 26-50% injury; 4 = 51-75% injury; and 5 = 

≥75% injury.

In the offi ce, the ordinal codes recorded by the fi eld crews are converted to percentage values representing the midpoint 

of each injury class as follows: 0 = 0; 1 = 3.5%; 2 = 16%; 3 = 38%; 4 = 63%; and 5 = 88%. Theoretically, the sitevalue has a range from zero to 100. In reality, the highest values are less than 25 and most are less than 5. A site with no 

injury has an index of zero.

The measurement intervals on the two scales are different. The intervals on the ordinal scale are equal; those on the 

percent scale are not. Midpoint values are used rather than ordinal values for the site-level index because (1) the 

midpoint percentage values bring the reader back to the original scale used by the fi eld crew to rate the injured plants; (2) 

the percentage values have some intuitive biological relevance, unlike the ordinal scale, which was developed largely as a 

matter of convenience for the fi eld crews; and (3) readers relate more easily to the percentage values than the ordinal scale.

It is understood that the data transformation introduces some error or misrepresentation into the reporting of the biosite_

index. Nevertheless, as long as the reader knows how the biosite-value was calculated, the midpoint percentage is still 

preferable because it provides a more meaningful image of ozone injury than would be provided by the ordinal scale.
31

SAS CODE: Biosite Tables

The following routine will generate summary statistics similar to those presented in Table 5.

The number of plots evaluated by State

The number of plants sampled by State

The percent of sampled plants in each HB injury severity class by State 

This is a highly defensible presentation of the ozone indicator data for State and regional reports.

/  fi lename o3_summary.sas Jan 2004 Barbara O'Connell** **/

/ Program to produce percent injured by severity by State ** ***/

/*** must fi rst run vouch03.sas, biovch03.sas, and biocor03.sas to create corrected data set****/

options ls=95 ps=1000 obs=max;

libname perm ‘c:\_barbo\2003\analysis\ozone\’;

data o3sum03; set perm.biocor03; *biocor03 is raw tally fi les corrected as per validation data;

 if qa_stat=1; *include only the regular crew data;

 if severity =. then severity=0;

 if amount>0 then nplants=1;

proc sort data=o3sum03; by State hex_num;

proc freq data=o3sum03;

tables severity/norow nocol nofreq nocum;

by State;

weight nplants;

title1 ‘2003 Ozone Severity’;

proc summary data=o3sum03; * to get the number of plots surveyed per State;

 class hex_num;

 id State;

 output out=all03;

data all03; set all03; total=_freq_; drop _type_ _freq_;

proc sort data=all03;

by State hex_num;

proc summary data=all03;

class State;

var hex_num;

output out=noplots n=;

proc print;

title1 ‘2003 Ozone plots’;

proc summary data=o3sum03; * to get the number of plants sampled;

class State;

var nplants;

output out=spcnt sum=;

proc print;

title1 ‘2003 Nbr. of Plants Sampled’;

run;

Note: The SAS routine was written by Barbara M. O’Connell, Forester, USDA Forest Service, Northern Research 

Station, FIA, Newtown Square, PA.

•

•

•
32

SAS CODE: Maps

The following routine will generate summary statistics that can be used to create a State or regional map of ozone 

biomonitoring sites with and without ozone injury. This type of map is useful for documenting status and change in 

the number and distribution of biosites with ozone injury across a State or region.

/**** fi lename biomap03.sas March 1998  /

/** * updated Jan 2004 Barbara O’Connell****/

/*** must fi rst run vouch03.sas, biovch03.sas, and biocor03.sas to create corrected data set****/

options ls=95 ps=1000 obs=max;

libname perm ‘c:\_barbo\2003\analysis\ozone\’;

data bio03; set perm.biocor03;

 if qa_stat=1; *include only the regular crew data;

proc summary nway data=bio03; *to create hex_num data set;

 class hex_num;

 id State;

 output out=all;

data all; set all; drop _type_ _freq_;

proc sort data=all; by hex_num ;

data temp; set bio03; if amount > .5; *include only injured data;

proc summary nway data=temp; *to create data set with hex avg injury and total nbr damaged;

 var amount severity;

 class hex_num ;

 output out=damaged mean= ;

data damaged; set damaged; damaged=_freq_; drop _type_ _freq_;

proc sort data=damaged; by hex_num;

data summary;

 merge all damaged;

 by hex_num ;

 if damaged=. then damaged=0; if amount < .5 then amount=0;

 if severity < .5 then severity=0;

data fi nal; set summary;

if amount = 0 then infect=2;

if amount > .5 then infect=1;

proc sort;

by State hex_num;

proc print r;

title1 ‘FHM 2003 Bioindicator’;

title2 ‘no. of plants with injury and avg amount/severity per plot’;

title4 ‘injury: 1=injury detected 2=no injury’;

var State hex_num infect damaged amount severity;

run;

Note: The SAS routine was written by Barbara M. O’Connell, Forester, USDA Forest Service, Northern Research 

Station, FIA, Newtown Square, PA.
33

Contact List for Ozone Data Management

National Indicator Advisors:

John Coulston, FS – Southern Research Station, Knoxville, TN, jcoulston@fs.fed.us

Gretchen Smith, UMass-Amherst, gcsmith@nrc.umass.edu

Information Management: For access to validated data fi les, summary statistics, and map products.

Brian Cordova, UNLV, cordovab@unlv.nevada.edu

Chuck Liff, FS – Rocky Mountain Research Station, Ogden, UT, cliff@fs.fed.us

John Coulston, FS – Southern Research Station, Knoxville, TN, jcoulston@fs.fed.us

National QA: For information on QA analyses of the fi eld data at the national level.

Jim Pollard, UNLV, pollardj@unlv.nevada.edu

Bill Smith, FS – RTP. bdsmith@fs.fed.us

Regional Analysts: For information on analyses and reports and for access to regional fi les.

Barb O’Connell, FS – Northern Research Station, Newtown Square, PA. boconnell@fs.fed.us 

Randy Morin, FS - Northern Research Station, Newtown Square, PA. rsmorin@fs.fed.us

Ed Jepsen, WI Department of Natural Resources. jepsee@dnr.state.wi.us

Mark Hansen, FS – Northern Research Station. St. Paul, MN. mhansen01@fs.fed.us

John Simpson, FS – Southern Research Station, Knoxville, TN. jsimpson03@fs.fed.us

Anita Rose, FS – Southern Research Station, Knoxville, TN. anitarose@fs.fed.us

Mark Ruby, FS – Rocky Mountain Research Station, Ogden, UT. mruby@fs.fed.us 

James Menlove, FS - Rocky Mountain Research Station, Ogden, UT. jmenlove@fs.fed.us

Ron Wanek, FS – Pacifi c Northwest Research Station, Portland, OR. rwanek@fs.fed.us

Sally Campbell, FS – Pacifi c Northwest Research Station, Portland, OR. Scampbell01@fs.fed.us

Regional Advisors: For information on ozone implementation, training, and QA procedures. 

Gretchen Smith, UMass-Amherst, gcsmith@nrc.umass.edu

Ed Jepsen, WI DNR, jepsee@dnr.state.wi.us

Dan Stratton, FS – Southern Research Station, Knoxville, TN. dstratton@fs.fed.us

Bill Dunning, FS – Rocky Mountain Research Station, Ogden, UT. bdunning@fs.fed.us

Sally Campbell, FS – Pacifi c Northwest Research Station, Portland, OR. scampbell01@fs.fed.us

Where to fi nd more information on the biomonitoring program and ozone data.

http://nrs.fs.fed.us/fi a/topics/ozone/default.asp This is the ozone indicator home page. Click on links to 

data, methods, ozone sensitive species, publications, contacts, and other information.

http://fi a.fs.fed.us/ This is the FIA home page. Enter ‘ozone’ in the search window for access to the 

ozone indicator home page; Click on FIA Data and Tools for access to fi eld data, standard summary 

tables, and the FIADB user guide for fi eld attributes. Click on FIA Library for access to the fi eld manual 

and fact sheet.

http://fhm.fs.fed.us/ This is the FHM home page. It includes fact sheets for all of the P3 forest health 

indicators.

FIA uses the Oracle Database Management System to process and store the ozone indicator data 

(also called NIMS – National Information Management System). Raw data from fi eld-collected tally 

fi les and sample voucher validation fi les are loaded onto fi ve standard tables (OZONE_PLOT_TBL, 

OZONE_VISIT, OZONE_SPECIES, OZONE_PLOT_NOTES, and OZONE_VALIDATION). 

Further processing computes indices and creates three standard summary tables (OZONE_BIOSITE_

SUMMARY, OZONE_PLOT_SUMMARY, and OZONE_SPECIES_SUMMARY), which are used 

as the presentation data in the FIADB (FIA database) after sensitive information has been stripped. 

The database structure is such that fl at fi les can be easily produced for users who do not have access 

to, or the capability of, database management on their own computers. ASCII data fi les (1994 to 

present), two core ozone maps, and a core list of ozone-sensitive tree and shrub species are available 
34

for download from the FIADB Data Mart (the data distribution system to the public). The fi rst 

map product is the national ozone risk map that provides an interpolated surface of probable ozone 

injury across the landscape. The second map product is an interpolated surface of ambient ozone 

concentrations. Data users select their area of interest (e.g., state, region, or eco-region) from these 

two map products, and use the procedures outlined in the ozone estimation document to calculate 

and interpret population metrics for the ozone indicator. If you have trouble accessing Web sites or 

data fi les, contact the National Ozone Advisor, the FIA analyst in your region, or Brian Cordova at 

cordovab@unlv.nevada.edu. 
Printed on Recycled Paper

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on 

the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial 

status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because 

all or part of an individual’s income is derived from any public assistance program. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternate means for communication 

of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 

(202)720-2600 (voice and TDD). To fi le a complaint of discrimination, write to USDA, Director, Offi ce 

of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call (800)795-3272 

(voice) or (202)720-6382 (TDD). USDA is an equal opportunity provider and employer. 

Smith, Gretchen C.; Smith, William D.; Coulston, John W. 2007. Ozone Bioindicator 

Sampling and Estimation. Gen. Tech. Rep. NRS-20. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northern Research Station. 34 p.

Ozone is an important forest stressor that has been measured at known phytotoxic 

levels at forest locations across the United States. The percent forest exhibiting 

negative impacts from ozone air pollution is one of the Montreal Process indicators 

of forest health and vitality. The ozone bioindicator data of the U.S. Forest Service 

Forest Inventory and Analysis Program (FIA) are the only source of information 

available that documents plant injury from air pollution using consistent protocols. 

This document introduces the FIA ozone indicator and describes the sampling and 

estimation procedures of the national biomonitoring program. We provide background 

material on ozone, examples of bioindicator summary statistics, a description of spatial 

interpolation, and methods to estimate status and change in forested areas with respect 

to the occurrence of ozone injury from ambient ozone concentrations. The goal is to 

provide guidance to analysts and researchers on ways to incorporate ozone bioindicator 

data into reports and research studies. Periodic recommendations to analysts on 

improved analytical techniques will be made. Analysts are encouraged to consult the 

companion user guide for additional guidance on interpreting the ozone biomonitoring 

data and reporting on the issue of ozone and forest health for the FIA program.

KEY WORDS: air quality, bioindicator species, biomonitoring, forest health, kriging, 

ozone, ozone sensitive, risk assessment, spatial interpolation
Capitalizing on the strengths of existing science 

capacity in the Northeast and Midwest to attain a more 

integrated, cohesive, landscape-scale research program