United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-333

May 1994

Monitoring Larval Populations of

the Douglas-Fir Tussock Moth and

the Western Spruce Budworm on

Permanent Plots: Sampling Methods

and Statistical Properties of Data

A.R. Mason and H.G. Paul
Authors R.R. MASON is

a principal insect ecologist and H.G. PAUL isa forestry technician,

Forestry and Range Sciences Laboratory, 1401 Gekeler Lane, La Grande, Oregon

97850.
Abstract Mason, A.R. ; Paul, H.G. 1994. Monitoring larval populations of the Douglas-fir

tussock moth and western spruce budworm on permanent plots: sampling

methods and statistical properties of data. Gen. Tech. Rep. PNW-GTR-333.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 22 p.

Procedures for monitoring larval populations of the Douglas-fir tussock moth and

the western spruce budworm are recommended based on many years experience

in sampling these species in eastern Oregon and Washington. It is shown that

statistically reliable estimates of larval density can be made for a population by

sampling host trees in a series of permanent plots in a geographical monitoring unit.

The most practical method is to estimate simultaneously densities on a plot of both

insect species by the nondestructive sampling of foliage on lower crown branches of

host trees. This can be done either by counting all larvae on sample branches or by

estimating the frequency of occurrence of a selected threshold number of larvae in

samples. Statistics are given on the expected within- and between-plot variances

and the number of sample plots needed in different sized monitoring units. In large

monitoring units, plot densities of tussock moth and budworm larvae usually are not

normally distributed, but they can be normalized by logarithmic transformation to

predict the probability of subpopulations of any given density occurring somewhere in

the unit. It is urged that sampling methods be consistent and that monitoring be done

annually to accumulate continuous databases that reflect the behavior of defoliator

populations over a long period.

Keywords: Ecological monitoring, population monitoring, sampling insects, Douglas-fir

tussock moth, Orgyia pseudotsugata, western spruce budworm, Choristoneura

occidentafis.
1 Introduction

1 Development of Sampling Methods and Data Sets

3 The Sampling Approach

3 Terminology and Definitions

4 Middle Crown Density as a Standard Index of Abundance

4 Permanent Sample Plots

5 Sample Branches

5 Age of Sampled Larvae and Timing

6 Beating Techniques and Equipment

6 Estimating Plot Density

6 Complete Count of Larvae in Branch Samples

7 Frequency of Occurrence of Larvae in Branch Samples

8 Converting Sample Mean to Middle Crown Density

9 Estimating and Interpreting Population Densities

9 Population Mean of Monitoring Unit

11 Number of Sample Plots

13 Population Normality and Predicting Defoliation

19 Conclusions

21 Acknowledgments

21 English Equivalents

21 Literature Cited

Contents
Introduction

Development of

Sampling Methods

and Data Sets

Monitoring is the systematic inventorying of insect populations over time. Its principal purpose is to track changes in the abundance of problem species to discover

temporal and spatial patterns in population fluctuation. In the short term, monitoring

information has immediate application for determining the present status of pest

populations and planning pest management activities. Cumulative monitoring data

also are needed for long-term research projects, such as building theoretical models

to predict population trends, performing time-series analyses to identify the underlying

causes of population change, and evaluating the long-term effects of pest management practices. Findings from these kinds of consistent efforts eventually will help in

developing practical prescriptions needed in modern pest management.

The Douglas-fir tussock moth (Orgyia pseudotsugata (McDunnough)) and western

spruce budworm (Choristoneura occidentalis Freeman) are the two most important

defoliators of Douglas-fir (pseudotsuga menziessi (Mirb.) Franco) and true firs

(Abies spp.) in the Western United States (Brookes and others 1978,1987). Various

methods have been used to monitor their populations (Dahlsten and others 1992,

Daterman and others 1979, Mason 1979, Shepherd and Otvos 1986, Srivastava and

others 1984, Sweeney and others 1990). In recent years, sampling larvae by beating

branches in the lower crown has become a popular technique. The success of this

method is due primarily to the advantage of not having to clip branches higher in

the tree and to the efficiency that comes with one person performing all phases of

sampling on a tree. The procedure, therefore, is especially efficient and cost-effective.

Although restricting sampling to only the lower crown foliage has sometimes been

criticized, the method consistently has produced accurate estimates of tussock moth

and budworm abundance and now has emerged as the method of choice for most

larval sampling in the Pacific Northwest.

Because lower crown sampling is relatively inexpensive, monitoring studies employing

this technique have been sustained for over 20 consecutive insect generations in

some areas. Such long-term databases will be critical in the future for determining

the overall response of defoliator populations to silvicultural treatments proposed

as part of east-side forest restoration and ecosystem management. New programs

concerned with managing the important pests affecting forest health should involve

maintaining the continuity of these already existing data sets.

In this report, we review the techniques recommended for monitoring tussock moth

and budworm larvae in the lower crown and summarize the statistical characteristics

of data encountered over many seasons of monitoring natural populations of these

insects.

The sampling procedures reported evolved over a long period of development and

have been field tested under many different forest conditions in the Western United

States. Most of the methodologies are already published; appropriate literature

citations are listed with their descriptions. The recommended techniques are efficient

and deliberately simplified to ensure that monitoring remains practical and can be

done at minimum expense. At the same time, they are statistically reliable and

proven procedures for monitoring tussock moth and budworm populations.
Figure 1-National Forests and approximate locations of monitored

populations.

Monitoring began in 1971 with a larval inventory in six study areas in a rising

population of the Douglas-fir tussock moth in the Blue Mountains of northeastern

Oregon. As sampling techniques improved and monitoring became more practical,

new study areas were established in other parts of the Northwest. Sampling was

extended in 1984 to include larvae of the western spruce budworm so that both

species could be monitored simultaneously on the same plots. As of 1992, annual

monitoring studies included 48 permanent plots representing the behavior of tussock

moth and budworm populations in parts of five National Forests in Oregon and

Washington (fig. 1). Over the 22 years from 1971 to 1992, 1,028 plot-years of

monitoring data were collected for the two species (table 1). This is one of the

most extensive databases available on the variability and abundance of these two

defoliators and contains invaluable information for planning new monitoring programs

and interpreting their results.

2
Umatilla (OR) 246 154

Wallowa-Whtman (OR) 124 80

Winema (OR) 134 59

Okanogan (WA) 68 52

Wenatchee (WA) 63 48

 ----------- --------- -­

Total 635 393

Table 1 - Number of plot-years of tussock moth and budworm

monitoring data collected in Oregon and Washington between

1971 and 1992

National Forest

Douglas-fir

tussock moth

Western spruce

budworma

- - - - - Plot-years - - - - -

a

Included in this group is the Modoc budworm, Choristoneura viridis Freeman,

which was the principal budworm species monitored in the Winema National

Forest.

The Sampling

Approach

Terminology and

Definitions

In explaining the approach recommended, some terms are used that have specific

meanings in the sampling protocol. The following definitions of key terms are given

to help in understanding how this nomenclature applies to the particular methods of

monitoring that are described:

Branch area -Area of the plane occupied by a foliated branch when the branch is

placed on a flat surface. A square meter of branch area is the standard unit of

measure for expressing larval density. In lower crown sampling, a square meter

of branch area is assumed to contain 9.3 45-cm branch tips, or 3.1 secondary

sampling units.

Density index- Estimated middle crown density, assumed to be correlated with the

true mean number of larvae at all crown levels.

Monitoring un ti - A specific geographical or management area for which an estimate

of population density is made.

Plot - An approximately 2-ha area occupied by host trees whose lower branches are

accessible for sampling.

Plot density - Estimated - mean number of larvae per square meter of branch area in

the middle crown of trees in a plot.

Population -The collection of all larvae of the Douglas-fir tussock moth or western

spruce budworm in the tree crowns of a defined monitoring unit.

Population density -Estimated mean number of larvae per square meter of branch

area in the middle crown of trees in a monitoring unit.

Primary sampling unit -A predetermined number of sample trees drawn from a plot.

Sample mean -The mean number of larvae per three-branch sampling unit in a plot.

Secondary sampling unit -Three 45-cm branch tips in the lower crown of a sample

tree in the primary sampling unit from which larvae are dislodged and counted.

3
Middle Crown Density

as a Standard Index

of Abundance

Permanent Sample Plots

4

The year-to-year abundance of tussock moth and budworm larvae traditionally has

been expressed by the number of individuals per standard unit of foliage or branch

area (Carolin and Coulter 1972, Mason 1970, Morris 1957, Srivastava and others

1984). Because foliage is a variable quantity as opposed to a fixed unit of space like

a hectare, such an expression of larval density is only an index of insect abundance

in relation to available food or branch habitat. This can present problems in the

analysis of long-term data sets, because changes in the quantity of foliage may alter

the index value even though there has been no change in the absolute number of

insects. Such relative indices of density, however, are more practical to estimate

than absolute population density and have a long history of successful use in forest

stands where the foliage base is relatively stable across years.

The random or systematic sampling of foliage throughout the whole tree crown is

completely impractical operationally. Density indices for tussock moth or budworm,

therefore, have traditionally been estimated for only the portion of the crown originating from the middle one-third of the bole on trees up to 15 m tall. This section

of the crown, commonly called the middle crown, contains a majority of the tree's

foliage and has been shown in numerous sampling studies to be a good indicator

of average larval density over the whole crown (Campbell and others 1984, Mason

1970, Srivastava and others 1984). The density of larvae in the middle crown,

therefore, is commonly used as the standard index of abundance for monitoring

both the Douglas-fir tussock moth and the western spruce budworm. Middle crown

density can be estimated either directly by drawing samples from the middle portion

of the crown or indirectly by predicting from samples taken from the more accessible

lower crown. Because the lower stratum of foliage within reach from the ground is

easy to sample and the number of larvae found there is a good predictor of middle

crown density, sampling lower branches is now the preferred technique for estimating the standard index of abundance (Mason 1977, 1987; Mason and others

1989; Torgersen and others 1993).

The monitoring of larval abundance is a two-stage process involving the sampling

of foliage in a small area to estimate a plot density and the sampling of a number

of plots in a large area to estimate a population density. Each plot necessarily consists of a cluster of the principal host trees, Douglas-fir and true firs, with foliage

low enough in the crown to be sampled from the ground. A plot is without explicit

boundaries but, in practice, is about 2-ha, within which a fixed number of trees

(primary sampling unit) are sampled. Trees sampled are selected indiscriminately

in proportion to their occurrence in the plot so that results reflect the species

composition of the area.

Resampling of the same plots annually is desirable to take advantage of previously

located sites, forest types, and tree sizes already identified as meeting monitoring

specifications. By using permanent plots, many of the same trees are sampled

each year, thus reducing the effect of spatial variation and increasing sensitivity for

detecting changes in insect density (Morris 1960). Analyses of overall population

trends also are most sensitive when data from permanent plots in succcessive years

can be treated as paired variates over a large monitored unit. Because the beating

of lower branches is nondestructive, plots can be resampled year after year without

damage to the trees.
Sample Branches

Age of Sampled Larvae

and Timing

Figure 2-Number of trees required to estimate the density of tussock moth or

budworm larvae on a plot with a standard error of 10, 15, or 20 percent of the

mean at a 68-percent probability level.

The sample on a host tree is three 45-cm branch tips (secondary sampling unit)

within reach from the ground. On average, 3.1 sampling units or 9.3 45-cm branch

tips of lower crown foliage comprise a square meter of branch area (Mason 1977,

1987). Depending on abundance of the two defoliators and the desired sampling

precision, samples from 20 to 50 trees on a plot are needed each year. Normally,

this sample size will give an estimate of plot density for either tussock moth or

budworm with a standard error that is less than 20 percent of the mean at the

68 percent probability level (fig. 2). For the same relative precision, however, more

samples are always needed at lower than at higher densities. The optimum combination of number of trees and permanent plots required in a particular situation can

be calculated, if needed, by a multistage sampling program that takes costs as well

as intraplot and interplot variances into consideration (Hazard and Stewart 1974).

Sampling comparable life stages each year is critical to the monitoring process. If

there are large differences in the larval stages sampled from year to year, serious

errors will be made in interpreting changes in population density. The larval stages

preferred for monitoring are instars I and II of tussock moth, frequently called early

larvae; and instars III, IV, and V of budworm, often called nominal fourth instars

(color plates A and B, respectively). Early larvae of tussock moth are favored

because they are more numerous than other instars and occur throughout the

foliage, albeit in varying proportions. As a result, they have a relatively low intraplot

variance and a higher probability of being found at low densities (Mason 1970).

Nominal fourth-instar budworm also are well distributed and, after leaving the buds,

are the youngest larvae to be dislodged easily from the foliage by beating (Srivastava

and others 1984).

5
Beating Techniques

and Equipment

Estimating Plot

Density

Complete Count

of Larvae in

Branch Samples

6

Early tussock moth larvae and nominal fourth instar budworm are found on the

foliage about the same time of the year and can be sampled simultaneously. Both

species normally reach these stages of development sometime after all buds have

burst on host trees and when new shoots have elongated at least 4 to 6 cm (color

plates C an D) (Wickman 1978). To assist in the correct timing of sampling, many

studies have related insect development to measurable indices reflecting the onset

of warm weather in the spring, such as the phenological status of host trees and

degree-day accumulation over 5.5 °C (Kemp and others 1986; Shepherd 1983;

Wickman 1976a, 1976b, 1977, 1988). Results show that well-timed sampling

usually occurs in the range of 278-389 degree-days C accumulation.

In general, sampling properly timed for tussock moth larvae also will be synchronized

for budworm larvae. The opposite is not always true, however, because budworm

larvae that emerged early in the spring may be active weeks before tussock moth

eggs hatch. An unseasonably cool spring can easily upset the normal synchrony

between the species by delaying tussock moth egg hatch while budworm larvae

continue to develop nearly on schedule. The timing of sampling, therefore, cannot

depend on indices alone but also should be based on local temperatures and direct

observation of insect development.

Branches are easily sampled for larvae by holding a portable drop cloth under a

45-cm portion of a branch tip and rapping the branch sharply with a beating stick

(color plates E and F). Free-living insect larvae and other arthropods on the branch

are dislodged onto the cloth where they can easily be observed and counted (color

plate G). In early summer after bud burst and when foliage is elongating, most

tussock moth and budworm larvae willdrop offthe foliage readily when disturbed.

Some budworm larvae, however, are more difficultto dislodge from webbed shoots

than others and may require extra-vigorous beating for a complete count.

A hand-held drop cloth used successfully for many years is a 56- by 114-cm lightweight canvas supported underneath by a frame of aluminum crossmembers. The

beating cloth can be unsnapped quickly from the frame for storage. Short lengths of

wooden dowel or PVC pipe can be used for beating the foliage. A stick 56 cm long

cut from 3/4-in PVC pipe is lightweight and will last for years. A complete description

of materials, construction, and other suggestions are given by Paul (1979).

The most common method for estimating plot density is by simple random sampling

of trees on the plot and counting all individuals in the three-branch sampling units.

The mean number of larvae per sampling unit, y, is then calculated for each insect

species by,

(1)

where Yi is the number of larvae in the sampling unit of the i'th tree and m is the

number of trees sampled per plot (Mason and others 1989). Sample variance among

three-branch sampling units, V(y), for the above cluster of trees assuming an infinite

number of sampling units therefore is,

(2)
Frequency of

Occurrence of Larvae

in Branch Samples

Figure 3-Relation between logarithmic-transformed values of intraplot variances

and mean plot densities in lower crown samples for the Douglas-fir tussock moth

and western spruce budworm.

In previous sampling operations, intraplot variances in the lower crown have been

almost identical for tussock moth and budworm populations so that, for practical

purposes, they can be considered to be the same (fig. 3).

Population density also can be estimated on a plot without having to count all larvae

in each sampling unit. The method was first developed for making quick estimates

of density in sparse populations of tussock moth (Mason 1977, 1987), but it has

been expanded to include the range of all natural densities of either the Douglas-fir

tussock moth or the western spruce budworm 1

(Mason and Beckwith 1990). The

technique is based on a statistical relation between the sample mean and the

frequency of occurrence of different minimum numbers of larvae in the sampling

units (Gerrard and Chiang 1970). The only measure required on a plot is a determination of the proportion of sampling units containing a specified threshold number

of larvae. Depending on the threshold selected, this usually can be done by counting

only a fraction of the total number of larvae on each sample. The mean number of

larvae per sampling unit, y, is then estimated by the equation,

where t is the selected threshold number and p is the proportion of sampling units

containing t or more larvae (fig. 4). The threshold number always must be large

enough so that p < 1.0 and can be chosen easily by using the average count of

larvae in the first four sampling units. This method of estimating a sample mean has

produced comparable results to that of making total counts and with less sampling

time (fig. 5). The sampling error associated with y is more difficult to estimate, but it

is similar in size to the error encountered in making complete counts for the same

number of trees sampled (Mason and Beckwith 1990).

1 Unpublished data. On file with: Forestry and Range

Sciences I_aboratory, 1401 Gekeler Lane, La Grande, OR

97850.

7
Figure 4- Estimated mean number of larvae per sampling unit in relation to the

proportion of samples infested at different threshold densities (t). Lines were

calculated by solutions of equation (3).

The example in table 2 uses actual counts of budworm larvae in a field plot and

compares the total count method with the frequency of occurrence procedure for

estimating density of larvae on the plot. Both methods gave similar estimates of

density and sampling precision, but one-third fewer larvae had to be counted with

the frequency of occurrence technique.

To assure comparability of data in different projects, mean densities estimated for

sampling units in the lower crown need to be converted to standard indices of abundance in the middle crown. This requires first expressing larval density in terms of a

square meter of branch area and then applying a correction factor that reflects the

vertical distribution of larvae of each species in the tree crown. The middle crown

density per square meter, M, for the plot is,

Converting Sample

Mean to Middle Crown

Density

(4)

where 3.1 is the average number of three-branch sampling units in a square meter, y

is the mean density of larvae per sampling unit, and R is the simple ratio of expected

middle crown to lower crown density. The distribution of tussock moth and budworm

larvae in the crown is naturally variable and changes with larval age (Campbell and

others 1984; Mason 1970, 1987). Annual monitoring, however, is recommended for

early summer when the predominant larval stages are nearly the same each year.

The correction factor, R, for these life stages has been examined in many sampling

studies and been found to be relatively consistent; that is, 2.0 for tussock moth

instars I and II and 2.23 for budworm instar IV (Mason 1970, 1987; Torgersen and

others, in press). Estimates of lower crown density per sampling unit, therefore, can

8
Estimating and

Interpreting

Population Densities

Population Mean of

Monitoring Unit

Figure 5-Comparison of mean number of western spruce budworm larvae per

sampling unit estimated in the lower crown by direct counts and by frequency of

occurrence (Mason and Beckwith 1990).

be converted to middle crown density per square meter by substituting one of these

constants for R in equation (4). The crown level correction is useful only for converting to a standard index of abundance to compare with other populations. Because

the same constants are used each year, the accuracy of the conversion has no

influence on interpreting annual fluctuations in population density on the same

monitoring plots.

Permanent plots are monitored to estimate the population density of a larger geographical unit. To ensure that plots adequately represent the unit, it is important

that they be selected objectively. The monitoring units might be relatively small, such

as a Ranger District or a silvicultural treatment area, or larger, such as a National

Forest or a geographical province; for example, a portion of the Blue Mountains or

the northern Cascade Range. Data from the successive monitoring of populations

on permanent plots are usually summarized in terms of these specific monitoring

units. The mean population density for a monitoring unit is simply the mean of the

individual plot densities defined by,

n

(5)

where M is the population mean of the monitoring unit, Mi is larval density on the i'th

plot of the unit, and n is the number of sample plots in the unit. The variability among

plots measured by interplot sampling variance therefore is,

(6)

9
Counts of budworm 11 20 7 14 20

larvae on sampling 19 14 10 20 3

units in order of 14 5 41 22 19

11 11 21 34 34

samplinga

25 19 20 20 19

Estimated by total Estimated by frequency of

Statistical parameter count of larvae occurrence of larvae> 13

Number of larvae counted 453 292

Proportion of samples with

13 or more larvae NA 0.72

Mean number of larvae per

sample (sample mean) 18.12 19.76

Standard error 1.79 2.24b

Percent precision of estimate 9.85 11.34

Mean number of larvae per

square meter in lower crownc 56.17 61.26

Mean number of larvae per

square meter in middle crown

(plot density)d 125.26 136.61

Table 2-Comparison of plot densities of western spruce budworm estimated

by total count of larvae on sampling units (equation 1 in text) and by frequency

of occurrence of 13 or more larvae on sampling units (equation 3 in text)

a Data collected from a plot of 25 trees near Fish Lake, Wallowa-Whitman National Forest, June 1987. b Calculated by method in Mason and Beckwith (1990).

C Number of larvae per sampling unit converted to number per square meter by multiplying by 3.1. d

Lower crown density corrected to middle crown density by multiplying by 2.23.

The population mean and interplot variance also are related by a simple power law,

where a and b are parameters reflecting the spatial variation of larval populations

over the monitoring unit (Taylor 1961). This relation is especially useful because

once the parameters have been determined, variance can be predicted for any

expected population mean.

Previous monitoring has generated a large database of tussock moth and budworm

densities from which the mean-variance relation using equation (7) has been determined for three different sized monitoring units (figs. 6, 7, and 8). Not surprisingly,

interplot variation increases for both species as the size of the area monitored

increases (table 3, page 16). Larger areas always introduce the possibility for more

variability as new habitats and environments are included. A case in point is the

unusually large parameter a in equation (7) for predicting budworm variance in

geographical provinces. This large value was the result of high variability of budworm

populations in the northern Cascade Range province where outbreak densities

persisted in the Okanogan National Forest but were absent from the nearby

Wenatchee National Forest.

10
Figure 6---Relation between logarithmic-transformed values of

interplot variances and mean population densities in Districtsized monitoring units.

Number of Sample Plots The interplot variation in a monitoring unit is important because it influences the

sample size needed for an estimate of population density at the desired level of

precision. Assuming infinite plots are available for sampling and a normal distribution

of densities in the monitoring unit, the approximate number of plots, n, required is,

where t is Student's t for a specified confidence probability, and E is the desired

standard error of estimate (Cochran 1977). By solving equation (7) for variance

and substituting in equation (8), the appropriate number of permanent plots in a

monitoring unit can be estimated for any specified sampling error.

11
Figure 7-Relation between logarithmic-transformed values of

interplot variances and mean population densities in Forestsized monitoring units.

For the same error, more plots will usually be needed for large monitoring units and

at low larval densities. Examples are given in figures 9,10, and 11 (pages 16-17)

where sample sizes generated from the previously derived equations for expected

variance (table 3) are plotted over a range of tussock moth and budworm densities.

Except for low larval densities in the province-sized monitoring unit, these calculations

show that budworm populations usually can be monitored with fewer plots than are

needed for tussock moth populations. In general, the maximum number of permanent

plots needed for monitoring both species together are 15 to 20 for District-sized units,

25 to 30 for the average Forest, and 50 to 60 for a larger province. These sample

sizes assume a precision of estimate of 20 percent of the mean and a confidence

level of 68 percent, which probably are adequate for the purposes of most larval

monitoring. Improving the levels of precision and confidence always requires more

plots to monitor, which would increase cost and possibly jeopardize continuity of the

program. It also should be remembered that a precise estimate usually is not as

12
Figure 8-Relation between logarithmic-transformed values of

interplot variances and mean population densities in provincesized monitoring units.

necessary at low densities, because the magnitude of the error on a numerical scale

is also small. Decisions on sample size will depend on many factors, not the least of

which are available resources, practicality, and the ultimate uses for the monitoring

data.

Population Normality

and Predicting

Defoliation

When populations are normally distributed, inferences can be made about the frequency of different larval densities in the monitoring unit even though they may not

be represented in the plot samples. The shape of the normal distribution curve, as

determined by the estimated population mean and variance, gives the probability of a

subpopulation of another density occurring somewhere in the monitoring unit. This is

measured by the distance of that density value from the population mean expressed

in terms of the number of standard deviations, called the standard normal deviate.

The probability, P, for any specified standard deviate, Z, under the normal curve

can be read from a table of cumulative standard normal distribution (table 4). The

percentile under the curve where values exceed the standard deviate, therefore, is

1-P.

13
Color Plate A-First instar of the Douglas-fir tussock moth.

Color Plate B-Fourth instar of the

western spruce budworm.

Color Plate C-New foliage development on grand fir (Abies grandis

(Dougl. ex D. Don) Lindl.) at the proper time for sampling first instar

tussock moths or nominal fourth instar budworms.

Color Plate D-New foliage

development on Douglas-fir at

the proper time for sampling first

instar tussock moths or nominal

fourth instar budworms.

14
Color Plates E and F-Sampling lower crown

branches with a drop-cloth and beating stick.

Color Plates G and H-Drop-cloth with larvae and dried

needles from a sample branch.

15
Douglas-fir tussock moth Western spruce budworm

Number Parameters Number Parameters

of of

Monitoring unit observations a b observations a b

District 91 0.87 1.82 53 0.52 1.79

National Forest

(Wallowa-Whitman,

Umatilla, Okanogan,

and Wenatchee) 51 1.32 1.90 35 1.02 1.91

Geographical province

(northern Blue

Mountains and

northern Cascades) 29 1.91 1.96 18 13.49 1.33

a Parameters a and b were calculated by linear regression after expressing the power curve logarithmically

as log V(M) =

log a + b log M.

Table 3-Statistics for the relation between population mean and interplot

variance for 3 sizes of monitoring units as expressed by the power curve,

Figure 9-Number of plots required to estimate population density for a Districtsized monitoring unit with a standard error of 20 percent of the mean and a

68-percent probability level.

16
Figure 10-Number of plots required to estimate population density for a

Forest-sized monitoring unit with a standard error of 20 percent of the mean

and a 68-percent probability level.

Figure 11-Number of plots required to estimate population density for a provincesized monitoring unit with a standard error of 20 percent of the mean and a

68-percent probability level.
z

p Z P Z P

0.0 0.5000 1.0 0.8413 2.0 0.9772

.1 .5398 1.1 .8643 2.1 .9821

.2 .5793 1.2 .8849 2.2 .9861

.3 .6179 1.3 .9032 2.3 .9893

.4 .6554 1.4 .9192 2.4 .9918

.5 .6915 1.5 .9332 2.5 .9938

.6 .7257 1.6 .9452 2.6 .9953

.7 .7580 1.7 .9554 2.7 .9965

.8 .7881 1.8 .9641 2.8 .9974

.9 .8159 1.9 .9713 2.9 .9981

Source: Abbreviated from Gilbert (1987).

Table 4--Cumulative probability, P, in normal distribution

for different standard normal deviates, Z

As an example, if 30 is the threshold number of larvae per square meter at which

visible defoliation appears and is the standard deviation of the plot densities,

the likelihood of defoliation somewhere in the monitoring unit for any population

mean and variance can be estimated by solving,

and then calculating the percentile (1-P) where density exceeds 30 larvae per square

meter. Because of the importance of the estimated mean and standard deviation in

defining the probabilities for a monitoring unit, the procedure will produce the most

reliable results from sample sizes of at least 30 plots that give good estimates of

these parameters.

Unfortunately, larval densities often are not distributed normally over the monitoring

unit but may be asymmetrical because of aggregations of larvae that skew the data

to the right. In these cases a lognormal distribution, which is usually achievable

by transforming the original data to logarithms, better describes the probability of

different densities. The standard normal deviate for the defoliation threshold density

is estimated the same as in equation (9), except that 30 is transformed to its natural

logarithm, and M and V(M) are calculated from transformed plot densities.

A useful guide for deciding when transformation is needed is the ratio of the standard

deviation to the population mean (coefficient of variation). As long as the ratio is less

than 1.2, even though data are not necessarily distributed normally, the original data

are preferred for analysis; otherwise a logarithmic transformation should be used

(Gilbert 1987).

In a test of 42 data sets of tussock moth and budworm densities in the Blue

Mountains and northern Cascades provinces, we found that only 14 (one-third)

initiallymet the criteria for normality. After employing the rule of a 1.2 coefficient

of variation, however, and transforming skewed data to natural logarithms, 39 data

sets met the condition of normality (table 5).

(9)

18
Douglas-fir tussock moth in the

northern Blue Mountains 5 10 15

Douglas-fir tussock moth in the

northern Cascade Range 4 5 9

Western spruce budworm in the

northern Blue Mountains 5 3 1 9

Western spruce budworm in the

northern Cascade Range 0 7 2 9

Total 14 25 3 42

Table 5-Frequency distribution of larval densities of Douglas-fir tussock

moth and western spruce budworm in province-sized monitoring unitsa

Insect species and

geographical province Normal Lognormalb Other Total

- - - - - - - Number of data sets - - - - - - -

a

Tests for normality were performed by computer analysis of normal probability plots using the

statistical package MINITAB, Release 8.0 (Ryan and Joiner 1976).

b Data transformed to natural logarithms before testing for normality.

Conclusions

An example of the usefulness of this statistical technique can be seen in its

application to a set of tussock moth densities collected from the Umatilla National

Forest in 1992 (table 6). Because of high variability among plot densities, the

coefficient of variation exceeded 1.2, thus requiring normalization of the data by a

logarithmic transformation. The results show that although the mean population

density of 4.63 larvae per square meter was far below the threshold for defoliation,

after transformation 5.4 percent of the area under the normal curve exceeded a

density of 30 larvae at which some visible defoliation would be likely. This prediction

was confirmed later in the summer by patchy tussock moth defoliation in a part of

the monitored area despite overall low densities in the Forest.

Monitoring is most valuable when it is used for building continuous databases that

record the behavior of defoliator populations over an extended period. Although this

often means sampling in years when population densities are low, these periods of

low numbers can be as important as outbreaks in analyzing the long-term behavior

of populations. Analyses of time-series data may be the only way to recognize important mechanisms embedded in the population's history that are essential for building

the predictive models needed in pest management (Berryman and Millstein 1990).

Accumulating continuous databases requires a financial commitment to monitoring

every year that, realistically, may be difficult to maintain. Nonetheless, this approach

is still the most practical, if not the only, way of diagnosing key processes underlying

the dynamics of defoliator populations (Berryman, in press). Long-term databases

also will be valuable for recording the possible effects of new silvicultural approaches

or changes in global climate on insect pests. There is no better way of answering

guestions on these subjects than to document the actual response of the populations

over a long period.

19

Distribution
Arithmetic plot densities (mean 0.54 0.01 0.29 0.48 0.87

number of larvae per square 1.02 .38 .38 2.30 .08

meter plus 0.01) .01 .01 19.06 22.23 11.03

24.07 .22 .42

Natural logarithms of plot -.62 -4.61 -1.24 -0.73 -.14

densities .02 -.97 -.97 .83 -2.53

-4.61 -4.61 2.95 3.10 2.40

3.18 -1.51 -.87

-

-

densities (population density): 4.63

Standard deviation 8.33

Coefficient of variation 1.80

densities: -.61

Standard deviation 2.49

Number of normal deviates

to defoliation density (In 30) 1.61

Visible defoliation percentile

(from table 4) .054

Table 6-Plot densities of Douglas-fir tussock moth larvae with statistical

calculations for a monitoring unit of 18 plots in the northern half of the

Umatilla National Forest in June 1992a

Statistical parameter

Mean of arithmetic plot

Mean of logarithmic plot

a

Logarithmic transformations are for normalizing data to estimate percentile of population with densities

capable of causing visible defoliation of trees.

Regardless of how strong a case can be made for defoliator monitoring, it is doubtful

that any program can survive for long unless the sampling system is relatively simple

and inexpensive to use. The methods we have described are a most efficient and

cost-effective way of monitoring tussock moth and budworm populations. Not only

can larval populations be censused over large units with statistically measured confidence limits, but their numbers can be interpreted directly in terms of the critical

threshold densities affecting trees and stands. The only requirement is having

representative stands with a stratum of lower crown host foliage accessible for

sampling from the ground. Dense even-aged stands where all the crowns are out of

reach, obviously, are not suited for monitoring by these methods. When acceptable

conditions are available, however, the regular monitoring of defoliator larvae should

be made a routine part of ecosystem management.

20
Acknowledgments

English Equivalents

Literature Cited

The idea that defoliator larvae might be monitored by beating lower crown foliage

over a portable drop cloth was first given to us 25 years ago by Dan Dotta and Dick

Hunt of the California Division of Forestry, who used a similar technique for quickly

evaluating tussock moth populations. In the intervening years, many colleagues

and assistants helped us to formalize and test procedures leading to the standard

methodologies that we now recommend. We thank all those who have participated

with this development through the years. Technical reviews of an early draft of this

paper were made by John W. Hazard, Statistical Consulting Service, Bend, OR;

Boyd E. Wickman and Torolf R. Torgersen, Pacific Northwest Research Station,

La Grande, OR; Donald W. Scott, Wallowa-Whitman National Forest, La Grande,

OR; Katharine A. Sheehan, Forest Pest Management, Pacific Northwest Region,

Portland, OR; and Julie C. Weatherby, Forest Pest Management, Intermountian

Region, Boise, ID.Their help in improving the final paper is much appreciated.

1 centimeter (cm) = 2.54 inches

1 degree-day C = 1.8 degree-days F

1 °C = [5(°F - 32)]+9

1 meter (m) = 39.7 inches

1 hectare (ha) = 2.47 acres

1 square meter (m2) = 1550 square inches

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21
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from lower-crown samples after treatment with Bacillus thuringiensis Berliner.

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23
Mason, R.R.; Paul, H.G. 1994. Monitoring larval populations of the Douglas-fir

tussock moth and western spruce budworm on permanent plots: sampling

methods and statistical properties of data. Gen. Tech. Rep. PNW-GTR-333.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 22 p.

Procedures for monitoring larval populations of the Douglas-fir tussock moth and

the western spruce budworm are recommended based on many years experience

in sampling these species in eastern Oregon and Washington. It is shown that

statistically reliable estimates of larval density can be made for a population by

sampling host trees in a series of permanent plots in a geographical monitoring unit.

The most practical method is to estimate simultaneously densities on a plot of both

insect species by the nondestructive sampling of foliage on lower crown branches of

host trees. For best results, sampling methods need to be consistent with monitoring

done annually to accumulate continuous databases that reflect the behavior of

defoliator populations over a long period.

Keywords: Ecological monitoring, population monitoring, sampling insects, Douglas-fir

tussock moth, Orgyia pseudotsugata, western spruce budworm, Choristoneura

occidentalis.

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